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Creating Business Value from Big Data, Analytics & Technology.

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Creating Business Value from Big Data, Analytics & Technology

Creating Business Value from Big Data, Analytics & Technology

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  • 1. Business Value Consulting for a PREDICTIVE and AGILE Enterprise STRATEGY + ANALYTICS + TECHNOLOGY ENABLING BIG DATA TRANSFORMATIONS FOR CONTINUOUS ADVANTAGE ™ rightedge ™ rightedge.com
  • 2. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Rightedge™ Confidential & Intellectual Property Material cannot be reproduced or distributed in any form without express written permission.
  • 3. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION C r e a t i n g B u s i n e s s Va l u e f r o m B i g D a t a , A n a l y t i c s & Te c h n o l o g y
  • 4. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION AGENDA ①  Big Data Phenomena (10 mins) ②  What's Disruptive with Big Data (10 mins) ③  Cases (25 mins) •  Battery Performance •  Casino Gaming ④  Cases (25 mins) •  Rail Sensor Data Analytics •  Advertising Analytics ⑤  Foundation Series Bootcamps (15 mins) ⑥  Closing Thoughts (5 mins) ⑦  Q & A (30 mins)
  • 5. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 5© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 5 Big Data Phenomena
  • 6. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION The Perfect Storm ①  LOTS OF DATA ②  COMPUTE POWER ③  MEMORY & STORAGE ④  INTERNET & CLOUD ⑤  SOCIAL+ LOC. + MOBILE
  • 7. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION What is Big Data? Structured Largely Unstructured Semi-structured Source: IBM and Oxford Survey: Getting Closer to Customers Tops Big Data Agenda, October 17, 2012 ü  People ü  Machines ü  Markets
  • 8. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION It’s a Big Data World Chart based on IDC and UC Berkeley Data Growth Estimates, Source: IDC & CosmoBC.com: http://techblog.cosmobc.com/2011/08/26/data-storage-infographic/ Petabyte PC Internet Time MobileMainframe Terabyte Data Volume Exabyte Zettabyte Machine 2011 Transactions M 2 M Interactions 2.0 Zettabytes in Enterprise Data Apps Patterns Information Insights Internet of Things Industrial Internet U G C Social Networks Sales of Goods & Services
  • 9. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Velocity VarietyVolume Ability to Make Sense of Data in Real-Time To Take Action What is Big Data Analytics? Tens of Billions of Events Terabytes to Petabytes to Exabytes Structured Semi-Structured Unstructured Binary Business Value Actionable Insights Leading To Superior Outcomes $ Adapted from Sources: Gartner, Cetas Analytics
  • 10. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Real-time Analytics Engine Unstructured Structured Semi- structured How To Make Sense of Data? Transac'ons   Logs   E-­‐mails   Social   Audio   Photo  &  Video   In-­‐Apps   Sensors   Actionable Insights Products   Inventory   Correlate   Predict   Recommend   •  Statistical Models •  Machine Learning •  Graph Algorithms •  Key Performance Indicators ….…. …. Ø  Volume Ø  Velocity Ø  Variety Ø  Variance
  • 11. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Optimization Genetic Algorithm (Compressions) Classification Neural Network (Models) Segmentation Machine Learning (Clusters) The Real-Time Engine InsightVisualization Dashboard (Views) Big Data S + SS + US Age Gender Income ……. FB Updates Tweets Real-time Business Analytics Engine ProductA ChannelX OfferP Analyst /Decision Maker Computing @ Scale @ Speed Statistical & Machine Modeling Data Mining Human Intelligence
  • 12. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 12© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 12 What’s Disruptive w/ Big Data?
  • 13. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Why is Big Data “Disruptive”? ①  Consumerization is “exponential” producer of Unstructured Data ②  Major cultural impact just as the Industrial & Internet Revolution ③  Real-time Customer/Market Knowledge will be a Competitive Edge ④  Real-time Data-Driven Decision-making will be Mandatory ⑤  Every Business must address it or Die
  • 14. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Workflow Centric Separating Application Logic and Data The New Business App Model New OLD Personalized User Experience Graphic Adapted From Gartner SSO -> APPS SSO -> DATA Data Tightly Coupled with App data data data data
  • 15. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION What is Changing Drastically? Decision-Making Process Big Data & Analytics Open Elastic IT LoB Manager Analyst IT •  Real-Time •  Predictive •  Closed-Loop DATA SCIENCETECHNOLOGY PROCESS PEOPLE
  • 16. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Enabling Context Driven Decision-Making What Businesses Need Now? 1 2 3 Predictive analytics Real-time analytics Investigative analytics - Predict What is going to happen - Know What is Happening Now -  Analyze What & Why it Happened
  • 17. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Business Impact Power of 1% Savings Driven by Real-Time Decisions
  • 18. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 18© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 18 Cases Predicting Battery Performance Casino Gaming Analysis
  • 19. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 19© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 19 Predicting Battery Performance
  • 20. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Forecasting vs. Prediction Forecasting Prediction Example Sales/Demand Forecast Likelihood of meeting forecasts Statement about the future Projection or Estimate Event that is likely to happen (probability) Basis Assumptions about future Insights about the future Usage For Planning in advance To take Pre-emptive action
  • 21. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Project Background 1.  Start-up Lithium Ion Battery Manufacturer 2.  4 Battery Models – 100-150 miles per charge 3.  First Target Use: Cars/Trucks – Racing, Commercial, Consumer 4.  Batteries in use in 1000+ Cars/Trucks 5.  Other Target Uses: Medical devices, Appliances, …
  • 22. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Hybrid & EV Battery Systems All Electric Hybrid Rechargeable Battery Packs Lead, Carbon, Nickel Hydride,… Heavy Lithium Ion Modules & Cells Require Safety Enclosure Lighter ~30-50 miles per gallon ~50-300 miles per charge ~$4,000+ 8 yrs, 100,000 miles ~$7000+ 8 yrs, 100,000 miles 48 lithium-ion modules. Each module contains 4 lithium-ion cells (192 cells) 28 modules. Each module contains 6 cells (168 cells)
  • 23. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Typical Hybrid & EV Battery Operation Typically operates in ONE of TWO Modes : Hybrid: High-power cycling (CS: Charge Sustaining) mode. (most common) EV: Continuous discharge (CD: Charge Depleting) mode
  • 24. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Framing the “Decision-Making” Problem ①  Forecast Battery Capacity Available to the ENERGY GRID (from all vehicles) •  Real-time Energy Supply & Demand Arbitrage ($$$$) •  Fleet Operational Cost Optimization ②  Predict Battery Performance (for each Battery Model) •  When is the overall system likely to fail (near to long term) •  Which Cell/Module is not performing as it should (real-time)
  • 25. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Analysis & Modeling Battery DATA SOURCES Electric Vehicle Driver Weather Traffic Real-Time Streaming Analytics Profiles, Logs Profiles, Logs Profiles, Logs Logs Logs Predicting Battery Failure Forecasting Battery Reserve Capacity Sensitivity Analysis Regression Analysis Trend Analysis Cohort Analysis
  • 26. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 26© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 26 Casino Gaming Analysis
  • 27. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Project Background 1.  10 Casinos Evaluated 2.  50 slot machines per location 3.  5-10 Games per slot machine 4.  ~500 Players Per Casino Per Day 5.  Over 5 TB of data captured per day
  • 28. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Marketing Questions that Needed Answers ①  What are Potential Profitable Segment (Players) Opportunities ②  What Advertisements to Target (bring him/her to the casino) ③  What Individual Offers to Recommend (in casino to incr. playing time = spend)
  • 29. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Some Interesting Player Stats… ①  Ave. Time Spent by Player (at Casino Slot Machines) 5 hours (in a day) ②  Ave. Spend (Slot Machine) by Player ~$100 /day ③  Ave. # of Slot Machines Played (in a Casino) 3
  • 30. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Casino Gaming Analysis & Modeling Casino DATA SOURCES Slot Machines Games Player Analytics Profiles, Logs Profiles, Logs Profiles, Logs Game, Logs Recommended Relevant Individual Offers (in-game) Identified 8 Potential (Player) Segments to Target (Behavioral + Psychographic) Cohort Behavior Analysis RFM Analysis Multivariate Analysis Cluster Analysis Rewards Program Play (Win/Loss) History Referrals Spend Patterns Play Patterns Real-time Exploratory
  • 31. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 31© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 31 Cases Railroad Sensor Data Analytics Predictive Advertising Analytics
  • 32. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 32© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 32 Foundation Series Bootcamps
  • 33. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Why You Should Care? Prepares YOU to understand, lead and drive Big Data Transformations in whatever ROLE you are in. Broaden your thinking (from silos), Align with Data-Driven Decision-Making, Develop NEW Skills THREE 1-day Bootcamps (in recommended order) 1.  Decision Maker Lens (POV) Learn how data-driven decisions are made using business frameworks 2.  Business Analyst Lens (POV) Know how data & analytical models are used in decisions 3.  Technologist Lens (POV( Understanding how Big Data Technologies enables data-driven decision-making Goal is to make YOU understand how data-driven decision-making impacts business value in your organization or your customer’s organization. Provides YOU with the knowledge, mindset and practical tools
  • 34. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Practical Learning Objectives This FOUNDATION SERIES program encourages YOU to apply the insights and best practices, learnt, in the context of your own organization (or your customers) including (but not limited to): ①  Define problems & solutions that create business value from the application of big data & analytics ②  Brainstorm sources & variety of data, use of statistical and machine learning models, Collective wisdom ③  Design Experiments to collect and analyze data in creative ways to optimize business value.
  • 35. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Content Focus This unique FOUNDATION SERIES program blends ①  Custom Curriculum (for corporate training) ②  Domain Specific Business frameworks, KPIs ③  Use case Examples, mini-cases, case studies, and ④  Brainstorming discussions ⑤  Check Lists (Questions to Ask) Participants learn how businesses use big data & analytics for decision-making effectively in critical functional areas such as strategy, customer support, sales, marketing, supply chain and IT.
  • 36. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Tied Together by F.A.I.T.H™ Methodology F A I T H Framing the business problem, formulating biz case, strategizing on scenarios Analysis & Modeling of the business problem with KVBI™, Relevant Data Insights Extraction, Interpretation and Validation Timely Action & Visual Reporting (using Technology) Harvesting Yield & KPI Monitoring for Closed Loop Feedback
  • 37. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Get F.A.I.T.H™ Certified Strategy + Analytics + Technology = Business Value F A I T H CONSISTENT. ITERATIVE. REPEATABLE. CLOSED-LOOP. Create, Grow, Build Data-Driven Decision-Making Mindsets
  • 38. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Sample Case: Starbucks Starbucks Starbucks wants to expand in Brazil in 2014. Wants to be Profitable in Year 1 of Expansion and Triple market share by Year 3. Your Team is asked to present an evidence based (data-driven) Market Expansion Strategy Recommendation Present in 40 mins Ilustrative
  • 39. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Mini Case: Starbucks Starbucks 1.  Who (think roles) would you want on your team ? 2.  List questions that needs answers (from data or otherwise) 3.  List Data Sources and Attributes you will need, use 4.  Identify Key Business Value Indicators (KVBI™) that will indicate profitability 5.  Determine Analytical Models to Use 6.  Identify Technology Infrastructure needed to support Strategy Illustrative Decision Making Task List
  • 40. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Competitive Analysis THINK ABOUT… Data (Available or not) that could enable your understanding of the 5 forces •  Data Sources (Internal, External) •  Data Attributes (Dimensions) Models that could surface insights on competitive position •  Statistical Models •  Prediction Models •  Recommendation Models Starbucks Illustrative
  • 41. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION THINK ABOUT… Data (Available or not) that could inform your STP (Segmentation, Targeting, Positioning) •  Data Sources (Internal, External) •  Data Attributes (Dimensions) Models that could surface insights on marketing mix (relative to self, competition) •  Statistical Models •  Prediction Models •  Recommendation Models Marketing Strategy Starbucks Illustrative
  • 42. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Bootcamp #1: Introduction to Data-Driven Decision-Making Every decision involves making assumptions about uncertainty and risk. Big Data & Analytics are transforming how decisions are made in every enterprise, from the start-up to the Global enterprise, to reduce uncertainty & risk. So every professional or employee in any company must understand how line of business (LoB) executives and managers make decisions in different departments - Strategy, HR, Marketing, Finance, Supply chain, IT and more. This Bootcamp is intended to give you a foundation on the business decision frameworks typically used in different functional areas by decision-makers. You also learn how decision frameworks are applied across different verticl business contexts and use cases. 1Decision Maker LensDEVELOP BUSINESS SENSE
  • 43. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Bootcamp #2: Introduction to Business Analytics As companies are inundated with large volumes, variety, and velocity of data the need to use real-time, batch and interactive forms of business analytics is becoming critical. Typically the business analyst and/or the data scientist is responsible for creating analytical models on the data for LoB decision-makers to use for making informed decisions. However, it is in the best interests of every professional and employee to get a fundamental understanding of the application of business analytics in different functional areas. This bootcamp is intended to provide a foundation on the application of business analytic models typically used in functional areas such as strategy, marketing, finance, supply chain, IT, customer support, and more. 2Business Analyst LensDEVELOP ANALYTICAL SENSE
  • 44. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Bootcamp #3: Introduction to Big Data Infrastructure Interestingly, Big Data is both hype and reality. However, every CXO, Senior Executives, LoB Managers, and even IT must have a fundamental understanding of what the key Big Data technologies are and how they could enable business value. This understanding is crucial to make the right investments that will create, generate, drive, and optimize business value and a competitive advantage. This bootcamp is intended to provide a foundation on the key Big Data technologies to invest in today and for the future to become a real-time (agile) and predictive enterprise. 3Technologist LensDEVELOP TECHNOLOGY SENSE
  • 45. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Top 10 Bootcamp Takeaways For YOU ①  Build Data-Driven Mindset with F.A.I.T.H – Consistent, Iterative, Repeatable, Closed-Loop System ②  How data-driven decisions are made using well-know strategy & analysis frameworks ③  What kinds and types of data & analytic models are potentially used in decision-making ④  What & How key technologies and applications are driving the big data revolution ⑤  Common challenges & pitfalls in using big data & analytics ⑥  Design controlled experiments to distinguish causality from correlation ⑦  Mini-cases, case studies & examples from strategy, marketing, supply chain, IT and other applications ⑧  Recognize application opportunities in your own department, industry or function ⑨  Identify organizational and cultural enablers & barriers to data-driven decision-making ⑩  Importance of customer privacy and data ownership (in the context of your role)
  • 46. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 46© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 46 Closing Thoughts
  • 47. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION REMEMBER What is Changing Drastically? Decision-Making Process Big Data & Analytics Open Elastic IT LoB Manager Analyst IT •  Real-Time •  Predictive •  Closed-Loop DATA SCIENCETECHNOLOGY PROCESS PEOPLE
  • 48. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Key Decision Areas (Driven by Big Data) ①  Customer Intelligence ②  Segmentation ③  Prediction and Recommendation ④  Dynamic Product Development & Innovation ⑤  Sensor Network Intelligence
  • 49. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Analytical Thoughts ①  Larger the Data Set Better the Prediction ②  Variety of Data = Richer, Deeper Insights ③  Trusting Predictions from Data Science ④  Real-time Segmentation & Targeting is non-trivial ⑤  Volume + Variety = Better Segmentation & Targeting ⑥  Human Intelligence Required to Pick Segments to Target!
  • 50. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Thank You! Balu Rajagopal balu@rightedge.com Questions ? Comments ? Please Email Me.
  • 51. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 51© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 51 Q & A
  • 52. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION CNBC: Rise of the Machines http://www.hulu.com/watch/536745 Segment 2