A-gram Issue 3

A-GRAM NOVEMBER 2021

Bi- Annual Magazine

Issue 3

ANALYTICS BEYOND DATA People, Process and Culture Bias in Analytics Data Science and analytics in post covid era Smarter, faster, more responsible AI

The Analytics and Business Computing Club

Foreword One of the key trends identified by Gartner for 2021 is the emergence of data and analytics as a core business function. Globally, business leaders are realizing the potential of tapping into data and analytics to accelerate growth, to aid them in better decision-making.

COVID-19 has brought in a new realization for the organizations that using traditional analytical techniques that rely heavily on huge volume of historical data to build models have become irrelevant. Business leaders who underestimate these complexities end up missing opportunities. However, with proper intervention in setting goals and strategies, data and analytics can be leveraged to consistently produce better business value.

As the sun shines brightly on the world of data and analytics, we at IIM Amritsar recognize the vitality of business analytical education, as we commenced the inaugural batch of MBA in business analytics. This year, we also conducted the inaugural IT and Analytics Conclave – Vyakriti, where prominent industry leaders came together and enlightened our students on various aspects of digital transformation that businesses are going through. All these new developments hold the future of analytics education at IIM Amritsar in good stead.

ABC- The Analytics and Business Computing Club of IIM Amritsar, with its enthusiastic coordinators and the systematic guidance of Prof. Sunil Reddy Kunduru, has been doing a great job of exposing our budding managers to the domain of analytics through a plethora of initiatives. It is with great pleasure that I present to you all “AGram”, the biannual magazine by ABC. The vision of this magazine is to stay atop the various trends and evolutions powered by analytics across the various domains of management.

I want to take this opportunity to thank all those involved for their valuable contribution in the conception of the third edition of A-Gram. I am optimistic that initiatives like these will go a long way in offering a unique perspective and delivering deep insights about the growing field of analytics and management, which will help managers lead in the new age of business.

Prof. R. Nagarajan Director - IIM Amritsar

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Table of Contents 01

Analytics Beyond Data: People, Process and Culture

PAGE 04

02

Three Pillars of Analytics and their Need in Post-Covid Era

PAGE 06

03

Bias in Analytics

PAGE 08

04

Smarter, Faster, More Responsible AI

PAGE 10

05

How AI will help make better Marketing Decisions

PAGE 14

06

Application of Tableau in Business Analytics

PAGE 18

07

A Brief Overview of AI and NLP in Data Science

PAGE 19

08

Club Activities

PAGE 22

09

Our Team

PAGE 23

10

Crossword

PAGE 24

11

Quiz

PAGE 25

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Faculty Article

Analytics Beyond Data: People, Process and Culture

Dr. Sunil Reddy Kunduru Assistant Professor IIM Amritsar

Let's get one thing very clear first. Data analytics cannot be done without data. Analytics is fundamentally data-driven.

In a sense, data is necessary to do analytics. This article is a commentary on the sufficiency of data in analytics. Of course, I would look at data in a broad sense to include the methods of analysis as well. I would also look at the question of sufficiency in a relatively loose way.

An obvious question first. If data is necessary but not sufficient for data analytics then what are the sufficient ingredients of data analytics? One of the obvious ingredients is the methods of data analysis. Data is only as good as the availability of appropriate methods that can help gain insights from the data.There are two types of methods that help gain insights from the data: computational and statistical methods. Computational methods that help you store, manage and retrieve data efficiently and reliably. On the other hand, you have methods of data analysis that includes statistical, mathematical and other heuristic methods.

Data and methods taken together is widely considered as the full landscape of analytics today. Undeniably, they constitute the bulk of what is needed for data analytics. As a result, the emphasis of the industry has been on collecting as much data as possible and building capabilities, in terms of both human and physical resources, to extract insights from the data. Once the focus is firmly on data and the methods of analysis of data, it is probable that you miss the big picture. Analytics becomes an end in itself. You hire the best talent that can work with data and draw insights from it. While all this might seem fine to many, the argument in this article is that we might have swung too far in this direction because we ignored the smaller, yet vital parts of what constitutes the analytics space. This is what we call analytics beyond data.

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Now that we have established that we are looking at analytics beyond the actual data analysis, lets break it into familiar levels of understanding. We will look at analytics from the perspective of people, process and culture. This is not a new way of looking at things in any measure. In fact, it is just an application of knowledge gained from research on organizational adoption of information technology over the last three decades. Adoption of IT does not end at procuring and deploying IT. It needs transformation of people, processes and culture of the organization. I will revisit these time-tested principles and the possible adaptions required when applying these principles to adoption of data analytics.

We need to train people to create data, work with data and take decisions based on data. Here, when I say people, I don't mean just the data analysts and data scientists. Data can be captured from every activity taking place in an organization. Of course, not all data have equal value. But value in data is not inherent, it has to be derived through working with data. Everyone in the organization must be trained to capture data, organize it and use it to gain insights into their own work. This is not as easy as it sounds. It can even backfire if one is not careful to recognize the fact that not every aspect of a person's work is codifiable.

There is an element of tacitness to every activity performed in an organization. While training people to capture, analyze and use data in decision-making, it is vital to remember that a significant part of the decision-making comes from the tacit knowledge gained through years of experience. Further, if we force people to take decisions only based on data, it is possible that they would not develop the soft skills that we learn from experience. In the long run, all the decisions will be mechanical and the

decisions would lose human touch. Anyone familiar with history of the game of chess knows what we lost once we have developed chess engines to tell us what the best move is in any given position. It became mechanical devoid of the personality and style that grandmasters of the previous era had.

It is important that we start thinking about how do we train everyone in the organization to capture data, work with data and use data in decision making. This does not mean everyone needs the same training that data analysts or data scientists have. However, they should be able adapt their work to further the cause of data-driven decision making at every level. They should be provided with the tools and training to do the same.

Processes are codifiable parts of work. Processes originated because some parts of work can be clearly defined, their components identified and their flow expressed in simple language and visual tools. As the organizations adopt data analytics, they have to start thinking about process re-engineering to capture greater value from the data. Business process re-engineering was very much a vital part of adoption of IT in organizations.

Organizations have just settled down after changing their processes to capture value from IT. It is pertinent that they re-engineer the processes to capture value from the data as well. It is quite possible that process re-engineering could be an ongoing activity that aims at capturing most value from any new organization-wide technology.

Process re-engineering can help capture data at the source in the best way possible. When I say the best way possible, I am referring to legal, ethical and practical issues related to gathering and using data. One the one hand you have intellectual property of the organization itself. On the other hand, you have the privacy of the employees and the customers. Finally, the resources that you can use to capture and gather data are limited.

Process reengineering can help capture and store data more efficiently while ensuring security of the data from theft and unauthorized use that may jeopardize the intellectual property of the organization or the privacy of its stakeholders. We need to think about the nature of process re-engineering when dealing with data analytics.

Culture is the most intangible, non-codifiable part of the organization. The culture is developed over a very long

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period of time and all the stakeholders of the organization shape its culture. Implementation of any organizationwide changes can come in conflict with the organizational culture. Culture is a product of the past and it preserves the past. As a result, when an organization adopts data analytics, trains its people and re-engineers its processes, it is possible and very probable that there would be resistance to these changes. The organizational culture simply does not allow these changes to happen.

Organizations and their leadership have to deal with this conflict.

Culture can only be changed gradually. However, it does not mean that we cannot take pro-active steps to change the culture. Organizations have been adapting their culture to suit the changing pace of their respective industries and the tastes of their customers. With data analytics, the primary change would come in terms of data-decision making at every level. As decision-making tools augment the individuals at different levels, it changes the way people interact and socialize in their work environment. For example, people from different divisions or functions interact to gather useful information. If data gathering becomes a norm and the data is readily available across divisions and functions, the need for interaction across divisions and functions reduces. This can lead to people receding into silos. On the other hand, if the culture persists and the information shared through interactions is different from the data coming through formal channels, it can lead to failure of adoption of the data analytics tools.

We need to understand what effect data analytics can have on the organizational culture and adapt accordingly.

In this article, I put forth a few ways in which data analytics is problem that goes beyond capturing data and using different methods to draw insights from it. The world of analytics is currently focusing on solving the problems with data and methods. However, now that we reaching a certain level of comfort in that area, it is pertinent to look beyond these immediate problems and learn from the past about the likely problems we could face in the future. I argue here that once we excel at data and methods, we would have to tackle problems with people, processes and culture. It is important to start thinking about these problems. History shows that excelling at these can give a firm competitive advantage as much as excelling at the core technology does.

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Students’ Corner

Three Pillars of Analytics and their Need in Post-Covid Era

Ms. Pragya Ram Nanwani Student - MBA06 IIM Amritsar

“The last ten years of IT have been about changing the way people work. The next ten years of IT will be about transforming your business.”?Aaron Levie, CEO of Box.

and performant analytics models, reliable and fit-forpurpose infrastructure is required.

Businesses have been transforming themselves for a long time, but, Coronavirus pandemic has accelerated the adoption of digital transformation like nothing before.To thrive in our new virtual business world, companies have been forced to ramp up their digital strategy quickly. Those that were unable to pivot and reset their business plan on time did not survive.

According to Gartner's 2019 CIO Agenda survey, firms that used AI increased from 4% to 14% between 2018 and 2019. Companies must look beyond the traditional use of CPU power to support their AI and Machine Learning applications using Graphics Processing Units as AI continues to grow. This will motivate them to design, train, and retrain their analytical models and run them more rapidly, resulting in better customer products and services.

In a post-covid era, businesses that progress digitally will engage more directly with customers, accelerate innovation, and hence demand a larger share of profit in their industries. Every business has the potential to become a digital one. Today's digitally changed businesses have an advantage, and only digital businesses will prosper in the future.

The goal always remained to create a data-driven organization. The times, however, have changed. Data must now guide businesses to survive in the unpredictable post-COVID-19 environment. The ability to quickly adapt and alter the business plan with personnel, goods, and processes based on actions, whether on the global market or within the company, is more apparent today. This means that advanced data analytics is essential for running realtime business processes.

Beyond data-driven, the goal now is to use advanced data analytics to drive real-time business planning. Data and business analysts must focus on strengthening the three pillars of data analytics: speed, performance, and agility, to alleviate dissatisfaction and provide a better analytics solution and experience for the organization. The foundation for enabling these pillars is the entire data architecture backed by a sound data strategy. To handle increasing data volumes and accommodate more advanced A-GRAM | 06

Speed

GPUs enable businesses to massively parallelize activities to assist the training of analytical solutions and/or inferencing, allowing them to finish numerous epochs in a quicker timescale and fine-tune the model. Furthermore, deploying GPUs in the cloud enables businesses to execute a variety of AI/ML workloads with the flexibility required for a cost-effective, scalable, and secure AI solution.

Whether a healthcare provider provides more accurate, faster diagnoses or a retailer provides more tailored customer experiences;more businesses are using AI. According to a 2018 Forrester survey of data and analytics decision-makers, 53% of businesses employ AI.

Performance Semi-structured and unstructured data are significantly more difficult to evaluate than structured data, yet they are far more common in businesses. The proliferation of IoT and social media content, according to IDC, will result in 80 percent of global data being unstructured by 2025.

Organizations need to bring a wide range of data together in a 360° view that gives deeper, more accurate, and precise analytics insights to stay competitive.

It's not always easy to tell what data is structured and what

is semi-structured or unstructured. Unstructured data is sometimes defined and processed in a structured manner (for example, log data in CSV format), whereas schemadefined data isn't always structured. These inconsistencies throw a monkey wrench into data analysis.

Data scientists and analysts with extensive experience in building the best models and methodologies for working with unstructured data can use AI algorithms to help discern value from large amounts of unstructured data.Enterprises must develop a long-term Data Architecture that allows them to move beyond data-driven analytics to drive business intelligence through advanced analytics.

Agility A data model should bring all important data and related tables from many data sources together so that analysts can query them all at once and in connection to one another.

Analysts' information and the value of their insights are limited if they do not do so.

To account for all of the data that analysts will need, data

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engineers, data analysts, and business stakeholders must interact with one another to establish business requirements, intended data uses, and prospective (current) data access limits. Continuous communication necessitates having open and honest dialogues, asking the correct questions, and agreeing on the business needs and timetables that everyone is working toward.

The evaluation of the business's operating environment should be at the center of the Data Strategy. Data Vault is one such example. Data vault helps enterprises efficiently grow their data quantities and respond to rapid business changes while maintaining a thorough data catalog and flexible data architecture. Because a history of the data is available, this is extremely valuable for compliance and auditing requirements.

Firm executives receive insights into where change may be welcome, where it is needed, and where it is crucial if the business is to survive as the pace of transformation and volumes of data continue to climb by examining how an organization is genuinely meeting the three pillars of data analytics.

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Students’ Corner

Bias in Analytics

Mr. Himadri Jana Student - MBA 07 IIM Amritsar

We want to believe that our decisions are founded on facts rather than a gut or a guess. However, this is not always the case, and our prejudices might have an impact on our thinking. Allowing for some predispositions can have a negative impact on results, even in the most data-driven companies. For everybody who works with data, eliminating the bias should be a key priority. Bias is a natural propensity that we all have, but it must be suppressed to the greatest extent feasible in order to make better decisions. Bias in data analytics can be avoided by asking the correct questions, which allow respondents to react without being influenced by outside factors, and by continuously improving algorithms. Mentioning below some biases which exist in data analytics: -

1)

3)

4)

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Aggregation Bias When a 'one size fits all' model is used to a population that is more diverse than the model allows for, this occurs. As a result, a single model variable may have multiple meanings for different groups within a population. It's crucial to strike a balance between your model's simplicity and performance.

5)

Evaluation Bias When the testing or benchmark data does not reflect the target population for which the model will be employed, this occurs. This suggests that your model works well for certain people in that population and not so well for others.

6)

Deployment Bias This occurs when there is a misalignment between the aim that the model was created to solve and how it is ultimately used. This could be in relation to the aim itself or the target population.

Representation Bias This happens when the data used to train your model and it does not accurately reflect the population for which it is be employed. This could be due to incorrect sampling procedures in your training dataset or an out-of-date training dataset. For e.g. In both underwriting and claims analytics, there may be a lack of representationof people with impairments in

Measurement Bias This happens when you're choosing the features and labels to include in the model. Bias occurs when the measurement technique is not uniform among groups within your target population. If certain groups generate more data to be measured than others, the same thing happens. Furthermore, your classification strategy may cause issues.

Confirmation Bias When researchers utilise the responses of respondents to corroborate their hypothesis, this is known as confirmation bias. Researchers will sometimes accept such evidence if it supports their own research and reject it if it contradicts it.

Confirmation bias has the disadvantage of supporting only one point of view and outright rejecting others, therefore reducing the company's focus. Researchers must be willing to study and reconsider respondents' responses, as well as get away with preconceived conceptions and beliefs, in order to reduce confirmation bias.

2)

the training data.

7)

Interpretation Bias Elizabeth Loftus of the University of California conducted a research in which she showed participants a DVD of car accidents. She then divided them into two groups and asked them to sit in different rooms. "What do you believe the speed of

the cars were when they hit?" she questioned the first group. "What do you believe the car's speed was when they smashed?" she questioned the second group. To heramazement, the second group's speed was faster than the first. When the identical question was asked differently, the two groups reacted differently.Similarly, if you have a blog where all of the material is contained on a single page, your bounce rate will be zero, which is acceptable, but may be mistakenly connected to bad blog performance. To avoid interpretation bias, one must examine the facts from all angles before coming to a judgement.

8)

Prediction Bias In early 2017, Chicago police began using 'predictive policing' to identify neighbourhoods' with a higher likelihood of experiencing violent crimes. The police were so reliant on data that they even sent warnings to persons who were more likely to commit a crime. In addition, a high number of police officers were allocated to areas where crime was more likely. There was a risk that such software would incorrectly categorize people based on their skin colour, race, gender, income, or the level of crime in their neighbourhood. To reduce such biases, the data must constantly be analysed by a human who is capable of making subtle distinctions that machines are not yet capable of.

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9) Information Bias In 2008, Google devised a novel challenge for early detection of flu in order to reduce its spread. The "Flu Trends" programme gathered search phrases used by people in a certain location. If there are more searches for flu symptoms, impacts, or remedies, the local health authorities will be alerted to take action.

However, a rise in keyword searches is not a reliable indicator of such things because an increase in searches for a sickness or flu does not always imply that persons in that region are infected. It could simply mean that people in one location are more willing to share information than people in another.

Bias in data analytics is still a serious issue, and advanced algorithms should be developed to help decrease it. Furthermore, data scientists should avoid being biased while gathering and analysing replies that are favourable to their research. They should be open to a variety of points of view in order to make better decisions.

None of these bias sources in analytics are groundbreaking. Insurers who have made data ethics a central part of their digital strategies will be familiar with them, as well as the issues that they present. This applies to the creation of their own models as well as the utilisation of models from commercial and in-sector partners.

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Students’ Corner

Smarter, Faster, More Responsible AI

Mr. Abhishekh Pahadi Student SIBM, Bengaluru

articles which human evaluators have difficulty distinguishing from articles written by humans. We'll see why GPT-3 in spite of being such a phenomenal invention is still in experimental phase and an early glimpse in changing the world.

Introduction Preamble Recent work in the field of NLP (Natural Language Processing) has shown dramatic breakthrough thanks to GPT-3; which stands for Generative Pre-trained Transformer, this is the 3rd version and both versions before this worked on same model by pre-training on a large corpus of text followed by fine-tuning on a specific task.

While typically task-agnostic in architecture, this method still requires task-specific datasets of tens of thousands of examples. This technology is developed by OpenAI which is US based for profit organization working in field of AI. GPT-3 is an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model. It was pre-trained with almost 570 billion GBs of text. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, questionanswering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, there are few datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web network. Finally, GPT-3 can also generate samples of news

Recent years in AI have shown a trend towards pre-trained language representations in NLP systems, mainly because they are agile, flexible and applied in increasingly taskagnostic ways. Earlier, single-layered representations were learnt using word vectors and fed to task-specific architectures. After that RNN (Recurrent Neural Networks) were used with multiple layers and contextual state representation to form stronger models. Now, pretrained recurrent or transformer language models have been fine-tuned omitting the need for task-specific architectures. However, major limitations to this approach are that while architecture might be task-agnostic, datasets still need to be task-specific to achieve stronger performance on desired tasks. Removing these limitations would be desirable for numerous reasons stated later in this article. Here is why models that are pre-trained with

Fig. 1 : Number of Examples in Context (K)

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in-context task-specific datasets show dramatic accuracy in unsupervised learning.

Figure 1 : Larger models make increasingly efficient use of in-context information. We show in-context learning performance on a simple task requiring the model to remove random symbols from a word, both with and without a natural language task description. The steeper “in-context learning curves” for large models demonstrate improved ability to learn a task from contextual information. We see qualitatively similar behavior across a wide range of tasks.

Figure 2 & 3 – the model was trained to write JSX code for the commands given, here it was asked to create a button for every color of rainbow and write an integral function of a to b with respect to time.

Features of GPT-3 GPT-3 is capable of doing or creating anything that has a language structure, more specifically it can write essays, summarize long paragraphs, translate languages, take memos and even writecomputer code. Here are few ways in which people have used GPT-3:

Fig. 4 Source : https://www.skynettoday.com/briefs/gpt3

Figure 4 – the model was trained grounded language understanding equations usually used in robotics, here it gives affordances as of things that can be done with cookie dough.

Approach The pre-training approach for GPT-3 included model, data, and training, similar to the process described in GPT2, with relatively straightforward scaling up of the model & dataset size, diversityand length of training. The systematic approach taken had 4 main factors as specified belowa.

Fig. 2

b. Few-shot : In this work the model is given a few demonstrations of the task at inference time but no weight updates are allowed. The main advantage of this technique is a reduced potential to learn an overly narrow distribution from a large but narrow fine-tuned dataset. The main disadvantage is that results from this method have so far been much worse than state-of-the-art fine-tuned models. Also, a small amount of task specific data is still required. c.

Fig. 3 Source : https://www.skynettoday.com/briefs/gpt3

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Fine-Tuning : The most common approach in recent years, it involves updating the weights of pre-trained model by training on supervised dataset to perform a specific task. This isn't used for GPT-3 and is a traditional or conventional training method.

One-shot : It is the same as few-shot except that only one demonstration is allowed. It accordingly affects the accuracy of the model. The advantage

is it saves the time of fine-tuning models with thousands of datasets.

given to the model. It predicts the output given only the natural language description of the task.

d. Zero-shot : This is where no demonstrations are

Fig. 5 : The figure above shows four methods for performing a task with language model Source : https://www.skynettoday.com/briefs/gpt3

Limitations Here is where the actual problem comes in because even though GPT-3 is capable of such impressive results, it does not “understand” which means it isn't sentient.

Simply put it lacks common sense. The major drawbacks found were -

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Lack of semantic understanding – It is devoid of pure common sense and if not trained with contextual datasets may give racist or sexist or xenophobic outputs.

Figure 6 – It shows that the right side output by GPT3 is racist, and needs proper training.

Source : https://analyticsindiamag.com/gpt-3-is-great-but-not-without-shortcomings/

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Students’ Corner

How AI will help make better Marketing Decisions

Mr. Abhimanyu Mukherjee Student KIIT School of Management

Introduction Today's business era is surrounded with technologies and digitalization. Artificial Intelligence is the most common advanced technologies that has been adopted by most of the sectors such as marketing, technology, healthcare, financial and others. This article will highlight the aspects of Artificial Intelligence in the context of marketing decision making process.

It has been ascertained that Artificial Intelligence is highly beneficial in the field of marketing for making effective decision that helps the organization to sail in the competitive market. Further, it has been discovered that AI not only helps in making marketing strategies or decision it also helps the organization to achieve marketing goals and objectives that helps in organizational profitability and growth (Stone et.al. 2020).

Figure : Global AI in Marketing Market, by region 2020-2027

Source: (Maximize Market Research,2021)

Artificial Intelligence Artificial Intelligence can be termed as machine intelligence capability to reproduce the cognitive outcomes of a human being or can be defined as a reproduction of human intelligence procedure by machines mainly computer systems. It has been identified that AI has been adopted by majority of the market segment in order to take the technological advancement benefits for better business profitability and growth. There are different of AI applications elements that comprises of speech recognition, expert systems, machine vision and natural language processing. It has been identified that the worldwide Artificial Intelligence market size is anticipated to reach USD 390.9 billion by 2025, in fact it is expected that market will be expand around CAGR of 46.2% from 2019 to 2025 (Scarlett Rose, 2020). Artificial Intelligence is known to be the most crucial technological advancement in the digital era that most of the big giant organization such as Apple, Facebook, Google, Amazon etc. has adopted AI in their business operation for better utilization of resources to gain profitability.

Below graph clearly depicts the market size of the AI application.

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Figure : AI market size 2017-2028 (USD Billion) Source: (Grand View Research, 2021)

Artificial Intelligence Marketing AI marketing basically is process of supporting intelligence technologies in order to gather crucial data, consumer perceptions, forecast consumer future moves and produce automated decisions that can directly or in directly affects marketing endeavor. In context to marketing, Additionally, it has been identified that AI marketing utilizes artificial intelligence advanced technologies in order to make robotic decisions mainly based data analysis, data collection, and other additional observations of consumer and targeted audience or accordance to the economic trends. Further it has been analysed that Artificial Intelligence nowadays adopted by most of the marketing organization with the motive of using marketing attempt wherever speed is required. AI tools specifically use data information and consumer profiles to understand the best communication technique for interacting with customers, then provide them automated tailored messages at the correct time without any interference from marketing group members for guarantee maximum efficiency. In today's competitive business world many marketers have implemented AI in their day-to-day business operations and decision-making process in order to perform further tactical tasks (Stupar et.al. 2021).

Fastest Growing Marketing Technologies

Figure : AI for growing marketing technologies Source: (Business Insider, 2018)

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Importance of AI in Marketing It has been observed that there is huge impact of artificial intelligence in the world of digital marketing. It is mainly because technologies have changed entire business practices as well as perceptions of the consumers. In fact, approximately 76% of consumer anticipate that organization should understand their preferences, expectations, needs and demand. In such cases Artificial Intelligence marketing authorize marketers to consumer or gather a vast amount of marketing data analytics by using online platforms like social media, web pages, emails etc. Few of them are mentioned below: Automation: mainly engages in making marketing automation system much intelligent that works effectively with marketing operations which can be lately utilized for taking crucial decision in regards to consumer interaction as well as for better outcomes.

Minimizing Errors: Artificial Intelligence in marketing helps organizations in reducing human errors which is very important in terms of data security. It has been seen many times that due to human errors crucial consumer data get lost.

Cost Saving: It has been identified that with the adequate use of AI in marketing operation wastage of resources can be controlled that save both cost and money which is the biggest benefit of AI in marketing as marketing projects includes huge cost (Fingent, 2019).

Use of AI in Marketing There is multiple use of AI in marketing sector that directly or indirectly help the organization in making crucial decision that leads to success and growth. Few of the major use of AI in marketing operations comprises of sales forecasting which is very important to meet consumer needs and demands, to understand consumer preferences: majorly use AI for ascertaining the informative data which is highly important for an organization to execute business and marketing strategy for business growth and profit. Additionally, some of the other uses of AI in marketing operations includes content generation, enhance advertising values, implement chatbots, speech recognition etc. all aims towards achieving organizational marketing targets in a beneficial manner (Marketing Evolution, 2021). Attach below is the image that clearly defines the multiple uses of AI system in different types of marketing activities

Figure : Usage of AI in Marketing Activities Source: (Digital Marketing Community,2019)

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AI in Marketing Decision making The most important aspect of AI that has been the biggest advantage of adopting AI in marketing sectors is that the automated process helps in ascertaining crucial data which are useful for making decision related to business operations of the organizations. It has been identified that nowadays business environment is loaded with lots of uncertainty and complexities. So, for making a single move in any project everything depends on decisions. Organizational success and failures depend on decision. An efficient decision can make the organization win in the most complex environment on other hand a single mistake in decision process can fail the organizational move in hassle free environment too. It has been analyzed that AI plays a crucial role in decision making process of a marketing organization. Decisions are taken on the basis of consumer trends, economic trends, financial status, consumer preferences etc. For a safe decision-making process AI is the best technological advancement that help organization to take real time decision regarding the bidding on programmatic media buys, select correct message, implementation of chatbots, predicting marketing analytics while analyzing, evaluating all critical data of consumers. AI in marketing decision making help firm to achieve organizational goals, objectives as well as helps in sustain in the competitive era.

Figure : Decision Making Process Using AI Source: (Khabar Express, 2021)

Conclusion

From the above essay it can be concluded that Artificial Intelligence plays a significant role in the decision-making process of a marketing organization. It has been identified that with the help of AI marketers are able to analyzes, compare and evaluate the informative data in a most systematic manner that directly helps in achieving the profit goals of the organization. Additionally, it is recommended that for better achievement of marketing strategies AI is highly beneficial as all marketing decision taken through AI implementation involves less risk factors in comparison to human decision-making process.

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Students’ Corner

Application of Tableau in Business Analytics

Mr. Ayush Kumar Student IIM Amritsar

Data visualization tool tableau is one of the fastest-growing tools used in Business Intelligence. By using this tool, the data is simplified and presented in an easy-to-understand format. It allows any level of professional in an organization to understand data created by Tableau easily.

Moreover, users without technical expertise can create customized dashboards using Tableau.

Data analysis with the Tableau tool is incredibly fast, and the visualizations are created in dashboards and worksheets.

Tableau can be used for the following purposes: ?

BI (Business Intelligence) ?

Data Visualization ?

Data Blending

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?

Data Collaboration ?

Visualization of queries ?

To create no-code data queries ?

To analyze data in real-time ?

To manage extensive size metadata ? to import data of large size

Organizations like Amazon, Walmart, Accenture, Lenovo, and so on widely use Tableau. Below is the dashboard created by me. It shows the stats of every player who ever played in the Indian Premier League. The below dashboard contains a pie chart, two donut charts, and a treemap. Also, the two slicers used show the name of the batsmen and the bowlers, through which that player's stats can be seen.

Students’ Corner

A Brief Overview of AI and NLP in Data Science

Mr. Rushikesh Anil Kothawade Student IIM Amritsar

Artificial Intelligence (AI) is the ability of machines to do intelligent activities in the same way that humans do. AI employs intelligence to complete automated tasks.

Machine Learning (ML) is the process of creating systems that can learn from their experiences. Artificial Intelligence is also a subset of it.

Artificial Intelligence consists of 3 Stages mainly: a) Stage 1 - Machine Learning: It is a set of algorithms that intelligent systems use to learn from their experiences.

A biologically inspired network of Artificial Neurons is known as a Neural Network (NN).

b) Stage 2 - Machine Intelligence: This is a sophisticated set of algorithms that computers use to learn from their experiences. Deep Neural Networks, for example. This is where AI is right now. c) Stage 3 - Machine Consciousness: This is when a machine learns from its own experiences rather than relying on external data.

Types of Artificial Intelligence 1) ANI stands for Artificial Narrow Intelligence and includes basic/role duties performed by chatbots and personal assistants such as Apple's Siri and Amazon's Alexa.

2) AGI stands for Artificial General Intelligence, which includes human-level functions such as those done by Uber's self-driving cars and Tesla's Autopilot. It entails the machines' continuous learning.

3) Artificial Super Intelligence (ASI) refers to intelligence that is far superior to that of humans.

Difference Between AI, NLP, ML, NN&DL Artificial Intelligence (AI) is the process of creating systems that can make intelligent decisions.

Natural Language Processing (NLP) is the process of creating designs that can understand language. Artificial Intelligence is a subset of it.

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Developing systems that use Deep Neural Networks on a large quantity of data is referred to as Deep Learning (DL).

Machine Learning is a subset of it.

What is Natural Language Processing?

Natural Language Processing (NLP) is defined as "the ability of machines to comprehend and interpret human language in the way it has been written or spoken."

The goal of NLP is to make computers/machines as intelligent as humans in terms of language comprehension.

NLP's ultimate goal is to bridge the gap between how humans communicate (natural language) and what computers can understand (machine language).

There are 3 different levels of linguistic analysis that can be done before performing NLP a) Syntax : What amount of given text is grammatically true. b) Semantics : What is the correct meaning of the text? c) Pragmatics : What is the actual purpose of the text?

NLP deals with various aspects of language such as; a) Phonology: It is the systematic organization of sounds in language. b) Morphology: It is a study of formation of words and how are they related to each other.

Approaches of NLP for better understanding semantic analysis; a) Distributional: It employs extensive statistical techniques of Machine Learning and Deep Learning.

b) Frame-Based: The sentences which are syntactically different but semantically identical are represented inside data structure (frame) for similar situations. c) Interactive: It involves a pragmatic approach where the user is responsible for teaching the computer to learn the language step-wise in an interactive learning environment. d) Theoretical Learning: In this, the sentences refer to the real word (the sun is yellow), and parts of the sentence can be integrated to represent the whole meaning out of it.

Why Do We Need NLP? With NLP, it is possible to perform a particular task like Automated Speechin stipulated time.

Due to the presence of extensive data around, we can make use of the computer's ability to run various algorithms to perform tasks in almost no time.

These tasks generally include other NLP applications like Automatic Text Summarization (i.e.,to generate a summary of given text) and Machine Translation (i.e.,translation of one language into another)

Overall Process of NLP: The procedure of Natural Language Processing involves two processes: a) Natural Language Understanding (NLU) b) Natural Language Generation (NLG) 1) Natural Language Understanding: Itis generally used to understand the meaning of the given text. The basic structure and the nature of each word inside the text must be understood for NLU. In order to understand its structure, NLU tries to resolve the following ambiguity present in natural language. a) Lexical Ambiguity : Words have multiple meanings b) Syntactic Ambiguity : Sentence having multiple parse trees. c) Semantic Ambiguity : Sentence having multiple meanings d) Anaphoric Ambiguity : Phrase or word which was mentioned previously but had a different meaning in the context.

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The meaning of each word is comprehended by using lexicons (i.e.,vocabulary) and a set of some grammatical rules. However, certain words have similar meanings (synonyms) and words with more than one meaning (polysemy).

2) Natural Language Generation: It creates readable text from structured data in the form of meaningful phrases and sentences. Natural language generation is a complex problem to solve.

Natural language generation can be broken down into three stages:a) Text Planning: The core content is ordered in structured data. b) Sentence Planning: To describe the flow of information, sentences are combined from structured data. c) Realization: To represent text, grammatically correct sentences are finally formed.

Dissimilarities between NLP and Text Mining or Text Analytics: The primary role of NLP is to identify the meaning and structure of the given text.

Text mining, also abbreviated as text analytics, uses pattern recognition to extract hidden information from text data.

Text mining is used to understand the basic structure (i.e.,syntax) of given text data. In contrast, natural language processing is used to understand the meaning (i.e.,semantics) of given text data.

For instance, I recently discovered my wallet near the bank. The goal of NLP is to figure out that 'bank' refers to a financial institution or a 'river bank.'

Chatbots or Automated Intelligent Agents? a) These are computer programs that you can communicate with via messaging apps, chat windows, or voice calling apps. b) These are intelligent digital assistants that help customers get answers to their questions in a costeffective, timely, and consistent manner.

Importance of Chatbots a) Chatbots are critical for comprehending changes in digital customer care services given, as well as many of

the most commonly asked questions. b) Chatbots are beneficial in particular situations, such as managing a high volume of similar requests with automated responses or when customer support demands are specialized to a given area and highly predictable. c) Natural language processing is a complicated field along with the intersection of artificial intelligence, computational linguistics, and computer science.

Getting started with NLP: The user has to import a file that containsthe written text.

Then the user should perform the following steps in order to execute the natural language processing. a) Sentence segmentation: It identifies some sentence boundaries in the given text, where one sentence ends and where another sentence begins. These sentences are often marked-ended with the punctuation mark '.' b) Tokenization: The aim is to identify different numbers, words,and other punctuation symbols. c) Stemming: It is used to take the raw text like 'eating,' which is further reduced to 'eat.' d) Part of speech (POS) tagging: Each word in a sentence is assigned with its respective part-of-speech tags, such as designating the word as a noun or adverb. e) Parsing: It helps divide the given text into various categories. To answer a question like this part of the

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sentence, modify another part of the sentence. f) Named Entity Recognition: It maps the entities such as persons, location, and time within the documents. g) Co-Reference resolution: It is all about defining the relationship of a given word in a sentence with a preceding and the succeeding sentence.

Further Key Application Areas of NLP a) Apart from Big Data, Log Analysis, and log mining, it has a wide range of other applications. b) Although the term 'natural language processing is not as well-known as 'big data' or machine learning,' we use it daily. c) Automatic Summarizer: The task of an automatic summarizer is to write a summary of the supplied text while ignoring extraneous information. d) Text classification: It is used to classify different journals and news pieces into different domains. It is also possible to classify multiple documents. Spam detection in emails is a famous example of text classification. e) Information Extraction: It proposes an email program to add events to the calendar automatically. f) Sentimental Analysis: It is a technique for predicting the subject of a text, such as whether it offers judgement, opinion, or reviews.

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Our Team

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Quiz 1. A proposed explanation for a phenomenon is ______________ a. Correlation b. R-Square c. Hypothesis d. P-Value 2. A mathematical tool that is used to determine whether the outcome of an experiment is the result of a relationship between specific factors or merely the result of chance __________ a. Correlation b. P-Value c. R-Square d. Statistical Significance 3. The branch of data analytics concerned with the prediction of future possibilities and trends_________ a. Prescriptive Analytics b. Predictive Analytics c. Descriptive Analytics d. Diagnostic Analytics 4. A voluminous amount of structured, semi-structured, and unstructured data that has the potential to be mined for information ____________ a. Small Data b. Big Data c. Meta Data d. Statistical Data 5. Which of the following is true about MEAN? a. Exact middle of the bell curve. b. Sum of all observation – MEAN = 0 c. Most frequently recorded observation d. None of the above 6. The shape of a normal distribution curve depends on ______________ a. Quartile deviation b. Mean c. Standard deviation d. Median 7. A: F-Distribution cannot take negative value.

B: There is only One parameter in F-Distribution. a. A is true and B is false. b. A is false and B is true. c. A and B both are true. d. A and B both are false.

8. The absolute measure of dispersion is ___________ a. Coefficient of dispersion b. Mean c. Coefficient of skewness d. Standard deviation

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