Five Trends in Analytics - How to Take Advantage Today - StampedeCon 2013

1. Five Key Trends in Business Analytics How to Take Advantage Today StampedeCon 2013 St. Louis, MO July 30, 2013 John Lucker, Principal Global Advanced Analytics & Modeling Market Leader JLucker@Deloitte.com - Twitter: @JohnLucker Deloitte Consulting LLP

2. - 2 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. About Today’s Speaker – John Lucker John Lucker Principal Deloitte Consulting (860) 725-3022 JLucker@deloitte.com •  Deloitte’s Global Advanced Analytics and Modeling Market Leader and a leader of the Deloitte Analytics Institute •  Provides clients with end-to-end strategy, business, operational, and technical consulting services in the areas of advanced business analytics, predictive modeling, data mining, scoring and rules engines, and numerous other advanced analytics business solution approaches. His clients are in many industries including insurance, banking and financial services, retail, consumer products, telecomm, healthcare, life sciences, media, hospitality and others. •  Author of more than 50 articles and papers on a variety of advanced analytics, predictive modeling and analytic business and technology topics •  Speaker at numerous global conferences on a variety of analytics and business solutions topics and published in a number of journals and professional publications. •  Co-inventor of US Patent 8,036,919 for a “Licensed Professional Scoring System and Method” and US Patent 8,145,507 for a “Commercial Insurance Scoring System and Method” and US Patent 8,200,511 for a “Method & System for Determining the Importance of Individual Variables in a Statistical Model” and two pending patents •  Education; University of Rochester – B.A.; University of Rochester – Simon School – M.B.A.

3. - 3 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Today’s Agenda – 5 Key Trends in Business Analytics 1.  Behavioral Economics 2.  Big Data Pragmatism 3.  Data Visualization 4.  Mobile Analytics 5.  Strategic Analytics

4. Behavioral Economics

5. - 5 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Analytics Helps Create a More Fact Based vs Gut Based Culture The problem is not that baseball professionals are stupid; it is that they are human. Like most people, including experts, they tend to rely on simple rules of thumb, on traditions, on habits, on what other experts seem to believe. -- Cass Sunstein & Richard Thaler review of Moneyball The most difficult subjects can be explained to the most slow-witted man if he has not formed any idea of them already; but the simplest thing cannot be made clear to the most intelligent man if he is firmly persuaded that he knows already, without a shadow of doubt, what is laid before him.” – Leo Tolstoy

6. - 6 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Cognitive Bias In Action – Availability Heuristic Every summer – Watch Out for Sharks! However: “It is more likely you will be killed by slipping on a wet floor” “During a 342 year period from 1670 to 2013 there were 1,022 US shark attacks” “During the same 342 year period there were only 36 fatal US shark attacks” Source: http://www.flmnh.ufl.edu/fish/sharks/statistics/GAttack/mapusa.htm

7. - 7 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Cognitive Bias In Action – Life Insurance Applications When applying for life insurance, do you engage in “risky activities” Cause of Death Odds of Dying (1 in) Swimming 56,587 Cycling 92,325 Running 97,455 Skydiving 101,083 Soccer 103,187 Hang-Gliding 116,000 Tennis 116,945 Marathon Running 126,626 Scuba Diving 200,000 Ping-Pong 250,597 Rock Climbing 320,000 Source: http://www.medicine.ox.ac.uk/bandolier/booth/Risk/sports.html

8. - 8 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Cognitive Bias In Action – Segment of One? – Sunk Cost Processes Fact: I haven’t obtained a new credit card in 23 years. Question: Over the past 30 months, I have received 140+ credit card offers from just two banks? Typical Response: We are very successful doing things as we have evolved them. We have made significant investments in customer segmentation. You are an exception and this doesn’t happen to most people.

9. - 9 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Cognitive Bias Examples from my Consulting Experience Examples •  Comment from a Financial Services executive in a pricing meeting: “I recommend we increase interest rates (APR) on our accessory motor lending products by 30 basis points this year because this summer will be very hot and the demand for motor “toys” will increase”. •  How does the executive know what the weather will be like in a few months? Can she be sure that demand for motor “toys” will go up? •  During a price optimization exercise a food retailer decided not to price different flavors of a product differently despite empirical evidence that some flavors were significantly more sensitive to price. They (wrongly) discussed that other food products were not priced that way. By pricing all flavors the same the company was unable to obtain a gross margin benefit of more than $4 million in the first year. •  Comment from a Retail executive: “we don’t care as much about the needs of men in our catalog and stores” because 80% of our customers are women. •  But what if sales are saturated for women and there could be a latent opportunity to sell more to men? If the infrastructure is there to sell to men (since 20% of sales is to men) then why not see if there is a way to increase male traffic to stores and catalog?

11. Big Data Pragmatism

12. - 12 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. What Is ‘Big Data’? •  Some people think of BD as anything that doesn’t fit in an Excel spreadsheet! •  In some simple contexts, BD is used to refer to any form of advanced statistical analytics or predictive modeling using diverse internal/external data sources •  In many contexts, BD is shorthand for the granular and numerous data sources used for analytics projects - big, small, old, new, structured, unstructured, etc. •  In more esoteric data contexts, some examples include geospatial, social/ sentiment, audio/video, mobile information, telematics, telephonic, internet searching, web logs, etc. Big Data refers to internal and external data that is multi- structured, generated from diverse sources in real-time and in large volumes making it beyond the ability of traditional technology to capture, manage and process within a tolerable amount of elapsed time

13. - 13 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Big Data – How Much Data is Generated Every Minute? •  It isn’t just the rate of creation of data that’s increasing, it’s consumption rate too •  This chart also shows one Big Data’s important facets – much of it is unstructured •  If you added up all the data we created or replicated last year, converted it to text and printed it as paperback-sized books, you’d get a pile stretching from the Earth to Pluto and back more than 16 times

14. - 14 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Big Data, Big Noise – But Perhaps Not Enough Focus on Strategy & Value Understanding Big Data as completely as possible and clearly identifying its links to various value levers is critical before designing any solutions - Value, Strategy, Decisions, Analytics, Execution, Assets InformationDemandInformationSupply Value-driven Strategies Value Chain Execution Information Assets Data Warehouse Insights, Analytics & Measurement Value Drivers Improvement Levers Data Sources Decisions Governance & Management

15. - 15 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Big Data’s Potential for High Return on Investment Big Data budget and investments are set to increase in the coming years although the returns on investments on Big Data investment are yet to be established, 15 Source: The Big Returns from Big Data, and Big Data Hits the Jackpot, Nucleus Research, April 2013; Big Data Executive Survey, NewVantage Partners, 31 December 2013 •  According to Nucleus Research, organizations can earn an incremental ROI of 241% by using Big Data capabilities •  Nucleus research cites increased business adoption is improved by business processes, efficiencies and decisions as one of the drivers and the ability to monitor the factors that impact a company, such as customer sentiment across customer segments – End-To-End Implementation and Integration •  In November 2013, alone, venture capital, and private equity invested $180 million in Big Data and analytics vendors 24% 36% 48% 36% 24% 22% 5% 6% Today 3 Years from now Less than $100,000 $100,000 - $1 Million $1 Million - $10 Million More than $10 Million Budget for big data

16. - 16 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. The Importance of the Big Data Trend & How to Capitalize On It 16 Source: British Retail Consortium (BRC) Analysis While Big Data has driven IT spending, hype should not drive the attention away from data quality, relevance, redundancy, and most importantly, insights Data Quality Data cleaning might take more effort than data analysis. The magnitude of bad data will get amplified with the increase in type of sources, and volume of data – but keep 80:20 Rule in mind Interpretability Substantial amount of data is unstructured and hence liable to different interpretation by different people and machines Business decisions stand the risk of being based on biased interpretations Relevance The amount of data stored will have contextual relevance with individuals analyzing the data. ‘One Man’s Signal is another Man’s Noise’ Privacy Issues As organizations capture more volumes of data from various sources, they are more susceptible to disturbing privacy concerns Especially as more and more consumer data is being used, organizations will have to be sensitive about the data they use Redundancy There are chances, organizations are capturing same data from multiple sources, multiple times e.g., Tweets, updates Organizations should be wary they are not investing in capturing one data point from multiple sources Novelty Most of the time, a lot of data captured from Big Data sources is already captured in existing data available with the enterprise Big Data investments should focus on finding new insights Dilutes Value Focus With Big Data hype, a lot of attention is going into collection, storage, and access of Big Data This has diverted attention from analysis and ultimate use of data Knowing what organizations want to do with the data might also be an important question to consider Avoid “Hoarding” Complex Big Data is still confusing to many professionals After the hype subsides, efforts into making it less complex, and user friendly should ensue

17. - 17 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Big Data Potential Roadmap A Big Data Roadmap begins with the decision makers and their strategic “crunchy” questions and then proceeds to the data sources and technologies that are required to address the needs. Identify Opportunities Assess Current Capabilities Identify and Define Use Cases Implement Pilots and Prototypes Adopt strategic ones in Production §  Identify strategic priorities and ask crunchy questions §  Assess §  Data and application landscape including archives §  Analytics and BI capabilities including skills §  Assess new technology adoptions §  IT strategy, priorities, policies, budget and investments §  Current projects §  Current data, analytics and BI problems §  Based on the assessments and business priorities identify and prioritize big data use cases §  Identify tools, technologies and processes to implement pilots. Define and compute metrics on incremental value added §  Prioritize and implement successful, high value initiatives in production 1 2 3 4 5

18. Data Visualization

19. - 19 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. What’s Important About Data Visualization Capabilities? •  What’s the business problem to be solved? •  Presents results of sophisticated analysis on large and complex data sets in understandable & actionable formats •  Makes insights accessible to a much broader audience based on user experience, appetite, aptitude, ineptitude •  Helps allocate the scarcest resource a decision maker has: attention •  Increases communications impact with key stakeholders (C-Suite, Boards, media, analysts, customers, etc.) •  Know Your Audience - is not for everyone – should allow easy interaction with data for those who want to dive deeper •  Capability requires appropriate information architecture, governance, security, software, staffing, training

20. - 20 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Visualization Possibilities are Limited Only by Imagination Streamgraph Force-directed graphs Tree Maps Sunburst Word Tag Cloud Bubble Chart Many Eye Bubble Chart Time Series Analysis Geospatial Parallel chord Calendar View Heat Maps

21. - 21 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Some Visualization Examples: SOMs 1.  SOMs help make sense of high dimensional and complex data 2.  A SOM places a high number of variables into a 2-D visual map where similar observations are plotted next to each other and grouped into segments. Here is a portion of a SOM where 700 variables describing purchase patterns from 10,000 stores have been analyzed to identify 46 segments. 3.  Overlays from variables of interest identify segments of opportunity. Here S17 was found to be an untouched segment.

22. - 22 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Some Visualization Examples: Network Relationship Maps Each doctor is a blue circle whose size is proportional to the amount of drugs prescribed. Doctors are linked if they share an organizational affiliation or have common patients. The thickness of the edge is proportional to the number of shared patients. The three red dots are individuals thought to be key influencers. This network graph shows that others may be equally or more influential.

23. - 23 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Some Visualization Examples: Pricing Waterfalls •  Highlights which cost-to-serve elements can be reduced in order to keep a larger portion of the list price •  Visually allows sales reps to determine what elements they can adjust in negotiations with a customer List Price $6.00 Order Size Discount Competitive Discount $5.78 Invoice Price Payment Terms Discount Annual Volume Bonus Off-Invoice Promotions $4.47 Pocket Price Co-op Advertising Freight $0.10 $0.12 $0.30 $0.37 $0.35 $0.20 $0.09 25.5% off list!

24. - 24 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Some Visualization Examples: Infographics http://www.youtube.com/watch?v=g5rGm6veAhg&feature=player_embedded&safety_mode=true&persist_safety_mode=1#!

26. - 26 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Should be a Governance Process for Visualization Pragmatism •  Stay alert for GIGO – try numerous approaches – fail often, learn fast •  Part Science / Part Art – need staff with refined right / left brains •  Tufte Type Rules – simplicity, color, proportion, substance, etc. •  Data Preparation – data needs to be in shape for visualization •  Limit Tool Investment – no requirement for big dollars •  Borrow Ideas – numerous examples and canned visualizations •  Avoid Excessive Diversity – many visualization types can doom effort •  Know Your Viewer – understand absorption / learning styles •  Eye on Value – as with any effort, look for ways to articulate value

27. Mobile Analytics

28. - 28 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Must Pay Attention to the Mobilization of Analytics •  Shift access to insight from “anytime anywhere” to “every time everywhere” •  Enable real-time collaboration and fact-based decision-making •  Improve field sales and service performance •  Drive more effective customer, market, employee interactions •  Point: If I can make stock market trades from my smartphone or tablet any time and anywhere, I should be able to perform analytics too.

29. - 29 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Mobile Analytics Pragmatism §  Integrate with overall analytics infrastructure §  Enable application building across popular mobile platforms §  Connect to back-end data sources made of internal, external, synthetic data sources §  Use native mobile device behaviors that all are familiar with §  Customize by role – e.g. executives won’t have the same requirements as salespeople, technicians, or managers §  Cultural considerations: §  Use a business-driven value roadmap to define success §  Constantly monitor implementation to meet business needs §  Be sure executives get distilled insight, not just mountains of data

30. Strategic Analytics

31. - 31 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. The “Crunchy Question” Approach to Realizing Analytic Value Crunchy questions are practical, detailed inquiries into tough business issues— roll-up-your-sleeves questions. Crunchy questions are designed to lay the groundwork for action Why Crunchy Questions? They crystalize the business challenges/issues that are at the heart of any Business Analytics Initiative Characteristics of Crunchy Questions §  Specific §  Relate to a particular business process and aligned with strategic goals §  Focus on optimizing or innovating, not merely informing §  Consider change relative to other indicators or processes §  Leverage and integrate internal and external inputs §  More forward or inward looking than backward looking §  More about differentiation than just comparison §  Consider various scenarios §  Actionable, i.e. more about “do it” than “prove it” §  Require advanced tools and techniques to answer “If its not at the heart of the business, it doesn’t count”

32. - 32 - Copyright © 2013 Deloitte Development LLC. Proprietary and Confidential. All rights reserved. Realizing Value from Big Data & Analytics is an End-To-End Process End-to-End Business Value 1. Business Strategy 2. Big Data Analytics 3. Biz & Ops Implement & Integration 4. Technology Integration 5. Org & Change Management 6. Performance Management

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