Power to the People: Analy0cs for All

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1 Arijit Sengupta CEO, BeyondCore, Inc. Power to the People: Analy0cs for All " Ten patents related to Advanced Analytics, Privacy/Security and BPaaS. " Previously worked at Oracle, Microsoft, Yankee Group and General Motors" Guest lectured at Stanford and other universities" Was written about in The World Is Flat 3.0, the New York Times, USA Today, San Jose Mercury News, and other leading publications. " MBA with Distinction from the Harvard Business School" Bachelor degrees with Distinction in Computer Science and Economics from Stanford University"

2 Power to the People: Analy0cs for All Clay Christensen in Consider the disrup*on technology has already introduced. The big data company BeyondCore can automa*cally evaluate vast amounts of data, iden*fy sta*s*cally relevant insights, and present them through an animated briefing, rendering the junior analyst role obsolete. Most Innova*ve 10 patents granted

3 The tempta*on to form premature theories upon insufficient data is the bane of our profession - Sherlock Holmes

4 But there are simply too many places to look! Assume there are 10 fields in the data + the outcome variable 1 variable at a *me (10 graphs) 2 variables at a *me (90 graphs) 3 variables at a *me (720 graphs) Who wants to review 820 graphs?

5 Customer Example: Fortune 500 Marke0ng Department Objective: Predict the most valuable customers based on their activity in first three months 1 st Order Analysis 2 st Order Analysis (8000 combina*ons in 4 mins.) 1.2 X more valuable 1.3 X more valuable 5 X more valuable Completely unprofitable 7.5 X more valuable Do campaign in Do campaign in

6 The Problem with N- order Sta0s0cs With Big Data the problem becomes worse Number of poten*al graphs an analyst would have to manually consider 10 Fields 50 Fields 100 Fields 250 Fields 1- Variable PaDerns Variable PaDerns 90 2,450 9,900 62, Variable PaDerns , ,200 15,438, Variable PaDerns 5,040 5,527,200 94,109,400 3,813,186,000 Reality check: not enough analysts or enough Jme to manually evaluate 4 billion graphs

7 Automated Analy0cs is the only solu0on for the current shortage in analy0cs talent McKinsey Global InsJtute Report We Need to Focus Here

8 Yesterday: Humans trained to serve the computer 2013 BeyondCore, Inc.

9 Today: Computers train to bemer serve humans 2013 BeyondCore, Inc.

10 No Sta0s0cs, No Computer Science, No Keyboard Then: Coding" Now: Selecting" Select the variable you want to learn about"

11 Sta0s0cs Made Comprehensible Through Stories Then: Math Now: AcJonable Examples

12 Tradi*onal Analysis works if you know the right ques*ons to ask but what if you don t?

13 The Future of Analy*cs Today

14 Simplifies Every Aspect of Analy0cs Typical Analy*cal Process. Democra*zed by BeyondCore Load, Clean, Transform Data Insight Discovery Predic*ve and Prescrip*ve Analysis Insight Visualiza*on BeyondCore Automa*cally: þ Learns data structures þ Handles data quality þ Simplifies data aggrega*on & transforma*on þ Evaluates millions of paderns þ Confirms stajsjcal validity of insights þ Guides users to key insights and drill downs þ Predicts outcomes & scenarios þ Explains Why þ Recommends best ac*ons þ Talks through insights like a human analyst þ Applies best prac*ces þ Highlights insights BeyondCore replaces overpriced junior analysts with Big Data, crunching vast amounts of informa*on to iden*fy trends.

15 The Solu0on: as presented by at Why did we use a hypothesis-free automated analysis for this Strata research? Too little time and resources and too many questions to consider. New BeyondCore 3.0 Feature

16 Micro- segmenta0on Analysis of 30 Million pa0ents for McKinsey Evaluated a Million variable combinations without pre-conceived notions to predict healthcare cost increases Would need to specifically look at young, female, diabe*cs to see this pajern

17 Automated Analyst Data Resiliency & Introspec*on Automated One- Click Analysis Two- Minute Animated Briefings Comprehensible Sta*s*cs

18 Why Chose For details, see: /WhySearsChoseBeyondCore.pdf 5 minutes from raw data to acjonable insight: In less than 5 minutes total, we had been set up on a cloud- based server, were able to load up our uniquely structured data, analyze it with a single click, and see the results of the automated analysis.

19 Micro- segmenta0on Analysis of 30 Million pa0ents for McKinsey Evaluated a Million variable combinations without pre-conceived notions to predict healthcare cost increases All discharges $4,200 average increase in annual medical costs post- inpajent discharge Organic disease Heart failure? Yes $17,800 No $3,600 Mental health Depressed? Yes $22,000 No $15,000 Compliance Filled anj- depressants in the following year? Less than 40% $33,000 More than 40% $17,500 See the full ar*cle at: hjp://strata.oreilly.com/?p=59582 and the video at hjp://youtu.be/utrpc4er2cq

20 Automated Analyst Overview Strong team: Market- tested solujon: used by 20 of the Fortune 100 in the first year since product release, highlighted by opinion- makers ( ) and press ( ) Unique secret sauce: knows which graphs are important and explains insights as a human analyst would Well- funded: $9M Series A led by and leading angels such as Audrey MacLean (Stanford), and Warren McFarlan (Harvard)

21 Automated Analyst For more informa*on contact: Most Innova*ve 8 patents granted

22 Backup Slides

23 BeyondCore Analysis of invoices on Objective: Reduce underpayment or late payment by analyzing adjustments / discrepancies in HP invoice data BC Analysis on HP Cloud BC Reports on HP Cloud Content Delivery Network 21,000 variable combina*ons evaluated Over 0.5 million calcula*ons 30 key insights auto-detected in minutes Smart- triaged ac*onable pajerns in invoice adjustments Iden*fied clear pajerns of spikes: Oct 285% spike for one customer Companies with highest average discrepancies shared non- obvious third order characteris*cs Jan 295% spike for another customer

24 United Health Group Claims Case Study (presented by UHC India CEO at IQPC conference) 1 Million Pa*ent Records Specific hospitals were paid more for certain procedures 138,000 variable combina*ons evaluated Example of auto-detected insight

25 Leading Hospital System Pa0ent Outcome (30+ hospitals) Objective: Analyze patient Length of Stay (LOS) patterns to detect opportunities for improving patient outcomes 247,000 pa*ent outcomes analyzed 534,000 variable combina*ons evaluated 35 key insights auto-detected For the exact same surgery, pa*ents in one hospital were recovering 9.3 days later Over 9 million calcula*ons For the same treatment, pa*ents with one insurance were staying 10 days longer

26 Leading Collec0ons Agency example: matching right methods to right segments For different Credit Score ranges different collec0on methods are more effec0ve 16 million transac*ons analyzed Credit Score < ,000 variable combina*ons evaluated Liquida*on rates for different collec*on methods Example of key insight auto-detected in minutes Credit Score > 650

27 Major Hospital System Financial Outcome (30+ Hospitals) Objective: Optimize Cash Flow by Analyzing Percent Paid = Payments / Expected Revenues 247,000 claims analyzed 678,000 variable combina*ons evaluated 27 key insights auto-detected For the exact same diagnosis, different insurers had very different payout rates Over 9 million calcula*ons Percentage paid, in aggregate, for different claim sizes by the exact same insurer

28 Fortune 100 Service Win Outcomes How successful were different partners in getting warranty contracts renewed? 1 million service opportuni*es analyzed 15,033 variable combina*ons evaluated 30 key insights auto-detected Success rates varied widely across regions Over 300 thousand calcula*ons For a key product, one region did much bejer while another did worse than usual

29 Top 5 Bank Mortgage Alloca0on Process (Outsourced to Top 10 outsourcing provider) Objective: Improve existing process that matched the most complex mortgages to the most experienced agents 21,000 mortgages analyzed 61,000 variable combina*ons evaluated Over 4 million calcula*ons 20 key insights auto-detected Iden*fied 9 specific ways for improving the exis*ng Complexity Score model for predic*ng mortgage processing *mes Delivered 4.7X improvement Automa*cally iden*fied that the best operators were cherry- picking the easiest mortgages

30 Shamrock Chicago Trading LLC (A division of the O Connor Investment Partnership) Objective: Leverage sentiment analysis to improve trades Analyzed multiple factors to determine non-obvious trading strategies" Seven different sentiment metrics from leading providers" Historical volatility of the stock " Implied volatility of the options on the stock" Average daily volume" Market cap" Dividend yield" Other traditional metrics" With BeyondCore, Shamrock was able to:" Validate the value of the sentiment metrics relative to trade profitability " Identify under exactly what circumstances which sentiment metric was the most actionable" Develop filters that optimized trade performance " For months we had debated whether sentiment analysis would help us improve trades. BeyondCore answered that question in minutes and automatically detected actionable trading strategies. - T Pruter"

31 BeyondCore Key Features! Automated Analysis: No coding, exploring, drilling required! Simple ExplanaJons: Shows the stories hidden in the data! PrescripJve AnalyJcs: Tells you what to do to deliver impact! Inherent Privacy: All the insights, none of the secrets! Simple Deployment: No installa*on required

32 Key Benefits: Why customers prefer BeyondCore Decision Criteria Compe*tors Strategic: Speed of analysis and quality of insights Require weeks or months to manually analyze data across hundreds of variable combinajons Automa*cally presents key insights by analyzing raw data in minutes or hours across millions of variable combinajons Financial: Total Cost of Ownership (TCO) per analysis A typical analyst conducts 10 analyses a year. Business users spend hours to understand analysis A typical analyst conducts 10+ analyses per month. Business users can understand the key insights of the analysis in just minutes OperaJonal Gap: Resources needed to enable a Big Data / Analy*cs strategy Require teams of trained analysts with deep business knowledge plus sta*s*cs and programming skills Users with business knowledge can learn to use BeyondCore in just 30 minutes. No sohware install required

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