Analytics September/October 2014 by Analytics Magazine

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Driving Better Business Decisions

septem ber/ o c tober 2014 Brought to you by:

Measuring Social Media Karma Five easy-to-extract metrics of posts, tweets, blogs, pings and uploads from a vast, unstructured world

ALSO INSIDE: • 10 myths of analytics and insights • High-performance analytics companies • Can technology transform government? • The value of ‘wrangling data’ skills Executive Edge Verisk Analytics CEO Scott Stephenson on innovation redefined: beyond investing in technology

Ins ide story

Slaying sacred cows Slaying metaphorical sacred cows is hard work, but that’s exactly what Will Towler does in this month’s lead feature article (“10 myths of analytics and insights”). Among the myths Towler debunks: “Knowledge is power,” “People are rational,” “You can’t manage what you can’t measure,” “Sound analytics drive sound decision-making” and my personal favorite, “Great insights sell themselves.” Any analytics professional who has ever bumped up against corporate decision-makers has been there, done that, been dismissed, picked up the pieces and analyzed what went wrong. By definition, metaphorical sacred cows are “considered to be exempt from criticism or questioning,” so they must be approached with caution. Yet Towler not only takes them on, he offers some valuable takeaways. For more click here. Speaking of measuring, Mu Sigma manager Kshira Saagar provides five fundamental measures that can serve as quick-wins to analyze your social media karma. Saagar notes that while almost every organization spends lots of financial and human resources trying to make sense of their social media actions (post, tweet, blog, ping, upload, etc.) and reactions (like, share, re-tweet, favorite, reblog and download), about half of chief 2

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marketing officers are unable to quantify social media impact on their companies. Warns Saagar: “In this interconnected world, a small social media ripple can have the impact of a tsunami on the eventual sales bottom line.” Harrison Schramm, CAP, author of “The Five-Minute Analyst” column, turned his textual data tools loose on an unusual target this issue: presidents’ State of the Union addresses over the years. Schramm used a method call the “FleschKincaid Grade Level” to calculate the readability and complexity of first-term State of the Union addresses by Presidents Madison, Lincoln, Clinton, Bush (George W.) and Obama. To see how they graded out, click here. In case you were wondering, the “CAP” after Schramm’s name indicates he’s a Certified Analytics Professional (CAP®). Developed by INFORMS, the CAP program includes an exam that is administered at more than 700 testing locations around the world. The CAP program was recently listed No. 1 by CIO magazine in an article titled, “11 Big Data Certifications That Will Pay Off.” For more on the CAP story, click here.

– 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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Features 30

10 myths of analytics and insights From traditional consumer research to emerging business intelligence, 10 fallacies to keep in mind to make the most of your analytical efforts. By Will Towler

High-performance analytics organizations To realize value from big data analytics, organizations need to integrate technology, tools and practices with existing analytics ecosystems. By Pramod Singh, Ritin Mathur and Srujana H.M.

Five measures of social media karma Return on investment: Quick-fix analyses and easy-to-extract metrics of posts, tweets, blogs, pings and uploads from vast, unstructured world. By Kshira Saagar

Can government lead innovation? The first U. S. chief technology officer claims great promise for public-private information technology partnerships. By Doug Samuelson

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Sodium Item cost President Stephen M. Robinson, University of Wisconsin-Madison (mg) President-Elect Fat (g) L. Robin Keller, University Calories of ($) Beef Patty 50 17 California, Irvine 220 $0.25 Past President Anne G. Robinson, Verizon Wireless Bun 330 9 Brian Denton,260 $0.15 Secretary University of Michigan Cheese 310 6 Nicholas G. Hall,70 $0.10 Treasurer Ohio State University Vice President-Meetings William “Bill” Klimack, Chevron Onions 1 2 10 $0.09 Vice President-Publications Eric Johnson, Dartmouth College Pickles 260 Vice President0 5 $0.03 Sections and Societies Paul Messinger, CAP, University of Alberta Lettuce 3 0 4 $0.04 Vice President Information Technology Bjarni Kristjansson, Maximal Software Ketchup Vice President-Practice 160 0 20 $0.02 Activities Jonathan Owen, CAP, General Motors Activities Tomato Vice President-International 3 0 Grace Lin, Institute 9 for Information Industry $0.04

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Vice President-Membership and Professional Recognition Ozlem Ergun, Georgia Tech Vice President-Education Joel Sokol, Georgia Tech 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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Analytics (ISSN 1938-1697) is published six times a year by the President Sales John Llewellyn Send your answer to puzzlor@gmail.com by October 15th&, Advertising 2014. The winner, chosen randomly from Institute for Operations Research and the Management Sciences john.llewellyn@mail.informs.org correct will society receive a $25 Amazon Gift Card. Past questions can be found at puzzlor.com. (INFORMS), theanswers, largest membership in the world dedicated Tel.: 770.431.0867, ext. 209 to the analytics profession. For a free subscription, register at Editor Peter R. Horner http://analytics.informs.org. Address other correspondence to peter.horner@mail.informs.org the editor, Peter Horner, peter.horner@mail.informs.org. The 770.587.3172 Johnexpressed Toczek in is Analytics the Sr. Director and Analytics for Aramark CorporationTel.: in the Global Operational Excellence opinions are thoseof of Decision the authors,Support and Assistant Donna group. He earned his BSc. in Chemicalits Engineering at Drexel University (1996) andEditor his MSc. in Brooks Operations Research from do not necessarily reflect the opinions of INFORMS, officers, donna.brooks@mail.informs.org Virginia Commonwealth (2005). Lionheart Publishing Inc. or theUniversity editorial staff of Analytics. Art Director Jim McDonald Analytics copyright ©2014 by the Institute for Operations jim.mcdonald@mail.informs.org Research and the Management Sciences. All rights reserved. Tel.: 770.431.0867, ext. 223 Advertising Sales Sharon Baker 6 | sharon.baker@mail.informs.org Tel.: 813.852.9942

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Innovation redefined: beyond investing in technology Innovation is about investing in new ways of thinking and empowering people to challenge the status quo.

By Scott Stephenson

One of the most overused words of the last decade is “innovation.” As with most buzzwords, the more it’s used, the less it tends to mean. But real innovation is what determines progress, and often as a result of progress, the definition of innovation changes with time. So, what does innovation mean now? First, let’s look at what it meant back in the day. Innovation meant taking risks, betting on something new. Today’s innovators likely still are risk takers – that, after all, is the nature of business. But we’ve advanced since then. Now, to cultivate and sustain success, the risk takers must also be risk mitigators. Today, taking risks and innovating go beyond investing in technology. Innovation is about investing in new ways of thinking and empowering people to challenge the status quo – whether to create a new product, find an unconventional solution or anticipate opportunities. One example that meets all those criteria is today’s mesh economy – companies that invent new ways for people to share products and services, driving traditional companies to rethink their approaches to innovation.

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Three Kinds of Innovation The Verisk Analytics innovation model consists of three subsets: • process innovation – drives efficiencies in business operations • product innovation – extends existing products with new functionality and capabilities • invention – redefines markets and creates new industry ideas We implement all three, with an emphasis on invention. Invention stems from our collaboration with customers, allowing us to gain a thorough understanding of what challenges confront the markets we serve and how we can find solutions to those issues. Equally, collaboration with our customers enables us to discover what works well and where the growth opportunities exist for them – and, consequently, what services we need to create, revive, or expand to meet their growing requirements. With customers deeply involved in our development process, we gain the benefit of real-time insights and reactions as they move through phases of innovation with us – from ideation to prototype to adoption. That kind of collaboration has redefined what innovation truly means today both at Verisk and for many other organizations. Innovation: What’s Next? Investing in innovation requires a 10

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culture that supports innovation, ensuring employees understand and rally around an organizational philosophy defining what innovation means and why it matters. The Verisk concept of innovation is based on our n+1 philosophy: To be competitive today, organizations must strive for what we call the n+1 data set. If a company’s data set has a certain number of elements — n — it must constantly be working to include one more. It must continually add elements, advancing toward the next layer, adding richness to its analysis. To be sure, such an approach requires investment – in data resources, in analytics, in technology, in people. But the return on investment is to thrive, rather than simply survive or ultimately fail. That’s true for all industries but especially so in data-driven industries such as insurance, healthcare and supply chain, among others. The n+1 philosophy can help a company answer such crucial questions as, What’s next? and What should we do to improve efficiency, reduce risk, indeed turn risk into opportunity and increase growth? For example, a comprehensive supply chain risk management strategy – along with the incorporation of an array of predictive analytic tools to measure and manage risk – often extends beyond the supply chain itself to encompass all major operations of the organization. In fact, modern w w w. i n f o r m s . o r g

predictive analytics is fast becoming a tool to recognize key trends, patterns and potential disruptions within supply chains. It’s a means to protect the enterprise’s most valuable assets while also creating sophisticated risk resilience and mitigation models. Exponential Benefits A recent PricewaterhouseCoopers survey indicates that over the next five years, the most innovative companies are set to grow at twice the pace of the global average and three times the rate of the least innovative. There’s a myriad of literature available about innovation, yet much is not particularly helpful because it tends to describe innovation as a single element. Quite the contrary, there are several different modes of innovation, and a company has to be clear about what it means when using the term. What kind of innovation is the company looking for? Innovation through the n+1 philosophy shows up everywhere in the form of this question: Is there a layer of data we’re not yet accessing that may drive us toward a better solution? One example can be found in catastrophe modeling. Historically, our business has tried to understand the probability of a range of perils, such as hurricanes. Companies need to understand both a hurricane’s location a na l y t i c s

and intensity. But we’re also aware there have been changes in the composition of the atmosphere that potentially can lead to an extreme weather event, such as a hurricane. That said, if the temperature of the surface of the ocean correlates with the amount of energy that can affect the next hurricane developing at sea, a company can use new parameters, such as sea surface temperatures, in its analysis. That’s the +1 layer. And each layer can set the stage for yet another. So, meaningful innovation today applies n+1 as its foundation and embraces a comprehensive dialog with customers to make sure a company truly understands its customers’ emerging needs. From the strength of that relationship, a business tends to realize a viable product and associated revenue much faster. And the benefits just accrue from there: Rather than aiming to build the solution of all solutions, a business can produce more refined iterations – one at a time – and then enjoy the rewards of one success after another. ❙ Scott Stephenson is president and chief executive officer of Verisk Analytics.

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Students, professionals need ‘data wrangling’ skills It seems irresponsible of business schools to continue to teach a core curriculum that does not reflect the increasingly central role of software programming.

By Vijay Mehrotra

As I write this, I am as usual frantically preparing for the new school year, which starts in just a few short days. I am once again teaching exclusively MBA students here at the University of San Francisco. This year, in addition to the core quantitative methods course and my longstanding applied statistics elective, I’m also teaching a new elective entitled “Introduction to Data Mining.” All of our analytics courses for MBA students are taught with a strong practical bent. Our core MBA course is entitled “Spreadsheets and Business Analytics,” and not surprisingly this course requires students to be very hands-on with Excel in building models and analyzing historical data. In addition, because both of my electives for this fall place a heavy emphasis on data analysis, both of them are built around the JMP software from SAS Institute. At this point, a short digression for a true confession: I have never liked computer programming. It’s not that I’m not capable of doing this kind of work – just ask me about the integration of GAMS with Mathematica in order to run numerical experiments for my dissertation! – but the reality is that programming is something that I do not for the most

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part enjoy. In my research, I am always looking for existing software tools (or graduate students) that I might be able to deploy to get my experiments done more quickly, and I will invariably write code from scratch only as a last resort. Given this aversion to programming, I am able to empathize with my MBA students, the vast majority of whom have little or no background with coding. Moreover, all of the same factors that have driven the proliferation of analytics in the business world have also made it

possible – and increasingly easier – for me to teach a lot of relatively sophisticated mathematical and statistical applications to these students. So I am thrilled that thanks to today’s faster hardware and user-friendly software, I can bring some important ideas and techniques to my MBA students in a hands-on way. A select few of my MBA students ultimately decide to take my “Analytics Consulting Projects” course [1], and that’s where the game changes radically. The reality is that there is virtually no

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All of the students learn that these capabilities are truly essential in order for them to be able to answer even moderately challenging business questions whose answers are data-driven.

way to do a meaningful real-world analytics project without doing a significant amount of data manipulation work to get the data into shape for whatever analysis needs to be done. As the New York Times’ Steve Lohr recently reported [2], this need for extensive “data wrangling” is as great for professional data science teams as it is for my students. This is where the fun begins. How do you manipulate data without writing a computer program? The most common first step is to use Excel to manipulate raw data into the required format, often in awkward and unnatural ways. This is not only because of Excel’s increasingly powerful capabilities for sorting, searching and summarizing but also because this is an environment with which they already have extensive experience and comfort. One of last year’s project teams, upon finally determining a particular statistical analysis that would provide the client with some unique insights, spent several hours perusing and posting on http://www.mrexcel. com/ before ultimately figuring out how to do the lookup/summary calculations that were needed to prepare the data. As the limitations of the Excel platform become apparent, some teams have no choice but to get educated on other tools such as Python, SQL and R. In class, we often hold short workshops to help support this learning process, some led by me and others organized by the students. But all of this consumes valuable time on the project calendars, and this data wrangling is often the source of a great deal of stress,

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especially since the problem statement and solution methodology is also evolving along the way for most projects. All of the students in this projects course ultimately learn that these capabilities are truly essential in order for them to be able to answer even moderately challenging business questions whose answers are data-driven. As such, after teaching this class for a second time last spring, I began thinking that a programming prerequisite might

be worth creating. The course that I had envisioned would be ruthlessly practical (fundamental programming logic, basics of relational databases and SQL, and examples of how to manipulate data in a few different environments including Python, SAS, SPSS and R). The goal would be to give them a little more skill – and a lot more confidence – in their ability to manage the data on subsequent projects. It turns out that I’m not the only business faculty member thinking about

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whether my students should learn to write code. A recent Business Week article entitled “B-Schools Finally Acknowledge: Companies Want MBAs Who Can Code” [3] provides a brief description of what several elite MBA programs are doing to provide their students with opportunities to study programming. Yet almost none of the examples that are cited in the article, ranging from a joint MBA and MS in computer science at Stanford to new elective courses being considered at Harvard, mention data management (with the exception of economist David Backus’ new elective in data visualization and Python being offered at NYU starting this fall). Instead, the focus of the article is on MBAs who want to pursue careers as product managers and tech entrepreneurs and what these elite programs are doing to support them. I would argue that there is something more profound here for business schools to consider. As Marc Andreessen famously pointed out in 2011, “More and more major businesses and industries are being run on software and delivered as online services – from movies to agriculture to national defense. …Six decades into the computer

revolution, four decades since the invention of the microprocessor and two decades into the rise of the modern Internet, all of the technology required to transform industries through software finally works …” In light of this reality, it seems irresponsible of business schools to continue to teach a core curriculum that largely does not reflect this increasingly central role. While a class or two in software programming will not solve that problem overnight, it is certainly a start. And any class that helps us create graduates that can wrangle a lot more of their own data should also be a big plus for our quantitatively oriented MBA students – and for their future employers. Vijay Mehrotra (vmehrotra@usfca.edu) is a professor in the Department of Business Analytics and Information Systems at the University of San Francisco’s School of Management. He is also a longtime member of INFORMS. Notes & REFERENCES 1. http://www.analytics-magazine.org/may-june2013/798-analyze-this-course-puts-students-in-theanalytics-game 2. For the complete article, see http://www.nytimes. com/2014/08/18/technology/for-big-data-scientistshurdle-to-insights-is-janitor-work.html?_r=0 3. http://www.businessweek.com/articles/2014-07-11/bschools-finally-acknowledge-companies-want-mbaswho-can-code

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INFO RMS in i t i at i ve s

An analytics maturity model that fits The fervor of big data and business analytics have led to a bumper crop of competing analytics maturity models that can be characterized as good, better and best.

By Aaron Burciaga, CAP

The Sears, Roebuck and Co. catalog once offered customers products in three tiers of quality: “good,” “better” and “best.” The price for each product corresponded to distinguishing features that made choices good, and others still better or best. It was an elegant solution for simpler times. These paper catalogs provided customers with an information model for prudent investments in products such as a pair of boots – fitting customers both literally and figuratively according to their needs, resources and size. The INFORMS Analytics Maturity Model or IAMM (see earlier articles in OR/MS Today [1] and in Analytics magazine [2]) is now available in a beta version (https:// analyticsmaturity.informs.org/). It emulates these “good,” “better” and “best” principles in two ways. First, the fervor of big data and business analytics have led to a bumper crop of competing analytics maturity models that can be characterized as “homegrown or proprietary” (good), “scholarly” (better) and “impartially developed by a respected panel of experts” (best) solutions. As the largest nonprofit association of analytics professionals with 11,000 members from across academia, business and government, INFORMS is uniquely qualified and positioned to

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Figure 1: IAMM themes and factors. define the new standard. Moreover, use of the IAMM is free; it is not associated with any commercial software or service. The second way in which the IAMM is reminiscent of Sears, Roebuck and Co. catalogs is that it efficiently organizes information to fit solutions to customer needs, resources and size. One size does not fit all. Thus, IAMM guides you, the user, toward developing an appropriately scaled analytics program making right-sized investment(s). During your assessment, the IAMM will guide you through a process to score analytic maturity in three crucial areas: 1) organization, 2) analytics capability and 3) data & infrastructure. Each one of these three themes is further defined by the four factors shown in Figure 1. In the spirit of TurboTax, WebMD and a na l y t i c s

other award-winning applications, the IAMM asks you to answer 12 fundamental questions about your use of analytics. Scores are entered on a 10-point scale that identifies beginning, developing and advanced analytics maturity. You can then view your scorecard along with an important benefit: customized recommendations, along with the ability to set goals, build a plan to incrementally develop the analytics maturity score and track progress over time. For example, you may find you are a 3/â&#x20AC;&#x153;beginnerâ&#x20AC;? in a category today. Using linked services, benchmarks and best practices, you can determine how and when you can achieve a 7/advanced level. This goal-setting feature identifies what specific actions, policies or investments are necessary to reach that level s e p t e m b e r / o c t o b e r 2 014

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of analytic maturity. It helps you, your clients and your executives to: 1) Develop and execute action plans; 2) justify resource investments; and 3) target key areas within the business. You will notice that context for each of your “as-is” and “to-be” scores is enriched by the benchmark information provided according to your industry – baselines by which you can compare your business’ current and goal states against other similar businesses. This unique feature of the IAMM helps you create or maintain a competitive advantage and provides both the rationale and justification for investments. If you need assistance developing your plan and improving your organization’s use of analytics, you can follow links to INFORMS services that can help you in the areas that are most important to you. The committee has received questions about the security of data input by users. The INFORMS IT department double encrypts the data to protect users’ privacy. No proprietary data is shared. IAMM celebrated its soft launch at the INFORMS Big Data Conference in San Jose, Calif., in June. INFORMS IT Director Nagaraj Reddi and I unveiled it for the many participants who turned to INFORMS for expertise in analytics. An active team of INFORMS practitioners from respected firms has been 20

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developing the IAMM for two years, carefully determining the needs of users and noting the INFORMS model’s unique position vis-à-vis other models. The IAMM Committee is chaired by Norm Reitter of CANA Advisors and includes, Rob Benson (Spinaker), John Poppelaars (ORTEC), Jim Williams (FICO) and Aaron Burciaga (Accenture), with INFORMS staffing by Executive Director Melissa Moore, Communications Director Barry List and IT Director Nagaraj Reddi. Just as Sears, Roebuck and Co. catalogs once met customer needs with tiered products, the INFORMS IAMM provides tailored assessments and solutions for right-sizing an analytics program. Complete your assessment today, fitting you and your business to good, better or best options for analytics maturity at https:// analyticsmaturity.informs.org/. ❙ Aaron Burciaga, CAP, is senior manager, analytics and operations research, at Accenture. Barry List, INFORMS director of communications, contributed to this article.

Notes & REFERENCES 1. Reitter, Norman, and List, Barry, 2013, “Analytics Maturity Model,” OR/MS Today, Vol. 40. No. 5 (October 2013). Available online at: https://www.informs.org/ ORMS-Today/Public-Articles/October-Volume-40Number-5/INFORMS-NEWS-Analytics-Maturity-Model 2. 1. Reitter, N., and List, B., 2013, “INFORMS Analytics Maturity Model,” (November/December 2013). Available online at: http://www.analytics-magazine. org/november-december-2013/904-informs-analyticsmaturity-model

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INFO RMS ini t i at i ve s

CIO Magazine: INFORMS’ CAP program ‘pays off’ INFORMS’ Certified Analytics Professional (CAP®) program was recently listed first by CIO magazine among the “11 Big Data Certifications That Will Pay Off.” Created by the largest society in the world for professionals in the fields of analytics, operations research and management science, INFORMS’ CAP exam is given at hundreds of computer-based testing centers worldwide through an agreement with Kryterion (a full-service provider of customizable assessment and certification products and services). Eligible candidates for the CAP certification exam can now choose to schedule their exam through INFORMS’ online testing center partner Kryterion, as well as strategic partnerships with colleges and universities and testing and training companies. Kryterion provides more than 700 testing locations (including 400 in the United States) in more than 100 countries. CAP exams can now be scheduled almost any day of the week and at a time and location that best suits the candidate. 22

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Exam locations can be found at http:// www.kryteriononline.com/host_locations/. Introduced in the spring of 2013, the CAP program was created by subject matter experts, many of whom are INFORMS members. The CAP credential is designed for general analytics professionals in early- to mid-career. The exam is based on a rigorous job task analysis, and it is vendor- and software-neutral. Candidates can apply at www.informs. org/applyforcertification. Upon payment, eligible candidates receive an online voucher to present on the Kryterion site. Benefits of analytics certification include the ability to advance one’s career by self-identifying as a professional that sets you apart from the competition, as well as a strong sense of satisfaction in earning an industry developed credential. The CAP designation demonstrates a continuing commitment to maintain or increase one’s level of knowledge through a mandatory continuing education obligation. w w w. i n f o r m s . o r g

The CAP program assists hiring managers in selecting analytics talent, and it shows that an organization hiring CAP professionals follows best analytics practice. The following individuals received CAP certification during the first half of 2014: Brian Backer, Johns Creek, Ga. A. Dwayne Ball, Lincoln, Neb. Eriketa Bakiri, Cincinnati Mehmet Begen, London, Ontario William Bentley, Alpharetta, Ga. David M. Blum, Arlington, Va. Brad Boehmke, Dayton, Ohio Alan Briggs, Odenton, Md. Aaron Burciaga, Fairfax, Va. Charles Burdick, Silver Spring, Md. Fernanda Maria Campello de Souza, Edmonton, Alberta Ibrahim Capar, Tuscaloosa, Ala. Andrew Churchill, Poolesville, Md. Kelly Cormican, Virginia Beach, Va. Elizabeth Dalton, Liberty Township, Ohio Chao Deng, Morrisville, N.C. Shannon Downs, Loveland, Ohio David Durkin, Roswell, Ga. Krista Elefante, Reston, Va. Rachel Ferst, San Mateo, Calif. Michele Fisher, Park South, South Africa Daniel Ford, Bethesda, Md. Nick Freeman, Tuscaloosa, Ala. Peter Furseth, Dallas, Texas Mark Gallagher, Fredericksburg, Va. a na l y t i c s

Ira Gershkoff, Ashburn, Va. Nathaniel Givens, Williamsburg, Va. Jessica Guo, Portland, Ore. Brian Harris, Alexandria, Va. Daniel Hudson, Hanover, Md. Stephen E. Hunt, Jr., Sumter, S.C. Alev Kaya, Summit, N.J. Dong-Gook Kim, Dalton, Ga. Andrew Kuoh, Washington, D.C. Jon Kidder, Mount Pleasant, S.C. Andrew Kiekhaefer, Coralville, Iowa David Krahl, San Jose, Calif. Robert Liebe, Stafford, Va. Michael Maguire, Washington, D.C. Craig Maxey, South Riding, Va. James Muccio, Fairfax, Va. David Munoz, Mexico D.F., Mexico Joseph Olah, Elkton, Md. Jonathan Owen, Beverly Hills, Mich. Sung-Hee Park, Dalton, Ga. Johann Pasion, Manchester, N.H. Jesse Pietz, Monument, Colo. Christopher Provan, Springfield, Va. Scott Radcliffe, Roswell, Ga. Nishit Shah, Maple, Ontario Scott Smith, Arlington, Mass. Wei Song, Mississauga, Ontario Jason Southerland, Fort Belvoir, Va. Eric Stephens, Nashville, Tenn. James Stone, Reading, Mass. Brando Sumayao, Chula Vista, Calif. Matthew Wagner, Silver Spring, Md. Rodney Zwainz, Crofton, Md. s e p t e m b e r / o c t o b e r 2 014

forum

Signs point to what analytics still needs If you don’t know where you are going, any road will take you there. Analytics need to convey and capture the right measures, such as key performance indicators in the organization’s executive dashboards.

By Jay Liebowitz

As we look at the growing field of analytics, it’s pretty clear that it can provide the signposts to help organizations gauge how well they are doing. In speaking about signposts, this reminds me of one of my pet peeves over the years – lack of proper signage, especially at airports. Nothing can be more frustrating than not finding your way to and within the airport. Case in point: Traveling from Montreal to the Burlington, Vt., airport, a sign says, “New York or Vermont.” If you follow the Vermont sign, which would be the most natural choice, it takes you all around the state of Vermont, and you’ll never make your flight out of Burlington (I know, it is the only flight I have ever missed in all my years of flying). It is even more annoying to follow signs at the airport that are blurred. That’s right, according to Alice Rawsthorn’s 2012 New York Times article, “Designers of the Signs that Guide You,” the new signs in the Vienna Airport are intentionally blurred. This can be troublesome for those who might have jet lag and haven’t slept well on the plane, aside from those who are vision impaired. Airport signage needs to be clear, concise and minimize customer dissatisfaction. Some airports are getting better with their signage. Instead of saying “Arrivals” and “Departures,” some say “Ticketing/

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Check-in” and “Passenger Pick-up.” However, some airports like Brussels, Belgium, are still using Helvetica type which is one of the poorest fonts for readability. And, with car rental return signs, drivers are still being confused at such airports in Orlando, Fla., and Florence, Italy, according to the blogs and newspaper accounts. In the same manner, analytics need to convey and capture the right measures, such as key performance indicators in the organization’s executive dashboards. It needs to not only report on what has happened (descriptive analytics), but also what will happen (predictive analytics) and ultimately what are the optimal conditions (prescriptive analytics for optimization). We can learn what still needs to be done in analytics by looking at airport signage to increase customer satisfaction. First, don’t get fancy with the airport signage – people want to be able to recognize the signs quickly (whether driving or catching flights in the airport). The signs need to clearly communicate the intent, both visually and content-wise. In much the same way, analytics should use the KISS philosophy (keep it simple, stupid) and provide the appropriate messages and signals. Second, design signs with the lowest common denominator in mind. That is, as a na l y t i c s

international and domestic visitors travel throughout airports, include universal symbols, colors and verbiage so that the typical traveler can understand. Analytics can also use this guidance in terms of their respective end users. Finally, continue to embed an analytics culture throughout the organization in the same way that airport signs should also be intuitive. Similar to using analytics for improving the business user’s experience, I am also trying to suggest ways to improve the airport signage for the average traveler. And, it’s not just in the airline industry; it applies across other transportation industries as well. For example, I noticed that there was an electronic sign at the front of each Amtrak car that stated, “Exit,” and whether the rest room was occupied. Why couldn’t the sign also include the train stop at each embarkation? Call me crazy, but I’m still “waiting for a sign.” ❙ Jay Liebowitz (jliebowitz@harrisburgu.edu) is the DiSanto Visiting Chair in Applied Business and Finance at Harrisburg University of Science and Technology, Harrisburg, Pa. He will be a keynote speaker at the Analytics Conference 2014 in Las Vegas on Oct. 20-21.

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Tr u th s & F u n da me nta ls

The need for not-for-profit organizations It is heart-warming to see providers and payers increasingly focused on health data and analytics to figure out better ways to deliver quality care without increasing cost.

By Rajib Ghosh

A couple of interesting developments happened during the past few months. Google launched its next healthcare initiative, Baseline Study, to do genetic analysis and determine what a healthy human body should be [1]. It will start with the genetic and molecular information from 175 volunteers, but it later will expand into thousands more. Google also announced its Google Fit platform and launched SDK. In the electronic medical record (EMR) space, Cerner, a known behemoth, bought the health IT business from Siemens after the latter decided to exit this vertical. Cerner’s acquisition is an interesting development. It shows that the EMR market is opening up for consolidation. A few large corporations, each with large market share, will eventually dominate the EMR marketplace catering to mostly large buyers. Too many options usually paralyze buying decisions. Apple decided to work closely with IBM to penetrate the enterprise software business. Healthcare has not been mentioned yet as part of the newly formed partnership, but this is certainly a possibility. In my last article, I mentioned how IBM’s strategy of “Watson in the cloud” for healthcare can receive a huge boost owing to the overwhelming preference of clinicians for Apple devices.

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Investment Increasing Investment in digital health technologies is also increasing at an unprecedented rate. It is heart-warming to see that providers and payers alike are becoming increasingly focused on health data and analytics to figure out better ways to deliver quality care for patients without increasing cost. Interestingly, this trend is not limited to the for-profit business world. Not-for-profit health plans, managed care businesses and community-based healthcare providers are also paying attention to the data that they have and analytics they need to become efficient in care delivery in the post Affordable Care Act (ACA) world. While challenges and jubilations in the for-profit healthcare organizations become newsworthy, data and analytics effort a na l y t i c s

of the not-for-profit community healthcare providers seldom make the news. This article focuses on the latter â&#x20AC;&#x201C; primarily community-based healthcare clinics, hospitals and managed care organizations â&#x20AC;&#x201C; that serve a large Medicaid population that is generally poor, underserved or both. Demand Growing But Not Supply ACA has started to have noticeable impact on the demand side of healthcare services, particularly in states that participated in the ACA-proposed Medicaid expansion program. Twenty-six states and Washington, D.C., have moved forward with the Medicaid expansion, leading to more than 3 million additional enrollees into Medicaid plans as of April 2014. Many of the j u ly / a u g u s t 2 014

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new enrollees were uninsured or underinsured earlier, causing high emergency room and hospitalization costs. According to projections, more than 15 million eligible people for Medicaid in participating states have joined the expansion program. While the expansion brought in new federal dollars to the respective state coffers in support of the safety net expansion, the key issue is that the growth in demand is not aptly matched by timely capacity expansion. An imminent scarcity of primary care providers in many states poses problems for the Analytics_AD_INFORMS.ai 1 8/12/2014 9:15:05 AM

proposed expansion. Even when physicians are available, competition from for-profit organizations makes them unaffordable to the not-for-profit health centers and hospitals. Additionally, Medicaid reimbursement rates are much lower than commercial plans, so specialists are not always willing to accept Medicaid patients. This creates issues for not-for-profit health plans in terms of maintaining a comprehensive provider network to address gaps in the patient care. ACA has some provisions to address the reimbursement issue, but is that enough?

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Desperate Need For Analytics The healthcare industry, both the notfor-profit and profit worlds, was traditionally not data- and analytics-driven. However, as the Medicaid patient population increases by leaps and bounds and community healthcare organizations cannot hire enough physicians to expand capacity, a precarious situation looms. Demonstrating quality in care delivery is now critical for community-based healthcare organizations to ensure sustained federal funding, but to do so within the constraints of available resources it is imperative that they utilize analytics to move toward population health management, efficient management of the high cost multi-chronic patients and in general take necessary steps toward improved staff productivity. In absence of coherent data and adequate analytics systems, this is turning out to be a monumental task for many organizations. The reactive nature of the healthcare industry is partly to blame for making a difficult situation worse. Everyone knew that this wave was coming, yet not enough was done to prepare. On the positive side, this has opened up new business opportunities for healthcare analytics companies who may consider turning their attention to notfor-profit segment of the U.S. healthcare industry, a segment that by 2019 is expected to address the healthcare needs of more than 50 million people [2]. a na l y t i c s

The non-profit healthcare segment also is in immediate need for analytics that can make sense of clinical, operational and financial data to provide descriptive, post-mortem analysis of the current situation and eventually guide the segment to maneuver the rough waters of the post-ACA world. This is a new healthcare environment that is comprised of an unprecedented number of patients who are waiting to be taken care of by a decreasing number of physicians, combined with funding sources that are under increased governmental scrutiny today and may not be available in the future. 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. Notes & REFERENCES 1. “Google’s New Moonshot Project: the Human Body.” 2. Anette L.Gardner, “Maintaining Clinic Financial Stability: Navigating Change, Leveraging Opportunities.”

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10 myths of analytics and insights By Will Towler

ith so much attention given to analytics and insights over the past few years, revisiting some of the industryâ&#x20AC;&#x2122;s longstanding myths seems in order. Whether you work with traditional consumer research methods or emerging business intelligence techniques, here are 10 fallacies to keep in mind to make the most out of your efforts: 1. Scientific evidence is proof History is full of inaccurate predictions and once believable theories that turned out to be wrong. Even Einstein, whose name is now synonymous with genius, 30

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at one point embraced the now obsolete static universe model. (Read more.) In marketing, perhaps no other blunder is more famous than the New Coke launch in 1985. Taste tests with close to 200,000 consumers indicated New Cokeâ&#x20AC;&#x2122;s taste was more popular than the original formula. However, Coca-Cola executives failed to consider the possibility that the importance of brand heritage trumped taste, and eventually the company reintroduced the original formula in response to negative public reaction. (Read more.) Takeaway: Science has always been subject to error, requiring an open mind to alternative possibilities. w w w. i n f o r m s . o r g

Figure 1: Correlations can potentially mask deeper relationships. 2. Knowledge is power In their book, “Big Data: A Revolution That Will Transform How We Live, Think, And Work,” Viktor MayerSchonberger and Kenneth Cukier contend that it doesn’t matter “why” there’s correlation, just that there “is” correlation. They provide an example of greater Pop-Tart sales during storms, a learning that WalMart has used to better merchandize. (Read more.) According to the authors, simply knowing that Pop-Tart sales are likely to increase is sufficient and doesn’t necessitate deeper investigation into underlying causes. While there’s no doubt this finding has valuable business implications, relying on correlations alone can limit broader applications and can even entail risk. Case in point: the 2008 financial a na l y t i c s

crisis. Analysis of AAA rated Collateralized Debt Obligations suggested they were sound investments (i.e., AAA = safe), but deeper analysis would have revealed danger. (Read more.) Takeaway: As the role of data in decision-making increases, never before has understanding underlying relationships been more important. 3. Correlation measures relationship strength A strong case can be made for questioning the extent to which correlation reflects a relationship between even seemingly interdependent variables. Consider U.S. healthcare expenditures and deaths from heart disease. Between 1960 and 2010, spending on healthcare in the United States increased more s e p t e m b e r / o c t o b e r 2 014

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than seven times after adjusting for inflation. In lockstep, deaths due to heart disease more than halved, making it easy to conclude that the two are closely related. Medical care advances are believed to have contributed to lower heart diseaserelated deaths through improved diagnosis and treatment. However, general lifestyle and diet changes also played significant roles. (Read more.) Furthermore, a review of other chronic disease trends reveals that some medical conditions, such as diabetes, have worsened (see Figure 1). There’s also myriad other factors potentially related to escalating healthcare costs, such as an aging population, greater administrative expenses and broader marketing pressures. For another take on how correlations potentially mask deeper relationships, check out Christopher Knittel and Aaron Smith’s paper “Ethanol Production And Gasoline Prices: A Spurious Correlation.” Takeaway: Seriously consider whether correlation truly reflects a relationship or simply masks the influence of one or more hidden intervening variables. 4. Random sampling ensures representation Unless you’re working with full universe coverage, some form of sampling is usually required. And while fantastic 32

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in theory, true randomness is difficult to achieve. Transactional data are constrained by membership and/or opt-outs; surveys face non-response; and social media content is subject to issues related to self-reporting. Beyond sampling, the challenge of unbiased representation is exacerbated by a number of factors ranging from human predispositions to herd mentality. Sinan Aral wrote a convincing piece in the MIT Sloan Management Review, for example, explaining the tendency for online customer reviews to be abnormally j-shaped rather than bell-curved. Referencing different studies, Aral explains how herd mentality can lead to a disproportionate concentration of positive ratings skewing online reviews over short and long terms. (Read more.) It’s another example of how things aren’t always what they appear to be. Takeaway: Accurate representation without some form of post hoc control is frequently illusive. 5. People are rational Humans act irrationally. As consumers, we often derive greater satisfaction from the same item if it costs more (not less); we let decoy options cause us to make suboptimal decisions (such as buying something bigger than we normally would); and we frequently stick w w w. i n f o r m s . o r g

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My th s o f A n a ly t i c s

to what weâ&#x20AC;&#x2122;re most familiar with (even if another choice is better for us). The popularity of behavioral economics reflects a growing interest in tapping irrationality for business and government. There are still plenty of questions about how best to merge economics and psychology, but their immutable relationship beyond traditional utilitarian theory is without doubt. (Read more.) Takeaway: Even the tightest models are fallible due to the unpredictability of human behavior.

often causing us to forget information and events. University of Illinois professor Brian Gonsalves conducted research in which he found subjects were able to recall information correctly only 54 percent of the time after being shown simple images and descriptions. Gonsalves explains that some people may remember the general context of an event, but may not encode specific details. (Read more.) Another study on the effectiveness of different advertising research methodologies found that less than 70 percent of respondents were able to accurately re6. Recall is a trusted form of call ad exposure. The research also rememory vealed that up to 25 percent of those who It doesnâ&#x20AC;&#x2122;t take advanced science to were not shown an ad incorrectly recalled know that our memories are imperfect, exposure (see Figure 2). (Read more.)

Figure 2: Not so total recall. 34

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Takeaway: Trusting recall as a research methodology brings with it serious limitations.

half.” The uncertainty that comes with advertising will probably never go away. One of the key reasons is the inability to accurately measure priming, in which exposure to one stimulus influences response to another. While the extent of priming is debated, that it exists is not. Our environment influences our subconsciousness through sights, sounds and smells. (Read more.) In addition, the effects of priming can be long-lasting, well beyond what one might expect. (Read more.) Takeaway: The return on some investments is not always immediate, often making short-term measurement a futile task.

marketing often track different metrics reviewed in isolation (e.g., sales might focus on funnel activities while marketing might be more concerned with brand and campaign assessment). Common or at least integrated metrics not only align teams but also ensure what’s being measured is most relevant. Strategy and customer experience consultant Christine Crandell recommends three groups of measures including end-to-end conversion, revenue diversity and outcome profitability. According to Crandell, “While there are literally hundreds of sales and marketing metrics that can be used, it comes down to (these) three that measure alignment and frame that all-too critical joint conversation with sales and marketing about what’s working and what isn’t.” (Read more.) Takeaway: Sales and marketing metrics should be shared or at least integrated in order to work toward the same goal and evaluate performance holistically.

8. Sales and marketing metrics are different

9. Sound analytics drive sound decision-making

Sales and marketing share the same overarching objective of driving profitable growth. Having common or at least integrated key performance indicators would seem reasonable, but sales and

The Corporate Executive Board recently conducted a study of 5,000 employees at 22 global companies and found that just over one-third of participants balance judgment and analysis,

7. You can’t manage what you can’t measure A century ago Lord Leverholm famously said, “I know half my advertising isn’t working, I just don’t know which

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key to effective decision-making. The other two-thirds either go exclusively with their gut or trust analysis over judgment. The study highlights the challenge that even sound insights can have in driving good decision-making due to broader organizational issues. According to an accompanying article in the Harvard Business Review, “At this very moment, there’s an odds-on chance that someone in your organization is making a poor decision on the basis of information that was enormously expensive to collect.” (Read more.) Takeaway: Quality output is only half the battle when it comes to impactful analytics and insights. The other half of the battle lies in an organization’s ability to take action effectively. 10. Great insights sell themselves You conducted a momentous research project or developed a groundbreaking business intelligence system. Why wouldn’t your work attract fans and lead to positive change? Industry track records, however, suggest most projects fail to accomplish their goals. (Read more.) Poor problem definition and operational snafus are common challenges. But even projects with clearly defined objectives and smooth implementation can fall short of expectations in the 36

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absence of effective communication and stakeholder engagement. According to the Project Management Institute, among organizations considered highly effective communicators, 80 percent of projects meet original goals vs. only 52 percent at their minimally effective counterparts. (Read more.) Takeaway: Projects without a wellcrafted stakeholder engagement and communication plan will likely have little chance of success from the get-go. New data sources and diagnostic capabilities continue to enhance the potential value available through analytics and insights. However, the longstanding truths of how to make an impact haven’t and likely won’t change. The fundamentals of statistics, human nature, stakeholder engagement and communication remain the same; and effectively leveraging their constructs largely determines project success. Will Towler is an analytics and insights consultant working in the Seattle area, and he has nearly 20 years experience in consumer research and business intelligence. For more, visit his website: www.insighttrends.com.

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Real izin g Va lu e

Building a highperformance big data analytics organization By (l-r) Pramod Singh, Ritin Mathur and Srujana H.M. “From Twitter feeds to photo streams to RFID pings, the big data universe is rapidly expanding, providing unprecedented opportunities to understand the present and peer into the future. Tapping its potential while avoiding its pitfalls doesn’t take magic; it takes a roadmap.” — Chris Berdik, author of “Mind over Mind”

he digital universe is expected to expand exponentially between 2013 and 2020, from 4.4 trillion gigabytes to 44 trillion gigabytes [1] a year, and this massive amount of data will significantly impact global industries. In nontraditional context, analyzing big 38

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data isn’t about managing more or diverse data. Rather it is about asking new questions, up skilling new capabilities, building new technological environments and devising holistic communication strategies to encompass the nuances associated with complexities of volumes of data. w w w. i n f o r m s . o r g

Numerous opportunities, as well as challenges, are associated with big data. Time savings can be achieved through real-time monitoring and forecasting of events that impact either business performance or operation. Significant cost savings over traditional analytical techniques can be achieved by adoption of big data due to usage of Hadoop clusters. While companies with business models predicated on the Internet have been the pioneers of developing big data analytics, other firms with more established

non-Internet-based models are also rapidly adopting big data analytics practices, typically in response to consumer and technology trends. With the emphatic big data explosion, it becomes imperative for organizations to assess and adopt big data analytics practices into their decision-making process. Big Data Tools and Technologies When considering an organizationâ&#x20AC;&#x2122;s needs for big data tools and technologies, it

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is useful to think of them in four dimensions. 1. Structured data management: Tools for managing high-volume structured data (for instance, clickstream data or machine or sensor data) are an important part of any big data technology stack. 2. Unstructured data management: The explosion in data volumes have been to a large extent a result of the rise in human information, which is typically comprised of social media data, videos, pictures and even text data from customer support logs. Tools and technologies to manage, analyze and make sense of this data stream are critical to build understanding and to correlate with other forms of structured data. 3. Analytics environment: Combining both structured and unstructured data, at scale, requires specialized tools and technologies to be able to merge these data sets and to be able to run analytical algorithms. Concepts such as in-database and in-memory analytics have greatly enhanced the ability to use large data sets for analysis at near real-time speeds and to combine the analytics environment within, for example, structured data management tools. 4. Visualization: Intuitive representation of data and results of analysis is a critical final component of the big data technology stack. This furthers the speed at which results are understood and insights derived. Tools and technologies 40

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that allow for quick drill down, investigative analysis are now pervasive and easily integrated into the analytics stack [2]. Most tools designed for data mining or conventional statistical analysis are not optimal for large data sets. A common hurdle to cross for most analytics organizations trying to leverage big data analytics is availability of big data technologies and platforms. Organizations usually start off by using open source technologies to gain experience and expertise. The big data analytics space, thankfully, provides many open source options for organizations. For example, Hadoop is a good starting place for being able to manage large data at scale. Combining this with NoSQL databases such as Hbase or MySQL can provide a good first step to get a feel for handling large data sets. Hadoop ecosystem tools like Hive, Pig, Sqoop, etc. allow data scientists to also get a feel for being able to query and analyze large data sets. R is an open source programming language and software environment designed for statistical computing and visualization [3]. For visualization, tools like d3.js allow for creative and varied visualization sets to help data scientists present results in an intuitive way. The challenge with using open source technologies though is two-fold. One, integrating these with a legacy enterprise w w w. i n f o r m s . o r g

stack is not easy, and most IT organizations don’t yet allow for easy integration. This integration quickly becomes critical when one moves beyond experimentation into solving real-world business problems that require multi-dimensional data, some of which might be in legacy enterprise data warehouse (EDW) environments [4]. Second, while strong use communities around open source technologies exist, the learning curve could be longer given the often less than user-friendly nature of these technologies.

Learning on open source requires a certain level of existing expertise, and beginners may find a learning approach based on open source harder. Integrated Big Data Analytics Platform Most analytics for business use cases rely on bringing together diverse data sets to analyze. With big data, these data sets are no longer limited to just structured data; they increasingly leverage unstructured data as well. This calls for a big data

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environment that allows data scientists to work seamlessly across data streams. Using an integrated environment provides a quicker, more scalable and integrated approach to analytics. This allows for a user-friendly environment for data scientists to learn new skills and adapt to working with and running analytics on large data sets. HP HAVEn, for example, brings together Hadoop, Autonomy, Vertica, HP Enterprise Security and any number of applications. Building Organizational Skills Providing for big data technologies and platforms sets the baseline for an organization. What needs to be done next is to have a focused effort across the organization to build the skills in these technologies. In contrast to traditional analytical organizations, big data organizations need to augment existing analytical staffs with data scientists who possess a higher level of technical capabilities, as well as the ability to manipulate big data technologies. These capabilities might include natural language processing and text mining skills; video, image and visual analytics experience; as well as the ability to code in scripting languages such as Python, Pig and Hivev. A data scientist in a big data analytics organization typically needs skills in three core areas: 1. business intelligence related skills to get to the data quickly, 2. statistics and 42

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analytical techniques to be able to analyze and, 3. business skills to be able to interpret analysis results in business terms. The time that an analytics organization has to respond to a business need is shrinking. This gives rise to a situation where you need all three skill sets in one person, which is hard to find. To guide skill development among the existing analyst community, HP developed competency centers aligned to each of the key technologies â&#x20AC;&#x201C; Vertica for structured data analytics, Autonomy for unstructured data analytics and Hadoop as a data lake. The competency centers cater to focused competency development through collaboration, training and live projects. These competency centers, composed of data scientists across the organization, created a skills framework and a big data curriculum to guide the skill development effort. Re-thinking Business Analytics With the right tools, technologies and skill sets, an organizationâ&#x20AC;&#x2122;s next step is deploying big data analytics to solve analytics questions in different application areas. A challenge some analytics organizations might have is getting their teams to think about how big data analytics applies to their business areas. Given the relative maturity of analytics solutions across most domains, teams sometimes have difficulty in assessing how big data could help. w w w. i n f o r m s . o r g

NORTHWESTERN ANALYTICS As businesses seek to maximize the value of vast new streams of available data, Northwestern University offers two master’s degree programs in analytics that prepare students to meet the growing demand for data-driven leadership and problem solving. Graduates develop a robust technical foundation to guide data-driven decision making and innovation, as well as the strategic, communication and management skills that position them for leadership roles in a wide range of industries and disciplines.

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At HP, our belief is that big data analytics impacts analytics in two ways: 1. helping answer existing/legacy questions in newer ways and, 2. addressing a range of newer questions and decisions organizations face today. An example of the existing/legacy question is the area of segmentation, a well-understood area in marketing and customer analytics. The challenge for most business analytics teams, though, is to look at segmentation with the fresh lens of big data analytics: How can big data help make segmentation better? Examples of how big data analytics can help address new questions is perhaps best exemplified when considering either social media analytics or machine/ sensor data analytics. These diverse areas are important aspects in business decision-making and impact such functions as marketing, manufacturing, customer service and R&D. Both require new analytical approaches to manage the large streams of data that get generated. Summary To realize value from big data analytics, organizations need to integrate technology, tools and practices with existing analytics ecosystems. The choices to make in terms of which tools to select and which skills to develop need careful consideration and have a long-term impact on an organization’s 44

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ability to integrate big data analytics. Analytics organizations should start by considering four key questions: 1. What technology and tools are needed? 2. What platform is best for integrating these technology choices with each other, as well as with legacy environments? 3. Which skills do we need to develop and how do we develop them? 4. How do we integrate all of the above into the business decision-making process? These questions require senior management time and attention. Addressing these issues comprehensively can reduce the barriers to success for analytics organizations looking to incorporate big data analytics. ❙ Pramod Singh (pramod.singh@hp.com) is director of Digital and Big Data Analytics at Hewlett-Packard (HP) and a member of INFORMS. He has a Ph.D. in mathematics from the University of Arkansas and an MBA in marketing. Ritin Mathur (ritin.mathur@ hp.com) is a senior manager of Big Data Analytics at HP. Srujana H.M. (srujana.h-m@hp.com) is a data scientist working on big data technology platforms at HP. All three are based in Bangalore, India. Notes & REFERENCES 1. Source: http://www.emc.com/about/news/ press/2014/20140409-01.htm 2. “Big Data Meets Big Data Analytics,” white paper, SAS Institute Inc., 2012. 3. Source: http://www.networkworld.com/article/2289422/ applications/9-open-source-big-data-technologies-towatch.html, last accessed on July 3, 2014. 4. Philip Russom, “Big Data Analytics,” TDWI Best Practices Report, Fourth Quarter, 2011. 5. Thomas H. Davenport and Jill Dyche, “Big Data in Big Companies,” paper, International Institute of Analytics, May 2013.

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Five measures of social media karma Return on investment: Quick wins and fundamental measures of posts, tweets, blogs, pings and uploads.

By Kshira Saagar

ost, tweet, blog, ping and upload are some of the poignant actions in the social media universe. Like, share, re-tweet, favorite, re-blog and download are the karmic reactions that help assess the validity and credibility of your actions in the same universe. Almost all organizations these days spend quite a lot of financial and human resources to understand and make sense of their social media actions, leading to the eventual reactions. And a surprising 49 percent of CMOs are unable to quantify social media impact on their companies. 46

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Facebook Insights, Twitter Analytics, YouTube Analytics, LinkedIn Analytics and Pinterest Analytics are some places that offer their own analytic platforms to measure your social media karma. But what should these measures be about? Let’s look at the five fundamental measures that will serve as quick-wins to analyze your social media karma. Measure 1: The “Who” – Who is the core group of your audience base? Getting a pulse of your audience and the crowd that comes looking for you is always the key. Instead of shooting in the w w w. i n f o r m s . o r g

dark with broadcasted content, a firm grip on who your audience is could enable a very easy way of chalking out content and tailoring the messages. Facebook Insights and other social media analytics platforms provide a complete breakdown of your follower group in terms of gender, age and location. The metric of interest here would be the population of your audience in each of these demographic buckets, resulting in your eureka moment of social media. Is your audience young, mobile and

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highly concentrated in specific urban locations? Or is it a completely different story? The answer to this question sets up the base for all forthcoming social media activities. Eventual insights from this exercise should help you do two things: 1) validate that you are talking to the right crowd of people at the right places; and 2) comprehensively understand the groups of people you have been ignoring but who seem to have considerable presence in your audience base.

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Your social media flock comes to your page with a specific intent in mind – is that information, offers, knowledge or just pure entertainment?

Measure 2: The “What” – What specific activities have any visible/tangible benefit? Understanding the mission of your audience is another significant step toward engaging them better. Your social media flock comes to your page with a specific intent in mind – is that information, offers, knowledge or just pure entertainment? Facebook Insights offers a way to gauge this by looking at the reach of each post and page. The adventurous-at-heart can download the entire Facebook Insights data at a post or page level and analyze what content works and what does not. The metric of interest here would be the reach of your posts – differentiated by text, image, video, links or other multimedia options. The way “reach” should be defined in such a way that it acts as a proxy for how many people “viewed” or “engaged” with the content. Facebook, LinkedIn and Twitter define reach intrinsically. For the others, which do not have an explicit “reach” metric, a proxy of number of views or number of clicks can be taken. An eventual insight from this exercise should help you precisely understand what the customer expects from your social media channel – is it an image with a collage of discount offers or is it a DIY video of how-to-use-a-productsmartly or is it plain old philosophy on things? Measure 3: The “Why” – Why does specific content work better than the others? A video or an image is not quite actionable in itself. What is more important is the content

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Figure 1: Five measures of social media karma. within the image, which makes the multimedia work. The metric of interest here would be a mixture of the sentimental response to your posts and the contextual messages contained in those posts. Sentiment analysis is a simple enough exercise in an open sourced platform like R or other online sources, which can provide a rundown of the positive and negative views for each post. A contextual analysis of each post based on its theme can help users understand why a specific post is more preferred compared to the others. An eventual insight from this exercise should help users figure out why a a na l y t i c s

customer likes a post and how they feel about it. Many social media listening centers set up by big corporate houses focus specifically on this part – real-time management of sentiments and assuaging the angry customer. Measure 4: The “When” – When does a time-bound game plan work according to the plan? Time is one of the most critical elements of a social media strategy. You cannot go around posting any time you want and any number of times you want. Any refined social media manager worth their salt would tell you that social media is an art more than a science, and a s e p t e m b e r / o c t o b e r 2 014

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The only true way of measuring the eventual success of any social media campaign is if it translates into real-world currency, one way or another.

bigger aspect of this art is knowing when to talk and when to stay silent. The metric of interest here would be a reach of posts (as discussed in Step 2) split by time of the day and day of the week for different channels. You should be able to figure out what time of day or day of week your post has maximum reach within your audience. One more thing of interest would be to analyze the activity period of your followers (possible in Facebook Insights and Twitter Analytics) and identify overlapping areas or gaps of improvement between you and your audience’s operating time. An eventual insight from this exercise should arm you better in terms of when to go onto your channels and update the posts. Many social media channels now offer the option to “preset” a post for a specific day and time in advance, which can be wisely programmed as a result of this analysis. Measure 5: The “How” – How is your audience responding to your efforts? Looking at CTR (click through rate) or jumping-with-joy (virtually) over follower count is not the only way to measure social media success. The only true way of measuring the eventual success of any social media campaign is if it translates into real-world currency, one way or another. Although it’d be a utopian exercise to tie every single social media activity to the green buck, there still exist alternate measures of monetary success.

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In this interconnected world, where a small social media ripple can have the impact of a tsunami on the eventual sales bottom line, it becomes imperative for analysts to know quick-fix analyses and easy-to-extract metrics.

The metric of interest here would be the simple overlap (correlation) between the sales at a point in time and the number of posts “around” the same point of time. It need not be strictly sales alone. It could be any metric that captures the essence of all the four points above; it could be new customers, overall customers, new mentions or increased engagement. An overlap of a specific success metric on a time-on-time basis with a post’s timings should help you figure out if your social media karma is beneficial or otherwise. An eventual insight from this exercise can help you better decide on the promotional budget for each type of post and the length of the promotion. One thing to remember, just like human karma, the efforts should not be estimated on an absolute immediate-time basis but with a practical lag (0-2 days) from the time of posting. In this interconnected world, where a small social media ripple can have the impact of a tsunami on the eventual sales bottom line, it becomes imperative for analysts to know quick-fix analyses and easy-to-extract metrics from the vast unstructured world of social media. It would be wiser for organizations to analyze data within the security of their own firewalls and derive some of the key metrics that are more useful for their purposes – at no extra cost at all. ❙ Kshira Saagar, a manager with Mu Sigma (www.mu-sigma. com), has considerable experience in analytics consulting with multiple Fortune 500 clients. His experience spans across technology, pharmaceutical and retail industries where he works closely with client teams and business executives in creating, operationalizing and driving consumption of analytics.

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Can government drive information innovation? The first U.S. chief technology officer claims analytics holds great promise for promoting public-private information technology partnerships

BY Doug Samuelson

an government be both smaller and better? Can analytics show the way? Aneesh Chopra, the first chief technology officer of the United States, thinks so. In a new book (“Innovative State: How New Technologies Can Transform Government”) [1], he argues that public-private partnership initiatives, utilizing new information technology, have already had large 54

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impacts in agencies ranging from the Veterans Administration to Health and Human Services and from Education to Energy. He cites numerous examples of successes, not confined to his own time in office, advancing these initiatives, although the period 2009-2012, when he was in office, he knows best and discusses in the most detail. He recounts the effort to improve appointment scheduling for veterans’ health and many federal-state cooperative w w w. i n f o r m s . o r g

efforts to update school 2. The Peter Principle: curricula and involve parPeople keep advancing ents in the process. And, of until they attain positions course, he claims considthey cannot fill competenterable success for various ly and then stick there, neiaspects of the new health ther improving nor getting care system. removed. These ideas did not 3. Oligarchism: Buoriginate with Chopra or reaucracies tend to make with the Obama administraself-preservation the overtion. Chopra traces many of riding priority, engenderthe initiatives to the work of ing stiff resistance to any a group that began meeting in the attempts to streamline. late 1980s, as a small number of 4. Olsonism: As renowned well-placed individuals from across economist Mancur Olson obthe political spectrum began meetserved, decision-making is often ing informally to formulate ways to inordinately influenced by feromake government both more efciously determined interest groups Aneesh ficient and more effective. James insisting on certain relatively small Chopra Pinkerton, a member of the group, wrote policies and resource allocations of great a book, “What Comes Next” [1995], that benefit to them. summarized the group’s approach. Pinker5. Information Infarction: Bureauton identified five general impediments to cratic decision-making fails because no implementing innovation: one in the bureaucracy can know all the 1. Parkinson’s Law: Work tends to relevant information. In a top-down, hierexpand to fill the time and resources availarchical structure, there is little incentive able, as organizations keep finding justifor people on the front line to present infications for more resources even when formation that threatens the status quo. their area of responsibility is shrinking. When these people do learn new, relevant Hence workforces grow even when the orinformation and pass it upward, the time it ganizations’ responsibilities do not. (Partakes for the information to travel up the kinson also noted that one reason for this chain, be considered and generate direcis that people in a hierarchy seek to multitions is often too long to generate meanply subordinates, not potential rivals.) ingful, timely benefits. a na l y t i c s

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While acknowledging the importance of these aspects of bureaucracy, Chopra nevertheless insists that the information technology revolution that began in the late 1990s has created the conditions for a new structure to break through the impediments. He built on his own background, first his education at The Johns Hopkins University and the Kennedy School of Government (Harvard), then as an entrepreneur and later as chief technology officer of the Commonwealth of Virginia in the Tim Kaine administration (2006-2010) to make the case (with a strong endorsement from Governor Kaine) to newly elected President Obama for how a national chief technology officer could help the country. He cites the “open innovation” concepts of Henry Chesbrough, professor of business administration at the University of California, Berkeley, as the moving force behind much of the high-tech innovation of private businesses in the 1990s. Chesbrough emphasized “giving more information to more people sooner” as the key idea. Applied to government, this means using government “to liberate or harness the energies of the private sector.” This approach involves four tool sets: 1. Open data: enabling the public to access more government digital data, not only for transparency but also, more important, so that the information can be incorporated in new products or services. 56

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2. Impatient convening: Government’s inviting the private sector to work collaboratively on standards that lower barriers to entry and foster competition. 3. Challenges and prizes: Widely inviting proposals to solve a particular problem, outside the cumbersome and often wasteful government procurement processes. 4. Attracting talent: Recruiting entrepreneurs into the government to manage the preceding three tool sets to focus on actual accomplishments and stimulate breakthrough results in a tight time frame. In his book, Chopra cites a number of examples of apparent successful and consequential implementation, including Department of Health and Human Services initiatives to make health data more widely available and useful; a San Francisco Bay Area project to make zoning information and requirements more readily available to prospective commercial tenants; a publicly available website to track and display legislative proposals in Virginia online; and the movement of the Federal Register, the official record of activities and proposed actions throughout the federal government, to a readily accessible and indexed public website. In recent comments about what he learned on his book tour, he says, “On my journey thus far, including stops on the ‘Daily Show’ and ‘Morning Joe,’ I’ve w w w. i n f o r m s . o r g

confronted a more pessimistic view that the hyper-partisanship in Washington is standing in the way of any meaningful progress. Perhaps my most memorable exchange took place at an event organized by my dear friend and convener-in-chief, Coach Kathy Kemper, on the growing interest in the ‘Internet of Things.’ “Supreme Court Justice Stephen Breyer expressed some skepticism as to our nation’s ability to fully harness the power of information technology, citing a paper he co-authored in the 1970s on ‘time of use’ pricing models that were expected to dramatically accelerate energy efficiency, but fell short, in part on the failure of our governance system. He mused of a new ‘call to action’ on elevating civics education. He asked why we couldn’t harness these technologies to cull together a 21st century civics curriculum to include a minilecture by President Obama on the original Magna Carta, opening up the treasure trove of artifacts held at the U.S. Constitution Center and across our network of libraries, among other ideas.” Chopra continues: “I responded with three points: First, that part of the problem on realizing the value of time-of-use pricing was an information gap between data held by the utilities on energy utilization (and the regulators on the specific rate plans) and the creativity of entrepreneurs a na l y t i c s

competing on how to best present that data for action by consumers. I shared an example from the book on Green Button (www.greenbuttondata.org), a voluntarilydesigned data standard adopted at first by three of California’s largest utilities to open up machine-readable access to energy usage data by consumers (and through Green Button Connect, their trusted third parties). Within a week of Green Button’s launch, an entrepreneur in New York City built ‘Watt Quiz,’ a game that pulled in rate and usage data to inform consumers on the best rate plan that would save them money without impacting their current utilization patterns (true low hanging fruit). “Second,” he went on, “I spoke of the governance model that has enabled this voluntary standard to scale. Rather than a single institution declaring such a policy be implemented – with the associated costs of likely a bloated IT acquisition – we pursued a version of former President Herbert Hoover’s vision of an ‘Associative State’ that emphasized government’s role as ‘convener’ rather than regulator, or direct investor. One phone call from me to PG&E’s CIO, Karen Austin, kicked off a series of voluntary collaborations that have since resulted in commitments by utilities serving 60 million households (over 100 million people) to adopt the Green Button data standard. SE P TEM B ER / OCTO B ER 2 014

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Rather than await some centrally designed civics curriculum for all to adopt, we’ve lowered barriers for everyone to deliver world-class civics instruction at a pace that is right for them.

“Third,” he concluded, “I highlighted our work with Education Secretary Arne Duncan on the ‘Open Education Data Initiative’ to flip the model of top-down curriculum development (whether for civics or other topics) to a more democratizing model harnessing a new ‘Learning Registry’ standard that empowers teachers and stakeholders to ‘tag’ any learning object on the Internet, including the ability to share peer ratings and reviews. Anyone can now contribute pieces and parts for a new civics course that can be more rapidly assembled for use by schools, non-profit institutions or parents. All at no charge. Rather than await some centrally designed civics curriculum for all to adopt, we’ve lowered barriers for everyone to deliver world-class civics instruction at a pace that is right for them. How did Justice Breyer respond? He said he would be buying my book.” Although Chopra’s enthusiasm is infectious and his examples are persuasive, serious issues remain. Long-time readers of OR/MS Today may remember reports of a broad, strongly backed initiative toward a standard electronic patient record in health care and other IT efforts 20 years ago [Samuelson, 1995]. Many knowledgeable people asserted, as we reported, that information problems were most likely the single biggest driver of both high costs and quality problems. Five years later, the National Institute of Medicine announced much the same conclusion. Still, very recent studies indicate that many of the same problems persist [James and Samuelson, 2013].

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INN OVATIVE STATE

Similarly, alert readers will note the irony of the disappointing developments, since the book appeared, in one of Chopra’s best examples, the VA scheduling system. Many of the improvements he claims really did take place – but not entirely system-wide. The rollout of the new healthcare system also provides both positive and negative evidence about his proposed approaches. (Much of the difficulty can be traced, he points out, to a single major procurement done “the old way,” resulting in a bad technical solution to one key component.) Substantial analysis remains to be done about how to get the more entrenched, change-resistant components of a large organization to go along with major innovation, or to come up with different improvements of their own. Chopra admits that he generated many more good ideas than he saw through to completion, leading his wife to call him “the Secretary of Memos.” One is led to suspect that individual leadership and persuasiveness play a larger role, and systems concepts a correspondingly lesser role, than Chopra and analytics professionals would like to admit. This implies that leadership style is one of the important subjects of study. Even more important, how to evaluate what proposed changes are working and how to focus on the real causes of problems is an ongoing challenge that analytics professionals are especially well qualified to address. 60

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Chopra also points out that the current news media culture and climate tends to focus on contentious issues and embarrassing shortcomings, while under-reporting large-scale but relatively slow-moving system changes. Here, too, analytics professionals could be helpful, by digesting meaningful information and presenting it to news outlets in ways they can readily utilize. Chopra’s book and public appearances are an attempt to do this, along with his active participation in electoral politics. (He ran, unsuccessfully, for lieutenant governor of Virginia last year and is currently very active in Senator Mark Warner’s re-election campaign.) In short, he doesn’t have all the answers – but he is definitely raising many of the right questions, and analytics professionals would do well to respond. 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 and a contributing editor of OR/MS Today and Analytics. Notes & REFERENCES 1. Aneesh Chopra, “Innovative State: How New Technologies Can Transform Government,” New York, Atlantic Monthly Press, 2014. 2. Brent James and Douglas A. Samuelson, “Change We Can Live With: Building the Data Capabilities and Analytics to Make Critical Improvements in Patient Safety and Wellness,” OR/MS Today, October 2013. 3. James Pinkerton, “What Comes Next: The End of Big Government – and the New Paradigm Ahead,” New York: Hyperion, 1995. 4. Douglas A. Samuelson, “Diagnosing the Real Health Care Villain,” OR/MS Today, February 1995.

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Analytics SAS and Hadoop take on the Big Data challenge. And win.

Why collect massive amounts of Big Data if you can’t analyze it all? Or if you have to wait days and weeks to get results? Combining the analytical power of SAS with the crunching capabilities of Hadoop takes you from data to decisions in a single, interactive environment – for the fastest results at the greatest value.

Read the TDWI report

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co n fer e n c e p r e v i e w

San Francisco conference to bridge data, decisions Special practice-oriented tracks will offer a view into how companies, large and small, have successfully implemented various analytics approaches to improve the bottom line.

By Candace “Candi” Yano

The 2014 INFORMS Annual Meeting in San Francisco, set for Nov. 9-12 and whose theme is “Bridging Data and Decisions,” promises to be one of the largest ever, with more than 5,000 technical presentations. Whether you are interested in pressing societal needs, including healthcare, energy and climate change, new developments in supply chain management and logistics, cutting-edge methodologies for optimization, or advances in stochastic processes and risk analysis, you will find hundreds of presentations to match your interests. Special practice-oriented tracks will offer a view into how companies, large and small, have successfully implemented various analytics approaches to improve the bottom line. Also featured are diverse plenary and keynote talks spanning a lecture by Nobel laureate Alvin Roth to a presentation on the Google Driverless Car

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Project. Other highlights include special lectures and sessions to commemorate what would have been the 100th birthday of the late George Dantzig. The conference will take place in two adjacent hotels, the Hilton San Francisco Union Square and the Parc 55 Wyndham, in close proximity to the city’s prime shopping district and near major tourist sites, major cultural attractions and world-class restaurants. Nearly 50 years ago, Scott McKenzie sang, “If you’re going to San Francisco,

CAREER CENTER The INFORMS Career Center offers employers expanded opportunities to connect to qualified O.R. and analytics professionals. For many years INFORMS Job Placement Service has helped both job seekers and recruiters make the right connections. INFORMS new center now offers a complete line of services to be used alone or in conjunction with the Job Fair at the 2014 Annual Meeting. Both give applicants and employers a convenient venue to connect. The Career Center is free to INFORMS member applicants.

be sure to wear some flowers in your hair.” The conference will offer still another suggestion: Be sure to come to the 2014 INFORMS Annual Meeting. It will be an exceptional venue for reconnecting with professional colleagues and making new ones. ❙ For more information, click here. Candi Yano is a professor in the Department of Industrial Engineering and Operations Research and in the Haas School of Business at the University of California-Berkeley and general chair of the INFORMS Annual Meeting in San Francisco.

SEARCHING? HIRING?

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• More analytics jobs • Preferred jobs • Featured jobs • Job alerts • Anonymous career profiles • Powerful searching capabilities

For info on job fair at the 2014 INFORMS Annual Meeting, http://meetings.informs.org/sanfrancisco2014/jobfair.html

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WSC 2014

WSC 2014: Exploring big data through simulation WSC has been the premier international forum for disseminating recent advances in the field of system simulation for more than 40 years.

By Stephen J. Buckley

The Winter Simulation Conference (WSC) has been the premier international forum for disseminating recent advances in the field of system simulation for more than 40 years, with the principal focus being discrete-event simulation and combined discrete-continuous simulation. In addition to a technical program of unsurpassed scope and high quality, WSC provides the central meeting place for simulation researchers, practitioners and vendors working in all disciplines and in industrial, governmental, military, service and academic sectors. WSC 2014 will be held Dec. 7-10 in Savannah, Ga., at the Westin Savannah Harbor Golf Resort & Spa and the adjacent Savannah International Trade & Convention Center. The appeal of simulation is its relevance to a diverse range of interests. WSC has always reflected this diversity, and WSC 2014 aligns with and expands upon this tradition. For those more inclined to the academic aspects of simulation, the conference offers tracks in modeling methodology, analysis methodology, simulation-based optimization, hybrid simulation and agent-based simulation. For those more inclined to the application of simulation, tracks include healthcare, manufacturing, logistics

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Is the largest association for analytics in the center of your professional network? It should be.

▪ Certification for Analytics Professionals ▪ A FREE Community Membership

to join online visit http://join.informs.org

▪ Continuing Ed courses for Analytics Professionals ▪ Online access to the latest in operations research and advanced analytics techniques ▪ Unsurpassed Networking Opportunities available in INFORMS Communities and at Meetings ▪ Subscriptions to online and print INFORMS Publications ▪ INFORMS Career Center - the industry’s leading job board

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WSC 2014

and supply chain management, military applications, business process modeling, project management and construction, homeland security and emergency response, environmental and sustainability applications, and networks and communications. The Modeling and Analysis of Semiconductor Manufacturing (MASM) is a conference-within-a-conference featuring a series of sessions focused on the semiconductor field. The Industrial Case Studies track affords industrial practitioners the opportunity to present their best practices to the simulation community. The Simulation Education track presents approaches to teaching simulation at education levels ranging from K-12 to graduate and professional workforce levels. Finally, WSC provides a comprehensive suite of introductory and advanced tutorials presented by prominent individuals in the field, along with a lively poster session, Ph.D. colloquium, a new attendee orientation and a distinguished speaker lunchtime program. The theme for WSC 2014, “Exploring Big Data Through Simulation,” is timely and relevant. The explosion of data throughout the world has created both opportunities and challenges to business and technical communities. In this conference, presenters will discuss how simulation can help. In addition to 66

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special tracks on big data simulation and decision-making and scientific applications, conference keynote speaker Robert Roser, head of scientific computing at Fermi National Accelerator Laboratory in Batavia, Ill., and one of the world’s leading experts on experimental particle physics, will speak about the recently discovered Higgs Boson particle and the role of simulation in the discovery. The military keynote speaker is Greg Tackett, director of the Ballistic Missile Defense Evaluation Directorate (BMDED) and the Ballistic Missile Defense System Operational Test Agency (BMDS OTA), U.S. Army Test and Evaluation Command, Redstone Arsenal, Alabama. The WSC is designed for professionals at all levels of experience across broad ranges of interest. The extensive cadre of exhibitors and vendor presentations, the meetings of various professional societies and user groups, along with the various social gatherings, give all attendees the opportunity to get acquainted with each other and to become involved in the ever-expanding activities of the international simulation community. ❙ For more, click here. Stephen J. Buckley is a research staff member at the IBM Thomas J. Watson Research Center in Yorktown Heights, N.Y., and general chair of WSC 2014.

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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速) exam

Present a Talk or Poster and Save

Present your work at the conference known for real-world analytics and save on registration. Case studies, best practice examples, and academic research with a practitioner orientation are all welcome.

Submission Deadlines Oral Presentations December 19, 2014 Poster Presentations January 16, 2015

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.

Missed this years conference? Review meeting highlights from 2014. meetings.informs.org/analytics2014

Five- M in u t e A n a lyst

State of the Union One interesting thing about text data is that the entire world of written word becomes your analytic garden. While exploring this garden, I thought it would be interesting to take a look at the presidents’ State of the Union addresses through the years.

By Harrison Schramm, CAP 68

Lately I’ve been interested in textual data, which has opened a whole new world of things to think – and write – about. One interesting thing about text data is that the entire world of written word becomes your analytic garden. While exploring this garden, I thought it would be interesting to take a look at the presidents’ State of the Union addresses through the years. The State of the Union is an annual report from the president of the United States to Congress. It can be a venue for rolling out new policies and strategies. We can safely assume that each administration takes the preparation and delivery of this speech very seriously, and puts the best resources they have into it. Therefore, the addresses may be considered a “snapshot” of the writing style of their time. The speeches can be found for all the presidents at a number of places; I used the American Presidency Project [1]. For this analysis, we consider the first term speeches by the following presidents: Madison, Lincoln, Kennedy, Clinton, Bush (George W.) and Obama. Calculating “readability” via machine methods seems difficult at first. Fortunately, there are a number of methods available. The one that I decided to use is the “Flesch-Kincaid Grade Level” [2], given by:

This test has several desirable properties; it is straightforward to calculate because word and syllable counts are easily counted by machine. Second,

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Figure 1: Flesch-Kincaid (FK) grade level of State of the Union a0ddresses for selected presidents. The data suggests a decline in complexity from Madison and Lincoln to present. The most recent three presidents (Clinton, G.W. Bush and Obama) are statistically indistinguishable as measured by FK (ANOVA p = .66). This is a reflection of the writing style of the time, more than the education level of the various presidents (and their staffs). Lincoln and Kennedy are similar (p = .25), while Madison was writing in a different grade level (p = .001).

it is invariant to the meaning of the specific passages, so it can be used equally against samples written in different styles or time periods. The second desirable property is also its main drawback. Specifically, the term “grade level” can be misleading, because it applies only to structure and not meaning. For example: “It is a far, far better thing that I do than I have ever done; it is a far, far better rest that I go to than I have ever known” [3], and “I went to the grocery store, bought

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some rye bread and ate it all up” [4]. The statements are clearly written at different intellectual levels, but both score 3.6 on the Flesch-Kincaid scale. Many packages are available to do this type of analysis; the analysis that follows was done using the “koRpus” [5] package for R. Our exploration of some presidents’ addresses are presented in Figure 1. For sake of comparison, Lincoln’s Gettysburg Address [6] has a grade level of 11.5, The Enchiridion by Epictetus [7] SE P TEM B ER / OCTO B ER 2 014

Five- M in u t e A n a lyst

has a grade level of 7.8, and my June Five-Minute Analyst column [8] has a grade level of 8.6. Conversely, “The Cat in the Hat” by Dr. Seuss, consisting of short sentences and monosyllabic words, scores -.36. Does This Matter? The writing style of the presidents is not only a reflection of themselves, but also of the times that they live and the audience to which they are speaking. It should not surprise us that in the modern era, presidents speaking to the entire electorate in real time via TV and radio have a lower grade level than Madison, who was speaking to a smaller audience. Those who wish for a more “intellectual” discourse with our leaders should consider the opening paragraph of Madison’s 1809 address: “At the period of our last meeting I had the satisfaction of communicating an adjustment with one of the principal belligerent nations, highly important in itself, and still more so as presaging a more extended accommodation. It is with deep concern I am now to inform you that the favorable

prospect has been over-clouded by a refusal of the British government to abide by the act of its minister plenipotentiary, and by its ensuing policy toward the United States as seen through the communications of the minister sent to replace him.” It will be interesting in future years to see if the apparent difficulty of texts stabilizes, increases or decreases. At this moment, I would believe all three outcomes. 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).

Notes & REFERENCES 1. http://www.presidency.ucsb.edu/sou.php#menu 2. Kincaid, J. P., 1975, “Derivation of New Readability Formulas for Navy Enlisted Personnel,” Research Branch Report 8-75, Millington, Tenn. 3. The closing of “A Tale of Two Cities” by Charles Dickens. 4. A sentence I just made up for this purpose. 5. http://cran.r-project.org/web/packages/koRpus/ index.html 6. http://www.abrahamlincolnonline.org/lincoln/ speeches/gettysburg.htm 7. As translated by George Long: http://www.ptypes. com/enchiridion.html 8. http://www.analytics-magazine.org/july-august2014/1080-five-minute-analyst-probabilistic-parkingproblems

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

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GREAT ANALYTICS PRACTICE “MEETING WITHIN A MEETING” AT 2014 INFORMS ANNUAL MEETING Think the INFORMS Annual Meeting is only for Academics? Think again.

Industry sessions span entire meeting November 9–12, 2014

Attend the Analytics Section Meet and Greet

Recology Plant Tour

Receive Individual Consulting

Daniel H. Wagner Prize: Excellence in O.R. Practice INFORMS Prize: Effective Integration in Organizations Industry Job Search Panel Discussion Recology is one of the nation’s 10 largest 100% employee-owned businesses, and has been recycling refuse for over 90 years. It piloted the first curbside compost collection program in the U.S., and launched the first program in the U.S. to create electricity using anaerobic digestion of source-separated food scraps.

Meet the people that care about what you care about. Monday, November 10, 6:15 – 7:15 pm

Analytics Magazine readers are invited to meet one-on-one or in small groups with INFORMS Analytics Section officers in San Francisco. We’ll help you arrange. For details, email gary.bennett@informs.org.

About the Competition The purpose of the competition is to bring forward, recognize, and reward outstanding examples of operations research and the management sciences in practice. The client organization that uses the winning work receives a prize citation; the authors of the winning work receive a cash award.

Call for Entries

A $15,000 Competition with a $10,000 First Prize Application Deadline: October 15, 2014

Key Dates for the Competition Wednesday, October 15, 2014

Deadline to provide a single pdf document containing a three page summary of your achievement, and a cover page with a 60-word abstract, and the name, address, phone number, and affiliation of each author.

Monday, December 16, 2014

Finalists will be selected based on the summaries and the INFORMS/CPMS verification process.

Friday, February 14, 2015

Deadline for finalists to provide a full written paper.

Monday, April 13, 2015

Each finalist group will give an oral presentation of their work in a special session at the INFORMS Conference on O.R. Practice in Huntington Beach, CA, April 12-14, 2015.

CPMS

The Prac ce Sec on of INFORMS

Entry Requirements Visit the website www.informs.org/edelmanaward for detailed information. Entries should report on a completed practical application and must describe results that had a significant, verifiable, and preferably quantifiable impact on the performance of the client organization. Finalist work will be published in the January-February 2016 issue of Interfaces. Any work you have done in recent years is eligible, unless it has previously been described by a Franz Edelman Award finalist. Previous publication of the work does not disqualify it. Anyone is eligible for the competition except a member of the judging panel.

E-mail Submissions

Please e-mail your submission to: trick@cmu.edu Michael Trick Chair, 2015 Edelman Award Competition Committee

The PuzzlOR & Thinking Analytically BY JOH N T PuzzlOR OCZEK The

& Thinking Analytically

BY JOHN TOCZEK

ThinGood k in gBurger A na ly t i ca lly Good Burger

Item Item Beef Patty Beef Patty Bun Bun Cheese Cheese Onions Onions Pickles Pickles Lettuce Lettuce Ketchup Ketchup Tomato Tomato

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Table 1: For the best burger, cost is no object.

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owner ofyour a fast food are restaurant with declinAs the owner of a fast food restaurantAs withthe declining sales, customers looking for something new and exciting on the menu. Your market research indicates that they want a burger that is loaded ing sales, your customers for something with everything as long as it meets certain health requirements. Moneyare is nolooking object to them.

As the owner of a fast food restaurant with declining sales, your customers are looking for something newwhat and exciting on the Your market The ingredient in the table is available to include onmenu. the burger. You must include new and exciting on thelist menu. Yourshows market research indicates that they want a burger thatresearch isatloaded least one of each and nocertain more than five of each item. You must use whole items (for example, with everything as long as item it meets health requirements. Money is no object to them. indicates that they want a burger that is loaded no with half servings of cheese). The final burger must contain less than 3000 mg of sodium, less than 150 grams of fat, and less than 3000 everything calories. as long as it meets certain health require-

The ingredient list in the table shows what is available to include on the burger. You must include at least one ofToeach itemcertain and no more than five ofMoney eachneed item. You must use whole items example, no maintain taste quality standards you’ll keep the servings of ketchup and(for lettuce the ments. istono object to them. same. Also, you’llThe needfinal to keep the servings of pickles andthan tomatoes same. half servings of cheese). burger must contain less 3000the mg of sodium, less than 150 grams of fat, and less than 3000 calories. The ingredient list in Table 1 shows what is avail-

able to burger include burger. You must include at least Question: What is the most expensive youon canthe make?

To maintain certain taste quality standards you’ll need to keep the servings of ketchup and lettuce the one ofofeach item no more than five of each item. same. Also, you’ll need to keep the servings pickles andand tomatoes the same. Send your answer to puzzlor@gmail.com by October 15th, 2014. The winner, chosen randomly fromservYou must use whole items (for example, no half correct answers, will receive a $25 Amazon Gift Card. Past questions can be found at puzzlor.com.

ingsburger of cheese). The final burger must contain less Question: What is the most expensive you can make?

than 3,000 mg of sodium, less than 150 grams of fat andbyless than153,000 calories. Send your answer to puzzlor@gmail.com October , 2014. The winner, chosen randomly from correct answers, will receive a $25 Amazon Gift Card. Past questions can be found at puzzlor.com. To maintain certain taste quality standards you’ll need to keep the servings of ketchup and lettuce the John Toczek is the Sr. Director of Decision Support and Analytics for Aramark Corporation in the Global Operational Excellence same. Also, you’ll(1996) needand tohis keep servings of pickles group. He earned his BSc. in Chemical Engineering at Drexel University MSc. the in Operations Research from Virginia Commonwealth University (2005). and tomatoes the same. John Toczek is the Sr. Director of Decision Support and Analytics for Aramark Corporation in the Global Operational Excellence group. He earned his BSc. in Chemical Engineering at Drexel University (1996) and his MSc. in Operations Research from th Virginia Commonwealth University (2005).

By John Toczek

John Toczek is the senior director

pXX of Decision Support and Analytics for

pXX

Question: What is the most expensive burger August 2014 you can make? OR/MS TODAY and Analytics Magazine

ARAMARK Corporation in the Global Operational Excellence group. He earned a bachelor of science degree Send your answer to puzzlor@gmail.com by in chemical engineering at Drexel Nov. 15. The winner, chosen randomly from correct University (1996) and a master’s degree in operations research from answers, will receive a $25 Amazon Gift Card. Past Virginia Commonwealth University OR/MS TODAY and Analytics Magazine August 2014 v1 questions can be found at puzzlor.com. (2005). He is a member of INFORMS.

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

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Fields of Fuel - A Multiplayer, Web-based Simulation Game A complex system of GAMS models is a centerpiece of this free web-based simulation game, which allows players to explore sustainability issues associated with bioenergy crop production. Biofuels and agronomic experts assisted in creating an accurate and realistic depiction of the system dynamics. • Players take on the role of farmers working to sustainably grow crops as energy resources, earn income and improve ecosystem services.

• Automated ‘bot’ players communicate with the optimization models via the GAMS Java API to evaluate which options will maximize their overall game score. • The game can be played in a variety of settings, but was primarly designed for use in high school and undergraduate classes.

For further information please visit http://www.fieldsoffuel.org or contact Steven Wangen - srwangen@wisc.edu

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