Analytics January/February 2015

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Data Visualization • What’s next: Seeing is believing • Realization: Big picture of big data

ALSO INSIDE: • 2015: Predictions for predictive analytics • Small analytics company takes on Ebola • Case study: Why cloud analytics works

Executive Edge Bill Gossman, head of ASA Corp., on why leadership is the real gamechanger for using data analytics

Ins ide story

Prediction: Big year for analytics Happy New Year! ’Tis the season to be making predictions about what the future holds for the analytics profession, and who better to do that than a trio of experts in predictive analytics who contributed their insights to this issue of Analytics magazine?

Glenn Wegryn, president of the Analytics Section of INFORMS and principal at Analytic Impact LLC, is the first to look into the crystal ball for his column, “Top 5 analytics predictions for 2015.”

Wegryn sees another winning year for the profession as the demand for analytics skills continues to grow. Wegryn expects analytics programs to continue to pop up at business schools throughout the United States in response to the demand, and that the INFORMS Certified Analytics Professional program will increase in importance as a certification of skills and experience.

Wegryn also expects the analytics community to move closer to a common definition of “analytics,” bridging the gap between the data-centric definition (What can the data tell us?) and the decisioncentric version (What is the problem we’re trying to solve?).

Next, Andrew Jennings, chief analytics officer at FICO, offers “Five more predictions for 2015.” Jennings starts by noting that since the hype around big data has 2

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finally crested, big data and analytics will become “business as usual” and “more companies will cultivate in-house analytic competencies and many will embrace analytics as an important part of their company cultures.”

Our third prognosticator, independent consultant and business advisor Rajib Ghosh, focuses on the healthcare industry in his column, “What to expect in 2015.” Ghosh predicts a “great year for healthcare analytics professionals,” and that providers, not payers, will drive the market for analytics.

“Gaining market share has become the new mantra for the insurance companies,” Ghosh writes. “They are also busy trying to find new business models for the future. Big providers are under immense pressure to improve their bottom line in the new world of the Affordable Care Act and government-driven payment reform.”

As for me, I’ll pass on making any predictions about what the future holds for analytics, both the profession and the online magazine you’re now reading.

Instead, I’ll quote another scientist you might have heard of: “I never think of the future, it comes soon enough.” – Albert Einstein

– Peter Horner, editor peter.horner@ mail.informs.org w w w. i n f o r m s . o r g

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Leadership: the real game changer Leaders who take a proactive initiative to data analytics set the stage for successful, actionable use of information.

By Bill Gossman

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The future of data visualization Nextgen technologies and evolving cognitive frameworks move data visualization from art to science.

By Will Towler

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Data visualization: Big picture of big data Realizing how quickly we understand what we see makes data visualization a key component of better decision-making.

By Nana S. Banerjee

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Analytics helps contain Ebola How one small data science and analytics company found a way to help contain the spread of the deadly virus.

By Douglas A. Samuelson and Brian Umana

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INFORMS Board of Directors President L. Robin Keller, University of California, Irvine

President-Elect Edward H. Kaplan, Yale University

Past President Stephen M. Robinson, University of Wisconsin-Madison

Secretary Brian Denton, University of Michigan

Treasurer Sheldon N. Jacobson, University of Illinois

Vice President-Meetings Ronald G. Askin, Arizona State University

Vice President-Publications Jonathan F. Bard, University of Texas at Austin

Vice President Sections and Societies Esma Gel, Arizona State University

Vice President Information Technology Marco Lübbecke, RWTH Aachen University Vice President-Practice Activities Jonathan Owen, CAP, General Motors Vice President-International Activities Grace Lin, Institute for Information Industry

Vice President-Membership

and Professional Recognition Ozlem Ergun, Georgia Tech

Vice President-Education Jill Hardin Wilson, Northwestern University

Vice President-Marketing,

Communications and Outreach E. Andrew “Andy” Boyd, University of Houston

Vice President-Chapters/Fora David Hunt, Oliver Wyman

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Departments 2 Inside Story 8 Analyze This!

14 What’s Trending 18 Healthcare Analytics 22 Viewpoint 26 Forum 30 INFORMS Initiatives 34 Newsmakers 66 Conference Preview 70 Five-Minute Analyst 74 Thinking Analytically Analytics (ISSN 1938-1697) is published six times a year by the Institute for Operations Research and the Management Sciences (INFORMS), the largest membership society in the world dedicated to the analytics profession. For a free subscription, register at http://analytics.informs.org. Address other correspondence to the editor, Peter Horner, peter.horner@mail.informs.org. The opinions expressed in Analytics are those of the authors, and do not necessarily reflect the opinions of INFORMS, its officers, Lionheart Publishing Inc. or the editorial staff of Analytics.

Analytics copyright ©2015 by the Institute for Operations Research and the Management Sciences. All rights reserved.

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Thoughts on collaboration and communication There seems to be violent agreement about the value of collaboration and communication, and yet we too often give it little more than lip service.

By Vijay Mehrotra

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The lead company in this hub is Type A Machines, a company whose specialty is the development and deployment of 3D printers. Type A Machines’ CEO Espen Sivertsen gave me a very interesting take on the benefits of this type of co-location, both for his company and for its partners. Perhaps the most obvious source of synergy: In addition to selling its hardware, Type A also runs a server farm that enables other companies within the hub to manage high-demand periods by doing some or all of their 3D printing literally right down the hall (not only without any shipping

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Analy ze T h i s !

costs but also without acquiring additional hardware that would likely sit idle during slower times).

In addition, by being physically close to innovators who are using its products, Type A receives constant feedback on where the reliability problems are with their leading-edge machines, what these customers are doing with 3D printing, and what their future needs might be. Similarly, by being in close proximity to Type A, the other hub companies have an awareness of its product direction, an opportunity to get access to early models of new printers, and the chance to collaboratively solve problems as they arise.

For example, Sivertsen shared an interesting story of a partner who needed to print something that was officially too big for Type A’s Series 1 printer. Through hallway discussions, the two companies came up with the idea of rotating the design by 45 degrees, after which they managed to successfully print a very valuable prototype for the partner right there in Type A’s server farm.

I had originally gone to BAAM looking for an analytics story about capturing and analyzing data to support improved product reliability, an area where I had done some research [1] in the past. However, for an industry in the very early stages of maturation, more of this type of data is clearly being transmitted in analog rather 10

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than digital format, between people who are deeply knowledgeable and passionate about their adjacent/overlapping areas of expertise.

Item: After a long career as an operations research leader at P&G [2], Glenn Wegryn is currently a principal at Analytics Impact LLC, as well as president of the Analytics Section of INFORMS. Though I have only met him briefly once, I have been a huge fan of his for a long time.

As one of the 1,000-plus members of the Analytics Section, I am thrilled to have someone of his experience and stature leading us. Given his strong track record of successful project outcomes, I definitely listen when he speaks [3].

Anyway, I recently came across an email from INFORMS containing Wegryn’s “Top 5 Analytics Predictions for 2015” (see page 14). While the whole list was interesting, No. 4 was the one that caught my eye: “Collaboration and Communication (aka the soft skills) will emerge as the difference-maker….”

Item: During the last week or so, I have seen a couple of interesting blog posts from Vincent Granville, the creator of Data Science Central (www.datasciencecentral.com), billed as “the leading social network for big data, business analytics and data science practitioners.” Granville is an w w w. i n f o r m s . o r g

active and innovative data scientist with a lot of interesting ideas, and I definitely pay attention to what he has to say.

In the first of these blog posts [4], Granville’s suggests that if you were trained as a statistician in the classical sense [as he was], very little of what you have learned is actually all that useful if you are trying to make a living as a data scientist (for the O.R. fans reading this, note that he does state that “data science uses some operations research”). Despite its intentionally

provocative title (“Data science without statistics is possible, even desirable”), this was actually a very thoughtful piece that argued for a less dogmatic and more utilitarian approach to tackling data. The crux of his intellectual objection is most clearly stated near the bottom of the article when he writes that, “old statistics use a top-down approach, from model and theory to data, while new statistics or data science use a bottom-up approach, from data to model or algorithm.”

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The second of these posts [5], featured a list of “10 data science predictions for 2015” originally created by the Institute for Advanced Analytics (http://iianalytics. com). This list also had a heavy emphasis on collaboration and communication.

commoditized quickly. Lest you imagine otherwise, take note of the last two predictions for 2015 on the IIA list: • Analytics, machine learning, and cognitive computing will increasingly take over the jobs of knowledge workers.

• Automated decision-making will So what? There seems to be violent come of age in 2015. agreement about the value of collaboration and communication, and yet we too In other words, innovate or die, my often give it little more than lip service. friends. And good luck trying to do it sucEven the term “soft skills” is inherently pe- cessfully in isolation. ❙ jorative, the modifier essentially mocking Vijay Mehrotra (vmehrotra@usfca.edu) is a the noun to which it is attached. professor in the Department of Business Analytics I would suggest looking at things a and Information Systems at the University of San Francisco’s School of Management. He is also a bit differently. Like Sivertsen and his collongtime member of INFORMS. leagues at Type A Machines, we as analytics professionals are constantly trying REFERENCES to gain acceptance for new technologies from people with different skills and capa1. Mehrotra, V., Grossman, T. A., “O.R. Process Skills Transform an Out of Control Call Center bilities than the ones who created them. into a Strategic Asset,” Interfaces, Vol. 39, No. 4, July-August 2009, pp. 346-352.

To do this successfully, we must culti2. See http://www.cbsnews.com/news/howvate our ability to see the world through operations-research-drives-success-at-pg/ for an excellent overview of Wegryn’s work at P&G. their eyes and to help them realize how 3. For example, check out his excellent presentation from the Spring 2012 INFORMS we might be able to help enable them to Analytics Conference entitled “Driving be successful. These are skills, with no Competitive Advantage with O.R.” at https:// www.informs.org/Community/Analytics/Videos/ modifier needed.

Analytics-Process-Presentations.

4. http://www.datasciencecentral.com/profiles/ But if I was required to add an adjecblogs/data-science-without-statistics-is-possibleeven-desirable tive for these skills, the word that comes to 5. http://www.datasciencecentral.com/profiles/ mind is “survival.” Because we will always blogs/10-data-science-predictions-for-2015 need to be innovating in collaboration with our allies and customers, for in the age of software we can expect solutions to be

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Wh at’ s t r e nd i ng

Top 5 analytics predictions for 2015 Glenn Wegryn, president of the Analytics Section of INFORMS and principal at Analytic Impact LLC, offers his top five analytics trends for 2015.

A middle ground needs to be found between the data-centric definition of analytics (What can the data tell us?) and the decision-centric version (What is the problem we’re trying to solve?).

BY Glenn Wegryn, CAP

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2014 proved to be a winning year for analytics.

Going by the number of conferences devoted to the topic of analytics and attendance, including this year’s record-breaking INFORMS Annual Meeting in San Francisco, all indications are that it will continue to prosper. Here I present five of the most important predictions regarding analytics capabilities for 2015.

1. What’s in a name? There will be continued effort, albeit not fully resolved in 2015, to converge on a common definition of what analytics is. INFORMS’ official definition is “... the scientific process of transforming data into insight for making better decisions.”

There are numerous other definitions or positions on what analytics is, but a middle ground needs to be found between the data-centric definition of analytics (What can the data tell us?) and the decision-centric version (What is the problem we’re trying to solve?).

Indeed, I view analytics as a bridge to converge the two in peaceful co-existence. Most importantly, it provides an easier point of entry for decision-makers to embrace, organize around, pay for and ultimately benefit from all of the tools in the shed. The

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2015

more we play in the same sandbox, the bigger the castle we can build together.

2. Business analytics programs will continue to grow. There are now more than 100 business schools in the United States that have, or have committed to launch, curriculum at the undergraduate and graduate levels with degrees or certificates in business analytics [1]. No doubt there will be more in 2015. Clearly the B-Schools have heard the call from McKinsey [2] and others [3] on the significant gap projected between the supply and demand for talent in the analytics space, particularly in the

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predict-and-decide advanced analytics skills. In 2015, the more established programs will dig deeper and continue to fine-tune the curriculum while newer programs will close the gap quickly. The more successful programs will leverage the breadth of academic disciplines (computer science, operations research, engineering, math and statistics, marketing, finance and others) to strengthen their programs.

3. Fraud and security. With the number of security breaches on major corporations being reported almost weekly, such as at Target, The Home Depot and j a n u a r y / f e b r u a r y 2 015

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more recently at Sony Pictures, there will be a significant increase in investment across the board in safeguarding commerce and privacy on the Internet. The importance of applying analytics methods – from using decision analysis to guide investment choices, to statistical methods, to detect-and-anticipate breaches and optimization models, to improve infrastructure design for safety, reliability and performance – will accelerate and continue to grow in 2015.

Communication includes effective use of visual capabilities from Tufte-proud graphics to interactive, data-rich, drilldown tools as a means, and not an end, to a better decision.

5. The INFORMS Certified Analytics Professional (CAP®) program will continue to increase in importance as a qualification of skills and experience. Longer term, as the supply of analytic talent catches up to demand, the CAP program will be recognized as 4. Collaboration and communica- an important differentiator for employtion (aka, the soft skills) will emerge as ment. Currently, the numbers who are the difference-maker not only in getting either certified or are signed up to take the best talent hired into the most coveted the exam exceeds the rate of the Projroles, but also in enabling more recogni- ect Management Professional (PMP) tion and value to organizations that uti- certification, over the same period [4]. lize analytics. Soft skills are important to To that end, INFORMS should begin educate, sell the value of, and, ultimately, a campaign targeted to the “buyer” of transform the culture within departments analytics to grow awareness of this cerand organizations. But more importantly, tification in their hiring decisions. ❙ these soft skills are essential to clearly To hear a podcast of Glenn Wegryn talking about convey the context of the problem and to his predictions, visit https://www.informs.org/podcast recommend a course of action for a deREFERENCES cision-maker to take. That can span from 1. Research from INFORMS Masters in Analytics intelligent bots interacting effectively with Committee, presented at the 2014 INFORMS Annual Meeting. users in an online application, to indepen2. McKinsey Institute, “Big Data: The next dent practitioners seeking to build repeat frontier for innovation, competition and productivity,” May 2011. business with a client, to boardroom ana3. InformationWeek Reports, “Big Data Widens lysts able to think on their feet and able Analytics Talent Gap.” 4. Presentation, meeting of INFORMS Board of to describe in plain language the options Directors, Nov. 9, 2014. available and facilitate a decision.

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Five more predictions for 2015 Dr. Andrew Jennings, chief analytics

3. Automation of modeling as well as

officer at FICO, sees a post-big-data world

the related reporting will be a priority.

in 2015. Predictive analytics and the big

The continued expansion of analytics is

data that fuels it become

predicated on making the process as sim-

deeply and broadly em-

ple as possible and codifying the steps that

bedded in business and

many analytic experts take for granted.

society – no longer a

This is not without some danger, but I

phenomenon but widely

believe we will see significant investment in

accepted as part of the

this area.

foundation. Here are a few of his predictions for the coming year:

4. The unstructured shall inherit the Earth. Analysis of unstructured data is coming of age, particularly in customer ser-

1. Big data and analytics are “busi-

vice. Every tweet, chat session, call center

ness as usual.” The hype around big data

conversation, and customer support email

has finally crested. Big data will always be

is going to be analyzed to accelerate

with us, and it will keep getting bigger. But

problem resolution, optimize scripts for

business users have come to the realiza-

sales people, enhance shopping experi-

tion that analytics is the key to unlocking

ences, make products less confusing, and

value from big data. In 2015, more compa-

increase compliance.

nies will cultivate in-house analytic competencies and many will embrace analytics as

5. The causation vs. correlation

an important part of their company cultures.

debate is becoming passé. In most cases whether the underlying relationship is causal

2. Predictive security will become an

or correlative is irrelevant. In business circles,

important tool in the effort to stop cyber

people want to make better decisions. They

criminals. With cybercrime and cyber ter-

need to know what business levers to pull

rorism on the rise, the good guys are finally

to get closer to the desired result; the why

moving beyond their reactive approach to

is secondary. Overall the important issue is

security. In 2015, police, military and in-

to gather good data and trust it. As famed

telligence agencies will employ analytics

statistician Bradley Efron said, “Those

to predict when, where and how the next

who ignore statistics are condemned to

attack will occur so such attacks can be

re-invent it.” ❙

thwarted before damage is done.

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healt h car e a na ly t i c s

What to expect in 2015 Four predictions for the healthcare analytics industry in the year ahead.

The demand for health informatics workers will increase at twice the rate of employment, yet the nation already faces a shortage of qualified candidates.

By Rajib Ghosh

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In my last article I reviewed the key achievements, opportunities and challenges facing the healthcare analytics industry in 2014. In this first article of 2015, I will share some of the anticipated developments in the marketplace that will impact the nascent industry.

1. Great year for healthcare analytics professionals As the demand for healthcare analytics increases, the demand for professionals with analytics experience will also grow. Health Catalyst, a healthcare analytics product and consulting company, found in a survey that healthcare analytics is the highest priority IT investment in the healthcare industry followed by population health and ICD-10. A recent report published by Burning Glass Technologies stated the demand for health informatics workers will increase at twice the rate of employment, yet the nation already faces a shortage of qualified candidates. This is good news for health informatics professionals with experience in new business models such as pay-for-performance or value-based purchasing. If you have a blend of those skills and experiences, you will be a hot commodity in 2015.

2. Interoperability will be a key focus area for health IT vendors and the government Interoperability is still a hot topic within the Office of the National Coordinator for Health Information Technology (ONC). In an updated release of the

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interoperability roadmap, ONC described one of its key criteria by 2017 is to enable providers to send, receive, find and use a basic set of essential health information.

By 2020, the roadmap projects, granular health information will be available to the providers. Interoperability, or lack thereof, is a key obstacle in creating a longitudinal view of a patient and deployment of population level analytics.

Patients can move from one health system to another during a course of 12 to 18 months. Meanwhile, patient’s medical records get locked in a fragmented

manner inside multiple electronic systems that do not talk to each other. Releasing data from such silos is the key to deriving benefits from the use of healthcare analytics. Until now, providers, pundits and politicians have blamed electronic health record (EHR) companies for their product strategy of keeping patient data locked into their own proprietary databases. Epic, the leader in market share among EHR companies, recently announced that it has hired a lobbyist firm to fix its “image” on Capitol Hill. Epic argues that its product can connect with

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healt h car e a na ly t i c s

Gaining market share has become the new mantra for the insurance companies.

They are also busy trying to find new business models for the future.

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many other systems including health information exchanges (HIEs).

Opposition groups argue that Epic can only interoperate with other Epic users; for everyone else there is a steep cost for interoperability. While some argue that fixing their product strategy is a better solution than following a PR strategy, the fact that Epic is doing this suggests that interoperability will be the topmost agenda for electronic health record companies in 2015.

EHR industry heavyweights including Epic are behind the recent launch of the “Argonaut Project.�

It is aimed at delivering some standard proposals around the utility of the new Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) technology that has the promise of sharing healthcare data on the Internet. The real impact of this project remains to be seen, but it is an encouraging sign.

3. Providers not payers will drive market for analytics For a long time payers were considered to be the growth driver of healthcare analytics because they have the total cost of care data. Payers can force network providers to share encounter data, which they use to triangulate with cost data to create meaningful analytics for predictive and preemptive population health management. But that perception is changing.

The insurance market in recent times has seen many twists and turns. Opening state- and federallevel insurance exchanges provided opportunities for payers in the form of government business, but it also brought fierce competition. Gaining market share has become the new mantra for the insurance

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companies. They are also busy trying to find new business models for the future.

Big providers are under immense pressure to improve their bottom line in the new world of the Affordable Care Act (ACA) and government-driven payment reform. They have to work hard to improve their quality ratings and emergency room readmissions to avoid Medicare penalties charged by the Center for Medicare and Medicaid (CMS). One estimate suggests that readmission penalties will cost hospitals $428 million in 2015, up from $227 million in 2014. For hospitals, large and medium, analytics will be their survival strategy. They will drive growth in the healthcare analytics market in 2015, not payers.

4. Care delivery transformation will increase demand for analytics Healthcare delivery organizations are undergoing rapid vertical and horizontal integrations. Health systems are merging, and various care delivery settings such as home health and wellness providers are increasingly brought under the umbrella of hospital systems. Care delivery will continue to undergo decentralization and transformation. The key to holding such multiple entities together will be the ability to use data and analytics to drive decision process. This trend will continue in 2015 and beyond, creating opportunities for analytics product a na l y t i c s

companies, along with technology and business consulting service providers.

Many argue that the political landscape change in the November election will halt this transformation in healthcare. I disagree.

The train has left the station. Healthcare organizations have already spent hundreds of millions of dollars buying new technology, investing in their workforce, offering incentives and restructuring their organizations in response to the ACA. Millions of people now have obtained insurance and have gotten used to the freedom of buying insurance from a marketplace where insurance companies compete. It will be hard, if not impossible, to take all that away.

I’m going to stop worrying about the what-if scenario, which may never materialize, and focus on fixing our broken healthcare system and plugging the loopholes. This is my resolution for 2015.

What’s yours? ❙ Rajib Ghosh (rghosh@hotmail.com) is an independent consultant and business advisor with 20 years of technology experience in various industry verticals where he had senior-level management roles in software engineering, program management, product management and business and strategy development. Ghosh spent a decade in the U.S. healthcare industry as part of a global ecosystem of medical device manufacturers, medical software companies and telehealth and telemedicine solution providers. He’s held senior positions at Hill-Rom, Solta Medical and Bosch Healthcare. His recent work interest includes public health and the field of IT-enabled sustainable healthcare delivery in the United States as well as emerging nations.

Follow Ghosh on twitter @ghosh_r.

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Cloud analytics works for me Using the public cloud practically eliminates the time lapse on infrastructure procurement.

When you have an ad hoc analytical project, internal IT dependency usually comes your way.

Until a few years back, we had to accommodate and account for it in our delivery timelines. In today’s world, we can avoid this altogether by opting for public cloud analytics.

The Problem

By Ganesh Moorthy

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Let’s assume that you are asked to analyze a significantly large data set and provide insights. Based on the data size, you determine that you’d require a clustered and distributed environment to be able to run the analysis within the pre-decided timeline. You ask your manager for help and he says to raise a request with IT. You raise the request in the ticketing system and check with IT. They answer that they don’t have it ready and will need to procure the system first.

You comply and ask how long will the process take?

The IT personnel responds, “Four weeks provided you get the budget approval from your department.”

Your timelines have just run amok!

If the above scenario is true in your case, then the situation is similar to what I have heard from several

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other analysts in different organizations.

While the IT department centrally manages all infrastructure, they mostly focus on normal operational efficiency goals.

Project specific ad hoc requirements usually do not fall under the preview of IT supply and demand hence is looked upon as ad-hoc. This is the norm in every service industry today. The 30,000-foot question is: What do we do?

Cloud Analytics to the Rescue The obvious answer comes in the form of using public cloud infrastructure

for your analytical needs. Using public cloud practically eliminates the time lapse on infrastructure procurement. In addition, analytics on public cloud comes in three different flavors: 1. The black-box approach allows you to consume already trained models as services; 2. The white box approach allows you to use the public cloud infrastructure for data storage, deploy analytical tools for performing descriptive or predictive needs and a visualization platform for reporting insights; and

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3. The grey box approach allows you to create an ensemble of models using statistical techniques such as decision trees and infer underlying rules for relationships.

In this case, while the actual implementation of statistical technique is black boxed, you still have control in determining the right path and relationships by varying the factors required for rules determination. All you need is to make a choice of determining which approach to take based on the problem definition.

Based on the infrastructure requirement, we looked into public cloud providers options and decided to go with Amazon Web Services. It took us just a couple of days (includes internal approval process) to get three large capacity nodes with Linux pre-installed. We wanted a clean system as we were building a custom natural language processing system to process the tweets.

It took us one week to deploy Apache Hadoop, Druid, R statistical tool, Zero MQ, Python and Java and two weeks to Custom Analytics on run through performance benchmark testPublic Cloud ing and an optimization exercise. In parAs part of an R&D engagement for allel, we also developed and deployed a one of our clients, we needed to perform custom HTML5 based visualization tool sentiment analysis on tweets in order for insights. to determine the problem context, keyAll in all, the entire exercise took a words, sentiments and topics. The main month and a half from start to finish for objective of this exercise was to deter- the initial proof of concept. The NLP algomine service-related issues and result- rithm development journey is still undering sentiment of customers. This being a way and being constantly optimized for social media analytics exercise, the solu- accuracy and performance. tion was to incorporate aspects of realWhat could have easily been a three time sentiment complex event processing months exercise if we had waited for along with data aggregation and historical internal infrastructure procurement, took descriptive analytics. only half the time to deliver. We also We determined that using a Twitter have both data ingestion and insights streaming service would be the best op- visualization on the public cloud along tion. This would require constantly listen- with backend processing components ing to the streaming service and would – not to mention, we did not make any result in collecting and analyzing up to depreciating investments, hence keeping 2,000 tweets a minute. the bottom line intact.

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Conclusion Cloud-based analytics or cloud analytics is an evolution in the making.

We are seeing traditional BI vendors providing cloud-based SAAS-BI services on one hand, while the others are providing specific niche services in social media analytics using third-party sourced data. In between, we have big cloud infrastructure providers, providing both infrastructure and analytical platform. On the most crucial aspects of data security, most of them have ironclad service-level

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ontinuing ducation

INTRODUCTION TO MONTE CARLO AND DISCRETE-EVENT SIMULATION Learn the basics of Monte Carlo and

discrete-event simulation, how to identify real-world problem types appropriate for

simulation, and develop skills and intuition for applying Monte Carlo and discrete-event simulation techniques.

agreements and adhere to them. Based on our own experience and listening from other engagements, I can confidently say, “Cloud analytics works for me,” provided you know how to make it work for you. ❙ Ganesh Moorthy is an associate director at Mu Sigma, where he serves as program manager/ senior solution architect for R&D engagements.

Moorthy has more than 16 years of experience in leading enterprise solution development for Fortune 500 clients. He is currently involved in building industrial Internet, augment reality and analytics and visualization platforms for both descriptive and predictive analytics.

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Visit the Website for the latest course schedule: informs.org/continuinged INFORMS Continuing Education program offers intensive, two-day in-person courses providing analytics professionals with key skills, tools, and methods that can be implemented immediately in their work environment.

These courses will give participants hands-on practice in handling real data types, real business problems and practical methods for delivering business-useful results.

visualizing data in order to derive insights and knowledge.

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CAP: How to become a modern-day analytics journeyman The practice of analytics is a craft, requiring special skills in its execution.

Certification has roots in medieval craft guilds, which established standards, set ethical guidelines for practice and functioned as professional associations. The Institute for Operations Research and the Management Sciences (INFORMS) is the largest professional society in the world for analytics, operations research and management science. INFORMS’ Certified Analytics Professional (CAP®) Program is an effort to establish standards and set guidelines. Certainly the practice of analytics is a craft, requiring special skills in its execution. Let me explain, and maybe by the end you’ll be interested in getting certified yourself.

Practicing Your Craft in the Middle Ages

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In the high Middle Ages, learning a craft began with apprenticeship, whereby teenage boys worked for a master craftsman in exchange for room, board and training. Apprentices could only practice their craft in their master’s workshop until they became journeymen, which required several years of experience and the production of a high-quality piece of work in that craft. However, once reaching journeyman status, skills became portable, an journeymen were granted letters or certificates that allowed them

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to travel to other towns to practice their craft, earn wages and learn from other masters.

After more years of practice and production of a masterpiece the journeyman becomes a master, if other masters agreed. The new master was then free to set up his own workshop. Guilds promoted their craft, set its norms or standards, and maintained a code of ethics related to its practice. They offered practitioners a portable credential that could signal their skills to those who wanted to do more

than just take their word. The travels of the journeymen had the added effect of disseminating new ways of practice, since they brought their accumulated knowledge to each new location and workshop.

INFORMS, a Modern-Day Guild?

INFORMS is certainly not a medieval guild, but it is a global organization of 11,000 members, of whom 50 percent are academics, 30 percent practitioners and 20 percent students. Members are highly educated – 96 percent have or

Winner: Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain Project Portfolio Planning at Intel Corporation Airline Crew Augmentation, Decades of Improvements from Sabre Vaccine Prioritization for Eective Pandemic Response Statistical and Optimization Techniques for Laundry Portfolio Optimization at P&G

2014 Wagner Prize winners Oleg Gusikhin (L) and David Simchi-Levi (R) with Wagner Prize Chair Allen Butler (C).

Gerrymandering for Justice: Redistricting U.S. Liver Allocation

Videos of 2014 Wagner Prize presentations are now available at:

http://livewebcast.net/INFORMS_AM_Wagner_Prize_2014/pres1/

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are earning an advanced degree, and 51 percent have Ph.D.s. INFORMS has taken on the championing of analytics in many ways, including establishing continuing education programs; an analytics maturity model; engagement with industry, government and academia; and certification.

The Institute’s decision to establish a certification program shares goals with the guilds: to advance the craft by introducing standards of quality, identify individuals with breadth of knowledge and encourage continued competency. While guilds were quasi-governmental in requiring membership to practice that craft (akin to governments requiring licenses for certain professions today), certification is a voluntary credential granted by a non-governmental body.

INFORMS conducted a job task analysis study by a panel of subject matter experts to define the knowledge, skills and abilities necessary to effectively practice analytics. They identified seven domains: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and model lifecycle management.

education, experience, exam, effectiveness and ethics. The education and analytics-related work experience combination required is three years for those with a master’s degree (any guess where that term originated?) in a related discipline (e.g., statistics, mathematics, computer science, etc.), five years with a bachelor’s degree in a related discipline and seven years with a degree in an unrelated discipline (waivers of the educational eligibility requirements will be considered on a case by case basis). The exam tests skills and knowledge in the seven JTA domains with 100 multiple-choice questions that must be completed in three hours or less. Computer-based testing is available at 700+ locations worldwide.

Analytics requires more than technical skills and knowledge, so a confirmation of effectiveness in the area of soft skills by an employer or client is required. And, CAP requires agreement with the code of ethics. For more details on applying see the CAP Candidate Handbook and the Complete CAP Study Guide.

Why Get Certified?

The target group is early to mid-career professionals, or “apprentices” seeking to What is Required for become “journeymen,” although thankCertification? fully today apprentices are paid! Those There are five “Es” of the Certified advanced in their careers may have a reAnalytics Professional (CAP®) program: sume whose mastery stands on its own, 28

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but early career professionals may find the differentiation offered by a portable, independent, third-party validation of knowledge of certain standards helpful.

Not unlike the journeyman certificate or letter, in this era of increasing demand for analytics professionals certification attests to knowledge and skills beyond a person’s own word. It demonstrates commitment to career and to craft and adherence to accepted standards. It can increase earnings potential and provide the personal satisfaction of achieving a milestone. INFORMS and CAP benefits

Introducing the

will endure for years to come.

For more on the CAP program, click here. ❙ Polly Mitchell-Guthrie (Polly.Mitchell-Guthrie@ sas.com) is the senior manager of the Advanced Analytics Customer Liaison Group in SAS’ Research and Development Division, where her team serves as a bridge between R&D and external customers and internal SAS divisions.

She serves as vice chair of the INFORMS Analytics Certification Board and has an MBA from the Kenan-Flagler Business School at the University of North Carolina at Chapel Hill. She is a member of INFORMS. A version of this article appeared in the December 2014 issue of Swiss Analytics Magazine. Reprinted with permission.

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Edelman Award, continuing education, IAA finalists INFORMS announces six finalists for the 2015 Edelman Award

Widely considered the “Super Bowl” of operations research, as well as INFORMS’ highest organizational honor, the Edelman Award acknowledges the best application of high-end analytics.

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INFORMS recently announced six finalists representing a diverse collection of applied analytics and advanced operations research projects that will compete for the 2015 Franz Edelman Award in a series of judged presentations at the INFORMS Conference on Business Analytics & Operations Research in Huntington Beach, Calif., in April.

Widely considered the “Super Bowl” of operations research, as well as INFORMS’ highest organizational honor, the Edelman acknowledges the best application of high-end analytics. The winner will be announced at an evening Edelman Gala held in conjunction with the conference.

The finalists and their presentations include: • IBM for “Predictive Cloud Computing with Big Data: Professional Golf and Tennis Forecasting” • Ingram Micro for “End-to-End Business Analytics and Optimization in Ingram Micro’s Two-Tier Distribution Business”

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• LMI/Defense Logistics Agency for “Peak and Next Gen: Effective Inventory Control for Items with Infrequent or Frequent, Highly Variable Demand” • Saudi Arabia Ministry of Municipal and Rural Affairs for “A Pilgrim Scheduling Approach to Increase Public Safety during Hajj” • Syngenta for “Good Growth through Advanced Analytics” • U.S. Army/Sandia National Laboratories for “The Capability Portfolio Analysis Tool (CPAT): A Fleet Management and Decision Analysis Tool” Now in its 44th year, the Franz Edelman Prize competition honors outstanding examples of analytics and operations research projects that transform companies, entire industries and people’s lives.

Using innovative advanced analytics methods, the nominated teams were instrumental in helping their respective institutes make better decisions, providing a disciplined way to which management can improve organizational performance in a wide variety of situations and across both public and private organizations.

For more information, click here.

2015 schedule of continuing ed analytics courses INFORMS’ continuing education program is designed specifically for those in the field of analytics and operations a na l y t i c s

research. INFORMS, the world’s largest organization for advanced analytics professionals, produces and disseminates data analytics and operations research information, training courses, certification, and best-in-class resources for our members and the industry at large.

Four upcoming intensive handson courses provide real take-away value. You will leave these courses with the skills, tools and methods that you can implement immediately in your work.

Included are two of the newest courses: Introduction to Monte Carlo and DiscreteEvent Simulation and Foundations of Modern Predictive Analytics. Following are the dates and locations for the 2015 February through May courses: Essential Practice Skills for Analytics Professionals, Feb. 26-27, San Jose, Calif.

Learn how to integrate and apply your analytical skills to real-world problemsolving for businesses and other organizations. This course provides approaches that you can apply immediately to a wide variety of settings, whether within your own organization or for an external client.

Data Exploration & Visualization, March 19-20, Dallas Hands-on training that focuses on the critical steps in the process of analyzing data: accessing and extracting data, cleaning and preparing data, exploring j a n u a r y / f e b r u a r y 2 015

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and visualizing data. This course uses several of the most popular software tools intensively, and provides an overview of the range of available software options.

Foundations of Modern Predictive Analytics, April 15-16, Huntington Beach, Calif.

Learn data mining techniques and tools that will allow you to make the link between business needs and your technical skills. This course gives you hands-on practice in handling real data types, real business problems and practical methods for delivering business-useful results.

Introduction to Monte Carlo and Discrete-Event Simulation, May 28-29, Washington, D.C. Identify real-world problem types appropriate for simulation, and develop skills and intuition for applying Monte Carlo and discrete-event simulation techniques. Via hands-on interactive sessions, you will investigate the use of Monte Carlo simulation in decision-making and the use of discrete-event simulation to solve mathematically intractable problems in stochastic modeling.

Don’t miss out on these courses.

For detailed course information on all INFORMS courses and to register, visit www.informs.org/continuinged or contact Thedra White at thedra.white@ informs.org or 443-757-3570.

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Innovative Applications in Analytics Award finalists The Analytics Section of INFORMS has named three finalists for its Innovative Applications in Analytics (IAA) Award. The Section’s flagship prize, the IAA recognizes creative and unique developments, applications or combinations of analytical techniques used in practice.

The three finalists, chosen by a panel of judges following a two-month verification process, will present their work at the INFORMS Conference on Business Analytics and Operations Research in Huntington Beach, Calif., in April 2015, where the winner will be announced.

The three finalists include: • “Simulation Approach for Aircraft Spare Engines & Engine Part Planning,” Jose A. Ramirez-Hernandez, Steven Oakley, Mei Zhang, Alejandro Scalise, American Airlines • “Intelligent Surgical Scheduling System,” Narges Hosseini, Kalyan Pasupathy, Yariv Marmor, Thomas Rohleder, Jeanne Huddleston, Paul Huddleston, Mayo Clinic Rochester • “Fusion Analytics for Public Transport Event Response,” IBM Research and the Land Transport Authority of Singapore For more about the competition, click here. ❙

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Lumina, Chevron earn DA honors Like many environmental decisions, the question of how to decommission California’s offshore oil platforms started out as highly controversial.

But, remarkably, almost all stakeholders, including oil companies and environmentalists, ended up supporting the same solution.

INFORMS society names ‘Rigs to reefs’ top solution Lumina Decision Systems, led by CEO Max Henrion, won the 2014 Decision Analysis Practice Award co-sponsored by the Decision Analysis Society of INFORMS and the Society of Decision Professionals for the project titled “A Multi-attribute Decision Analysis for Decommissioning California’s Offshore Oil Platforms.” Co-authored by Brock Bernstein and Syya Swamy, Henrion delivered the awardwinning presentation at the 2014 INFORMS Annual Meeting in San Francisco in November.

The award recognizes outstanding applications of decision analysis to significant decisions. It considers the quality of the analysis, its impact on the decision, and the importance and benefit of that decision.

“This was a great demonstration of how decision analysis can bring together parties with significantly different points of view and objectives to find a ‘win-win’ solution,” says Frank Koch, chair of the award committee.

Like many environmental decisions, the question of how to decommission California’s offshore oil platforms started out as highly controversial. But, remarkably, almost all stakeholders, including oil companies and environmentalists, ended up supporting the same solution. The original leases required the oil companies to remove the entire rigs at the end

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of their productive life. Some are up to 1,200 feet deep, making removal very costly with substantial environmental impacts. These rigs have developed their own rich ecosystems, with commercially and ecologically important rockfish species, making them popular with sea lions, recreational divers and other marine life forms.

An interdisciplinary team created a decision tool in Lumina’s Analytica software to explore and evaluate decommissioning options to assist the California Ocean Science Trust. The two

main options examined were complete removal, as required by the original leases, and conversion to an artificial reef, by cutting structures at 85 feet below the water line to prevent interference with shipping. Partial removal reduces costs by up to $500 million and helps to preserve marine ecosystems. Eventually the stakeholders reached near consensus on the “rigs to reefs� policy option. California lawmakers almost unanimously passed enabling legislation, signed into law by then-Gov. Arnold Schwarzenegger in 2010.

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“By clearly identifying the a gala celebration markissues, synthesizing the best ing the 50th anniversary of multi-disciplinary science, decision analysis. The celdaylighting the uncertainty ebration, which included and providing unbiased rean afternoon program on view, the tool was successthe history and future of ful in distilling the rhetoric decision analysis followed to meaningful discussion of by an evening dinner gala, trade offs and values,” says was held Nov. 8 in San “This decision Skyli McAfee, director of CaliFrancisco in conjunction fornia Ocean Science Trust. with the 2014 INFORMS analysis was one “Further, the tool was made Annual Meeting. of the most available to the public; its The award recognizes challenging and, assumptions and approach “consistent and sustained in the end, were transparent. Constituexcellence in decisionsatisfying ents had the opportunity to making throughout an projects I have import various scenarios and organization.” learn the best approach. With Chevron was cited for worked on.” this tool, sound legislation its “unique and long-term — Max Henrion was passed that will serve commitment to making deCalifornia and our marine cision quality an organiresources well.” zational competency contributing to the “This decision analysis was one of company’s outperforming its peer group the most challenging and, in the end, in total shareholder return for both the last satisfying projects I have worked on,” five- and 10-year periods. Chevron began adds Henrion. “It is truly an honor to have its decision analysis initiative more than our work recognized in this way.”

15 years ago. Today, more than 25,000 employees have been trained in the disChevron receives inaugural cipline. Decision analysis is the discipline Raiffa-Howard Award incorporating decision theory, methodThe Society of Decision Profession- ology and professional practice guiding als (SDP) presented the inaugural Raiffa- decision-making under uncertainty.”

Howard Award for Organizational Decision The awards’ namesakes – ProfesQuality to the Chevron Corporation during sor Howard Raiffa of Harvard University 36

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and Professor Ronald Howard of Stanford University – are widely considered co-founders of the discipline of decision analysis, which crystallized with key contributions from Raiffa and Howard in 1964. Both were on hand for the celebration, along with more than 300 other decision analysis luminaries and guests.

“While elements of decision analysis have existed for hundreds of years, it was the work of [Raiffa and Howard] beginning in 1964 that brought it into mainstream use by giving us a logical and systematic set of principles to enhance our clarity of thought and enable us to act with confidence in the face of uncertainty,” says Bill Klimack, president of the SDP, an association of professional practitioners. “Corporations and government entities now have the tools to make complex decisions.”

“Decision analysis has by now helped many thousands of decision-makers, and has created many billions of dollars of value for the economy through better business decisions and better government policies,” adds Jeff Keisler, president of the Decision Analysis Society (DAS), a subdivision of INFORMS. “The Society of Decision Professionals is delighted to honor professors Howard and Raiffa for their work that has made an indelible impression on our field and our lives,” adds Jack Kloeber, vice president of SDP. a na l y t i c s

Klimack, Keisler and Kloeber are active members of INFORMS as well as SDP. INFORMS was a sponsor of the gala celebration along with DAS. Decision analysis emerged as a distinct academic field in 1964, building on developments in statistical decision theory and game theory by Raiffa and dynamic probabilistic systems by Howard.

In that year, Raiffa began teaching the first university course in decision analysis within the Economics Department at Harvard and began preparing material for his 1968 book, “Decision Analysis.”

Also in 1964, but independently, Howard conducted the first professional application of the field he called “decision analysis,” which he described in his 1966 paper, “Decision Analysis: Applied Decision Theory.” Since then, major corporations have adopted decision analysis to improve multibillion-dollar capital decisions. ❙ Source: Raiffa-Howard Award Program

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Leadership: The real game changer for using data analytics

By Bill Gossman mall- and medium-size businesses (SMBs) are no different than their larger counterparts in recognizing the growing importance of data analytics. Some view the cost as prohibitive because data analytics, and specifically predictive analytics, is rooted in statistics and sophisticated mathematics that require special skills or expensive outsourcing. Others are reluctant to embrace data analytics because they question whether

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data can be strategically analyzed to create actionable responses rather than fishing expeditions that may or may not yield anything of value.

The business reality is that these positions have very little to do with data, data science or technology. It is a lack of executive guidance and direction – in other words, a people problem. With strong corporate leadership and managerial judgment, an effective analytics strategy is likely to emerge. w w w. i n f o r m s . o r g

The Analytics Quandary for Leaders An analytical focus can be an ultimate game changer for companies, which is why the concentration should be on established objectives rather than amassing data that may or may not be relevant.

It is at this point that leadership finds itself at a crossroads of two conflicting scenarios, both of which have drawbacks.

One scenario takes the form of a “wait and see” approach before deciding if investment in analytics offers an acceptable return on investment – a risky approach.

Delays can impact marketplace positioning and create an edge for competitors.

The second scenario focuses on the here and now of tactical day-to-day operations in lieu of a broader business strategy that leverages data.

An underlying assumption on the part of upper management that applies to both scenarios is erroneous. The assumption: Analytics is only possible with big and costly infrastructures that require significant management and resource commitments. The truth is significantly different.

While data is certainly a huge entity, size in this case is not as important as determining relevance. The idea is to keep matters manageable. The best way to do that is to focus on the low-hanging fruit of analytics – the data that is most easily accessible and valuable for establishing achievable goals. a na l y t i c s

The best-known example of this type of leadership is graphically displayed in the film “Moneyball.” Analyzing data about ballplayer skills that other general managers overlooked enabled Oakland’s Billy Beane to become one of the first baseball executives to leverage its value in a much more stringent, scientific way – a classic example of motivational leadership in analytics.

Leadership’s Data Usability Cost has and always will be a factor in analytics decision-making, but thanks to decades of technological advancements, data analytics are now available at prices that small and medium-sized businesses should be willing to consider for the most obvious and important reason: positive impact on the bottom line. A report published by The Economist describes a “strong link” between a company’s financial success and its use of analytics.

A survey released by the publication noted that 53 percent of respondents in its strategic group reported a higher financial success rate than their peers who did not take advantage of strategic data management (Briody, 2011). The report indicated that what it called “data collectors” (those who do not fully leverage the data at their disposal) and “data wasters” (under-users of collected data) are at risk of coming out on the short end financially.

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Even limited analysis of minimal levels of data can make a significant difference to the bottom line, especially when leadership clearly establishes goals beforehand.

that is not so readily apparent. “The biggest barrier is the assumption that analytics is a data science and technology problem when it’s really a leadership and managerial problem,” says Florian Zettelmeyer, director of the data analytics program at the Kellogg School of Management, Northwestern University. “The managerial class has to understand the problems that need to be solved and how analytics can help solve them.”

When leadership fails to implement a decision management strategy and convey its importance throughout the company, it unwittingly bypasses a major growth opportunity. Data constantly flows into and around an organization. Leveraging its specific components requires analytical execution and deployment in areas of maximum benefit by a committed staff. This is the essence of opportunity management. Effective decision management uses analytics to achieve better outcomes from customer interactions. The value, of course, depends on how well all these factors are applied.

Recognize the Unrecognizable Even limited analysis of minimal levels of data can make a significant difference to the bottom line, especially when leadership clearly establishes goals beforehand. The objective for data analytics is not quantity of data but quality of outcomes: e.g., solving business problems, generating customer information or analyzing market conditions.

Results should be sustainable, which means placing the emphasis on strategic instead of tactical.

Analytical approaches tend to vary depending upon 40

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the size of the company. Larger corporations generally engage statisticians in addition to a business intelligence team as part of their overall strategy. Most SMBs cannot afford to contract with statisticians, but have other avenues to maximize business intelligence, most notably automated programs. Some add BI expertise to their staffs, but are uncertain what their next big step should be. At this stage, leadership has to be prepared to resolve inevitable internal conflicts about analytical direction.

What is needed is a strategy to integrate analytics into decision management beginning with two very simple and achievable practices: focus and common sense. “You must have a long-term plan and a strategy that works with it,” says David Hufnagel, chief operations officer and vice president of Congressional Federal Credit Union (CFCU). “If you don’t work in concert with the plan, it becomes inoperable with no results.” The credit union has relied on automated analytics for the last three years to drive a proactive outreach to its 45,000 members. “It’s not about being unique with each individual,” Hufnagel says. “It’s about identifying what is relevant to our members.”

The CFCU experience is an example of focus and common sense for maximizing the value of data analytics through technological innovation. The idea is to have the technology focus on data that a na l y t i c s

directly relates to company goals. There is no reason to go data fishing in the hopes of snagging information that may be vital someday. This isn’t to suggest that such effort has no value. Rather, it’s more about priority, available time and resources.

Clarify ROI and Analytical Options Sometimes return on investment from automated analytical software is difficult to establish, which is why it can be the biggest barrier to a go/no-go decision.

Nonetheless, analytics success stories are everywhere. Leslie Deich, a professional financial services specialist, says she found the technology to be extremely effective in detecting consumer fraud, which helped protect GE Consumer Capital, Genworth Financial and Fannie Mae from what could have been significant losses. “We needed to find fraud patterns in the data and this made everything easier because it was able to locate the patterns in very quick fashion,” Deich says. “If you don’t know the data or are unfamiliar with it, this will help you see the relationships.”

CFCU’s David Hufnagel relies on opportunity management “to get a 360-degree view” of its membership. “It’s meant better targeting (and) now we’re seeing greater acceptance of offers,” he says. “It allows for greater efficiency with each campaign.” j a n u a r y / f e b r u a r y 2 015

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Leaders who take a strategic and proactive initiative when it comes to data analytics set the stage for the successful and actionable use of information.

These experiences demonstrate the ability of companies to achieve their desired outcomes through programs that maximize qualitative and quantitative analytics without having to worry about programming and algorithms. “Analytics are not disembodied truth,” says Northwestern’s Zettelmeyer.

“They answer those questions managers are most concerned about.”

Pathway to Opportunity Leaders who take a strategic and proactive initiative when it comes to data analytics set the stage for the successful and actionable use of information. The starting point for this type of initiative is a focus on management and oversight.

Analytics may be among the most powerful tools for executive decision-making and business management. They are at their best when flexible, focused and applied in tandem with decision management best practices that can be augmented and enhanced as needed. Executives and managers have a pathway to opportunity management regardless of corporate size and the ability to streamline business flow to improve growth opportunities and increase profitability if they are willing to take advantage of it. ❙ Bill Gossman (bill.gossman@asacorp.com) is president of Advanced Software Applications (ASA Corp.) in Pittsburgh, Pa. ASA provides analytical and decision solutions that help businesses grow revenue, improve efficiencies and mitigate risks.

REFERENCES Briody, Dan, 2011, “Big data: Harnessing a game-changing asset,” a report by the Economist Intelligence Unit, sponsored by SAS.

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Catch the Wave: Real-World Analytics Solutions INFORMS CONFERENCE ON

USINESS ANALYTICS & PERATIONS RESEARCH

Huntington Beach APRIL 12-14, 2015

LEARN HOW ANALYTICS AND O.R. CAN MAXIMIZE THE VALUE OF YOUR DATA TO DRIVE BETTER BUSINESS DECISIONS. √ Real-world use of descriptive, predictive, and prescriptive analytics √ Focus on big data, marketing analytics, healthcare applications √ Most rigorous and real world analytics conference offered √ Administration of Certified Analytics Professional (CAP®) exam √ Not just what to do but HOW TO DO IT Only INFORMS can provide an analytics and O.R. conference backed up by the best minds in industry and academia. Hand-picked speakers take you through case studies on how analytics can maximize the value of your data, driving better business decisions, and impacting the bottom line.

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data v is ua li z at i o n

The future of data visualization

By Will Towler

D

ata visualization is entering a new era. Emerging sources of intelligence, theoretical developments and advances in multidimensional imaging are reshaping the potential value that analytics and insights can provide, with visualization playing a key role. The principles of effective data visualization won’t change. However, nextgen technologies and evolving cognitive frameworks are opening new horizons, moving data visualization from art to science.

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Looking back, much attention has been given to the principles of effective data visualization, such as substance, context and actionability. As timeless tenets that will continue to be important, regardless of medium or format, a brief review seems in order: • As with any form of communication, effectively conveying a message with data requires that it be substantive. And while creative visuals can enhance interest and memory, embellishment can’t make up for lack of substance. According to purist w w w. i n f o r m s . o r g

Figure 1: Billion Dollar O’Gram.

Edward Tufte, “Every single pixel should testify directly to content.” Learn more.

• Visualization should be accurate and contextual. David McCandless’s Billion Dollar O’Gram provides an example of how greater meaning can be added by incorporating the bigger picture. According to McCandless, “Absolute figures in a connected world don’t give you the whole picture. They’re not as true as they could be. We need relative figures that are connected to other data so that we can see a fuller picture.” Learn more.

• More than anything else, data visualization should facilitate decision-making, a goal that is difficult to achieve for many. According to a recent KPMG study, while data and analytics are deemed

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Source: David McCandless http://informationisbeautiful.net

increasingly important to organizations, generating actionable insights remains a top challenge. Learn more.

Looking forward, nextgen technologies and evolving cognitive frameworks will boost the role that data visualization can play in organizations and society.

Consider the Internet of Things, Network and Complexity Theories, and recent developments in multidimensional visualization: The Internet has transformed the way we visualize information through a better understanding of networks and an explosion in profile, behavioral and attitudinal data. Sociograms, for example, have gone from relatively simple graphs to multifaceted relational maps, as illustrated j a n u a r y / f e b r u a r y 2 015

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in Figure 2 and Figure 3, courtesy of the Journal of Social Structure and the Leadership Learning Community.

The Internet of Things is expected to have a similar impact, with billions of connected devices capturing human and machine activity. Fully capitalizing on the data generated will require further advances in our ability to synthesize and display spatiotemporal activities.

Network Theory has been in use for decades, with its earliest applications largely in social structure analysis. More recently,

Figure 2: Pre-Internet sociogram.

Source: Journal of Social Structure

Figure 3: Internet-age sociogram.

Source: Leadership Learning Community

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Figure 4: Global spread of infectious diseases.

Network Theory is being applied to understand relationships and interactions in a variety of domains, such as crime prevention and disease management.

Dirk Brockmann and Dirk Helbling’s work modeling the spread of infectious diseases provides an example of the power that Network Theory holds.

In their article, “The Hidden Geometry of Complex, Network-Driven Contagion Phenomena,” in Science magazine, the authors wrote: “The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes.

The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an a na l y t i c s

Source: Science (http://www.uvm.edu/~cdanfort/ csc-reading-group/brockmann-science-2013.pdf)

intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns.”

By providing insight into the workings of dynamic systems with interdependent elements, Complexity Theory can help us identify trends that might lead to unexpected tipping points such as environmental disasters. Complexity Theory can also support the decoding of intricate structural dependencies such as economic and market forces. In “The Atlas Of Economic Complexity,” for example, the work of Cesar Hidalgo, Ricardo Hausmann and j a n u a r y / f e b r u a r y 2 015

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Figure 5: Depiction of economic complexity. project members illustrates how understanding the composition of a country’s cumulative industrial knowledge can explain economic development in ways not possible through more traditional linear econometric frameworks.

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Source: The Observatory of Economic Complexity; http://atlas.media.mit.edu/atlas/

The vast majority of data visualizations today are two-dimensional. However, that’s changing with creative use of color and size, combination of space and time, and advanced computer graphics.

For instance, neuroscientists Emmanuelle w w w. i n f o r m s . o r g

The Internet of Things

Tens of billions of devices will be connected to the Internet in the next decade.

From smart appliances and wearables to automobile sensors and environmental monitors, the Internet of Things will provide unprecedented insight into what’s happening around us. High-throughput, interconnected data streams will help us improve safety, drive operational efficiencies and better understand consumer demand.

In the words of Kevin Ashton, who first coined the term “the Internet of Things” in his seminal 2009 RFID Journal article, “The Internet of Things has the potential to change the world, just as the Internet did. Maybe even more so.”

Network Theory

Network Theory builds on Graph Theory, which applies algorithms to understand and model pair-wise relationships between objects. Network Theory examines relationship symmetry, with the existence of asymmetric relationships providing grounds to predict the likely spread of information (social network analysis), dissect complex disorders (biological network analysis), find the shortest path between two points (network optimization) and identify target objects based on their behavior (link analysis).

Complexity Theory

Complexity Theory posits that many systems are characterized by complex, non-linear interactions that evolve dynamically and often unpredictably. Known as the “butterfly effect,” small perturbations in one state (“here”) can result in large repercussions in a seemingly unrelated state (“there”). According to Complexity Theory, it’s impossible to predict with certainty a future state, but it is possible to understand the structure and potential states of complex systems.

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Figure 6: The 5-D colorimetric technique.

Tognoli and Scott Kelso developed a five-dimensional model known as the 5-D colorimetric technique, that provides a dynamic and comprehensive view of brain activity through spatiotemporal display and color coding. Another example is Microsoft’s Holograph, an interactive 3-D platform that can render static and

Source: Florida Atlantic University, Center for Complex Systems and Brain Sciences; http://www.ccs.fau.edu/hbbl3/?p=1013

dynamic images above or below a plane for more natural exploration and manipulation of complex data. And commentary from team members Curtis Wong and David Brown posted on Microsoft News suggests that Holograph may one day allow users to actually reach inside a visual and interact with it.

Multidimensional Visualization

The adage “a picture is worth a thousand words� gained credence from our ability to process visuals more easily than text. Visualization has also been shown to improve learning and recall, and can portray complex concepts and relationships more easily than can text. Recent developments in computer graphics are making possible visualizations that enable the integration, manipulation and exploration of dynamic multidimensional data sets. Multidimensional visualizations allow users to not only examine data from new perspectives but also interact with it more effectively.

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Figure 7: Microsoft’s Holograph, an interactive 3-D platform.

Source: Microsoft; http://microsoft-news.com/microsoft-research-talks-about-holograph-aninteractive-3-d-data-visualization-research-platform/

As the world becomes increasingly interconnected and interdependent, opportunities to generate value through data visualization will only increase. The Internet of Things will have a profound effect on the role that data visualization can play in organizations and society, improving our ability to understand how humans and machines interact with each other and the environment. Application of evolving cognitive frameworks, such as Network and

Complexity Theories, will help us better reflect dynamic and intricate structural dependencies. And advances in multidimensional visualization will allow us to more effectively synthesize and explore spatiotemporal conditions. â?™ Will Towler (wjtowler@hotmail.com) is an analytics and insights specialist. The views expressed in this article are those of the author and do not necessarily represent the views of an employer or business partners.

Request a no-obligation INFORMS Member Benefits Packet For more information, visit: http://www.informs.org/Membership

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Data visualization: The big picture of big data

By Nana S. Banerjee

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he 1977 film “Powers of Ten” portrays the universe as an arena of both continuity and change. The short documentary, selected by the Library of Congress as being “culturally, historically or aesthetically significant,” and written and directed by Charles and Ray Eames, begins with a 1-meter distant shot of a man laying by a picnic setting and steadily moves out until it reveals the very edge of the known universe. Then, at a rate of 10-to-the-tenth meters per 52

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second, the film rushes us back toward Earth to the reclined man’s hand and further down to the level of a carbon atom on his skin. That fascinating journey into the macro and then micro demonstrates visually the importance of scale and, in a metaphysical sense, the importance of visualization itself.

The importance of data visualization becomes more obvious when viewed within the context of how the human brain works. Much has been written in recent years about how the processes of the w w w. i n f o r m s . o r g

Figure 1: Natural log of relative operating speeds.

Source: “The User Illusion: Cutting Consciousness Down to Size,” by Tor Nørretranders (Penguin Press Science)

brain and how understanding those processes can provide profound insights.

In his best-selling 2012 book “Thinking, Fast and Slow,” Nobel laureate Daniel Kahneman introduces the terms System 1 and System 2. The terms differentiate between the information processing that occurs in the human subconscious and conscious minds. System 1 addresses the functions that are uncontrolled and effortless. System 2 comprises functions that are controlled and require effort to engage. In action, System 1 allows us to instantaneously recognize facial expressions – visual processing.

In contrast, System 2 allows us to make complex decisions or apply reason.

A little more than a decade before the release of Kahneman’s book, Danish a na l y t i c s

physicist Tor Nørretranders, in his book “The User Illusion: Cutting Consciousness Down to Size,” converts the “bandwidth of human senses” to computer terms. He explains just why data visualization (a manifestation of System 1) is perhaps the most powerful form of data interpretation.

Nørretranders demonstrates that when assessing the “language of the mind,” the sense of sight simply operates at an order of magnitude faster than the sense of touch (similar to the bandwidth associated with a network of computers), which in itself operates at an order of magnitude faster than the sense of smell. As such, the sense of smell operates at an order of magnitude faster than the sense of taste (which has a bandwidth similar to a calculator)! j a n u a r y / f e b r u a r y 2 015

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Realizing how quickly we understand and internalize what we see is at the foundation of what makes data visualization such an important aspect of how we analyze information and make better decisions.

Realizing how quickly we understand and internalize what we see is at the foundation of what makes data visualization such an important aspect of how we analyze information and make better decisions. That said, the mechanisms behind data visualization create a powerful tool to design effective visualizations to suit any context – whether that tool is a simple, static bar chart or something vastly more complex, multidimensional and interactive. As such, the science behind data visualization ranges from the fundamentals of how we literally see to the complexities of cognitive psychology.

Combining the science with the art – how best to portray the intent of any particular visualization – winds up somewhere on a curve between presentation and exploration. The difference between presentation and exploration can be described as the difference between presenting a known story in a data set using analysis and exploring a notyet-understood data set using a visual examination. Henry David Thoreau said, “It’s not what you look at that matters, it’s what you see.” With data visualization, the significance of the quote is quite literal. It’s a fully formed discipline that requires multiple skills – among them, the knowledge of statistics, ideas of space, design and topography, and a deep subject matter expertise in the sector being served.

From Theory to Practice Currently, for any company that deals with a titanic amount of data, data visualization is and will remain an absolutely fundamental tool. Verisk

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Analytics is a prime example – collecting and maintaining highly granular data on several billions of insurance policies and claims, credit card and debit card transactions, real estate, health services, government and human resources. While many consumer-centric firms have long been skilled in collecting information, they now generate and acquire exponentially growing, disparate and complex quantities of data – and depend on that data in many ways for their very survival in today’s marketplace.

Much of the talk today about data management and analysis and its effect on how business gets done targets the science of analytical modeling. In that pursuit, many firms indeed have come far, and yet they still have farther to go. Large, well-capitalized firms (such as banks, insurers, retailers) spend considerable resources and energy collecting and storing data, not just because they produce a lot of it but more likely because the regulatory environment mandates storing much of it.

These firms haven’t spent nearly enough effort aggregating their data across functional silos, integrating internal data with third-party data, analyzing the data, and distributing the resulting insights to people who can take action on it.

As an example from the retail sector, imagine that a retailer is looking to assess the effectiveness of a particular a na l y t i c s

promotional campaign at its retail stores through the holiday season. The management team at the retailer would invariably want to know: Do we know the baseline sales at our stores and our competitor stores before the promotional period? Let’s say maybe. Do we know how shoppers at our stores respond to promotional offers in the regular season? That’s another maybe. Do we know what the weather was like and if it played a role in affecting shopper turnout at our stores during the campaign period? That’s one more maybe. Would all those pieces of information come together at the same time and be presented to management in a manner that’s easy to analyze? That’s highly unlikely. And that’s a great example of where visualization becomes so helpful.

As consumers of information, we’re all demanding visualization in our own way. We’ve started to reject the culture of sound bites and nonsynthesized statistics that agenda-driven interest groups have inundated us with in the last two decades.

Visualization allows us to map the information in a way that leads to better decision-making – easier and faster. The 2012 InformationWeek Business Intelligence, Analytics and Information Management Survey, conducted in late 2011, indicated nearly half (45 percent) of the 414 respondents cited “ease-of-use challenges with j a n u a r y / f e b r u a r y 2 015

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Figure 2: A detailed inundation map of the New England coastline depicted in Figure 2 shows the surge footprint of Hurricane Sandy in blue. The orange circles represent clustered locations by actual number, with the largest circles containing the most individual locations. Where the blue and orange overlap, the map illustrates where Sandy had the greatest impact. Such maps help companies determine the extent of floods and resulting losses.

Source: AIR Touchstone® zoomed-in surge

complex software/less-technically savvy employees” as the second-biggest barrier to adopting business intelligence/analytics products – fractionally behind the biggest barrier, “data quality problems,” cited by 46 percent of respondents.

Mother Nature’s Infographics Catastrophe risk management has come a long way in its 25-year history, and the sophistication of those analytics goes well beyond the numbers in a database.

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insight into what drives risk.

Looking back to Superstorm Sandy, healthcare officials in New York City, in advance of the storm, were trying to decide whether to evacuate hospitals. In the end, many chose not to move patients before the storm. Unfortunately, numerous hospitals were then catastrophically flooded, and patients had to be moved during the worst of the deluge.

Certainly, myriad factors go into assessing a situation like that, but as analytics and their visualization become w w w. i n f o r m s . o r g

increasingly sophisticated, they’ll be able to help risk-bearing organizations, including insurers and local authorities, develop appropriate prescriptions for mitigating risk – by providing the contextual detail for better-informed decisions.

Today’s advanced climate models are capable of effectively projecting the impact of storms as they get closer to coastlines or geographic regions. Such models can assess the total number of homeowners expected to be affected, when an event is expected to worsen, and when it will be safe for insurance personnel to move into the area. The visualization models enable the decision-maker or assessor to evaluate locations at the individual building level. That capability facilitates a preplanning process and allows companies to communicate proactively with policyholders so they can take certain loss control measures – such as boarding windows, reducing chance of fire, and so on – to mitigate damage. Such models are also allowing insurers to readily project and visualize the impact of fallen trees on power lines serving a group of policyholders.

Given the complexity associated with climate change and the inherent difficulty in the assimilation of evolving and interdependent data, our dependence on a sophisticated and constantly improving visualization capability is far too great to be denied. a na l y t i c s

The Sight in Business Insight Unquestionably, the tried-and-true bar, line and pie charts have served us well. But when the complexities of relationships are more nuanced and the data becomes more unstructured, visual analytics need to become more dynamic, multidimensional and customized.

For lenders and insurers, visualization can help identify a range of data issues quickly – from a high-level view of exposure location to exposure composition and completeness, including breakdowns by profile of the entities at risk (customers, businesses, properties, vehicles and so on). Visual link analysis technology helps discover critical, previously hidden connections within data. Seeing those connections – within proprietary data, in data from external sources or through a combination of sources – provides insight and knowledge to make decisions. The technology finds all data elements applicable to a question and draws a picture of the connections among those elements, revealing previously invisible relationships. The contextual approach provides a multidimensional understanding of profitability, customer behavior, and industry trends.

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involved. Mapping techniques often find thousands of errors in a fraction of the normal time. Mapping also finds red flags in claims data. Fraud investigators at financial institutions often use visual link analysis to assist in their inquiries. For example, a money-laundering investigator monitors each check, credit card or ATM withdrawal over a specific threshold, and the technology helps in instantly flagging irregular patterns, revealing potential sources of fraud or money laundering. Seeing those connections – within company data, in data from external sources or through a combination of sources can give claims investigators insight and knowledge to help make better decisions.

Visualization is useful in insurance for commercial fleet and personal auto policyholders. Telematics programs use sensors to determine factors as simple as distance (vehicle miles traveled) and as sophisticated as camera-based recording. Devices transmit and store the resultant collection for immediate or deferred analysis, meaningful interpretation and visualization. Although the use of telematics data and visualization is in its early

stages, the usage-based insurance (UBI) opt-in rate is expected to increase to 20 percent over the next five years, according to one recent industry poll. Other polls consistently show that two-thirds of consumers are open to telematics-based insurance policies, especially if there’s the potential for premium discounts. Among newer consumers of vehicle insurance — the Gen Ys and the Millennials — the use of telematics and visualization technology is almost expected.

While throughout history and in the present day there is always that rare breed with the unique and innate ability to quickly make sense of disparate sources of information and data, the mortals among us are blessed to be living at a time replete with the data and tools to make those connections for us in a fraction – enabling us not only to make better business decisions but maybe even allow us to see the as yet unforeseen. ❙ Dr. Nana Banerjee is a group executive of Verisk Analytics. He serves as president of Argus Information and Advisory Services and as chief analytics officer of Verisk Analytics.

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Analy tic s i n Ac t i o n

How analytics helps contain Ebola

By Douglas A. Samuelson and Brian Umana (l-r)

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he outbreak of Ebola disease in West Africa, with some cases spreading elsewhere, has been a source of concern and frustration throughout the world.

This is the story about how a small data science and analytics company found a way to help contain the spread of the virus.

In September 2014, Brian Umana, a consultant at Illumina Consulting Group 60

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(ICG), had a conversation with company CEO David Waldrop about the Ebola epidemic. They each expressed concern about the scale of the human suffering, but also about facets of security and public health that could limit the spread of the epidemic. Given the nature of their earlier work in real-time streaming data analytics, they eventually began speaking about various ways one might apply data w w w. i n f o r m s . o r g

science and analytics to the problem.

They were not the first to do this, nor in any way did they take the most dramatic or significant action. However, within days of the conversation, ICG began to make a small contribution to safety in the U.S. response to the “maritime vector” of the Ebola virus.

Waldrop built a new-use case for LUX software, ICG’s principal product, a high-volume data streaming and pattern recognition tool. What Waldrop had noticed was that detection and prevention measures for potential maritime- and port-based spread of the Ebola virus appeared less rigorous than corresponding detection and prevention measures used in air travel. No one would dispute that it is essential to monitor airports where passengers are arriving from Ebolastricken countries as a measure for containing the spread of the disease. West African seaports and merchant shipping, however, are another potential means for spreading the disease abroad, a topic that has drawn far less attention.

Waldrop used the software to draw an area of interest off the coast of West Africa, enclosed by a second area of interest encompassing all the water outside of the first area but still inside of a specific radius of the West African coast.

Waldrop instructed LUX that any time a boat crossed from Area 1 (the port area) a na l y t i c s

to Area 2 (farther from the coast) and then exited Area 2 to create a map-based alert of the boat’s location, satellite data, name, flag, stated destinations and other information. He then implemented more sophisticated versions of this model. The result was additional notice to ports, and to authorities outside of the specific ports in question, about boats that should receive specific attention. Waldrop’s experiment worked, and he promptly donated the results to a federal agency.

Owing to the highly contagious nature of Ebola and the close quarters onboard ships, there’s a high risk of an outbreak rapidly affecting an entire crew. Such a ship entering port would pose a grave health threat to persons in the port and surrounding area. U.S. authorities and shippers are aware of the Ebola Maritime Vector threat, but detection and prevention measures appear less rigorous than those in the air travel sector. The rules in effect for maritime traffic were and are that ships must file a notice of arrival 96 hours prior to U.S. landfall, and those notices must include declaring visits made to any Ebola risk ports during the ship’s last five port visits. In addition, ship captains or owners are required to self-report any communicable disease onboard during the 15-day period prior to entering port.

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The scenario created compelling incentives for non-compliance with disease reporting, as ship owners face major commercial revenue losses if their ships are quarantined.

may be boarded, inspected and possibly quarantined. This scenario created compelling incentives for non-compliance with disease reporting, as ship owners face major commercial revenue losses if their ships are quarantined, or if contract conditions are not met owing to delays, or if the ship is diverted and fails to reach the port specified in a bill of lading. Taking this situation into account, Waldrop’s experiment with LUX helped address some issues of public safety in ports. Two federal agencies looked at the information. It brought the maritime aspect of the problem to their attention and demonstrated a relatively easy way to pay attention to it. They asked about the data sources and began monitoring them. The ships were monitored on the LUX watch dashboard. This procedure provided several days of advance warning about any ship that had visited West Africa and was headed to a U.S. port.

Ebola Maritime Vector Threat To mitigate the Ebola threat, LUX uses two real-time data streams to perform analysis. The first is data streamed from internationally mandated [1] automatic identification system (AIS) reporting. AIS, installed on all merchant ships in the world, is especially relevant to monitoring ships traveling to and from West Africa ports. The second source is data generated by the Global Data on Events, Location and Tone (GDELT) project [2].

The LUX Ebola Maritime Vector monitoring methodology works as follows:

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Figure 1: Illustration of the LUX user interface. The outer AOI provides alerts on arrivals and departures from the zone of concern. The inner AOI monitors activity in and around port areas.

• A user defines geographic areas of interest (AOI). In this case, two such areas are displayed on the map as shown in Figure 1.

• The user then writes rules instructing the software to generate alerts on any inbound or outbound ship crossing the boundaries of the AOIs. • When an alert is first received on an inbound or outbound ship, AIS data is used to determine the ship’s name, its AIS unique identifier number and other data such as course, speed, flag, declared destination port and cargo embedded in the AIS reporting.

• Once the ship is identified, the user

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writes another rule containing the ship’s name and AIS identifier instructing the software to track and generate alerts on its location and activity.

• LUX tracks the ship to its next port (declared or not) and sends the user alerts based on rules and geographic areas of interest related to that port. A useful tool in this regard is the dynamic area of interest (DAOI). The DAOI is centered on the ship itself and moves with it. A DAOI of any radius may be established.

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• The user then instructs the software to generate alerts on information collected by GDELT that matches the user’s rule parameters such as reports of Ebola cases, quarantining of ships, illness among ship crew members, etc. This is a potential source for discovering Ebola’s spread through the maritime vector.

• LUX also provides the means to detect abnormal behavior, such as a ship deviating from course, diverting to a port other than its declared port destination, or, particularly, cessation of its AIS reporting stream.

• If a ship’s AIS reporting were to cease, the software’s forecasting analytics would still provide an estimated track and the ship’s progress along it.

• To facilitate monitoring of multiple ships (thousands), rules alerts can be sent to a user-established watch board.

The watch board displays, as color-coded cells, aggregates of AIS alert reporting on as many individual ships as desired.

It also monitors and displays GDELT alerts. In this way a user may be relieved of constantly monitoring activity for situational awareness and let LUX take up that task 24/7. When the number or type of alerts reaches a user specified threshold, the watch board changes the color of the appropriate watch item cells as a visual notification. An audible tone may be added as an additional notification aid.

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What this story highlights is the vast amount of information available at the fingertips of everyone in the analytics community, the ability to act quickly on that information when it is properly applied and brought to the right people’s attention, and the potential benefit of such analysis and action. ❙ Doug Samuelson (samuelsondoug@yahoo.com) is president and chief scientist of InfoLogix, Inc., in Annandale, Va., and a senior operations research analyst with Group W, Inc., in Merrifield and Triangle, Va., supporting the Marine Corps Combat Development Command (MCCDC). He is a longtime member of INFORMS. Brian Umana (brian.umana@icgsolutions.com), a consultant at Illumina Consulting Group, has worked in software analytics, political analytics and media.

REFERENCES 1. International Maritime Organization’s International Convention for the Safety of Life at Sea 2. GDELT is the largest, most comprehensive and highest resolution open source database of human society ever created. GDELT monitors and analyzes the world’s news media from nearly every corner of every country in print, broadcast and web formats, in over 100 languages, every moment of every day.

Subscribe to Analytics It’s fast, it’s easy and it’s FREE! Just visit: http://analytics.informs.org/

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$

ting n e v n i e R alue V

INFORMS third conference on the health sector brings together researchers and stakeholders around the most current work in healthcare operations research, systems engineering, and analytics in one highly-focused conference.

Cross-cultural view of healthcare systems and analysis of operational impacts.

Structured networking opportunities, including birds-of-a-feather discussion groups and facilitated networking over lunch.

Collegial, small-scale setting in a vibrant location in one of the fastest growing cities in the U.S.

HEALTHCARE 2015 NASHVILLE TENNESSEE Abstract Submissions Now Open!

http://meetings.informs.org/healthcare2015/abstract-submission INFORMS Healthcare 2015 invites submissions on research and applications in the healthcare arena. We welcome submissions in either oral or poster formats, which will be reviewed by the Committee. There is a limit of one oral presentation per presenting/lead author. Authors may present a poster as well as one oral presentation.

JULY 29-31 OMNI HOTEL GENERAL CHAIRS Robert Dittus, Vanderbilt University M. Eric Johnson, Vanderbilt University PROGRAM CHAIR Vikram Tiwari, Vanderbilt University INVITED SESSIONS CHAIR Eva Lee, Georgia Institute of Technology

DEADLINE FOR SELECTED PRESENTATIONS March 1, 2015

SPONSORED SESSIONS CHAIR Mehmet Begen, Western University, Canada

DEADLINE FOR POSTER PRESENTATIONS March 15, 2015

HAS STUDENT PAPER COMPETITION CHAIRS Anita Tucker, Brandeis University Baris Ata, University of Chicago

http://meetings.informs.org/healthcare2015

ADVISORY COMMITTEE Dr. Warren Sandberg, Vanderbilt University R. Lawrence Van Horn, Vanderbilt University

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Business Analytics and O.R. Conference set for Huntington Beach The conference will celebrate the best in the profession, headlined by the prestigious Franz Edelman Award competition and the Franz Edelman Gala.

By Manoj Chari

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INFORMS will return to Huntington Beach, Calif., for the 2015 INFORMS Conference on Business Analytics and Operations Research, which will be held April 12-14 at the Hyatt Regency Resort & Spa. The conference, with the breadth of its coverage and the depth and quality of its presentations, should be of great interest to practitioners of analytics and operations research, whether they work in the public sector or private industry. It will also appeal to academics who are training future analytics practitioners or have research interests that would benefit from exposure to cutting-edge work in industry and government.

The conference will run from Sunday to Tuesday, with the main conference program held on Monday and Tuesday. The Sunday sessions focus on vendor technology workshops, the INFORMS Professional colloquium and the UPS George D. Smith finalist presentations. Plenaries from industry luminaries and thought leaders [such as keynote speaker William Ruh, VP of GE Software; see accompanying sidebar] headline the main conference program.

The conference will celebrate the best in the profession, headlined by the prestigious Franz Edelman Award competition and the Franz Edelman Gala, both

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Huntington Beach, Calif., will provide the backdrop for the INFORMS Conference on Business Analytics and Operations Research in April.

of which will be held on Monday. There will also be an invited session over two days that will feature presentations by finalists and winners of various other INFORMS prizes such as the Daniel H. Wagner Prize, the INFORMS Prize, the Analytics Innovation Prize and UPS George D. Smith Prize.

The main program will cover a wide range of topics in analytics and operations research practice with tracks on marketing analytics, unstructured data analytics, real-time decision systems, supply chain and logistics, revenue management and pricing, managing risk and decision analysis. Attendees typically have a choice of at least eight parallel, a na l y t i c s

high-quality presentations to choose from among these areas and can tailor their conference itinerary to their professional interests.

Additionally, software tutorials from vendor sponsors run parallel to the main sessions, allowing attendees an opportunity to hone their skills in that arena.

Poster sessions on both days offer further possibilities for attendees to present their work to their peers in an informal setting.

Conference receptions, meals, breaks and other events are designed to offer plenty of networking opportunities.

In summary, analytics and O.R. professionals who attend this conference will hear about the latest and greatest j a n u a r y / f e b r u a r y 2 015

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in practice, learn new ideas and best practices, expand their professional and personal networks and celebrate their profession. Local area students are particularly encouraged to attend and take advantage of the reduced conference registration and professional exposure afforded by this conference.

Look for announcements and updates on the INFORMS website and the conference website for up to date information about the conference. â?™ Manoj Chari is chair of the 2015 INFORMS Conference on Business Analytics and Operations Research.

GE’s Ruh to deliver conference keynote William Ruh, vice president of GE Software, will deliver the keynote address at the 2015 INFORMS Conference on Business Analytics and Operations Research set for April 1214 in Huntington Beach, Calif.

Ruh joined GE in 2011 to lead a renewed focus on software and analytics and to drive the global strategy, direction, operations and portfolio of softwarebased services that harness streams of customized performance data for reaching new levels of productivity. As part of this charter, GE Software is creating an ecosystem of strategic partnerships and investments, developing a common software platform (PredixTM) for powering industrial Internet offerings,

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and establishing a portfolio of shared services including cyber security, data science, user experience, cloud and agile development for building more sustainable and safe products.

Prior to joining GE, Ruh was vice president at Cisco, where he held global responsibility for developing advanced services and solutions. He has more than 30 years of industry experience in enterprise application integration and object-oriented technology.

Ruh is an accomplished author and a frequent speaker on such topics as emerging business models, cloud computing, analytics, mobile computing, agile development, large-scale distributed systems and M2M communications.

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Journals INFORMS PubsOnLine Suite is your personal link to all areas of OR/MS research and discoveries giving you a serious competitive advantage. Subscribe to INFORMS PubsOnLine Suite and save!

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Five - M in u t e A na lyst

Battleship The research question is: How can we tell if another player is being dishonest?

By Harrison Schramm, CAP

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I had the opportunity to play Battleship with two boys in my family. For those who may not be familiar, Battleship is a board game played in two stages.

First, each player places their fleet of five ships covering a total of 17 squares on a 10x10 grid without their opponent seeing. Ships may be placed along rows or columns but not diagonals. Then, players call out grid squares to “shoot” at, and the other player reports if it is a “hit” or a “miss.” It is presumed that the players are honest. The research question is: How can we tell if another player is being dishonest? (Note: this project was much more fun than I expected it to be!)

The basics: When there are no hits on the board, 17/100 = 17 percent of the spaces are occupied. A first approximation or “naïve” plan would say that the expected number of shots until the first hit is 1/.17 = 5.8. This is slightly inaccurate, because the shots are not independent. A finite population adjustment shows that the expected number of shots is approximately

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Figure 1: A scheme for covering the Battleship board in 33 shots. There are similar schemes for covering the board in 50 and 20 shots.

5.3. This is a minor – but noticeable – difference in the number of times to first hit. I would begin to seriously suspect (95 percent confidence) that my opponent was cheating if I did not score a hit in the first 15 attempts, and would be almost certain (99 percent confidence) he was cheating if I did not score a hit in the first 22 shots.

Can we do better? Yes! Each ship is rectangular, and can only inhabit squares that are along ranks or files (like rooks in chess). The way to handle this is to pick shots along diagonals (like bishops

in chess). If we have already shot and missed in a particular square, say, A1, then we don’t have to shoot at A2 or B1.

For example, if we are most interested in hitting ships three or more lengths in size – submarine, cruiser, battleship and carrier – then we may “skip two” and “cover” the board in 33 shots (see Figure 1).

We can think of this as superimposing a couple of different games on top of each other; for example, in the carrier case (skip four), it would look something like this:

Figure 2: Picture of the “superimposed” Battleship game. Conceptually, the carrier is occupying one square and is playing on the 20-square board (left), while the rest of the fleet are occupying their respective sizes on the 100 square board. Each shot removes one square off the respective boards. a na l y t i c s

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The general method for determining the probability of hit in the next round, given no hits in the preceding rounds, is to determine the probability of hit for each ship type, and the key intuition is that for ships that are three or larger, the scheme shown in Figure 1 effectively “covers” three spaces on the board after the first shot, which always covers just one (why?). The general formula is:

possibility of missing the two-square “corvette” ship, but will certainly hit all the others. This is an area ripe for further analysis.

Defending along the rows: What does it mean to be “maximally random?” There are different interpretations, but one way might be to make the groupings along the rows and columns of the board fit a Poisson distribution with λ=1.7. A perfect fit is not possible because at least one of the rows or columns will have a “5” due to the carrier, which only has a .3 probThe formulae for the other spacing are ability of occurrence. This turns out to be similar. an optimization problem that exceeds the We can compare these approaches, bounds of the Five-Minute Analyst. This and see that they all perform remarkably problem is very similar to solving Sudoku similar; the five-space approach is slightly with optimization. Below is a candidate better for achieving the first hit. solution and its performance measured by the Poisson metric.

In conclusion, this piece has brought up more areas for analysis on this topic than presented answers. Hopefully you will think differently about Battleship next time you play.

Bonus: If you don’t have your computer handy while you are figuring out Figure 3: Performance of various skipwhich strategy has the greatest rate of ping schemes for scoring the first “hit” in improvement, you can appeal to calculus.

Battleship.

The proposed strategies are of the form We have only considered the time until the first hit. While beyond the bounds of . Differentiate, yielding this analysis, my sense is that “skip two,” as depicted in Figure 1, is the optimum and evaluate at a convenient game playing strategy. This strategy has a place, say, n = 1 . ❙ 72

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Figure 4: A possible laydown of Battleship. This was developed using a spreadsheet to sum the rows and columns and generate the histogram shown in Figure 5.

Figure 5: Performance of the laydown shown in Figure 4. The ideal distribution is shown in gray, with the row and column distributions shown in blue and orange, respectively.

Harrison Schramm (harrison.schramm@gmail.com) is an operations research professional in the Washington, D.C., area. He is a member of INFORMS and a Certified Analytics Professional (CAP).

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Electrifying

Figure 1: Where to place substations?

By John Toczek John Toczek is the senior director of Decision Support and Analytics for ARAMARK Corporation in the Global Operational Excellence group. He earned a bachelor of science degree in chemical engineering at Drexel University (1996) and a master’s degree in operations research from Virginia Commonwealth University (2005). He is a member of INFORMS.

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A new city is being built that will include 20 distinct neighborhoods as shown by the house icons in the map. As part of the planning process, electricity needs to be connected to each of the neighborhoods.

The city has been allocated funds to put in three electrical substations to service the electrical needs of the neighborhoods. The substations are represented by the three electrical box icons to the right of the map. Because laying electrical line to each neighborhood is expensive, the placement of the substations on the map requires careful consideration.

A neighborhood will be serviced by the nearest electrical substation. A neighborhood may only be connected to one substation. The substations may be placed in any cell (including the same cell as an existing neighborhood). The cost of electrical wiring is $1 million per kilometer. Distances are measured using a direct line between cells, which are each one kilometer apart. For example, the distance between cell A1 and B2 is 1.41 kilometers.

Question: What is the minimum cost required to connect all neighborhoods to electricity?

Send your answer to puzzlor@gmail. com by March 15. The winner, chosen randomly from correct answers, will receive a $25 Amazon.com Gift Card. Past questions can be found at puzzlor.com. â?™

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OPTIMIZATION GENERAL ALGEBRAIC MODELING SYSTEM High-Level Modeling The General Algebraic Modeling System (GAMS) is a high-level modeling system for mathematical programming problems.

GAMS is tailored for complex, large-scale modeling applications, and allows you to build large maintainable models that can be adapted quickly to new situations. Models are fully portable from one computer platform to another.

State-of-the-Art Solvers GAMS incorporates all major commercial and academic state-of-

GAMS Integrated Developer Environment for editing, debugging, solving models, and viewing data.

the-art solution technologies for a broad range of problem types.

West Point Academy Scheduler Since 1802 the United States Military Academy (USMA) at West Point has trained cadets to become Army officers. Every year USMA schedules more than 250 courses and examinations for 4,000+ cadets. Each cadet is given a careful balance of academic, military, and physical education.

Student-centric planning, unique requirements, and a vast catalog of business rules makes course and examination scheduling a very unusual and challenging problem. Since 2001 USMA has used GAMS webbased models to support their complex scheduling process. Originally developed on-site by GAMS experts, a GAMS application is now part of West Point’s academy management system. This application assigns courses to students based on a variety of constraints, identifies conflicts, and suggests schedules for each student’s term-end examinations.

Combined with human intervention, the GAMS application has reduced a previous scheduling process which required several weeks to the work of a few hours.

For further information please contact David Monroe David.Monroe@usma.edu

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