Analytics, Data Science, & Artificial Intelligence Systems for Decision Support, 11th Ramesh Sharda

Analytics, Data Science, & Artificial Intelligence Systems for Decision Support, 11th By Ramesh Sharda

Email: Richard@qwconsultancy.com

Chapter 1: An Overview of Analytics, and AI

Learning Objectives for Chapter 1 • • • • • • •

Understand the need for computerized support of managerial decision making Understand the development of systems for providing decision-making support Recognize the evolution of such computerized support to the current state of analytics/data science and artificial intelligence Describe the business intelligence (BI) methodology and concepts Understand the different types of analytics and review selected applications Understand the basic concepts of artificial intelligence (AI) and see selected applications Understand the analytics ecosystem to identify various key players and career opportunities

CHAPTER OVERVIEW The business environment (climate) is constantly changing, and it is becoming more and more complex. Organizations, both private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions, some of which are very complex. Making such decisions may require considerable amounts of relevant data, information, and knowledge. Processing these in the framework of the needed decisions must be done quickly, frequently in real time, and usually requires some computerized support. As technologies are evolving, many decisions are being automated, leading to a major impact on knowledge work and workers in many ways. This book is about using business analytics and artificial intelligence (AI) as a computerized support portfolio for managerial decision making. It concentrates on the theoretical and conceptual foundations of decision support as well as on the commercial tools and techniques that are available. The book presents the fundamentals of the techniques and the manner in which these systems are constructed and used. We follow an EEE (exposure, experience, and exploration) approach to introducing these topics. The book primarily provides exposure to various analytics/AI techniques and their applications. The idea is that students will be inspired to learn from how various organizations have employed these 1 .

technologies to make decisions or to gain a competitive edge. We believe that such exposure to what is being accomplished with analytics and that how it can be achieved is the key component of learning about analytics. In describing the techniques, we also give examples of specific software tools that can be used for developing such applications.

However, the book is not limited to any one software tool, so students can experience these techniques using any number of available software tools. We hope that this exposure and experience enable and motivate readers to explore the potential of these techniques in their own domain. To facilitate such exploration, we include exercises that direct the reader to Teradata University Network (TUN) and other sites that include teamoriented exercises where appropriate. In our own teaching experience, projects undertaken in the class facilitate such exploration after students have been exposed to the myriad of applications and concepts in the book and they have experienced specific software introduced by the professor. This chapter has the following sections:

CHAPTER OUTLINE 1.1 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company 1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics 1.3 Decision-Making Processes and Computer Decision Support Framework 1.4 Evolution of Computerized Decision Support to Business Intelligence/ Analytics/Data Science 1.5 Analytics Overview 1.6 Analytics Examples in Selected Domains 1.7 Artificial Intelligence Overview 1.8 Convergence of Analytics and AI 1.9 Overview of the Analytics Ecosystem 1.10 Plan of the Book 1.11 Resources, Links, and the Teradata University Network Connection

ANSWERS TO END OF SECTION REVIEW QUESTIONS      Opening Vignette Questions 1. It is said that KONE is embedding intelligence across its supply chain and enables

smarter buildings. Explain.

KONE uses a variety of IoT applications to record and communicate a wide variety of systems status and performance information that can then be used to identify issues and collect important data for future applications.

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2. Describe the role of IoT in this case.

IoT allows for the collection of multiple discrete points of data throughout the systems that can be used in a variety of applications.

3. What makes IBM Watson a necessity in this case?

IBM Watson serves to both collect and analyze the wide variety of information presented. It can then communicate this information to other systems and establish patterns based on the data collected.

4. Check IBM Advanced Analytics. What tools were included that relate to this case?

The tools available have many possible applications to the case, specifically the ability to evaluate the data collected across a large number of systems and different parameters.

5. Check IBM cognitive buildings. How do they relate to this case?

This solution uses many similar technologies that appears to focus primarily on the ability to detect issues and potential issues within the building.

Section 1.2 Review Questions 1. Why is it difficult to make organizational decisions?

Organizational decisions may be difficult to make due to a complex process necessary to both identify and define the problem as well as evaluate the host of different possible solutions.

2. Describe the major steps in the decision-making process.

• • • •

1.Define the problem (i.e., a decision situation that may deal with some difficulty or with an opportunity).

2. Construct a model that describes the real-world problem.

3. Identify possible solutions to the modeled problem and evaluate the solutions.

4. Compare, choose, and recommend a potential solution to the problem.

3. Describe the major external environments that can impact decision making.

• •

Political factors. Major decisions may be influenced by both external and internal politics. An example is the 2018 trade war on tariffs.

Economic factors. These range from competition to the genera and state of the economy. These factors, both in the short and long run, need to be considered.

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Sociological and psychological factors regarding employees and customers.

These need to be considered when changes are being made.

Environment factors. The impact on the physical environment must be assessed in many decision-making situations.

4. What are some of the key system-oriented trends that have fostered IS-supported

decision making to a new level?

Computer applications have shifted from merely processing transaction and monitoring activities to actively analyzing and seeking solution to problems through cloud-based systems.

5. List some capabilities of information technologies that can facilitate managerial

decision making.

• Group communication and collaboration • Improved data management.

• Managing giant data warehouses and Big Data • Analytical support.

• Overcoming cognitive limits in processing and storing information • Knowledge management.

• Anywhere, anytime support.

Section 1.3 Review Questions 1.

List and briefly describe Simon’s four phases of decision making.

Simon’s four phases of decision making are intelligence, design, choice, and implementation.

• Intelligence consists of gathering information by examining reality, then identifying and defining the problem. In this phase problem ownership should also be established.

• Design consists of determining alternatives and evaluating them. If the evaluation will require construction of a model, that is done in this phase as well.

• The choice phase consists of selecting a tentative solution and testing its validity.

• Implementation of the decision consists of putting the selected solution into effect.

2.

What is the difference between a problem and its symptoms?

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Problems arise out of dissatisfaction with the way things are going. It is the result of a difference or gap between what we desire and what is or is not happening. A symptom is how a problem manifests itself. A familiar personal example is a high temperature (symptom) and an illness (problem). It is necessary to diagnose and treat the underlying illness. Attempting to relieve the temperature works if the illness is one which the body’s defenses can cure, but, can be disastrous in other situations. A business example: high prices (problem) and high unsold inventory level (symptom). Another is quality variance in a product (symptom) and poorly calibrated or worn-out manufacturing equipment (problem).

3.

Why is it important to classify a problem?

Classifying a problem enables decision makers to use tools that have been developed to deal with problems in that category, perhaps even including a standard solution approach.

4.

Define implementation.

Implementation involves putting a recommended solution to work, but not necessarily implementing a computer system.

5.

What are structured, unstructured, and semistructured decisions? Provide two examples of each.

• Structured problem, the procedures for obtaining the best (or at least a good enough) solution are known. Examples would include commonly and historically addressed issues and problems within a business or industry.

• Unstructured decisions are fuzzy, complex problems for which there are no cut-and-dried solution methods. Examples would include issues or problems within a business or industry that combined multiple structured problems or problems where the necessary data or research is not readily available.

• Unstructured problem is one where the articulation of the problem or the solution approach may be unstructured in itself. Examples would include problems within the business or industry where the definition of the problem itself is not agreed upon where the data is not readily available and there may currently exist no ability to collect that data.

6.

Define operational control, managerial control, and strategic planning. Provide two examples of each.

• Operational control focuses on the day to day monitoring and control over plans with existing measures and defined actions. Examples may include monitoring Accounts Receivable or controlling inventory.

• Managerial control focuses on short-term control over existing plans where existing actions and measures may be defined, that may also require individual 5 .

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or group decision-making to apply or amend to meet the required result.

Examples may include preparing budgets and negotiating contracts.

Strategic planning focuses on mid and long term planning that directs the core activities and initiatives of the business. Examples may include decisions to make major purchases or conduct research and development.

7.

What are the nine cells of the decision framework? Explain what each is for.

The nine cells of the decision framework (see figure 1.2) aligns the three types of decisions (structured, semistructured and unstructured) with the three types of control (operational, managerial and strategic). Each of these cells can provide information about the types of decisions that need to be made based on the availability of information on past decisions or data for decision-making as well as the level of the decision-making involved.

8.

How can computers provide support for making structured decisions?

Computers can be instrumental in providing information for structured decisions because they can be used to collect the underlying data needed for the decision as well as providing a known system to abstract analyze and classify possible actions or results.

9.

How can computers provide support for making semistructured and unstructured decisions?

In these situations, computers can be used to collect the underlying information needed for decision as well as potentially applying some of the learnings from past solutions that may exist. Additionally they may provide the computational ability to conduct a thorough analysis of the identified problem.

Section 1.4 Review Questions 1. List three of the terms that have been predecessors of analytics.

These terms include decision support systems (DSS), executive information systems (EIS) and business intelligence (BI).

2. What

was the primary difference between the systems called MIS, DSS, and Executive Information Systems?

The primary differences between the systems are the amount of information available for analysis as well as the sophistication of the display and problem solving capabilities of each.

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3. Did DSS evolve into BI or vice versa?

Systems and products referred to as DSS transitioned into the BIA label, although both are content free expressions and mean different things to different professionals.

4. Define BI.

Business intelligence (BI) is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies.

5. List and describe the major components of BI.

There are three major components to BI: • the data warehouse environment that organizes summarizes and standardizes business data • the business analytic environment which uses the data warehouse to access and manipulate data to display results • the performance and strategy component that utilizes information from the analytic environment to create more detailed analyses and strategy 6. Define OLTP.

Online transaction processing (OLTP) systems handle a company’s routine ongoing business.

7. Define OLAP.

Online analytical processing (OLAP) systems are used to process information and research requests.

8. List some of the implementation topics addressed by Gartner’s report.

The Gartner report proposed splitting planning and executing into four categories; business organization functionality and infrastructure components.

9. List some other success factors of BI.

Other success factors may include ease of availability of software and solutions for self-service, integration of DI into the corporate culture and appropriate integration between various BI tools.

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Section 1.5 Review Questions 1.

Define analytics.

The term replaces terminology referring to individual components of a decision support system with one broad word referring to business intelligence. More precisely, analytics is the process of developing actionable decisions or recommendations for actions based upon insights generated from historical data.

Students may also refer to the eight levels of analytics and this simpler descriptive language: “looking at all the data to understand what is happening, what will happen, and how to make the best of it.”

2.

What is descriptive analytics? What various tools are employed in descriptive analytics?

Descriptive analytics refers to knowing what is happening in the organization and understanding some underlying trends and causes of such occurrences. Tools used in descriptive analytics include data warehouses and visualization applications.

3. How is descriptive analytics different from traditional reporting?

Descriptive analytics gathers more data, often automatically. It makes results available in real time and allows reports to be customized.

4. What is a DW? How can DW technology help in enabling analytics?

A data warehouse, introduced in Section 1.7, is the component of a BI system that contains the source data. As described in this section, developing a data warehouse usually includes development of the data infrastructure for descriptive analytics— that is, consolidation of data sources and making relevant data available in a form that enables appropriate reporting and analysis. A data warehouse serves as the basis for developing appropriate reports, queries, alerts, and trends.

5. What is predictive analytics? How can organizations employ predictive analytics?

Predictive analytics is the use of statistical techniques and data mining to determine what is likely to happen in the future. Businesses use predictive analytics to forecast whether customers are likely to switch to a competitor, what customers are likely to buy, how likely customers are to respond to a promotion, and whether a customer is creditworthy. Sports teams have used predictive analytics to identify the players most likely to contribute to a team’s success.

6. What is prescriptive analytics? What kind of problems can be solved by prescriptive

analytics?

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Prescriptive analytics is a set of techniques that use descriptive data and forecasts to identify the decisions most likely to result in the best performance. Usually, an organization uses prescriptive analytics to identify the decisions or actions that will optimize the performance of a system. Organizations have used prescriptive analytics to set prices, create production plans, and identify the best locations for facilities such as bank branches.

7.

Define modeling from the analytics perspective.

As Application Case 1.6 illustrates, analytics uses descriptive data to create models of how people, equipment, or other variables operate in the real world. These models can be used in predictive and prescriptive analytics to develop forecasts, recommendations, and decisions.

8.

Is it a good idea to follow a hierarchy of descriptive and predictive analytics before applying prescriptive analytics?

As noted in the analysis of Application Case 1.5, it is important in any analytics project to understand the business domain and current state of the business problem.

This requires analysis of historical data, or descriptive analytics. Although the chapter does not discuss a hierarchy of analytics, students may observe that testing a model with predictive analytics could logically improve prescriptive use of the model.

9.

How can analytics aid in objective decision making?

As noted in the analysis of Application Case 1.4, problem solving in organizations has tended to be subjective, and decision makers tend to rely on familiar processes.

The result is that future decisions are no better than past decisions. Analytics builds on historical data and takes into account changing conditions to arrive at fact-based solutions that decision makers might not have considered.

10.

What is Big Data analytics?

The term Big Data refers to data that cannot be stored in a single storage unit.

Typically, the data is arriving in many different forms, be they structured, unstructured, or in a stream. Big Data analytics is analytics on a large enough scale, with fast enough processing, to handle this kind of data.

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What are the sources of Big Data?

Major sources include clickstreams from Web sites, postings on social media, and data from traffic, sensors, and the weather.

12.

What are the characteristics of Big Data?

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Today Big Data refers to almost any kind of large data that has the characteristics of volume, velocity, and variety. Examples include data about Web searches, such as the billions of Web pages searched by Google; data about financial trading, which operates in the order of microseconds; and data about consumer opinions measured from postings in social media.

13.

What processing technique is applied to process Big Data?

One computer, even a powerful one, could not handle the scale of Big Data. The solution is to push computation to the data, using the MapReduce programming paradigm.

Section 1.6 Review Questions 1.

What are three factors that might be part of a PM for season ticket renewals?

Examples might include ticket cost, marketing and team success.

2.

What are two techniques that football teams can use to do opponent analysis?

Examples might include frequency of running plays and individual athlete trends and matchups.

3.

What other analytics uses can you envision in sports?

Many examples exist including maintenance of facilities and accuracy of referees.

4.

Why would a health insurance company invest in analytics beyond fraud detection? Why is it in its best interest to predict the likelihood of falls by patients?

There are many possible applications, for example insurance companies may want to evaluate causes for conditions so that those conditions can be avoided.

An excellent example of this is patient falls. Having this information allows for preventive measures to be taken before a fall occurs.

5.

What other applications similar to prediction of falls can you envision?

Student responses will vary that may include prediction of other conditions such as cancer.

6.

How would you convince a new health insurance customer to adopt healthier lifestyles (Humana Example 3)?

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Data can be used to demonstrate to a customer that adoption of a healthier lifestyle may limit the negative experiences associated with various conditions or diseases.

7.

Identify at least three other opportunities for applying analytics in the retail value chain beyond those covered in this section.

Student responses will vary.

8.

Which retail stores that you know of employ some of the analytics applications identified in this section?

Student responses will vary.

9.

What is a common thread in the examples discussed in image analytics?

In each analysis a detailed understanding of both the image data and other supplementary data sources were used to create solutions.

10.

Can you think of other applications using satellite data along the lines presented in this section?

Student responses will vary.

Section 1.7 Review Questions 1.

2.

3.

What are the major characteristics of AI? • Technology that can learn to do things better over time.

• Technology that can understand human language.

• Technology that can answer questions.

List the major benefits of AI. • Significant reduction in the cost of performing work. This reduction continues over time while the cost of doing the same work manually increases with time.

• Work can be performed much faster.

• Work is consistent in general, more consistent than human work.

• Increased productivity and profitability as well as a competitive advantage are the major drivers of AI. What are the major groups in the ecosystem of AI? List the major contents of

each.

• Major Technologies include machine learning, deep learning and intelligent agents.

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• • •

•

Knowledge-based technologies include expert systems, recommendation engines, chat bots, virtual personal assistants and robo advisors.

Biometric related technologies include natural language processing and other biometric recognition technologies support theories, tools and platforms include a variety of disciplines such as computer science, cognitive science, control theory, linguistics, mathematics, neuroscience, philosophy, psychology, and statistics.

Tools and platforms include the various software applications and systems available from a wide number of vendors.

4.

Why is machine learning so important?

Machine learning presents the promise of creating more effective and accurate solutions to problems without the direct intervention of individuals.

5.

Differentiate between narrow and general AI. Narrow AI focuses on a specific, defined domain whereas general AI may cross multiple domains and become more powerful as it is refined.

6.

Some say that no AI application is strong. Why? No AI currently performs the full range of human cognitive capabilities.

7.

Define assisted intelligence, augmented intelligence, and autonomous intelligence.

• Assisted intelligence is the equivalent of week AI and works within narrow domains.

• Augmented intelligence use computer abilities to extend human cognitive abilities.

• Automated intelligence perform a broad range of functions without human intervention.

8.

What is the difference between traditional AI and augmented intelligence?

These systems are designed to extend human capabilities as opposed to replacing them.

9.

Relate types of AI to cognitive computing.

Not addressed in this chapter, but students may note that both can be designed to perform tasks.

10.

List five major AI applications for increasing the food supply.

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Examples include increasing productivity of farm equipment, improved planting and harvesting, improving food nutrition, reducing the cost of food processing, driverless machines, picking fruits and vegetables, pest control improvements and weather monitoring.

11.

List five contributions of AI in medical care.

Examples include disease prediction, tracking medication intake, telepresence, improved diagnostics, more efficient supply chains, personal diagnoses, providing medical information and others.

Section 1.8 Review Questions 1.

What are the major benefits of intelligent systems convergences?

This convergence allows for a greater number of overall features and applications to more complex problems as multiple systems can be combined.

2.

Why did analytics initiatives fail at such a high rate in the past?

Responses will vary but may focus on a lack of availability of data, lack of processing tools and complexity of the required analysis.

3.

What synergy can be created by combining AI and analytics?

AI may be used to automatically locate, visualize and narrate important items and can be used to create predictions that can be compared to actual performance.

These activities will free up time for more analytics.

4.

Why is Big Data preparation essential for AI initiatives?

AI works best when it has access to robust data sources. Properly preparing big data for use in AI allows data to be used completely and effectively.

5.

What are the benefits of adding IoT to intelligent technology applications?

The primary benefit is the inclusion of additional data that can be used for various types of analysis.

6.

Why it is recommended to use blockchain in support of intelligent applications?

The use of block chain technology can add security to data in a distributed network.

Section 1.9 Review Questions 13 .

(This section has no review questions.)

Section 1.10 Review Questions (This section has no review questions.)

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION  Application Case 1.1: Making Elevators Go Faster!

1. Why this is an example relevant to decision making?

This is an example of how the symptoms may not directly reveal the problem (perceived versus actual wait time being the issue).

2. Relate this situation to the intelligence phase of decision making.

This situation demonstrates how the intelligence phase of decision-making is important because detailed problem identification is necessary in order to create a satisfactory solution.

Application Case 1.2: SNAP DSS Helps OneNet Make Telecommunications Rate Decision (No questions in this case) Application Case 1.3: Silvaris Increases Business with Visual Analysis and RealTime Reporting Capabilities 1. What was the challenge faced by Silvaris?

Material prices changed rapidly and it was necessary to receive a real-time view of data without moving that data to a separate reporting format.

2. How did Silvaris solve its problem using data visualization with Tableau?

Tableau allow the company to easily connect and visualize live data and create dashboards for reporting purposes.

Application Case 1.4: Siemens Reduces Cost with the Use of Data Visualization 1. What challenges were faced by Siemens visual analytics group?

The group needed to provide a wide range of reports for different organizational needs while maintaining consistency and self-service ability.

2. How did the data visualization tool Dundas BI help Siemens in reducing cost?

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The system allowed them to create highly interactive dashboards that enabled early detection of issues.

Application Case 1.5: Analyzing Athletic Injuries 1. What types of analytics are applied in the injury analysis?

In this example both reporting and predictive analysis were included.

2. How do visualizations aid in understanding the data and delivering insights into the data?

These visualizations made understanding and depicting the information easier by displaying healing time based on position, severity of injury or injuries healing time treatment offered in the associated healing time etc.

3. What is a classification problem?

An issue that occurs in this case when the type of healing category is incorrectly identified, leading to an incorrect prediction of healing time.

4. What can be derived by performing sequence analysis?

Student responses may vary, but in this example it may be possible to predict how one injury may result in other injuries later.

Application Case 1.6: A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Date 1. Why would reallocation of inventory from one customer to another be a major issue for discussion?

This action may require a discount to the first customer or may result in the delay that may jeopardize the customer relationship.

2. How could a DSS help make these decisions?

A DSS system would provide greater visibility into actual inventories, expected inventories and potential customer implications of reallocation of inventory.

Application Case 1.7: A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Date 1. What is the purpose of knowing how much ground is covered by green foliage on a farm? In a forest?

In a farm setting, this may indicate the level of plant growth. In a forest setting, this may provide details on how the forest is evolving.

2. Why would image analysis of foliage through an app be better than a visual check?

It will provide a more consistent quantitative estimate than individual qualitative perceptions of growth.

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3. Explore research papers to understand the underlying algorithmic logic of image analysis. What did you learn?

Student research and responses will vary. Results may indicate that there are different methods of analysis and that this is a rapidly changing field.

4. What other applications of image analysis can you think of?

Student responses will vary.

Application Case 1.8: AI Increases Passengers’ Comfort and Security in Airports and Borders 1. List the benefits of AI devices to travelers.

Benefits will include faster processes such as recognition, more accurate processes and providing additional services.

2. List the benefits to governments and airline companies.

Benefits will include more accurate, faster and more cost efficient services being provided.

3. Relate this case to machine vision and other AI tools that deal with people’s biometrics This case provides an example of how machine vision and other AI tools can be used as a part of biometric recognition systems that more quickly and accurately identify individuals as they enter an airport.

Application Case 1.9: Robots Took the Job of Camel-Racing Jockeys for Societal Benefits 1. It is said that the robots eradicated the child slavery. Explain.

This is because robots have replaced children who in the past may have been kidnapped to act as jockeys.

2. Why do the owners need to drive by their camels while they are racing?

This is necessary for the camels to react and run. Additionally owners can vary their interaction with the camel based on how the camel is performing in comparison to the others in the race.

3. Why not duplicate the technology for horse racing?

Student opinions and responses will vary, but may focus on the lack of child slavery in Western horseracing.

4. Summarize ethical aspects of this case (Read Boddington, 2017). Do this exercise after you have read about ethics in Chapter 14.

Student responses will vary.

Application Case 1.10: Amazon Go Is Open for Business

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1. Watch the video. What did you like in it, and what did you dislike?

Student preferences will vary.

2. Compare the process described here to a selfcheck available today in many supermarkets and “big box” stores (Home Depot, etc.).

The major difference is that products are scanned as they are added to a bag, as opposed to using a checkout kiosk.

3. The store was opened in downtown Seattle. Why was the downtown location selected?

This location was selected because of the proximity of a large number of potential customers.

4. What are the benefits to customers? To Amazon?

Customers benefit from the ability to quickly purchase items without a shipping time.

Amazon is able to capture additional sales that may not have been available before due to immediate needs.

5. Will customers be ready to trade privacy for convenience? Discuss.

Student responses will vary, but may focus on the lack of privacy in existing webbased sales.

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION   1.

Survey the literature from the past six months to find one application each for DSS, BI, and analytics. Summarize the applications on one page, and submit it with the exact sources.

Student responses and research will vary.

2.

Your company is considering opening a branch in China. List typical activities in each phase of the decision (intelligence, design, choice, and implementation) regarding whether to open a branch.

While student responses may vary, typical answers may include: • Intelligence - data collection on customers and markets, identification of overall objective, statements of problems to be solved prior to opening the branch • Design - setting criteria for the decisions to be made, creating a decision model, identification of alternatives and outcomes • Choice - sensitivity analysis of choices, selection of solution to the problems planning for implementation • Implementation - opening the new branch in China

3.

You are about to buy a car. Using Simon’s (1977) four phase model, describe your activities at each step in making the decision.

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While student responses may vary, typical answers may include: • Intelligence - understanding needs for a car, collection of information on different models, definition of the problem • Design - setting selection criteria for a car, generating a decision model based on criteria • Choice - using the model to make a selection • Implementation - purchasing the car

4.

Explain, through an example, the support given to decision makers by computers in each phase of the decision process.

While student responses may vary, typical answers may include: • Intelligence - collection and formatting of data • Design - identification of potential criteria and calculations required for a model • Choice - calculation of the model and sensitivity analysis

5.

Comment on Simon’s (1977) philosophy that managerial decision making is synonymous with the whole process of management. Does this make sense?

Explain. Use a real-world example in your explanation.

Student responses and opinions will vary. Students may note that much of management is the understanding of challenges and the creation of solutions to those challenges. Some students may note that managing others may not be approached in this fashion, although it may be. Student examples will vary based on their own types of experience in or with management roles.

6.

Review the major characteristics and capabilities of DSS. How does each of them relate to the major components of DSS? A DSS includes a variety of characteristics with associated capabilities. Each of these capabilities may be housed in one or more DSS system components. The arrangement of this architecture will vary based on system. The characteristics of the DSS are listed below: • Provides support for semistructured or unstructured problems • Supports managers at all levels • Supports individuals and groups • Supports interdependent or sequential decisions • Supports intelligence, design, choice, and implementation • Support variety of decision processes and styles • Is adaptable and flexible • Provides interactivity, ease of use • Improves effectiveness and efficiency • Provides complete human control of the process • Provides ease of development by end users 18 .

• Provides models and analysis • Provides data access • Can be standalone, integrated, and Web-based tool 7.

List some internal data and external data that could be found in a DSS for a university’s admissions office.

Student responses will vary, but may include some of the following examples: • internal data - application information, results of application essays • external data - high school GPA, results from standardized tests

8.

Distinguish BI from DSS. A DSS is typically built to support the solution of a certain problem or to evaluate an opportunity. This is a key difference between DSS and BI applications. In a very strict sense, business intelligence (BI) systems monitor situations and identify problems and/or opportunities using analytic methods. Business intelligence (BI) is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. It is, like DSS, a content-free expression, so it means different things to different people.

9.

Compare and contrast predictive analytics with prescriptive and descriptive analytics. Use examples.

Predictive analytics aims to determine what will likely happen in the future, whereas descriptive analytics describe what has happened in the past. Prescriptive analytics seeks to recognize what is currently going on as well as creating forecasts.

Student examples will vary, but may include: • predictive analytics - using existing data from a DW to create a forecast of future events • descriptive analytics - using existing data from DW to describe what is happened in the past • prescriptive analytics - using live or current data to understand current operations and forecast future results to aid in decision-making

10.

Discuss the major issues in implementing BI. Student responses will vary, but may focus on several issues that have occurred in implementing BI. These issues may include: • availability of data • ability to format and use data • ability to use data from multiple sources • ability to determine root problems • time required for analysis 19 .

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ability to quickly create ongoing analyses

ANSWERS TO END OF CHAPTER EXCERCISES   Teradata University Network and Other Hands-On Exercises 1. Go to the TUN site teradatauniversitynetwork.com. Using the site password your instructor provides, register for the site if you have not already previously registered.

Log on and learn the content of the site. You will receive assignments related to this site. Prepare a list of 20 items on the site that you think could be beneficial to you.

Student reports will vary based on interest.

2.

Go to. Explore the Sports Analytics page, and summarize at least two applications of analytics in any sport of your choice.

Student reports will vary based on selection of applications.

3.

Go to. The TUN site, and select “Cases, Projects, and Assignments.” Then select the case study “Harrah’s High Payoff from Customer Information.” Answer the following questions about this case: a. What information does the data mining generate? b. How is this information helpful to management in decision making? (Be specific.) c. List the types of data that are mined. d. Is this a DSS or BI application? Why? Student reports will vary.

4.

Go to teradatauniversitynetwork.com and find the paper titled “Data Warehousing Supports Corporate Strategy at First American Corporation” (by Watson, Wixom, and Goodhue). Read the paper, and answer the following questions: a. What were the drivers for the DW/BI project in the company? b. What strategic advantages were realized? c. What operational and tactical advantages were achieved? d. What were the critical success factors for the implementation? e. What data analysis techniques are employed in the project? Comment on some initiatives that resulted from data analysis. f. What are the different prediction problems answered by the models? g. List some of the actionable decisions taken that were based on the prediction results. h. Identify two applications of Big Data analytics that are not listed in the article.

Student evaluation of the paper will vary.

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Go to http://analytics-magazine.org/issues/digitaleditions and find the January/February 2012 edition titled “Special Issue: The Future of Healthcare.”

Read the article “Predictive Analytics—Saving Lives and Lowering Medical Bills.”

Answer the following questions: a. What problem is being addressed by applying predictive analytics? b. What is the FICO Medication Adherence Score? c. How is a prediction model trained to predict the FICO Medication Adherence Score HoH? Did the prediction model classify the FICO Medication Adherence Score? d. Zoom in on Figure 4, and explain what technique is applied to the generated results. e. List some of the actionable decisions that were based on the prediction results.

5.

Student analysis of the report will vary.

6. Go to http://analytics-magazine.org/issues/digitaleditions, and find the January/February 2013 edition titled “Work Social.” Read the article “Big Data, Analytics and Elections,” and answer the following questions: a. What kinds of Big Data were analyzed in the article’s Coo? Comment on some of the sources of Big Data. b. Explain the term integrated system. What is the other technical term that suits an integrated system? c. What data analysis techniques are employed in the project? Comment on some initiatives that resulted from data analysis. d. What are the different prediction problems answered by the models? e. List some of the actionable decisions taken that were based on the prediction results. f. Identify two applications of Big Data analytics that are not listed in the article.

Student analysis of the report will vary.

6.

Search the Internet for material regarding the work of managers and the role analytics plays in it. What kinds of references to consulting firms, academic departments, and programs do you find? What major areas are represented? Select five sites that cover one area, and report your findings.

Student searches and reports will vary

7.

Explore the public areas of dssresources.com. Prepare a list of its major available resources. You might want to refer to this site as you work through the book.

Student list will vary based on the time the search is conducted.

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8.

9.

Go to microstrategy.com. Find information on the five styles of BI. Prepare a summary table for each style.

Student summaries will vary.

Go to oracle.com, and click the Hyperion link under Applications. Determine what the company’s major products are. Relate these to the support technologies cited in this chapter.

Student reports will vary based on the time the analysis is conducted.

10.

Go to the TUN questions site. Look for BSI videos. Review the video of “Case of Retail Tweeters.” Prepare a one-page summary of the problem, proposed solution, and the reported results. You can also find associated slides on slideshare.net.

Student papers will vary.

11.

Review the Analytics Ecosystem section. Identify at least two additional companies in at least five of the industry clusters noted in the discussion.

Student selection of companies will vary.

12.

The discussion for the analytics ecosystem also included several typical job titles for graduates of analytics and data science programs. Research Web sites such as datasciencecentral.com and tdwi.org to locate at least three similar job titles that you may find interesting for your career.

Student research and career interests will vary.

13.

Go to Brainspace at MIT lab brainspace.com. View the video about “Augmented Human Intelligence.” Find the activities that deal with the enabling of meaningful combination of people and machines. Write a report.

Student reports will vary.

14.

Find information about IBM Watson’s activities in the healthcare field. Write a report.

Student reports will vary based on the date the research is conducted.

15.

Examine Daniel Power’s DSS Resources site at dssresources.com . Take the Decision Support Systems Web Tour (dssresources.com/tour/index.html).

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Explore other areas of the Web site. List at least three recent resources related to analytics. What topics do these cover?

Student perceptions of the resources will vary.

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Chapter 2: Artificial Intelligence Concepts, Drivers, Major Technologies, and Business Applications Learning Objectives for Chapter 2 1. Understand the concepts of artificial intelligence (AI) 2. Become familiar with the drivers, capabilities, and benefits of AI 3. Describe human and machine intelligence 4. Describe the major AI technologies and some derivatives 5. Discuss the manner in which AI supports decision making 6. Describe AI applications in accounting 7. Describe AI applications in banking and financial services 8. Describe AI in human resource management 9. Describe AI in marketing 10. Describe AI in production-operation management

CHAPTER OVERVIEW Artificial intelligence (AI), which was a curiosity for generations, is rapidly developing into a major applied technology with many applications in a variety of fields. OpenAI’s (an AI research institution described in Chapter 14) mission states that AI will be the most significant technology ever created by humans. AI appears in several shapes and has several definitions. In a crude way, it can be said that AI’s aim is to make machines exhibit intelligence as close as possible to what people exhibit, hopefully for the benefit of humans. The latest developments in computing technologies drive AI to new levels and achievements. For example, IDC Spending Guide (March 22, 2018) forecasted that worldwide spending on AI will reach $19.1 billion in 2018. It also predicted annual double-digit spending growth for the near future. According to Sharma (2017), China expects to be the world leader in AI, with a spending of $60 billion in 2025. For the business value of AI, see Greig (2018). In this chapter, we provide the essentials of AI, its major technologies, its support for decision making, and a sample of its applications in the major business functional areas.

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CHAPTER OUTLINE 2.1 Opening Vignette: INRIX Solves Transportation Problems 2.2 Introduction to Artificial Intelligence 2.3 Human and Computer Intelligence 2.4 Major AI Technologies and Some Derivatives 2.5 AI Support for Decision Making 2.6 AI Applications in Accounting 2.7 AI Applications in Financial Services 2.8 AI in Human Resource Management (HRM) 2.9 AI in Marketing, Advertising, and CRM 2.10 AI Applications in Production-Operation Management (POM)

ANSWERS FOR END OF SECTION REVIEW QUESTIONS Section 2.1 Opening Vignette Review Questions 1. Explain why traffic may be down while congestion is up (see the London case at inrix.com/uk-highways-agency/).

Congestion may be caused by other reasons such as accidents and weather.

2. How does this case relate to decision support?

Information is provided to various types of decision makers, some in real time. The system also includes some automated decision making.

3. Identify the AI elements in this system.

Data is collected, some automatically. AI algorithms process the data to make predictions and suggest routes. The system makes inferences based on past drivers’ behavior.

4. Identify developments related to AI by viewing the company’s press releases from the most recent four months at inrix.com/press-releases. Write a report.

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Open-ended answers.

5. According to INRIX, the new mobile traffic app is a threat to Waze. Explain why.

It provides similar recommendations but with more accuracy (more diversified data).

It also provides recommendations for future dates. Waze does not.

6. Go sitezeus.com/data/inrix and describe the relationship between INRIX and Zeus.

View the 2:07 min. video at sitezeus.com/data/inrix/. Why is the system in the video called a “decision helper”?

The capabilities of INRIX and Zeus are compatible, so synergy is created. Note that both are improved with time.

Section 2.2 Review Questions 1. Define AI. Machines that have human-like thought processes. Ability to immitate human behavior.

2. What are the major aims and goals of AI? Study of human thought processes and understand what intelligence is so as to transfer them to machines. Perceive and properly read environmental changes make machines creative.

3. List some characteristics of AI. Can facilitate human work, increase productivity, do not get tired, can work in risky environments. Machines that attempt to exhibit intelligent behavior.

4. List some AI drivers.

Cost savings, high speed, competition, capable technologies

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5. List some benefits of AI applications.

Consistent quality, non-stop production, ever increasing funcionalities, ability to learn from experience.

6. List some AI limitations.

Lack of human touch and feel, ignoring non-tasks surroundings, can cause damage.

7. Describe the artificial brain.

Machine that is desired to be intelligent, creative and self-aware as humans.

8. List the three flavors of AI and describe augmentation.

Assisted, autonomous, and augmented. Augmented refers to combining different levels and types of AI solutions.

Section 2.3 Review Questions 1. What is intelligence?

It is composed of complex concepts such as reasoning, logic, ability to learn and solve problems.

2. What are the major capabilities of human intelligence? Which are superior to that of AI machines?

Make sense of ambiguous information, respond quickly to new situations, prioritize information, and reason. Express emotions and solve problems.

3. How intelligent is AI? AI is not yet as intelligent as humans. But it is getting more and more intelligent and in certain areas is even more successful (e.g., complex games, diagnosis). AI’s goal is in solving structured problems.

4. How can we measure AI’s intelligence?

Use Turing Tests. Compare computer generated answers to those made by humans and to standards.

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5. What is the Turing Test and what are its limitations?

Given same tasks to human and computers with knowing which is which. Try to determine which is which. The test measures only Q&A. It measures only some parts of intelligence.

6. How can one measure the intelligence level of a vacuum cleaner?

You need to set criteria of performance (e.g., ability to recognize objects) and determine the ability of the machine to make appropriate decisions when the cleaner discovers obstacles.

Section 2.4 Review Questions 1. Define intelligent agents and list some of their capabilities.

Autonomous small computer programs for conducting routine tasks. For example, spelling checker, price discovery. They are quick, inexpensive, consistent, and reduce the information overload burden.

2. Prepare a list of applications of intelligent agents.

Approvs small loans, match people to jobs, assist people with computer work, match supply and demand.

3. What is machine learning? How can it be used in business?

Ability to identify pattern by learning from experience. Monitor sense and analyze data in the computing environment. Self adjust to changes by learning from example. The lessons learned are used for diagnosis and predictions in business areas, medicine, etc.

4. Define deep learning.

Ability to learn ‘deeper’ than regular machine learning and thus solve more complex problems.

Uses most powerful learning algorithms. Supports machine vision, robotics and voice understanding.

5. Define robotics and explain its importance for manufacturing and transportation.

Robotics combines several AI technologies (e.g., machine vision, voice recognition) to make autonomous decisions and performing mechanical tasks. Thus, they can speed up many tasks

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ranging from assembly to welding to transporting things. Robots also play a role in self driving vehicles.

6. What is NLP? What are its two major formats?

Natural language processing is the capability of a computer to analyze human language so that the computer can understand its meaning (voice or speech understanding) and able to generate human language (speech generation) after data processing by the computer.

7. Describe machine translation of languages. Why it is important in business?

Once a human language is understood, it can be translated into other languages (e.g., use Google Translate). This enables people to understand messages and websites written in other languages. This can support global trade and communication and collaboration.

8. What are knowledge systems?

Knowledge systems are used for autonomous decisions and in providing answers to queries (e.g., Alexa). They provide advice based on stored knowledge.

9. What is cognitive computing?

In order to study the human thought process (an AI goal) scientist uses the knowledge about the human brain to create, for example, self-learning machines. In addition, such knowledge is used for teaching machines to reason.

10. What is augmented reality?

Real time integration of digital information and the user’s environment (e.g., vision voice).

Such integration enables to catch information from the environment (e.g. photos) and then learn about related characteristics, as well as process it in other ways.

Section 2.5 Review Questions 1. Distinguish between fully automated and supported decision making.

Fully automated decisions do not require human colaboration, the computer does it all. In decision support, the computer provides help in some steps of the decision making process (e.g., in generative alternatives, predicting consequences).

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2. List the benefits of AI for decision support.

Enable quicker decisions, predict potential results of alternatives, consolidate relevant data, enable collaboration of group decision makers.

3. What factors influence the use of AI for decision support?

Type of decision, cost, urgency of getting a solution, possibility of matching of AI tool to type of problem.

4. Relate AI to the steps in the classical decision-making process.

1) AI is used in diagnosing problems and in comparing performance to standards.

2) AI assists in generating alternatives. AI predicts consequences of alternatives.

3) Solutions are compared, and the best one is selected.

4) Finally, AI can assist in implementation.

5. What are the necessary conditions for AI to be able to automate decision making?

Structured situations, possibility of significant cost and or time saving, chance of acceptance of the AI solution, fairly routine situations, lack of human experts on site, and strong management support.

6. Describe Schrage’s four models.

1) Autonomous advisor provides suggestions on best courses of action, and strategies which must be approved by humans.

2) Autonomous outsource makes outsourcing decisions. In this case, all data must be clear and include decision rules (e.g., If-Then must be provided to the machines).

3) People-machine collaboration requires two partners. The machine makes the entire decisions. However, humans need to deal with the constraints. Training of people for the collaboration is needed.

4) Complete machine autonomy. Here, the entire processes are fully automated.

Section 2.6 Review Questions 1. What are the major reasons for using AI in accounting?

Increase productivity and speed of routine activities. Reduce elapsed time and increasing consistency. Total cost reduction. Provide competitive advantage.

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2. List some applications big accounting firms use.

Improving auditing, tax calculation, fraud detection, verifications, claims verifications, compliance verification, projects’ evaluations, predictions, and quality assessment.

3. Why do big accounting firms lead the use of applied AI? To attract more business, to increase their productivity and to gain a competitive edge.

Also, they have large R&D budgets.

4. What are some of the advantages of using AI cited by the ICAEW report?

Solve difficult accounting problems, provide inexpensive and better data support for decision making, generating insights from analysis, free time of accountants, detect fraud, task verifications, checking accuracy of contracts.

5. How may the job of the accountant be impacted by AI? The accountant will have more time to innovate and perform complex tasks. Some accountants will lose their jobs (if they do routine, repetitive tasks).

Section 2.7 Review Questions 1. What are the new ways that banks interact with customers by using AI? Interaction via chatbots (e.g., offer real time online conversation). Make real time offers online. Banks offer machine advisory services. Facial recognition in branches, so bankers know who the customers are when they see them (they do not have to ask).

2. It is said that financial services are more personalized with AI support. Explain.

Computer vision can recognize the customer in the physical bank. No need to ask. There may be a better match when replying to customers’ queries.

3. What back-office activities in banks are facilitated by AI? Processing large amounts of data (e.g., claims), processing payments, and doing the bookeeping.

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4. How can AI contribute to security and safety?

By predicting security breaches and discovering fraud cases quickly.

5. Wha are the role of chatbots and virtual assistants in financial services?

Chatbots can provide assistance to customers (e.g., answer queries, direct where to go next). Personal virtual assistants can suggest investment activities.

6. How can IBM Watson help banking services?

Watson can analyze big data and provide suggestions for strategy and for problem solving. Also, it can facilitate compliance.

7. Relate Salesforce Einstein to CRM in financial services.

Customer relativity is critical in dealing with claims. Salesforce Einstein is discussed in Section 2.6.

8. How can AI help in processing insurance claims?

AI can expedite claims processing. It also can predict accident-prone drivers. Computer vision can facilitate the reporting of accident damages. Also, accuracy increased. Also, accidents can be simulated and analyzed.

Section 2.8 Review Questions 1. List the activities in recruiting and explain the support provided by AI to each.

Finding candidates by evaluating resumes is quickly done. Also, assigning applications to positions and conducting testing. Screen resumes posted on the Web. Create model resumes that can be compared to resumes of applicants. Chatbots help with information delivery, save time for recruiters.

2. What are the benefits rewarded to recruiters by AI? Easier to find talents and do so faster. Better market for jobs and applicants, identify the best employees internally (in-house).

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3. What are the benefits to job seekers?

Easier to be discovered. A best match of applicants to positions. Shorter wait time for appointment decisions.

4. How does AI facilitate training?

One way is to use chatbots as tutors. Also, chatbots can be used for personalized paced learning.

5. How is performance evaluation of employees improved by AI? By breaking tasks into small portions, and using AI, it is more accurate and faster to evaluate perfomance and treat areas that need improvements.

6. How can companies increase retention and reduce attrition with AI? AI can discover what makes employees happier. Also, AI can be used to figure out why employees are not happy. AI can predict tendencies to leave and find remedies.

7. Describe the role of chatbots in supporting HRM. Provide information to new and existent employees. Help in recruiting and training.

Some day it may be used to comfort sad employees.

Section 2.9 Review Questions 1. List 5 of the 15 applications of Davis (2016). Comment on each.

Product recommendation - using recommenders (Chapter 12) is popular Fraud detection - done extensively by credit card issuers Producer pricing – AI helps in checking and changing prices based on supply and demand and on competition Speech recognition – helps to provide customer service and sell in natural languages Image recognition – used in market research and in defect detection 2. Which of the 15 applications relate to sales?

Product recommendation

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Smart sales engine Language translation Sales forecast Chatbot advisors 3. Which of the 15 applications relate to advertising?

Social semantics for learning about customers’ needs. Target one-to-one ads. Customer segmentation. Content generation.

4. Which of the 15 applications relate to customer service and CRM? Product recommendation Smart search Social semantics Website design Predictive customer services (the effectiveness of) 5. For what are the prediction capabilities of AI used?

Determine pricing and advertisement stragies. Help in new product design. Predict the success of certain ads. Predict consumers’ attitudes towards new products. Predict consumer behavior (e.g., towards ads, prices). Predict sales volume.

6. What is Salesforce’s Einstein?

AI-based personal advisor for customers and vendors. Has powerful analytical and prediction capabilities. Improves customer engagement and interaction.

7. How can AI be used to improve CRM? Predicting the impact of different CRM options. Providing assistance via chatbots.

Enables discussions among customers and with the vendors. Provides voice communication which is preferred by customers.

Section 2.10 Review Questions 1. Describe the role of robots in manufacturing.

Robots are used in assembly lines (e,g., cars), for material handling, can do welding.

They also work in toxic environments and improve the supply chain.

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2. Why use AI in manufacturing?

It saves time and permits work to be done in hazardous environments. Provides competitive edge. Minimizes interruptions, and people-related problems. Can do certain tasks much better than humans (e.g., inspection).

3. Describe the Bollard et al. implementation model.

It is a five step model that begins with business process improvement. Then, certain processes are outsourced. Deploys AI and analytics to support decision making, automates as much as possible. Digitizes the customers’ experiences.

4. What is an intelligent factory?

Highly automated factory where machines make most of the work in an integrated fashion and can make many decisions. Can produce large volumes quickly.

5. How are a company’s internal and external logistics supported by AI technologies?

To begin with, robots can do material handling (Amazon’s internal order fulfillment).

Partners’ activities are better coordinated, and transportation can be better managed and controlled. External transports are controlled by IoT (e.g., at DHL). Machine learning helps optimizing shipments. Finally, logistics may include optimal inventory management and automatic replenishment.

ANSWERS TO APPLICATION CASES Answers to Case 2.2 1. Discuss the benefits of combining machine learning with other AI technologies.

They used 100 variables and defined intelligent performance levels in each. Then, they compared these to the performance of the machine.

2. How can machine learning improve marketing?

It is able to self-clean floors. Not able to deal with unforseen obstacles such as a dog.

3. Discuss the opportunities of improving human resource management.

Deep learning can increase the learning capabilities overtime. For example, dealing with rarely seen obstacles and dealing with multifactor environments.

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4. Discuss the benefits for customer service.

Open-ended answer.

Answers to Case 2.3 1. Why use machine learning for predictions?

One can get more accurate predictions that can be changed quickly; predictions are used extensively in decision making in many areas.

2. Why use machine learning for detections?

Detecting fraud, maintenance problems, health issues, etc. are difficult and must be done quickly. Detecting in real time (e.g., computer security breach, illness) can be very useful.

3. What specific decisions were supported in the five cases? a) Predict which drivers are more likely to be involved in accidents (insurance issue) b) Improving satellite image quickly (for several purposes) c) Detecting illegal overfishing (compliance issue) d) Deteching fraud in using credit cards (finance issue) e) Detecting defects in food processing. (manufacturing issue)

Answers to Case 2.4 1. What are the characteristics of the tasks for which AI is used?

Tasks that require processing of very large amounts of structured data that take a long time to complete. Also, tasks that do report generation which is fairly standard, but tedious. Tasks that require huge amounts of different data (e.g., legal, tax preparation, and auditing tasks).

2. Why do the big accounting firms use different implementation strategies?

They may have different clients, tasks, and strategies. Since work is paid by the clients, the firms try to make the clients happy. Also, they may have different constructs. Finally, all this is new, so the firms experiment with different implementation strategies.

Answers to Case 2.5

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1. What are Einstein’s advantages to US Bank?

The bank needs superb customer service and one-to-one advertisement and customer service. The identificaiton of customers and matching offers of services were provided by Einstein CRM. The machine also helped in matching customers and services.

2. What are its advantages to customers?

Customer receive more and better attention. They wait less in line and they can get quick explanations and answers to queries. Customers feel more satisfied when bankers understand their needs.

3. What are the benefits of voice communication?

It is more natural than typed communication and faster to ask and get a reply.

Answers to Case 2.6 1. What types of decisions are supported?

Screening applicants and their resumes. Creating profiles of desired jobs and matching them with applicants. So recruitment decisions can be made faster and better. AI can also help in performance evaluation and in promotion decisions.

2. Comment on the human–machine collaboration.

Human-machine collaboration, as will be seen in Chapter 13 and 14, can be very beneficial. The HRM employees can use the machines for decision support and for answering questions made by employees.

3. What are the benefits to recruiters? To applicants?

Recruiters can save time and be more consistent. Also, they can do a more accurate and unbiased performance evaluation. Advanced AI can assist in identifying incorrect information provided by applicants. Appropriate applicants can be discovered among the many on the Web. Applicants face an unbiased evaluation and a usually quicker turnaround.

4. Which tasks in the recruiting process can be fully automated?

Screening large numbers of resumes online can be fully automated. Also, providing information by chatbots.

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5. What are the benefits of such automation?

Saving time and money. Also, the accuracy of information provided by chatbots is consistent andt is less subject to legal cases if innacurate information has been provided.

Answers to Case 2.7 1. Identify all AI technologies used in the Food Assistant.

Chatbots, computer vision, personal assistant, machine learning recommender.

2. List the benefits to the customers.

Make customers happy, provide immediate answers while shopping in supermarkets. Get advice about food use.

3. List the benefits to Kraft Foods.

Make customers happy. Can learn about consumers’ behavior and loyalty. Expand mobile marketing, vendors can better assess customers’ reaction to promotions. Finally, vendors can better influence consumers to buy their products via targeted ads and the personalized advice provided.

4. How is advertising done?

Via targeted ads and the personalized advice provided.

5. What role is “behavioral pattern recognition” playing?

AI makes inferences about what specific customers like, and then recommends promotions. One method of AI is collaborative filtering.

6. Compare Kraft’s Food Assistant to Amazon.com and Netflix recommendation systems.

Amazon uses an algorithm to tell shoppers what other shoppers that bought the same item bought in addition. Netflix suggests what videos to watch, based also on what smilar customers watched. Kraft’s Assistant interacts with customers and evaluates their response. Also, Kraft uses voice communication.

ANSWERS TO TECHNOLOGY INSIGHT CASES Questions for Discussion: 2.1 Technology Insight 1. What is the basic premise of augmented intelligence?

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Improve assisted AI by extending human cognitive capabilities.

2. List the major differences between augmented intelligence and assisted AI applications.

Assisted AI works only in narrow, well-defined domains (structured), augmented combine machines, and people intelligence. Dealing with more complex situations.

3. What are some benefits of augmented intelligence?

Generates better predictions and recommendations, works faster, and is more accurate.

4. How does the technology relate to cognitive computing?

Assists in solving complex problems. Extending human cognitive capabilities.

Questions for Discussion: 2.2 Technology Insight 1. Differentiate between the autonomous advisor and the people–machine collaboration models.

The autonomous advisor is based on data-driven management. The algorithm generates strategies and makes recommendations. Actions must be approved by humans. In peoplemachine collaboration, people not only approve the recommendations of the algorithms, but are also involved in implementation.

2. In all four models, there are some degrees of people–machine interaction. Discuss.

While machines can make decisions, humans need to design them, supervise execution, interpret results, and improve them over time. The least involvement of humans is in model #4.

3. Why it is easier to use model #4 for investment decisions than, for example, marketing strategies?

There are less variables in investment decisions and they are usually more structured.

Also, most of the information in investment decisions is quantitative and can decoded.

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4. Why is it important for data scientists to work with top management in autonomous AI machines?

Data scientists provide the analysis whose results managers view for making decisions.

Using autonomous machines requires full understanding by the scientists of the decision making process and also the use of the autonomous machines.

ANSWERS TO QUESTION FOR DISCUSSION (End of Chapter) 1. Discuss the difficulties in measuring the intelligence of machines.

There are several variables that need to be measured against standards which may difficult to establish. With many variables, it is necessary to give each of them a weight, and this may be difficult. Some of these may be impacted by the physical enviroment and the skills of the employees that work with the machines.

2. Discuss the process that generates the power of AI. The power of AI is provided by the method used and the technology and algorithms applied. For example, what knowledge is used and how it is extracted, stored and applied.

Also, in learning-based AI, the process includes the sources of knowledge and the learning mechanisms, algorithms and procedures.

3. Discuss the differences between machine learning and deep learning.

Machine learning is done by examining examples by parsing the data in examples and then learning from the new data and applying them to make decisions such as pattern recognition. The machines can adjust their capabilities to changes in the environment. Deep learning can be viewed as a subset of machine learning. Deep learning tries to mimic the human brain. It uses fresh data to learn, so it can use self-direction to solve difficult problems so it is useful in autonomous vehicles. Its key motto is continous learning.

4. Describe the difference between machine vision and computer vision.

Machine vision is based on what cameras “see.” It then provides images of automated processes (e.g., inspection). It is important in processes of robotics and autonomous vehicles. It is an engineering subfield. Computer vision is a computer subfield that processes digital information from images and videos. It also deals with 3D images.

Analysis of the images is used for decision making.

5. How can a vacuum cleaner be as intelligent as a six-year-old child?

The machine can handle certain situations (e.g., deal with obstacles) as well as the child.

Of course the comparison is related only to limited tasks (such as dealing with obstacles).

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6. Why are NLP and machine vision so prevalent in industry?

The knowledge about both technologies is abundant. There are many applications that are easy to justify (cost benefit). Also, they are easy to implement. Machine and computer vision components are fairly simple. Voice recognition is fairly mature technology which has been in use for decades.

7. Why are chatbots becoming very popular?

Chatbots can look like small people and they use natural language. When they have a large knowledge base (such as Alexa and Google Assistant), they can provide fairly accurate advice at a reasonable cost per usage. Chatbots can be used for both general purposes (like Alexa) or for specialized knowledge in a narrow domain (e.g., guide people in airports).

Finally, people like them.

8. Discuss the advantages and disadvantages of the Turing Test.

It is a logical and simple test. Its results can be easily measured (e.g., in percents, or levels).

It is inexpensive.

However, it is good only to Q&A dialog and it requires a large database as well as an intelligent human expert. It does not cover all aspects of intelligence.

9. Why is augmented reality related to AI? Augmented reality integrates digital information with the users’ environment in real time (e.g., vision and voice). The technology uses scene recognition, machine learning, NLP and even gesture recognition. It is available on some smart phones. It is used extensively in architectural design of furniture and building and in their sales.

10. Discuss the support that AI can provide to decision makers.

AI can support the individual steps in decision making as well as in automating the entire process. Steps such as problem (task) identification, brainstorming of finding alternative solutions, and selecting appropriate action to changes in the environment may be complex.

AI can partially or fully automate these steps. Executing these steps may require expertise or complex data manipulation and analysis.

11. Discuss the benefits of automatic and autonomous decision making.

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The two major benefits are cost reduction and fast execution. Cost reduction comes from either use of less people, or use of lower skilled employees. Also, employees working 24/7 are inexpensive. Finally, the decisions are consistent. For example, self-driving vehicles cause little or almost no accidents.

12. Why is general (strong) AI considered to be “the most significant technology ever created by humans”?

Strong AI can result in highly intelligent technologies that will enable machines to do many tasks that can benefit humans. Ultimately, people will have to work very little, served by robots. Also, strong AI will improve medical research, making people healthier and live longer. Also, more diversed entertainment will be delivered so quality of life will be drastically improved.

13. Why is the cost of labor increasing, whereas the cost of AI is declining?

The cost of labor rises with inflation and in areas of shortage of skilled labor. Workers demand higher wages. Cost of AI declines due to innovations, competition among producers, cheaper designs, and better knowledge.

14. If an artificial brain someday contains as many neurons as the human brain, will it be as smart as a human brain? (students need to do extra research) Probably not. While more neurons can improve several machine activities, it may nor be enough to increase creativity, show emotions and exhibit other human capabilities.

However, in certain areas, machines will be able to be smart or even smarter than humans.

15. Distinguish between dumb robots and intelligent ones.

Dumb robots are trained to execute one or a few tasks (e.g., move materials, weld a point). They cannot handle complex tasks or deal with malfunctions in processes which intelligent robots can do. Intelligent robots can deal with changing environments by stopping work or providing a solution to fix a problem (e.g., watch the Bumblebee movie, 2018).

16. Discuss why applications of natural language processing and computer vision are popular and have many uses.

Refer to question #6. In addition, both technologies have been around for long time.

Machine vision has been extended to computer vision where even more applications exist. Both technologies are easy to explain and usually they support employees by making their job easier. However, recent applications, especially computer vision, may

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replace humans.

ANSWERS TO EXERCISES (End of Chapter) 1. Go to itunes.apple.com/us/app/public-transit-app-moovit/id498477945?mt=8.

Compare Moovit operations to the operation of INRIX. Moovit works for travel by bus or train. It is a geolocation tool that tells you, for example, when to exit a bus. You must have cellular data service for your cell phone. It is similar to Waze and INRIX, but it is not as sophisticated. Yet, it is good for public transportation.

It is a free app. It is also tells you how to get from where you are to desired locations in many big cities by using public transportation.

2. Go to sitezeus.com and view the 2:07 min. video. Explain how the technology works as a decision helper.

The site provides “location intelligence” which is the process of driving meaningful insights from geospatial data relationships in order to solve related problems. It assists in making location-related decisions. You can also create 3D terrain maps of many locations in the world. The SiteZeus technology works with machine learning. The technology provides retailers’ and brands’ capabilities for improving decision making (e.g., predictive power).

3. Go to Investopedia and learn about investors’ tolerance. Then, find out how AI can be used to contain this risk, and write a report.

Known as risk tolerance, it is the risk investors are willing to withstand. Solutions depend on the degree of risk tolerance. One way is to optimize portfolios by combining AI and risk methods. All the Big Accounting firms provide advice and tools.

4. In 2017, McKinsey & Company created a five-part video titled “Ask the AI Experts: What Advice Would You Give to Executives About AI?” View the video and summarize the advice given to the major issues discussed. (Note: This is a large class project.)

This is a class project. Different videos can be allocated to different groups.

5. Watch the McKinsey & Company video (3:06 min.) on today’s drivers of AI at youtube.com/watch?v=yv0IG1D-OdU and identify the major AI drivers. Write a report.

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The power of technology, the ability to collect data and advancement in machine learning and deep learning are major drivers.

6. Go to the Web site of the Association for the Advancement of Artificial Intelligence aaai.org/home.html and describe its content. Compare it to that of ai.sri.com and csail.mit.edu/.

The content of sites keep changing. So the answers will depend on when this exercise is assigned. Instructors could give guidelines.

7. Go to crosschx.com and find information about Olive. Explain how it works, what are its limitations and advantages and which types of decisions it automates and which it only supports.

Olive is an AI assistant for different management tasks in the healthcare industry (e.g., check prior authorization, appointment reminder). Go to oliveai.com/meetolive/. The answer keeps changing with time. It can automate many tasks in front and back office tasks. The name of the company has been changed from Crosshx to Olive.

8. Go to waze.com and moovitapp.com and find their capabilities. Summarize the help they can provide users.

The capabilities are improving with time. Waze is now a global app that helps drivers to navigate while moovit allows directions for public transportation users.

9. Go to sentient.ai. Find its products that facilitate e-commerce. Write a report.

Scientists can build powerful distributed AI software platforms to create solutions for complex problems. It is based on sentient theory of perceiving and responding to changes in the environment (e.g., noise, light). .

10. Go to artificialbrain.org and report the latest progress there.

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The answers depend on when the students will access the site. It is a very comprehensive site and the instructor should provide guidelines of how to organize the answers.

11. Find recent information on research that is aimed to measure artificial intelligence. Write a report.

The answers depend on the timing when the students will access sites. It is a very comprehensive site and the instructor should provide a guideline of how to organize the answers.

12. Go to salesforce.com and find recent developments in AI Einstein. Why it is so popular?

The answers depend on when the students will work on this assignment. The capabilities of the software are improving with time.

13. Find the latest information on IBM Watson’s advising activities. Write a report.

IBM Watson’s capabilites are ever increasing so the answers depend on when the assignment is made. (Check ibm.com/watson). Concentrate on health. Watch YouTube videos.

14. Find information on the use of AI in iPhones. Explore the role of Edge AI. Write a report.

The usage of AI in iPhones is constatnly increasing (e.g., see Simonte, Wired Magazine August 12, 2018). Several dozen applications exist and more are growing (Perez, et.al., Tech Crunch, July 6, 2018). For Edge AI, see edgeaisummit.com.

15. Explore the AI-related products and services of Nuance Inc. (nuance.com). Explore the Dragon voice recognition product.

Nuance’s products and services keep changing. In general, it is a voice technology provider. The products of this company can understand, analyze, anticipate, reason, and resolve. Dragon voice recognition is a leading product. Well known in the medical field (dictating by physicians), it is good for training.

16. Go to Netradyne report at cs_netradyne.com/

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and read about the use of its product for road safety. Write a report.

Netradyne provides ‘our story.’ The company combines computer vision, IoT, and machine learning. Applicants are related to self-driving of commercial vehicles. For how the company’s product improves safety, see News provided by Netradyne in December 2017. Then, the product was called “Driver Assistance System.”

17. Go to salesforce.com and investigate the capabilities of Gecko HRM. Relate it to Salesforce Einstein. Provide examples of two applications.

Gecko HRM capabilities are ever changing. It is a social collaboration site. It replaces spreadsheets, providing intuitive, friendly and modular HRM applications. It can be combined with Einstein (from the same company) to extend applications to include analysis and predictions. Examples of applications are: managing travel orders and costs, performance management, and recruitment and onboarding.

18. Enter “McKinsey.com/quarterly/the-five-fifty”. Scroll to find “Real world AI”. Then click on view edition.What do you see?

There is a list of potential impacts on by AI organized in 19 global sectors. Biggest impact is on retail and banking.

19. Find material on the impact of AI on advertising. Write a report. Go to strategicsourceror.com/2018/03/giant-scale-supply-chains-can-make.html.

Summarize the use of AI. The areas covered are marketing and sales (up to $200 billion; customer service and risk management ($100 billion)); fraud and analysis and use of analytics.

In similar reports and discussion papers from McKinsey Institute, one can find more examples and discussions.

20. Find material on the impact of AI on advertising.

The impact of AI on advertising is a diversified topic covered by dozens of articles (e.g., in Forbes.com, clickz.com, ogilvy.com). the impact is ever changing. Instructors may use this as a class project, dividing the assignment to subtopics.

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21. Go to strategicsourceror.com/2018/03/giant-scale-supply-chains-can-make.html.

Summarize the use of AI. Strategic sourceror (News, March 16, 2018) claims that AI can improve decision making (procurement and sourcing) by expediting the movements along the supply chains. Sourcing opportunities are discovered quickly. Machine learning is a major AI technology for this purpose. Application areas include hotel and manufacturing.

Chapter 3: Nature of Data, Statistical Modeling, and Visualization

Learning Objectives for Chapter 3 1. Understand the nature of data as they relate to business intelligence (BI) and analytics 2. Learn the methods used to make real-world data analytics ready 3. Describe statistical modeling and its relationship to business analytics 4. Learn about descriptive and inferential statistics 5. Define business reporting and understand its historical evolution 6. Understand the importance of data/information visualization 7. Learn different types of visualization techniques 8. Appreciate the value that visual analytics brings to business analytics 9. Know the capabilities and limitations of dashboards CHAPTER OVERVIEW

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In the age of Big Data and business analytics in which we are living, the importance of data is undeniable. Newly coined phrases such as “data are the oil,” “data are the new bacon,” “data are the new currency,” and “data are the king” are further stressing the renewed importance of data. But the type of data we are talking about is obviously not just any data. The “garbage in garbage out—GIGO” concept/principle applies to today’s Big Data phenomenon more so than any data definition that we have had in the past. To live up to their promise, value proposition, and ability to turn into insight, data have to be carefully created/identified, collected, integrated, cleaned, transformed, and properly contextualized for use in accurate and timely decision making.

Data are the main theme of this chapter. Accordingly, the chapter starts with a description of the nature of data: what they are, what different types and forms they can come in, and how they can be preprocessed and made ready for analytics. The first few sections of the chapter are dedicated to a deep yet necessary understanding and processing of data. The next few sections describe the statistical methods used to prepare data as input to produce both descriptive and inferential measures. Following the statistics sections are sections on reporting and visualization. A report is a communication artifact prepared with the specific intention of converting data into information and knowledge and relaying that information in an easily understandable/digestible format. Today, these reports are 1 .

visually oriented, often using colors and graphical icons that collectively look like a dashboard to enhance the information content. Therefore, the latter part of the chapter is dedicated to subsections that present the design, implementation, and best practices regarding information visualization, storytelling, and information dashboards. This chapter has the following sections: CHAPTER OUTLINE 3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing 3.2 Nature of Data 3.3 Simple Taxonomy of Data 3.4 Art and Science of Data Preprocessing 3.5 Statistical Modeling for Business Analytics 3.6 Regression Modeling for Inferential Statistics 3.7 Business Reporting 3.8 Data Visualization 3.9 Different Types of Charts and Graphs 3.10 Emergence of Visual Analytics 3.11 Information Dashboards

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 3.1 Review Questions 1.

What does SiriusXM do? In what type of market does it conduct its business?

Sirius XM is a satellite radio provider that provides services worldwide. It’s content is distributed through serious XM enabled car stereos as well as through the Internet, smart phones and other technologies.

2.

What were its challenges? Comment on both technology and data-related challenges.

The company noted challenges relating to changes in its target market (reduction of disposable income), the need to convert secondary buyers of automobiles into customers and the potential applications of its purchase of Agreo.

3.

What were the proposed solutions?

The company proposed several shifts in its business model to meet these challenges. First was an increase in personalized interactions with customers.

Second was a better understanding of the technology needs of external partners.

Third was the creation of an integrated data solution across the company to better understand customer and partner needs.

2 .

4.

How did the company implement the proposed solutions? Did it face any implementation challenges?

To implement this solution the company brought many existing operations inhouse and selected Teradata as a solution provider. The solution involved more consistent data collection, the use of in-house analytics using this data, and marketing based on the results from the research. While the case does not specifically mention any implementation challenges, this appears to be a significant effort that required several implementation steps to accomplish.

5.

What were the results and benefits? Were they worth the effort/investment?

The results allow the company to see metrics on campaign success in real time in addition to better visibility of all data types across the organization. This allows the company to better understand the effectiveness of different marketing campaigns and operational activities. These results satisfy the initial requirements and therefore are probably viewed as worth the effort and investment.

6.

Can you think of other companies facing similar challenges that can potentially benefit from similar data-driven marketing solutions?

Student responses will vary, but any customer facing business that uses data as a primary means of targeting marketing campaigns may benefit.

Section 3.2 Review Questions 1.

How do you describe the importance of data in analytics? Can we think of analytics without data?

Data is an absolute requirement for analytics, it is the “raw material” that analytics uses. There is no analytics without data.

2.

Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?

The inputs to modern data analytics can include data of many types including business process data, social media data and IoT data. Outputs are similarly diverse and can take the form of reports, dashboards, and other knowledge applications.

3.

Where do the data for business analytics come from?

Data comes from a variety of sources that can include business process information, data from the Internet or social media, information from the Internet of Things.

3 .

4.

In your opinion, what are the top three data-related challenges for better analytics?

Student opinions will vary but may include; data availability, data cleanliness, integration of different data types, effective data storage, effective data analytics, and usable reports or visualizations.

5.

What are the most common metrics that make for analytics-ready data?

There are several metrics that define analytics ready data, and they include: • Data source reliability.

• Data content accuracy.

• Data accessibility • Data security and data privacy.

• Data richness.

• Data consistency • Data currency/data timeliness.

• Data granularity • Data validity.

• Data relevancy

Section 3.3 Review Questions 1. What are data? How do data differ from information and knowledge?

Data (datum in singular form) refers to a collection of facts usually obtained as the result of experiments, observations, transactions, or experiences. Data can consist of numbers, letters, words, images, voice recordings, and so on, as measurements of a set of variables (characteristics of the subject or event that we are interested in studying). Data are often viewed as the lowest level of abstraction from which information and then knowledge is derived. Information and knowledge is derived from data, but only after review and analysis.

2. What are the main categories of data? What types of data can we use for BI and analytics?

3.

Data can be classified as structured or unstructured. Structured data can be used in data mining algorithms and can be classified as categorical or ordinal data.

Unstructured data is composed of a combination of different types of content including text, images, voice and web content.

3. not?

Can we use the same data representation for all analytics models? Why, or why

4 .

Different types of data should be represented in different ways for different analytics models. Data should be represented in a format that allows conclusions to be drawn from it and so white data sources can be compared to other like data sources.

4.

What is a 1-of-N data representation? Why and where is it used in analytics?

Sometimes nonnumeric data is converted into a numeric form to allow for ease of analysis. This is typically done when nominal or ordinal variables have consistent possible values and a numeric analysis would provide a better understanding of the data.

Section 3.4 Review Questions 1.

Why are the original/raw data not readily usable by analytics tasks?

Raw data is often dirty, misaligned, overly complex, and inaccurate.

2.

What are the main data preprocessing steps?

The main data preprocessing steps are: • Data consolidation • Data cleaning • Data transformation • Data reduction

3.

What does it mean to clean/scrub the data? What activities are performed in this phase?

This is the second step of data preprocessing, often referred to as data cleaning. In this step missing values are addressed, noise in the data is identified and reduced and erroneous data is eliminated.

4.

Why do we need data transformation? What are the commonly used data transformation tasks?

Data transformation is necessary so that data can be effectively analyzed. It may also be necessary when data from multiple sources are used. Common steps in this process include normalizing the data, aggregating data and constructing new attributes as needed.

5.

Data reduction can be applied to rows (sampling) and/or columns (variable selection). Which is more challenging?

5 .

Data reduction that eliminates columns is the least challenging because it reduces the number of variables while not requiring a secondary analysis of samples.

Section 3.5 Review Questions 1.

What is the relationship between statistics and business analytics?

Statistics (statistical methods and underlying techniques) is usually considered as part of descriptive analytics. Some of the statistical methods can also be considered as part of predictive analytics, such as discriminant analysis, multiple regression, logistic regression, and k-means clustering.

2.

What are the main differences between descriptive and inferential statistics?

The main difference between descriptive and inferential statistics is the data used in these methods—whereas descriptive statistics is all about describing the sample data on hand, inferential statistics is about drawing inferences or conclusions about the characteristics of the population.

3.

List and briefly define the central tendency measures of descriptive statistics.

• • •

4.

The arithmetic mean (or simply mean or average) is the sum of all the values/observations divided by the number of observations in the data set.

The median is the measure of center value in a given data set The mode is the observation that occurs most frequently.

List and briefly define the dispersion measures of descriptive statistics.

• • •

The range is the difference between the largest and the smallest values in a given data set (i.e., variables).

Variance is a method used to calculate the deviation of all data points in a given data set from the mean.

The standard deviation is calculated by simply taking the square root of the variations

5.

What is a box-and-whiskers plot? What types of statistical information does it represent?

The box-and-whiskers plot (or simply a box plot) is a graphical illustration of several descriptive statistics about a given data set. The box plot shows the centrality (median and sometimes also mean) as well as the dispersion (the density of the data within the middle half—drawn as a box between the first and third quartiles), the minimum and maximum ranges (shown as extended lines from the box, looking like whiskers, that are calculated as 1.5 times the upper or 6 .

lower end of the quartile box), and the outliers that are larger than the limits of the whiskers. A box plot also shows whether the data are symmetrically distributed with respect to the mean or sway one way or another.

6. What are the two most commonly used shape characteristics to describe a data distribution?

• •

Skewness is a measure of asymmetry (sway) in a distribution of the data that portrays a unimodal structure—only one peak exists in the distribution of the data.

Kurtosis focuses more on characterizing the peak/tall/skinny nature of the distribution. Specifically, kurtosis measures the degree to which a distribution is more or less peaked than a normal distribution.

Section 3.6 Review Questions 1.

What is regression, and what statistical purpose does it serve?

Regression is a statistical technique to model the dependence of a variable (response or output variable) on one (or more) explanatory (input) variables.

2.

What are the commonalities and differences between regression and correlation?

Both methods attempt to describe the association between two (or more) variables, these two terms are often confused by professionals and even by scientists. Correlation makes no a priori assumption of whether one variable is dependent on the other(s) and is not concerned with the relationship between variables; instead it gives an estimate on the degree of association between the variables. On the other hand, regression attempts to describe the dependence of a response variable on one (or more) explanatory variables where it implicitly assumes that there is a one-way causal effect from the explanatory variable(s) to the response variable, regardless of whether the path of effect is direct or indirect.

3.

What is OLS? How does OLS determine the linear regression line?

The Ordinary Least Squares (OLS) method aims to minimize the sum of squared residuals (squared vertical distances between the observation and the regression point) and leads to a mathematical expression for the estimated value of the regression line (which are known as b parameters).

4.

List and describe the main steps to follow in developing a linear repression model.

A linear regression model begins with tabulated data and then goes through three steps prior to deployment. The first step is data assessment where scatterplots and 7 .

correlations are often viewed to better understand the data. The second step is model sitting were data may be transformed and some parameters may be estimated. The third step is the model assessment where assumptions are tested and model fit is evaluated.

5.

What are the most commonly pronounced assumptions for linear regression?

There are five commonly pronounced assumptions for linear regression. They are: • Linearity • Independence • Normality • Constant variance • Multicollinearity

6.

What is logistics regression? How does it differ from linear regression?

Logistic regression aims to regress to a mathematical function that explains the relationship between the response variable and the explanatory variables using a sample of past observations (training data). Logistic regression differs from linear regression with one major point: its output (response variable) is a class as opposed to a numerical variable.

7.

What is time series? What are the main forecasting techniques for time-series data?

A time series is a sequence of data points of the variable of interest, measured and represented at successive points in time spaced at uniform time intervals. The main forecasting technique aims to evaluate and identify any trends over the timeseries to forecast future events.

Section 3.7 Review Questions 1.

What is a report? What are reports used for?

A report is any communication artifact prepared with the specific intention of conveying information in a digestible form to whoever needs it whenever and wherever. It is typically a document that contains information (usually driven from data) organized in a narrative, graphic, and/or tabular form, prepared periodically (recurring) or on an as-needed (ad hoc) basis, referring to specific time periods, events, occurrences, or subjects.

2.

What is a business report? What are the main characteristics of a good business report?

8 .

Business reports can fulfill many different (but often related) functions. Here are a few of the most prevailing ones: • To ensure that all departments are functioning properly.

• To provide information.

• To provide the results of an analysis.

• To persuade others to act.

• To create an organizational memory 3.

Describe the cyclic process of management, and comment on the role of business reports.

Management is a cyclic function where decision-makers evaluate information, make decisions, trigger actions and then evaluate the resulting information.

Business reports are one format of this information that is evaluated and can be created from operational data.

4.

List and describe the three major categories of business reports.

• • •

5.

Metric management reports - evaluate business performance based on predefined outcome-oriented metrics.

Dashboard type reports - present performance indicators on a one-page dashboard typically using graphs, charts and colors.

Balanced scorecard type reports - present an integrated view of organizational success across multiple stakeholders.

What are the main components of a business reporting system?

The main components of the business reporting system include the ability to capture relevant data, the ability to store that data and the ability to generate relevant reports from that data store.

Section 3.8 Review Questions 1.

What is data visualization? Why is it needed?

Data visualization has been defined as “the use of visual representations to explore, make sense of, and communicate data” (Few, 2007). It is useful because it allows a faster, more simplified method to understand information.

2.

What are the historical roots of data visualization?

Despite the fact that predecessors to data visualization date back to the second century AD, most developments have occurred in the last two and a half centuries, predominantly during the last 30 years (Few, 2007). Although visualization has not been widely recognized as a discipline until fairly recently, today’s most popular visual forms date back a few centuries. Geographical 9 .

exploration, mathematics, and popularized history spurred the creation of early maps, graphs, and timelines as far back as the 1600s, but William Playfair is widely credited as the inventor of the modern chart, having created the first widely distributed line and bar charts in his Commercial and Political Atlas of 1786 and what is generally considered to be the first time-series line chart in his Statistical Breviary published in 1801.

3.

Carefully analyze Charles Joseph Minard’s graphical portrayal of Napoleon’s march. Identify and comment on all the information dimensions captured in this ancient diagram.

Student perceptions and ability to read French will vary, but may focus on the length of march, the size of Army and battlefield temperature.

4.

Who is Edward Tufte? Why do you think we should know about his work?

A researcher who is catalogued historical and modern visualizations of information. His work may provide inspiration for visualizations that can be created.

5.

What do you think is the “next big thing” in data visualization?

Student projections will vary, but may focus on richer visualizations or visualizations that can change in real time.

Section 3.9 Review Questions 1.

Why do you think there are many different types of charts and graphs?

There are a large variety of chart and graph types due to the variety of information that needs to be displayed and the potential reasons for its display.

2.

What are the main differences among line, bar, and pie charts? When should you use one over the others?

Mine charts are typically used for timeseries data, bar charts are useful for nominal or numerical data that can be easily split into categories, and pie charts are typically used to indicate percent allocations from a whole.

3.

Why would you use a geographic map? What other types of charts can be combined with it?

Geographic maps are typically used when visualization of data from a geographic standpoint is useful. These maps are typically combined with other types of charts and graphs to display information in each of the geographic locations.

10 .

4.

Find and explain the role of two types of charts that are not covered in this section.

Student research and responses will vary.

Section 3.10 Review Questions 1.

What are the main reasons for the recent emergence of visual analytics?

They enable the users of business analytics and BI systems to better communicate relationships, add historical context, uncover hidden correlations, and tell persuasive stories that clarify and call to action.

2.

Look at Gartner’s Magic Quadrant for Business Intelligence and Analytics Platforms. What do you see? Discuss and justify your observations.

Student analysis will vary, but the visualization classifies different software application based on their ability to execute as well as their completeness of vision. Based on their scores in these two variables they are classed in one of four quadrants which include challengers, leaders, niche players and visionaries.

3.

What is the difference between information visualization and visual analytics?

Visual analytics is the combination of visualization and predictive analytics

Section 3.11 Review Questions 1.

What is an information dashboard? Why is it so popular?

Dashboards provide visual displays of important information that is consolidated and arranged on a single screen so that the information can be digested at a single glance and easily drilled in and further explored.

2.

What are the graphical widgets commonly used in dashboards? Why? Some examples of widgets can include embedded charts and graphs, traffic lights and gauges. These widgets are typically used because they allow for the easy comparison of data or comparison of data to a prescribed metric.

3.

List and describe the three layers of information portrayed on dashboards.

These layers of information include:

11 .

• • •

Monitoring: Graphical, abstracted data to monitor key performance metrics.

Analysis: Summarized dimensional data to analyze the root cause of problems.

Management: Detailed operational data that identify what actions to take to resolve a problem.

4.

What are the common characteristics of dashboards and other information visuals?

Some common components include; visual components, transparency to users, combination of multiple types of data into a single view, drill downs or drill through’s, dynamic display of up-to-date information, and the minimal amount of required customization.

5.

What are the best practices in dashboard design?

The best practices for dashboard design include: • Benchmark Key Performance Indicators with Industry Standards • Wrap the Dashboard Metrics with Contextual Metadata • Validate the Dashboard Design by a Usability Specialist • Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard • Enrich the Dashboard with Business-User Comments • Present Information in Three Different Levels • Pick the Right Visual Construct Using Dashboard Design Principles • Provide for Guided Analytics

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION

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Application Case 3.1: Verizon Answers the Call for Innovation: The Nation’s Largest Network Provider uses Advanced Analytics to Bring the Future to its Customers 1. What was the challenge Verizon was facing?

The telecommunications market in the US is very competitive, and in order to maintain their competitive advantage, Verizon needed to ensure that they continued to use data and analytics to effectively market to new and existing customers.

12 .

2. What was the data-driven solution proposed for Verizon’s business units?

Verizon used a Teradata solution that allowed them to analyze customer and business unit data to identify new revenue sources, predictive turn in the core mobile business and forecast mobile phone plans.

3. What were the results?

Verizon has been able to continue to leverage its use of data analytics to innovate and disrupt the current marketing channels.

Application Case 3.2: Improving Student Retention with Data-Driven Analytics 1. What is student attrition, and why is it an important problem in higher education?

Student attrition occurs when students drop out of classes or academic programs.

It is important in higher education because it indicates a lack of student success and may also have financial and or regulatory implications.

2. What were the traditional methods to deal with the attrition problem?

Traditional methods focused on reacting to student drops when they occurred.

3. List and discuss the data-related challenges within the context of this case study.

The proposed solution required a significant amount of data to be analyzed to generate predictive information about potential student drops. There were several data related challenges as part of the solution. The first challenge was the variety and sourcing of needed data. The analytic approach needed to be evaluated and assessed to ensure that the methodologies used to predict attrition were correct and that the different variables involved were properly weighted.

4. What was the proposed solution? What were the results?

The proposed solution uses data from a number of different sources as well as several different analytic methodologies to predict students that may be at risk of attrition. Based on the results of the study, given sufficient data with the proper variables data mining methods are capable of predicting freshman student attrition with approximately 80% accuracy.

Application Case 3.3: Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems

13 .

1. What were the challenges the Town of Cary was facing?

Prior to the use of the new system, the town had a difficult time identifying if there was a water loss issue in transmission or at a customer location.

Additionally, the cost of manual meter readings was significant.

2. What was the proposed solution?

The proposed solution used wireless water meters that were able to continually report on water use. These reports, in addition to historical information, could help identify when water use in any particular area indicated a potential leak. The system also allowed for the elimination of manual meter readings.

3. What were the results?

The solution was a success, and met the stated goals of being able to identify potential weeks early and reducing ongoing cost of meter reading.

4. What other problems and data analytics solutions do you foresee for towns like Cary?

Student opinions will vary, but similar systems may be used in other municipal areas such as natural gas delivery and garbage pickup.

Application Case 3.4: Predicting NCAA Bowl Game Outcomes 1. What are the foreseeable challenges in predicting sporting event outcomes (e.g., college bowl games)?

There are a large number of variables that can potentially affect the outcome not only of individual games but team seasons as well. One major challenge is the selection of the variables to be used in creating a predictive analysis of these events.

2. How did the researchers formulate/design the prediction problem (i.e., what were the inputs and output, and what was the representation of a single sample—row of data)?

Specific details are provided in table 3.5 of the case. 36 variables were used. An example variable is when loss percentage which evaluates whether the home team wins or loses a game this is presented as a percentage.

3. How successful were the prediction results? What else can they do to improve the accuracy?

The results were generally fairly accurate, between 75 and 86%. It is possible that the results could be improved by the addition of more variables.

14 .

Application Case 3.5: Flood of Paper Ends at FEMA 1. What is FEMA, and what does it do?

FEMA is the Federal Emergency Management Agency, they coordinate disaster response in the event of a national disaster.

2. What are the main challenges that FEMA faces?

One of the main challenges to be addressed was access to insurance information in areas that had been flooded so that claims could be made quickly and payments sent to victims of the disaster.

3. How did FEMA improve its inefficient reporting practices?

The agency adopted a new analytics system from Bureau that that houses all relevant insurance information and allows for easy data and report retrieval.

Application Case 3.6: Macfarlan Smith Improves Operational Performance Insight with Tableau Online 1. What were the data and reporting related challenges that Macfarlan Smith faced?

The company had a number of data and reporting issues. These included data being dispersed across multiple systems and in multiple formats, data quality being suspect on some of these platforms, and reporting solutions were difficult to customize.

2. What were the solution and the obtained results/ benefits?

The company selected tableau as a solution. This solution allowed them to centralize data storage and easily create reports and perform analyses on the fly.

As a result, the company has significantly better reporting capabilities, reports can be produced in much less time and there is no longer concern about data validity.

Application Case 3.7: Dallas Cowboys Score Big with Tableau and Teknion 1. How did the Dallas Cowboys use information visualization?

The team sought to better understand its merchandising operations, and use data analytics to become more profitable in this area.

2. What were the challenge, the proposed solution, and the obtained results?

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The team selected Microsoft to provide the solution and used a combination of Tableau and Tecknion products. All data is managed centrally, and analysis is easily performed in tableau. The team is now able to monitor all merchandising activities from manufacturer to end customer and better understand the product lifecycle.

Application Case 3.8: Visual Analytics Helps Energy Supplier Make Better Connections 1. Why do you think energy supply companies are among the prime users of information visualization tools?

Energy supply companies may be one of the prime users of information visualization tools because of the detailed and rapidly changing markets in which they operate.

2. How did Electrabel use information visualization for the single version of the truth?

By using a consolidated system, it was possible to remove ambiguity and performance issues. This created better reliability and believability in the information presented.

3. What were their challenges, the proposed solution, and the obtained results?

The company selected a SAS Visual Analytics product that allowed him for centralization of data and reporting while eliminating technical slowdowns and ambiguity in the data itself. The company now has reduced data preparation time, created clear graphic insights for invoicing and better understands workload management.

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION

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1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.

Data is an absolute requirement for analytics, it is the “raw material” that analytics uses. There is no analytics without data.

2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?

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The inputs to modern data analytics can include data of many types including business process data, social media data and IoT data. Outputs are similarly diverse and can take the form of reports, dashboards, and other knowledge applications.

3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?

Data comes from a variety of sources that can include business process information, data from the Internet or social media, information from the Internet of Things.

4. What are the most common metrics that make for analytics-ready data?

There are several metrics that define analytics ready data, and they include: • Data source reliability.

• Data content accuracy.

• Data accessibility • Data security and data privacy.

• Data richness.

• Data consistency • Data currency/data timeliness.

• Data granularity • Data validity.

• Data relevancy

5. What are the main categories of data? What types of data can we use for BI and analytics?

Data can be classified as structured or unstructured. Structured data can be used in data mining algorithms and can be classified as categorical or ordinal data.

Unstructured data is composed of a combination of different types of content including text, images, voice and web content.

6. Can we use the same data representation for all analytics models (i.e., do different analytics models require different data representation schema)? Why, or why not?

Different types of data should be represented in different ways for different analytics models. Data should be represented in a format that allows conclusions to be drawn from it and so white data sources can be compared to other like data sources.

7. Why are the original/raw data not readily usable by analytics tasks?

Raw data is often dirty, misaligned, overly complex, and inaccurate.

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8. What are the main data preprocessing steps? List and explain their importance in analytics.

The main data preprocessing steps are: • Data consolidation - in this step data is assessed, collected, selected, filtered, integrated and unified • Data cleaning - in this step missing values are addressed, noise is identified and reduced, erroneous data is eliminated • Data transformation - in this step data is normalized, aggregated and new attributes may be constructed • Data reduction - in this step attributes or records may be reduced and skewed data is balanced 9. What does it mean to clean/scrub the data? What activities are performed in this phase?

This is the second step of data preprocessing, often referred to as data cleaning. In this step missing values are addressed, noise in the data is identified and reduced and erroneous data is eliminated.

10. Data reduction can be applied to rows (sampling) and/ or columns (variable selection). Which is more challenging? Explain.

Data reduction that eliminates columns is the least challenging because it reduces the number of variables while not requiring a secondary analysis of samples.

11. What is the relationship between statistics and business analytics? (Consider the placement of statistics in a business analytics taxonomy.)

Statistics (statistical methods and underlying techniques) is usually considered as part of descriptive analytics. Some of the statistical methods can also be considered as part of predictive analytics, such as discriminant analysis, multiple regression, logistic regression, and k-means clustering.

12. What are the main differences between descriptive and inferential statistics?

The main difference between descriptive and inferential statistics is the data used in these methods—whereas descriptive statistics is all about describing the sample data on hand, inferential statistics is about drawing inferences or conclusions about the characteristics of the population.

13. What is a box-and-whiskers plot? What types of statistical information does it represent?

The box-and-whiskers plot (or simply a box plot) is a graphical illustration of several descriptive statistics about a given data set. The box plot shows the 18 .

centrality (median and sometimes also mean) as well as the dispersion (the density of the data within the middle half—drawn as a box between the first and third quartiles), the minimum and maximum ranges (shown as extended lines from the box, looking like whiskers, that are calculated as 1.5 times the upper or lower end of the quartile box), and the outliers that are larger than the limits of the whiskers. A box plot also shows whether the data are symmetrically distributed with respect to the mean or sway one way or another.

14. What are the two most commonly used shape characteristics to describe a data distribution?

• •

Skewness is a measure of asymmetry (sway) in a distribution of the data that portrays a unimodal structure—only one peak exists in the distribution of the data.

Kurtosis focuses more on characterizing the peak/tall/skinny nature of the distribution. Specifically, kurtosis measures the degree to which a distribution is more or less peaked than a normal distribution.

15. List and briefly define the central tendency measures of descriptive statistics.

• • •

The arithmetic mean (or simply mean or average) is the sum of all the values/observations divided by the number of observations in the data set.

The median is the measure of center value in a given data set The mode is the observation that occurs most frequently.

16. What are the commonalities and differences between regression and correlation?

Both methods attempt to describe the association between two (or more) variables, these two terms are often confused by professionals and even by scientists. Correlation makes no a priori assumption of whether one variable is dependent on the other(s) and is not concerned with the relationship between variables; instead it gives an estimate on the degree of association between the variables. On the other hand, regression attempts to describe the dependence of a response variable on one (or more) explanatory variables where it implicitly assumes that there is a one-way causal effect from the explanatory variable(s) to the response variable, regardless of whether the path of effect is direct or indirect.

17. List and describe the main steps to follow in developing a linear regression model.

A linear regression model begins with tabulated data and then goes through three steps prior to deployment. The first step is data assessment where scatterplots and correlations are often viewed to better understand the data. The second step is model sitting were data may be transformed and some parameters may be estimated. The third step is the model assessment where assumptions are tested and model fit is evaluated.

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18. What are the most commonly pronounced assumptions for linear regression? What is crucial to the regression models against these assumptions?

There are five commonly pronounced assumptions for linear regression. They are: • Linearity - This assumption states that the relationship between the response variable and the explanatory variables is linear.

• Independence - This assumption states that the errors of the response variable are uncorrelated with each other.

• Normality - This assumption states that the errors of the response variable are normally distributed.

• Constant variance - This assumption, also called homoscedasticity, states that the response variables have the same variance in their error regardless of the values of the explanatory variables.

• Multicollinearity - This assumption states that the explanatory variables are not correlated.

19. What are the commonalities and differences between linear regression and logistic regression?

Logistic regression aims to regress to a mathematical function that explains the relationship between the response variable and the explanatory variables using a sample of past observations (training data). Logistic regression differs from linear regression with one major point: its output (response variable) is a class as opposed to a numerical variable.

20. What is time series? What are the main forecasting techniques for time-series data?

A time series is a sequence of data points of the variable of interest, measured and represented at successive points in time spaced at uniform time intervals. The main forecasting technique aims to evaluate and identify any trends over the timeseries to forecast future events.

21. What is a business report? Why is it needed?

Business reports can fulfill many different (but often related) functions. Here are a few of the most prevailing ones: • To ensure that all departments are functioning properly.

• To provide information.

• To provide the results of an analysis.

• To persuade others to act.

• To create an organizational memory 22. What are the best practices in business reporting? How can we make our reports stand out?

Best practices in the generation of business reports include ensuring that 20 .

data/information/analysis is correct, results are recent, comparisons are made appropriately, information is presented in an intuitive fashion and important issues are highlighted. Business reports can be made to stand out by properly addressing these best practices and ensuring that they contain information that is usable and actionable by decision-makers.

23. Describe the cyclic process of management, and comment on the role of business reports.

Management is a cyclic function where decision-makers evaluate information, make decisions, trigger actions and then evaluate the resulting information.

Business reports are one format of this information that is evaluated and can be created from operational data.

24. List and describe the three major categories of business reports.

• • •

Metric management reports - evaluate business performance based on predefined outcome-oriented metrics.

Dashboard type reports - present performance indicators on a one-page dashboard typically using graphs, charts and colors.

Balanced scorecard type reports - present an integrated view of organizational success across multiple stakeholders.

25. Why has information visualization become a centerpiece in BI and business analytics? Is there a difference between information visualization and visual analytics?

There are a large variety of chart and graph types due to the variety of information that needs to be displayed and the potential reasons for its display. Visual analytics is the combination of visualization and predictive analytics.

26. What are the main types of charts/graphs? Why are there so many of them?

There is great diversity in the types of graphs and charts that are available to display information because there are so many different types of information to potentially display, as well as methods to compare this information to other information or established metrics. The main types of charts and graphs in use include: • line chart • bar chart • pie chart • scatterplot • bubble chart • histogram 21 .

• • • • • • •

Gantt chart PERT chart geographic map bullet graph heat map highlight table Tree map

27. How do you determine the right chart for a job? Explain and defend your reasoning.

There are a large number of potential charts that are available for use in presenting information from data analysis. The correct chart is the one that best displays the information/data you have generated in a manner that is easy to understand and that answers the route question or problem that your research attempts to answer. Charts should display only the information needed to make an informed decision, and should use a format that is consistent with this type of problem or decision-making.

28. What is the difference between information visualization and visual analytics?

Visual analytics is the combination of visualization and predictive analytics.

29. Why should storytelling be a part of your reporting and data visualization?

One of the major hurdles of presenting research and analysis is ensuring that your audience understands both the conclusion and the method that was relied upon to generate that conclusion. By focusing on and treating your report as a story, you can work through a comfortable progression of the data to the analysis to the conclusion and suggestion. This process should be comfortable for your audience to engage with and understand.

30. What is an information dashboard? What does it present?

Dashboards provide visual displays of important information that is consolidated and arranged on a single screen so that the information can be digested at a single glance and easily drilled in and further explored.

31. What are the best practices in designing highly informative dashboards?

The best practices for dashboard design include: • Benchmark Key Performance Indicators with Industry Standards • Wrap the Dashboard Metrics with Contextual Metadata • Validate the Dashboard Design by a Usability Specialist • Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard • Enrich the Dashboard with Business-User Comments • Present Information in Three Different Levels 22 .

• •

Pick the Right Visual Construct Using Dashboard Design Principles Provide for Guided Analytics

32. Do you think information/performance dashboards are here to stay? Or are they about to be outdated? What do you think will be the next big wave in BI and business analytics in terms of data/information visualization?

Student opinions will vary, but may focus on the benefits of dashboards and their ability to quickly provide information on important business metrics. Based on their current utility it can be argued that they are here to stay.

ANSWERS TO END OF CHAPTER EXCERCISES

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Teradata University and Other Hands-on Exercises 1. Download the “Voting Behavior” data and the brief data description from the book’s Web site. This is a data set manually compiled from counties all around the United States. The data are partially processed, that is, some derived variables have been created.

Your task is to thoroughly preprocess the data by identifying the error and anomalies and proposing remedies and solutions. At the end, you should have an analytics-ready version of these data. Once the preprocessing is completed, pull these data into Tableau (or into some other data visualization software tool) to to extract useful visual information from it. To do so, conceptualize relevant questions and hypotheses (come up with at least three of them) and create proper visualizations that address those questions of “tests” of those hypotheses.

Student analysis and reports will vary.

2. Download Tableau (at tableau.com, following academic free software download instructions on the site). Using the Visualization_MFG_Sample data set (available as an Excel file on this book’s Web site), answer the following questions: a. What is the relationship between gross box office revenue and other movierelated parameters given in the data set? b. How does this relationship vary across different years? Prepare a professionallooking written report that is enhanced with screenshots of your graphic findings.

Student experience and reports on use will vary.

3. Go to teradatauniversitynetwork.com. Look for an article that deals with the nature of data, management of data, and/or governance of data as it relates to BI and analytics, and critically analyze the content of the article.

Student selection of article and thus report will vary.

4. Go to UCI data repository (archive.ics.uci.edu/ml/ datasets.html) and identify a large 23 .

data set that contains both numeric and nominal values. Using Microsoft Excel or any other statistical software: a. Calculate and interpret central tendency measures for each and every variable. b. Calculate and interpret the dispersion/spread measures for each and every variable.

Student selection of data and responses based on that data will vary.

5. Go to UCI data repository (archive.ics.uci.edu/ml/ datasets.html) and identify two data sets, one for estimation/regression and one for classification. Using Microsoft Excel or any other statistical software: a. Develop and interpret a linear regression model. b.

Develop and interpret a logistic regression model.

Student selection of data and regression model from that data will vary.

6. Go to KDnuggest.com and become familiar with the range of analytics resources available on this portal. Then identify an article, a white paper, or an interview script that deals with the nature of data, management of data, and/or governance of data as they relate to BI and business analytics, and critically analyze the content of the article.

Student selection of article or white paper will vary, and thus report will vary.

7. Go to Stephen Few’s blog, “The Perceptual Edge” (perceptualedge.com). Go to the section of “Examples.” In this section, he provides critiques of various dashboard examples. Read a handful of these examples. Now go to dundas.com. Select the “Gallery” section of the site. Once there, click the “Digital Dashboard” selection. You will be shown a variety of different dashboard demos. Run a couple of them. a. What types of information and metrics are shown on the demos? What types of actions can you take? b. Using some of the basic concepts from Few’s critiques, describe some of the good design points and bad design points of the demos.

Student selection of dashboards and response to those dashboards will vary.

8. Download an information visualization tool, such as Tableau, QlikView, or Spotfire. If your school does not have an educational agreement with these companies, a trial version would be sufficient for this exercise. Use your own data (if you have any) or use one of the data sets that comes with the tool (such tools usually have one or more data sets for demonstration purposes). Study the data, come up with several business problems, and use data visualization to analyze, visualize, and potentially solve those problems.

Student selection of tool will vary, and thus reports on the interface will vary.

9. Go to teradatauniversitynetwork.com. Find the “Tableau Software Project.” Read the description, execute the tasks, and answer the questions.

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Student reports will vary based on their perceptions and the date at which they evaluate the software.

10. Go to teradatauniversitynetwork.com. Find the assignments for SAS Visual Analytics. Using the information and step-by-step instructions provided in the assignment, execute the analysis on the SAS Visual Analytics tool (which is a Webenabled system that does not require any local installation). Answer the questions posed in the assignment.

Student selection of assignments will vary, creating different reports.

11. Find at least two articles (one journal article and one white paper) that talk about storytelling, especially within the context of analytics (i.e., data-driven storytelling). Read and critically analyze the article and paper, and write a report to reflect your understanding and opinions about the importance of storytelling in BI and business analytics.

Student selection of articles, and thus reports, will vary.

12. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic- specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations.

Student selection of topic and data set will vary, creating different responses.

Team Assignments and Role-Playing Projects 1. Analytics starts with data. Identifying, accessing, obtaining, and processing of relevant data is the most essential task in any analytics study. As a team, you are tasked to find a large enough real-world data (either from your own organization, which is the most preferred, or from the Internet that can start with a simple search, or from the data links posted on KDnuggets.com), one that has tens of thousands of rows and more than 20 variables to go through, and document a thorough data preprocessing project. In your processing of the data, identify anomalies and discrepancies using descriptive statistics methods and measures, and make the data analytics ready. List and justify your preprocessing steps and decisions in a comprehensive report.

Student work and reports will vary.

2. Go to a well-known information dashboard provider Web site (dundas.com, idashboards.com, enterprise- dashboard.com). These sites provide a number of examples 25 .

of executive dashboards. As a team, select a particular industry (e.g., healthcare, banking, airline). Locate a handful of example dashboards for that industry. Describe the types of metrics found on the dashboards. What types of displays are used to provide the information? Using what you know about dashboard design, provide a paper prototype of a dashboard for this information.

Student selection and thus reports will vary.

3. Go to teradatauniversitynetwork.com. From there, go to University of Arkansas data sources. Choose one of the large data sets, and download a large number of records (this could require you to write an SQL statement that creates the variables that you want to include in the data set). Come up with at least 10 questions that can be addressed with information visualization. Using your favorite data visualization tool (e.g., Tableau), analyze the data, and prepare a detailed report that includes screenshots and other visuals.

Student analysis and report will vary based on selection.

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Chapter 4: Predictive Analytics/ Machine Learning

Learning Objectives for Chapter 5 1.

2.

3.

4.

5.

6.

7.

Define data mining as an enabling technology for business analytics Understand the objectives and benefits of data mining Become familiar with the wide range of applications of data mining Learn the standardized data mining processes Learn different methods and algorithms of data mining Build awareness of existing data mining software tools Understand the privacy issues, pitfalls, and myths of data mining

CHAPTER OVERVIEW Generally speaking, data mining is a way to develop intelligence (i.e., actionable information or knowledge) from data that an organization collects, organizes, and stores.

A wide range of data mining techniques is being used by organizations to gain a better understanding of their customers and their operations and to solve complex organizational problems. In this chapter, we study data mining as an enabling technology for business analytics and predictive analytics; learn about the standard processes of conducting data mining projects; understand and build expertise in the use of major data mining techniques; develop awareness of the existing software tools; and explore privacy issues, common myths, and pitfalls that are often associated with data mining.

CHAPTER OUTLINE 4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime 4.2 Data Mining Concepts 4.3 Data Mining Applications 4.4 Data Mining Process 4.5 Data Mining Methods 4.6 Data Mining Software Tools 4.7 Data Mining Privacy Issues, Myths, and Blunders

1 .

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 4.1 Review Questions 1.

Why do law enforcement agencies and departments like the Miami-Dade Police Department embrace advanced analytics and data mining?

Utilizing large and information-rich data (that they collect on a daily basis) to optimize their services allows the department to perform more policing with a smaller budget. Additionally it allows them to reevaluate cold cases where patterns in the data may not have been apparent to detectives.

2.

What are the top challenges for law enforcement agencies and departments like the Miami-Dade Police Department? Can you think of other challenges (not mentioned in this case) that can benefit from data mining?

The department is faced with an increased workload with budgets that are shrinking or remaining constant. Other challenges may include changes in demographics or changing laws that very the amount or type of enforcement that they must perform.

3.

What are the sources of data that law enforcement agencies and departments like the Miami-Dade Police Department use for their predictive modeling and data mining projects?

The department was able to leverage existing data on repeat offenders.

4.

What type of analytics do law enforcement agencies and departments like the Miami-Dade Police Department use to fight crime?

This department and other agencies will primarily use predictive analytics to evaluate potential repeat offenders.

5.

What does “the big picture starts small” mean in this case?

Explain.

2 .

The department chose to begin their data mining operations using a small-scale test to prove that it works. From these beginnings it would be possible to expand the system.

Section 4.2 Review Questions 1.

Define data mining. Why are there many different names and definitions for data mining?

Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, and artificial learning techniques to extract and identify useful information and subsequent knowledge from large sets of data.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.

Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining.

2.

What recent factors have increased the popularity of data mining?

Following are some of the most pronounced reasons: • More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace.

• General recognition of the untapped value hidden in large data sources.

• Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc.

• Consolidation of databases and other data repositories into a single location in the form of a data warehouse.

• The exponential increase in data processing and storage technologies.

• Significant reduction in the cost of hardware and software for data storage and processing.

• Movement toward the de-massification (conversion of information resources into nonphysical form) of business practices.

3.

Is data mining a new discipline? Explain.

Although the term data mining is relatively new, the ideas behind it are not. Many of the techniques used in data mining have their roots in traditional statistical analysis and artificial intelligence work done since the early part of 1980s. New or increased use of data mining applications makes it seem like data mining is a new discipline.

3 .

In general, data mining seeks to identify four major types of patterns: Associations, Predictions, Clusters and Sequential relationships. These types of patterns have been manually extracted from data by humans for centuries, but the increasing volume of data in modern times has created a need for more automatic approaches. As datasets have grown in size and complexity, direct manual data analysis has increasingly been augmented with indirect, automatic data processing tools that use sophisticated methodologies, methods, and algorithms. The manifestation of such evolution of automated and semiautomated means of processing large datasets is now commonly referred to as data mining.

4.

What are some major data mining methods and algorithms?

Generally speaking, data mining tasks can be classified into three main categories: prediction, association, and clustering. Based on the way in which the patterns are extracted from the historical data, the learning algorithms of data mining methods can be classified as either supervised or unsupervised. With supervised learning algorithms, the training data includes both the descriptive attributes (i.e., independent variables or decision variables) as well as the class attribute (i.e., output variable or result variable). In contrast, with unsupervised learning the training data includes only the descriptive attributes. Figure 5.3 (p. 198) shows a simple taxonomy for data mining tasks, along with the learning methods, and popular algorithms for each of the data mining tasks.

5.

What are the key differences between the major data mining tasks?

Prediction: the act of telling about the future. It differs from simple guessing by taking into account the experiences, opinions, and other relevant information in conducting the task of foretelling. A term that is commonly associated with prediction is forecasting. Even though many believe that these two terms are synonymous, there is a subtle but critical difference between the two. Whereas prediction is largely experience and opinion based, forecasting is data and model based. That is, in order of increasing reliability, one might list the relevant terms as guessing, predicting, and forecasting, respectively. In data mining terminology, prediction and forecasting are used synonymously, and the term prediction is used as the common representation of the act.

Classification: analyzing the historical behavior of groups of entities with similar characteristics, to predict the future behavior of a new entity from its similarity to those groups Clustering: finding groups of entities with similar characteristics Association: establishing relationships among items that occur together Sequence discovery: finding time-based associations Visualization: presenting results obtained through one or more of the other methods Regression: a statistical estimation technique based on fitting a curve defined by a mathematical equation of known type but unknown parameters to existing data 4 .

Forecasting: estimating a future data value based on past data values.

Section 4.3 Review Questions 1.

What are the major application areas for data mining?

Applications are listed near the beginning of this section (pp. 201-203): CRM, banking, retailing and logistics, manufacturing and production, brokerage and securities trading, insurance, computer hardware and software, government and defense, travel, healthcare, medicine, entertainment, homeland security and law enforcement, and sports.

2.

Identify at least five specific applications of data mining and list five common characteristics of these applications.

This question expands on the prior question by asking for common characteristics.

Several such applications and their characteristics are listed on pp. 201-203.

3.

What do you think is the most prominent application area for data mining? Why? Students’ answers will differ depending on which of the applications (most likely banking, retailing and logistics, manufacturing and production, government, healthcare, medicine, or homeland security) they think is most in need of greater certainty. Their reasons for selection should relate to the application area’s need for better certainty and the ability to pay for the investments in data mining.

4.

Can you think of other application areas for data mining not discussed in this section? Explain.

Students should be able to identify an area that can benefit from greater prediction or certainty. Answers will vary depending on their creativity.

Section 4.4 Review Questions 1.

What are the major data mining processes?

Similar to other information systems initiatives, a data mining project must follow a systematic project management process to be successful. Several data mining processes have been proposed: CRISP-DM, SEMMA, and KDD.

2.

Why do you think the early phases (understanding of the business and understanding of the data) take the longest in data mining projects?

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Students should explain that the early steps are the most unstructured phases because they involve learning. Those phases (learning/understanding) cannot be automated. Extra time and effort are needed upfront because any mistake in understanding the business or data will most likely result in a failed BI project.

3.

List and briefly define the phases in the CRISP-DM process.

CRISP-DM provides a systematic and orderly way to conduct data mining projects. This process has six steps. First, an understanding of the data and an understanding of the business issues to be addressed are developed concurrently.

Next, data are prepared for modeling; are modeled; model results are evaluated; and the models can be employed for regular use.

4.

What are the main data preprocessing steps? Briefly describe each step and provide relevant examples.

Data preprocessing is essential to any successful data mining study. Good data leads to good information; good information leads to good decisions. Data preprocessing includes four main steps (listed in Table 5.1 on page 209): • data consolidation: access, collect, select and filter data • data cleaning: handle missing data, reduce noise, fix errors • data transformation: normalize the data, aggregate data, construct new attributes • data reduction: reduce number of attributes and records; balance skewed data

5.

How does CRISP-DM differ from SEMMA? The main difference between CRISP-DM and SEMMA is that CRISP-DM takes a more comprehensive approach—including understanding of the business and the relevant data—to data mining projects, whereas SEMMA implicitly assumes that the data mining project’s goals and objectives along with the appropriate data sources have been identified and understood.

Section 4.5 Review Questions 1.

Identify at least three of the main data mining methods.

Classification learns patterns from past data (a set of information—traits, variables, features—on characteristics of the previously labeled items, objects, or events) in order to place new instances (with unknown labels) into their respective groups or classes. The objective of classification is to analyze the historical data stored in a database and automatically generate a model that can predict future behavior.

6 .

Cluster analysis is an exploratory data analysis tool for solving classification problems. The objective is to sort cases (e.g., people, things, events) into groups, or clusters, so that the degree of association is strong among members of the same cluster and weak among members of different clusters.

Association rule mining is a popular data mining method that is commonly used as an example to explain what data mining is and what it can do to a technologically less savvy audience. Association rule mining aims to find interesting relationships (affinities) between variables (items) in large databases.

2.

Give examples of situations in which classification would be an appropriate data mining technique. Give examples of situations in which regression would be an appropriate data mining technique.

Students’ answers will differ, but should be based on the following issues.

Classification is for prediction that can be based on historical data and relationships, such as predicting the weather, product demand, or a student’s success in a university. If what is being predicted is a class label (e.g., “sunny,” “rainy,” or “cloudy”) the prediction problem is called a classification, whereas if it is a numeric value (e.g., temperature such as 68°F), the prediction problem is called a regression.

3.

List and briefly define at least two classification techniques.

• Decision tree analysis. Decision tree analysis (a machine-learning technique) is arguably the most popular classification technique in the data mining arena.

• Statistical analysis. Statistical classification techniques include logistic regression and discriminant analysis, both of which make the assumptions that the relationships between the input and output variables are linear in nature, the data is normally distributed, and the variables are not correlated and are independent of each other.

• Case-based reasoning. This approach uses historical cases to recognize commonalities in order to assign a new case into the most probable category.

• Bayesian classifiers. This approach uses probability theory to build classification models based on the past occurrences that are capable of placing a new instance into a most probable class (or category).

• Genetic algorithms. The use of the analogy of natural evolution to build directed search-based mechanisms to classify data samples.

• Rough sets. This method takes into account the partial membership of class labels to predefined categories in building models (collection of rules) for classification problems.

4.

What are some of the criteria for comparing and selecting the best classification technique?

• The amount and availability of historical data

7 .

• The types of data, categorical, interval, ration, etc.

• What is being predicted -- class or numeric value • The purpose or objective

5.

Briefly describe the general algorithm used in decision trees.

A general algorithm for building a decision tree is as follows: 1. Create a root node and assign all of the training data to it.

2. Select the best splitting attribute.

3. Add a branch to the root node for each value of the split. Split the data into mutually exclusive (nonoverlapping) subsets along the lines of the specific split and mode to the branches.

4. Repeat steps 2 and 3 for each and every leaf node until the stopping criteria is reached (e.g., the node is dominated by a single class label).

6.

Define Gini index. What does it measure?

The Gini index and information gain (entropy) are two popular ways to determine branching choices in a decision tree. The Gini index measures the purity of a sample. If everything in a sample belongs to one class, the Gini index value is zero.

7.

Give examples of situations in which cluster analysis would be an appropriate data mining technique.

Cluster algorithms are used when the data records do not have predefined class identifiers (i.e., it is not known to what class a particular record belongs).

8.

What is the major difference between cluster analysis and classification?

Classification methods learn from previous examples containing inputs and the resulting class labels, and once properly trained they are able to classify future cases. Clustering partitions pattern records into natural segments or clusters.

9.

What are some of the methods for cluster analysis?

The most commonly used clustering algorithms are k-means and self-organizing maps.

10.

Give examples of situations in which association would be an appropriate data mining technique.

Association rule mining is appropriate to use when the objective is to discover two or more items (or events or concepts) that go together. Students’ answers will differ.

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Section 4.6 Review Questions 1.

What are the most popular commercial data mining tools?

Examples of these vendors include IBM (IBM SPSS Modeler), SAS (Enterprise Miner), StatSoft (Statistica Data Miner), KXEN (Infinite Insight), Salford (CART, MARS, TreeNet, RandomForest), Angoss (KnowledgeSTUDIO, KnowledgeSeeker), and Megaputer (PolyAnalyst). Most of the more popular tools are developed by the largest statistical software companies (SPSS, SAS, and StatSoft).

2.

Why do you think the most popular tools are developed by statistics companies?

Data mining techniques involved the use of statistical analysis and modeling. So it’s a natural extension of their business offerings.

3.

What are the most popular free data mining tools?

Probably the most popular free and open source data mining tool is Weka. Others include RapidMiner and Microsoft’s SQL Server.

4.

What are the main differences between commercial and free data mining software tools?

The main difference between commercial tools, such as Enterprise Miner and Statistica, and free tools, such as Weka and RapidMiner, is computational efficiency. The same data mining task involving a rather large dataset may take a whole lot longer to complete with the free software, and in some cases it may not even be feasible (i.e., crashing due to the inefficient use of computer memory).

5.

What would be your top five selection criteria for a data mining tool? Explain.

Students’ answers will differ. Criteria they are likely to mention include cost, user-interface, ease-of-use, computational efficiency, hardware compatibility, type of business problem, vendor support, and vendor reputation.

Section 5.7 Review Questions 1.

What are the privacy issues in data mining?

Data that is collected, stored, and analyzed in data mining often contains information about real people. This includes identification, demographic, financial, personal, and behavioral information. Most of these data can be accessed through some third-party data providers. In order to maintain the privacy

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and protection of individuals’ rights, data mining professionals have ethical (and often legal) obligations.

2.

How do you think the discussion between privacy and data mining will progress?

Why? As technology advances and more information about people becomes easier to get, the privacy debate will adjust accordingly. People’s expectations about privacy will become tempered by their desires for the benefits of data mining, from individualized customer service to higher security. As with all issues of social import, the privacy issue will include social discourse, legal and legislative decisions, and corporate decisions. The fact that companies often choose to selfregulate (e.g., by ensuring their data is de-identified) implies that we may as a society be able to find a happy medium between privacy and data mining.

(Answers will vary by student.)

3.

What are the most common myths about data mining?

Data mining provides instant, crystal-ball predictions.

Data mining is not yet viable for business applications.

Data mining requires a separate, dedicated database.

Only those with advanced degrees can do data mining.

Data mining is only for large firms that have lots of customer data.

4.

What do you think are the reasons for these myths about data mining?

Students’ answers will differ. Some answers might relate to fear of analytics, fear of the unknown, or fear of looking dumb.

5.

What are the most common data mining mistakes/blunders? How can they be minimized and/or eliminated?

• • • • • • • • •

Selecting the wrong problem for data mining.

Ignoring what your sponsor thinks data mining is and what it really can and cannot do.

Leaving insufficient time for data preparation. It takes more effort than one often expects.

Looking only at aggregated results and not at individual records.

Being sloppy about keeping track of the mining procedure and results.

Ignoring suspicious findings and quickly moving on.

Running mining algorithms repeatedly and blindly. It is important to think hard enough about the next stage of data analysis. Data mining is a very hands-on activity.

Believing everything you are told about data.

Believing everything you are told about your own data mining analysis.

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•

Measuring your results differently from the way your sponsor measures them.

Ways to minimize these risks are basically the reverse of these items.

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION

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Application Case 4.1: Visa Is Enhancing the Customer Experience while Reducing Fraud with Predictive Analytics and Data Mining 1.

What challenges were Visa and the rest of the credit card industry facing?

The company sought to reduce the significant amount of credit card fraud, while minimizing false positives that would upset customers.

2.

How did Visa improve customer service while also improving concepts related to retention of fraud?

By using customer data to better understand customer spending patterns and practices it was possible for the company to identify superior products for some customers based on those habits.

3.

What is in-memory analytics, and why was it necessary?

In memory analysis requires significant computing power but reduces the need to move data and perform additional model iterations making it much faster and more accurate.

Application Case 4.2: American Honda Uses Advanced Analytics to Improve Warranty Claims 1.

How does American Honda use analytics to improve warranty claims?

The system evaluates warranty repair requests and based on data related to repairs, parts, customers and other variables. The system is able to rate warranty requests as incomplete inaccurate or noncompliant; meaning the system will flag requests that appear to be fraudulent. Additionally, the new system now enables managers to quickly aggregate and generate report warranty data.

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2.

In addition to warranty claims, for what other purposes does American Honda use advanced analytics methods?

In addition to providing information on warranty claims, the data compiled by the system can also be used to evaluate trends in the fleet, and predict future needs related to parts or repairs.

3.

Can you think of other uses of advanced analytics in the automotive industry?

You can search the Web to find some answers to this question.

Student responses will vary, but may include reminders for scheduled maintenance or indications of part wear before failure.

Application Case 4.3: Predictive Analytic and Data Mining Help Stop Terrorist Funding 1.

How can data mining be used to fight terrorism? Comment on what else can be done beyond what is covered in this short application case.

The application case discusses use of data mining to detect money laundering and other forms of terrorist financing. Other applications could be to track the behavior and movement of potential terrorists, as well as text mining emails, blogs, and social media threads.

2.

Do you think that, although data mining is essential for fighting terrorist cells, it also jeopardizes individuals’ rights to privacy?

Yes, because it inevitably involves tracking personal and financial data of individuals. (As an opinion question, students’ answers will vary.)

Application Case 4.4: Data Mining Helps in Cancer Research 1.

How can data mining be used for ultimately curing illnesses like cancer?

Even though cancer research has traditionally been clinical and biological in nature, in recent years data-driven analytic studies have become a common complement. In medical domains where data- and analytics-driven research have been applied successfully, novel research directions have been identified to further advance the clinical and biological studies. Using data mining techniques, researchers are able to identify novel patterns, paving the road toward a cancerfree society.

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2.

What do you think are the promises and major challenges for data miners in contributing to medical and biological research endeavors?

According to the American Cancer Society, half of all men and one-third of all women in the United States will develop cancer during their lifetimes; approximately 1.5 million new cancer cases will be diagnosed in 2013. Cancer is the second most common cause of death in the United States and in the world, exceeded only by cardiovascular disease. Data mining shows tremendous promise for helping to understand cancer, leading to better treatment and saved lives. Data mining is not meant to replace medical professionals and researchers, but to complement their invaluable efforts to provide data-driven new research directions and to ultimately save more lives. Without the cooperation and feedback from the medical experts, data mining results are not of much use. The patterns found via data mining methods should be evaluated by medical professionals who have years of experience in the problem domain to decide whether they are logical, actionable, and novel to warrant new research directions.

Application Case 4.5: Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People’s Healthcare Decisions 1. What does Influence Health do?

Influence Health, Inc. provides the healthcare industry’s only integrated digital consumer engagement and activation platform. It enables providers, employers, and payers to positively influence consumer decision making and health behaviors well beyond the physical care setting through personalized and interactive multichannel engagement.

2. What were the company’s challenges, proposed solutions, and obtained results?

The company is caught in in the specific business model of US-based healthcare.

Patients expect a personalized experience, but at the same time there are enormous cost pressures related to care. The company uses IBM predictive analytics technology to help clients discover the factors that may have the most influence on patients healthcare decisions. Using this data they are able to predict which specific healthcare services will be needed and they are able to boost revenues and response rates by improving outcomes for these procedures.

3. How can data mining help companies in the healthcare industry (in ways other than the ones mentioned in this case)?

Student responses will vary, but may focus on the ability of providers to help predict potential causes of illness for specific clients and counsel them to make lifestyle changes that may decrease the probability of these issues occurring.

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Application Case 4.6: Data Mining Goes to Hollywood: Predicting Financial Success of Movies 1.

Why is it important for Hollywood professionals to predict the financial success of movies?

The movie industry is the “land of hunches and wild guesses” due to the difficulty associated with forecasting product demand, making the movie business in Hollywood a risky endeavor. If Hollywood could better predict financial success, this would mitigate some of the financial risk.

2.

How can data mining be used to predict the financial success of movies before the start of their production process?

The way Sharda and Delen did it was to use data from movies between 1998 and 2005 as training data, and movies of 2006 as test data. They applied individual and ensemble prediction models, and were able to identify significant variables impacting financial success. They also showed that by using sensitivity analysis, decision makers can predict with fairly high accuracy how much value a specific actor (or a specific release date, or the addition of more technical effects, etc.) brings to the financial success of a film, making the underlying system an invaluable decision aid.

3.

How do you think Hollywood performed, and perhaps is still performing, this task without the help of data mining tools and techniques?

Most is done by gut feel and trial-and-error. This may keep the movie business as a financially risky endeavor, but also allows for creativity. Sometimes uncertainty is a good thing.

Application Case 4.7: Predicting Customer Buying Patterns—The Target Story 1.

What do you think about data mining and its implications concerning privacy?

What is the threshold between knowledge discovery and privacy infringement?

There is a tradeoff between knowledge discovery and privacy rights. Retailers should be sensitive about this when targeting their advertising based on data mining results, especially regarding topics that could be embarrassing to their customers. Otherwise they risk offending these customers, which could hurt their bottom line. (Answers will vary by student.)

2.

Did Target go too far? Did they do anything illegal? What do you think they should have done? What do you think they should do now (quit these types of practices)?

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Target might have made a tactical mistake, but they certainly didn’t do anything illegal. They did not use any information that violates customer privacy; rather, they used transactional data that most every other retail chain is collecting and storing (and perhaps analyzing) about their customers. Indeed, even the father apologized when realizing his daughter was actually pregnant. The fact is, we live in a world of massive data, and we are all as consumers leaving traces of our buying behavior for anyone to see. (Answers will vary by student.)

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION 1.

  

Define data mining. Why are there many names and definitions for data mining?

Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, and artificial learning techniques to extract and identify useful information and subsequent knowledge from large sets of data.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.

Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining.

2.

What are the main reasons for the recent popularity of data mining?

Following are some of the most pronounced reasons: • More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace.

• General recognition of the untapped value hidden in large data sources.

• Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc.

• Consolidation of databases and other data repositories into a single location in the form of a data warehouse.

• The exponential increase in data processing and storage technologies.

• Significant reduction in the cost of hardware and software for data storage and processing.

• Movement toward the de-massification (conversion of information resources into nonphysical form) of business practices.

3.

Discuss what an organization should consider before making a decision to purchase data mining software.

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Before making a decision to purchase data mining software, organizations should consider the standard criteria to use when investing in any major software: cost/benefit analysis, people with the expertise to use the software and perform the analyses, availability of historical data, a business need for the data mining software.

4.

Distinguish data mining from other analytical tools and techniques.

Students can view the answer in Figure 5.1 (p. 193), which shows that data mining is a composite or blend of multiple disciplines or analytical tools and techniques.

5.

Discuss the main data mining methods. What are the fundamental differences among them?

Three broad categories of data mining methods are prediction (classification or regression), clustering, and association.

Prediction is the act of telling about the future. It differs from simple guessing by taking into account the experiences, opinions, and other relevant information in conducting the task of foretelling. A term that is commonly associated with prediction is forecasting. Even though many believe that these two terms are synonymous, there is a subtle but critical difference between the two. Whereas prediction is largely experience and opinion based, forecasting is data and model based.

Classification is analyzing the historical behavior of groups of entities with similar characteristics, to predict the future behavior of a new entity from its similarity to those groups.

Clustering is finding groups of entities with similar characteristics.

Association is establishing relationships among items that occur together.

The fundamental differences are:

6.

•

Prediction (classification or regression) predicts future cases or conditions based on historical data.

•

Clustering partitions pattern records into natural segments or clusters.

Each segment’s members share similar characteristics.

•

Association is used to discover two or more items (or events or concepts) that go together.

What are the main data mining application areas? Discuss the commonalities of these areas that make them a prospect for data mining studies.

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Applications are listed near the beginning of section 5.3: CRM, banking, retailing and logistics, manufacturing and production, brokerage and securities trading, insurance, computer hardware and software, government and defense, travel, healthcare, medicine, entertainment, homeland security and law enforcement, and sports.

The commonalities are the need for predictions and forecasting for planning purposes and to support decision making.

7.

Why do we need a standardized data mining process? What are the most commonly used data mining processes?

In order to systematically carry out data mining projects, a general process is usually followed. Similar to other information systems initiatives, a data mining project must follow a systematic project management process to be successful.

Several data mining processes have been proposed: CRISP-DM, SEMMA, and KDD.

8.

Discuss the differences between the two most commonly used data mining processes.

The main difference between CRISP-DM and SEMMA is that CRISP-DM takes a more comprehensive approach—including understanding of the business and the relevant data—to data mining projects, whereas SEMMA implicitly assumes that the data mining project’s goals and objectives along with the appropriate data sources have been identified and understood.

9.

Are data mining processes a mere sequential set of activities? Explain.

No. Even though these steps are sequential in nature, there is usually a great deal of backtracking. Because the data mining is driven by experience and experimentation, depending on the problem situation and the knowledge/experience of the analyst, the whole process can be very iterative (i.e., one should expect to go back and forth through the steps quite a few times) and time consuming. Because latter steps are built on the outcome of the former ones, one should pay extra attention to the earlier steps in order not to put the whole study on an incorrect path from the onset.

10.

Why do we need data preprocessing? What are the main tasks and relevant techniques used in data preprocessing?

Data preprocessing is essential to any successful data mining study. Good data leads to good information; good information leads to good decisions. Data preprocessing includes four main steps (listed in Table 5.1 on page 209): • data consolidation: access, collect, select and filter data 17 .

• • •

11.

data cleaning: handle missing data, reduce noise, fix errors data transformation: normalize the data, aggregate data, construct new attributes data reduction: reduce number of attributes and records; balance skewed data

Discuss the reasoning behind the assessment of classification models.

The model-building step also encompasses the assessment and comparative analysis of the various models built. Because there is not a universally known best method or algorithm for a data mining task, one should use a variety of viable model types along with a well-defined experimentation and assessment strategy to identify the “best” method for a given purpose.

12.

What is the main difference between classification and clustering? Explain using concrete examples.

Classification learns patterns from past data (a set of information—traits, variables, features—on characteristics of the previously labeled items, objects, or events) in order to place new instances (with unknown labels) into their respective groups or classes. The objective of classification is to analyze the historical data stored in a database and automatically generate a model that can predict future behavior. Classifying customer-types as likely to buy or not buy is an example.

Cluster analysis is an exploratory data analysis tool for solving classification problems. The objective is to sort cases (e.g., people, things, events) into groups, or clusters, so that the degree of association is strong among members of the same cluster and weak among members of different clusters. Customers can be grouped according to demographics.

13.

Moving beyond the chapter discussion, where else can association be used?

Students’ answers will vary.

14.

What are the privacy issues with data mining? Do you think they are substantiated?

Data that is collected, stored, and analyzed in data mining often contains information about real people. This includes identification, demographic, financial, personal, and behavioral information. Most of these data can be accessed through some third-party data providers. In order to maintain the privacy and protection of individuals’ rights, data mining professionals have ethical (and often legal) obligations. As time goes on, this will continue to be a public debate.

As technology advances and more information about people becomes easier to get, the privacy debate will adjust accordingly. People’s expectations about 18 .

privacy will become tempered by their desires for the benefits of data mining, from individualized customer service to higher security. As with all issues of social import, the privacy issue will include social discourse, legal and legislative decisions, and corporate decisions. The fact that companies often choose to selfregulate (e.g., by ensuring their data is de-identified) implies that we may as a society be able to find a happy medium between privacy and data mining.

15.

What are the most common myths and mistakes about data mining?

• • • • •

Data mining provides instant, crystal-ball predictions.

Data mining is not yet viable for business applications.

Data mining requires a separate, dedicated database.

Only those with advanced degrees can do data mining.

Data mining is only for large firms that have lots of customer data.

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION

  

Teradata University Network (TUN) and Other Hands-On Exercises 1. Visit teradatauniversitynetwork.com. Identify case studies and white papers about data mining. Describe recent developments in the field of data mining and predictive modeling.

Student selection of cases and thus reports will vary., 2. Go to teradatauniversitynetwork.com. Locate Web seminars related to data mining. In particular, locate and watch a seminar given by C. Imhoff and T. Zouqes. Then answer the following questions: a. What are some of the interesting applications of data mining? b. What types of payoffs and costs can organizations expect from data mining initiatives?

Student responses and perceptions of the video will vary.

3. For this exercise, your goal is to build a model to identify inputs or predictors that differentiate risky customers from others (based on patterns pertaining to previous customers) and then use those inputs to predict new risky customers. This sample case is typical for this domain. The sample data to be used in this exercise are in Online File W4.1 in the file CreditRisk.xlsx. The data set has 425 cases and 15 variables pertaining to past and current customers who have borrowed from a bank for various reasons. The data set contains customer-related information such as financial standing, reason for the loan, employment, demographic information, and the outcome or dependent variable for credit standing, classifying each case as good or bad based on the institution’s past experience.

Take 400 of the cases as training cases and set aside the other 25 for testing. Build a decision tree model to learn the characteristics of the problem. Test its performance on 19 .

the other 25 cases. Report on your model’s learning and testing performance. Prepare a report that identifies the decision tree model and training parameters as well as the resulting performance on the test set. Use any decision tree software. (This exercise is courtesy of StatSoft, Inc., based on a German data set from ftp.ics.uc,i.edu/pub/machinelearning-databases/ statlog/german renamed CreditRisk and altered.)

Student work in building this model will vary based on the students approach.

4. For this exercise, you will replicate (on a smaller scale) the box-office prediction modeling explained in Application Case 4.6. Download the training data set from Online File W4.2, MovieTrain.xlsx, which is in Microsoft Excel format. Use the data description given in Application Case 4.6 to understand the domain and the problem you are trying to solve. Pick and choose your independent variables. Develop at least three classification models (e.g., decision tree, logistic regression, neural networks). Compare the accuracy results using 10-fold cross-validation and percentage split techniques, use confusion matrices, and comment on the outcome. Test the models you have developed on the test set (see Online File W4.3, MovieTest.xlsx). Analyze the results with different models, and find the best classification model, supporting it with your results.

Student work on this predictive model will vary based on their approach.

5. This exercise introduces you to association rule mining. The Excel data set baskets1ntrans.xlsx has around 2,800 observations/records of supermarket transaction products data. Each record contains the customer’s ID and products that they have purchased. Use this data set to understand the relationships among products (i.e., which products are purchased together). Look for interesting relationships and add screenshots of any subtle association patterns that you might find. More specifically, answer the following questions.

• Which association rules do you think are most important?

• Based on some of the association rules you found, make at least three business recommendations that might be beneficial to the company. These recommendations can include ideas about shelf organization, up-selling, or crossselling products. (Bonus points will be given to new/innovative ideas.)

• What are the Support, Confidence, and Lift values for the following rule?

Wine, Canned Veg → Frozen Meal Student analysis and the reports of that analysis will vary.

6. In this assignment, you will use a free/open source data mining tool, KNIME (knime.org), to build predictive models for a relatively small Customer Churn Analysis data set. You are to analyze the given data set (about the customer retention/attrition behavior for 1,000 customers) to develop and compare at least three prediction (i.e., classification) models. For example, you can include decision trees, neural networks, SVM, k nearest neighbor, and/or logistic regression models in your comparison. Here are the specifics for this assignment:

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• Install and use the KNIME software tool from (knime.org).

• You can also use MS Excel to preprocess the data (if you need to/want to).

• Download CustomerChurnData.csv data file from the book’s Web site.

• The data are given in comma-separated value (CSV) format. This format is the most common flat-file format that many software tools can easily open/handle (including KNIME and MS Excel).

• Present your results in a well-organized professional document.

• Include a cover page (with proper information about you and the assignment).

• Make sure to nicely integrate figures (graphs, charts, tables, screenshots) within your textual description in a professional manner. The report should have six main sections (resembling CRISP-DM phases).

• Try not to exceed 15 pages in total, including the cover (use 12-point Times New Roman fonts, and 1.5line spacing).

Student use and perception of the software package will vary.

Team Assignments and Role-Playing Projects 1. Examine how new data capture devices such as RFID tags help organizations accurately identify and segment their customers for activities such as targeted marketing.

Many of these applications involve data mining. Scan the literature and the Web and then propose five potential new data mining applications that can use the data created with RFID technology. What issues could arise if a country’s laws required such devices to be embedded in everyone’s body for a national identification system?

Team results and reports will vary.

2. Interview administrators in your college or executives in your organization to determine how data mining, data warehousing, Online Analytics Processing (OLAP), and visualization tools could assist them in their work. Write a proposal describing your findings. Include cost estimates and benefits in your report.

Interviews, and thus reports, will vary based on the individuals interviewed.

3. A very good repository of data that has been used to test the performance of many data mining algorithms is available at ics.uci.edu/~mlearn/MLRepository.html. Some of the data sets are meant to test the limits of current machine-learning algorithms and to compare their performance with new approaches to learning. However, some of the smaller data sets can be useful for exploring the functionality of any data mining software, such as RapidMiner or KNIME. Download at least one data set from this repository (e.g., Credit Screening Databases, Housing Database) and apply decision tree or clustering methods, as appropriate. Prepare a report based on your results. (Some of these exercises, especially the ones that involve large/challenging data/problem may be used as semester-long term projects.)

Student research will cause variance in the reports written.

21 .

4. Large and feature-rich data sets are made available by the U.S. government or its subsidiaries on the Internet. For instance, see a large collection of government data sets (data.gov), the Centers for Disease Control and Prevention data sets (www.cdc.gov/DataStatistics), Surveillance, Cancer.org’s Epidemiology and End Results data sets (http://seer.cancer.gov/data), and the Department of Transportation’s Fatality Analysis Reporting System crash data sets (www.nhtsa.gov/FARS). These data sets are not preprocessed for data mining, which makes them a great resource to experience the complete data mining process. Another rich source for a collection of analytics data sets is listed on KDnuggets.com (KDnuggets.com/datasets/index.html).

Student perceptions of these data sets and their results working with them will vary.

5. Consider the following data set, which includes three attributes and a classification for admission decisions into an MBA program:

a. Using the data shown, develop your own manual expert rules for decision making. b. Use the Gini index to build a decision tree. You can use manual calculations or a spreadsheet to perform the basic calculations. c. Use an automated decision tree software program to build a tree for the same data.

Student creation of rules and decision trees will vary based on their approach.

Internet Exercises 1. Visit the AI Exploratorium at cs.ualberta.ca/~aixplore. Click the Decision Tree link.

Read the narrative on basketball game statistics. Examine the data, and then build a 22 .

decision tree. Report your impressions of its accuracy. Also explore the effects of different algorithms.

Student responses will vary based on the date the site is evaluated an individual perceptions.

2. Survey some data mining tools and vendors. Start with fico.com and egain.com.

Consult dmreview.com, and identify some data mining products and service providers that are not mentioned in this chapter.

Vendors selected by students will vary.

3. Find recent cases of successful data mining applications. Visit the Web sites of some data mining vendors, and look for cases or success stories. Prepare a report summarizing five new case studies.

Cases selected by students will vary, and thus final reports will vary.

4. Go to vendor Web sites (especially those of SAS, SPSS, Cognos, Teradata, StatSoft, and Fair Isaac) and look at success stories for BI (OLAP and data mining) tools. What do the various success stories have in common? How do they differ?

Student analysis of these cases will vary based on the date the site is visited and the success stories evaluated.

5. Go to statsoft.com (now a Dell company). Download at least three white papers on applications. Which of these applications might have used the data/text/Web mining techniques discussed in this chapter?

Student selection of white papers will cause variance in final reports.

6. Go to sas.com. Download at least three white papers on applications. Which of these applications could have used the data/text/Web mining techniques discussed in this chapter?

Student selection of white papers will cause variance in final reports.

7. Go to spss.com (an IBM company). Download at least three white papers on applications. Which of these applications could have used the data/text/Web mining techniques discussed in this chapter?

Student selection of white papers will cause variance in final reports.

8. Go to teradata.com. Download at least three white papers on applications. Which of these applications could have used the data/text/Web mining techniques discussed in this chapter?

23 .

Student selection of white papers will cause variance in final reports.

9. Go to fico.com. Download at least three white papers on applications. Which of these applications could have used the data/text/Web mining techniques discussed in this chapter?

Student selection of white papers will cause variance in final reports.

10. Go to salfordsystems.com. Download at least three white papers on applications.

Which of these applications could have used the data/text/Web mining techniques discussed in this chapter?

Student selection of white papers will cause variance in final reports.

11. Go to rulequest.com. Download at least three white papers on applications. Which of these applications could have used the data/text/Web mining techniques discussed in this chapter?

Student selection of white papers will cause variance in final reports.

12. Go to KDnuggets.com. Explore the sections on applications as well as software.

Find names of at least three additional packages for data mining and text mining.

Results will vary based on the date the website is visited in the types of applications selected by the student.

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Chapter 5: Machine-Learning Techniques for Predictive Analytics Learning Objectives for Chapter 5 1.

2.

3.

4.

5.

6.

7.

8.

Understand the basic concepts and definitions of artificial neural networks (ANN) Learn the different types of ANN architectures Understand the concept and structure of support vector machines (SVM) Learn the advantages and disadvantages of SVM compared to ANN Understand the concept and formulation of k-nearest neighbor (kNN) algorithm Learn the advantages and disadvantages of kNN compared to ANN and SVM Understand the basic principles of Bayesian learning and Naïve Bayes algorithm Learn the basics of Bayesian Belief Networks and how they are used in predictive analytics 9. Understand different types of ensemble models and their pros and cons in predictive analytics CHAPTER OVERVIEW Predictive modeling is perhaps the most commonly practiced branch in data science and business analytics. It allows decision makers to estimate what the future holds by means of learning from the past (i.e., historical data). In this chapter, we study the internal structures, capabilities/limitations, and applications of the most popular predictive modeling techniques, such as artificial neural networks, support vector machines, knearest neighbor, Bayesian learning, and ensemble models. Most of these techniques are capable of addressing both classification- and regression-type prediction problems. Often, they are applied to complex prediction problems where other, more traditional techniques are not capable of producing satisfactory results. In addition to the ones covered in this chapter, other notable prediction modeling techniques include regression (linear or nonlinear), logistic regression (for classification-type prediction problems), and different types of decision trees (covered in Chapter 4).

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CHAPTER OUTLINE 5.1 Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedures 5.2 Basic Concepts of Neural Networks 5.3 Neural Network Architectures 5.4 Support Vector Machines 5.5 Process-Based Approach to the Use of SVM 5.6 Nearest Neighbor Method for Prediction 5.7 Naïve Bayes Method for Classification 5.8 Bayesian Networks 5.9 Ensemble Modeling

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 5.1 Review Questions 1.

Why is it important to study medical procedures? What is the value in predicting outcomes?

Demand for healthcare services is increasing because of the aging population, but the supply side is having problems keeping up with the level and quality of service. Healthcare systems ought to significantly improve their operational effectiveness (doing the right thing, such as diagnosing and treating accurately) and efficiency (doing it the right way, such as using the least amount of resources and time). Clinical decision support systems that use the outcome of data mining studies can support healthcare managers and/or medical professionals in making accurate and timely decisions to optimally allocate resources in order to increase the quantity and quality of medical services.

2.

What factors do you think are the most important in better understanding and managing healthcare? Consider both managerial and clinical aspects of healthcare.

Healthcare systems ought to significantly improve their operational effectiveness (doing the right thing, such as diagnosing and treating accurately) and efficiency (doing it the right way, such as using the least amount of resources and time).

Effectiveness is probably more of a clinical concern, while efficiency is more of a managerial concern.

3.

What would be the impact of predictive modeling on healthcare and medicine?

Can predictive modeling replace managerial or medical personnel?

Clinical decision support systems that use the outcome of data mining studies (such as the ones presented in this case study) are shown to be useful and 2 .

reasonably accurate predictors, especially if used in combination. These are not meant to replace healthcare managers and/or medical professionals. Rather, they intend to support them in making accurate and timely decisions to optimally allocate resources in order to increase the quantity and quality of medical services. There still is a long way to go before we can see these decision aids being used extensively in healthcare practices. Among others, there are behavioral, ethical, and political reasons for this resistance to adoption. Maybe the need and the government incentives for better healthcare systems will expedite the adoption.

4.

What were the outcomes of the study? Who can use these results? How can the results be implemented?

The main outcome of this study was to show the efficacy of data mining in predicting the outcome and in analyzing the prognostic factors of complex medical procedures such as CABG surgery. The study showed that using a number of prediction methods (as opposed to only one) in a competitive experimental setting has the potential to produce better predictive as well as explanatory results. SVM, ANN, and both C5 and CART decision trees were used.

5.

Search the Internet to locate two additional cases where predictive modeling is used to understand and manage complex medical procedures.

Open-ended question, answer will be determined by the search results each student selects.

Section 5.2 Review Questions 1.

What is an ANN? An ANN (artificial neural network) is a computer program that models a biological neural network to learn to recognize patterns, and then recognize them in new data.

2.

What are the most common ANN architectures? How do they differ from each other?

The most common ANN architectures include feedforward (multilayer perceptron with backpropagation), associative memory, recurrent networks, Kohonen’s selforganizing feature maps, and Hopfield networks. The simplest architectures are feedforward networks, which involve information flowing unidirectionally from input layer to hidden layers to output layer. More complex architectures are recurrent; these have many connections in every direction between the layers and neurons, creating a complex connection structure. Kohonen networks SOM 3 .

networks provide a way to represent multidimensional data in much lower dimensional spaces, usually one or two dimensions. One of the most interesting aspects of SOM is that they learn to classify data without supervision (i.e., no output vectors). Hopfield networks are interconnected networks of nonlinear neurons, and are effective in solving complex constraint optimization problems.

With Hopfield networks, each neuron is connected to every other neuron within the network.

3.

What types of business problems can be solved with ANN? Student responses related to specific problems will vary, but generally the type of problems that work best are ones with large data sets and pre-existing solutions that can be tested against.

Section 5.3 Review Questions 1.

What are the most popular neural network architectures?

There are several neural network architectures designed to solve different types of problems (Haykin, 2009). The most common ones include feedforward (multilayer perceptron with backpropagation), associative memory, recurrent networks, Kohonen’s self- organizing feature maps, and Hopfield networks.

2.

What types of problems are solved with Kohonen SOM ANN architecture?

The Kohonen SOM ANN architecture learns to classify data without supervision (i.e., there is no output vector). Because of its self-organizing capability, SOM are commonly used for clustering tasks where a group of cases is assigned an arbitrary number of naturals groups.

3.

How does Hopfield ANN architecture work? To what type of problems can it be applied?

Hopfield ANN architecture represents highly interconnected networks of nonlinear neurons can be extremely effective in solving complex computational problems. These networks were shown to provide novel and quick solutions to a family of problems stated in terms of a desired objective subject to a number of constraints (i.e., constraint optimization problems).

Section 5.4 Review Questions 1.

How do SVM work?

Support vector machines (SVM) are supervised learning methods that produce input-output functions from a set of labeled training data. Both classification 4 .

functions and regression functions are possible in SVMs, and these can be either linear or nonlinear functions. For example, given a classification-type prediction problem, linear classifiers (hyperplanes) can separate the data into multiple subsections, each representing one of the classes. If there are n classes to group data into, then the hyperplane will have n-1 dimensions.

2.

What are the advantages and disadvantages of SVM? SVMs are popular because of their superior predictive power and their theoretical foundation. SVMs have demonstrated highly competitive performance in numerous real-world prediction problems. A significant advantage of SVMs is that while ANNs may suffer from multiple local minima, the solutions to SVMs are global and unique. Two more advantages of SVMs are that they have a simple geometric interpretation and give a sparse solution. The reason that SVMs often outperform ANNs in practice is that they successfully deal with the “over fitting” problem, which is a big issue with ANNs. One disadvantage with SVM is the selection of the kernel type and kernel function parameters. A second and perhaps more important limitation of SVMs are the speed and size, both in the training and testing cycles. Model building in SVMs involves complex and timedemanding calculations. From the practical point of view, perhaps the most serious problem with SVMs is the high algorithmic complexity and extensive memory requirements of the required quadratic programming in large-scale tasks.

3.

What is the meaning of “maximum margin hyperplanes”? Why are they important in SVM? Although many linear classifiers (hyperplanes) can separate the data into multiple subsections, only one hyperplane achieves the maximum separation between the classes. This is the hyperplane whose distance from the nearest data points is maximized. The trick is to find the parallel hyperplanes that separate the classes and whose margin of distance is at a maximum. The assumption is that the larger the margin or distance between these parallel hyperplanes, the better the generalization power of the classifier.

4.

What is “kernel trick”? How is it used in SVM? The kernel trick is a method for converting a linear classifier algorithm into a nonlinear one by using a nonlinear function to map the original observations into a higher-dimensional space; this makes a linear classification in the new space equivalent to nonlinear classification in the original space. This is what enables the general hyperplane approach to SVMs (which are inherently linear) to solve nonlinear classification problems. Common kernel types are polynomial, radial basis function (RBF), Gaussian RBF, and sigmoid.

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Section 5.5 Review Questions 1.

What are the main steps and decision points in developing a SVM model?

First, numericize the data. Each data instance must be represented as a vector of numeric values, including the categorical variable (the classification). Second, normalize (scale) the data. This prevents larger-magnitude attributes from dominating the others during the learning process. Next, select the kernel type and the kernel parameters. Finally, deploy the model.

2.

How do you determine the optimal kernel type and kernel parameters?

You can do this experimentally, trying different ones out and comparing results.

Often the RBF is a good start. Deciding on the best parameters for a kernel involves a parameter search method, such as cross-validation or grid search.

3.

Compared to ANN, what are the advantages of SVM? A significant advantage of SVMs is that while ANNs may suffer from multiple local minima, the solutions to SVMs are global and unique. Two more advantages of SVMs are that they have a simple geometric interpretation and give a sparse solution. The reason that SVMs often outperform ANNs in practice is that they successfully deal with the “over fitting” problem, which is a big issue with ANNs.

4.

What are the common application areas for SVM? Conduct a search on the Internet to identify popular application areas and specific SVM software tools used in those applications.

Common applications of SVM include text and hypertext categorization, image classification, protein classification for medical science, and hand-written character recognition. Many of the same software products that include neural network algorithms also include SVM algorithms (e.g., MATLAB, SPSS).

(Student answers will vary.)

Section 5.6 Review Questions 1.

What is special about the kNN algorithm?

The k-nearest neighbor algorithm is among the simplest of all machine-learning algorithms. It is easy to understand (and explain to others) what it does and how it does it.

2.

What are the advantages and disadvantages of kNN as compared to ANN and SVM?

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Compared to both ANN and SVM, the k-nearest neighbor algorithm is very simple to learn and implement. But it is a lazy learner, often reaching local rather than global minima/maxima. In addition, the accuracy of the kNN algorithm can be significantly different with different values of k. Furthermore, the predictive power of the kNN algorithm degrades with the presence of noisy, inaccurate, or irrelevant features.

3.

What are the critical success factors for a kNN implementation?

One critical factor is selection of the best similarity metric for determining what is a “nearest” neighbor. A second is the selection of the correct parameter (i.e., the k value). This can be done using cross-validation.

4.

What is a similarity (or distance measure)? How can it be applied to both numerical and nominal valued variables?

A similarity measure is a mathematically calculable distance metric. Given a new case, kNN makes predictions based on the outcome of the k neighbors closest in distance to that point. Therefore, to make predictions with kNN, we need to define a metric for measuring the distance between the new case and the cases from the examples. Two popular approaches are Euclidean and rectilinear. Both of these assume numerical data points. There are ways to measure distance for nonnumerical (nominal) data as well. In the simplest case, for a multi-value nominal variable, if the value of that variable for the new case and that for the example case are the same, the distance would be zero, otherwise one. In cases such as text classification, more sophisticated metrics exist, such as the overlap metric (or Hamming distance).

5.

What are the common applications of kNN? Image recognition, DNA sequencing, pattern recognition, statistical classification, Internet marketing, and plagiarism detection are some of the possible applications.

(Student answers will vary.)

Section 5.7 Review Questions 1.

What is special about the Naïve Bayes algorithm? What is the meaning of “Naïve” in this algorithm?

Naïve Bayes is a simple probability-based classification method (a machinelearning technique that is applied to classification-type prediction problems) derived from the well-known Bayes theorem. The word “Naïve” comes from its strong, somewhat unrealistic, assumption of independence among the input variables.

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2.

What are the advantages and disadvantages of Naïve Bayes compared to other machine-learning methods Advantages include the ability to be developed very efficient efficiently and effectively in a machine-learning environment. A disadvantage is the underlying, unrealistic assumption of independence among the input variables.

3.

What type of data can be used in Naïve Bayes algorithm? What type of predictions can be obtained from it?

The method requires the output variable to have nominal values. Although the input variables can be a mix of numeric and nominal types, the numeric output variable needs to be discretized via some type of binning method before it can be used in a Bayes classifier.

4.

What is the process of developing and testing a Naïve Bayes classifier?

Step 1. Obtain the data, clean the data, and organize them in a flat file format Step 2. Make sure that the variables are a nominal; if not, the numeric variables need to go through a data transformation.

Step 3. Calculate the prior probability of all class labels for the dependent variable.

Step 4. Calculate the likelihood for all predictor variables and their possible values with respect to the dependent variable. In the case of mixed variable types, each variable’s likelihood is estimated with the proper method that applies to the specific variable type.

Section 5.8 Review Questions 1.

What are Bayesian networks? What is special about them?

Bayesian networks (BN) are supportive of self-activated, multidirectional propagation of evidence that converges rapidly to a globally-consistent equilibrium. BN is a powerful tool for representing dependency structure in a graphical, explicit, and intuitive way. It reflects the various states of a multivariate model and their probabilistic relationships.

2.

What is the relationship between Naïve Bayes and Bayesian networks?

A BN does not assume independence among the input variables.

3.

What is the process of developing a Bayesian networks model?

Step 1. Compute the conditional mutual information function for each (i, j ) pair Step 2. Build a complete undirected graph and use a conditional mutual information function to annotate the weight of an edge connecting xi to xj.

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Step 3. Build a maximum weighted spanning tree.

Step 4. Convert the undirected graph into a directed one by choosing a root variable and setting the direction of all edges to be outward from it.

Step 5. Construct a TAN model by adding a vertex labeled by C and an arc from C to each xi.

4.

What are the advantages and disadvantages of Bayesian networks compared to other machine-learning methods?

One advantage is its adaptability, it is possible to start building a network with a limited understanding of the model and expanded as new information becomes available. Additionally, the method has good applicability as a completed BN offers a holistic view of all relationships. A possible disadvantage could be in accuracy if existing probabilities upon which it is constructed are not exact (although research points to approximate probabilities being surprisingly good).

5.

What is Tree Augmented Naïve (TAN) Bayes and how does it relate to Bayesian networks?

Tree Augmented Naïve (TAN) Bayes is a more recent and popular method for learning the structure of the network. The TAN method is an updated version of the Naïve Bayes classifier that uses tree structure to approximate the interactions between predictor variables and the target variable.

Section 5.9 Review Questions 1.

What is a model ensemble, and where can it be used analytically?

Ensembles (or more appropriately called model ensembles or ensemble modeling) are combinations of the outcomes produced by two or more analytics models into a compound output. Ensembles are primarily used for prediction modeling when the scores of two or more models are combined to produce a better prediction.

2.

What are the different types of model ensembles?

Model ensembles can be classified into four groups in two dimensions: homogeneous or heterogeneous types and bagging or boosting.

3.

Why are ensembles gaining popularity over all other machine-learning trends?

Their popularity is growing because they continually are able to create accurate and robust analytic models and their effectiveness is improved in parallel with rapidly improving software and hardware capabilities.

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4.

What is the difference between bagging- and boosting-type ensemble models?

Bagging and boosting employ slightly different strategies to utilize the training data set and to achieve the goal of building the best possible prediction model ensemble. Bagging uses a bootstrap sample of cases to build decision trees whereas boosting uses the complete training data set. Whereas bagging creates independent, simple trees to ensemble, boosting creates dependent trees that collectively contribute to the final ensemble.

5.

What are the advantages and disadvantages of ensemble models Ensemble models have the advantages of accuracy, robustness, reliability/stability and coverage when designed correctly. Unfortunately they have the disadvantages of complexity, lack of transparency, high computational load and are more difficult to deploy.

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION

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Application Case 5.1: Neural Networks Are Helping to Save Lives in the Mining Industry 1.

How did neural networks help save lives in the mining industry?

The Council for Scientific and Industrial Research (CSIR) in South Africa developed a device with an embedded neural network that assists any miner in making an objective decision when determining the integrity of the hanging wall.

This helps prevent death and injury from rock falls, a common danger to miners.

2.

What were the challenges, the proposed solution, and the obtained results?

In the mining industry, most of the underground injuries and fatalities are due to rock falls (i.e., fall of hanging wall/roof). The method that has been used for many years in the mines when determining the integrity of the hanging wall is to tap the hanging wall with a sounding bar and listen to the sound emitted. An experienced miner can differentiate an intact/solid hanging wall from a detached/loose hanging wall by the sound that is emitted. But this method is subjective. The proposed solution is to provide miners with a device that uses a trained neural network to record and classify sounds to identify a hanging wall as either intact or detached.

The multilayer perceptron-type ANN architecture that was built achieved better than 70 percent prediction accuracy on sample data. At this point, the system is in its prototype-testing phase.

Application Case 5.2: Predictive Modeling Is Powering the Power Generators

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1.

What are the key environmental concerns in the electric power industry?

Even though some energy-generation methods are favored over others, all forms of electricity generation have positive and negative aspects. Some are environmentally favored but are economically unjustifiable; others are economically superior but environmentally prohibitive. In a market economy, the options with fewer overall costs are generally chosen above all other sources. It is not clear yet which form can best meet the necessary demand for electricity without permanently damaging the environment. Current trends indicate that increasing the shares of renewable energy and distributed generation from mixed sources has the promise of reducing/balancing environmental and economic risks.

2.

What are the main application areas for predictive modeling in the electric power industry?

Predictive modeling can be used to optimize operational parameters to produce cleaner combustion and more stable flame temperatures. Another application is to predict problems, such as failures or maintenance issues, before they happen.

Modeling can also be used to reduce NOx emissions.

3.

How was predictive modeling used to address a variety of problems in the electric power industry?

See answer to question #2.

Application Case 5.3: Identifying Injury Severity Risk Factors in Vehicle Crashes with Predictive Analytics 1.

How does sensitivity analysis shed light on the black box (i.e., neural networks)?

Sensitivity analysis techniques provide a clear interpretation of how a neural network does what it does; that is, specifically how (and to what extent) the individual inputs factor into the generation of specific network output. This process extracts the cause-and-effect relationships among the inputs and the outputs of a trained neural network model.

2.

Why would someone choose to use a black-box tool like neural networks over theoretically sound, mostly transparent statistical tools like logistic regression?

ANN are known to be superior in capturing highly nonlinear complex relationships between predictor and target variables, assuming a linear relationship is often an oversimplification of the problem.

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3.

In this case, how did neural networks and sensitivity analysis help identify injuryseverity factors in traffic accidents?

ANN and sensitivity analysis helped estimate the significance of the crash factors on the level of injury severity sustained by the driver. This study was a two-step process. In the first step, the testers developed a series of prediction models (one for each injury severity level) to capture the in-depth relationships between the crash-related factors and a specific level of injury severity. In the second step, they conducted sensitivity analysis on the trained neural network models to identify the prioritized importance of crash-related factors as they relate to different injury severity levels.

The study revealed that the variable seatbelt use was the most important determinant for predicting higher levels of injury severity but it was one of the least significant predictors for lower levels of injury severity. Other interesting findings involved gender (good predictor for low injury severity, but not for high) and age (vice versa).

Application Case 5.4: Efficient Image Recognition and Categorization with kNN 1.

Why is image recognition/classification a worthy but difficult problem?

Application areas of image recognition and categorization range from agriculture to homeland security, personalized marketing to environmental protection. Image recognition is an integral part of an artificial intelligence field called computer vision. While the field of visual recognition and category recognition has been progressing rapidly, much remains to be done to reach human-level performance.

Current approaches are capable of dealing with only a limited number of categories (100 or so categories) and are computationally expensive.

2.

How can kNN be effectively used for image recognition/classification applications? kNN classifiers are natural in this setting, and have computational advantages over SVMs. But they suffered from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. By combining kNN with SVM, you can improve performance while maintaining computational advantage.

Another possible hybrid is combining kNN with Naïve Bayes algorithm.

Application Case 5.5: Predicting Disease Progress in Crohn’s Disease Patients: A Comparison of Analytics Methods 1.

What is Crohn’s disease and why is it important?

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Inflammatory bowel disease (IBD), which includes Crohn’s disease and ulcerative colitis (UC), impacts 1.6 million Americans. Crohn’s disease causes chronic inflammation and damages the gastrointestinal tract.

2.

Based on the findings of this Application Case, what can you tell about the use of analytics in chronic disease management?

This study was able to show that disease can be managed in real time by using decision support tools that rely on advanced analytics to predict the future inflammation state, which would then allow for medical intervention prospectively. With this information, healthcare providers can improve patient outcomes by intervening early and making necessary therapeutic adjustments that would work for the specific patient.

3.

What other methods and data sets might be used to better predict the outcomes of this chronic disease?

Student examples and conjecture will vary.

Application Case 5.6: To Imprison or Not to Imprison: A Predictive AnalyticsBased Decision Support System for Drug Courts 1.

What are drug courts and what do they do for the society?

Drug courts are our courts specifically designed to deal with drug offenses in the hope that offenders can be diverted from the criminal justice system through treatment and other outreach.

2.

What are the commonalities and differences between traditional (theoretical) and modern (machine-learning) base methods in studying drug courts?

Both systems use the same data sets in order to evaluate offenders and their success in drug courts. Traditional methods can only provide descriptive results, whereas modern methods can provide predictive results that are significantly more reliable.

3.

Can you think of other social situations and systems for which predictive analytics can be used?

Student experiences and responses will vary.

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION

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1. What is an artificial neural network and for what types of problems can it be used?

2. Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar?

The physical aspects of biological neural networks are not mimicked by ANNs. ANNs mimic the functional aspects of biological networks at an elementary level.

3. What are the most common ANN architectures? For what types of problems can they be used?

The most common ANN architectures include feedforward (multilayer perceptron with backpropagation), associative memory, recurrent networks, Kohonen’s self-organizing feature maps, and Hopfield networks. The simplest architectures are feedforward networks, which involve information flowing unidirectionally from input layer to hidden layers to output layer. More complex architectures are recurrent; these have many connections in every direction between the layers and neurons, creating a complex connection structure. Kohonen networks SOM networks provide a way to represent multidimensional data in much lower dimensional spaces, usually one or two dimensions.

One of the most interesting aspects of SOM is that they learn to classify data without supervision (i.e., no output vectors). Hopfield networks are interconnected networks of nonlinear neurons, and are effective in solving complex constraint optimization problems.

With Hopfield networks, each neuron is connected to every other neuron within the network.

4. ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode.

Learning in supervised mode is simpler. Since we know the desired output and the value of inputs as well as all the algorithms that express relationships in the system, it is possible to compute values of output for given weights. By comparing actual and desired weights one can see the difference. This difference can be reduced by adjusting the values of the weights. Ideally the differences should be driven to zero. Finding the ideal value of the weights is called the learning or training.

In unsupervised situations, the desired outputs are not specified. Therefore, the process is only semi-automatic (i.e., a human must examine the result to determine when the training needs to stop). Weights and other parameters can be adjusted once the outputs are examined.

5. What are SVM? How do they work?

Support vector machines (SVM) are supervised learning methods that produce inputoutput functions from a set of labeled training data. Both classification functions and regression functions are possible in SVMs, and these can be either linear or nonlinear functions. For example, given a classification-type prediction problem, linear classifiers 14 .

(hyperplanes) can separate the data into multiple subsections, each representing one of the classes. If there are n classes to group data into, then the hyperplane will have n-1 dimensions.

6. What are the types of problems that can be solved by SVM? SVMs are popular because of their superior predictive power and their theoretical foundation. SVMs have demonstrated highly competitive performance in numerous realworld prediction problems. A significant advantage of SVMs is that while ANNs may suffer from multiple local minima, the solutions to SVMs are global and unique. Two more advantages of SVMs are that they have a simple geometric interpretation and give a sparse solution. The reason that SVMs often outperform ANNs in practice is that they successfully deal with the “over fitting” problem, which is a big issue with ANNs. One disadvantage with SVM is the selection of the kernel type and kernel function parameters. A second and perhaps more important limitation of SVMs are the speed and size, both in the training and testing cycles. Model building in SVMs involves complex and time-demanding calculations. From the practical point of view, perhaps the most serious problem with SVMs is the high algorithmic complexity and extensive memory requirements of the required quadratic programming in large-scale tasks.

7. What is the meaning of “maximum-margin hyperplanes”? Why are they important in SVM? Although many linear classifiers (hyperplanes) can separate the data into multiple subsections, only one hyperplane achieves the maximum separation between the classes.

This is the hyperplane whose distance from the nearest data points is maximized. The trick is to find the parallel hyperplanes that separate the classes and whose margin of distance is at a maximum. The assumption is that the larger the margin or distance between these parallel hyperplanes, the better the generalization power of the classifier.

8. What is the kernel trick and how does it relate to SVM? The kernel trick is a method for converting a linear classifier algorithm into a nonlinear one by using a nonlinear function to map the original observations into a higherdimensional space; this makes a linear classification in the new space equivalent to nonlinear classification in the original space. This is what enables the general hyperplane approach to SVMs (which are inherently linear) to solve nonlinear classification problems. Common kernel types are polynomial, radial basis function (RBF), Gaussian RBF, and sigmoid.

9. What are the specific steps to follow in developing an SVM model?

First, numericize the data. Each data instance must be represented as a vector of numeric values, including the categorical variable (the classification). Second, normalize (scale) the data. This prevents larger-magnitude attributes from dominating the others during the

15 .

learning process. Next, select the kernel type and the kernel parameters. Finally, deploy the model.

10. How can the optimal kernel type and kernel parameters be determined?

You can do this experimentally, trying different ones out and comparing results. Often the RBF is a good start. Deciding on the best parameters for a kernel involves a parameter search method, such as cross-validation or grid search.

11. What are the common application areas for SVM? Conduct a search on the Internet to identify popular application areas and specific SVM software tools used in those applications.

Common applications of SVM include text and hypertext categorization, image classification, protein classification for medical science, and hand-written character recognition. Many of the same software products that include neural network algorithms also include SVM algorithms (e.g., MATLAB, SPSS). (Student answers will vary.)

12. What are the commonalities and differences, advantaged and disadvantages between ANN and SVM? A significant advantage of SVMs is that while ANNs may suffer from multiple local minima, the solutions to SVMs are global and unique. Two more advantages of SVMs are that they have a simple geometric interpretation and give a sparse solution. The reason that SVMs often outperform ANNs in practice is that they successfully deal with the “over fitting” problem, which is a big issue with ANNs. 13. Explain the difference between a training and a testing data set in ANN and SVM. Why do we need to differentiate them? Can the same set be used for both purposes? Why or why not?

The same set cannot be used for both purposes because that would create a “self-fulfilling prophecy:” having learned to separate two parts of the input set from each other, correct behavior would then be tested by simply confirming that it has done so. This is not meaningful. It is necessary to test whether or not it has learned to separate the two parts of the overall universe under investigation from each other.

For example, suppose an aerial surveillance ANN is designed to tell tanks from rocks and is thus trained with photographs of those two items. Suppose, hypothetically, that the photographs of tanks were (because of differences in the time they were taken, equipment used, etc.) slightly darker than those of rocks. The ANN might successfully learn to separate the photographs in the training set into the two appropriate parts. A person simply confirming that it did so, using those same photographs, would have no way of knowing that it might have just learned to tell darker photographs from lighter ones. It is necessary to test the ANN with a totally different set of photographs to confirm that it can actually identify tanks.

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14. Everyone would like to make a great deal of money on the stock market. Only a few are very successful. Why is using an SVM or ANN a promising approach? What can they do that other decision support technologies cannot do? How could SVM or ANN fail?

The factors that lead to changes in stock prices are imperfectly known. Attempts to understand them have not been successful. However, it is reasonable to suppose that those factors, while we may not understand them, do exist. An ANN might be able to find them in a mass of data because it has no preconceived notions about what they should be.

Other decision support technologies are driven by human guidance in some way and rely ultimately on human decision makers to identify the relevant factors.

An ANN could fail in this application if it cannot identify the relevant factors, or if it identifies a set of factors that would have predicted stock movements during one time period (when the market was driven by one set of factors) but are not useful in predicting them in another period (when it is driven by different factors). Attempting to use factors derived from analysis of an earlier period to guide investments in a later one could be a recipe for bankruptcy.

15. What is special about the kNN algorithm?

The k-nearest neighbor algorithm is among the simplest of all machine-learning algorithms. It is easy to understand (and explain to others) what it does and how it does it.

16. What are the advantages and disadvantages of kNN as compared to ANN and SVM? Compared to both ANN and SVM, the k-nearest neighbor algorithm is very simple to learn and implement. But it is a lazy learner, often reaching local rather than global minima/maxima. In addition, the accuracy of the kNN algorithm can be significantly different with different values of k. Furthermore, the predictive power of the kNN algorithm degrades with the presence of noisy, inaccurate, or irrelevant features.

17. What are the critical success factors for a kNN implementation?

One critical factor is selection of the best similarity metric for determining what is a “nearest” neighbor. A second is the selection of the correct parameter (i.e., the k value).

This can be done using cross-validation.

18. What is a similarity (or distance) measure? How can it be applied to both numerical and nominal valued variables?

A similarity measure is a mathematically calculable distance metric. Given a new case, kNN makes predictions based on the outcome of the k neighbors closest in distance to that point. Therefore, to make predictions with kNN, we need to define a metric for measuring the distance between the new case and the cases from the examples. Two popular approaches are Euclidean and rectilinear. Both of these assume numerical data points. There are ways to measure distance for non-numerical (nominal) data as well. In 17 .

the simplest case, for a multi-value nominal variable, if the value of that variable for the new case and that for the example case are the same, the distance would be zero, otherwise one. In cases such as text classification, more sophisticated metrics exist, such as the overlap metric (or Hamming distance).

19. What are the common (business and scientific) applications of kNN? Conduct a Web search to find three realworld applications that use kNN to solve the problem.

Image recognition, DNA sequencing, pattern recognition, statistical classification, Internet marketing, and plagiarism detection are some of the possible applications.

(Student answers will vary.)

20. What is special about the Naïve Bayes algorithm? What is the meaning of “Naïve” in this algorithm?

Naïve Bayes is a simple probability-based classification method (a machine-learning technique that is applied to classification-type prediction problems) derived from the well-known Bayes theorem. The word “Naïve” comes from its strong, somewhat unrealistic, assumption of independence among the input variables.

21. What are the advantages and disadvantages of Naïve Bayes compared to other machine-learning methods?

Advantages include the ability to be developed very efficient efficiently and effectively in a machine-learning environment. A disadvantage is the underlying, unrealistic assumption of independence among the input variables.

22. What type of data can be used in a Naïve Bayes algorithm? What type of predictions can be obtained from it?

The method requires the output variable to have nominal values. Although the input variables can be a mix of numeric and nominal types, the numeric output variable needs to be discretized via some type of binning method before it can be used in a Bayes classifier.

23. What is the process of developing and testing a Naïve Bayes classifier?

Step 1. Obtain the data, clean the data, and organize them in a flat file format Step 2. Make sure that the variables are a nominal; if not, the numeric variables need to go through a data transformation.

Step 3. Calculate the prior probability of all class labels for the dependent variable.

Step 4. Calculate the likelihood for all predictor variables and their possible values with respect to the dependent variable. In the case of mixed variable types, each variable’s likelihood is estimated with the proper method that applies to the specific variable type.

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24. What are Bayesian networks? What is special about them?

Bayesian networks (BN) are supportive of self-activated, multidirectional propagation of evidence that converges rapidly to a globally-consistent equilibrium. BN is a powerful tool for representing dependency structure in a graphical, explicit, and intuitive way. It reflects the various states of a multivariate model and their probabilistic relationships.

25. What is the relationship between Naïve Bayes and Bayesian networks?

A BN does not assume independence among the input variables.

26. What is the process of developing a Bayesian networks model?

Step 1. Compute the conditional mutual information function for each (i, j ) pair Step 2. Build a complete undirected graph and use a conditional mutual information function to annotate the weight of an edge connecting xi to xj.

Step 3. Build a maximum weighted spanning tree.

Step 4. Convert the undirected graph into a directed one by choosing a root variable and setting the direction of all edges to be outward from it.

Step 5. Construct a TAN model by adding a vertex labeled by C and an arc from C to each xi.

27. What are the advantages and disadvantages of Bayesian networks compared to other machine-learning methods?

One advantage is its adaptability, it is possible to start building a network with a limited understanding of the model and expanded as new information becomes available.

Additionally, the method has good applicability as a completed BN offers a holistic view of all relationships. A possible disadvantage could be in accuracy if existing probabilities upon which it is constructed are not exact (although research points to approximate probabilities being surprisingly good).

28. What is Tree Augmented Naïve (TAN) Bayes and how does it relate to Bayesian networks?

Tree Augmented Naïve (TAN) Bayes is a more recent and popular method for learning the structure of the network. The TAN method is an updated version of the Naïve Bayes classifier that uses tree structure to approximate the interactions between predictor variables and the target variable.

29. What is a model ensemble, and analytically where can it be used?

Ensembles (or more appropriately called model ensembles or ensemble modeling) are combinations of the outcomes produced by two or more analytics models into a compound output. Ensembles are primarily used for prediction modeling when the scores of two or more models are combined to produce a better prediction.

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30. What are the different types of model ensembles?

Model ensembles can be classified into four groups in two dimensions: homogeneous or heterogeneous types and bagging or boosting.

31. Why are ensembles gaining popularity over all other machine-learning trends?

Their popularity is growing because they continually are able to create accurate and robust analytic models and their effectiveness is improved in parallel with rapidly improving software and hardware capabilities.

32. What is the difference between bagging- and boosting-type ensemble models?

Bagging and boosting employ slightly different strategies to utilize the training data set and to achieve the goal of building the best possible prediction model ensemble. Bagging uses a bootstrap sample of cases to build decision trees whereas boosting uses the complete training data set. Whereas bagging creates independent, simple trees to ensemble, boosting creates dependent trees that collectively contribute to the final ensemble.

33. What are the advantages and disadvantages of ensemble models?

Ensemble models have the advantages of accuracy, robustness, reliability/stability and coverage when designed correctly. Unfortunately they have the disadvantages of complexity, lack of transparency, high computational load and are more difficult to deploy.

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ANSWERS TO END OF CHAPTER EXERCISES

Teradata University Network (TUN) and Other Hands-On Exercises 1. Go to the Teradata University Network Web site (teradatauniversitynetwork.com) or a URL given by your instructor. Locate Web seminars related to data mining and neural networks. Specifically, view the seminar given by Professor Hugh Watson at the SPIRIT2005 conference at Oklahoma State University; then, answer the following questions: a. Which real-time application at Continental Airlines might have used a neural network? b. What inputs and outputs can be used in building a neural network application? c. Given that its data mining applications are in real time, how might Continental implement a neural network in practice? d. What other neural network applications would you propose for the airline industry?

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Student research and reports will vary.

2. Go to the Teradata University Network Web site (teradatauniversitynetwork.com) or a URL given by your instructor. Locate the Harrah’s case. Read the case and answer the following questions: a. Which of the Harrah’s data applications are most likely implemented using neural networks? b. What other applications could Harrah’s develop using the data it collects from its customers? c. What are some concerns you might have as a customer at this casino?

Student research and reports will vary.

3. A bankruptcy-prediction problem can be viewed as a problem of classification. The data set you will be using for this problem includes five ratios that have been computed from the financial statements of real-world firms. These five ratios have been used in studies involving bankruptcy prediction. The first sample includes data on firms that went bankrupt and firms that did not. This will be your training sample for the neural network.

The second sample of 10 firms also consists of some bankrupt firms and some nonbankrupt firms. Your goal is to use neural networks, SVM, and nearest neighbor algorithms to build a model using the first 20 data points and then to test its performance on the other 10 data points. (Try to analyze the new cases yourself manually before you run the neural network and see how well you do.) The following tables show the training sample and test data you should use for this exercise.

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Describe the results of the neural network, SVM, and nearest neighbor model predictions, including software, architecture, and training information.

Student research and reports will vary.

4. The purpose of this exercise is to develop models to predict the type of forest cover using a number of cartographic measures. The given data set (see Online Supplements) includes four wilderness areas found in the Roosevelt National Forest of northern 22 .

Colorado. A total of 12 cartographic measures were utilized as independent variables; seven major forest cover types were used as dependent variables. This is an excellent example for a multi-class classification problem. The data set is rather large (with 581,012 unique instances) and feature rich. As you will see, the data are also raw and skewed (unbalanced for different cover types). As a model builder, you are to make necessary decisions to preprocess the data and build the best possible predictor. Use your favorite tool to build the models for neural networks, SVM, and nearest neighbor algorithms, and document the details of your results and experiences in a written report.

Use screenshots within your report to illustrate important and interesting findings. You are expected to discuss and justify any decision that you make along the way.

Student research and reports will vary.

5. Go to UCI Machine-Learning Repository (archive.ics. uci.edu/ml/index.php), identify four data sets for classification-type problems, and use these data sets to build and compare ANN, SVM, kNN, and Naïve Bayes models. To do so, you can use any analytics tool. We suggest you use a free, open-source analytics tool such as KNIME (knime.org) or Orange (orange.biolab.si). Prepare a well-written report to summarize your findings.

Student research and reports will vary.

6. Go to Google Scholar (scholar.google.com). Conduct a search to find two papers written in the last five years that compare and contrast multiple machine-learning methods for a given problem domain. Observe commonalities and differences among their findings and prepare a report to summarize your understanding.

Student research and reports will vary.

Team Assignments and Role-Playing Projects 1. Consider the following set of data that relates daily electricity usage to a function of the outside high temperature (for the day):

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a. Plot the raw data. What pattern do you see? What do you think is really affecting electricity usage? b. Solve this problem with linear regression Y = a + bX (in a spreadsheet). How well does this work? Plot your results. What is wrong? Calculate the sum-ofthesquares error and R2. c. Solve this problem by using nonlinear regression. We recommend a quadratic function, Y = a + b1X + b2X2. How well does this work? Plot your results. Is anything wrong? Calculate the sum-ofsquares error and R2. d. Break the problem into three sections (look at the plot). Solve it using three linear regression models, one for each section. How well does this work? Plot your results. Calculate the sum-of-squares error and R2. Is this modeling approach appropriate? Why or why not?

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e. Build a neural network to solve the original problem. (You might have to scale the X and Y values to be between 0 and 1.) Train the network (on the entire set of data) and solve the problem (i.e., make predictions for each of the original data items). How well does this work? Plot your results. Calculate the sumof-squares error and R2. f. Which method works best and why?

Team research and reports will vary.

2. Build a real-world neural network. Using demo software downloaded from the Web (e.g., NeuroSolutions at neurodimension.com or another neural network tool/site), identify real-world data (e.g., start searching on the Web at archive.ics.uci.edu/ml/index.php or use data from an organization with which someone in your group has a contact) and build a neural network to make predictions. Topics might include sales forecasts, predicting success in an academic program (e.g., predict GPA from high school ranking and SAT scores, being careful to look out for “bad” data, such as GPAs of 0.0) or housing prices; or survey the class for weight, gender, and height and try to predict height based on the other two factors. You could also use U.S. Census data by state on this book’s Web site or at census.gov to identify a relationship between education level and income. How good are your predictions? Compare the results to predictions generated using standard statistical methods (regression). Which method is better? How could your system be embedded in a decision support system (DSS) for real decision making?

Team research and reports will vary.

3. For each of the following applications, would it be better to use a neural network or an expert system? Explain your answers, including possible exceptions or special conditions. a. Diagnosis of a well-established but complex disease b. Price lookup subsystem for a high-volume merchandise seller c. Automated voice inquiry processing system d. Training of new employees e.

Handwriting recognition Team research and reports will vary.

4. Consider the following data set, which includes three attributes and a classification for admission decisions into an MBA program:

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a. Using the data given here as examples, develop your own manual expert rules for decision making. b. Build and test a neural network model using your favorite data mining tool.

Experiment with different model parameters to “optimize” the predictive power of your model. c. Build and test a support vector machine model using your favorite data mining tool. Experiment with different model parameters to “optimize” your model’s predictive power. Compare the results with ANN and SVM. d. Report the predictions on the last three observations from each of the three classification approaches (ANN, SVM, and kNN). Comment on the results. e. Comment on the similarity and differences of these three prediction approaches. What did you learn from this exercise?

Team research and reports will vary.

5. You have worked on neural networks and other data mining techniques. Give examples of the use of each of them. Based on your knowledge, how would you differentiate among these techniques? Assume that a few years from now you will come across a situation in which neural network or other data mining techniques could be used to build an interesting application for your organization. You have an intern working with you to do the grunt work. How will you decide whether the application is appropriate for a neural network or another data mining model? Based on your homework assignments, what specific software guidance can you provide so that your intern is productive for you quickly? Your answer for this question might mention the specific software, describe how to go about setting up the model/neural network, and validate the application.

Team research and reports will vary.

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ANSWERS TO END OF CHAPTER INTERNET EXERCISES

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1. Explore the Web sites of several neural network vendors, such as California Scientific Software (calsci.com), NeuralWare (neuralware.com), and Ward Systems Group (wardsystems.comv), and review some of their products. Download at least two demos and install, run, and compare them.

Student research and reports will vary.

2. A very good repository of data that have been used to test the performance of neural network and other machine-learning algorithms can be accessed at https://archive.ics.uci.edu/ml/index.php. Some of the data sets are really meant to test the limits of current machine-learning algorithms and compare their performance against new approaches to learning. However, some of the smaller data sets can be useful for exploring the functionality of the software you might download in Internet Exercise 1 or the software that is available at StatSoft.com (i.e., Statistica Data Miner with extensive neural network capabilities). Download at least one data set from the UCI repository (e.g., Credit Screening Databases, Housing Database). Then apply neural networks as well as decision tree methods as appropriate. Prepare a report on your results. (Some of these exercises could also be completed in a group or even as semester-long projects for term papers and so on.)

Student research and reports will vary.

3. Go to calsci.com and read about the company’s various business applications. Prepare a report that summarizes the applications.

Student research and reports will vary.

4. Go to nd.com. Read about the company’s applications in investment and trading.

Prepare a report about them.

Student research and reports will vary.

5. Go to nd.com. Download the trial version of NeuroSolutions for Excel and experiment with it using one of the data sets from the exercises in this chapter. Prepare a report about your experience with the tool.

Student research and reports will vary.

6. Go to neoxi.com. Identify at least two software tools that have not been mentioned in this chapter. Visit Web sites of those tools and prepare a brief report on their capabilities.

Student research and reports will vary.

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7. Go to neuroshell.com. Look at Gee Whiz examples. Comment on the feasibility of achieving the results claimed by the developers of this neural network model.

Student research and reports will vary.

8. Go to easynn.com. Download the trial version of the software. After the installation of the software, find the sample file called Houseprices.tvq. Retrain the neural network and test the model by supplying some data. Prepare a report about your experience with this software.

Student research and reports will vary.

9. Visit tibco.com. Download at least three white papers of applications. Which of these applications might have used neural networks?

Student research and reports will vary.

10. Go to neuralware.com. Prepare a report about the products the company offers.

Student research and reports will vary.

11. Go to ibm.com. Download at least two customer success stories or case studies that use advanced analytics or machine learning. Prepare a presentation for your understanding of these application cases.

Student research and reports will vary.

12. Go to sas.com. Download at least two customer success stories or case studies that use advanced analytics or machine learning. Prepare a presentation for your understanding of these application cases.

Student research and reports will vary.

13. Go to teradata.com. Download at least two customer success stories or case studies where advanced analytics or machine learning is used. Prepare a presentation for your understanding of these application cases.

Student research and reports will vary.

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Chapter 6: Deep Learning and Cognitive Computing Learning Objectives for Chapter 6 1. Learn what deep learning is and how it is changing the world of computing 2. Know the placement of deep learning within the broad family of artificial intelligence (AI) learning methods 3. Understand how traditional “shallow” artificial neural networks (ANN) work 4. Become familiar with the development and learning processes of ANN 5. Develop an understanding of the methods to shed light into the ANN black box 6. Know the underlying concept and methods for deep neural networks 7. Become familiar with different types of deep learning methods 8. Understand how convolutional neural networks (CNN) work 9. Learn how recurrent neural networks (RNN) and long short-memory networks (LSTM) work 10. Become familiar with the computer frameworks for implementing deep learning 11. Know the foundational details about cognitive computing 12. Learn how IBM Watson works and what types of application it can be used for CHAPTER OVERVIEW Artificial intelligence (AI) is making a re-entrance into the world of commuting and in our lives, this time far stronger and much more promising than before. This unprecedented re-emergence and the new level of expectations can largely be attributed to deep learning and cognitive computing. These two latest buzzwords define the leading edge of AI and machine learning today. Evolving out of the traditional artificial neural networks (ANN), deep learning is changing the very foundation of how machine learning works. Thanks to large collections of data and improved computational resources, deep learning is making a profound impact on how computers can discover complex patterns using the self-extracted features from the data (as opposed to a data scientist providing the feature vector to the learning algorithm). Cognitive computing— first popularized by IBM Watson and its success against the best human players in the game show Jeopardy!—makes it possible to deal with a new class of problems, the type of problems that are thought to be solvable only by human ingenuity and creativity, ones that are characterized by ambiguity and uncertainty. This chapter covers the concepts, methods, and application of these two cutting-edge AI technology trends.

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CHAPTER OUTLINE 6.1 Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence 6.2 Introduction to Deep Learning 6.3 Basics of “Shallow” Neural Networks 6.4 Process of Developing Neural Network–Based Systems 6.5 Illuminating the Black Box of ANN 6.6 Deep Neural Networks 343 6.7 Convolutional Neural Networks 6.8 Recurrent Networks and Long Short-Term Memory Networks 6.9 Computer Frameworks for Implementation of Deep Learning 6.10 Cognitive Computing

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ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 6.1 Review Questions 1. What is fraud in banking?

Fraud in banking is when a customer attempts to access an account that they do not have permission to access for the retrieval of funds or the mining of information.

2. What are the types of fraud that banking firms are facing today?

Student research and experience will vary. Some common types of fraud include stolen credit cards and fraudulent access to online accounts.

3. What do you think are the implications of fraud on banks and on their customers?

The implications of fraud are huge for banks and their customers because of the large monetary value involved and the importance of trust in the banking relationship.

4. Compare the old and new methods for identifying and mitigating fraud.

Old methods of fraud did not catch a significant number of the illegal activities and also generated a large number of false positives. New methods are significantly more effective and can balance the need for detection against excessive false positives.

5. Why do you think deep learning methods provided better prediction accuracy?

The system is able to provide better prediction accuracy because it is able to access and utilize a larger amount of data quickly and in real time, but also because the detection framework can evolve over time to become more accurate and recognize new types of fraud.

6. Discuss the trade-off between false positive and false negative (type 1 and type 2 errors) within the context of predicting fraudulent activities.

False negatives indicate that a fraud was able to be committed, whereas a false positive will create a customer service issue and inconvenience an account holder. Any system must try to balance between these two to ensure that fraud is not occurring, but at the same time the level of customer inconvenience is not so much that it would create a larger business issue.

Section 6.2 Review Questions 1. What is deep learning? What can deep learning do?

Deep learning, the newest branch of AI and machine-learning, attempts mimic the thought process of humans using mathematical algorithms to learn from data.

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2. Compared to traditional machine learning, what is the most prominent difference of deep learning?

The performance of traditional machine-learning algorithms such as decision trees, support vector machines, logistic regression, and neural networks relies heavily on the representation of the data. What deep learning has added to the classic machine-learning methods is in fact the ability to automatically acquire the knowledge required to accomplish such informal tasks and consequently extract some advanced features that contribute to the superior system performance.

3. List and briefly explain different learning methods in AI. These details are nicely summarized in figure 6.4. In knowledge-based systems, manually created representations provide the framework for evaluating information. In classic machine learning, manually created features provide the foundation for mapping of those features and then output. In generic representational learning features are created automatically and then mapping is performed from those features. In deep learning features are also created automatically but move beyond a single stage process to create more advanced features first before completing mapping functions.

4. What is representation learning, and how does it relate to deep learning?

Representation learning techniques entail one type of machine learning (which is also a part of AI) in which the emphasis is on learning and discovering features by the system in addition to discovering the mapping from those features to the output/target. Deep learning is a branch of representation learning.

Section 6.3 Review Questions 1. How does a single artificial neuron (i.e., PE) work?

This process is aided by referring to figure 6.5. Inputs enter the neuron and encounter the adjustable weight and bias. A calculation weight function is applied to the input as is a net input function. The output of the net input function then goes through another function called the transfer function for conversion and the production of actual output.

2. List and briefly describe the most commonly used ANN activation functions.

Most commonly used AAN and activation functions are: • Hard limit - operation as a baseline but increases to a set point if the condition is met • Linear - operation increases as condition increases • Log-Sigmoid - operation increases on a log scale as condition increases

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Positive linear - operation has a baseline but then increases linearly after a condition is met It is very helpful to refer to table 6.1 3. What is MLP, and how does it work?

MLP is multi- layer perception and represents neurons with multiple inputs as seen in figure 6.6. In this type of neuron each input would have its own adjustable weight.

4. Explain the function of weights in ANN. Weights define the impact that a given input has on a neuron in the next layer. As such, they embody what the network has learned so far. As a network learns, its weights are adjusted.

5. Describe the summation and activation functions in MLP-type ANN architecture.

The summation function determines the total input to a neuron by calculating the weighted sum of its individual input values. Its output is input to the transformation (transfer) function. The transformation (or transfer) function determines the output of a neuron from its input (i.e., output of the summation function).

Many possible transformation functions exist. Most are sigmoid functions. (A sigmoid function is a non-decreasing function whose value is in the range 0–1.) A simple transformation function produces an output of 0 for inputs up to a threshold value, an output of 1 from that value up.

Section 6.4 Review Questions 1. List the nine steps in conducting a neural network project.

These are shown in the flowchart of Figure 6.9. After testing (step 8), it is possible to return to a previous step: Step 1:Collect data Step 2:Separate (data) into training and test sets Step 3: Define a network structure Step 4: Select a learning algorithm Step 5:Set parameters and values Step 6:Initialize weights and start training Step 7:Stop training, freeze the network weights Step 8:Test the trained network Step 9:Implementation: use the network with new cases 2. What are some of the design parameters for developing a neural network?

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Some design parameters to consider include the type of network to employ, the number of nodes (input, hidden, and output) and layers, the types of transformation functions within each neuron, the original weight settings, and the acceptable delta (error) level.

3. Draw and briefly explain the three-step process of learning in ANN. In supervised learning, the learning process is inductive; that is, connection weights are derived from existing cases. The usual process of learning involves three tasks: 1. Compute temporary outputs.

2. Compare outputs with desired targets.

3. Adjust the weights and repeat the process.

Student drawings will closely resemble figure 6.12.

4. How does backpropagation learning work?

Backpropagation (short for back-error propagation) is the most widely used supervised learning algorithm in neural computing. Backpropagation involves a feedforward network with one or more hidden layers. Backpropagation involves supervised learning because predetermined correct answers for each training pattern are given and compared to the results given by the backpropagation algorithm. The difference between the correct answer and the output (known as the delta value) is calculated, and weights of the output nodes are adjusted accordingly. Weight adjustment also proceeds backward to the hidden layers; hence the term “backpropagation.” This process repeats, continuously testing against training patterns, until the delta value is sufficiently low.

5. What is overfitting in ANN learning? How does it happen, and how can it be mitigated?

Overfitting happens when the trained model is highly fitted to the training data set but performs poorly with regard to external data sets. A large group of strategies known as regularization strategies is designed to prevent models from overfitting by making changes or defining constraints for the model parameters or the performance function.

6. Describe the different types of neural network software available today.

Many commercial ANN software products function like software shells. They provide a set of standard architectures, learning algorithms, and parameters, along with the ability to manipulate the data. Some development tools can support up to several dozen network paradigms and learning algorithms. Most of the leading data mining tools (e.g., SAS Enterprise Miner, IBM SPSS Modeler, Statistica Data Miner) include neural network learning algorithms. Some specialized neural network products include California Scientific (BrainMaker), NeuralWare, NeuroDimension Inc., Ward Systems Group (Neuroshell), and Megaputer. Others are implemented as spreadsheet add-ins. In addition,

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there are class libraries and APIs for languages such as Java and C++. Mathematical applications such as MATLAB also include neural network algorithms.

Section 6.5 Review Questions 1. What is the so-called black-box syndrome?

ANN are typically thought of as black boxes, capable of solving complex problems but lacking the explanation of its capabilities. This phenomenon is commonly referred to as the “black-box” syndrome.

2. Why is it important to be able to explain an ANN’s model structure?

It is important to be able to explain a model’s “inner being”; such an explanation offers assurance that the network has been properly trained and will behave as desired once deployed in a business intelligence environment. Such a need to “look under the hood” might be attributable to a relatively small training set (as a result of the high cost of data acquisition) or a very high liability in case of a system error.

3. How does sensitivity analysis work in ANN? Sensitivity analysis is a method for extracting the cause-and-effect relationships among the inputs and the outputs of a trained neural network model. In the process of performing sensitivity analysis, the trained neural network’s learning capability is disabled so that the network weights are not affected.

4. Search the Internet to find other methods to explain ANN methods. Report the result Open-ended answer, depending on what a student finds.

Section 6.6 Review Questions 1. What is meant by “deep” in deep neural networks? Compare deep neural networks to shallow neural networks.

Deep neural networks have a large number of hidden layers in addition to a large number of neurons in each layer. This allows for a much more complex processing activity than was available in a shallow neural network and is supported by improvements in hardware technology.

2. What is GPU? How does it relate to deep neural networks?

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A GPU is a graphical processing unit that is able to be used for AI purposes and allows neural networks with over 1 million neurons to function. They are significantly faster than traditional CPUs for this purpose.

3. How does a feedforward multilayer perceptron-type deep network work?

These networks are simply large-scale neural networks that can contain many layers of neurons and handle tensors as their input. These models are called feedforward because the flow of information that goes through them is always forwarding and no feedback connections (i.e., connections in which outputs of a model are fed back to itself) are allowed.

4. Comment on the impact of random weights in developing deep MLP. Optimization of the performance (loss) function in many real applications of deep MLPs is a challenging issue. The problem is that applying the common gradient-based training algorithms with random initialization of weights and biases that is very efficient for finding the optimal set of parameters in shallow neural networks most of the time could lead to getting stuck in the locally optimal solutions rather than catching the global optimum values for the parameters. As the depth of network increases, chances of reaching a global optimum using random initializations with the gradient-based algorithms decrease.

5. Which strategy is better: more hidden layers versus more neurons?

Whereas theoretically it is still an open research question, practically using more layers in a network seems to be more effective and computationally more efficient than using many neurons in a few layers.

Section 6.7 Review Questions 1. What is CNN? CNN is a convolutional neural network that includes at least one layer involving a convolutional weight function instead of the general matrix multiplication.

2. For what type of applications can CNN be used?

CNN was initially designed for computer vision applications but is also applicable to non-image data sets as well.

3. What is convolution function in CNN and how does it work?

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The convolution function uses the concept of parameter sharing which in addition to computational efficiency allows for weights parameters to be shared thus reducing processing load in systems with a large number of weight parameters.

4. What is pooling in CNN? How does it work?

Generally, a convolution layer is followed by another layer known as the pooling layer.

The purpose of a pooling layer is to consolidate elements in the input matrix to produce a smaller output matrix while maintaining the important features. Normally, a pooling function involves an r * c consolidation window that moves around the input matrix and in each move calculates some summary statistics of the elements involved in the consolidation window so that it can be put in the output image.

5. What is ImageNet and how does it relate to deep learning?

ImageNet is an ongoing research project that provides researchers with a large database of images, each linked to a set of synonym words from WordNet. ImageNet is a huge database for developing image processing–type deep networks.

6. What is the significance of AlexNet? Draw and describe its architecture.

AlexNet was one of the first convolutional networks designed for image classification using the ImageNet data set. It was composed of five convolution layers followed by three fully connected layers. One of the contributions of this architecture that made its training remarkably faster and computationally efficient was the use of rectified linear unit (ReLu) transfer functions in the convolution layers instead of the traditional sigmoid functions.

7. What is GoogLeNet? How does it work?

Google Lens uses deep learning artificial neural network algorithms (along with other AI techniques) to deliver information about the images captured by users from their nearby objects. This involves identifying the objects, products, plants, animals, and locations and providing information about them on the Internet.

8. How does CNN process text? What are word embeddings, and how do they work?

CNN processes text using the same approach as is used for processing images. Word in beddings are also known as word vectors where the relative position of words in text is given weight for additional context during processing.

9. What is word2vec, and what does it add to traditional text mining Word2vec is a two-layer neural network that gets a large text corpus as the input and converts each word in the corpus to a numeric vector of any given size with very interesting features. Although word2vec itself is not a deep learning algorithm, its outputs 9 .

already have been widely used in many deep learning research and commercial projects as inputs. By providing such a meaningful representation of textual data, in recent years, word2vec has driven many deep learning–based text mining projects in a wide range of contexts (e.g., medical, computer science, social media, marketing), and various types of deep networks have been applied to the word embeddings created by this algorithm to accomplish different objectives.

Section 6.8 Review Questions 1. What is RNN? How does it differ from CNN? RNN is a recurrent neural network. RNNs are the type of neural networks that have memory and that apply that memory to determine their future outputs It differs from CNN in that it has these defined feedback methods.

2. What is the significance of “context,” “sequence,” and “memory” in RNN? Our and ends are useful because they are able to include context in their processing. In this circumstance context refers to an understanding of the past sequence of events and the ability to use memory to hold these past events and use them in consideration for decisions.

3. Draw and explain the functioning of a typical recurrent neural network unit.

Student drawings will vary, but will closely resemble figure 6.31 and describe how past inputs are considered before outputs are generated.

4. What is the LSTM network, and how does it differ from RNNs? LSTM (long short-term memory) networks have been widely used in many sequence modeling applications, including image captioning, handwriting recognition and generation, parsing, speech recognition, and machine translation. LSTM differs from RNN in that it has the ability to process longer term dependencies.

5. List and briefly describe three different types of LSTM applications.

• • •

Image captioning - the ability to label video images using context of previous screens Speech recognition - the ability to recognize spoken words accurately utilizing their context within a sentence.

Machine translation - the ability to translate speech to text from one language to another

6. How do Google’s Neural Machine Translation and Microsoft Skype Translator work

10 .

These voice translation services include both speech recognition and machine translation in real time. Users can speak in different languages and their conversation is recognized and then translated for the other listener.

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Section 6.9 Review Questions 1. Despite the short tenure of deep learning implementation, why do you think there are several different computing frameworks for it?

Perceptions and opinions will vary will likely focus on the power and utility of these frameworks.

2. Define CPU, NVIDIA, CUDA, and deep learning, and comment on the relationship between them.

CPU - central processing unit NVIDIA - developer of GPU hardware CUDA - programming language used on NVIDIA GPU hardware Deep learning - the newest branch of AI and machine-learning, attempts mimic the thought process of humans using mathematical algorithms to learn from data.

Much of the progress in deep learning is due to the availability of GPU hardware that allows for detailed learning architectures.

3. List and briefly define the characteristics of different deep learning frameworks.

The operation of these libraries mostly relies on a parallel computing platform and application programming interface (API) developed by NVIDIA called Compute Unified Device Architecture (CUDA), which enables software developers to use GPUs made by NVIDIA for general-purpose processing. In fact, each deep learning framework consists of a high-level scripting language (e.g., Python, R, Lua) and a library of deep learning routines usually written in C (for using CPUs) or CUDA (for using GPUs).

4. What is Keras, and how is it different from the other frameworks?

Keras (https:// keras.io/) is an open-source neural network library written in Python that functions as a high-level application programming interface (API) and is able to run on top of various deep learning frameworks including Theano and TensorFlow. In essence, Keras just by getting the key properties of network building blocks (i.e., type of layers, transfer functions, and optimizers) via an extremely simple syntax automatically generates syntax in one of the deep learning frameworks and runs that framework in the backend.

Section 6.10 Review Questions 1. What is cognitive computing, and how does it differ from other computing paradigms?

Cognitive computing makes a new class of problems computable. It addresses highly complex situations that are characterized by ambiguity and uncertainty. Instead of

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seeking results as other paradigms may, cognitive computing seeks to augment human capability to find solutions.

2. Draw a diagram and explain the conceptual framework of cognitive computing. Make sure to include inputs, enablers, and expected outcomes in your framework.

Student drawings will differ but will closely resemble figure 6.36.

3. List and briefly define the key attributes of cognitive computing.

•

Adaptive: Cognitive systems must be flexible enough to learn as information changes and goals evolve. The systems must be able to digest dynamic data in real time and make adjustments as the data and environment change.

•

Interactive: Human-computer interaction (HCI) is a critical component in cognitive systems. Users must be able to interact with cognitive machines and define their needs as those needs change. The technologies must also be able to interact with other processors, devices, and cloud platforms.

•

Iterative and stateful: Cognitive computing technologies can also identify problems by asking questions or pulling in additional data if a stated problem is vague or incomplete. The systems do this by maintaining information about similar situations that have previously occurred. •

•

Contextual: Understanding context is critical in thought processes, so cognitive systems must understand, identify, and mine contextual data, such as syntax, time, location, domain, requirements, and a specific user’s profile, tasks, or goals.

Cognitive systems may draw on multiple sources of information, including structured and unstructured data and visual, auditory, or sensor data.

4. How does cognitive computing differ from ordinary AI techniques?

• • • •

Technologies - both technologies utilize machine learning, and LP, neural networks and deep learning: but cognitive computing adds text mining and sentiment analysis capabilities offered - while AI seeks to find hidden patterns in data sources, cognitive computing attempts to simulate the human thought process to find solutions purpose - AI seeks to automate complex processes whereas cognitive computing seeks to augment human capability industries - both have application in most industries, but the focus of cognitive computing is currently in customer service, marketing, healthcare, entertainment and services

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5. What are the typical use cases for cognitive analytics?

The typical use cases for cognitive computing include the following: • Development of smart and adaptive search engines • Effective use of natural language processing • Speech recognition • Language translation • Context-based sentiment analysis • Face recognition and facial emotion detection • Risk assessment and mitigation • Fraud detection and mitigation • Behavioral assessment and recommendations 6. Explain what the terms cognitive analytics and cognitive search mean.

Cognitive analytics is a term that refers to cognitive computing–branded technology platforms, such as IBM Watson, that specialize in processing and analyzing large, unstructured data sets.

Cognitive search is the new generation search method that uses AI (advanced indexing, NLP, and machine learning) to return results that are much more relevant to users.

7. What is IBM Watson and what is its significance to the world of computing?

IBM Watson is perhaps the smartest computer system built to date. In 2010, an IBM research team developed Watson, an extraordinary computer system—a novel combination of advanced hardware and software—designed to answer questions posed in natural human language.

8. How does Watson work?

Watson uses DeepQA: • Massive parallelism. Watson needed to exploit massive parallelism in the consideration of multiple interpretations and hypotheses.

• Many experts. Watson needed to be able to integrate, apply, and contextually evaluate a wide range of loosely coupled probabilistic questions and content analytics.

• Pervasive confidence estimation. No component of Watson committed to an answer; all components produced features and associated confidences, scoring different question and content interpretations. An underlying confidenceprocessing substrate learned how to stack and combine the scores.

• Integration of shallow and deep knowledge. Watson needed to balance the use of strict semantics and shallow semantics, leveraging many loosely formed ontologies.

9. List and briefly explain five use cases for IBM Watson 14 .

Selections and descriptions will vary, but five potential use areas described in the chapter include: • healthcare and medicine • security • finance • retail • education • government • research

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION

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Application Case 6.1: Finding the Next Football Star with Artificial Intelligence 1. What does SciSports do? Look at its Web site for more information.

SciSports is a provider of intelligence and analytics to professional football organizations.

2. How can advanced analytics help football teams?

Analytics can provide a number of benefits by allowing teams to better understand gameplay overall including player and team strengths and weaknesses, competitor analysis, game play analysis and predictive modeling for player selection.

3. What is the role of deep learning in solutions provided by SciSports?

The company uses deep learning in several solutions including learning the best layouts for players in corner kicks him and managing the compilation of 3D imaging by better understanding ball movements.

Application Case 6.2: Gaming Companies Use Data Analytics to Score Points with Players 1. What are the main challenges for gaming companies?

The videogame industry is highly competitive, and gaming companies must ensure that they are providing a product that meets their customers needs. As a part of this marketing companies have determined that understanding and connecting with players can drive sales and loyalty.

2. How can analytics help gaming companies stay competitive?

Analytics allows gaming companies to better understand their players activities and 15 .

desires. Better understanding in these areas allows player behaviors to be monetized more completely; this includes running more effective marketing campaigns, improving retention and using results to improve future games.

3. What types of data can gaming companies obtain and use for analytics?

Giving companies are able to access large amounts of information on gamers for titles that are played online. This information can include duration of play, time of play, types of games played and the size of a gamers network.

Application Case 6.3: Artificial Intelligence Helps Protect Animals from Extinction 1. What is WildTrack and what does it do?

WildTrack is an artificial intelligence system that is designed to aid humans in the tracking of animals.

2. How can advanced analytics help WildTrack?

The system is able to provide detailed information on animals based on their tracks, but is also able to learn and provide more complete and accurate information over time.

3. What are the roles that deep learning plays in this application case?

The ability to better understand patterns in the tracks and become more accurate and detailed is a feature set provided by deep learning.

Application Case 6.4: Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents 1. How does sensitivity analysis shed light on the black box (i.e., neural networks)?

Sensitivity analysis techniques provide a clear interpretation of how a neural network does what it does; that is, specifically how (and to what extent) the individual inputs factor into the generation of specific network output. This process extracts the cause-andeffect relationships among the inputs and the outputs of a trained neural network model.

2. Why would someone choose to use a black-box tool such as neural networks over theoretically sound, mostly transparent statistical tools like logistic regression?

ANN are known to be superior in capturing highly nonlinear complex relationships between predictor and target variables, assuming a linear relationship is often an oversimplification of the problem.

3. In this case, how did neural networks and sensitivity analysis help identify injuryseverity factors in traffic accidents?

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ANN and sensitivity analysis helped estimate the significance of the crash factors on the level of injury severity sustained by the driver. This study was a two-step process. In the first step, the testers developed a series of prediction models (one for each injury severity level) to capture the in-depth relationships between the crash-related factors and a specific level of injury severity. In the second step, they conducted sensitivity analysis on the trained neural network models to identify the prioritized importance of crash-related factors as they relate to different injury severity levels.

The study revealed that the variable seatbelt use was the most important determinant for predicting higher levels of injury severity but it was one of the least significant predictors for lower levels of injury severity. Other interesting findings involved gender (good predictor for low injury severity, but not for high) and age (vice versa).

Application Case 6.5: Georgia DOT Variable Speed Limit Analytics Help Solve Traffic Congestions 1. What was the nature of the problems that GDOT was trying to solve with data science?

The department wanted to evaluate the effectiveness of a proof of concept variable speed limit (VSL) pilot program.

2. What type of data do you think was used for the analytics?

Student opinions will vary but most likely will include minimum speed, maximum speed, average speed, number of vehicles and number of complete stops (traffic jams).

3. What were the data science metrics developed in this pilot project? Can you think of other metrics that can be used in this context?

The two new metrics that were developed as part of this study included an evaluation of frequency and duration of slowdown locations as well as a better understanding of turbulence created by bottlenecks. Student ideas on additional metrics will vary.

Application Case 6.6: From Image Recognition to Face Recognition 1. What are the technical challenges in face recognition?

Face recognition is difficult because the goal is to identify an individual as opposed to a class using moving 3D images.

2. Beyond security and surveillance purposes, where else do you think face recognition can be used?

This is an open ended question student ideas will be different.

3. What are the foreseeable social and cultural problems with developing and using face recognition technology?

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It is possible that extensive use of facial recognition can be an issue of privacy, with individuals being easily tracked whenever they are in public.

Application Case 6.7: Deliver Innovation by Understanding Customer Sentiment 1. Why do you think sentiment analysis is gaining overwhelming popularity?

Student opinions will vary, but may focus on the importance of understanding consumer sentiments as it relates to many aspects of life including commerce and politics.

2. How does sentiment analysis work? What does it produce?

Sentiment analysis works by analyzing text and notes available from individuals in some relation to the topic being addressed. Information on products and features gleaned categorized and analyzed. This results in a better understanding of how a product is perceived and how it meets customer needs.

3. In addition to the specific examples in this case, can you think of other businesses and industries that can benefit from sentiment analysis? What is common among the companies that can benefit greatly from sentiment analysis?

Student opinions on other examples will vary, but one commonality may be that companies that deal directly with the public where consumers have a choice in product or service offering may benefit significantly.

Application Case 6.8: IBM Watson Competes against the Best at Jeopardy!

1. In your opinion, what are the most unique features about Watson?

Student opinions and responses will vary.

Watson is a question answering (QA) computer system developed by an IBM Research team and named after IBM’s first president as part of a project called DeepQA. What makes it special is that it is able to compete at the human champion level in real time on the TV quiz show, Jeopardy!; in fact, in 2011, it was able to defeat Ken Jennings, who held the record for the longest winning streak in the game. Like Deep Blue has done with chess, Watson is showing that computer systems are getting quite good at demonstrating human-like intelligence.

2. In what other challenging games would you like to see Watson compete against humans? Why? Student opinions and selections will vary.

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3. What are the similarities and differences between Watson’s and humans’ intelligence?

The DeepQA architecture involves massive parallelism, many experts, pervasive confidence estimation, and integration of the-latest-and-greatest in text analytics, involving both shallow and deep semantic knowledge. As implemented in Watson, DeepQA brings more than 100 different techniques for analyzing natural language, identifying sources, finding and generating hypotheses, finding and scoring evidence, and merging and ranking hypotheses. More important than any particular technique is the combination of overlapping approaches that can bring their strengths to bear and contribute to improvements in accuracy, confidence, and speed.

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION

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1. What is deep learning? What can deep learning do that traditional machine-learning methods cannot?

Deep learning, the newest branch of AI and machine-learning, attempts mimic the thought process of humans using mathematical algorithms to learn from data.

2. List and briefly explain different learning paradigms/ methods in AI. These details are nicely summarized in figure 6.4. In knowledge-based systems, manually created representations provide the framework for evaluating information. In classic machine learning, manually created features provide the foundation for mapping of those features and then output. In generic representational learning features are created automatically and then mapping is performed from those features. In deep learning features are also created automatically but move beyond a single stage process to create more advanced features first before completing mapping functions.

3. What is representation learning, and how does it relate to machine learning and deep learning?

Representation learning techniques entail one type of machine learning (which is also a part of AI) in which the emphasis is on learning and discovering features by the system in addition to discovering the mapping from those features to the output/target. Deep learning is a branch of representation learning.

4. List and briefly describe the most commonly used ANN activation functions.

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Most commonly used AAN and activation functions are: • Hard limit - operation as a baseline but increases to a set point if the condition is met • Linear - operation increases as condition increases • Log-Sigmoid - operation increases on a log scale as condition increases • Positive linear - operation has a baseline but then increases linearly after a condition is met It is very helpful to refer to table 6.1 5. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN. MLP is multi- layer perception and represents neurons with multiple inputs as seen in figure 6.6. In this type of neuron each input would have its own adjustable weight.

6. List and briefly describe the nine-step process in conducting a neural network project.

These are shown in the flowchart of Figure 6.9. After testing (step 8), it is possible to return to a previous step: Step 1:Collect data Step 2:Separate (data) into training and test sets Step 3: Define a network structure Step 4: Select a learning algorithm Step 5:Set parameters and values Step 6:Initialize weights and start training Step 7:Stop training, freeze the network weights Step 8:Test the trained network Step 9:Implementation: use the network with new cases 7. Draw and briefly explain the three-step process of learning in ANN. In supervised learning, the learning process is inductive; that is, connection weights are derived from existing cases. The usual process of learning involves three tasks: 1. Compute temporary outputs.

2. Compare outputs with desired targets.

3. Adjust the weights and repeat the process.

Student drawings will closely resemble figure 6.12.

8. How does the backpropagation learning algorithm work?

Backpropagation (short for back-error propagation) is the most widely used supervised learning algorithm in neural computing. Backpropagation involves a feedforward network with one or more hidden layers. Backpropagation involves supervised learning because predetermined correct answers for each training pattern are given and compared 20 .

to the results given by the backpropagation algorithm. The difference between the correct answer and the output (known as the delta value) is calculated, and weights of the output nodes are adjusted accordingly. Weight adjustment also proceeds backward to the hidden layers; hence the term “backpropagation.” This process repeats, continuously testing against training patterns, until the delta value is sufficiently low.

9. What is overfitting in ANN learning? How does it happen, and how can it be prevented?

Overfitting happens when the trained model is highly fitted to the training data set but performs poorly with regard to external data sets. A large group of strategies known as regularization strategies is designed to prevent models from overfitting by making changes or defining constraints for the model parameters or the performance function.

10. What is the so-called black-box syndrome? Why is it important to be able to explain an ANN’s model structure?

ANN are typically thought of as black boxes, capable of solving complex problems but lacking the explanation of its capabilities. This phenomenon is commonly referred to as the “black-box” syndrome.

11. How does sensitivity analysis work in ANN? Search the Internet to find other methods to explain ANN methods.

Sensitivity analysis is a method for extracting the cause-and-effect relationships among the inputs and the outputs of a trained neural network model. In the process of performing sensitivity analysis, the trained neural network’s learning capability is disabled so that the network weights are not affected.

12. What is meant by “deep” in deep neural networks? Compare deep neural network to shallow neural network.

Deep neural networks have a large number of hidden layers in addition to a large number of neurons in each layer. This allows for a much more complex processing activity than was available in a shallow neural network and is supported by improvements in hardware technology.

13. What is GPU? How does it relate to deep neural networks?

A GPU is a graphical processing unit that is able to be used for AI purposes and allows neural networks with over 1 million neurons to function. They are significantly faster than traditional CPUs for this purpose.

14. How does a feedforward multilayer perceptron–type deep network work?

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These networks are simply large-scale neural networks that can contain many layers of neurons and handle tensors as their input. These models are called feedforward because the flow of information that goes through them is always forwarding and no feedback connections (i.e., connections in which outputs of a model are fed back to itself) are allowed.

15. Comment on the impact of random weights in developing deep MLP. Optimization of the performance (loss) function in many real applications of deep MLPs is a challenging issue. The problem is that applying the common gradient-based training algorithms with random initialization of weights and biases that is very efficient for finding the optimal set of parameters in shallow neural networks most of the time could lead to getting stuck in the locally optimal solutions rather than catching the global optimum values for the parameters. As the depth of network increases, chances of reaching a global optimum using random initializations with the gradient-based algorithms decrease.

16. Which strategy is better: more hidden layers versus more neurons?

Whereas theoretically it is still an open research question, practically using more layers in a network seems to be more effective and computationally more efficient than using many neurons in a few layers.

17. What is CNN? CNN is a convolutional neural network that includes at least one layer involving a convolutional weight function instead of the general matrix multiplication.

18. For what type of applications can CNN be used?

CNN was initially designed for computer vision applications but is also applicable to non-image data sets as well.

19. What is the convolution function in CNN, and how does it work?

The convolution function uses the concept of parameter sharing which in addition to computational efficiency allows for weights parameters to be shared thus reducing processing load in systems with a large number of weight parameters.

20. What is pooling in CNN? How does it work?

Generally, a convolution layer is followed by another layer known as the pooling layer.

The purpose of a pooling layer is to consolidate elements in the input matrix to produce a smaller output matrix while maintaining the important features. Normally, a pooling function involves an r * c consolidation window that moves around the input matrix and

22 .

in each move calculates some summary statistics of the elements involved in the consolidation window so that it can be put in the output image.

21. What is ImageNet, and how does it relate to deep learning?

ImageNet is an ongoing research project that provides researchers with a large database of images, each linked to a set of synonym words from WordNet. ImageNet is a huge database for developing image processing–type deep networks.

22. What is the significance of AlexNet? Draw and describe its architecture.

AlexNet was one of the first convolutional networks designed for image classification using the ImageNet data set. It was composed of five convolution layers followed by three fully connected layers. One of the contributions of this architecture that made its training remarkably faster and computationally efficient was the use of rectified linear unit (ReLu) transfer functions in the convolution layers instead of the traditional sigmoid functions.

23. What is GoogLeNet? How does it work?

Google Lens uses deep learning artificial neural network algorithms (along with other AI techniques) to deliver information about the images captured by users from their nearby objects. This involves identifying the objects, products, plants, animals, and locations and providing information about them on the Internet.

24. How does CNN process text? What is word embeddings, and how does it work?

CNN processes text using the same approach as is used for processing images. Word in beddings are also known as word vectors where the relative position of words in text is given weight for additional context during processing.

25. What is word2vec, and what does it add to the traditional text mining?

Word2vec is a two-layer neural network that gets a large text corpus as the input and converts each word in the corpus to a numeric vector of any given size with very interesting features. Although word2vec itself is not a deep learning algorithm, its outputs already have been widely used in many deep learning research and commercial projects as inputs. By providing such a meaningful representation of textual data, in recent years, word2vec has driven many deep learning–based text mining projects in a wide range of contexts (e.g., medical, computer science, social media, marketing), and various types of deep networks have been applied to the word embeddings created by this algorithm to accomplish different objectives.

26. What is RNN? How does it differ from CNN?

23 .

RNN is a recurrent neural network. RNNs are the type of neural networks that have memory and that apply that memory to determine their future outputs. It differs from CNN in that it has these defined feedback methods.

27. What is the significance of context, sequence, and memory in RNN? Our and ends are useful because they are able to include context in their processing. In this circumstance context refers to an understanding of the past sequence of events and the ability to use memory to hold these past events and use them in consideration for decisions.

28. Draw and explain the functioning of a typical recurrent neural network unit.

Student drawings will vary, but will closely resemble figure 6.31 and describe how past inputs are considered before outputs are generated.

29. What is LSTM network, and how does it differ from RNNs? LSTM (long short-term memory) networks have been widely used in many sequence modeling applications, including image captioning, handwriting recognition and generation, parsing, speech recognition, and machine translation. LSTM differs from RNN in that it has the ability to process longer term dependencies.

30. List and briefly describe three different types of LSTM applications.

• • •

Image captioning - the ability to label video images using context of previous screens Speech recognition - the ability to recognize spoken words accurately utilizing their context within a sentence.

Machine translation - the ability to translate speech to text from one language to another

31. How do Google’s Neural Machine Translation and Microsoft Skype Translator work?

These voice translation services include both speech recognition and machine translation in real time. Users can speak in different languages and their conversation is recognized and then translated for the other listener.

32. Despite its short tenure, why do you think deep learning implementation has several different computing frameworks?

Perceptions and opinions will vary will likely focus on the power and utility of these frameworks.

33. Define and comment on the relationship between CPU, NVIDIA, CUDA, and deep learning.

24 .

CPU - central processing unit NVIDIA - developer of GPU hardware CUDA - programming language used on NVIDIA GPU hardware Deep learning - the newest branch of AI and machine-learning, attempts mimic the thought process of humans using mathematical algorithms to learn from data.

Much of the progress in deep learning is due to the availability of GPU hardware that allows for detailed learning architectures.

34. List and briefly define the characteristics of different deep learning frameworks.

The operation of these libraries mostly relies on a parallel computing platform and application programming interface (API) developed by NVIDIA called Compute Unified Device Architecture (CUDA), which enables software developers to use GPUs made by NVIDIA for general-purpose processing. In fact, each deep learning framework consists of a high-level scripting language (e.g., Python, R, Lua) and a library of deep learning routines usually written in C (for using CPUs) or CUDA (for using GPUs).

35. What is Keras, and how does it differ from other frameworks?

Keras (https:// keras.io/) is an open-source neural network library written in Python that functions as a high-level application programming interface (API) and is able to run on top of various deep learning frameworks including Theano and TensorFlow. In essence, Keras just by getting the key properties of network building blocks (i.e., type of layers, transfer functions, and optimizers) via an extremely simple syntax automatically generates syntax in one of the deep learning frameworks and runs that framework in the backend.

36. What is cognitive computing and how does it differ from other computing paradigms?

Cognitive computing makes a new class of problems computable. It addresses highly complex situations that are characterized by ambiguity and uncertainty. Instead of seeking results as other paradigms may, cognitive computing seeks to augment human capability to find solutions.

37. Draw a diagram and explain the conceptual framework of cognitive computing. Make sure to include inputs, enablers, and expected outcomes in your framework.

Student drawings will differ but will closely resemble figure 6.36.

38. List and briefly define the key attributes of cognitive computing.

•

Adaptive: Cognitive systems must be flexible enough to learn as information changes and goals evolve. The systems must be able to digest dynamic data in real time and make adjustments as the data and environment change.

25 .

•

Interactive: Human-computer interaction (HCI) is a critical component in cognitive systems. Users must be able to interact with cognitive machines and define their needs as those needs change. The technologies must also be able to interact with other processors, devices, and cloud platforms.

•

Iterative and stateful: Cognitive computing technologies can also identify problems by asking questions or pulling in additional data if a stated problem is vague or incomplete. The systems do this by maintaining information about similar situations that have previously occurred. •

•

Contextual: Understanding context is critical in thought processes, so cognitive systems must understand, identify, and mine contextual data, such as syntax, time, location, domain, requirements, and a specific user’s profile, tasks, or goals.

Cognitive systems may draw on multiple sources of information, including structured and unstructured data and visual, auditory, or sensor data.

39. How does cognitive computing differ from ordinary AI techniques?

• • • •

Technologies - both technologies utilize machine learning, and LP, neural networks and deep learning: but cognitive computing adds text mining and sentiment analysis capabilities offered - while AI seeks to find hidden patterns in data sources, cognitive computing attempts to simulate the human thought process to find solutions purpose - AI seeks to automate complex processes whereas cognitive computing seeks to augment human capability industries - both have application in most industries, but the focus of cognitive computing is currently in customer service, marketing, healthcare, entertainment and services

40. What are the typical use cases for cognitive analytics?

The typical use cases for cognitive computing include the following: • Development of smart and adaptive search engines • Effective use of natural language processing • Speech recognition • Language translation • Context-based sentiment analysis • Face recognition and facial emotion detection • Risk assessment and mitigation • Fraud detection and mitigation • Behavioral assessment and recommendations 41. What is cognitive analytics? What is cognitive search?

26 .

Cognitive analytics is a term that refers to cognitive computing–branded technology platforms, such as IBM Watson, that specialize in processing and analyzing large, unstructured data sets.

Cognitive search is the new generation search method that uses AI (advanced indexing, NLP, and machine learning) to return results that are much more relevant to users.

42. What is IBM Watson, and what is its significance to the world of computing?

IBM Watson is perhaps the smartest computer system built to date. In 2010, an IBM research team developed Watson, an extraordinary computer system—a novel combination of advanced hardware and software—designed to answer questions posed in natural human language.

43. How does IBM Watson work?

Watson uses DeepQA: • Massive parallelism. Watson needed to exploit massive parallelism in the consideration of multiple interpretations and hypotheses.

• Many experts. Watson needed to be able to integrate, apply, and contextually evaluate a wide range of loosely coupled probabilistic questions and content analytics.

• Pervasive confidence estimation. No component of Watson committed to an answer; all components produced features and associated confidences, scoring different question and content interpretations. An underlying confidenceprocessing substrate learned how to stack and combine the scores.

• Integration of shallow and deep knowledge. Watson needed to balance the use of strict semantics and shallow semantics, leveraging many loosely formed ontologies.

44. List and briefly explain five use cases for IBM Watson.

Selections and descriptions will vary, but five potential use areas described in the chapter include: • healthcare and medicine • security • finance • retail • education • government • research

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ANSWERS TO END OF CHAPTER EXERCISES

Teradata University Network (TUN) and Other Hands-On and Internet Exercises 27 .

1. Go to the Teradata University Network Web site (teradatauniversitynetwork.com).

Search for teaching and learning materials (e.g., articles, application cases, white papers, videos, exercises) on deep learning, cognitive computing, and IBM Watson. Read the material you have found. If needed, also conduct a search on the Web to enhance your findings. Write a report on your findings.

Student research and reports will vary.

2. Deep learning is relatively new to the world of analytics. Its application cases and success stories are just starting to emerge in the Web. Conduct a comprehensive search on your school’s digital library resources to identify at least five journal articles where interesting deep learning applications are described. Write a report on your findings.

Student reports will vary.

3. Most of the applications of deep learning today are developed using R- and/or Pythonbased open-source computing resources. Identify those resources (frameworks such as Torch, Caffe, TensorFlow, Theano, Keras) available for building deep learning models and applications. Compare and contrast their capabilities and limitations. Based on your findings and understanding of these resources, if you were to develop a deep learning application, which one would you choose to employ? Explain and justify/defend your choice.

Student selections and analysis will vary.

4. Cognitive computing has become a popular term to define and characterize the extent of the ability of machines/ computers to show “intelligent” behavior. Thanks to IBM Watson and its success on Jeopardy!, cognitive computing and cognitive analytics are now part of many realworld intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.

Student research, analysis and reports will differ.

5. Download KNIME analytics platform, one of the most popular free/open-source software tools from knime. org. Identify the deep learning examples (where Keras is used to build some exemplary prediction/classification models) in its example folder. Study the models in detail. Understand what it does and how exactly it does it. Then, using a different but similar data set, build and test your own deep learning prediction model.

Report your findings and experiences in a written document.

Student reports will vary.

6. Search for articles related to “cognitive search.” Identify at least five pieces of written material (a combination of journal articles, white papers, blog posts, application cases, 28 .

etc.). Read and summarize your findings. Explain your understanding of cognitive search and how it differs from regular search methods.

Student selection of articles and thus reports will differ.

7. Go to Teradata.com. Search and find application case studies and white papers on deep learning and/or cognitive computing. Write a report to summarize your findings, and comment on the capabilities and limitations (based on your understanding) of these technologies.

Students will select and write on different cases.

8. Go to SAS.com. Search and find application case studies and white papers on deep learning and/or cognitive computing. Write a report to summarize your findings, and comment on the capabilities and limitations (based on your understanding) of these technologies.

Students will select and write on different cases.

9. Go to IBM.com. Search and find application case studies and white papers on deep learning and/or cognitive computing. Write a report to summarize your findings, and comment on the capabilities and limitations (based on your understanding) of these technologies.

Students will select and write on different cases.

10. Go to TIBCO.com or some other advanced analytics company Web site. Search and find application case studies and white papers on deep learning and/or cognitive computing. Write a report to summarize your findings, and comment on the capabilities and limitations (based on your understanding) of these technologies.

Student research, analysis and reports will differ.

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Chapter 7: Text Analytics, Text Mining, and Sentiment Analysis Learning Objectives for Chapter 7 1. Describe text mining and understand the need for text mining 2. Differentiate among text analytics, text mining, and data mining 3. Understand the different application areas for text mining 4. Know the process of carrying out a text mining project 5. Appreciate the different methods to introduce structure to text-based data 6. Describe sentiment analysis 7. Develop familiarity with popular applications of sentiment analysis 8. Learn the common methods for sentiment analysis 9. Become familiar with speech analytics as it relates to sentiment analysis 10. Learn three facets of Web analytics—content, structure, and usage mining 11. Know social analytics including social media and social network analyses CHAPTER OVERVIEW This chapter provides a comprehensive overview of text analytics/mining and Web analytics/mining along with their popular application areas such as search engines, sentiment analysis, and social network/media analytics. As we have been witnessing in recent years, the unstructured data generated over the Internet of Things (IoT) (Web, sensor networks, radio-frequency identification [RFID]–enabled supply chain systems, surveillance networks, etc.) are increasing at an exponential pace, and there is no indication of its slowing down. This changing nature of data is forcing organizations to make text and Web analytics a critical part of their business intelligence/analytics infrastructure.

CHAPTER OUTLINE 7.1 Opening Vignette: Amadori Group Converts Consumer Sentiments into Near-Real-Time Sales 7.2 Text Analytics and Text Mining Overview 7.3 Natural Language Processing (NLP) 7.4 Text Mining Applications 7.5 Text Mining Process 7.6 Sentiment Analysis 1 .

7.7 Web Mining Overview 7.8 Search Engines 7.9 Web Usage Mining 7.10 Social Analytics

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 7.1 Review Questions 1. According to the vignette and based on your opinion, what are the challenges that the food industry is facing today?

Student perceptions may vary, but some common themes related to the challenges faced by the food industry could include the changing nature and role of food in people’s lifestyles, the shift towards pre-prepared or easily prepared food, and the growing importance of marketing to keep customers interested in brands.

2. How can analytics help businesses in the food industry to survive and thrive in this competitive marketplace?

Analytics can serve dual purposes by both tracking customer interest in the brand as well as providing valuable feedback on customer preferences. An analytics system can be used to evaluate the traffic to various brand marketing campaigns (website or social) that play a pivotal role in ensuring that products are being shown to new potential buyers and reminding existing customers of their value. An analytics system can also be used to help gather customer feedback and perception information on a brand in general or products in particular. This valuable information can be used as a part of both marketing and product design.

3. What were and still are the main objectives for Amadori to embark into analytics?

What were the results?

The company’s main objectives were to market more effectively to potential customers and create direct communications through social media and other channels with current customers to start a dialogue. The case illustrates how an analytics system integrated with thoughtful website design can help a company meet these goals.

4. Can you think of other businesses in the food industry that utilize analytics to become more competitive and customer focused? If not, an Internet search could help find relevant information to answer this question.

Student opinions and Web searches will vary, but will show similar strategies for packaged foods as well as fast foods in the US.

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Section 7.2 Review Questions 1.

What is text analytics? How does it differ from text mining?

Text analytics is a concept that includes information retrieval (e.g., searching and identifying relevant documents for a given set of key terms) as well as information extraction, data mining, and Web mining. By contrast, text mining is primarily focused on discovering new and useful knowledge from textual data sources. The overarching goal for both text analytics and text mining is to turn unstructured textual data into actionable information through the application of natural language processing (NLP) and analytics. However, text analytics is a broader term because of its inclusion of information retrieval. You can think of text analytics as a combination of information retrieval plus text mining.

2.

What is text mining? How does it differ from data mining?

Text mining is the application of data mining to unstructured, or less structured, text files. As the names indicate, text mining analyzes words; and data mining analyzes numeric data.

3.

Why is the popularity of text mining as an analytics tool increasing?

Text mining as a BI is increasing because of the rapid growth in text data and availability of sophisticated BI tools. The benefits of text mining are obvious in the areas where very large amounts of textual data are being generated, such as law (court orders), academic research (research articles), finance (quarterly reports), medicine (discharge summaries), biology (molecular interactions), technology (patent files), and marketing (customer comments).

4.

What are some popular application areas of text mining?

• Information extraction. Identification of key phrases and relationships within

text by looking for predefined sequences in text via pattern matching.

• Topic tracking. Based on a user profile and documents that a user views, text

mining can predict other documents of interest to the user.

• Summarization. Summarizing a document to save time on the part of the reader.

• Categorization. Identifying the main themes of a document and then placing the

document into a predefined set of categories based on those themes.

• Clustering. Grouping similar documents without having a predefined set of

categories.

• Concept linking. Connects related documents by identifying their shared

concepts and, by doing so, helps users find information that they perhaps would not have found using traditional search methods.

• Question answering. Finding the best answer to a given question through knowledge-driven pattern matching.

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Section 7.3 Review Questions 1.

What is NLP? Natural language processing (NLP) is an important component of text mining and is a subfield of artificial intelligence and computational linguistics. It studies the problem of “understanding” the natural human language, with the view of converting depictions of human language (such as textual documents) into more formal representations (in the form of numeric and symbolic data) that are easier for computer programs to manipulate.

2.

How does NLP relate to text mining?

Text mining uses natural language processing to induce structure into the text collection and then uses data mining algorithms such as classification, clustering, association, and sequence discovery to extract knowledge from it.

3.

What are some of the benefits and challenges of NLP? NLP moves beyond syntax-driven text manipulation (which is often called “word counting”) to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context. The challenges include: •

• • •

• •

Part-of-speech tagging. It is difficult to mark up terms in a text as corresponding to a particular part of speech because the part of speech depends not only on the definition of the term but also on the context within which it is used.

Text segmentation. Some written languages, such as Chinese, Japanese, and Thai, do not have single-word boundaries.

Word sense disambiguation. Many words have more than one meaning.

Selecting the meaning that makes the most sense can only be accomplished by taking into account the context within which the word is used.

Syntactic ambiguity. The grammar for natural languages is ambiguous; that is, multiple possible sentence structures often need to be considered. Choosing the most appropriate structure usually requires a fusion of semantic and contextual information.

Imperfect or irregular input. Foreign or regional accents and vocal impediments in speech and typographical or grammatical errors in texts make the processing of the language an even more difficult task.

Speech acts. A sentence can often be considered an action by the speaker. The sentence structure alone may not contain enough information to define this action.

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4.

What are the most common tasks addressed by NLP? Following are among the most popular tasks: • Question answering.

• Automatic summarization.

• Natural language generation.

• Natural language understanding.

• Machine translation.

• Foreign language reading.

• Foreign language writing.

• Speech recognition.

• Text-to-speech.

• Text proofing.

• Optical character recognition.

Section 7.4 Review Questions 1.

List and briefly discuss some of the text mining applications in marketing.

Text mining can be used to increase cross-selling and up-selling by analyzing the unstructured data generated by call centers.

Text mining has become invaluable for customer relationship management.

Companies can use text mining to analyze rich sets of unstructured text data, combined with the relevant structured data extracted from organizational databases, to predict customer perceptions and subsequent purchasing behavior.

2.

How can text mining be used in security and counterterrorism?

Students may use the introductory case in this answer.

In 2007, EUROPOL developed an integrated system capable of accessing, storing, and analyzing vast amounts of structured and unstructured data sources in order to track transnational organized crime.

Another security-related application of text mining is in the area of deception detection.

3.

What are some promising text mining applications in biomedicine?

As in any other experimental approach, it is necessary to analyze this vast amount of data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of

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automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.

Section 7.5 Review Questions 1.

What are the main steps in the text mining process?

• • •

2.

See Figure 7.6 (p. 309). Text mining entails three tasks: Establish the Corpus: Collect and organize the domain-specific unstructured data Create the Term–Document Matrix: Introduce structure to the corpus Extract Knowledge: Discover novel patterns from the T-D matrix

What is the reason for normalizing word frequencies? What are the common methods for normalizing word frequencies?

The raw indices need to be normalized in order to have a more consistent TDM for further analysis. Common methods are log frequencies, binary frequencies, and inverse document frequencies.

3.

What is SVD? How is it used in text mining?

Singular value decomposition (SVD), which is closely related to principal components analysis, reduces the overall dimensionality of the input matrix (number of input documents by number of extracted terms) to a lower dimensional space, where each consecutive dimension represents the largest degree of variability (between words and documents) possible

4.

What are the main knowledge extraction methods from corpus?

The main categories of knowledge extraction methods are classification, clustering, association, and trend analysis.

Section 7.6 Review Questions 1.

What is sentiment analysis? How does it relate to text mining?

Sentiment analysis tries to answer the question, “What do people feel about a certain topic?” by digging into opinions of many using a variety of automated tools. It is also known as opinion mining, subjectivity analysis, and appraisal extraction Sentiment analysis shares many characteristics and techniques with text mining.

However, unlike text mining, which categorizes text by conceptual taxonomies of 6 .

topics, sentiment classification generally deals with two classes (positive versus negative), a range of polarity (e.g., star ratings for movies), or a range in strength of opinion.

2. What are the most popular application areas for sentiment analysis? Why? Customer relationship management (CRM) and customer experience management are popular “voice of the customer (VOC)” applications. Other application areas include “voice of the market (VOM)” and “voice of the employee (VOE).”

3.

What would be the expected benefits and beneficiaries of sentiment analysis in politics?

Opinions matter a great deal in politics. Because political discussions are dominated by quotes, sarcasm, and complex references to persons, organizations, and ideas, politics is one of the most difficult, and potentially fruitful, areas for sentiment analysis. By analyzing the sentiment on election forums, one may predict who is more likely to win or lose. Sentiment analysis can help understand what voters are thinking and can clarify a candidate’s position on issues.

Sentiment analysis can help political organizations, campaigns, and news analysts to better understand which issues and positions matter the most to voters. The technology was successfully applied by both parties to the 2008 and 2012 American presidential election campaigns.

4. What are the main steps in carrying out sentiment analysis projects?

The first step when performing sentiment analysis of a text document is called sentiment detection, during which text data is differentiated between fact and opinion (objective vs. subjective). This is followed by negative-positive (N-P) polarity classification, where a subjective text item is classified on a bipolar range. Following this comes target identification (identifying the person, product, event, etc. that the sentiment is about). Finally come collection and aggregation, in which the overall sentiment for the document is calculated based on the calculations of sentiments of individual phrases and words from the first three steps.

5. What are the two common methods for polarity identification? What is the main difference between the two?

Polarity identification can be done via a lexicon (as a reference library) or by using a collection of training documents and inductive machine learning algorithms. The lexicon approach uses a catalog of words, their synonyms, and their meanings, combined with numerical ratings indicating the position on the NP polarity associated with these words. In this way, affective, emotional, and attitudinal phrases can be classified according to their degree of positivity or negativity. By contrast, the training-document approach uses statistical analysis 7 .

and machine learning algorithms, such as neural networks, clustering approaches, and decision trees to ascertain the sentiment for a new text document based on patterns from previous “training” documents with assigned sentiment scores.

Section 7.7 Review Questions 1.

What are some of the main challenges the Web poses for knowledge discovery?

• The Web is too big for effective data mining.

• The Web is too complex.

• The Web is too dynamic.

• The Web is not specific to a domain.

• The Web has everything.

2.

What is Web mining? How does it differ from regular data mining or text mining?

Web mining is the discovery and analysis of interesting and useful information from the Web and about the Web, usually through Web-based tools. Text mining is less structured because it’s based on words instead of numeric data.

3.

What are the three main areas of Web mining?

The three main areas of Web mining are Web content mining, Web structure mining, and Web usage (or activity) mining.

4.

Identify three application areas for Web mining (at the bottom of Figure 8.1).

Based on your own experiences, comment on their use cases in business settings.

(Since there are several application areas, this answer will vary for different students. Following is one possible answer.)

Three possible application areas for Web mining include sentiment analysis, clickstream analysis, and customer analytics. Clickstream analysis helps to better understand user behavior on a website. Sentiment analysis helps us understand the opinions and affective state of users on a system. Customer analytics helps to provide solutions for sales, service, marketing, and product teams, and optimize the customer life cycles. The use cases for these applications center on user experience, and primarily affect customer service and customer relationship management functions of an organization.

5.

What is Web content mining? How can it be used for competitive advantage?

Web content mining refers to the extraction of useful information from Web pages. The documents may be extracted in some machine-readable format so that automated techniques can generate some information about the Web pages.

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Collecting and mining Web content can be used for competitive intelligence (collecting intelligence about competitors’ products, services, and customers), which can give your organization a competitive advantage.

6.

What is Web structure mining? How does it differ from Web content mining?

Web structure mining is the process of extracting useful information from the links embedded in Web documents. By contrast, Web content mining involves analysis of the specific textual content of web pages. So, Web structure mining is more related to navigation through a website, whereas Web content mining is more related to text mining and the document hierarchy of a particular web page.

Section 7.8 Review Questions 1. What is a search engine? Why are search engines critically important for today’s businesses?

A search engine is a software program that searches for documents (Internet sites or files) based on the keywords (individual words, multi-word terms, or a complete sentence) that users have provided that have to do with the subject of their inquiry. This is the most prominent type of information retrieval system for finding relevant content on the Web. Search engines have become the centerpiece of most Internet-based transactions and other activities. Because people use them extensively to learn about products and services, it is very important for companies to have prominent visibility on the Web; hence the major effort of companies to enhance their search engine optimization (SEO).

2. What is a Web crawler? What is it used for? How does it work?

A Web crawler (also called a spider or a Web spider) is a piece of software that systematically browses (crawls through) the World Wide Web for the purpose of finding and fetching Web pages. It starts with a list of “seed” URLs, goes to the pages of those URLs, and then follows each page’s hyperlinks, adding them to the search engine’s database. Thus, the Web crawler navigates through the Web in order to construct the database of websites.

3. What is “search engine optimization”? Who benefits from it?

Search engine optimization (SEO) is the intentional activity of affecting the visibility of an e-commerce site or a website in a search engine’s natural (unpaid or organic) search results. It involves editing a page’s content, HTML, metadata, and associated coding to both increase its relevance to specific keywords and to remove barriers to the indexing activities of search engines. In addition, SEO efforts include promoting a site to increase its number of inbound links. SEO

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primarily benefits companies with e-commerce sites by making their pages appear toward the top of search engine lists when users query.

4. What things can help Web pages rank higher in search engine results?

Cross-linking between pages of the same website to provide more links to the most important pages may improve its visibility. Writing content that includes frequently searched keyword phrases, so as to be relevant to a wide variety of search queries, will tend to increase traffic. Updating content so as to keep search engines crawling back frequently can give additional weight to a site. Adding relevant keywords to a Web page’s metadata, including the title tag and metadescription, will tend to improve the relevancy of a site’s search listings, thus increasing traffic. URL normalization of Web pages so that they are accessible via multiple URLs. Using canonical link elements and redirects can help make sure links to different versions of the URL all count toward the page’s link popularity score.

Section 7.9 Review Questions 1. What are the three types of data generated through Web page visits?

• • •

Automatically generated data stored in server access logs, referrer logs, agent logs, and client-side cookies User profiles Metadata, such as page attributes, content attributes, and usage data.

2. What is clickstream analysis? What is it used for?

Analysis of the information collected by Web servers can help us better understand user behavior. Analysis of this data is often called clickstream analysis. By using the data and text mining techniques, a company might be able to discern interesting patterns from the clickstreams.

3. What are the main applications of Web mining?

• • • • • •

Determine the lifetime value of clients.

Design cross-marketing strategies across products.

Evaluate promotional campaigns.

Target electronic ads and coupons at user groups based on user access patterns.

Predict user behavior based on previously learned rules and users’ profiles.

Present dynamic information to users based on their interests and profiles.

4. What are commonly used Web analytics metrics? What is the importance of metrics?

There are four main categories of Web analytic metrics: 10 .

• • • •

Website usability: How were they using my website? These involve page views, time on site, downloads, click map, and click paths.

Traffic sources: Where did they come from? These include referral websites, search engines, direct, offline campaigns, and online campaigns.

Visitor profiles: What do my visitors look like? These include keywords, content groupings, geography, time of day, and landing page profiles.

Conversion statistics: What does all this mean for the business? Metrics include new visitors, returning visitors, leads, sales/conversions, and abandonments.

These metrics are important because they provide access to a lot of valuable marketing data, which can be leveraged for better insights to grow your business and better document your ROI. The insight and intelligence gained from Web analytics can be used to effectively manage the marketing efforts of an organization and its various products or services.

Section 7.10 Review Questions 1. What is meant by social analytics? Why is it an important business topic?

From a philosophical perspective, social analytics focuses on a theoretical object called a “socius,” a kind of “commonness” that is neither a universal account nor a communality shared by every member of a body. Thus, social analytics in this sense attempts to articulate the differences between philosophy and sociology.

From a BI perspective, social analytics involves “monitoring, analyzing, measuring and interpreting digital interactions and relationships of people, topics, ideas and content.” In this perspective, social analytics involves mining the textual content created in social media (e.g., sentiment analysis, natural language processing) and analyzing socially established networks (e.g., influencer identification, profiling, prediction). This is an important business topic because it helps companies gain insight about existing and potential customers’ current and future behaviors, and about the likes and dislikes toward a firm’s products and services.

2. What is a social network? What is the need for SNA? A social network is a social structure composed of individuals/people (or groups of individuals or organizations) linked to one another with some type of connections/relationships. Social network analysis (SNA) is the systematic examination of social networks. Dating back to the 1950s, social network analysis is an interdisciplinary field that emerged from social psychology, sociology, statistics, and graph (network) theory.

3. What is social media? How does it relate to Web 2.0?

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Social media refers to the enabling technologies of social interactions among people in which they create, share, and exchange information, ideas, and opinions in virtual communities and networks. It is a group of Internet-based software applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content.

4. What is social media analytics? What are the reasons behind its increasing popularity?

Social media analytics refers to the systematic and scientific ways to consume the vast amount of content created by Web-based social media outlets, tools, and techniques for the betterment of an organization’s competitiveness. Data includes anything posted in a social media site.

5. How can you measure the impact of social media analytics?

First, determine what your social media goals are. From here, you can use analysis tools such as descriptive analytics, social network analysis, and advanced (predictive, text examining content in online conversations), and ultimately prescriptive analytics tools.

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION

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Application Case 7.1: Netflix: Using Big Data to Drive Big Engagement: Unlocking the Power of Analytics to Drive Content and Consumer Insight 1. What does Netflix do? How did they evolve into this current business model?

Netflix is a provider and creator of streaming digital content for end-users. The company began as a subscription-based DVD rental company and expanded into digital streaming.

Changes in the digital streaming market made it more economically beneficial for the company to also expand into content creation.

2. In the case of Netflix, what was it meant to be data-driven and customer-focused?

Four Netflix, being data-driven means that decisions on new content being created or licensed (as well as maintaining existing licenses) should be based on data directly related to customer preferences (based on actual use as well as preferred genres). The decision on how to spend these limited licensing dollars must be made to create the greatest benefit for the greatest number of users in order to maintain current subscriptions as well as drive new sign-ups. This focus on customer demands also meets the aspect of customer focus.

3. How did Netflix use Teradata technologies in its analytics endeavors?

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The Teradata solution provides two distinct advantages for Netflix. The first is the ability to use an existing, robust cloud-based system to perform analytics functions. This provides security both in terms of redundancy as well as confidence in the technology itself. The second is the ability to use Teradata’s robust discovery engine to perform analytics functions to create a better understanding of customer types and their preferences.

Application Case 7.2: AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Add Value for Advertisers in a Multichannel World 1. What are the common challenges that broadcasting companies are facing today? How can analytics help to alleviate these challenges?

Broadcasters are faced with the need to maintain the attention of viewers by providing quality program that meets their current desires, desires that are always in flux. An analytics system can help to alleviate these challenges by evaluating viewers current tastes and desires for future programming.

2. How did AMC leverage analytics to enhance its business performance?

AMC has been able to successfully leverage analytics to better understand their customers and use this understanding to market effectively to them as well as ensure that they provide content that meets viewers desires. These analytics systems allow the broadcaster to better understand what different customers want in specific groupings, but also how to market their services effectively to individuals within these groupings.

3. What were the types of text analytics and text minisolutions developed by AMC networks? Can you think of other potential uses of text mining applications in the broadcasting industry?

These details are not discussed in the case, but students may be able to identify some of this information through independent research. In general, the analytics being used incorporate data from internal and external sources to determine viewership of individual programs as well as purchases of digital programs.

Application Case 7.3: Mining for Lies 1.

Why is it difficult to detect deception?

Humans tend to perform poorly at deception-detection tasks. This phenomenon is exacerbated in text-based communications. Although some people believe that they can readily identify those who are not being truthful, a summary of deception research showed that, on average, people are only 54 percent accurate in making veracity determinations.

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2.

How can text/data mining be used to detect deception in text?

Through a process known as message feature mining, statements are transcribed for processing, then cues are extracted and selected. Text processing software identifies cues in statements and generates quantified cues. Classification models are trained and tested on quantified cues, and based on this, statements are labeled as truthful or deceptive (e.g., by law enforcement personnel). The featureselection methods along with 10-fold cross-validation allow researchers to compare the prediction accuracy of different data mining methods (for example, neural networks).

3.

What do you think are the main challenges for such an automated system?

One challenge is that the training the system depends on humans to ascertain the truthfulness of statements in the training data itself. You can’t know for sure whether these statements are true or false, so you may be using incorrect training samples when “teaching” the machine learning system to predict lies in new text data. (This answer will vary by student.)

Application Case 7.4: The Magic Behind the Magic: Instant Access to Information Helps the Orlando Magic Up their Game and the Fan’s Experience 1. According to the application case, what were the main challenges the Orlando Magic was facing?

The primary challenges are both financial, with a need to maximize both ticket sales and concessions/merchandising. These challenges can be addressed by tailoring services to meet the needs of customers.

2. How did analytics help the Orlando Magic to overcome some of its most significant challenges on and off the court?

In order to meet the challenge of ensuring that season ticket purchases continue, the team used a predictive analysis decision tree model to identify customers that may not renew, and those individuals could receive additional sales and marketing attention.

Additionally, coaching staff use analytic tools to better understand all aspects of the game itself including player strengths and weaknesses and opponent strategies.

3. Can you think of other uses of analytics in sports and especially in the case of the Orlando Magic? You can search the Web to find some answers to this question.

Student research and responses will vary but may include the idea to use analytics when selecting new players or determining if players ready for contract renewal should be retained.

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Application Case 7.5: Research Literature Survey with Text Mining 1.

How can text mining be used to ease the task of literature review?

Text mining enables a semiautomated analysis of large volumes of published literature. Clustering was used in this study to identify the natural groupings of the articles, and list the most descriptive terms that characterized those clusters. This led to discovery and exploration of interesting patterns using tabular and graphical representation of their findings. Use of text and data mining can thus speed up and simplify the literature review process for academic researchers.

2.

What are the common outcomes of a text mining project on a specific collection of journal articles? Can you think of other potential outcomes not mentioned in this case?

Common outcomes include identifying natural clusters of similar articles, helping to identify the optimal number of cluster classifications. Using text mining, you can answer questions such as “Are there clusters that represent different research themes specific to a single journal?” and “Is there a time-varying characterization of those clusters?” Text mining also has other possible applications in literature reviews. For example, sentiment analysis can help to identify positive and negative judgments. Text mining can be used to build taxonomies of concepts and terms within and between research articles. You can find common themes by author as well as by journal.

Application Case 7.6: Creating a Unique Digital Experience to Capture Moments That Matter at Wimbledon 1. How did Wimbledon use analytics capabilities to enhance viewers’ experience?

Wimbledon undertook a number of analytics projects in order to enhance the viewer experience for the tournament. One project was a redesign of the website for the tournament. Based on their understanding of current users they were able to determine that the majority of users were still accessing the site using desktop browsers, and so were able to work on optimizing the experience for that platform (as well as accommodating the growth in mobile users). Next they were able to tap in to a huge amount of data that was being collected or had been collected over time. Data and analysis from the match came in in real time and was analyzed and displayed as information to viewers. Additionally, NLP systems allowed the tournament to search its vast archives of facts and details that could be retrieved and presented in real time.

Finally, Wimbledon used their understanding of the potential demand for this service to make appropriate decisions about housing both the broadcasting and analytics portions of the service in the cloud to provide the necessary bandwidth and security.

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2. What were the challenges, proposed solution, and obtained results?

Many of the challenges were technical, but those were overcome through the use of existing, trusted IT partners to provide services and create a unique digital presence. The results were very positive, with a significant number of online visitors and a 98% growth in total visits over the previous year.

Application Case 7.7: Delivering Individualized Content and Driving Digital Engagement: How Barbour Collected More Than 49,000 New Leads in One Month with Teradata Interactive 1. What does Barbour do? What was the challenge Barbour was facing?

Barbour is an English heritage and lifestyle brand renowned for waterproof outerwear that has recently expanded into other luxury fashion goods. The company’s challenge was establishing a direct relationship with its customers because in the past all sales had been made through distributors. While in e-commerce site was launched in 2013, connecting with customers in a crowded digital market space was difficult.

2. What was the proposed analytics solution?

The company worked with partner Teradata to create a lead nurturing program. The goal was both to drive immediate sales as well as to gather information that could create more meaningful long-term relationships with customers. The company had access to historical customer information that was generally seen as incomplete. Teradata was able to begin with this information and create marketing campaigns that also collected additional data that allowed for future, more personalized content to be driven to customers through a customer lifecycle program. These activities were integrated with existing marketing and social media campaigns.

3. What were the results?

The results were very positive and over a one-month period the company was able to collect more than 49,700 leads within their primary European regions. In addition to very positive responses (60% click through rates) the company was also able to collect and begin analysis on additional customer data that can be used in the lifecycle program.

Application Case 7.8: Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy 1. How can social media analytics be used in the consumer products industry?

Social media analytics can be incredibly useful for consumer products because it allows the producers/retailers to better understand their customers and connect with them 16 .

individually based on their preferences.

2. What do you think are the key challenges, potential solutions, and probable results in applying social media analytics in consumer products and services firms?

It’s Student opinions will vary, but responses will focus on: • Challenges - crowded market spaces with data in multiple formats from multiple sources • potential solutions - focus on analytics and the ability to better understand customer needs at both the aggregate and individual level • probable results - can be very positive if analytic efforts are successful in identifying customer preferences and targeting individual customers or unique groups with marketing or social media content that drives interest in the brand

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION

  

1. Explain the relationship among data mining, text mining, and sentiment analysis.

Technically speaking, data mining is a process that uses statistical, mathematical, and artificial intelligence techniques to extract and identify useful information and subsequent knowledge (or patterns) from large sets of data. Data mining is the general concept. Text mining is a specific application of data mining: applying it to unstructured text files. Sentiment analysis is a specialized form of text and data mining that identifies and classifies terms in text sources according to sentiment (e.g. judgment, opinion, and emotional content).

2. In your own words, define text mining, and discuss its most popular applications.

Students’ answers will vary.

3. What does it mean to induce structure into text-based data? Discuss the alternative ways of inducing structure into them.

Text mining, like other data mining approaches, are inductive approaches for finding patterns and trends in data. One difference between text mining and other data mining approaches is the use of natural language processing.

Text-based data is inherently unstructured and must be converted to a structured format for predictive modeling or other type of analysis. The third step of the 3step text mining process utilizes inductive algorithms to classify and structure the corpus of text sources so that knowledge can be extracted.

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Four possible approaches for inducing structure in text in order to extract knowledge are (a) classification (grouping terms into predefined categories), (b) clustering (coming up with “natural” groupings), (c) association rule learning (finding frequent combinations of terms), and (d) trend analysis (recognizing concept distributions based on specific collections of documents).

4. What is the role of NLP in text mining? Discuss the capabilities and limitations of NLP in the context of text mining.

NLP is an important component of text mining. It studies the problem of “understanding” the natural human language, with the view of converting depictions of human language (such as textual documents) into more formal representations (in the form of numeric and symbolic data) that are easier for computer programs to manipulate. The goal of NLP is to move beyond syntaxdriven text manipulation (which is often called “word counting”) to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context.

5. List and discuss three prominent application areas for text mining. What is the common theme among the three application areas you chose?

National security and counterterrorism: The FBI’s system is expected to create a gigantic data warehouse along with a variety of data and text mining modules to meet the knowledge discovery needs of federal, state, and local law enforcement agencies.

Biomedical: A system extracts disease–gene relationships from literature accessed via MEDLINE. Marketing: Text mining can be used to increase cross-selling and up-selling by analyzing the unstructured data generated by call centers.

The value of all of these applications is significantly increased by extracting knowledge from huge volumes of text-data and documents through the use of text mining tools.

6. What is sentiment analysis? How does it relate to text mining?

Sentiment analysis tries to answer the question, “What do people feel about a certain topic?” by digging into opinions of many using a variety of automated tools. It is also known as opinion mining, subjectivity analysis, and appraisal extraction.

Sentiment analysis shares many characteristics and techniques with text mining.

However, unlike text mining, which categorizes text by conceptual taxonomies of 18 .

topics, sentiment classification generally deals with two classes (positive versus negative), a range of polarity (e.g., star ratings for movies), or a range in strength of opinion.

7. What are the common challenges with which sentiment analysis deals?

Sentiment that appears in text comes in two flavors: explicit, where the subjective sentence directly expresses an opinion (“It’s a wonderful day”), and implicit, where the text implies an opinion (“The handle breaks too easily”). Implicit sentiment analysis is harder to analyze because it may not include words that are obviously evaluations or judgments. Another challenge involves the timeliness of collection/analysis of textual data coming from a wide variety of data sources. A third challenge is the difficulty of identifying whether a piece of text involves sentiment or not, especially with implicit sentiment analysis. The same sorts of issues involving text mining in natural language settings also apply to sentiment analysis.

8. What are the most popular application areas for sentiment analysis? Why? Customer relationship management (CRM) and customer experience management are popular “voice of the customer (VOC)” applications. Other application areas include “voice of the market (VOM)” and “voice of the employee (VOE).”

9. What are the main steps in carrying out sentiment analysis projects?

The first step when performing sentiment analysis of a text document is called sentiment detection, during which text data is differentiated between fact and opinion (objective vs. subjective). This is followed by negative-positive (N-P) polarity classification, where a subjective text item is classified on a bipolar range. Following this comes target identification (identifying the person, product, event, etc. that the sentiment is about). Finally come collection and aggregation, in which the overall sentiment for the document is calculated based on the calculations of sentiments of individual phrases and words from the first three steps.

10. What are the two common methods for polarity identification? Explain.

Polarity identification can be done via a lexicon (as a reference library) or by using a collection of training documents and inductive machine learning algorithms. The lexicon approach uses a catalog of words, their synonyms, and their meanings, combined with numerical ratings indicating the position on the NP polarity associated with these words. In this way, affective, emotional, and attitudinal phrases can be classified according to their degree of positivity or negativity. By contrast, the training-document approach uses statistical analysis and machine learning algorithms, such as neural networks, clustering approaches,

19 .

and decision trees to ascertain the sentiment for a new text document based on patterns from previous “training” documents with assigned sentiment scores.

11. Discuss the differences and commonalities between text mining and Web mining.

Text mining is a specific application of data mining: applying it to unstructured text files. Web mining is a specific application of data mining: applying it to information on and about the Web (content, structure, and usage).

12. In your own words, define Web mining, and discuss its importance.

Students’ answers will vary.

13. What are the three main areas of Web mining? Discuss the differences and commonalities among these three areas.

Web mining consists of three areas: Web content mining, Web structure mining, and Web usage mining.

Web content mining refers to the automatic extraction of useful information from Web pages. It may be used to enhance search results produced by search engines.

Web structure mining refers to generating interesting information from the links included in Web pages. Web structure mining can also be used to identify the members of a specific community and perhaps even the roles of the members in the community.

Web usage mining refers to developing useful information through analysis of Web server logs, user profiles, and transaction information.

14. What is a search engine? Why is it important for businesses?

A search engine is a software program that searches for documents (Internet sites or files) based on the keywords (individual words, multi-word terms, or a complete sentence) that users have provided that have to do with the subject of their inquiry. This is the most prominent type of information retrieval system for finding relevant content on the Web. Search engines have become the centerpiece of most Internet-based transactions and other activities. Because people use them extensively to learn about products and services, it is very important for companies to have prominent visibility on the Web; hence the major effort of companies to enhance their search engine optimization (SEO).

15. What is SEO? Who benefits from it? How?

20 .

Search engine optimization (SEO) is the intentional activity of affecting the visibility of an e-commerce site or a website in a search engine’s natural (unpaid or organic) search results. It involves editing a page’s content, HTML, metadata, and associated coding to both increase its relevance to specific keywords and to remove barriers to the indexing activities of search engines. In addition, SEO efforts include promoting a site to increase its number of inbound links. SEO primarily benefits companies with e-commerce sites by making their pages appear toward the top of search engine lists when users query.

16.What is Web analytics? What are the metrics used in Web analytics?

Web analytics, also called Web usage mining, can be considered a part of Web mining, and aims to describe what has happened on the website (employing a predefined, metrics-driven descriptive analytics methodology). There are four main categories of Web analytic metrics: • • • •

Website usability: How were they using my website? These involve page views, time on site, downloads, click map, and click paths.

Traffic sources: Where did they come from? These include referral websites, search engines, direct, offline campaigns, and online campaigns.

Visitor profiles: What do my visitors look like? These include keywords, content groupings, geography, time of day, and landing page profiles.

Conversion statistics: What does all this mean for the business? Metrics include new visitors, returning visitors, leads, sales/conversions, and abandonments.

These metrics are important because they provide access to a lot of valuable marketing data, which can be leveraged for better insights to grow your business and better document your ROI. The insight and intelligence gained from Web analytics can be used to effectively manage the marketing efforts of an organization and its various products or services.

17. Define social analytics, social network, and social network analysis. What are the relationships among them?

Social analytics involves monitoring, analyzing, measuring, and interpreting digital interactions and relationships of people, topics, ideas, and content. It involves mining the textual content created in social media (e.g., sentiment analysis, natural language processing) and analyzing socially established networks (e.g., influencer identification, profiling, prediction). A social network is a social structure composed of individuals/people (or groups of individuals or organizations) linked to one another with some type of connections/relationships.

Social network analysis (SNA) is the systematic examination of social networks, and is an interdisciplinary field that emerged from social psychology, sociology, statistics, and graph (network) theory. Social analytics therefore combines text analysis for content and sentiment in online communications with social network 21 .

analysis to identify and analyze relationships between individuals in a community.

18. What is social media analytics? How is it done? Who does it? What comes out of it?

Social media analytics refers to the systematic and scientific ways to consume the vast amount of content created by Web-based social media outlets, tools, and techniques for the betterment of an organization’s competitiveness. It is done using many analytic methods, including text mining, sentiment analysis, and social network analysis. Companies use it to get a better understanding of their customer base, and can gain financial and competitive advantages from doing so.

Governments use it to track potential terrorist threats, which can lead to enhanced national security. Social scientists use it to get a better understanding of how communities and societies work, which can provide guidance on how to best manage these societies.

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ANSWERS TO END OF CHAPTER EXERCISES

Teradata University Network (TUN) and Other Hands-on Exercises 1. Visit teradatauniversitynetwork.com. Identify cases about text mining. Describe recent developments in the field. If you cannot find enough cases at the Teradata University Network Web site, broaden your search to other Web-based resources.

Student selection of cases will vary and create differences in reports.

2. Go to teradatauniversitynetwork.com to locate white papers, Web seminars, and other materials related to text mining. Synthesize your findings into a short written report.

Student selection and perspectives on different reports will generate a variety of findings.

3. Go to teradatauniversitynetwork.com and find the case study named “eBay Analytics.”

Read the case carefully and extend your understanding of it by searching the Internet for additional information, and answer the case questions.

Student interpretation and analysis of the information will vary.

4. Go to teradatauniversitynetwork.com and find the sentiment analysis case named “How Do We Fix an App Like That?” Read the description, and follow the directions to download the data and the tool to carry out the exercise.

Work on this exercise will vary.

22 .

5. Visit teradatauniversitynetwork.com. Identify cases about Web mining. Describe recent developments in the field. If you cannot find enough cases at the Teradata University Network Web site, broaden your search to other Web-based resources. 6.

Browse the Web and your library’s digital databases to identify articles that make the linkage between text/Web mining and contemporary business intelligence systems.

Student selection of cases and their analysis will differ.

Team Assignments and Role-Playing Projects 1. Examine how textual data can be captured automatically using Web-based technologies. Once captured, what are the potential patterns that you can extract from these unstructured data sources?

Team selections of technology will create variations in their reports.

2. Interview administrators at your college or executives in your organization to determine how text mining and Web mining could assist them in their work. Write a proposal describing your findings. Include a preliminary cost–benefit analysis in your report.

Team interviews of administrators will vary and create differences in their findings.

3. Go to your library’s online resources. Learn how to download attributes of a collection of literature (journal articles) in a specific topic. Download and process the data using a methodology similar to the one explained in Application Case 7.5.

Processes at individual institutions will vary as well the articles downloaded.

4. Find a readily available sentiment text data set (see Technology Insights 7.2 for a list of popular data sets) and download it onto your computer. If you have an analytics tool that is capable of text mining, use that. If not, download RapidMiner (http://rapid-i.com) and install it. Also install the Text Analytics add-on for RapidMiner. Process the downloaded data using your text mining tool (i.e., convert the data into a structured form). Build models and assess the sentiment detection accuracy of several classification models (e.g., support vector machines, decision trees, neural networks, logistic regression). Write a detailed report in which you explain your findings and your experiences.

Selection of different data sets will result in different models and variances in the final report.

5. Examine how Web-based data can be captured automatically using the latest technologies. Once captured, what are the potential patterns that you can extract from these content-rich, mostly unstructured data sources?

23 .

Selection of technologies will vary based on student preferences in the data search.

Internet Exercises 1. Find recent cases of successful text mining and Web mining applications. Try text and Web mining software vendors and consultancy firms and look for cases or success stories. Prepare a report summarizing five new case studies.

Student research and reports will vary.

2. Go to statsoft.com. Select Downloads, and download at least three white papers on applications. Which of these applications might have used the data/text/Web mining techniques discussed in this chapter?

Student research and reports will vary.

3. Go to sas.com. Download at least three white papers on applications. Which of these applications might have used the data/text/Web mining techniques discussed in this chapter?

Student research and reports will vary.

4. Go to ibm.com. Download at least three white papers on applications. Which of these applications might have used the data/text/Web mining techniques discussed in this chapter?

Student research and reports will vary.

5. Go to teradata.com. Download at least three white papers on applications. Which of these applications might have used the data/text/Web mining techniques discussed in this chapter?

Student research and reports will vary.

6. Go to clarabridge.com. Download at least three white papers on applications. Which of these applications might have used text mining in a creative way?

Student research and reports will vary.

7. Go to kdnuggets.com. Explore the sections on applications as well as software. Find names of at least three additional packages for data mining and text mining.

Student research and reports will vary.

24 .

8. Survey some Web mining tools and vendors. Identify some Web mining products and service providers that are not mentioned in this chapter.

Student research and reports will vary.

9. Go to attensity.com. Download at least three white papers on Web analytics applications. Which of these applications might have used a combination of data/ text/Web mining techniques?

Student research and reports will vary.

25 .

Chapter 8: Prescriptive Analytics: Optimization and Simulation Learning Objectives for Chapter 8 1. Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics 2. Understand the basic concepts of analytical decision modeling 3. Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support 4. Describe how spreadsheets can be used for analytical modeling and solutions 5. Explain the basic concepts of optimization and when to use them 6. Describe how to structure a linear programming model 7. Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking 8. Understand the concepts and applications of different types of simulation 9. Understand potential applications of discrete event simulation

CHAPTER OVERVIEW This chapter extends the analytics applications beyond reporting and predictive analytics.

It includes coverage of selected techniques that can be employed in combination with predictive models to help support decision making. We focus on techniques that can be implemented relatively easily using either spreadsheet tools or by using stand-alone software tools. Of course, there is much additional detail to be learned about management science models, but the objective of this chapter is to simply illustrate what is possible and how it has been implemented in real settings. We present this material with a note of caution: Modeling can be a difficult topic and is as much an art as it is a science. The purpose of this chapter is not necessarily for you to master the topics of modeling and analysis. Rather, the material is geared toward gaining familiarity with the important concepts as they relate to prescriptive analytics and their use in decision making. It is important to recognize that the modeling we discuss here is only cursorily related to the concepts of data modeling. You should not confuse the two. We walk through some basic concepts and definitions of decision modeling. We next introduce the idea of modeling directly in spreadsheets. We then discuss the structure and application of two successful time-proven models and methodologies: linear programming and discrete event simulation. As noted earlier, one could take multiple courses just in these two topics, but our goal is to give you a sense of what is possible. This chapter includes the following sections: 1 .

CHAPTER OUTLINE 8.1 Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts 8.2 Model-Based Decision Making 8.3 Structure of Mathematical Models for Decision Support 8.4 Certainty, Uncertainty, and Risk 8.5 Decision Modeling with Spreadsheets 8.6 Mathematical Programming Optimization 8.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking 8.8 Decision Analysis with Decision Tables and Decision Trees 8.9 Introduction to Simulation 8.10 Visual Interactive Simulation

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 9.1 Review Questions 1. What decision was being made in this vignette?

The city of Philadelphia was issuing bus routes to vendors and needed to determine a process of awarding bids that maximized ROI for the city.

2. What data (descriptive and or predictive) might one need to make the best allocations in this scenario?

The vendors were evaluated based on five variables: cost, capabilities, reliance, financial stability, and business acumen. Each vendor submitted a proposal with a different price for different routes. Some vendors specified a minimum number of routes, and if that minimum wasn’t met, their cost would increase.

3. What other costs or constraints might you have to consider in awarding contracts for such routes?

Selection of other constraints may vary but could include information on quality/satisfaction, reliability etc.

4. Which other situations might be appropriate for applications of such models?

Student opinions on other potential situations may vary, but this system would work well in other cases where bids were being awarded or where contracts were being granted.

2 .

Section 8.2 Review Questions 1.

List three lessons learned from modeling.

This question refers to lessons found in the examples in this section, not to lessons learned from modeling in general. Many examples could be cited, among them these three: PillowTex learned how a simulation model can lead to lean manufacturing and save millions of dollars. IBM was able to use a combination of weather and sensor data to build a river system simulation application that could simulate thousands of river branches at a time for flood prediction and irrigation management. TurboRouter, a DSS for ship routing and scheduling, claims that over the course of just a three-week period, a company used this model to better utilize its fleet, generating additional profit of $1–2 million.

2.

List and describe the major issues in modeling.

Major modeling issues include: • problem identification and environmental analysis: scanning the environment to figure out what problems exist and can be solved via a model • variable identification: identifying the critical factors in a model and their relationships • forecasting: predicting the future • model categories: selecting the right type of model for the problem or subproblem • model management: coordinating a firm’s models and their use • knowledge-based modeling: how to take advantage of human knowledge in modeling

3.

What are the major types of models used in DSS? The major types from Table 9.1 follow.

• Optimization with few alternatives • Optimization via an algorithm • Optimization via an analytical formula • Simulation • Heuristics (“rules of thumb”) • Predictive models • Other models

4.

Why are models not used in industry as frequently as they should or could be?

There are several reasons. The most important is that useful models are often complex, requiring major investments of time and money as well as scarce, expensive expertise.

3 .

5.

What are the current trends in modeling?

One recent trend is the development of model libraries and solution technique libraries. Another is an increasing emphasis toward developing and using Web tools and software to access and even run software to perform modeling, optimization, and simulation. There is a continuing trend toward making analytics models completely transparent to the decision maker. There is also a trend to build a model of a model to help in its analysis. Influence diagram help in this respect.

Section 8.3 Review Questions 1.

What is a decision variable?

A decision variable is a data element controlled by the decision maker, whose possible values describe alternative courses of action.

2.

List and briefly discuss the three major components of linear programming.

Of the four components of any decision support mathematical model, linear programming uses result (outcome) variables, decision variables, and uncontrollable variables (parameters). Linear programming models do not use the fourth component, intermediate result variables.

There are other possible answers that do not tie in to the concepts of this section.

For example, one could say that the three major components of a linear programming problem are its objective function, its decision variables, and its constraints. You might wish to connect this question to Section 4.8.

3.

Explain the role of intermediate result variables.

An intermediate result variable reflects an intermediate result of the mathematical model. It is a result variable, and therefore is influenced by decision and uncontrollable variables in the mathematical model. An intermediate variable also influences other result variables in the mathematical model. For example, in a human resources system, employee salaries (decision variable) affect employee satisfaction (intermediate result variable), which further affects productivity (result variable).

4 .

Section 8.4 Review Questions 1.

Define what it means to perform decision making under assumed certainty, risk, and uncertainty.

• • •

2.

Decision making under assumed certainty: the values of all variables affecting the decision, including future values, are known or can be assumed to be known.

Decision making under risk: the exact value of decision variables is not known, but their statistical probability distributions are known (or can be assumed to be).

Decision making under uncertainty: even these distributions are not known.

How can decision-making problems under assumed certainty be handled?

Decision-making problems under assumed certainty can be handled by mathematical models that will yield an optimum solution.

3.

How can decision-making problems under assumed uncertainty be handled?

Decision-making problems under uncertainty can be handled, first, by trying to get more information. If this is not possible or if the uncertainty remains after this has been done, it is necessary to evaluate the outcomes of many possible values of the decision variables. The choice then depends on the decision maker’s attitude toward risk. For example, one solution may have a higher expected value than another, but also a higher likelihood of a negative outcome. A manager with an inclination to gamble might choose that solution, whereas a more conservative and risk-averse manager would choose a solution that offers a lower probable return but also minimizes the likelihood of loss.

4.

How can decision-making problems under assumed risk be handled?

Decision-making problems under assumed risk can be handled by taking the probability distributions of the decision variables and working through the model with those distributions. The result will be a distribution of outcomes.

Note that the mean of the distribution of outcomes is often not the outcome that would be obtained under assumed certainty if each variable had its mean value.

This is because, in a complex model, decision variables interact in complex ways and effect is not always immediately obvious.

Another approach to problems under assumed risk is to select a random value from each decision variable’s probability distribution and to run the model, under assumed certainty, with those values. This is repeated until a statistical distribution of results is obtained. This approach is used when a model is too complex to work through with statistical distributions directly.

5 .

Section 8.5 Review Questions 1.

What is a spreadsheet?

This section does not define the concept, relying on student’s experience to tell them what a spreadsheet is. Given the audience of this book, that should normally be appropriate. Instructors who assign this question with no further explanation should expect some students to search for definitions (finding one or more like the above two) whereas others come up with their own and still others use descriptive phrases from the text. If a specific approach is wanted, that should be stated in advance.

2.

What is a spreadsheet add-in? How can add-ins help in DSS creation and use?

Spreadsheet add-ins are small programs designed to extend the capabilities of a spreadsheet package. They help in DSS creation and use because many add-ins are designed specifically for that purpose, often by structuring and solving specific types of models.

3.

Explain why a spreadsheet is so conducive to the development of DSS. A spreadsheet is conducive to DSS development because it provides an easily understood metaphor for the computation and typically incorporates many powerful modeling functions.

Section 8.6 Review Questions 1.

List and explain the assumptions involved in LP. The LP approach involves both economic and technical assumptions.

Economic assumptions are: • Returns from different allocations can be measured by a common unit (e.g., dollars, utility).

• The return from any allocation is independent of other allocations.

• The total return is the sum of the returns yielded by the different activities.

• All data are known with certainty.

• Resources are to be used in the most economical manner.

Technical assumptions are: • • •

The objective function (that is to be maximized) is a linear combination of the outputs.

Each output (decision variable) uses a linear combination of the inputs.

Constraints on inputs are linear (or constant, which is a special case of linear) inequalities.

6 .

The requested explanations should show that the student understands each of the concepts, and is not simply parroting items from a list.

2.

List and explain the characteristics of LP. Linear programming is an optimization method used to solve problems in which the objective function and the constraints are all linear. It rests on the assumptions listed in the previous answer. LP problems can be solved by a wide variety of software for all popular computers.

3.

Describe an allocation problem.

An allocation problem is a linear programming problem in which limited resources can be allocated among several possible uses, each of which yields a known return per unit and are subject to constraints.

4.

Define the product-mix problem.

The product-mix problem is a linear programming problem in which a variety of different products are made from common resources. Each product requires a known resource mix and has a known profitability. Some resources are limited.

Total profitability is to be maximized.

A product-mix problem can be viewed as an allocation problem. The difference is that a person formulating the problem as a product-mix problem is typically interested in the quantities of each product to be produced, while a person formulating the same problem as an allocation problem is usually interested in the quantities of each resource to be used. The solutions are identical, and each approach can produce both answers.

5.

Define the blending problem.

The blending problem is a linear programming problem in which resources can be used in different ways to create a desired end product. The ways in which the resources combine to create the characteristics of the end product are known.

Total cost of the end product is to be minimized.

6.

List several common optimization models.

Several common optimization models are listed at the end of the section. These include the assignment problem, dynamic programming, goal programming, investment, linear and integer programming, network models for planning and scheduling, nonlinear programming, replacement (capital budgeting), simple inventory models (e.g., economic order quantity) and transportation (minimizing cost of shipments). A motivated student will be able to find others through a Web search.

7 .

Section 8.7 Review Questions 1.

List some difficulties that may arise when analyzing multiple goals.

• • • • • •

2.

It is usually difficult to obtain an explicit statement of the organization’s goals.

The importance of specific goals may change over time or in different situations.

Goals and subgoals are viewed and weighted differently by different people and in different parts of the organization.

Goals change in response to changes in the organization and its environment.

The relationship between alternatives and their role in determining goals may be difficult to quantify.

Participants assess the importance (priorities) of the various goals differently.

List the reasons for performing sensitivity analysis.

Sensitivity analysis attempts to assess the impact of a change in input data or parameters on the result variable(s). Reasons for using it listed in this section include: • Revising models to eliminate too-large sensitivities • Adding details about sensitive variables or scenarios • Obtaining better estimates of sensitive external variables • Altering a real-world system to reduce actual sensitivities • Accepting and using the sensitive (and hence vulnerable) real world, leading to the continuous and close monitoring of actual results Another reason for using sensitivity analysis is to understand the sensitivity of different decision choices to variations in external conditions. One alternative may promise excellent results under nominal conditions but performs poorly if conditions vary from the nominal. Another may not turn out quite as well if all goes according to plan but will perform better if conditions vary from it. For example, one possible supplier may offer low prices for deliveries according to a pre-planned schedule but impose large penalties if the schedule is changed; another might have a higher base price but lower penalties for changing the delivery schedule.

3.

Explain why a manager might perform what-if analysis.

A manager might perform what-if analysis to find out what will happen if a particular action is taken. A manager might also perform what-if analysis to try out several alternatives, choosing the one that works out best.

8 .

4.

Explain why a manager might use goal seeking.

A manager might use goal seeking to find values of decision variables that enable him or her to meet a predetermined criterion. For example, a manager may need to find an advertising budget that will reach X households at a cost of not over $Y.

Section 8.8 Review Questions 1.

What is a decision table?

A decision table is a way to organize information in a systematic way to prepare it for analysis. Typically, decisions are shown along one axis, states of nature on the other. The range of outcomes that can result from any decision, given the possible states of nature, can then be easily seen in one column (or row).

2.

What is a decision tree?

A decision tree is an alternative representation of a decision situation, in which choices and states of nature are shown as alternating nodes along the branches of a tree. The range of outcomes that can result from any decision, given the possible states of nature, can then be easily seen by following all branches from that decision to their ends.

3.

How can a decision tree be used in decision making?

By showing the decision maker the possible outcomes that could result from a given choice, the tree gives the decision maker information by which to compare choices.

4.

Describe what it means to have multiple goals.

Having multiple goals means that a decision maker hopes to obtain the best possible combination of several factors, all of which depend on the decision to be made. For example, a student may want to find an instructor who is entertaining, has a good reputation for teaching, grades easily, assigns little homework, and whose section meets at convenient times. It will be difficult, if not impossible, to achieve all these at the same time. Multiple goals require willingness to compromise.

(You may want to defer this question until students have read Section 9.9, or to refer them forward to it at this point.)

9 .

Section 8.9 Review Questions 1.

List the characteristics of simulation.

• • • • •

2.

An imitation of reality rather than a representation of it A technique for conducting experiments Fewer simplifications than with most other methods Descriptive rather than normative; users can use simulation results to search if desired Usually used for problems too complex for numerical optimization methods

List the advantages and disadvantages of simulation.

Advantages: • The theory is fairly straightforward.

• Time can be compressed a great deal, quickly giving a manager some feel as to the long-term effects of many policies.

• Simulation is descriptive rather than normative. This allows a manager to pose what-if questions. Managers can use trial-and-error quickly, at little expense, accurately, and with low risk.

• A manager can experiment to determine which decision variables and which parts of the environment are really important, and with different alternatives.

• An accurate simulation model requires intimate knowledge of the problem, thus forcing its builder to interact with the manager. This leads to better understanding of the problem and the potential decisions available.

• The model is built from the manager’s perspective.

• No generalized understanding is required of the manager; as every component in the model corresponds to part of the real system.

• Simulation can handle a wide variety of problem types, such as inventory and staffing, as well as higher-level managerial functions, such as longrange planning.

• Simulation can include most real complexities of problems; simplifications are not needed. For example, it can use real probability distributions rather than approximate theoretical ones.

• Simulation automatically produces many important performance measures.

• Simulation is often the only DSS modeling method that can readily handle relatively unstructured problems.

• Simulation generally can include the real complexities of problems; simplifications are not necessary.

• Simulation automatically produces many important performance measures.

10 .

• •

Simulation is often the only DSS modeling method that can readily handle relatively unstructured problems.

There are some relatively easy-to-use simulation packages. These include add-in spreadsheet packages, influence diagram software, Java-based (and other Web development) packages, and visual interactive simulation systems.

Disadvantages: • An optimal solution cannot be guaranteed, though relatively good ones are generally found.

• Simulation model construction can be a slow and costly process, although newer modeling systems are easier to use than ever.

• Solutions and inferences from a simulation study are usually not transferable to other problems because the model incorporates unique problem factors. It doesn’t generalize.

• Simulation is sometimes so easy to explain to managers that analytic methods are often overlooked. (This is not a disadvantage of the method, but a practical caution about using it.)

• Simulation software sometimes requires special skills because of its complexity. Some simulation packages require the model developer to be, in effect, a programmer.

3.

List and describe the steps in the methodology of simulation.

The steps are shown in Figure 10.4. They are: • Define the problem • Construct the simulation model • Test and validate the model • Design the experiments • Conduct the experiments • Evaluate the results • Implement the results

4.

List and describe the types of simulation.

Important types of simulation include probabilistic simulation, time-dependent and time-independent simulation, and visual simulation.

Probabilistic simulation is simulation where one or more of the independent variables is described by a statistical distribution.

Time-dependent and time-independent simulations are distinguished by whether or not it is important, in order to model the system correctly, to know exactly when events occur.

11 .

Visual simulation is the graphical display of computerized results and is one of the most successful developments in computer-human interaction and problem solving.

Section 8.10 Review Questions 1.

Define visual simulation and compare it to conventional simulation.

Visual simulation uses graphical representation to show the situation to the end user. It does everything a conventional simulation does, which is any technique for conducting experiments (such as what-if analyses) with a digital computer on a model of a management system, but does it using visually pleasing and informative representation.

Visual interactive simulation is a type of visual simulation in which input is provided directly to the visual representation of the system, with results visible immediately or nearly so.

2.

Describe the features of VIS (i.e., VIM) that make it attractive for decision makers.

The main attraction of VIS (VIM) is the graphical display. This type of interaction can help managers learn about the decision-making situation, though some people respond to graphical displays better than others. Non-technical managers, who may not understand a situation from data in tables or graphs, can grasp what goes on in a visual presentation easily.

3.

How can VIS be used in operations management?

According to the text, VIS (VIM) “has been used in several operations management decisions.” Examples given there include plant operations and waiting lines. The two are related, as waiting line buildup indicates an unbalanced plant. With VIS, a manager can see work in progress piling up at one stage of a process—which is far less expensive than watching it pile up in the real factory!

4.

How is an animated film like a VIS application?

An animated film is (with modern animation techniques; this question is not about hand-drawn animations of a few decades ago) a computer-generated representation of a physical system, as is a VIS application. While some animated films use computers simply to generate final artwork and to fill in the gaps between key frames created manually, others use complex algorithms to simulate realistic motion of animated people or animals, calculate trajectories of physical objects, and determine what happens when two objects interact (e.g., a vehicle

12 .

hitting a wall). These are true VIS applications, even if their purpose is not to support management decision making.

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION

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Application Case 8.1: Canadian Football League Optimizes Game Schedule (This application case has no discussion questions.)

Application Case 8.2: Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions 1. What were the main challenges faced by Ingram Micro in developing a BIC? Ingram faced challenges related to their data capture and accuracy, issues with successfully implementing a CRM system, and resistance to the idea of demand pricing.

2. List all the business intelligence solutions developed by Ingram to optimize the prices of their products and to profile their customers.

Ingram developed BI solutions to optimize prices, these include a price optimization tool known as IMPRIME and a digital marketing platform known as Intelligence INGRAM. 3. What benefits did Ingram receive after using the newly developed BI applications?

The use of IMPRIME led to a $757 million growth in revenue and a $18.8 million increase in gross profits.

Application Case 8.3: American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids for Shipment Routes 1.

Besides reducing the risk of overpaying or underpaying suppliers, what are some other benefits AA would derive from its “should be” model?

As a model for evaluating bids, the DSS could be used to streamline and standardize AA’s RFQ process. It also helps to give users a better understanding and quicker analysis of different bids, and potentially leads to better relations with suppliers.

2.

Can you think of other domains besides air transportation where such a model could be used?

13 .

Since this is a model for determining fair payment to suppliers, it can apply to most supplier relationships involving product inventory. Any industry involving a supply chain could benefit from models like AA’s should-cost model.

3.

Discuss other possible methods with which AA could have solved its bid overpayment and underpayment problem.

AA could use other BI-related approaches besides decision analysis via DPL. For example, this problem may have been formulated using a linear programming approach, multi-criteria decision model, or some other optimization approach.

Even text analytics could be useful; by searching past RFQ bids from suppliers, AA could get a better idea of the bid process.

Application Case 8.4: Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children with Families 1. What were the challenges faced by PAE while making adoption matching decisions?

The existing matching system based on pairs of attributes did not provide enough recommendations for adoption and/or matches that were unsuccessful.

2. What features of the new spreadsheet tool helped PAE solve their issues of matching a family with a child?

The new system Inc. additional attributes that were significant and when added to existing information provided better matching decisions and a higher percent of children getting a permanent home.

Application Case 8.5: Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes 1. What were the challenges faced by Metro Meals on Wheels Treasure Valley related to meal delivery before adoption of the spreadsheet-based tool?

The first challenge was the time sensitive nature of the service, food that was not delivered within 90 minutes became cold. More significantly, the process of managing stops for the volunteers became very unwieldy and time-consuming.

2. Explain the design of the spreadsheet-based model.

The new system collects basic information about each customer and most significantly their address. The spreadsheet then uses VBA to access the MapQuest API and charts customers location and generates a workable number of stops based on distances and driving times.

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3. What are the intangible benefits of using the Excel-based model to Metro Meals on Wheels?

The primary intangible benefit was the increase in volunteer satisfaction and greater retention of volunteers.

Application Case 8.6: Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians 1. What was the issue faced by the Regional Neonatal Associates group?

The physicians needed an improved system to schedule their availability at the NICU. The system needed to ensure constant coverage but also balance Dr. workload and account for individual preferences for shift types.

2. How did the HPSM model solve all of the physician’s requirements?

A new model was formed and named the Hybrid Preference Scheduling Model (HPSM).

For satisfying the equality requirement of six physicians, the model first calculated one week’s workload and divided it for nine weeks for them. This way, the work was divided equally for all six physicians. The workload for the three remaining physicians was distributed in the nine-week schedule according to their preference. The resulting schedule was reviewed by the physicians and they found the schedule more acceptable.

Application Case 8.7: Steel Tubing Manufacturer Uses a Simulation-Based Production Scheduling System 1. Explain the advantages of using Simio’s simulation model over traditional methods.

The Simio simulation model that takes into consideration all the operational complexity, manufacturing material matching algorithms, and deadline considerations.

2. In what ways has the predictive analysis approach helped management achieve the goals of analyzing the production schedules?

In addition to solving the scheduling issue, this risk based system can also warn schedulers about specific orders potentially being delivered late and allow for changes to be made to rectify potential issues.

3. Besides the steel manufacturing industry, in what other industries could such a modeling approach help improve quality and service?

Student ideas will vary, but may include other areas of manufacturing or anywhere 15 .

custom products are created for customers (like a bakery).

Application Case 8.8: Cosan Improves Its Renewable Energy Supply Chain Using Simulation 1. What type of supply chain disruptions might occur in moving the sugar cane from the field to the production plants to develop sugar and ethanol?

Student ideas may vary, but may include disruptions related to harvesting the sugarcane, loading it onto trucks transporting it in the trucks (and the associated issues with the trucks themselves), offloading it at the refinery and moving it into the refinery process.

2. What types of advanced planning and prediction might be useful in mitigating such disruptions?

Student ideas may vary, but could include an understanding of the causes of these disruptions and how they can be mitigated, an understanding of the frequency and associated time of each disruption, and an understanding of how a single disruption can affect the remainder of the supply chain.

Application Case 8.9: Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assessment 1. In situations such as what this case depicts, what other approaches can one take to analyze investment decisions?

Student ideas may vary, but could include other inventory systems that would be more labor intensive but require a lower upfront cost (barcoding for example).

2. How would one save time if an RFID chip can tell the exact location of a product in process?

Because this data is gathered automatically and in real time it is possible to compare the predicted/scheduled position of a product in process and quickly compare it to its actual location. Slowdowns over and accepted buffer could trigger the need to evaluate production and possibly intervene.

3. Research to learn about the applications of RFID sensors in other settings. Which one do you find most interesting?

Student research and reports will vary.

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ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION 1.

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How does prescriptive analytics relate to descriptive and predictive analytics??

Prescriptive analytics involves using an analytical model to help guide a decision maker in making a decision. It answers the question “what should we do?” In contrast, descriptive and predictive analytics answer the questions “what has happened?” and “what could happen?” respectively. Prescriptive analytics is focused on decision-making.

2.

Explain the differences between static and dynamic models. How can one evolve into the other?

A static model describes relationships among parts of a system at a point in time.

A dynamic model describes relationships among parts of a system as it moves through time, with its state at one instant influencing (together with its inputs) its state at the next.

The relationships that hold in a static model at a point in time must also hold in a dynamic model of the same system at every time. To modify a static model into a dynamic model, it is necessary to add relationships between time periods so that the static model in one time period depends, in an appropriate way, on the values of system variables in the prior time period rather than standing on its own. This may increase the model’s complexity dramatically and make it harder, if not impossible, to solve.

3.

What is the difference between an optimistic approach and a pessimistic approach to decision making under assumed uncertainty?

An optimist makes the choice that has the best best-case outcome, even if it gives worse results under other conditions. The pessimist makes the choice as the best worst-case outcome, even if it gives worse results under other conditions.

For example: Suppose one choice has equally probable results of +$80 and +$30. Another has equally probable results of +$120 and –$10. (The expected value of both is +$55, so a rational decision maker with a linear utility function would call it a tie.) An optimist will choose the second because, in the best case, it will yield +$120: better than the best-case result of +$80 for the other option. A pessimist will choose the first because, at worst, it will yield +$30: better than the worst-case result of –$10 for the other choice.

4.

Explain why solving problems under uncertainty sometimes involves assuming that the problem is to be solved under conditions of risk.

Solving problems under uncertainty means the decision maker has no idea of likelihood of one outcome or another. However, decision makers usually have some idea of the relative probability of certain influencing factors. By assuming approximate probabilities (changing the situation to one of risk even if the 17 .

probabilities are not really known), the decision maker can predict the results to be expected from a course of action. Sensitivity analysis can then be used, if desired, to determine how much the prediction depends on the accuracy of the estimates.

5.

Excel is probably the most popular spreadsheet software for PCs. Why? What can we do with this package that makes it so attractive for modeling efforts?

The reasons why Excel is the most popular spreadsheet program for PCs have more to do with industry dynamics and Lotus’s slowness in porting 1-2-3 to Windows than with its technical merits. Although undoubtedly a good program, it achieved dominance in the mid-1990s when it was not markedly superior to competitors. Its superiority over most competing products today is due to the limited development resources available for products with far smaller market shares and came about only after its dominance was already well established. A full discussion of how and why Excel became the most popular PC spreadsheet program is beyond the scope of a DSS book or course.

Once a program such as Excel achieves a dominant market share, factors such as size and inertia of its user community, the infrastructure that builds up around it (add-ins, books, etc.) and (in its case) bundling into Microsoft Office combine to help it maintain that position.

Excel’s attractiveness for modeling efforts is due to five factors: the intuitiveness of the spreadsheet metaphor, its ubiquity (which makes Excel models easy to share), user familiarity with it, its flexibility, and its power. You can do many things with Excel. Its ease of use facilitates creating ad-hoc DSS. Macros allow complex procedures to be automated, with VBA available when macros fall short.

Built-in goal-seeking and scenario analysis capabilities enable a model, once developed, to become part of a larger problem-solving process.

In short: (nearly) everyone has Excel, and Excel has what (nearly) everyone needs.

6.

Explain how decision trees work. How can a complex problem be solved by using a decision tree?

A decision tree is an alternative representation of a decision situation, in which choices and states of nature are shown as alternating nodes along the branches of a tree. The range of outcomes that can result from any decision, given the possible states of nature, can then be easily seen by following all branches from that decision to their ends. By showing the decision maker the possible outcomes that could result from a given choice, the tree gives the decision maker information by which to compare choices. (This question is essentially the same as Review Questions 2 and 3 of Section 9.8, combined.)

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7.

Explain how LP can solve allocation problems.

LP solves allocation problems by using mathematical relationships (inputs required per unit of output) under known constraints (resource availability, etc.) to find the allocation of finite resources that produces the greatest value (highest value of the objective function).

8.

What are the advantages of using a spreadsheet package to create and solve LP models? What are the disadvantages?

Advantages: The spreadsheet framework is well understood by many modern managers, linear programming models fit well into the tabular format, solutions can be stored directly in the spreadsheet, and it is possible to explain linear programming using what-if analysis directly in a spreadsheet for small problems.

Spreadsheet models are not bound by the theoretical limitations of the linear programming approach, so they can (for example) work with nonlinear constraints.

Disadvantages: Large problems are difficult to create, debug, modify, and interpret. The fact that spreadsheets are a general-purpose tool, not a specialized one, makes them slower than LP software, and special structures, such as for network optimization problems, cannot be exploited for efficient solution.

9.

What are the advantages of using an LP package to create and solve LP models?

What are the disadvantages?

Advantages: The software was designed to do that one thing, it can be quite fast, the best packages can interface with large-scale databases, and some systems even feature modeling languages to enter problems.

Disadvantages: The output can be cryptic, it may require an analyst to operate and even develop the model, the software can be cumbersome to operate, and limitations of a particular package can make it difficult to solve large problems.

10.

What is the difference between decision analysis with a single goal and decision analysis with multiple goals (i.e., criteria)? Explain in detail the difficulties that may arise when analyzing multiple goals.

In a single goal we compare only one result (e.g., profit). In multiple goals we compare several results. Since in the latter case we rarely find an alternative that is superior along all the criteria, it is necessary to prioritize goals, weight them, or combine them in some other way in order to make a choice.

11.

Explain how multiple goals can arise in practice.

It is rare to have only one objective in making a business or personal decision. In choosing a flight, for example, people consider schedule, price, airline preference, 19 .

and more. When multiple stakeholders are involved, which is common in business decisions, there are even more goals to compromise among. (Consider, for example, three business travelers trying to pick a single flight. They might agree on cost, but have different schedule preferences and want frequent flyer credit on different airlines.)

12.

Compare and contrast what-if analysis and goal seeking.

What-if analysis begins with conditions and determines their result. Goal seeking begins with a desired result and determines the conditions that will produce it.

That often calls for using what-if analysis in an iterative process: assume some conditions, examine the result, on that basis choose other conditions, examine their result, and continue (using an intelligent search process that considers how prior condition changes affected the result) until the desired result is reached.

13. Describe the general process of simulation.

The steps are shown in Figure 10.4. They are: • Define the problem • Construct the simulation model • Test and validate the model • Design the experiments • Conduct the experiments • Evaluate the results • Implement the results 14. List some of the major advantages of simulation over optimization and vice versa.

Advantages: Simulation can deal with realistic, complex situations (it models risk), simulation theory is straightforward, time is compressed, there is no need for restrictive assumptions, and it fits how managers think (models are built from the manager’s perspective).

Disadvantages: An optimal solution is not guaranteed; construction can be slow; and simulation software is often not user-friendly, requiring programming.

15. Many computer games can be considered visual simulation. Explain why.

Many computer games simulate a fictional (realistic or fantasy) environment and the action that takes place in it. They use visual presentation to show the progress of the simulation. This enhances the understanding of the environment and how the game is played, often providing immediate feedback as to the success or failure of a player action.

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16. Explain why VIS is particularly helpful in implementing recommendations derived by computers.

Implementation is a process of human change. People must be motivated to change. They are more likely to accept change if they see its benefits. VIS makes it possible to see those benefits.

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ANSWERS TO END OF CHAPTER EXERCISES

Teradata University Network (TUN) and Other Hands-on Exercises 1. Explore teradatauniversitynetwork.com, and determine how models are used in the BI cases and papers.

Student research and reports will vary.

2. Create the spreadsheet models shown in Figures 8.3 and 8.4. a. What is the effect of a change in the interest rate from 8% to 10% in the spreadsheet model shown in Figure 8.3? b. For the original model in Figure 8.3, what interest rate is required to decrease the monthly payments by 20% ? What change in the loan amount would have the same effect? c. In the spreadsheet shown in Figure 8.4, what is the effect of a prepayment of $200 per month? What prepayment would be necessary to pay off the loan in 25 years instead of 30 years?

Student models and analysis will vary.

3. Solve the MBI product­mix problem described in this chapter, using either Excel’s Solver or a student version of an LP solver, such as Lindo. Lindo is available from Lindo Systems, Inc., at lindo.com; others are also available— search the Web. Examine the solution (output) reports for the answers and sensitivity report. Did you get the same results as reported in this chapter? Try the sensitivity analysis outlined in the chapter; that is, lower the righthand side of the CC-8 marketing constraint by one unit, from 200 to 199. What happens to the solution when you solve this modified problem? Eliminate the CC-8 lowerbound constraint entirely (this can be done easily by either deleting it in Solver or setting the lower limit to zero) and re-solve the problem. What happens? Using the original formulation, try modifying the objective function coefficients and see what happens.

Student analysis and insight will differ.

4. Investigate via a Web search how models and their solutions are used by the U.S. Department of Homeland Security in the “war against terrorism.” Also investigate how other governments or government agencies are using models in their missions.

21 .

Student selection of the subject, research and subsequent analysis will vary.

5. This problem was contributed by Dr. Rick Wilson of Oklahoma State University. The recent drought has hit farmers hard. Cows are eating candy corn! You are interested in creating a feed plan for the next week for your cattle using the following seven nontraditional feeding products: Chocolate Lucky Charms cereal, Butterfinger bars, Milk Duds, vanilla ice cream, Cap’n Crunch cereal, candy corn (because the real corn is all dead), and Chips Ahoy cookies.

Their per pound cost is shown, as is the protein units per pound they contribute, the total digestible nutrients (TDN) they contribute per pound, and the calcium units per pound.

You estimate that the total amount of nontraditional feeding products contribute the following amount of nutrients: at least 20,000 units of protein, at least 4,025 units of TDN, at least 1,000 but no more than 1,200 units of calcium. There are some other miscellaneous requirements as well.

• The chocolate in your overall feed plan (in pounds) cannot exceed the amount of nonchocolate poundage. Whether a product is considered chocolate or not is shown in the table (YES = chocolate, NO = not chocolate).

• No one feeding product can make up more than 25% of the total pounds needed to create an acceptable feed mix.

• There are two cereals (Chocolate Lucky Charms and Cap’n Crunch). Combined, they can be no more than 40% (in pounds) of the total mix required to meet the mix requirements. Determine the optimal levels of the seven products to create your weekly feed plan that minimizes cost. Note that all amounts of products must not have fractional values (whole numbered pounds only).

Student analysis and work will vary.

6. This exercise was also contributed by Dr. Rick Wilson of Oklahoma State University to illustrate the modeling capabilities of Excel Solver. National signing day for rugby recruiting season 2018 has been completed. Now, as the recruiting coordinator for the San Diego State University Aztec rugby team, it is time to analyze the results and plan for 2019. You’ve developed complex analytics and data collection processes and applied them for the past few recruiting seasons to help you develop a plan for 2019. Basically, you have divided the area in which you actively recruit rugby players into eight different regions. Each region has a per­target cost, a “star rating” (average recruit “star” ranking, from 0 to 5, similar to what Rivals uses for football), a yield or acceptance rate 22 .

percentage (the percentage of targeted recruits who come to SDSU), and a visibility measure, which represents a measure of how much publicity SDSU gets for recruiting in that region, measured per target (increased visibility will enhance future recruiting efforts).

Your goal is to create a LINEAR mathematical model that determines the number of target recruits you should pursue in each region in order to have an estimated yield (expected number) of at least 25 rugby recruits for next year while minimizing cost.

(Region 1 with yield of 40%: if we target 10 people, the expected number that will come is .4*10 = 40.) In determining the optimal number of targets in each region (which, not surprisingly, should be integer values), you must also satisfy the following conditions: • No more than 20% of the total targets (not the expected number of recruits) should be from any one region.

• Each region should have at least 4% of the total targets (again, not the expected number of recruits, but the number of targets).

• The average star rating of the targets must be at least equal to 3.3.

• The average visibility value of the targets must be at least equal to 3.5.

• Off on the recruiting trail you go!

Student calculations, analysis and subsequent reports will differ.

7. This exercise was also contributed by Dr. Rick Wilson of Oklahoma State University.

You are the Water Resources Manager for Thirstiville, OK, and are working out the details for next year’s contracts with three different entities to supply water to your town.

Each water source (A, B, C) provides water of different quality. The quality assessment is aggregated together in two values P1 and P2, representing a composite of contaminants, such as THMs, HAAs, and so on. The sources each have a maximum of water that they can provide (measured in thousands of gallons), a minimum that we must purchase from them, and a per-thousand-gallon cost.

23 .

On the product end, you must procure water such that you can provide three distinct water products for next year (this is all being done at the aggregate “city” level). You must provide drinking water to the city, and then water to two different wholesale clients (this is commonly done by municipalities). The table below shows requirements for these three products, and the “sales” or revenue that you get from each customer (by thousand gallons, same scale as the earlier cost). For each of the three water products/customers, MIN is the minimum that we have to provide to each, MAX is the maximum that we can provide (it is reasonable to be provided with a targeted range of product to provide to our customers), the maximum P1 and P2 weighted average for the water blended together for each quality “category” (the contaminants) per customer, and the sales price.

Yes, the second wholesale customer (WSale 2) will take as much water as you can blend together for them. Obviously, water from all three sources will need to be blended together to meet the Thirstiville customer requirements. There is one more requirement: for each of the three products (drinking water and the two wholesale clients), Source A and Source B both individually (yes, separately) must make up at least 20% of the total amount of the production of that particular water type. We do not have such a requirement for Source C. Create an appropriate LP model that determines how to meet customer water demand for next year while maximizing profit (sales less costs).

Summarize your results (something more than telepathy—say, some sort of table of data beyond the model solution?) It must use words ( ) and indicate how much water we should promise to buy from our three sources. Integers are not required.

Student calculations, analysis and final reports will vary.

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Chapter 9: Big Data, Cloud Computing, and Location Analytics: Concepts and Tools Learning Objectives for Chapter 9 1. Learn what Big Data is and how it is changing the world of analytics 2. Understand the motivation for and business drivers of Big Data analytics 3. Become familiar with the wide range of enabling technologies for Big Data analytics 4. Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics 5. Compare and contrast the complementary uses of data warehousing and Big Data technologies 6. Become familiar with in-memory analytics and Spark applications 7. Become familiar with select Big Data platforms and services 8. Understand the need for and appreciate the capabilities of stream analytics 9. Learn about the applications of stream analytics 10. Describe the current and future use of cloud computing in business analytics 11. Describe how geospatial and location-based analytics are assisting organizations CHAPTER OVERVIEW

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Big Data, which means many things to many people, is not a new technological fad. It has become a business priority that has the potential to profoundly change the competitive landscape in today’s globally integrated economy. In addition to providing innovative solutions to enduring business challenges, Big Data and analytics instigate new ways to transform processes, organizations, entire industries, and even society altogether. Yet extensive media coverage makes it hard to distinguish hype from reality.

This chapter aims to provide a comprehensive coverage of Big Data, its enabling technologies, and related analytics concepts to help understand the capabilities and limitations of this emerging technology. The chapter starts with a definition and related concepts of Big Data followed by the technical details of the enabling technologies, including Hadoop, MapReduce, and NoSQL. We provide a comparative analysis between data warehousing and Big Data analytics. The last part of the chapter is dedicated to analytics, which is one of the most promising value propositions of Big Data analytics. stream This chapter contains the following sections: 1 .

CHAPTER OUTLINE 9.1 Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods 9.2 Definition of Big Data 9.3 Fundamentals of Big Data Analytics 9.4 Big Data Technologies 9.5 Big Data and Data Warehousing 9.6 In-Memory Analytics and Apache SparkTM 9.7 Big Data and Stream Analytics 9.8 Big Data Vendors and Platforms 9.9 Cloud Computing and Business Analytics 9.10 Location-Based Analytics for Organizations

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 9.1 Review Question 1. What problem did customer service cancellation pose to AT’s business survival?

The company was churning through customers to quickly and there were concerns about the financial impacts.

2. Identify and explain the technical hurdles presented by the nature and characteristics of AT’s data.

The company allowed cancellations at many different contact points and would wants to record information about why customers were canceling at each of these points.

Unfortunately this resulted in a multitude of different types of data that would be collected. Additionally due to the large number of cancellations a large amount of data was being generated.

3. What is sessionizing? Why was it necessary for AT to sessionize its data?

While not discussed in the case, sessionizing is ordering customer interactions into a flow so that the flow of actions can be visualized and analyzed. This is what is seen in figure 2 .

9.2. It was important for the company to sessionizing the data so that they could visualize the path the customers took to cancellation.

4. Research other studies where customer churn models have been employed. What types of variables were used in those studies? How is this vignette different?

Student research and results will vary.

5. Besides Teradata Vantage, identify other popular Big Data analytics platforms that could handle the analysis described in the preceding case. (Hint: see Section 9.8.)

Student research and results will vary.

Section 9.2 Review Questions 1.

Why is Big Data important? What has changed to put it in the center of the analytics world?

As more and more data becomes available in various forms and fashions, timely processing of the data with traditional means becomes impractical. The exponential growth, availability, and use of information, both structured and unstructured, brings Big Data to the center of the analytics world. Pushing the boundaries of data analytics uncovers new insights and opportunities for the use of Big Data.

2.

How do you define Big Data? Why is it difficult to define?

Big Data means different things to people with different backgrounds and interests, which is one reason it is hard to define. Traditionally, the term “Big Data” has been used to describe the massive volumes of data analyzed by huge organizations such as Google or research science projects at NASA. Big Data includes both structured and unstructured data, and it comes from everywhere: data sources include Web logs, RFID, GPS systems, sensor networks, social networks, Internet-based text documents, Internet search indexes, detailed call records, to name just a few. Big data is not just about volume, but also variety, velocity, veracity, and value proposition.

3.

Out of the Vs that are used to define Big Data, in your opinion, which one is the most important? Why? Although all of the Vs are important characteristics, value proposition is probably the most important for decision makers’ “big” data in that it contains (or has a greater potential to contain) more patterns and interesting anomalies than “small” data. Thus, by analyzing large and feature rich data, organizations can gain greater business value that they may not have otherwise. While users can detect the patterns in small data sets using simple statistical and machine-learning 3 .

methods or ad hoc query and reporting tools, Big Data means “big” analytics. Big analytics means greater insight and better decisions, something that every organization needs nowadays. (Different students may have different answers.)

4.

What do you think the future of Big Data will be like? Will it lose its popularity to something else? If so, what will it be?

Big Data could evolve at a rapid pace. The buzzword “Big Data” might change to something else, but the trend toward increased computing capabilities, analytics methodologies, and data management of high volume heterogeneous information will continue. (Different students may have different answers.)

Section 9.3 Review Questions 1.

What is Big Data analytics? How does it differ from regular analytics?

Big Data analytics is analytics applied to Big Data architectures. This is a new paradigm; in order to keep up with the computational needs of Big Data, a number of new and innovative analytics computational techniques and platforms have been developed. These techniques are collectively called high-performance computing, and include in-memory analytics, in-database analytics, grid computing, and appliances. They differ from regular analytics which tend to focus on relational database technologies.

2.

What are the critical success factors for Big Data analytics?

Critical factors include a clear business need, strong and committed sponsorship, alignment between the business and IT strategies, a fact-based decision culture, a strong data infrastructure, the right analytics tools, and personnel with advanced analytic skills.

3.

What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?

Traditional ways of capturing, storing, and analyzing data are not sufficient for Big Data. Major challenges are the vast amount of data volume, the need for data integration to combine data of different structures in a cost-effective manner, the need to process data quickly, data governance issues, skill availability, and solution costs.

4.

What are the common business problems addressed by Big Data analytics?

Here is a list of problems that can be addressed using Big Data analytics: • • •

Process efficiency and cost reduction Brand management Revenue maximization, cross-selling, and up-selling 4 .

• • • • • • •

Enhanced customer experience Churn identification, customer recruiting Improved customer service Identifying new products and market opportunities Risk management Regulatory compliance Enhanced security capabilities

Section 9.4 Review Questions 1.

What are the common characteristics of emerging Big Data technologies?

They take advantage of commodity hardware to enable scale-out, parallel processing techniques; employ nonrelational data storage capabilities in order to process unstructured and semistructured data; and apply advanced analytics and data visualization technology to Big Data to convey insights to end users.

2.

What is MapReduce? What does it do? How does it do it?

MapReduce is a programming model that allows the processing of large-scale data analysis problems to be distributed and parallelized. The MapReduce technique, popularized by Google, distributes the processing of very large multistructured data files across a large cluster of machines. High performance is achieved by breaking the processing into small units of work that can be run in parallel across the hundreds, potentially thousands, of nodes in the cluster. The map function in MapReduce breaks a problem into sub-problems, which can each be processed by single nodes in parallel. The reduce function merges (sorts, organizes, aggregates) the results from each of these nodes into the final result.

3.

What is Hadoop? How does it work?

Hadoop is an open source framework for processing, storing, and analyzing massive amounts of distributed, unstructured data. It is designed to handle petabytes and exabytes of data distributed over multiple nodes in parallel, typically commodity machines connected via the Internet. It utilizes the MapReduce framework to implement distributed parallelism. The file organization is implemented in the Hadoop Distributed File System (HDFS), which is adept at storing large volumes of unstructured and semistructured data.

This is an alternative to the traditional tables/rows/columns structure of a relational database. Data is replicated across multiple nodes, allowing for fault tolerance in the system.

5 .

4.

What are the main Hadoop components? What functions do they perform?

Major components of Hadoop are the HDFS, a Job Tracker operating on the master node, Name Nodes, Secondary Nodes, and Slave Nodes. The HDFS is the default storage layer in any given Hadoop cluster. A Name Node is a node in a Hadoop cluster that provides the client information on where in the cluster particular data is stored and if any nodes fail. Secondary nodes are backup name nodes. The Job Tracker is the node of a Hadoop cluster that initiates and coordinates MapReduce jobs or the processing of the data. Slave nodes store data and take direction to process it from the Job Tracker.

Querying for data in the distributed system is accomplished via MapReduce. The client query is handled in a Map job, which is submitted to the Job Tracker. The Job Tracker refers to the Name Node to determine which data it needs to access to complete the job and where in the cluster that data is located, then submits the query to the relevant nodes which operate in parallel. A Name Node acts as facilitator, communicating back to the client information such as which nodes are available, where in the cluster certain data resides, and which nodes have failed.

When each node completes its task, it stores its result. The client submits a Reduce job to the Job Tracker, which then collects and aggregates the results from each of the nodes.

5.

What is NoSQL? How does it fit into the Big Data analytics picture?

NoSQL, also known as “Not Only SQL,” is a new style of database for processing large volumes of multi-structured data. Whereas Hadoop is adept at supporting large-scale, batch-style historical analysis, NoSQL databases are mostly aimed at serving up discrete data stored among large volumes of multi-structured data to end-user and automated Big Data applications. NoSQL databases trade ACID (atomicity, consistency, isolation, durability) compliance for performance and scalability.

Section 9.5 Review Questions 1.

What are the challenges facing data warehousing and Big Data? Are we witnessing the end of the data warehousing era? Why or why not?

What has changed the landscape in recent years is the variety and complexity of data, which made data warehouses incapable of keeping up. It is not the volume of the structured data but the variety and the velocity that forced the world of IT to develop a new paradigm, which we now call “Big Data.” But this does not mean the end of data warehousing. Data warehousing and RDBMS still bring many strengths that make them relevant for BI and that Big Data techniques do not currently provide.

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2.

What are the use cases for Big Data and Hadoop?

In terms of its use cases, Hadoop is differentiated two ways: first, as the repository and refinery of raw data, and second, as an active archive of historical data. Hadoop, with their distributed file system and flexibility of data formats (allowing both structured and unstructured data), is advantageous when working with information commonly found on the Web, including social media, multimedia, and text. Also, because it can handle such huge volumes of data (and because storage costs are minimized due to the distributed nature of the file system), historical (archive) data can be managed easily with this approach.

3.

What are the use cases for data warehousing and RDBMS? Three main use cases for data warehousing are performance, integration, and the availability of a wide variety of BI tools. The relational data warehouse approach is quite mature, and database vendors are constantly adding new index types, partitioning, statistics, and optimizer features. This enables complex queries to be done quickly, a must for any BI application. Data warehousing, and the ETL process, provide a robust mechanism for collecting, cleaning, and integrating data.

And, it is increasingly easy for end users to create reports, graphs, and visualizations of the data.

4.

In what scenarios can Hadoop and RDBMS coexist?

There are several possible scenarios under which using a combination of Hadoop and relational DBMS-based data warehousing technologies makes sense. For example, you can use Hadoop for storing and archiving multi-structured data, with a connector to a relational DBMS that extracts required data from Hadoop for analysis by the relational DBMS. Hadoop can also be used to filter and transform multi-structural data for transporting to a data warehouse, and can also be used to analyze multi-structural data for publishing into the data warehouse environment. Combining SQL and MapReduce query functions enables data scientists to analyze both structured and unstructured data. Also, front end query tools are available for both platforms.

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Section 9.6 Review Questions 1. What are some of the unique features of Spark as compared to Hadoop?

Hadoop utilizes the batch processing framework and lacks real time processing capabilities, whereas Spark is a unified analytics engine that can execute both batch and streaming data .

2. Give examples of companies that have adopted Apache Spark. Find new examples online.

Examples include Uber, Pinterest, Netflix, Yahoo, and eBay. Uber uses Apache SparkTM to detect fraudulent trips at scale. Pinterest measures user engagement in real-time using Apache SparkTM. The recommendation engine of Netflix also utilizes the capabilities of Apache SparkTM. Yahoo, one of the early adopters of Apache SparkTM, has used it for creating business intelligence applications. Finally, eBay has used Apache SparkTM for data management and stream processing.

Searches for updated users will vary based on the date of the search.

3. Run the exercise as described in this section. What do you learn from this exercise?

Student outcomes and results will vary.

Section 9.7 Review Questions 1. What is a stream (in the Big Data world)?

A stream can be thought of as an unbounded flow or sequence of data elements, arriving continuously at high velocity. Streams often cannot be efficiently or effectively stored for subsequent processing; thus Big Data concerns about Velocity (one of the six Vs) are especially prevalent when dealing with streams. Examples of data streams include sensor data, computer network traffic, phone conversations, ATM transactions, web searches, and financial data.

2. What are the motivations for stream analytics?

In situations where data streams in rapidly and continuously, traditional analytics approaches that work with previously accumulated data (i.e., data at arrest) often either arrive at the wrong decisions because of using too much out-of-context data, or they arrive at the correct decisions but too late to be of any use to the organization. Therefore it is critical for a number of business situations to analyze the data soon after it is created and/or as soon as it is streamed into the analytics system. It is no longer feasible to “store everything.” Otherwise, analytics will either arrive at the wrong decisions because of using too much out-of-context data, or at the correct decisions but too late to be of any

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use to the organization. Therefore it is critical for a number of business situations to analyze the data as soon as it is streamed into the analytics system.

3. What is stream analytics? How does it differ from regular analytics?

Stream analytics is the process of extracting actionable information from continuously flowing/streaming data. It is also sometimes called “data in-motion analytics” or “realtime data analytics.” It differs from regular analytics in that it deals with high velocity (and transient) data streams instead of more permanent data stores like databases, files, or web pages.

4. What is critical event processing? How does it relate to stream analytics?

Critical event processing is a method of capturing, tracking, and analyzing streams of data to detect events (out of normal happenings) of certain types that are worthy of the effort. It involves combining data from multiple sources to infer events or patterns of interest. An event may also be defined generically as a “change of state,” which may be detected as a measurement exceeding a predefined threshold of time, temperature, or some other value. This applies to stream analytics because the events are happening in real time.

5. Define data stream mining. What additional challenges are posed by data stream mining?

Data stream mining is the process of extracting novel patterns and knowledge structures from continuous, rapid data records. Processing data streams, as opposed to more permanent data storages, is a challenge. Traditional data mining techniques can process data recursively and repetitively because the data is permanent. By contrast, a data stream is a continuous flow of ordered sequence of instances that can only be read once and must be processed immediately as they come in.

6. What are the most fruitful industries for stream analytics?

Many industries can benefit from stream analytics. Some prominent examples include ecommerce, telecommunications, law enforcement, cyber security, the power industry, health sciences, and the government.

7. How can stream analytics be used in e-commerce?

Companies such as Amazon and eBay use stream analytics to analyze customer behavior in real time. Every page visit, every product looked at, every search conducted, and every click made is recorded and analyzed to maximize the value gained from a user’s visit.

Behind the scenes, advanced analytics are crunching the real-time data coming from our clicks, and the clicks of thousands of others, to “understand” what it is that we are interested in (in some cases, even we do not know that) and make the most of that information by creative offerings.

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8. In addition to what is listed in this section, can you think of other industries and/or application areas where stream analytics can be used?

Stream analytics could be of great benefit to any industry that faces an influx of relevant real-time data and needs to make quick decisions. One example is the news industry. By rapidly sifting through data streaming in, a news organization can recognize “newsworthy” themes (i.e., critical events). Another benefit would be for weather tracking in order to better predict tornados or other natural disasters. (Different students will have different answers.)

9. Compared to regular analytics, do you think stream analytics will have more (or less) use cases in the era of Big Data analytics? Why? Stream analytics can be thought of as a subset of analytics in general, just like “regular” analytics. The question is, what does “regular” mean? Regular analytics may refer to traditional data warehousing approaches, which does constrain the types of data sources and hence the use cases. Or, “regular” may mean analytics on any type of permanent stored architecture (as opposed to transient streams). In this case, you have more use cases for “regular” (including Big Data) than in the previous definition. In either case, there will probably be plenty of times when “regular” use cases will continue to play a role, even in the era of Big Data analytics. (Different students will have different answers.)

Section 9.8 Review Questions 1. Identify some of the key Big Data technology vendors whose key focus is on-premise Hadoop platforms.

Student search results will vary, but may include Qubole, Yash & HortonWorks.

2. What is special about the Big Data vendor landscape? Who are the big players?

The Big Data vendor landscape is developing very rapidly. It is in a special period of evolution where entrepreneurial startup firms bring innovative solutions to the marketplace. Cloudera is a market leader in the Hadoop space. MapR and Hortonworks are two other Hadoop startups. DataStax is an example of a NoSQL vendor. Informatica, Pervasive Software, Syncsort, and MicroStrategy are also players. Most of the growth in the industry is with Hadoop and NoSQL distributors and analytics providers. There is still very little in terms of Big Data application vendors. Meanwhile, the next-generation data warehouse market has experienced significant consolidation. Four leading vendors in this space—Netezza, Greenplum, Vertica, and Aster Data—were acquired by IBM, EMC, HP, and Teradata, respectively. Mega-vendors Oracle and IBM also play in the Big Data space, connecting and consolidating their products with Hadoop and NoSQL engines.

3. Search and identify the key similarity and differences between cloud providers’ analytics offerings and analytics providers’ presence on specific cloud platforms.

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The results of student research will vary based on the date of the research and the provider selected.

4. What are some of the features of a platform such as Teradata Vantage?

Some results will vary based on the data search, but current features listed at https://www.teradata.com/Products/Software/Vantage include: •

best analytics engine

•

tool integration

•

scalability

Section 9.9 Review Questions 1. Define cloud computing. How does it relate to PaaS, SaaS, and IaaS? Cloud computing offers the possibility of using software, hardware, platform, and infrastructure, all on a service-subscription basis. Cloud computing enables a more scalable investment on the part of a user. Like PaaS, etc., cloud computing offers organizations the latest technologies without significant upfront investment.

In some ways, cloud computing is a new name for many previous related trends: utility computing, application service provider grid computing, on-demand computing, software as a service (SaaS), and even older centralized computing with dumb terminals. But the term cloud computing originates from a reference to the Internet as a “cloud” and represents an evolution of all previous shared/centralized computing trends.

2. Give examples of companies offering cloud services. 3. How does cloud computing affect BI? Companies offering such services include 1010data, LogiXML, and Lucid Era. These companies offer feature extract, transform, and load capabilities as well as advanced data analysis tools. Other companies, such as Elastra and Rightscale, offer dashboard and data management tools that follow the SaaS and DaaS models.

4. How does DaaS change the way data is handled?

In the DaaS model, the actual platform on which the data resides doesn’t matter. Data can reside in a local computer or in a server at a server farm inside a cloud-computing environment. With DaaS, any business process can access data wherever it resides.

Customers can move quickly thanks to the simplicity of the data access and the fact that they don’t need extensive knowledge of the underlying data.

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5. What are the different types of cloud platforms?

The three different types of cloud platforms are IaaS, PaaS and SaaS. 6. Why is AaaS cost-effective?

AaaS in the cloud has economies of scale and scope by providing many virtual analytical applications with better scalability and higher cost savings. The capabilities that a service orientation (along with cloud computing, pooled resources, and parallel processing) brings to the analytic world enable cost-effective data/text mining largescale optimization, highly-complex multi-criteria decision problems, and distributed simulation models.

7. Name at least three major cloud service providers.

Examples includes Amazon, IBM Cloud, Microsoft Azure, Google App Engine and Openshift.

8. Give at least three examples of analytics-as-a-service providers.

Examples include IBM Cloud, MineMyText.com, SAS Viya, Tableau and Snowflake.

Section 9.10 Review Questions 1. How does traditional analytics make use of location-based data?

Traditional analytics produce visual maps that are geographically mapped and based on the traditional location data, usually grouped by the postal codes. The use of postal codes to represent the data is a somewhat static approach for achieving a higher level view of things.

2. How can geocoded locations assist in better decision making?

They help the user in understanding “true location-based” impacts, and allow them to view at higher granularities than that offered by the traditional postal code aggregations. Addition of location components based on latitudinal and longitudinal attributes to the traditional analytical techniques enables organizations to add a new dimension of “where” to their traditional business analyses, which currently answer questions of “who,” “what,” “when,” and “how much.” By integrating information about the location with other critical business data, organizations are now creating location intelligence (LI).

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3. What is the value provided by geospatial analytics?

Geospatial analysis gives organizations a broader perspective and aids in decision making. Location intelligence (LI) is enabling organizations to gain critical insights and make better decisions by optimizing important processes and applications. By incorporating demographic details into locations, retailers can determine how sales vary by population level and proximity to other competitors; they can assess the demand and efficiency of supply chain operations. Consumer product companies can identify the specific needs of the customers and customer complaint locations, and easily trace them back to the products. Sales reps can better target their prospects by analyzing their geography.

4. Explore the use of geospatial analytics further by investigating its use across various sectors like government census tracking, consumer marketing, and so forth.

Students’ answers will vary.

5. Search online for other applications of consumer-oriented analytical applications.

Students’ answers will vary.

6. How can location-based analytics help individual consumers?

If a user on a smart phone enters data, the location sensors of the phone can help find others in that location who are facing similar circumstances, as well as local companies providing services and products that the consumer desires. The user can thus see what others in their location are choosing, and the opportunities for meeting his or her needs. Conversely, the user’s behaviors and choices can then contribute information to other consumers in the same location.

7. Explore more transportation applications that may employ location-based analytics.

Students’ answers will vary.

8. What other applications can you imagine if you were able to access cell phone location data?

Students’ answers will vary.

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ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION

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Application Case 9.1: Big Data Analytics Helps Luxottica Improve Its Marketing Effectiveness 1.

What does Big Data mean to Luxottica?

For Luxottica, Big Data includes everything they can find about their customer interactions (in the form of transactions, click streams, product reviews, and social media postings). They see this as constituting a massive source of business intelligence for potential product, marketing, and sales opportunities.

2.

What were their main challenges?

Because Luxottica outsourced both data storage and promotional campaign development and management, there was a disconnect between data analytics and marketing execution. Their competitive posture and strategic growth initiatives were compromised for lack of an individualized view of their customers and an inability to act decisively and consistently on the different types of information generated by each retail channel.

3.

What was the proposed solution, and the obtained results?

Luxottica deployed the Customer Intelligence Appliance (CIA) from IBM Business Partner Aginity LLC. This product is built on IBM PureData System for Analytics. This solution helps Luxottica highly segment customer behavior and provide a platform and smart database for marketing execution systems, such as campaign management, e-mail services, and other forms of direct marketing.

Anticipated benefits include a 10% improvement in marketing effectiveness, identifying the highest valued customers, and the ability to target customers based on preference and history.

Application Case 9.2: Overstock.com Combines Multiple Datasets to Understand Customer Journeys 1. What are some of the different marketing campaigns a company might run to woo customers? What format might data about these campaigns take?

There are a wide variety of different types of marketing campaigns, but some of those mentioned within the case include targeted online and direct mail campaigns, advertising through various channels, growing the loyalty program by providing different customer incentives and so on.

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2. By visualizing the most common customer paths to sales, how would you use that information to make decisions on the future marketing campaigns?

By visualizing these paths you would be able to determine the effectiveness or necessity of each of the different types of marketing campaigns and determine if there was important interplay between them to generate purchase events.

3. What other applications of such path analysis techniques can you think of?

Student opinions and ideas will vary, but many retail businesses use a similar marketing mix a variety of different advertising methods.

Application Case 9.3: eBay’s Big Data Solution 1.

Why is Big Data a big deal for eBay? eBay is the world’s largest online marketplace, and its success requires the ability to turn the enormous volumes of data it generates into useful insights for customers. Big Data is essential for this effort.

2.

What were the challenges, the proposed solution, and the obtained results? eBay was experiencing explosive data growth and needed a solution that did not have the typical bottlenecks, scalability issues, and transactional constraints associated with common relational database approaches. The company also needed to perform rapid analysis on a broad assortment of the structured and unstructured data it captured. eBay’s solution includes NoSQL via Apache Cassandra and DataStax Enterprise. It also uses integrated Apache Hadoop analytics that come with DataStax. The solution incorporates a scale-out architecture that enables eBay to deploy multiple DataStax Enterprise clusters across several different data centers using commodity hardware. Now, eBay can more cost effectively process massive amounts of data at very high speeds. The new architecture serves a wide variety of new use cases, and its reliability and fault tolerance has been greatly enhanced.

3.

Can you think of other e-commerce businesses that may have Big Data challenges comparable to that of eBay? Any large company with a significant online presence will have similar challenges as eBay. Amazon and Walmart are two of the largest.

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Application Case 9.4: Understanding Quality and Reliability of Healthcare Support Information on Twitter 1. What was the data scientists’ main concern regarding health information that is disseminated on the Twitter platform?

The primary concern was the quality and accuracy of the information that was being distributed.

2. How did the data scientists ensure that nonexpert information disseminated on social media could indeed contain valuable health information?

The scientists evaluated the type of information that was distributed and the size of the following of the user for both influential and non-influential users. They found that influential users typically provided higher quality information with more discussion of facts.

3. Does it make sense that influential users would share more objective information whereas less influential users could focus more on subjective information? Why? Student opinions will vary, but it may be that users gain influence by providing higher quality information and other users are able to identify this information themselves.

Application Case 9.5: Using Natural Language Processing to analyze customer feedback in TripAdvisor reviews 1. How did the predictive modelling help TripAdvisor?

The company was able to use historical information on user reviews to predict the type of review they would give a particular restaurant.

2. Why was Spark used?

Spark was used because of its ability for parallel processing and in memory processing.

Application Case 9.6: Salesforce Is Using Streaming Data to Enhance Customer Value 1. Are there areas in any industry where streaming data is irrelevant?

Student opinions will vary, but it would be difficult to find an industry.

2. Besides customer retention, what are other benefits of using predictive analytics?

Companies are able to make predictions and decisions about their consumers more rapidly. This ensures that businesses target, attract, and retain the right customers and maximize their value.

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Application Case 9.7: Using Social Media for Nowcasting Flu Activity 1. Why would social media be able to serve as an early predictor of flu outbreaks?

Social media can be used because individuals will mention if they become sick and this information, along with the users geographic location, can be analyzed with models that have historically been used for disease propagation.

2. What other variables might help in predicting such outbreaks?

Student opinions will vary, but some examples might include the density level of individuals in metropolitan areas and the percentage of individuals active on the particular social media platform.

3. Why would this problem be a good problem to solve using Big Data technologies mentioned in this chapter?

This is a very complex problem and process since data needs to be extracted for a secondary use from a social media platform, geographic information tagged and then an analysis performed.

Application Case 9.8: Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse 1. What could be the reasons behind the health disparities across gender?

Students opinions on these topics will vary. One possible answer is that individuals have different lifestyles related to their gender that affect these results.

2. What are the main components of a network?

A network is comprised of a defined set of items called nodes, which are linked to each other through edges.

3. What type of analytics was applied in this application?

To compare the comorbidities, a network analysis approach was applied.

Application Case 9.9: Major West Coast Utility Uses Cloud-Mobile Technology to Provide Real-Time Incident Reporting

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1. How does cloud technology impact enterprise software for small and mid-size businesses?

It is made deployment of software easier without issues related to hardware, installation and backups. All of the business logic and configurations are stored in the cloud, the solution itself can act as a stand-alone system for customers who have no backend systems.

2. What are some of the areas where businesses can use mobile technology?

Mobile technology is ideal when employees themselves are mobile either due to the nature of their work or because they are away from the office.

3. What types of businesses are likely to be the forerunners in adopting cloud-mobile technology?

In addition to businesses that have compelling needs to communicate with employees that are separate from an office location, businesses that have needs for distributed applications without large budgets or IT staffs may benefit.

4. What are the advantages of cloud-based enterprise software instead of the traditional on-premise model?

There are number of benefits including the ability to use familiar hardware that does not need to be standardized, the ability to easily perform software updates, and store data in the cloud (for both ease of access and backup reasons).

5. What are the likely risks of cloud versus traditional on-premise applications?

Student opinions will vary, but there will always be a concern about data security and privacy.

Application Case 9.10: Great Clips Employs Spatial Analytics to Shave Time in Location Decisions 1. How is geospatial analytics employed at Great Clips?

Great Clips depends on a growth strategy that is driven by rapidly opening new stores in the right locations and markets. They use geospatial analysis to help analyze the locations based on the requirements for a potential customer base, demographic trends, and sales impact on existing franchises in the target location. They use their Alteryx-based solution to evaluate each new location based on demographics and consumer behavior data, aligning with existing Great Clips customer profiles and the potential revenue impact of the new site on the existing sites.

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2. What criteria should a company consider in evaluating sites for future locations?

Major criteria include potential customer base, demographic trends, and sales impact on existing franchises in the target location.

3. Can you think of other applications where such geospatial data might be useful?

Geospatial data can be used to help customers find the right location (for example, the closest Great Clips location). It is certainly relevant for other companies in a variety of industries. Analyzing customer profiles and applying these to geographic information can assist with many retail firms. Another possibility is utilizing geospatial analysis to find locations for manufacturing facilities; in this case you would be looking for supplier and raw materials’ locations more than customer locations. In the consumer market, geospatial analysis can help users in a variety of applications; for example finding the best locations for restaurants or stores catering to the customer’s desires. (Student answers will vary.)

Application Case 9.11: Starbucks Exploits GIS and Analytics to Grow Worldwide 1. What type of demographics and GIS information would be relevant for deciding on a store location?

Information such as trade areas, retail clusters and generators, traffic, and demographics is important in deciding the next store’s location.

2. It has been mentioned that Starbucks encourages its customers to use its mobile app.

What type of information might the company gather from the app to help it better plan operations?

The company will be able to determine where the user is ordering from, and the distance to the nearest Starbucks from that location.

3. Will the availability of free Wi-Fi at Starbucks stores provide any information to Starbucks for better analytics?

Student ideas and responses will vary.

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION 1.

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What is Big Data? Why is it important? Where does Big Data come from?

Traditionally, the term “Big Data” has been used to describe the massive volumes of data analyzed by huge organizations like Google or research science projects at NASA. But for most businesses, it’s a relative term: “Big” depends on an 19 .

organization’s size. In general, Big Data exceeds the reach of commonly used hardware environments and/or capabilities of software tools to capture, manage, and process it within a tolerable time span for its user population. Big Data has become a popular term to describe the exponential growth, availability, and use of information, both structured and unstructured. Big data is important because it is there and because it is rich in information and insight that, if effectively tapped, can lead to better business decisions and improved company performance. Much has been written on the Big Data trend and how it can serve as the basis for innovation, differentiation, and growth. Big Data includes both structured and unstructured data, and it comes from everywhere: data sources include Web logs, RFID, GPS systems, sensor networks, social networks, Internet-based text documents, Internet search indexes, and detailed call records, to name just a few.

Big data is not just about volume, but also variety, velocity, veracity, and value proposition.

2.

What do you think the future of Big Data will be? Will it lose its popularity to something else? If so, what will it be?

Big Data could evolve at a rapid pace. The buzzword “Big Data” might change to something else, but the trend toward increased computing capabilities, analytics methodologies, and data management of high volume heterogeneous information will continue. (Different students may have different answers.)

3.

What is Big Data analytics? How does it differ from regular analytics?

Big Data analytics is analytics applied to Big Data architectures. This is a new paradigm; in order to keep up with the computational needs of Big Data, a number of new and innovative analytics computational techniques and platforms have been developed. These techniques are collectively called high-performance computing, and include in-memory analytics, in-database analytics, grid computing, and appliances. They differ from regular analytics which tend to focus on relational database technologies.

4.

What are the critical success factors for Big Data analytics?

Critical factors include a clear business need, strong and committed sponsorship, alignment between the business and IT strategies, a fact-based decision culture, a strong data infrastructure, the right analytics tools, and personnel with advanced analytic skills.

5.

What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?

Traditional ways of capturing, storing, and analyzing data are not sufficient for Big Data. Major challenges are the vast amount of data volume, the need for data integration to combine data of different structures in a cost-effective manner, the need to process data quickly, data governance issues, skill availability, and solution costs.

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6.

What are the common business problems addressed by Big Data analytics?

Here is a list of problems that can be addressed using Big Data analytics: • • • • • • • • • •

7.

Process efficiency and cost reduction Brand management Revenue maximization, cross-selling, and up-selling Enhanced customer experience Churn identification, customer recruiting Improved customer service Identifying new products and market opportunities Risk management Regulatory compliance Enhanced security capabilities

In the era of Big Data, are we about to witness the end of data warehousing?

Why? What has changed the landscape in recent years is the variety and complexity of data, which made data warehouses incapable of keeping up. It is not the volume of the structured data but the variety and the velocity that forced the world of IT to develop a new paradigm, which we now call “Big Data.” But this does not mean the end of data warehousing. Data warehousing and RDBMS still bring many strengths that make them relevant for BI and that Big Data techniques do not currently provide.

8.

What are the use cases for Big Data/Hadoop and data warehousing/RDBMS? In terms of its use cases, Hadoop is differentiated two ways: first, as the repository and refinery of raw data, and second, as an active archive of historical data. Hadoop, with their distributed file system and flexibility of data formats (allowing both structured and unstructured data), is advantageous when working with information commonly found on the Web, including social media, multimedia, and text. Also, because it can handle such huge volumes of data (and because storage costs are minimized due to the distributed nature of the file system), historical (archive) data can be managed easily with this approach.

Three main use cases for data warehousing are performance, integration, and the availability of a wide variety of BI tools. The relational data warehouse approach is quite mature, and database vendors are constantly adding new index types, partitioning, statistics, and optimizer features. This enables complex queries to be done quickly, a must for any BI application. Data warehousing, and the ETL process, provide a robust mechanism for collecting, cleaning, and integrating data.

And, it is increasingly easy for end users to create reports, graphs, and visualizations of the data.

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9.

Is cloud computing “just an old wine in a new bottle?” How is it similar to other initiatives? How is it different?

In some ways, cloud computing is a new name for many previous related trends: utility computing, application service provider grid computing, on-demand computing, software as a service (SaaS), and even older centralized computing with dumb terminals. But the term cloud computing originates from a reference to the Internet as a “cloud” and represents an evolution of all previous shared/centralized computing trends.

Cloud computing offers the possibility of using software, hardware, platform, and infrastructure, all on a service-subscription basis. Cloud computing enables a more scalable investment on the part of a user. Like PaaS, etc., cloud-computing offers organizations the latest technologies without significant upfront investment.

10.

What is stream analytics? How does it differ from regular analytics?

Stream analytics is the process of extracting actionable information from continuously flowing/streaming data. It is also sometimes called “data in-motion analytics” or “real-time data analytics.” It differs from regular analytics in that it deals with high velocity (and transient) data streams instead of more permanent data stores like databases, files, or web pages).

11.

What are the most fruitful industries for stream analytics? What is common to those industries?

Many industries can benefit from stream analytics. Some prominent examples include e-commerce, telecommunications, law enforcement, cyber security, the power industry, health sciences, and the government.

12.

Compared to regular analytics, do you think stream analytics will have more (or fewer) use cases in the era of Big Data analytics? Why? Stream analytics can be thought of as a subset of analytics in general, just like “regular” analytics. The question is, what does “regular” mean? Regular analytics may refer to traditional data warehousing approaches, which does constrain the types of data sources and hence the use cases. Or, “regular” may mean analytics on any type of permanent stored architecture (as opposed to transient streams). In this case, you have more use cases for “regular” (including Big Data) than in the previous definition. In either case, there will probably be plenty of times when “regular” use cases will continue to play a role, even in the era of Big Data analytics. (Different students will have different answers.)

13. What are the potential benefits of using geospatial data in analytics? Give examples.

Geospatial analytics gives organizations a broader perspective and aids in decision making. Geospatial data helps companies with managing operations, targeting customers, and deciding on promotions. It also helps consumers directly, making use 22 .

of integrated sensor technologies and global positioning systems installed in their smartphones. Using geospatial data, companies can identify the specific needs of the customers and customer complaint locations, and easily trace them back to the products. Another example is in the telecommunications industry, where geospatial analysis can enable communication companies to capture daily transactions from a network to identify the geographic areas experiencing a large number of failed connection attempts of voice, data, text, or Internet.

14. What types of new applications can emerge from knowing locations of users in real time? What if you also knew what they have in their shopping cart, for example?

One prominent application is in the emerging area of reality mining, which uses location-enabled devices for finding nearby services, locating friends and family, navigating, tracking of assets and pets, dispatching, and engaging in sports, games, and hobbies. Adding shopping cart knowledge will enhance the application’s ability to provide targeted information to a customer; for example, the app could find prices for similar products in nearby stores.

15. How can consumers benefit from using analytics, especially based on location information?

Consumer-oriented analytics based on location information fit into two major categories: (a) GPS navigation and data analysis, and (b) historic and current location demand analysis. Consumers benefit from analytics-based applications in many areas, including fun and health, as well as enhanced personal productivity.

16. “Location-tracking–based profiling is powerful but also poses privacy threats.”

Comment.

Students’ answers will differ. Privacy threats relate to user-profiling, intrusive use of personal information, and not being able to control what is being collected.

17. Is cloud computing “just an old wine in a new bottle?” How is it similar to other initiatives? How is it different?

In some ways, cloud computing is a new name for many previous related trends: utility computing, application service provider grid computing, on-demand computing, software as a service (SaaS), and even older centralized computing with dumb terminals. But the term cloud computing originates from a reference to the Internet as a “cloud” and represents an evolution of all previous shared/centralized computing trends.

Cloud computing offers the possibility of using software, hardware, platform, and infrastructure, all on a service-subscription basis. Cloud computing enables a more scalable investment on the part of a user. Like PaaS, etc., cloud-computing offers organizations the latest technologies without significant upfront investment.

18. Discuss the relationship between mobile devices and social networking.

23 .

Mobile social networking enables social networking where members converse and connect with one another using cell phones or other mobile devices.

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ANSWERS TO END OF CHAPTER EXCERCISES

Teradata University Network (TUN) and Other Hands-on Exercises 1. Go to teradatauniversitynetwork.com, and search for case studies. Read cases and white papers that talk about Big Data analytics. What is the common theme in those case studies?

Student research and reports will vary 2. At teradatauniversitynetwork.com, find the SAS Visual Analytics white papers, case studies, and hands-on exercises. Carry out the visual analytics exercises on large data sets and prepare a report to discuss your findings.

Student performance of tasks will vary as will their analysis.

3. At teradatauniversitynetwork.com, go to the Sports Analytics page. Find applications of Big Data in sports. Summarize your findings.

Student research and reports will differ 4. Go to teradatauniversitynetwork.com, and search for BSI Videos that talk about Big Data. Review these BSI videos, and answer the case questions related to them.

5. Go to the teradata.com and/or asterdata.com Web sites. Find at least three customer case studies on Big Data, and write a report where you discuss the commonalities and differences of these cases.

Student selection of case studies and the resulting reports will differ.

6. Go to IBM.com. Find at least three customer case studies on Big Data, and write a report where you discuss the commonalities and differences of these cases.

Student selection of cases will cause differences in the reports.

7. Go to claudera.com. Find at least three customer case studies on Hadoop implementation, and write a report where you discuss the commonalities and differences of these cases.

Students will select different cases.

24 .

8. Go to mapr.com. Find at least three customer case studies on Hadoop implementation, and write a report where you discuss the commonalities and differences of these cases.

Students will select different cases.

9. Go to hortonworks.com. Find at least three customer case studies on Hadoop implementation, and write a report in which you discuss the commonalities and differences of these cases.

Student selection and analysis will vary.

10. Go to marklogic.com. Find at least three customer case studies on Hadoop implementation, and write a report where you discuss the commonalities and differences of these cases.

Student selection and analysis will vary.

11. Go to youtube.com. Search for videos on Big Data computing. Watch at least two.

Summarize your findings.

Students will select different videos and have different reactions.

12. Go to google.com/scholar, and search for articles on stream analytics. Find at least three related articles. Read and summarize your findings.

Student summaries will vary based on the data search and the articles found.

13. Enter google.com/scholar, and search for articles on data stream mining. Find at least three related articles. Read and summarize your findings.

Selection of articles will vary.

14. Enter google.com/scholar, and search for articles that talk about Big Data versus data warehousing. Find at least five articles. Read and summarize your findings.

Student research will differ.

15. Location-tracking–based clustering provides the potential for personalized services but challenges for privacy. Divide the class into two parts to argue for and against such applications.

Student opinions and debate positions will be different.

16. Enter YouTube.com. Search for videos on cloud computing, and watch at least two.

Summarize your findings.

Students will find and select different videos.

25 .

17. Enter Pandora.com. Find out how you can create and share music with friends.

Explore how the site analyzes user preferences.

Students will have different impressions of the service.

18. Enter Humanyze.com. Review various case studies and summarize one interesting application of sensors in understanding social exchanges in organizations.

Students will select different case studies and have different reactions.

19. The objective of the exercise is to familiarize you with the capabilities of smartphones to identify human activity. The data set is available at archive.ics.uci.edu/ml/datasets/ Human+Activity+Recognition+Using+Smartphones. It contains accelerometer and gyroscope readings on 30 subjects who had the smartphone on their waist. The data is available in a raw format and involves some data preparation efforts. Your objective is to identify and classify these readings into activities like walking, running, climbing, and such. More information on the data set is available on the download page. You may use clustering for initial exploration and to gain an understanding of the data. You may use tools like R to prepare and analyze this data.

Student analysis and subsequent reports will be different.

26 .

Chapter 10: Robotics: Industrial and Consumer Applications Learning Objectives for Chapter 10 1.

2.

3.

4.

5.

6.

Discuss the general history of automation and robots Discuss the applications of robots in various industries Differentiate between industrial and consumer applications of robots Identify common components of robots Discuss impacts of robots on future jobs Identify legal issues related to robotics

CHAPTER OVERVIEW Chapter 2 briefly introduced robotics, an early and practical application of concepts developed in AI. In this chapter, we present a number of applications of robots in industrial as well as personal settings. Besides learning about the already deployed and emerging applications, we identify the general components of a robot. In the spirit of managerial considerations, we also discuss the impact of robotics on jobs as well as related legal issues. Some of the coverage is broad and impacts all other artificial intelligence (AI), so it may seem to overlap a bit with Chapter 14. But the focus in this chapter is on physical robots, not just software-driven applications of AI. This chapter has the following sections: CHAPTER OUTLINE 10.1 Opening Vignette: Robots Provide Emotional Support to Patients and Children 10.2 Overview of Robotics 10.3 History of Robotics 10.4 Illustrative Applications of Robotics 10.5 Components of Robots 10.6 Various Categories of Robots 10.7 Autonomous Cars: Robots in Motion 10.8 Impact of Robots on Current and Future Jobs 10.9 Legal Implications of Robots and Artificial Intelligence

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ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 10.1 Review Questions 1. What characteristics would you expect to have in a robot that provides emotional support to patients?

Robots that provide emotional support to patients would need to be accepted by patients in order to be effective. To meet this need a robot would have to be capable of two way communication. This would include the ability to speak to the patient as well as understand the patient’s speech. Additionally, the robot would need to understand nonverbal cues given by the patient. Finally, the robot would need an appearance that was welcoming and acceptable to the patient.

2. Can you think of other applications where robots such as the Huggable can play a helpful role?

Student opinions will vary, but it may also be possible to use similar robots at other times of crisis as well as an adjunct to ongoing childcare.

3. Visit the website https://www.universal-robots.com/case-stories/aurolab/ to learn about collaborative robots. How could such robots be useful in other settings?

Student ideas and opinions on other possible uses and settings will vary greatly.

Section 10.2 Review Questions 1. Define robot.

There are many competing definitions that are largely based on context. A common definition is machine or physical device or software that with the cooperation of AI can accomplish a responsibility autonomously.

2. What is the difference between automation and autonomy?

Automation is performing preselected repetitive tasks, whereas autonomy is the capability of adaptation to new situations.

3. Give examples of robots in use. Find recent applications online and share with the class Student research and examples will vary greatly based on their own interests and the date the search is performed.

Section 10.3 Review Questions

2 .

1. Identify some of the key milestones in the history of manufacturing that have led to the current interest in robotics.

• • • • • • • • • • • • • •

320 BC - Aristotle stated, “If every tool, when ordered, or even of its own accord, could do the work that befits it, then there would be no need either of apprentices for the master workers or of slaves for the lords.”

1495 - Leonardo Da Vinci drafted strategies and images for a robot that looked like a human 1700 -1900, various automatons were created 1893 - “Steam Man,” a prototype for a humanoid robot, was proposed by Canadian professor George Moore 1898 - Nikola Tesla exhibited a submarine prototype 1913 - the world’s first moving conveyor belt assembly line was started by Henry Ford.

1920 - the term robot was coined by Karel Capek 1950s - innovators were creating machines that could handle dangerous, repetitive tasks 1950s - the first commercial robotic arm, Planetbot, was developed 1960s - Ralph Mosher and his team created two remotely operated robotic arms, Handyman and Man-mate 1963 – Shakey, a mobile robot was developed 1976 - during NASA’s mission to Mars, a Viking lander was created for the atmospheric conditions of Mars 1986 - the first LEGO-based educational products were put on the market by Honda.

1994 - Dante II, an eight-legged walking robot built by Carnegie Mellon University, collected the volcanic gas sample from Mount Spur

2. How would Shakey’s capabilities compare to today’s robots?

While Shakey’s actual capabilities would seem primitive today, it’s form and general structure is consistent with many current efforts 3. How have robots helped with space missions?

Robots have assisted on many space missions by collecting data in environments that are too harsh or dangerous for humans. An example would be the Viking lander used in the Mars missions in 1976.

Section 10.4 Review Questions 1. Identify applications of robots in agriculture.

An example provided in this section is Mahindra & Mahindra Ltd. seeking to improve the process of harvesting tabletop grapes. Another example is AGROBOT, a company

3 .

engaged in the business of agricultural robots, has developed a robot that can harvest strawberries at any place.

2. How could a social support robot such as Pepper or MEDi be useful in healthcare?

Both of these social support robots could have benefits within the healthcare system.

Both could provide social support to individuals who were in the hospital or seeking similar care. They could provide very routine functions, but more importantly provide social support that otherwise would not be provided or that would be provided by the limited availability healthcare staff.

3. Based on the illustrative applications of robots in this section, build a matrix where the rows are the robots’ capabilities and the columns are industries. What similarities and differences do you observe across these robots?

Student creations of matrices will vary based on their perception of the important features of each of the different robot categories.

Section 10.5 Review Questions 1. What are the common components of a robot?

The common components of a robot include the power controller, sensors, effectors (or rover or manipulator), navigator (or actuator system) and the controller/CPU. 2. What is the function of sensors in a robot?

Sensors are used to direct a robot in its surrounding.

3. How many different types of sensors might exist in a robot?

A large number of different sensors may exist in any particular robot because each type of sensor may provide different feedback on the environment. For example, force sensors, ultrasound sensors, distance sensors, laser scanners, etc. may all exist on the same robot.

4. What is the function of a manipulator?

A manipulator is a type of a sector that can be used to interact, move, reposition and alter something in the external environment. For example the mechanical hand on a manufacturing robot would be a manipulator.

Section 10.6 Review Questions 1. Identify some key categories of robots.

The key categories of robots include preset robots, collaborative robots, standalone 4 .

robots, remote-controlled robots, and supplementary robots.

2. Define and illustrate the capabilities of a cobot.

Cobots are the robots that can collaborate with human workers, assisting them to achieve their goals. There are various functions of collaborative robots. Depending on the usage, the collaborative robots are used. Collaborative robots have various applications in manufacturing as well as the medical industry.

3. Distinguish between a preset robot and a stand-alone robot. Give examples of each.

Preset robots are preprogrammed. They have been designed to perform the same task over time, whereas stand-alone robots have a built-in AI system and work independently without much interference from humans. An example of a preset robot may be an industrial welding arm. An example of a stand-alone robot may be a standalone vacuum cleaner.

Section 10.7 Review Questions 1. What are some of the key technology advancements that have enabled the growth of self-driving cars?

Examples of key technologies that enable the growth in self driving cars include the development of mobile phones, fast wireless Internet, computer centers and cars, electronic navigation and maps, and the field of deep learning.

2. Give examples of regulatory issues in self-driving cars.

Several questions about liability include these: What will a license involve? Will new drivers be required to get traditional licenses even if they are not drivers? What about young people, or older people with disabilities? What will be required to operate these new vehicles?

3. Conduct online research to identify the latest developments in autonomous car deployment. Give examples of positive and negative developments.

Student research will vary based on the date of the research and their interest in particular topics.

4. Which type of self-driving vehicles are likely to have the most disruptive effect on jobs, and why?

Autonomous trucks will have a massive disruptive effect on jobs in the transportation industry.

5 .

Section 10.8 Review Questions 1. Which jobs are most at risk of disappearing as the result of the new robotics revolution?

Student list of specific jobs may vary, but the general categories include jobs in logistics, transportation, customer service, inspectors and possibly some white-collar positions 2. Identify at least three new categories of jobs that are likely to result in a significant number of new employees.

Student list of specific positions will vary but general categories will include developers, technicians, robot specific maintenance workers etc.

3. Are the tasks undertaken by data labelers just for one time or longer lasting?

As such image applications grow, the need for labelers will also increase. These workers are also needed for continuous improvement of the robot or AI algorithms by recording false positives or newer examples.

4. Research the concepts of UBI and SIS. Student research and opinions on these controversial topics will vary.

Section 10.9 Review Questions 1. Identify some of the key legal issues for robotics and AI. Some of the key legal issues for robotics and AI include tort liability, patents, property, taxation, practice of law, constitutional law, professional certification and law enforcement.

2. Liability for harm (tort liability) is an obvious early question for any technology. What are some of the key challenges in identifying such liability?

The key challenge will be a determination of who is at fault when an injury occurs. Is the AI responsible? Is its owner responsible? Is the company that created it responsible?

3. Recent news about illegal intervention in elections has led to the discussion about who is responsible for damage control. When chatbots and automated social media systems have the ability to propagate “fake news,” who should be required to monitor them and prevent such action?

Student opinions on this controversial topic will differ widely.

4. What are some of the law enforcement issues in employing AI?

6 .

Examples of law enforcement issues associated with employing AI include access to data to determine where crimes are being committed, classifying crimes by category discretion in prosecuting infractions, constitutionality, and use of AI in the judiciary.

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION

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1. Based upon the current state of the art of robotics applications, which industries are most likely to embrace robotics? Why? Lists of specific jobs or industries will vary by student, but generally industries that will except robotics and AI will do so because the use of these technologies will provide a higher quality product or service at a lower cost than can be provided with human.

Additionally other tasks may be automated if they are dangerous, uncomfortable or disliked by humans.

2. Watch the following two videos: https://www. youtube.com/watch?v=GHc63Xgc0-8 and https:// www.youtube.com/watch?v=ggN8wCWSIx4 for a different view on impact of AI on future jobs. What are your takeaways from these videos? What is the more likely scenario in your view? How can you prepare for the day when humans indeed may not need to apply for many jobs?

Student reactions and opinions on these videos will differ.

3. There have been many books and opinion pieces written about the impact of AI on jobs and ideas for societal responses to address the issues. Two ideas were mentioned in the chapter – UBI and SIS. What are the pros and cons of these ideas? How would these be implemented?

Student opinions related to these controversial topics, and how they should or should not be implemented, will vary greatly.

4. There has been much focus on job protection through tariffs and trade negotiation recently. Discuss how and why this focus may or may not address the job changes coming due to robotics and AI technologies.

Student perceptions and opinions on this political topic will differ.

5. Laws rely on incentive structures to encourage prosocial behavior. For example, criminal law encourages compliance by punishing those who break the law. Patent law incentivizes creation of new technologies by offering inventors a period of limited monopoly during which they can exclusively use their invention. To what extent do these (and other) incentives make sense when applied to AI? How can incentive structures be created to encourage AI devices to behave in prosocial manners?

7 .

Student essays will vary.

6. To what extent do extralegal considerations come into play with regard to the above issues? Are there moral (or religious) dimensions to be considered when determining whether AI should be given rights similar to those of a person? Would AI-assisted law enforcement or court action erode faith in the criminal justice system and judiciary?

Student opinions on these social issues will vary.

7. Adopting policies that maximize the value of AI encourages future development of these technologies. Such a course, however, is not without drawbacks.

For instance, determining that a “robot tax” is not a preferred policy choice would increase the incentive to adopt a robot workforce and improve any relevant technologies.

Elevating the state of robotics is a laudable goal, but in this instance, it would come at the anticipated cost of reduced public funds. How should trade-offs such as these be evaluated? Where should encouragement of technological progress (especially regarding AI) fall in the hierarchy of government priorities?

Student ideas on the implementation of these technologies and there ramifications will differ significantly.

ANSWERS TO END OF CHAPTER EXERCISES

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1. Identify applications other than those discussed in this chapter where Pepper is being used for commercial and personal purposes.

Student research and resulting reports will vary.

2. Go through specifications of MAARS at https://www. qinetiq-na.com/wpcontent/uploads/brochure_ maars.pdf. What are the functions of MAARS? Student research on this topic and their opinions about its future will differ.

3. Conduct online research to find at least one new robotics application in agriculture.

Prepare a brief summary of your research: the problem addressed, technology summary, results achieved if any, and lessons learned.

Student research will vary.

4. Conduct online research to find at least one new robotics application in healthcare.

Prepare a brief summary of your research: the problem addressed, technology summary, results achieved if any, and lessons learned.

Student research and summaries in this topic area will differ.

8 .

5. Conduct online research to find at least one new robotics application in customer service. Prepare a brief summary of your research: the problem addressed, technology summary, results achieved if any, and lessons learned.

Student research and their results will vary.

6. Conduct online research to find at least one new robotics application in an industry of your choice. Prepare a brief summary of your research: the problem addressed, technology summary, results achieved if any, and lessons learned.

Student research will vary based on the date of the research and the selection of technology by the student.

7. Conduct research to identify the most recent developments in self-driving cars.

Results will differ based on the date the research is conducted.

8. Conduct research to learn and summarize any new investments and partnerships in self-driving cars.

Results will differ based on the date the research is conducted.

9. Conduct research to identify any recent examples of legal issues regarding self-driving cars.

Results will differ based on the date the research is conducted.

10. Conduct research to identify any other new types of jobs that would be enabled by AI and robotics beyond what was covered in the chapter.

Student selection of topic areas for research will affect final reports.

11. Conduct research to report on the latest projections for job losses due to robotics and AI. 12. Identify case stories for each of the legal dimensions identified by Schuster (2018) in Section 10.9.

Student research and reports will vary.

9 .

Chapter 11: Group Decision Making, Collaborative Systems, and AI Support

Learning Objectives for Chapter 12 1. Understand the basic concepts and processes of group work, communication, and collaboration 2. Describe how computer systems facilitate team communication and collaboration in an enterprise 3. Explain the concepts and importance of the time/ place framework 4. Explain the underlying principles and capabilities of groupware, such as group support systems (GSS) 5. Understand how the Web enables collaborative computing and group support of virtual meetings 6. Describe collective intelligence and its role in decision making 7. Define crowdsourcing and explain how it supports decision making and problem solving 8. Describe the role of AI in supporting collaboration, group work, and decision making 9. Describe human–machine collaboration 10. Explain how teams of robots work CHAPTER OVERVIEW In this chapter, we present several topics related to group decision support and collaboration. People work together, and groups (or teams) make many of the complex decisions in organizations. The increase in organizational decision-making complexity drives the need for meetings and group work. Supporting group work in which team members may be in different locations and working at different times emphasizes the important aspects of communications, computer-mediated collaboration, and workplace methodologies. Group support is a critical aspect of decision support systems (DSS).

Effective computer-supported group support systems have evolved to increase gains and decrease losses in task performance and underlying processes. New tools and methodology are used to support teamwork. These include collective intelligence, crowdsourcing, and different types of AI. Finally, human–machine and machine–machine collaboration are increasing the power of collaboration and problem solving. All these are presented in the following sections: 1 .

CHAPTER OUTLINE 11.1 Opening Vignette: Hendrick Motorsport Excels with Collaboration Teams 11.2 Making Decisions in Groups: Characteristics, Processes, Benefits, and Dysfunctions 11.3 Supporting Group Work and Team Collaboration with Computerized Systems 11.4 Electronic Support to Group Communication and Collaboration 11.5 Direct Computerized Support for Group Decision Making 11.6 Collective Intelligence and Collaborative Intelligence 11.7 Crowdsourcing as a Method for Decision Support 11.8 Artificial Intelligence and Swarm AI Support of Team Collaboration and Group Decision Making 11.9 Human–Machine Collaboration and Teams of Robots

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 11.1 Review Questions 1. What were the major drivers for the use of Microsoft’s Teams?

The team needed a very flexible solution that provided the ability to have meaningful and data rich communication during a race, but also a more detailed and thorough analysis of a seasons worth of data in the off-season.

2. List some discussions held during the racing season, and how they were supported by the technology.

An example discussion that would’ve been held during the racing season was an evaluation of the amount of fuel that needed to be put into the car the next time he came into the pits.

3. List decisions held during the off-season, and how they were supported by the technology.

In the off-season data could be analyzed from the previous season including more indepth and thoughtful analysis of larger data sets.

4. Discuss why Microsoft Teams was selected, and explain how it supports teamwork group decision making.

The software product was selected because it allowed for synchronous chat communication during races that could be augmented by sharing data and files. In the offseason it allowed for sharing an analysis of information and links to Office 365 products and the creation of a collaboration hub.

2 .

5. Trace communication and collaboration within and between groups.

Different groups would communicate with each other in different ways. The methods of communication would also be determined based on the type of information being shared (simple questions and conversations versus the analysis of large data sets).

6. Specify the function of Microsoft Teams workspace.

The workspace provides the ability for real-time chat that additionally can be linked to data files and documents. Larger documents and data sets can also be shared and analyzed using onboard tools (like Microsoft Excel).

7. Watch the video at youtube.com/watch?time_continue=108&v=xnFdM9IOaTE and summarize its content.

Student evaluations of the movie will vary.

Section 11.2 Review Questions 1.

Define groupwork.

Groupwork is work done by two or more people together.

2.

List five characteristics of groupwork.

The text gives the characteristics listed below. A correct answer can consist of any five: • A group performs a task (sometimes decision making, sometimes not).

• Group members may be located in different places.

• Group members may work at different times.

• Group members may work for the same organization or for different organizations.

• A group can be permanent or temporary.

• A group can be at one managerial level or can span several levels.

• There can be synergy (leading to process and task gains) or conflict in groupwork.

• There can be gains and/or losses in productivity from groupwork.

• The task may have to be accomplished very quickly.

• It may be impossible or too expensive for all the team members to meet in one place, especially when the group is called for emergency purposes.

• Some of the needed data, information, or knowledge may be located in many sources, some of which may be external to the organization.

• The expertise of non-team members may be needed.

3 .

• •

3.

Groups perform many tasks; however, groups of managers and analysts frequently concentrate on decision making.

The decisions made by a group are easier to implement if supported by all (or at least most) group members.

Describe the process of a group meeting for decision making.

The text lists the items listed below as “activities and processes” that characterize meetings. Other answers, that are more descriptive of the meeting process, can also be correct. If you have specific requirements in this regard, it might be well to state them.

• • • • • • •

The decision situation is important, so it is advisable to make it in a group in a meeting.

A meeting is a joint activity engaged in by a group of people typically of equal or nearly equal status.

The outcome of a meeting depends partly on the knowledge, opinions, and judgments of its participants and the support they give to the outcome.

The outcome of a meeting depends on the composition of the group and on the decision-making process the group uses.

Differences in opinions are settled either by the ranking person present or, often, through negotiation or arbitration.

The members of a group can be in one place, meeting face-to-face, or they can be a virtual team, in which case they are in different places while in a meeting.

The process of group decision making can create benefits as well as dysfunctions.

Section 11.3 Review Questions 1.

Why do companies use computers to support groupwork?

Because it can help a group follow a process, save cost, expedite decisions, support virtual teams, improve access to external experts, and improve the decision-making process overall.

4 .

2. What is GSS? Group support systems (GSS) are appropriate communication methods and technologies needed for groups to collaborate effectively.

3. Describe the importance of collaboration for decision making.

Collaboration is important for decision-making because it allows for the sharing and discussion of ideas and the realization of the benefits possible from group work.

4.

Describe the components of the time/place framework.

The two components of the time/place framework are time and place. Either can be the same or different. The four combinations that result (same time/place, same time/different place, different time/same place, different time/place) define the framework.

Section 11.4 Review Questions 1.

Define groupware Groupware is computerized tools have been developed to provide group support.

2.

List the major groupware tools and divide them into synchronous and asynchronous types.

Synchronous (real-time) groupware tools include audio teleconferencing, videoconferencing, data conferencing, Web conferencing, whiteboards, screen sharing, and instant video.

Asynchronous groupware tools include blogs, wikis, discussion groups, autoresponders, workflow software, interactive portals, and online workspaces.

Other asynchronous communication tools, such as e-mail, can be useful to groupwork but are not true “groupware.”

3.

Identify specific tools for Web conferencing and their capabilities.

Web-based systems allow improved, electronically supported virtual meetings.

Sources of online meetings and presentation tools include webex.com, gotomeeting.com, Adobe.com, and Skype.com. Microsoft Office has a built-in capability for virtual meetings. These systems feature Webinars, screen sharing, audioconferencing, videoconferencing, polling, and question-and-answer sessions. Mobile phones allow live meetings through applications such as Facetime.

4. Describe collaborative workflow.

Collaborative workflow refers to software products that address project-oriented and collaborative processes.

5 .

5. What is collaborative workspace? What are its benefits?

A collaborative workspace is where people can work together from any location at the same or at a different time.

6. Describe social collaboration.

Social collaboration refers to collaboration conducted within and between socially oriented groups.

Section 11.5 Review Questions 1.

Define GDSS and list the limitations of the initial GDSS software.

A GDSS is “an interactive computer-based system that facilitates the solution of semistructured and unstructured problems by a group of decision makers.” Its goal is “to improve the productivity of decision-making meetings, either by speeding up the decision-making process, by improving the quality of the resulting decisions, or both.”

The initial GDSS software had these three major limitations: 1.

Its use was limited to face-to-face meetings.

2.

Its use depended on a special “decision room” facility. This was expensive.

3.

It was designed to support a single, clearly defined, narrow task.

2.

Define GSS and list its benefits.

A group support system (GSS) is “any combination of hardware and software that enhances groupwork either in direct or indirect support of decision making. GSS is a generic term that includes all forms of collaborative computing.”

GSS benefits are improving the productivity and effectiveness of meetings by streamlining and speeding up the decision-making process (efficiency) and/or improving the quality of the decision (effectiveness). This is achieved by providing support for group members in generating and exchanging ideas, opinions and preferences, using specific features such as parallelism and anonymity.

The book provides a list of twelve GSS support activities. Students may be attracted to this list to answer this question. This would be incorrect, since activities are not benefits.

3.

List process gain improvements made by GSS. GSS addressed limitations of earlier-generation GDSS by adding support for virtual teams and by providing indirect support as well as decision-making tools.

6 .

Along with providing structure to decision making and ready access to information, a GSS supports activities such as idea generation, consensus building, conflict resolution, voting, and anonymous ranking of alternatives. A complete GSS is considered a specially designed information system, but many capabilities today are embedded in standard productivity tools. Modern systems also have been made easy to use, thanks to a Windows-based graphical user interface or Web browser interface. GSS are building up their capabilities to operate in both asynchronous and synchronous modes.

4.

Define decision room.

A decision room is an electronic meeting room with networked PCs and a large public screen at the front of the room. It may have software that runs over a LAN. A server PC is attached to a large-screen projection system and connected to the network to display the work at individual workstations and aggregated information from the facilitator’s workstation.

5.

Describe Web-based GSS. Web-based GSS is a system in which groupware allows group members to work from any location at any time, using their computers or mobile devices. It often includes audioconferencing and videoconferencing.

6. Describe how GDSS supports brainstorming and idea generation.

GDSS systems support parallel processing of information and idea generation, which is the foundation of brainstorming.

Section 11.6 Review Questions 1. What is collective intelligence (CI)?

Collective intelligence (CI) refers to the total intelligence of a group. It is also refers to as the wisdom of the crowd.

2. List the major benefits of CI. The major benefits are the ability to solve complex problems and/or design new products and services that result from innovations.

3. How is CI supported by computers?

Collective intelligence can be supported by many of the tools and platforms group interaction and group decision-making. In addition, the Internet, intranet, and the IoT (Chapter 13) play a major role in facilitating CI by enabling people to share knowledge and ideas.

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4. How can CI change work and life?

CI allows more people to have more engagement and involvement in organizational decision making.

5. How can CI impact organization structure and decision making?

Organizations’ structures tend to be flatter, and more decisions are delegated to teams.

All this results in decentralized workplaces.

6. The Carnegie case described how standard collaboration tools create a collective intelligence infrastructure. The WRESTORE case described a modeling analytical framework that enables stakeholders to collaborate. What are the similarities and differences between the two cases?

Both cases are similar in that they collect and publicly display ideas from individuals.

The cases are different because in the Carnegie case all users actively participated whereas in the second case a small sub set of users contributed to solutions.

7. Describe collaborative intelligence.

Collaborative intelligence has the following 10 components: (1) willingness to share, (2) knowing how to share, (3) being willing to collaborate, (4) knowing what to share, (5) knowing how to build trust, (6) understanding team dynamics, (7) using correct hubs for networking, (8) mentoring and coaching properly, (9) being open to new ideas, and (10) using computerized tools and technology.

8. How do you create business value from collective intelligence?

Groups and team members provide ideas and insights. To excel, organizations must utilize people’s knowledge, some of which is created by collective intelligence.

Section 11.7 Review Questions 1. Define crowdsourcing.

Crowdsourcing refers to outsourcing tasks to a large group of people (crowd).

2. Describe the crowdsourcing process.

1. Identify the problem and the task(s) to be outsourced.

2. Select the target crowd (if not an open call).

3. Broadcast the task to the crowd (or to an unidentified crowd in an open call).

4. Engage the crowd in accomplishing the task (e.g., idea generation, problem solving).

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5. Collect user-generated content.

6. Have the quality of submitted material evaluated by the management that initiated the request, by experts, or by a crowd.

7. Select the best solution (or a short list).

8. Compensate the crowd (e.g., the winning proposal).

9. Implement the solution.

3. List the major benefits of the technology.

Technology solutions allow for the capture classification and analysis of potential solutions.

4. List some areas for which crowdsourcing is suitable.

• • • • • • • •

Logo design—Design Bill Problem solving—InnoCentive, NineSigma, IdeaConnection Business innovation—Chardix • Brand names—Name This Product and manufacturing design—Pronto ERP Data cleansing—Amazon Mechanical Turk Software testing—uTest Trend watching—TrendWatching Images—Flickr Creative Commons

5. Why may you need a vendor to crowdsource the problem-solving process?

Vendors will typically have a large group of pre-registered solvers available. Additionally vendors will be able to assist with the initial call for information, data capture and data classification and analysis.

Section 11.8 Review Questions 1. Relate the use of AI to the activities in Figure 11.1.

1. Meeting preparation. AI is used to find a convenient time for meetings to take place.

AI can assist in scheduling meetings so that all can participate.

2. Problem identification. AI technologies are used for pattern recognition that can identify areas that need attention. AI can be used in other types of analysis to identify potential or difficult to pinpoint problems.

3. Idea generation. AI is known for its quest for creativity. Team members can increase their creativity when they use AI for support.

4. Idea organization. Natural language processing (NLP) can be used to sort ideas and organize them for improved evaluation.

5. Group interaction and collaboration. AI can facilitate communication and collaboration among group members. This activity is critical in the process of arriving at a consensus.

Also, Swarm AI (see the end of this section) is designed to increase interactions among group members so their combined wisdom is elevated.

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6. Predictions. AI supports predictions that are required to assess the impact of the ideas generated regarding performance and/or impacts in the future. Machine learning, deep learning, and Swarm AI are useful tools in this area.

7. Multinational groups. Collaboration among people located in different countries is on the rise. AI enables group interaction of people who speak different languages, in real time.

8. Bots are useful in supporting meetings. Group members may consult Alexa and other bots. Chatbots can provide answers to queries in real time.

9. Other advisors. IBM Watson can provide useful advice during meetings, supplementing knowledge provided by participants and by Alexa 2. Discuss the different ways that AI can facilitate group collaboration.

1. Virtual assistants increase employees’ productivity, creativity, and job satisfaction.

Bots also enable employees to focus on high-value tasks.

2. Bots are accepted as part of workers’ teams.

3. Bots improve conference calls. They also can take meetings notes and schedule meetings.

4. AI can use facial recognition to sign in eligible people to meetings.

5. Personal characteristics are likely to influence how people feel about AI in the workplace.

6. Employees in general like to have AI in their teams.

7. Security is a major concern when AI, such as virtual assistants, is used in teams.

8. The major AI tools that are most useful are NLP and voice response; AI can also summarize the key topics of meetings and understand participants’ needs. AI can be aware of organizational goals and workers’ skills and can make suggestions accordingly.

3. How can AI support group evaluation of ideas?

AI can facilitate communication and collaboration among group members. This activity is critical in the process of arriving at a consensus. Also, Swarm AI (see the end of this section) is designed to increase interactions among group members so their combined wisdom is elevated.

4. How can AI facilitate idea generation?

AI is known for its quest for creativity. Team members can increase their creativity when they use AI for support.

5. What is the analogy of swarm AI to swarms of living species?

The term swarm intelligence refers to the collective behavior of decentralized, selforganized systems, natural or artificial (per Wikipedia). Such systems consist of things (e.g., ants, people) interacting with each other and their environment. A swarm’s actions are not centrally controlled, but they lead to intelligent behavior. In nature, there are many examples (e.g., ant colonies, fish schools) of such behaviors.

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6. How is swarm AI used to improve group work and to initiate group predictions?

Natural groups were observed to amplify their group intelligence by forming swarms.

Social creatures, including people, can improve the performance of their individual members when working together as a unified system. In contrast with animals and other species whose interactions among group members are natural, people need technology to exhibit swarm intelligence. This concept is used in studies and implementation of AI and robotics. The major applications are in the area of predictions.

Section 11.9 Review Questions 1. Why is there an increase in human–machine collaboration?

As the available technologies advance, this type of collaboration allows for greater efficiency and effectiveness in the tasks selected.

2. List some benefits of such collaboration.

There are a large number of benefits but some of them include the ability to use strengths of each member of the collaboration, the ability to hand off tasks to the member best suited to complete it, the ability to increase workload and throughput, and the ability to produce solutions that would not be possible as individuals.

3. Describe how collaborating robotics can be used in manufacturing.

Different robotic systems can work together in a manufacturing environment to perform discrete and different tasks as a part of an overall process. Individual robots can be assigned to perform tasks that are best suited to their construction and available feature set.

4. Discuss the use of teams of robots.

Robots can be effective when used as a team because different robotics systems can have different purposes and strengths that work well towards a common goal.

5. What will do robots on Mars?

Robots have and will continue to be used on Mars to collect data in locations that are inhospitable to humans.

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ANSWERS TO APPLICATION CASE QUESTIONS

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Application Case 11.1: Collaborative Modeling for Optimal Water Management: The Oregon State University Project 1. Crowdsourcing is used to find information from a crowd. Why is it needed in this case? (see Section 11.7 if you are not familiar with crowdsourcing).

With such a diverse group of stakeholders, crowdsourcing could be used to collect a broad set of potential ideas, problems and solutions.

2. How does WRESTORE act as a CI tool?

The tool seeks input from users based on their own perceptions and preferences.

3. Debate centralized control versus participative collaboration. Cite the pros and cons of each.

Students will have different positions and arguments in this debate.

4. Why it is difficult to manage water resources?

It is difficult to manage water resources because of the large, diverse group of stakeholders and potential uses. Additionally calculation and prediction of overall use is very complex.

5. How can an optimization/simulation/AI model support group work in this case?

AI can be used for both simulation and optimization using a combination of known factors relating to use patterns as well as the needs and preferences of groups participating in the simulation.

Application Case 11.2: How InnoCentive Helped GSK Solve a Difficult Problem 1. Why did GSK decide to crowdsource?

The question under consideration was very detailed due to the large number of diseases and the corresponding potential aspects of the new treatment.

2. Why did the company use InnoCentive?

GSK was selected do to their fit with the project and because they had solvers already in place for similar projects 3. Comment on the global nature of the case.

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Solvers were sourced globally, this helps to ensure that a diverse set of opinions and potential solutions were evaluated 4. What lessons did you learn from this case?

Student responses will vary, but students may note that even companies with significant internal competence may choose to crowd source to evaluate complex ideas.

5. Why do you think a small $5000 reward is sufficient Student opinions will vary, it is possible that solvers were acting in this case for the good of potential patients, not payment.

Application Case 11.3: XPRIZE Optimizes Visioneering 1. Why is the group discussion in this case complex?

Finding the top global problems can be a very complex challenge due to a large number of variables.

2. Why is getting a consensus when top experts are involved more difficult than when non-experts are involved?

This is not directly addressed in the case, but students may suggest that it is due to the strong feelings of experts and difficulty in creating rank ordered lists.

3. What was the contribution of swarm AI? The swarm AI to generate each group’s synergy with the AI algorithms acting as moderators.

4. Compare simple voting to swarm AI voting.

It allowed for the use of the swarm thinking together feature that provides more collaborative information as opposed to individual voting.

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION

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1. Explain why it is useful to describe group work in terms of the time/place framework.

Because the type of information system needed to support the groupwork depends on where the work is in terms of this framework.

2. Describe the kinds of support that groupware can provide to decision makers.

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The support can be divided into three categories: General (can be either synchronous or asynchronous) • Built-in e-mail, messaging system • Browser interface • Joint Web-page creation • Sharing of active hyperlinks • File sharing (graphics, video, audio, or other) • Built-in search functions (by topic or keyword) • Workflow tools • Use of corporate portals for communication, collaboration, and search • Shared screens • Electronic decision rooms • Peer-to-peer networks Synchronous (same-time) • Instant messaging (IM) • Videoconferencing, multimedia conferencing • Audio conferencing • Shared whiteboard, smart whiteboard • Instant video • Brainstorming • Polling (voting), and other decision support (consensus builder, scheduler) Asynchronous (different times) • Workspaces • Threaded discussions • Users can receive/send e-mail, SMS • Users can receive activity notification alerts, via e-mail or SMS • Users can collapse/expand discussion threads • Users can sort messages (by date, author, or read/unread) • Auto responder • Chat session logs • Bulletin boards, discussion groups • Use of blogs, wikis, and wikilogs • Collaborative planning and/or design tools • Use of bulletin boards 3. Explain why most groupware is deployed today over the Web. Group members can work from any location at any time. Also, the software is relatively inexpensive, and it can run on computers and mobile devices that now offer sufficient power at a low cost.

4. Explain in what ways physical meetings can be inefficient. Explain how technology can make meetings more effective.

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Almost all of the reasons involve people. Some don't take meetings seriously. Other issues are that a large amount (perhaps as high as 90 percent) of the information discussed at a meeting is either not remembered or remembered incorrectly. This could become an open-ended answer if you let the students describe their meeting experiences.

How can effective meetings be run? Some items that might be included are: have an agenda, keep to the subject, record the minutes of each meeting, send out a meeting notice, and ask meeting participants to summarize results.

5. Explain how GDSS can increase some benefits of collaboration and decision making in groups and eliminate or reduce some losses.

GDSS provides support to reduce the negative effects of group decision making and increase the positive effects. For example, anonymity means more equal participation, whereas parallelism saves time. By moving quickly, people get the urge to contribute.

There might be less free-riding. There are many more benefits.

6. The initial term for group support system (GSS) was group decision support system (GDSS). Why was the word decision dropped? Does this make sense? Why, or why not?

Because GSS can help even when there is no decision to be made. For instance, it can help group members communicate. Many meetings are not held to make a formal decision. Researchers noticed this and changed the name of the technology to reflect reality.

7. Discuss why Microsoft SharePoint is considered a workspace. What kind of collaboration does it support? 8. Reese (2017) claims that swarm AI can be used instead of polls for market research. Discuss the advantages of swarm AI. In what circumstances would you prefer each method? (Read “Polls vs. Swarms” at Unanimous AI.) 9. What is a collaborative robot? What is an uncollaborative one?

A collaborative robot is one that works as a part of the team (with humans or other robots) to complete a task. An un-collaborative robot is one that works wholly autonomous will he.

10. Discuss the ways in which social collaboration can improve work in a digital workplace.

Collective intelligence (CI) refers to the total intelligence of a group. It is also refers to as the wisdom of the crowd. People in a group are using their skills and knowledge for solving problems and providing new insights and ideas. The major benefits are the ability to solve complex problems and/or design new products and services that result from innovations.

11. Provide an example of using analytics to improve decision making in sport.

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Student examples will vary.

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ANSWERS TO end of chapter EXERCISES

1. Go to realtimeboard.com. How can the site support idea creation and brainstorming?

Student analysis and responses will vary.

2. Investigate how researchers are trying to develop collaborative computer systems that portray or display nonverbal communication factors (e.g., images).

Student research and the resulting reports will vary.

3. For each of the software packages Skype Business and WebEx, check the trade literature and the Web for details and explain how each includes computerized collaborative support system capabilities.

Student selection of sources and the resulting analysis will differ.

4. Compare Simon’s four-phase decision-making model to the steps in using GDSS. Student analysis and results will differ.

5. A major claim in favor of wikis is that they can replace e-mail, eliminating its disadvantages (e.g., spam). Go to socialtext.com and review such claims. Find other supporters of switching to wikis. Then find counter arguments and conduct a debate on the topic.

Students will have different opinions on these topics.

6. Search the Internet to identify sites that describe methods for making meetings more effective and efficient.

Student research and reports will vary.

7. Enter MIT Center for CI and review some of its recent activities. Write a report.

Student research and reports will vary.

8. Debate the issue of the quality of crowdsourcing results. Start by viewing youtube.com/ watch?v=JJHAHQmiI3c.

Student perceptions of the video will be different as well their positions in a debate.

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9. Find information about Yammer (a Microsoft company). Why is it considered a social collaboration tool? Why is it popular? Write a report.

Student research and reports will vary.

10. Enter Dropbox.com and find its collaboration tools. Write a summary.

Student research and reports will vary.

11. Read Pena (2017). Examine the 12 benefits of collaboration. Which are related to social collaboration?

Student evaluations and the resulting reports will differ.

12. Compare Microsoft’s Universal Translator to Google’s Translator. Concentrate on face-to-face conversation in real time.

Student research will vary based on the date of the research and subsequent analysis will differ.

13. Write a report on the issue of whether crowdsourcing produces superior decisions.

Use Quora for help. Find other sources.

Student research and reports will vary.

14. Investigate the status of IBM Connections Cloud. Examine all the collaboration and communication features. How does the product improve productivity? Write a report.

Student research and reports will vary.

15. Compare Microsoft Teams to Spark Teams. Write a report.

Student research and reports will vary.

16. Enter crowdtap.com and read Kurzer (2018) paper. Explain how the platforms work.

Relate the material about crowdsourcing and collective intelligence. Write a report.

Student research and reports will vary.

17. Go to technologyreview.com and look at the May 8, 2017, video (17:42 min.) “Next Generation HumanMachine Collaboration.” Write a report.

Students will have different perceptions and analyses of the video, and their subsequent reports will vary.

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Chapter 12: Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors Learning Objectives for Chapter 12 1.

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Describe recommendation systems Describe expert systems Describe chatbots Understand the drivers and capabilities of chatbots and their use Describe virtual personal assistants and their benefits Describe the use of chatbots as advisors Discuss the major issues related to the implementation of chatbots

CHAPTER OVERVIEW Advancement in artificial intelligence (AI) technologies and especially natural language processing (NLP), machine and deep learning and knowledge systems, coupled with the increased quality and functionalities of other intelligent systems, and mobile devices and their apps, have driven the development of chatbots (bots) for inexpensive and fast execution of many tasks related to communication, collaboration, and information retrieval. The use of chatbots in business is increasing rapidly, partly because of their fit with mobile systems and devices. As a matter of fact, sending messages is probably the major activity in the mobile world. In the last two to three years, many thousands of bots have been placed into service worldwide by both organizations (private and public) and individuals. Many people refer to these phenomena as the chatbot revolution. Chatbots today are much more sophisticated than those of the past. They are extensively used, for example, in marketing; customer, government, and financial services; healthcare; and in manufacturing. Chatbots make communication more personal than faceless computers and excel in data gathering. Chatbots can stand alone or be parts of other knowledge systems.

We divide the applications in this chapter into four categories: expert systems, chatbots for communication and collaboration, virtual personal assistants (native products, such as Alexa), and chatbots that are used as professional advisors. Some implementation topics of intelligent systems are described last. This chapter has the following sections:

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CHAPTER OUTLINE 12.1 Opening Vignette: Sephora Excels with Chatbots 12.2 Expert Systems and Recommenders 12.3 Concepts, Drivers, and Benefits of Chatbots 12.4 Enterprise Chatbots 12.5 Virtual Personal Assistants 12.6 Chatbots as Professional Advisors (Robo Advisors) 12.7 Implementation Issues

ANSWERS TO SECTIONS REVIEW QUESTIONS Section 12.1 Questions for the Opening Vignette 1. List and discuss the benefits of bots to the company.

Better customer service at a reduced cost. Provide recommendations of best matched products. Customers are happier since they can visualize potential changes due to different products. Customers becomedmore loyal.

2. List and discuss the benefits of bots to customers.

Easy for customers to understand due to Q&A mode. More engagement with the company. Use of voice. Use of photos help. Users can experiment online. Can experiment with the virtual assistant. Can use natural language for voice communication.

3. Why were the bots deployed via Messenger and Kik? Users, especially younger ones, like to be on messaging platforms such as Messenger. So, it is easy for users to access the company’s bots. In addition, companies can use messaging systems to collect information about users and engage them.

4. What would happen to Sephora if competitors use a similar approach?

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The company will have to make its bots even more knowledgeable and offer new and improved services – instantly!

Section 12.2 Review Questions 1. Define expert systems.

An expert system is a computerized, fully automated, advisory system for a fairly defined and usually rule-based domain.

2. What is the major objective of ES?

To provide computerized advice where expertise is not readily available, expensive, or where one needs expertise for self-training.

3. Describe experts.

An expert is a person that is very knowledgeable about an area in a specific domain.

Experts have authoritative knowledge.

4. What is expertise?

Expertise is the knowledge embedded in experts or transferred to computers. It knows how to solve problems, execute complex tasks, and answer questions related to the expertise domain. Expertise is acquired through experience and education/training.

5. List some areas especially amenable to ES.

Routine areas such as approve small loans, conduct a prescreening of applicants, simple equipment failure diagnosis, selection among small number of routine alternatives.

6. List the major components of ES and describe each briefly.

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Knowledge acquisition – the collection of the relevant knowledge Expert - the contributor of the knowledge Knowledge base – the place where the knowledge is stored Inference engine – the procedures and technology that create the output provided to users 7. Why is ES usage on the decline?

It is limited in what it can do. It may be too expensive due to changes that need to be done for specific applications and customers.

8. Define recommendation systems and describe their operations and benefits.

These systems provide product or service or action advices to users, usually on a one-toone basis. These systems (such as in Case 12.3) attempt to learn something (e.g., tastes, needs, likes) about the customers they service, and target advice or answer or some recommendations to the customers. The major benefits are the low cost per recommendation; ability to get recommendation at any place, anytime; consistency of the recommendation; ability to generate recommendations at a low cost and high speed, and advice can be delivered by voice in natural language.

9. How do recommendation systems relate to AI?

a. They learn about the customers’ preferences b. They acquire the knowledge needed to make recommendations c. They build the algorithms that match the customers’ preferences to the recommendation d. They build an NLP for the person-machine dialogue

Section 12.3 Review Questions 1. Define chatbots and describe their use.

Chatbots are computerized systems that frequently look like people and can use writing or voice to interact with people. Usually they use Q&A mode. Their major use is as

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advisory systems, but they can be used to perform simple tasks and can even comfort people in distress.

2. List the major components of chatbots.

They are built on top of chat platforms (like Facebook Messenger). They need to have knowledge, they need to process natural language capability, so they need NLP, and they need a dialog (such as Dialogflow, or Wit.ai). They also need a Dialog Manager. The knowledge is inserted into the system by Custom Integration.

3. What are the major drivers of chatbot technology?

The low cost of a device that can serve many people (e.g., by providing information in airports, museums, corporations, etc.).

4. How do chatbots work?

They are similar to recommendation systems. They are mostly in Q&A mode. Once a question is received, the NLP translates it into machine language. The processing unit and the dialog manager try to match an answer from the knowledge base (content). Then a reply is delivered by voice generation to the users.

5. Why are chatbots considered AI machines?

They need to have intelligence to answer questions and provide information in natural languages. Meaning, they need to have appropriate knowledge and process it correctly.

They also use AI to conduct NLP dialog. Some specialized chatbots have computer vision capabilities.

Section 12.4 Review Questions 1. Describe some marketing bots.

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Sephora (Opening vignette – uses several bots for marketing). Alibaba offers a fashion advisor. There are thousands of other bots that help marketing products.

Many are on Facebook Messenger and other messaging companies. Also, see answer to Case 12.4. For a comprehensive directory, see topbots.com/brands/, and hubspot.com/bots.

2. What can bots do for financial services?

Provide information to customers, enable payments and money transfers, verify eligibility, book appointments, provide investment advice (Section 2.5). Assist in selfservice applications, customer acquisitioning, and much more.

3. How can bots assist shoppers?

They can provide information about products and services, they can place orders, they can check eligibility of funding, and may provide product instructions. Customers can also get help in stores and online (e-commerce: see virtualspirits.com/ecommercechatbot-how-chatbots-help-customers-shipping-delivery.aspx).

4. List some benefits of enterprise chatbots.

They can provide information to employees regarding safety and labor regulations. Bots can facilitate learning and training by acting as personal assistants to employees who need to relearn and update their knowledge. Employees can do it at their own pace.

Bots can be partners, working together with employees, providing instructions and directions.

5. Describe the sources of knowledge for enterprise chatbots.

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Knowledge can be extracted from enterprise experts, from manuals, from training documentation, and from partners that a company is doing business with. Some knowledge can be purchased from outside vendors and from experts.

Section 12.5 Review Questions 1. Describe an intelligent virtual personal assistant.

This is a chatbot that assists individual people by providing instructions and answers to general questions in life. Well-known are Alexa and Google Assistant. The knowledge is centralized and maintained in the cloud. As the name indicates, the machine helps people by providing advice and information about many topics.

2. Describe the capabilities of Amazon’s Alexa.

The capabilities are ever-increasing. They are divided into general information and into skills. The general capabilities can be accessed by all users. The skills may be provided to a designated audience. This exercise can be assigned as a class exercise.

3. Relate Amazon’s Alexa to Echo.

Echo is the main speaker of Alexa. It is a voice controlled virtual assistant with which people can control smart homes (Chapter 13) and other systems. Echo will connect to a desired app on Alexa. All you have to do is just say “Alexa,” which will provide the knowledge to users.

4. Describe Echo Dot and Tap. These are two different speakers that support Alexa. Echo Dot is now (2019) in its 3rd generation. Tap Echo is not available anymore from Amazon. It used to be a wireless Echo speaker that is been discontinued. There are several new models of Echo (e.g., Echo Auto, Echo Plus. This can be a class project on all Echos.

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5. Describe Apple’s Siri and Google’s Assistant.

These assistants compete with Alexa. In 2019, Google Assistant was in a class with Alexa, while Siri has less capabilities. Tests in early 2019 show that in certain areas, Google Assistant was more knowledgeable than Alexa.

6. How is the knowledge of personal assistants maintained?

Some of the knowledge is permanent, other keep changing. For example, distance between cities is fixed, but temperature is variable. Some of the knowledge is collected by sensors, others are provided by experts.

7. Explain the relationship between virtual personal assistants and chatbots.

The chatbot is usually a private virtual personal assistant that has its own small database. The owners need to update the knowledge. The public assistants (e.g. Alexa, Siri) serve many clients at home and work. Chatbots are usually much smaller in scope and owned by an organization for their members.

Section 12.6 Review Questions 1. Define robo advisor.

Robo Advisors are machines that provide advice on how to invest in stocks, based on the objectives provided to them by investors.

2. Explain how robo advisors work for investments.

The Robo is basically a bot with knowledge on an ETFs portfolios of stocks. The knowledge comes from sets of If-then rules, “If you are of a certain age and your

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investment objective is portfolio growth” etc., then these stocks which are included in these ETF will be in your portfolio.

3. Discuss some of the shortcomings of robo advisors for investments.

One shortage is that they include ETFs and not individual stocks. Also, they consider only investors’ preferences which are in their knowledge base. They may not be able to answer some questions that investors have. The robo advisors basically have a prepared set of questions that they ask the investors and based on the answers, the robo advisors create a portfolio. The robo advisors may change the portfolio over time if market conditions change.

4. Explain the people-machine collaboration in robo advising.

The collaboration comes via the questions and answers session. Since only ETFs are considered, there is very little room for justification and explanation.

5. Describe IBM Watson as an advisor.

IBM Watson is a powerful advisor on certain topics (e.g., in medical research). The product also helps to solve difficult business problems. In the solution, Watson uses its cognitive capabilities with IBM’s analytical capabilities (of IBM QRadar product).

It is a Q&A system.

Section 12.7 Review Questions 1. Explain the quality issues.

The quality of chatbots is generally increasing with time. It depends on how much money in invested in the bot. The more the operators of the bots are willing to invest, the more accurate will be the output provided by the bots.

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2. What determines the quality of Robo Advisors?

Like in any machine advisory, the quality of the advice depends on the quality of the knowledge and the input provided by those seeking advice.

3. List some steps in the construction of Alexa Smart Home system.

a. Get an Echo Speaker b. Find an appropriate location. The speaker needs to hear you and you need to hear the speaker c. Set up all the smart devices d. Make all the connections e. Test and fine tune the systems

4. What is Microsoft’s Azura Bot Service?

Azura Bot Service is used to build bots quickly. It has a set of templates that lead the builder to build bots with different capabilities (e.g., natural language understanding, Q&A).

ANSWERS TO APPLICATION CASES Application Case 12.1: ES Aid in Identification of Chemical, Biological and Radiological Agents 1. How can the CBR Advisor assist in making quick decisions?

The system can provide a timely response since it contains the expertise needed to identify the elements used in a terrorist attack. The ES provides the step-by-step procedures to identify the toxic agents (i.e. type, composition, etc.). The system provides advice to top level management, educational material, and material for research activities.

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2. What characteristics of the CBR Advisor make it an expert system?

First, the system contains the knowledge of experts, which is transferred to a machine. Second, the knowledge can be organized so that it can be used by nonexperts whenever needed. The knowledge is packaged in such a way that it can be used by different professionals in case of an emergency.

3. What could be other situations in which similar expert systems can be employed?

Natural disasters such as earthquake or fire. In such a case, well prepared staff will respond much better if they consult the ES.

Application Case 12.2: VisiRule

Questions for Case 12.2 1. Which of the limitations of early ES have been solved by the VisiRule system?

Introducing ‘ease of use’ via diagramming. Developing a hybrid process using decision trees for an easy addition of rules. Also, VisiRule provides an easy update of the knowledge base and it adds dual delivery using machine learning. It provides a chatbot for easy communication. There is no need for a programmer (or knowledge engineer).

Finally, VisiRule provides a self-assessment tool and explanation and justification are easily generated.

2. Compare Figures 12.2 and 12.1. What are the differences between the creation (Fig. 12.2) and the development (Fig. 12.1) subsystems?

While the process is basically similar, having a development part and a consultation part, the ease of use (via diagramming) and the improved quality (via machine learning), makes the VisiRule product superior.

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3. Compare Figures 12.2 and 12.1. What are the differences between the delivery part (Fig. 12.2) and the consultation part (Fig. 12.1) subsystems?

The delivery (called deployment in Fig. 12.2, consultation in Fig. 12.1) shows the users’ activities. In both cases, it is based on Q&A. The answers are generated from the rules (knowledge). The construction side (Fig. 12.1) is called the learning (from experts or from data in Fig. 12.2). The major differences are in the learning side (machine learning) and in chatbots not shown in the figures, in the interaction (Fig. 12.2).

4. Identify all AI technologies and list their contribution to the VisiRule system.

Machine learning is added for creation of If-then rules directly from data. Machine learning also supports the dual delivery. Machine learning discovers hidden patterns that are used to form predictive decision models. Finally, chatbots help communication with the users.

5. List some benefits of this ES to users.

There is ease of use on one hand and better quality of advice on the other hand. Also, the product has more capabilities than in traditional ES. The users have more control over the systems since they can update them by themselves.

Application Case 12.3: Netflix Recommender: A Critical Success Factor Questions for Case 12.3 1. Why is the recommender system useful? (Relate it to one-to-one targeted marketing.)

Recommender tries to find the best match to the need of an individual customer; such match is attempted by targeted advertisement.

2. Explain how recommendations are generated.

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It is required to find the ‘likes’ of the customer. This can be done by analyzing previous purchasing, asking questions (expensive), finding what similar customers stream, etc.

3. Amazon disclosed its recommendation algorithms to the public, but Netflix did not.

Why?

Amazon operates in a less competitive environment. People shop for specific items. The recommendation is less influencing than a recommendation from Netflix. Also, the Amazon algorithm is simple (“People that purchased this product also purchased…”) so the algorithm is easily duplicated.

4. Identify some research activities that attempt to “mimic the human brain.”

The answer will depend on when the assignment is given. To find information, search on futureism.com.

5. Explain the changes due to the globalization move of the company.

Due to cultural differences the recommendation is more difficult. Lack of historical data in certain countries (cultures) forces Netflix to change the recommendation method. Also, import and export regulations need to considered, as well as the global security issues, pricing, and technology used.

Application Case 12.4: WeChat’s Super Chatbot

Questions for Case 12.4 1. Find some recent activities that WeChat does.

WeChat is keep changing. So, the answer will be determined on when the assignment is made. Also, it belongs to Tencent in China. Not all is translated into English. The

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product is available in both in English and Chinese. For a good source, go to abacusnews.com and search WeChat. Users can even access government sources.

2. What makes this chatbot so unique?

The major benefits are the ease of use and the many activities one can do on this messaging system. Interactions are the diverse shopping capabilities. In fact, several of the tasks are available today on Alexa and Google Assistant. So, WeChat is not only a diversified messaging system, but it is also a virtual personal assistant. It combines capabilities of several popular apps (e.g., Instagram, Facebook, WhatsApp, and Venmo).

Users can freely switch between iOS and Android.

3. Compare the bot of WeChat to bots offered by Facebook.

In WeChat we can build chatbots via subscription accounts or service accounts. Most popular are customer support bots. You can use smartloop.ai to do the job. For details, see Hossain Fuad: blog.recime.io/building-a-wechat-bot-8801c85cdb2o, (2017).

On Facebook, you can build bots on the Messenger platform fairly easily. It will take only a few minutes. It is used extensively for marketing (e.g., lead generation) sales, finance, etc. For comprehensive coverage, see Beck (2018): clearvoice.com/blog/buildfacebook-chatbot-10-minutes/.

Application Case 12.5: How Vera Gold Mark Uses Chatbots to Increase Sales Answers to Questions for Case 12.5 1. List the benefits to VGM.

VGM is able to have inexpensive sale bots 24/7, which is critical for a company that sells globally. Also, the bots provide consistent information. Instead of conducting an up-todate of the knowledge of many salespeople, here the company updates one bot. Sales increase due to the 24/7 service and due to a better one-to-one alignment or property with customers.

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2. List the benefits to buyers.

Customers feel that they get better service and a more honest one (the communication with the bot is documented in writing). They get offers that better match their needs due to the information collected by the bots. Also, 24/7 open business is important.

3. What is the role of Kenyt Technologies?

Kenyt Technologies created the hardware and software necessary for the chatbot (specialized for real estate sales).

Application Case 12.6: Transvania Airlines Uses Bots for Communication Answers to Questions for Case 12.6 1. What drives consumer preference for mobile devices and chat?

Many travelers are young and use mobile devices, and texting is in their culture. These people are usually on Facebook and also are getting used to connecting to vendors online.

2. Why was the bot placed on Facebook Messenger?

Given the high usage of Facebook Messenger and the ease of getting to and from the bot without leaving Messenger, it makes this place ideal.

3. What were the benefits of using Cognizant?

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Cognizant is a major technology that is experienced in Messenger and in installing bots and placing them in operation. The bot needed to be connected with many external and internal information systems, some of which are changing in real time. So, a large and general IT vendor was needed.

4. What is the advantage of buying a ticket from a bot rather than from an online store?

It is faster and more natural for young people. Also, users carry their cell phone 24/7.

They also may like to converse with bots.

Application Case 12.7: Betterment, the Pioneer of Financial Robo Advisors Answers to Questions for Case 12.7 1. What are Betterment’s benefits to investors?

Investors pay relatively little for the advice they are getting from Betterment (and similar services). The advice can be personalized to meet the investors’ objectives. It is also being monitored and changed accordingly. Betterman’s services are easily accessible online. One limitation is that the investments are only in ETFs and not in individual stocks.

2. Compare Betterment to its major competitors (see Eule, 2017).

Most of the competitors are financial institutions (e.g., Schwab) that for them robo advisory is a secondary business. However, over time, some competitors may move closer to Betterment.

3. What are the benefits of adding the human touch (i.e., compared to pure automation and to only human service)?

The human touch makes users more confident. They can get explanations not offered by the machines and some added services. Users have to pay extra money for these services.

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This added flexibility is needed in many cases and helps Betterment to have more customers. Pure human advice is much more expensive.

4. Find some new information about Betterment. Write a report.

The answer expands on the time the assignment is given to students. New capabilities are added all the time.

Discussion Questions: Technology Insight 12.1: Chatbot’s Platform Providers 1. What is the difference between a regular enterprise bot and a platform?

A regular enterprise bot is an application. The platform is used to build or modify bots.

2. Discuss the benefits of ChattyPeople.

ChattyPeople’s accounts of companies are easily connected to social media pages. Thus, it is possible to transfer money to/from contacts using PayPal, Apple Pay, etc.

ChattyPeople also supports messaging.

3. Discuss the need for Kudi.

Kudi enables making payments directly from messaging apps. In addition, Kudi provides for payment reminders. This bot is highly secured.

4. Discuss the reasons for consumers preferring messaging platforms.

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Messaging platforms are popular because they provide free opportunity to talk, chat online, exchange photos, have group conferences, and more. They are also user friendly and archive past dialogs.

ANSWERS TO QUESTIONS FOR DISCUSSION: (END OF CHAPTER) 1. Some people say that chatbots are inferior for chatting. Others disagree. Discuss.

Some people do not like to talk to machines; others love it. As chatbots get smarter, more people will like them. But many people may never accept them since they do not show emotions. Also, some people do not trust machines.

2. Discuss the financial benefits of chatbots.

Most chatbots cost very little and can save companies a lot of money. The major cost is the update of the knowledge. So as long as the cost of updating is not too large and/or the number of users is very large, using chatbots will show positive cost-benefit. However, in some cases, it is difficult to measure the financial benefits (e.g., in customer satisfaction).

3. Discuss how IBM Watson will reach 1 billion people by 2018 and what the implications of that are.

Watson is probably in use today (2019) by over one billion people, which increases the revenue of IBM. There is an agreement about the product benefits, especially in the medical and health fields. Watson combines powerful analytical capabilities with diversified advisory services. It looks like more and more companies and individuals use it.

4. Discuss the limitation of chatbots and how to overcome them.

A major limitation is the size and quality of the knowledge. The chatbots need (1) to understand exactly what people say, i.e. natural language understanding; and (2) to have

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the knowledge to respond. Advancement in NLP will help the communication problem and deep learning may improve the responses.

5. Discuss what made ES popular for almost 30 years before their decline.

ES was popular only in very narrow domains. Changes in the domain environment made them useless unless quick changes were made to the knowledge. Also, knowledge acquisition was very expensive. Better advisory systems and improved technologies will make most traditional ES obsolete.

6. Summarize the difficulties in knowledge acquisition from experts (also consult Chapter 2).

It is difficult to find qualified experts, since they are busy and charge very high fees.

Also, experts may be biased and have difficulty to communicate their expertise. Many times, it is necessary to engage knowledge engineers in the process. These may be expensive, difficult to find, and biased at times.

7. Compare the ES knowledge-refining system with knowledge improvement in machine learning.

ES knowledge refinement is based on feedback collected manually from users of the ES. In machine learning, improvement is achieved by automatically analyzing more and more related data.

8. Discuss the difference of enterprises’ use of chatbots internally and externally.

The external use is mostly with customers for customer service and marketing purposes.

There may be some use with suppliers’ employees. Internally, the bots are used mostly for supporting employees, for HRM, safety issues, training and education and financial

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applications in the enterprise.

9. Some people say that without a virtual personal assistant, a home cannot be smart. Why?

A smart home operates on the control of many devices. Personal assistants sense the home environment and can take appropriate actions to adjust for changes. The assistant also converses with people and can take actions based on what they learn from communicating with people.

10. Compare Facebook Messenger virtual assistant project M with that of competitors.

Project M was shut down by Facebook when the book was in print. Other vendors did not offer similar services. Project M was a text-based virtual assistant facilitated by humans whose job it was to train the AI system. The assistance was provided by a bot.

11. Examine Alexa’s skill in ordering drinks from Starbucks.

Alexa skill is used for ordering drinks from Starbucks. This skill starts by a simple request “Alexa, tell Starbucks to brew my regular order.” It is getting additional features with time. It is called Starbucks Skill. It is similar to the skill that you use to order from Pizza Hut.

12. Discuss the advantages of robo advisors over human advisors. What are the disadvantages?

Robo advisors are available 24/7 at very low fee. They are more consistent than humans.

However, they cannot deal with complex investments. They are confined to ETFs and cannot include individual stocks in a portfolio. Finally, they may not be accurate in interpreting the users’ goals in an investment.

13. Explain how marketers can reach more customers with bots.

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First, they provide 24/7 service using bots. They also may provide language translation to customers who may be more comfortable in certain languages. Marketers place the company’s bots on messaging services for ease of the use by users who can also chat by voice with the chatbots.

14. Are robo advisors the future of finance? Debate; start with Demmissie (2017).

Instructions should arrange two teams for this debate. New materials can be collected.

Remember: a. Robots will be smarter in the future b. Investment advice may be more complex (to include stocks, bonds) c. There will be more companies that will offer robo advisors d. New innovations will come

15. Research the potential impact of chatbots on work and write a summary.

The chatbots will take some jobs from people. But in many cases, they will just expand customer service to 24/7. The issue of job loss is discussed in Chapter 14 for all AI tools.

Some HRM jobs may disappear and the job of receptionists in an organization to welcome visitors will be fully automated. Also, certain advisory service will be partially or fully automated. A new kind of relationship is being developed where employees consult the chatbot before they provide service, or answer customers’ questions.

ANSWERS TO EXERCISES (END OF CHAPTER) 1. Compare the chatbots of Facebook and WeChat. Which has more functionalities?

Both messaging services keep changing. So, the answers are open. During 2018 and early 2019, WeChat has had more functionalities. The answers will change with time.

2. Enter <URL>nuance.com</URL> and find information about Dragon Medical Advisor.

Describe its benefits. Write a report.

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The Medical Advisor provides several guidance: Clinical, ED, HCC and Pediatric. The Advisor helps physicians to improve documentation quality. This helps physicians to have more time for patients, enable earlier discovery of what is wrong and deliver better health care. The advice is provided in a workflow system that integrates evidence-based advice with real-time point-of-care data. The products are based on NLP (e.g., for dictating by physicians) and computerized imaging documents. The company also provides data security solutions. Major benefits are achieved in data integration, quality and security of data. The product automatically provides real-time quality feedback to physicians while they are documenting patients’ encounters.

3. Enter <URL>shopadvisor.com/our-platform</URL> and review the platform’s components. Examine the product’s capabilities and compare them with those of two other shopping advisors.

ShopAdvisor provides vendors, brands and agencies with proximity marketing solutions. It does this by bringing consumers (via their smartphones) to the providers and their products.

The advice frequently brings more customers to the vendors/brands. The components depending on the services provided, such as: ‘Ad-Buy optimization’, ‘sales lift analysis’ and ‘product/store finder’. There are not direct competitors; several companies provide one or two of the six services provided by ShopAdvisor.

4. Enter <URL>chatbots.org/</URL> and joint a forum of your interest. Also, explore research issues of your interest. Write a report.

Answers depend on what forums the student will select.

5. There is intense competition between all major tech companies regarding their virtual personal assistants. New innovations and capabilities appear daily. Research the status of these assistants for Amazon, Apple, Microsoft, Google, and Samsung. Write a report.

The answer will depend on when the students will enter the Websites. This can be a group assignment when each assistant can be assigned to a different person or a group. The class may select criteria for evaluation, such as accurate answers to the same query.

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6. Some people believe that chatbots will change how people interact with the Internet and browse online. Prepare a report regarding this.

One change will be voice interaction. Voice commands and queries will be in natural language. Interaction will be simplified using chatbot advice. Other areas: chatbots can be used as an interface to the IoT, chatbot companions for dementia patients and making medical diagnosis faster.

In summary, chatbots can be considered a new paradigm of human-machine interface (i.e. conversational interface). Chatbots are, and will improve, human-machine interactions.

7. Explain why is Amazon’s Echo is needed to work with Alexa? Read <URL>howtogeek.com/253719/do-i-need-an-amazon-echo-to-use-alexa/</URL>. Write a report.

Alexa is an Amazon’s Voice Service that acts as a virtual personal assistant. Echo is the NLP voice interface to Alexa. So, Alexa itself is a service, the brain that contains knowledge; together with Echo it is a virtual personal advisor. The two terms are frequently confused and Echo is frequently referred to as Alexa.

8. Find out how Simon Property Group is using chatbots across over 200 shopping malls. Write about the benefits to different types of users and to the company.

Simon Property Group is a large real estate company that owns retail properties all over the US; in many cases entiren shopping malls (208 in 2019). The company launched chatbots on Messenger for all the malls they manage. The objective is to enhance customer service and provide customers an easy and fast way to get information about each mall and each store there. Based on interactions with customers, the bot can provide predictive marketing data.

Also, from the data analysis the company recommended to customers new stores to visit.

Each bot is kind of a concierge for any shopper at Simons’ malls. In addition to providing information, bots are used to engage with customers proactively. Each mall has an individual bot.

9. Read recent information about enterprise bots. Write a report.

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Open-ended answers.

10. Enter <URL>gravityinvestments.com/digital-advice-platform-demo</URL>. Would you invest in this project? Research and write a report.

It is an institutional robo-advisor company similar to Betterment. In order to decide about investing in the company, the student needs to find information about the performance of the company’s recommendations and compare it to other performances (e.g. Dow Jones, S&P 500).

11. Enter <URL>visirule.co.uk</URL> and find all products it has for expert systems. List them and write a short report.

The list is changing. In February 2019, the product list was VisiRule 365, VisiRule and VisiRule for creating business rules, one for health care and one for law firms. Also, there is VisiRule for authors. They all evolved around graphical tools for creating rules for different ES products. Also available is mobile deployment, cloud-based delivery, interactive mode and more. Most products are based on AI.

12. Research the role of chatbots in helping patients with dementia.

Chatbots are used as companions to dementia patients. One such bot is Endurance; see endurancerobots.com and aiin.healthcare/topics (look for robotic animals that comfort dementia patients). The chatbots are good in engaging patients as well as the elderly.

13. Find information on Baidu’s Melody chatbot and how it works with Baidu Doctor.

Melody is a medical conversational chatbot that helps Chinese doctors diagnose diseases.

The bot looks like a doctor, asks the physician to describe the patient’s symptoms. Then, Melody will ask additional questions leading to a diagnosis. Melody is embedded in Baidu Doctor, which enables patient-doctor dialog. Patients can contact local doctors, make appointments, ask questions, etc. Similar bots are available in other countries.

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14. Pose a question related to a chatbot on <URL>quora.com</URL>. Summarize the answers received in a report.

Open-ended, depends on the query placed by each student.

15. Nina is an intelligent chatbot from Nuance Communication Inc. that works for Alexa Internet of Things (IoT), smart homes, and more. Find information and write a report about Nina’s capabilities and benefits.

Nina is a voice-operated virtual assistant regarding digital issues. It needs to be adopted to vendors’ communication channels (website, voice, text messaging app, and even TV).

Nina enables intelligent dialog between customers and brands. It provides self-service experience. Nina gets to be smarter by using machine learning experiences. It is considered as a top enterprise virtual assistant.

One variety of Nina is Nina for Amazon Alexa. This product integrates with IoT and delivered as Alexa’s skill. Nina is integrated with smart home IoT devices. See details at globenewswire.com/news of June 1, 2017.

16. Microsoft partners with the government of Singapore to develop chatbots for e-services. Find out how this is done.

These bots help citizens to ask questions from government agencies and complete simple tasks (such as fill in forms). It makes the use of e-government easy, since there is no need to scroll through different menus to search for information. Also, there is less need to place calls. The government can collect information about the citizens and improve its services.

17. Study the Tommy Hilfiger Facebook Messenger bot. Find out how it is (and was) used in the company’s marketing campaigns.

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Enter Messenger and find items you want to buy (e.g., shoes). Then, search for the chatbot.

18. Two comprehensive building tools for chatbots are Botsify and Personality Forge (<URL>personalityforge.com</URL>). Compare the tools. Write a report.

Personality Forge is not a tool for building bots (it is used for personality tests). Botsify is a popular tool for designing chatbots by non-technical users. No coding is needed; you create chatbots for most messaging systems (e.g. Messenger, Slack). The bots can have machine learning capabilities. You can also add a chatbot to your website. Now (2019) it is Botsify 2.0.

19. Find information about the Alibaba-backed robo advisor Youyu by Yunfeng’s Investment.

What is unique about this service? Start by visiting <URL>http://www.internationaladviser.com/news/1035281/alibaba-backed-retail-robo-adviser-youyu-launches-honkkong/</URL>.

Note This URL may be inactive. There are changes there.

The robo-advisor is in Hong Kong, launched by Yunfeng Financial Group. Initially it has been designed to encourage direct-to-consumer investment in mutual funds. The bot is called Youyu, which means to ‘have fish’ in Chinese. It can be used on a smartphone. The chatbot is still evolving. Alibaba sponsors another robo-advisor in Hong Kong by Magnum Research Ltd.

20. Enter <URL>exsys.com</URL>. Select three case studies and explain why they were successful.

The answers depend on what applications the students will choose.

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21. It is time now to build your own bot. Consult with your instructor about which software to use. Have several bots constructed in your class and compare their capabilities. Use Microsoft’s Azure if you have some programming experience.

Open-ended answers. It can be a group project. We recommend using Botsify. It is simple and easy to use.

Chapter 13: The Internet of Things as a Platform for Intelligent Applications

Learning Objectives for Chapter 13 1.

2.

3.

4.

5.

6.

7.

8.

9.

Describe the IoT and its characteristics Discuss the benefits and drivers of IoT Understand how IoT works Describe sensors and explain their role in IoT applications Describe typical IoT applications in a diversity of fields Describe smart appliances and homes Understand the concept of smart cities, their content, and their benefits Describe the landscape of autonomous vehicles Discuss the major issues of IoT implementation

CHAPTER OVERVIEW

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The Internet of Things (IoT) has been in the technology spotlight since 2014. Its applications are emerging rapidly across many fields in industry, services, government, and the military (Manyika et al., 2015). It is estimated that 20 to 50 billion “things” will be connected to the Internet by 2020–2025. The IoT connects large numbers of smart things and collects data that are processed by analytics and other intelligent systems. The technology is frequently combined with artificial intelligence (AI) tools for creating smart applications, notably autonomous cars, smart homes, and smart cities.

This chapter contains the following sections:

1 .

CHAPTER OUTLINE 13.1 Opening Vignette: CNH Industrial Uses the Internet of Things to Excel 13.2 Essentials of IoT 13.3 Major Benefits and Drivers of IoT 13.4 How IoT Works 13.5 Sensors and Their Role in IoT 13.6 Selected IoT Applications 13.7 Smart Homes and Appliances 13.8 Smart Cities and Factories 13.9 Autonomous (Self-driving) Vehicles 13.10 Implementing IoT and Managerial Considerations

2 .

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 13.1 Review Questions 1. Why is the IoT the only viable solution to CNH’s problems?

The IoT provides the capability for CNH to monitor a large number of vehicles, with multiple types of data readings, across a large physical area.

2. List and discuss the major benefits of IoT. From the case, the major benefits for CNH of IoT are: •

ability to monitor all vehicles

•

ability to monitor vehicles across a large geographic area

•

ability to receive information from a variety of sensors on each vehicle

•

provides information on optimization of equipment

•

predicts fuel supply

•

alerts owners of preventative maintenance

•

indicates when trucks are overloaded

•

provides information on planning for deliveries or farming

•

provides warnings of impending breakdowns

3. How can CNH’s product development benefit from the collected data about usage?

The product development group will have a wealth of information about actual usage of the products that they can then use when redesigning current products or developing new products. For example, the group will be able to better understand the climates and duration of use during harvest. Additionally, the group will be able to identify any part that appears to cause breakdowns more frequently.

4. It is said that the IoT enables telematics and connected vehicles. Explain.

The IoT allows for the collection of a wide variety of information about vehicle location, use and condition. These pieces of information can then be used to assist operators in planning routes, timelines and scheduled maintenance.

3 .

5. Why is IoT considered the “core of the future business strategy”?

Much of business is built on the ability to make effective decisions, and those decisions are based on data. The IoT provides the opportunity for a wealth of data in a variety of different areas to be collected, and this data can then be used to make appropriate business decisions.

6. It is said that the IoT will enable new services for CNH (e.g., for sales and collaboration with partners). Elaborate.

Student responses will vary, but some examples may include the ability to schedule both routine and nonroutine maintenance as indicated, the ability to recommend more appropriate products based on real-world use, assist in planning logistics services based on data received, and other potential services.

7. View Figure 13.3 (The process of IoT) and relate it to the use of IoT at CNH. This figure provides information on how IoT functions, and examples from CNH can be drawn from it. In the CNH case, the vehicles have numerous sensors that provide data through wireless systems to the company servers. This information is collected stored and transferred. Data can then be analyzed to provide insights in real time (for example if a truck is overloaded) as well as more long-term analysis (types of conditions equipment is operated in).

8. Identify decision support possibilities.

Both operators and the company can use information provided by the system to make decisions about the use of the equipment as well as gain insight about how the equipment is used. Operators can use information about fuel economy to make decisions about when to refuel. The company can use information about the average temperature in which the equipment is used to make determinations on product design for better longevity and function in individual parts.

9. Which decisions made by the company and its customers are supported by IoT? The case indicates that delivery trucks can be connected to planners with delivery sources and destinations. Additionally, the performance of people who drive the vehicles can be analyzed and recommendations made on methods to improve the vehicles efficiency.

Section 13.2 Review Questions 1. What is IoT? The IoT is a network of connected computing devices including different types of objects (e.g., digital machines). Each object in the network has a unique identifier (UID), and it is

4 .

capable of collecting and transferring data automatically across the network. 2. List the major characteristics of IoT.

3. Why is IoT important?

The IoT is important because it represents a major disruptive technology that has the ability to cut costs create new business models and improve quality of products and services.

4. List some changes introduced by IoT. Several examples are given, but some include the ability to know where an individual is within a secure location, the ability to track vehicles to optimize routing, and the ability to monitor machines to predict needed maintenance.

5. What is the IoT ecosystem?

The IoT ecosystem describes all of the potential applications of IoT. This includes applications (also referred to as verticals) platforms and enablement (also referred to as horizontals) and building blocks. Figure 13.1 displays this is a graphic 6. What are the major components of an IoT technology?

IoT technology can be divided into four major blocks •

Hardware

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Connectivity

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software backend

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applications

Section 13.3 Review Questions 1. List the benefits of IoT for enterprises.

The chapter identifies the following benefits for enterprises: •

Reduces cost by automating processes.

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Improves workers’ productivity.

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Creates new revenue streams.

5 .

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Optimizes asset utilization (e.g., see the opening vignette).

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Improves sustainability.

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Changes and improves everything.

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May anticipate our needs (predictions).

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Enables insights into broad environments (sensors collect data).

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Enables smarter decisions/purchases.

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Provides increased accuracy of predictions.

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Identifies problems quickly (even before they occur).

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Provides instant information generation and dissemination.

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Offers quick and inexpensive tracking of activities.

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Makes business processes more efficient.

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Enables communication between consumers and financial institutions.

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Facilitates growth strategy.

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Fundamentally improves the use of analytics (see the opening vignette).

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Enables better decision making based on real-time information.

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Expedites problem resolution and malfunction recovery.

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Supports facility integration.

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Provides better knowledge about customers for personalized services and marketing.

2. List the benefits of IoT for consumers.

The chapter identifies many benefits for enterprises, and some of these are also of direct benefit to consumers: •

Reduces cost by automating processes.

•

Optimizes asset utilization (e.g., see the opening vignette).

•

Improves sustainability.

6 .

•

Changes and improves everything.

•

May anticipate our needs (predictions).

•

Provides increased accuracy of predictions.

•

Identifies problems quickly (even before they occur).

•

Provides instant information generation and dissemination.

•

Offers quick and inexpensive tracking of activities.

•

Enables communication between consumers and financial institutions.

•

Enables better decision making based on real-time information.

•

Expedites problem resolution and malfunction recovery.

•

Supports facility integration.

3. List the benefits of IoT for decision making.

The chapter identifies the following benefits for enterprises that are directly related to improved decision-making: •

Optimizes asset utilization (e.g., see the opening vignette).

•

May anticipate our needs (predictions).

•

Enables insights into broad environments (sensors collect data).

•

Enables smarter decisions/purchases.

•

Provides increased accuracy of predictions.

•

Identifies problems quickly (even before they occur).

•

Provides instant information generation and dissemination.

•

Fundamentally improves the use of analytics (see the opening vignette).

•

Enables better decision making based on real-time information.

4. List the major drivers of IoT. 7 .

The following are the major drivers of the IoT according to the section: •

The number of “things”—20 to 50 billion—may be connected to the Internet by 2020–2025.

•

Connected autonomous “things”/systems (e.g., robots, cars) create new IoT applications.

•

Broadband Internet is more widely available, increasing with time. • The cost of devices and sensors is continuously declining.

•

The cost of connecting the devices is decreasing.

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Additional devices are created (via innovations) and are interconnected easily (e.g., see Fenwick, 2016).

•

More sensors are built into devices.

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Smartphones’ penetration is skyrocketing.

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The availability of wearable devices is increasing.

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The speed of moving data is increasing to 60 THz. • Protocols are developing for IoT (e.g., WiGig).

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Customer expectations are rising; innovative customer services are becoming a necessity.

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The availability of IoT tools and platforms is increasing.

•

The availability of powerful analytics that are used with IoT is increasing.

Section 13.4 Review Questions 1. Describe the major components of IoT. According to figure 13.2, the major components include: •

Hardware - this includes the physical devices, sensors and actuators were data are produced and recorded.

•

Connectivity - this provides a hub that collects data from sensors and transmits it to the cloud for analysis.

•

Software - this allows for the connected data to be managed.

8 .

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Applications - this allows for the data to be turned into meaningful information through analysis.

2. Explain how the IoT works following the process illustrated in Figure 13.3.

Figure 13.3 illustrates the process through which the IoT operates.

•

Things communicate data to the Internet ecosystem through sensors and wireless systems

•

Information flows to the cloud where it is collected stored and transferred. In this step analysis, mining and processing may also be performed.

•

This can result in intelligent knowledge which can then be subject to decisionmaking (possibly using machine learning) that may generate innovations and possibly actions

•

These actions may also be informed by people, machines or other systems

•

The results of these actions may then be said back in to the original Things to improve performance, usability, information gathering, etc.

3. How does IoT support decision making?

The IoT supports decision-making by providing a large amount of readily available data for analysis. This data can provide a real perspective on multiple situations and decisionmaking can then focus on the best outcome without concern for the quantity, accuracy, availability or quality of data.

Section 13.5 Review Questions 1. Define sensor.

A sensor is an electronic device that automatically collects data about events or changes in its environment.

2. Describe the role of sensors in IoT. In many IoT applications single or multiple sensors are present. They serve to collect data to send to other electronic devices for processing.

3. What is RFID? What is a RFID sensor?

RFID is a generic technology that refers to the use of radio-frequency waves to identify objects. An RFID sensor is an upgraded RFID tag that wirelessly communicates with readers providing additional information.

4. What role does the RFID perform in IoT? 9 .

RFID provides another source of data to IOT systems as well as providing an additional technology that can be used for sensors to wirelessly transmit information.

5. Define smart sensor and describe its role in IoT. A smart sensor is one that senses the environment and processes the input it collects by using its built-in computing capabilities

Section 13.6 Review Questions 1. Describe several enterprise applications.

Student selections will vary but may include a discussion of the French national railway system, Hilton hotel, Ford, Tesla, Johnny Walker, Apple and Starbucks.

2. Describe several marketing and sales applications.

Student selections will vary but may include discussions related to systems that disruptively collect data, provide real-time personalization, provide environmental attribution, and complete conversation paths.

3. Describe several customer service applications.

Student selections will vary but may include applications such as smart homes and appliances, smart cities and autonomous vehicles.

Section 13.7 Review Questions 1. Describe a smart home.

A smart home is a home with automated components that are interconnected (frequently wirelessly), such as appliances, security, lights, and entertainment, and are centrally controlled and able to communicate with each other.

2. What are the benefits of a smart home?

Smart homes are designed to provide their dwellers with comfort, security, low energy cost, and convenience. They can communicate via smartphones or the Internet. The control can be in real time or at any desired intervals.

3. List the major smart appliances.

Some examples of smart home appliances included in the chapter are: •

lighting

•

television 10 .

•

energy management

•

water control

•

speakers and chat bots

•

home entertainment

•

alarm clocks

•

vacuum cleaners

•

cameras

•

refrigerators

•

home security

4. Describe how Nest works.

Nest is a popular smart home set of applications that includes a number of programmable self-learning, sensor driven, Wi-Fi enabled products. This includes a learning thermostat, a smoke detector and security system. These applications can be integrated together and programmed to support the needs of the consumer and controlled centrally through nest.com.

5. Describe the role of bots in smart homes B’s ots may be used in smart homes to meet and anticipate needs of consumers. In the future, AI based smart home systems may also be able to perform household tasks and manage processes for individuals.

Section 13.8 Review Questions 1. Describe smart city.

In smart cities, digital technologies (mostly mobile based) facilitate better public services for citizens, better utilization of resources, and less negative environmental impact.

2. List some benefits of a smart city to the residents.

In general, smart cities will provide residents better access to public services and better utilization of resources. Specific examples could include free access to citywide Wi-Fi, use of bike shares, checking out materials from a library and paying for bus rides.

3. What is the role of IoT in smart city initiatives?

11 .

IoT can provide fundamental data gathering functions in a smart city by tracking condition availability and use of items and providing a framework through which they can be provided to residents.

4. How are analytics combined with IoT? Why? The IoT can provide a wealth of data for improved decision-making. Analytic applications are typically combined in an IoT system to provide insight and actionable information to improve processes, services, and other functions.

5. Describe smart and cognitive buildings.

Smart or cognitive buildings are able to learn the behavior of various building systems in order to optimize their use. This has the advantage of being able to diagnose abnormal situations and propose remedies, provide better customer personalization and more efficiently use energy.

6. What is a smart factory?

A smart factory is a flexible system that can self-optimize performance across a broader network, self-adapt to and learn from new conditions in real or near real time, and autonomously run entire production processes.

7. Describe technology support to smart cities A variety of technologies exist to support smart cities. These technologies include systems that provide access to public benefits (Wi-Fi and libraries), transportation, ecommerce payments, infrastructure monitoring and analytics systems.

Section 13.9 Review Questions 1. What are self-driving vehicles? How are they related to the IoT? Self-driving vehicles are personal and commercial vehicles that are driven by technology systems that eliminate the need for human control of the vehicle. These vehicles rely on a large number of sensors to perform these complicated driving activities and this use of sensors is a natural portion of the IoT. 2. What are the benefits of self-driving vehicles to drivers, society, and companies?

A large number of potential benefits exist and can include convenience, safety, reduced costs, information sharing, better understanding of resource use, reduction of waste etc.

3. Why are Uber and similar companies interested in self-driving vehicles?

Self-driving cars may allow these companies to extend their existing business model to incorporate the use of driverless vehicles when human driven vehicles are unavailable, too far away or not economically feasible.

12 .

4. What AI technologies are needed to support autonomous vehicles?

A variety of AI technologies are necessary to support autonomous vehicles. These technologies can include but are not limited to vehicle awareness, mapping, driving and collision avoidance and occupant comfort.

5. What are flying cars?

Flying cars are consumer level vehicles designed to fly as opposed to operate on established roadways.

6. List some implementation issues of autonomous vehicles.

Some implementation issues that exist include the cost of 3D map technologies, the capabilities of AI software, customer and insurance acceptance, technology research expenses the overall structure of the IoT to support these initiatives.

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION

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Application Case 13.1: Using Sensors, IoT, and AI for Environmental Control at the Athens, Greece, International Airport 1. What is the role of IoT in the project?

IoT system provides environmental monitoring analysis and reporting of background information related to air pollution.

2. What is the role of sensors?

The sensors provide background information relating to a number of environmental factors including temperature, humidity, atmosphere pressure, ozone level, particulate matter and aircraft location.

3. What are the benefits of the project?

The project enables the airport to better understand environmental and pollution conditions so that improvements in systems, infrastructure and design can help reduce pollution overall.

Application Case 13.2: Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets to Predict Failures

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1. What type of information would likely be collected by an oil and gas drilling platform?

Many types of data could be collected, but some would include pressure, temperature, flow rate and many others.

2. Does this application fit the three V’s (volume, variety, velocity) of Big Data? Why or why not?

This project does that these parameters because of the large number of potential types of data to be captured and analyzed, the wide variety of those types of information and the fact that these systems are continuously running and processing large quantities of oil or natural gas.

3. Which other industries (list five) could use similar operational measurements and dashboards?

Student responses will vary, but may include pipelines, water wells, oil refineries, water purification plants, and waste management facilities.

Application Case 13.3: Amsterdam on the Road to Become a Smart City 1. Watch the video at youtube.com/watch?v= FinLi65Xtik/ and comment on the technologies used.

Student perceptions and comments on the video will vary.

2. Get a copy of the MIT case study at sloanreview. mit.edu/case-study/data-driven-citymanagement/. List the steps in the process and the applications that were likely used in IoT. Student evaluations of the case study will vary.

3. Identify the smart components used in this project.

The smart components identified in the case include smart mobility, smart living, smart society, smart areas, smart economy, big and open data, infrastructure, and living labs.

Application Case 13.4: How IBM Is Making Cities Smarter Worldwide 1. List the various services that are improved by IoT in a smart city.

Examples from the case include resource allocation, water management, transportation, decision-making, traffic management, utilities management, emergency management, and forecasting crime.

14 .

2. How do the technologies support decision making?

The technologies provide accurate, real-time data that can be analyzed to make better decisions.

3. Comment on the global nature of the examples.

Student responses will vary, but it may be noted that these systems are being adopted worldwide which indicates a large amount of interest and recognition of the possible benefits.

Application Case 13.5: Waymo and Autonomous Vehicles 1. Why did Waymo first use simulation?

The use of the simulation was a part of him a DARPA and US Department of Defense grant.

2. Why was legislation needed?

Legislation was needed because this type of activity on public roads had not been envisioned and was not included in current law.

3. What is the Early Rider Program?

In 2018 passengers volunteered to participate in the service and were picked up by self driving bands.

4. Why will it take years before regular car owners will be able to enjoy a ride in the back seat of their self-driving cars?

There are a variety of reasons why implementation of the technology may take a while, examples include technical reasons (improvements in the technology, AI and sensors), acceptance (by individuals and government) and legislation (making driverless cars acceptable everywhere).

5. Why are Lyft, Uber, and Avis interested in self-driving cars Driverless cars would allow these companies to expand their current business models by providing a different transportation alternatives when others are unavailable, too expensive or inconvenient.

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ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION

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1. Compare the IoT with regular Internet.

In contrast with the regular Internet that connects people to each other using computing technology, the IoT connects “things” (physical devices and people) to each other and to sensors that collect data.

2. Discuss the potential impact of autonomous vehicles on our lives.

Student perspectives will vary, but may focus on an increase in convenience, a reduction in accidents and a reduction in costs.

3. Why must a truly smart home have a bot?

Student perceptions will vary, but many will argue that the addition of bots to a smart home provide an extra level of convenience for customers by reducing time and effort needed to perform household tasks.

4. Why is the IoT considered a disruptive technology?

The IoT is a disruptive technology because it has the ability to radically change the way that decisions are made, products and services are delivered, and the type of products, services and experiences that can be provided to customers and businesses.

5. Research Apple Home Pod. How does it interact with smart home devices?

Student responses will vary based on the date of research. Students may find that Apple products integrate with other Apple home products as well as third-party products to create smart home systems across multiple applications.

6. Alexa is now connected to smart home devices such as thermostats and microwaves.

Find examples of other appliances that are connected to Alexa and write a report.

Student reports will vary based on the date the report is researched, current information can be found at www.amazon.com/smart-home-devices/.

7. Discuss the objective of smart cities to conserve the earth’s limited resources.

Smart cities have the ability to optimize the use of several resources required by residents these resources can include applications related to water use, energy use and pollution.

By using these resources efficiently (and minimizing pollution) smart cities can have an impact on the environment.

8. What are the major uses of IoT?

16 .

McKinsey’s Global Institute (Bughin et al., 2015) identifies the following issues: •

organizational alignment

•

interoperability challenges

•

security

•

privacy

•

connection of data silos

•

preparation of existing IT architectures

•

management

•

connected customers

9. Accidents involving driverless cars slow down the implementation of the technology.

Yet, the technology can save hundreds of thousands of lives. Is the slowdown (usually driven by politicians) justifiable? Discuss.

Student opinions and perceptions will vary.

 

ANSWERS TO END OF CHAPTER EXCERCISES

1. Go to theinternetofthings.eu and find information about the IoT Council. Write a summary of it.

Student reports will vary.

2. Go to https://www.ptc.com/en/resource-center or other sources, and select three IoT implemented cases. Write a summary of each.

Student selection of cases and their summaries will vary.

3. AT&T is active in smart city projects. Investigate their activities (solutions). Write a summary.

Student reports will vary based on the date of research.

4. It is said that the IoT will enable new customer service and B2B interactions. Explain how.

17 .

One of the major benefits of the IOT is the ability for businesses to gather and analyze large amounts of data relating to customer use of products. Analysis of this information will lead to insights into customer needs and preferences. This information can then be used to enrich customer service with assistance in using existing products better or additional products and services that could meet their needs more fully.

5. The IoT has a growing impact on business and e-commerce. Find evidence. Also read Jamthe (2016).

Student research and summaries will vary.

6. Find information about Sophia, a robot from Hanson Robotics. Summarize her capabilities.

Student perceptions and reports will vary based on the date of research. Current information is available at https://www.hansonrobotics.com/sophia/.

7. Examine the Ecobee thermostat and its integration with Alexa. What are the benefits of the integration? Write a report.

Student reports will vary based on the date of research and their perceptions. Current information is available at https://www.ecobee.com.

8. Enter smartcitiescouncil.com. Write a summary of the major concept found there; list the major enablers and the type of available resources.

Student research, reports and perceptions will vary.

9. Find the status of Bill Gates’s futuristic smart city. What are some of its specific plans?

Reports on this smart city will vary based on the date and types of research gathered.

10. City Brain is the name of Alibaba’s platform for smart cities. One project has been adopted in China and Malaysia. Find information and write a report.

Student research and the content of their reports will vary.

11. Find the status of delivering pizza by self-driving cars. Check Domino’s Pizza news.

Current status information will vary based on the date of student research.

12. India has many IoT applications, including projects for 100 smart cities. Read the 2016 status report atenterpriseinnovation.net/article/internet-thingsnext-big-wave-india1270947471/ and find more recent information about it. Why do you think IoT is so widespread in India? Write a report.

18 .

Student reports will vary.

13. Read the Blue Hill report (Park, 2017) and summarize all the issues related to IoT. Student analysis and reflections on this report will vary.

14. Find the status of smart cities as it is related to IoT and Cisco. Write a report.

Student reports and perceptions will vary based on the type of information researched and the date of that research.

15. Watch the video atyoutube.com/ watch?v=ZJr0X3XBMmA (14:36 min.). Write a summary about the five smart devices.

Student perceptions and reports on devices will vary.

16. Watch the video “Smart Manufacturing” (22 min.) at youtube.com/watch?v=SfVUkGoCA7s and summarize the lessons learned.

Student perceptions and reports will vary.

17. The competition for creating and using autonomous cars is intensifying globally. Find 12 companies that are competing in this field.

Reports will vary greatly based on the type of information researched and the date of that research.

18. Enter McKinsey Global Institute mckinsey.com/mgi/ overview and find recent studies on IoT. Prepare the summary.

Student selection of cases, and the case is available will vary based on the date of research.

19. AT&T is trying to connect autonomous vehicles to smart cities. Find information on the progress of this project. Identify the benefits and the difficulties.

Progress on this initiative will vary over time and student reports will reflect this.

19 .

Chapter 14: Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts Learning Objectives for Chapter 14 1.

2.

3.

4.

5.

6.

Describe the major implementation issues of intelligent technologies Discuss legal, privacy and ethical issues Understand the deployment issues of intelligent systems Describe the major impacts on organizations and society Discuss and debate the impacts on jobs and work Discuss the arguments of utopia and dystopia in a debate of the future of robots and artificial intelligence (AI) 7. Discuss the potential danger of mathematical models in analytics 8. Describe the major influencing technology trends 9. Describe the highlights of the future of intelligent systems CHAPTER OVERVIEW In this concluding chapter, we cover a variety of issues related to the implementation and future of intelligent systems. We begin our coverage with technological issues such as security and connectivity. Then, we move to managerial issues that cover legality, privacy, and ethics. We next explore the impacts on organizations, society, work and jobs. Then, we review technology trends that point to the future. Note. In this chapter we refer to all technologies covered in this book as intelligent technologies or intelligent systems. This chapter has the following sections: CHAPTER OUTLINE 14.1 Opening Vignette: Why Did Uber Pay $245 Million to Waymo?

14.2 Implementing Intelligent Systems: An Overview 14.3 Legal, Privacy, and Ethical Issues 14.4 Successful Deployment of Intelligent Systems 14.5 Impacts of Intelligent Systems on Organizations 740 14.6 Impacts on Jobs and Work 14.7 Potential Dangers of Robots, AI, and Analytical Models 14.8 Relevant Technology Trends 14.9 Future of Intelligent Systems

1 .

ANSWERS TO END OF SECTION REVIEW QUESTIONS

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Section 14.1 Review Questions 1. Identify the legal issues involved in this case.

The primary legal issue centers around the intellectual property of self driving cars created by Waymo’s and the distinction between that intellectual property and the industry know-how developed by former employees.

2. Why do you think Waymo agreed to take Uber’s shares instead of money?

It may have been difficult to value the intellectual property at issue, and offering stock provided a way to extract future value from that property while not curtailing current operations.

3. What is the meaning of intellectual property in this case?

Intellectual property in this case included specific files on knowledge methods and construction of self driving cars as well as insiders general understanding of how those cars were being developed and built.

4. The presiding federal judge said at the end: “This case is now ancient history.” What did he mean to say?

Student interpretations of this will vary, but may focus on the fact that by the time this case was settled significant progress had been made in the associated technologies.

5. Summarize the potential damages to the two parties if they had continued with the legal dispute.

Both parties ran the risk of losing the associated intellectual property in the lawsuit, as well as continuing to accrue significant legal costs.

6. Summarize the benefits of the settlement to both sides.

Both sides were able to maintain intellectual property rights and cut their legal costs.

Waymo was able to benefit in future possible economic benefits from the intellectual property (via stock) while not slowing Uber’s current pace of development.

Section 14.2 Review Questions 1. List the major steps in the implementation process.

• •

Step 1 Need assessment Step 2 Preparations 2 .

• • •

Step 3 System acquisition Step 4 System development Step 5 Impact assessment

2. Why is implementation an important subject?

While making the correct business decision is critical, so is the implementation of new programs, processes and services. If implementation is not handled well these new initiatives may operate sub-optimally or may fail altogether.

3. Describe the major impact areas of intelligent systems.

Intelligent systems are impacting all our lives and many businesses and other organizations. These changes are occurring in almost all aspects of businesses and organizations to help improve products and services, reduce costs and expand markets.

Section 14.3 Review Questions 1. List some legal issues of intelligent systems.

• • • • •

What is the value of an expert opinion in court when the expertise is encoded in a computer?

Who is liable for wrong advice (or information) provided by an intelligent application?

What happens if a manager enters an incorrect judgment value into an analytic application and the result is damage or a disaster?

Who owns the knowledge in a knowledge base?

Can management force experts to contribute their expertise?

2. Describe privacy concerns in intelligent systems.

In general, privacy is the right to be left alone and the right to be free from unreasonable personal intrusions. The Internet, in combination with large-scale databases, has created an entirely new dimension of accessing and using data. The inherent power in systems that can access vast amounts of data can be used for the good of society. For example, by matching records with the aid of a computer, it is possible to eliminate or reduce fraud, crime, government mismanagement, tax evasion, welfare cheating, family-support filching, employment of illegal aliens, and so on. The same is true on the corporate level.

Private information about employees may aid in better decision making, but the employees’ privacy may be affected. Similar issues are related to information about customers.

3. In your view, who should own the data about your use of a car? Why? Student opinions will vary, but arguments will revolve around individual privacy concerns versus the need to know of employers.

3 .

4. List ethical issues in intelligent systems.

Representative ethical issues that could be of interest in MSS implementations include the following: • • • • • • • • • • • • •

Electronic surveillance Ethics in DSS design Software piracy Invasion of individuals’ privacy Use of proprietary databases Use of intellectual property such as knowledge and expertise Exposure of employees to unsafe environments related to computers Computer accessibility for workers with disabilities Accuracy of data, information, and knowledge Protection of the rights of users Accessibility to information Use of corporate computers for non-work-related purposes How much decision making to delegate to computers

5. What are the 10 commandments of computer/information systems?

1. Thou shalt not use a computer to harm other people.

2. Thou shalt not interfere with other people’s computer work.

3. Thou shalt not snoop around in other people’s files.

4. Thou shalt not use a computer to steal.

5. Thou shalt not use a computer to bear false witness.

6. Thou shalt not use or copy software for which you have not paid.

7. Thou shalt not use other people’s computer resources without authorization.

8. Thou shalt not appropriate other people’s intellectual output.

9. Thou shalt not think about the social consequences of the program you write.

10. Thou shalt not use a computer in ways that show consideration and respect.

Section 14.4 Review Questions 1. Describe the systems deployment process.

KPMG’s approach includes the following four steps: • Establishing priority areas for technological innovation.

• Developing a strategy and a plan for the employees.

• Identify providers and partners for plans’ execution.

• Establishing a strategy and plans to realize benefits from the digital labor initiatives.

4 .

2. Discuss the role of top management in deploying intelligent systems.

It is critical for top management to understand the entirety of the intelligent system that they are implementing with specific focus on tactical and strategic opportunities, organizational redesign, and integration into organizational structure and the effect on jobs.

3. Why is connectivity such an important issue?

These systems by their nature require constant access to designated networks (or the Internet) to function. In many cases, the real time data involved also necessitates a highspeed connection to ensure that all data reaches the appropriate software quickly.

4. Describe system development issues.

Shchutskaya (2017) cites the following three major problems: 1. Development approach. Business analytic and AI systems require an approach different from that of other IT/computer systems. Specifically, it is necessary to identify and deal with different and frequently large data sources. It is necessary to cleanse and curate these data. Also, if learning is involved, one needs to use machine training. Thus, special methodologies are needed.

2. Learning from data. Many AI and business analytics involve learning. The quality of the input data determines the quality of the applications. Also, the learning mechanism is important. Therefore, data accuracy is critical. In learning, systems must be able to deal with changing environmental conditions. Data should be organized in databases, not in files.

3. No clear view is available of how insights are generated. AI, IoT, and business analytic systems generate insights, conclusions, and recommendations based on the analysis of the data collected. Given that data are frequently collected by sensors and there are different types of them, we may not have a clear view of the insights that are generated.

5. Discuss the importance of security and safety, and how to protect them.

Most of these systems are designed to be managed and updated in the cloud and thus are part of the Internet that exposes them to hackers. This potential must be clearly acknowledged during the design and implementation of systems so that appropriate consideration of the potential threats and methods to minimize or eliminate them are incorporated into design.

6. Describe some issues in intelligent systems adoption.

There are many possible issues related to the adoption of intelligent systems, but some include employee resistance, lack of sufficient resources, lack of planning and coordination, etc.

5 .

Section 14.5 Review Questions 1. List the impacts of intelligent systems on managerial tasks.

Less expertise (experience) is required for making many decisions. Faster decision making is possible because of the availability of information and the automation of some phases in the decision-making process. Less reliance on experts and analysts is required to provide support to top executives. Power is being redistributed among managers. (The more information and analysis capability they possess, the more power they have.)

Support for complex decisions allows decisions to be made faster and of better quality.

Information needed for high-level decision making is expedited or even self-generated.

Automation of routine decisions or phases in the decision-making process (e.g., for frontline decision making and using ADS) may eliminate some managers, especially middle level managers. Routine and mundane work can be done using an analytic system, freeing up managers and knowledge workers to do more challenging tasks.

2. Describe new organizational units that are created because of intelligent systems.

One change in organizational structure is the possibility of creating an analytics department, a BI department, or a knowledge management department in which analytics play a major role. This special unit can be combined with or replace a quantitative analysis unit, or it can be a completely new entity.

3. Identify examples of analytics and AI applications used to redesign workspace or team behavior.

When a company introduces a data warehouse and BI, the information flows and related business processes (e.g., order fulfillment) are likely to change because information flows change. For example, before IBM introduced e-procurement, it restructured all related business processes, including decision making, searching inventories, reordering, and shipping.

4. How is cognitive computing affecting industry structure and competition?

The use of cognitive computing is having a great impact on industries due to the ability to enable more tasks to be completed by humans and shifting some tasks to systems that may have been completed by humans in the past. This results in the ability to process significantly more information at a greatly increased pace which could allow for better organizational decision-making.

5. Describe the impacts of intelligent systems on competition.

The use of intelligent systems can allow organizations to effectively utilize significantly more data in the completion of decision making processes. This should result in better decisions that will have positive organizational impacts and aid in a firm’s overall level 6 .

of competitiveness in its industry. Organizations that do not adopt these technology run the risk of becoming less competitive. Him 6. Discuss the impact of intelligent systems on decision making Analytics can change the manner in which many decisions are made and can consequently change managers’ jobs. They can help managers gain more knowledge, experience, and expertise, and consequently enhances the quality and speed of their decision making. In particular, information gathering for decision making is completed much more quickly when analytics are in use. This affects both strategic planning and control decisions, changing the decision-making process and even decision-making styles.

Section 14.6 Review Questions 1. Summarize the arguments of why intelligent systems will take away many jobs.

The primary argument is that intelligent systems can automate the cognitive tasks required in many jobs and provide this at a lower cost as well as higher-speed/accuracy level than existing workers.

2. Discuss why job losses may not be catastrophic.

This argument centers on the bullies that intelligent systems will change the nature of work, causing the loss of many current jobs, and will create a number of new positions that support these systems or the improvements/changes that they usher in.

3. How safe is your job? Be specific.

Student responses will vary based on their position and their perception of the impact of intelligent systems.

4. How may intelligent systems change jobs?

It is projected that intelligent systems may eliminate many low skilled jobs by automating them, but will increase the importance of high skilled jobs that can only be performed by humans.

5. In what ways may work be changed?

Intelligent systems may change the dynamic of how humans and AI work together.

6. Discuss some measures to deal with the changes brought by intelligent systems.

Manyika (2017) made the following suggestions for policymakers: 7 .

1. Use learning and education to facilitate the change.

2. Involve the private sector in enhancing training and retraining.

3. Have governments provide incentives to the private sector so employees can invest in improved human capital.

4. Encourage private and public sectors to create appropriate digital infrastructure.

5. Innovative income and wage schemes need to be developed.

6. Carefully plan the transition to the new work. Deal properly with displaced employees.

7. Properly handle new technology-enabled technologies.

8. Focus on new job creation, particularly digital jobs.

9. Properly capture the productivity increase opportunities.

7. One of the areas of potential job loss is due to autonomous vehicles. Discuss the logic of this.

The belief is that autonomous vehicles will be able to provide the required services at a lower cost, faster total time and with a reduction of accidents. If this is the case, there is significant economic incentive to move to autonomous vehicles as quickly as possible.

Section 14.7 Review Questions 1. Summarize the major arguments of the Utopia camp.

AI will support humans and enable innovations. AI also will partner with humans.

2 him. Summarize the major arguments of the Dystopia camp.

AI will become more intelligent and powerful than humans and replace them.

3. What is the friendly AI? AI machines should be designed so that they will benefit humans rather than harm them 4. What is Open AI? Relate it to the dystopia vision.

Open AI is a nonprofit organization tasked with enacting the path to safe artificial general intelligence (AGI).

5. What are the potential risks in using modeling and analytics?

O’Neil (2016) argues that analytics may serve to increase inequality and threaten democracy through the use of models that are nontransparent with ill-defined objectives and no self-correcting mechanisms.

8 .

Section 14.8 Review Questions 1. Identify three of the Gartner 10 that are mostly related to analytics and data science.

Student perception of the most important factors may vary.

2. Intelligent Apps and Analytics.

5. Empowered Cloud 9. Augmented Analytics.

2. Identify three of the Gartner 10 that are most related to AI and machine learning.

Student perception of the most important factors may vary.

1. AI Foundation and Development.

2. Intelligent Apps and Analytics.

3. Intelligent and Autonomous Things.

3. Identify three of the Gartner 10 that are most related to IoT, sensors, and connectivity.

Student perception of the most important factors may vary.

2. Intelligent Apps and Analytics.

3. Intelligent and Autonomous Things.

5. Empowered Cloud

4. Identify three technologies related to analytics from the other predictions list and explore them in more detail. Write a report.

Student selections and reports will vary.

5. Identify three data science–related technologies from the long list and explore them in more detail. Write a report.

Student selections and reports will vary.

6. Identify three AI-related technologies from the long list and explore them in more detail. Write a report.

Student selections and reports will vary.

7. Describe ambient computing and its potential contribution to intelligent systems.

9 .

Ambient computing (or paradigm computing) refers to electronic environments (e.g., network devices such as sensors) that are sensitive and responsive to people and their environments and can support people in whatever task they are doing.

Section 14.9 Review Questions 1. Describe the AI activities of major U.S. tech companies.

Student responses will vary based on the date of response and amount of research performed. Examples of current AI activities for US tech companies (such as Google, Apple, Facebook, Microsoft and IBM) are seen on pages 760 through 761.

2. Describe the work by Chinese giant companies.

Student responses will vary based on the date of response and amount of research performed. Examples of current AI activities for Chinese tech companies (such as Tencent, Baidu and Alibab) are seen on pages 761 through 762.

3. Describe Alibaba’s approach to AI (The ET Brain model).

The approach consists of three parts: technologies, capabilities and applications.

Technologies include systems such as big data processing, neural networks, and advanced data processing. These technologies support brain capabilities. Brain capabilities include sections such as reasoning, machine learning, cognitive perception and strategic decision-making. These capabilities can then benefit applications and innovations in a number of areas including smart cities, travel, fashion, medical, environment etc.

ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION

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Application Case 14.1: How 1-800-Flowers.com Uses Intelligent Systems for Competitive Advantage 1. Why it is necessary to provide better customer experience today?

The company is in a highly competitive market, and customer satisfaction drives repeat business which has a major impact on financial success.

2. Why do data need sophisticated analytical tools?

Sophisticated tools allow for better personalization for customers. By better understanding customer needs and being able to suggest more appropriate products, the 10 .

company is able to maintain/increase customer satisfaction.

3. Read the “Key benefit of SAS Marketing Automation.” Which benefits do you think are used by 1-800-Flowers.com and why?

Student perceptions of the report will vary and so will their selection of the benefits that could aid this particular company.

Application Case 14.2: White-Collar Jobs That Robots Have Already Taken 1. Watch the 4:22 min. video about an interview with Palmer, at linkedin.com/pulse/5jobsrobots-take-first-shelly-palmer/. Discuss some of the assertions made regarding doctors.

Student perceptions about the assertions will vary.

2. Discuss the possibility of your checkup by a robot-diagnostician. How would you feel?

Student perception and opinions will vary.

3. With the bombardment of fake news and their biased creators, it may be wise to replace all of them by intelligent machines. Discuss such a possibility.

Student perceptions of this option will vary, but may focus around the ability to effectively lead out fake news versus alternative viewpoints.

4. You are a defendant in a crime you did not commit. Would you prefer a traditional lawyer or one equipped with an AI e-discovery machine? Why? Student perception and opinions will vary.

Application Case 14.3: How Alibaba.com Is Conducting AI 1. Relate cloud computing to AI at Alibaba.

Alibaba believes that all AI in the future will be conducted in a cloud environment, and thus the AI system that it is developing must be based in the cloud.

2. Explain the logic of the ET Brain model.

The approach consists of three parts: technologies, capabilities and applications.

Technologies include systems such as big data processing, neural networks, and advanced data processing. These technologies support brain capabilities. Brain capabilities include sections such as reasoning, machine learning, cognitive perception 11 .

and strategic decision-making. These capabilities can then benefit applications and innovations in a number of areas including smart cities, travel, fashion, medical, environment etc.

3. Search the Web to find recent Alibaba activities in the AI field.

Results will vary based on the date of search.

4. Read Lashinsky (2018). Why is Alibaba in such strong competition with Tencent?

Student perceptions, analysis and reports on this paper will vary.

ANSWERS TO END OF CHAPTER QUESTIONS FOR DISCUSSION

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1. Some say that analytics in general dehumanize managerial activities, and others say they do not. Discuss arguments for both points of view.

Students’ answers will differ. A common criticism of traditional data-processing systems is their negative effects on people’s individuality. Such systems are criticized as being impersonal: They may dehumanize and depersonalize activities that have been computerized because they reduce or eliminate the human element that was present in noncomputerized systems. Some people feel a loss of identity; they feel like just another number. On the bright side, one of the major objectives of analytics is to create flexible systems and interfaces that allow individuals to share their opinions and knowledge and work together with computers. Despite all these efforts, some people are still afraid of computers, so they are stressed; others are mostly afraid of their employers watching what they do on the computer.

2. Diagnosing infections and prescribing pharmaceuticals are the weak points of many practicing physicians. It seems, therefore, that society would be better served if analyticsbased diagnostic systems were used by more physicians. Answer the following questions: a. Why do you think such systems are used minimally by physicians?

Students’ answers will differ. Some reasons are: physicians do not understand and therefore do not trust the ES; malpractice insurance does not cover recommendations made by MYCIN; administrators will not invest in it; physicians fear they will be replaced or earn less. b. Assume that you are a hospital administrator whose physicians are salaried and report to you. What would you do to persuade them to use an intelligent system?

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Students’ answers will differ. Students should identify a combination of positive (more money) and negative (calm their fears) motivators c. If the potential benefits to society are so great, can society do something that will increase doctors’ use of such intelligent systems?

Probably not--or at least not yet.

3. What are some of the major privacy concerns in employing intelligent systems on mobile data?

Loss of privacy is a key concern in employing analytics on mobile data. If someone can track the movement of a cell phone, the privacy of that customer is a big issue. Some of the app developers claim that they only need to gather aggregate flow information, not individually identifiable information. But many stories appear in the media that highlight violations of this general principle. Sometimes, retailers provide information on their customers to the federal government, in violation of their stated privacy policies.

Legally, the right of privacy is not absolute. The public’s right to know is superior to the individual’s right to privacy. For example, the USA PATRIOT Act broadens the government’s ability to access student information and personal financial information without any suspicion of wrongdoing. Location information from devices has been used to locate victims and criminals, so provides a social good. But at what point is the information not the property of the individual?

4. Identify some cases of violations of user privacy from current literature and their impact on data science as a profession.

Student selection of cases of privacy violation will vary based on their searches and date of those searches.

5. Some fear that robots and AI will kill all of us. Others disagree. Debate the issue.

Student opinions in this debate will vary greatly!

6. Some claim that AI is overhyped. Debate the issue. Place a question on Quora and analyze five responses.

Student opinions on this question and the responses received on Quora will vary.

7. Some claim that AI may become a human rights issue (search for Safiya Noble).

Discuss and debate.

Student research and reports will vary 8. Discuss the potential impact of the GDPR on privacy, security, and discrimination.

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Student research and reports will vary.

9. Discuss ethics and fairness in machine learning. Start by reading Pakzad (2018).

Student analysis and discussion of this paper will vary based on their viewpoints and personal opinions.

10. Should robots be taxed like workers? Read Morris (2017) and write about the pros and cons of the issue.

Student analysis and reports on this topic will vary.

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ANSWERS TO END OF CHAPTER EXERCISES

1. Identify ethical issues related to managerial decision making. Search the Internet, join discussion groups/ blogs, and read articles from the Internet. Prepare a report on your findings.

Ethical issues related to management decision-making and MIS in general are seen in sources such as the “Ten Commandants” of computer ethics. Other items may include: • Electronic surveillance.

• Ethics in business intelligence (BI) and AI systems design.

• Software piracy.

• Invasion of individuals’ privacy.

• Use of proprietary databases and knowledge bases.

• Use of personal intellectual property such as knowledge and expertise for the benefits of companies and the payment to the contributors.

• Accuracy of data, information, and knowledge.

• Protection of the rights of users.

• Accessibility to information by AI users.

• The amount of decision making to delegate to intelligent machines.

Student selection of specific ethical issues and reports will vary 2. Search the Internet to find examples of how intelligent systems can facilitate activities such as empowerment, mass customization, and teamwork.

Student search results and reports will vary 3. Investigate the American Bar Association’s Technology Resource Center (americanbar.org/groups/departments_offices/legal_technology_resources.html) and 14 .

nolo.com. What are the major legal and societal concerns regarding intelligent systems?

How are they being dealt with?

Student research and selection of issues will vary, this will greatly affect the content of their reports.

4. Explore several sites related to healthcare (e.g., WebMD. com, who.int). Find issues related to AI and privacy. Write a report on how these sites suggest improving privacy.

Student research and selection of issues will vary, this will greatly affect the content of their reports.

5. Go to Humanyze.com. Review various case studies and summarize one interesting application of sensors in understanding social exchanges in organizations.

Student selection of the case study will vary and will affect their reports.

6. Research the issue of voice assistants and privacy protection. Start by reading Collins (2017) and Huff (2017).

Student reports will vary.

7. Is granting advanced robots rights a good or bad idea? Read Kottasova (2018) for a start.

Student perceptions and opinions will vary.

8. Face and voice recognition applications are mushrooming. Research the state of their regulation in a country of your choice. Use the United States if your country is not regulating.

Student research will vary greatly based on the date of the research.

9. Research the ethical issues of self-driving cars. Start by reading Himmelreich (2018).

Student perceptions and opinions on this issue will vary.

10. Is your organization ready for AI? Research this issue and find all major activities that it includes.

Student responses will vary based on the type and industry of the organization that they affiliate with.

11. Research the role of IoT as a tool for providing connectivity between sensors and analytics. Write a report.

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Student reports will vary.

12. Some people say that robots and chatbots may increase insurance risk and fees.

Research this and write a report.

Student research and reports will vary.

13. Watch the video at youtube.com/watch?v=wwuovuCfDU/ and comment about the robot’s potential impacts.

Student perception of the video and their reflections on it will vary.

14. Research the issue stated in quotation marks: “When will robots rebel?” and “Will AI take control of the plant?” Start by reading Maguire (2017) and read advancedmp. com/artificial-intelligence/. Write a report.

Student opinion and the resulting reports on this topic will vary greatly!

15. Read Chui et al. (2016) and research the areas in which machines can replace humans and where they cannot (yet). Find changes since 2016. Write a report.

Student reports will vary based on the date of research and their selection of areas of possible replacement.

16. Watch the 3:38 min. video at youtube.com/watch?v=781Mlkxyql/. Relate it to Musk’s predictions about robots reigning in this world (Section 14.7).

Student perceptions and responses will vary.

17. Read the SAS report on AI ethics at sas.com/en_us/ insights/articles/analytics/artificial-intelligenceethics.html. Comment on each of the three proposed steps. Also comment on the human-machine collaboration in problem solving.

Student reports will vary.

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