Data to Dollars™ - Practical Analytics in the Big Data Era Jaime Fitzgerald April 2012

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  • 1. FSIUG WEBINAR Jaime Fitzgerald, Founder & President of Fitzgerald Analytics Turning Data to Dollars™ in the Era of "Big Data": How to avoid common pitfalls of managing large  volumes of data, sidestep "big data hype," and  capitalize on new opportunities Date: April 18, 2012 Time: 2:00 – 3:00 ESTMore and more technologists are getting excited about "Big Data", which they often define ashaving greater volume, greater variety, and greater velocity than traditional data assets.Although "Big Data" has great potential to spur innovation, the enabling technology andanalytics create new challenges and risks. Organizations are investing significant time andmoney in "Big Data" strategies, tactics, teams and tools. Yet, despite the hype, most "Big Data"initiatives have not generated concrete and positive ROI.
  • 2. • Message from our  President, Rich  Bouthilette • Message from our Today’s Agenda Quest Education  Specialist, Jenn Abney • Webinar • Q&A 
  • 3. The Financial Services Industry User Group   (FSIUG)• Quest Affliated ‐ Independent User Group  • Comprised of Financial Services Instutions that  have licensed an Oracle ERP product• Main Purpose:  Provide ways for members to  share implementation strategies and product  experiences and help them shorten the  learning curve related to maximizing their ERP  platform
  • 4. Recent Activities• User Group meetings at various conferences  such as Collaborate and Open World• Held a successful Financial Services Industry  Symposium last summer at Adelphi University  in Long Island• Had a Kiosk at Oracle’s Financial Services  Industry Meeting in February in NYC
  • 5. Upcoming Plans• Lunch and Learns – Suggestions for topics?• Webinars• Financial Services Industry track at Reconnect – Peoplesoft Product focused event happening in late  August in Hartford, CT – Submit FSI related abstracts – Send abstracts or ideas to me of what you want to  hear:   Richard Bouthillette   rbouthillette@amica.com 800/652‐6422  x24037
  • 6. Reconnect• Jennifer Abney, Education Specialist, Quest  International User Group
  • 7. August 27‐29, 2012 Connecticut Convention Center Hartford, Connecticut  USA QuestDirect.org/RECONNECT• PeopleSoft RECONNECT is a new PeopleSoft-focused event, replacing our Regional events. This new event will offer in- depth education into PeopleSoft product modules in a way that isn’t possible at COLLABORATE due to space limitations.• What content will be available? o Granular content within PeopleSoft modules like: o HCM o Financials o Supply Chain o Tools & Technology o Upgrades o Enhancement discussions with Oracle development and support. o SIG meetings around the featured product modules.
  • 8. Turning Data to Dollars™ in the Era of "Big Data"• Jaime Fitzgerald, Founder and President, Fitzgerald Analytics April 18, 2012 Architects of Fact‐Based Decisions™
  • 9. Nice to Meet You! Data to Dollars™ specialist.   Creator of a structured methodology and  toolkit to accomplish this.   Will share further at Reconnect! • Key Mission is to  Find & unlock opportunities via data, technology, people, + processes. Principles:Jaime Fitzgerald @jfitzgerald “Begin with the End in Mind” (Covey) “Quality is Free” (McGregor)
  • 10. Table of ContentsIntroduction 1. Big Data… Big Results? 2. Data to Dollars™ 3. Implications of Big Data 4. Key Takeaways and Questions
  • 11. Transforming Data to Dollars™It’s a journey… 1 2 Small Data Big Data Product of Alberta 3 Really Big Data Product of everywhere
  • 12. Defining Big Data: “Three Vs”"Big Data“ is often defined as data with: greater volume… greater variety… and/or greater velocity….
  • 13. Another Way to Define “Big Data”What are the optimal methods to accomplish your goal? Traditional approaches Big‐data approaches • Centralized • Distributed Data storage • Relational DBs (tables) • Non‐relational DBs (key‐value pairs) Data access • SQL queries • Map‐reduce and custom algorithms • Centralized • Distributed Data analysis • Standardized analytics • Custom analytics • MS SQL Server • Hadoop • Oracle • BigTable Typical tools • Tableau • Riak • Excel pivot tables • Amazon S3Note that this definition hinges on methods applied, not on dataset sizes: 800GB Can Be  80GB Can Be  “Traditional” “Big Data”
  • 14. My Perspective Towards “Big Data”Skeptical (of the hype)… ….yet Cautiously Optimistic! Big Data Product of Alberta
  • 15. Big Data Hype – Does is Cause a Problem?“Data is the New Oil”  – World Economic Forum Report 
  • 16. The Potential is Real…It’s Just Not Easy to Get
  • 17. Table of ContentsIntroduction 1. Big Data… Big Results? 2. Data to Dollars™ 3. Implications of Big Data 4. Key Takeaways and Questions
  • 18. Will Big Data Unlock Big Results?• It depends…• ...on the  principles you  work by. Stephen Covey
  • 19. Beginning with the End in Mind 1. Your Goal 2. Insight You Need 3. Analytic Methods 4. Data You Need 5. Tools, Platforms, Technology,  People, and Processes
  • 20. “A Journey of a Thousand Miles….” 2 1 Fitzgerald Analytics: Converting Data to Dollars™ Better Data Better Analysis Better Results 3 Worth The Trip!
  • 21. Key Steps in the Journey to Results 1. Data 2. Analytics 3. Results Data Governance  Better Decisions Analysis Insight Data Management  Better Processes Data Quality  More Customers New Data Source   Happier Customers Acquisition
  • 22. Table of ContentsIntroduction 1. Big Data… Big Results? 2. Data to Dollars™ 3. Implications of Big Data 4. Key Takeaways and Questions
  • 23. Simplify Your Analytic Process via “Causal Clarity” • …Clearly defining “Cause and Effect” is the most crucial enabler of analysis  that is simple, efficient and high impact. 1 2 3 Define  Define Business  Define Goal Model CausalityInputs  Usually net profit  Products / services  Aka “drivers tree”  Can be anything!:  How sold / how   Makes the causal  – Marketing ROI delivered model visual – Non‐profit impact  To what customers – Customer   At what price satisfaction  Cost structure (fixed vs.  – Etc. variable)  Known KPIs and  rationale for them
  • 24. Here’s a Simple Example• A simple example… Volume . . .  Revenues Price . . .  Profit COGS . . .  Costs SG&A . . . 
  • 25. Causality Flow and Strategy Planning• Causality flow and strategy planning move in opposite directions… Causality flows this way… Volume . . .  Revenues Price . . .  Profit COGS . . .  Costs SG&A . . .  … but strategy is best developed in this direction (“Beginning with the End in Mind”)
  • 26. “Causal Clarity”• If cause and effect are clear, practical analytics becomes feasible 1. Drivers of  2. Optimized by  3. Unlocking  Results… Analysis & Data… Better Results Better  Revenue Key Decisions Decisions Costs Risks Key Business  Better  Processes Processes Profit Causes Effects
  • 27. Causal Models: A Simple “Base Case”• Each business model has an inherent “causal model,” but the  “core branches” are similar Example: Drivers  of Net Profit Revenue less Your  Has Cost of Revenue Gross ProfitBusiness  Operating Costs Model less Net Profit Marketing Other Costs Overhead Other
  • 28. A Point of OpportunityHere is an opportunity to enhance ROI on Marketing + Sales efforts: Point of Opportunity: “Efficiency of New Client Acquisition” Key Driver / KPI:  Acquisition Cost per New Client Formula:  [spending on new client marketing]/[# New Clients) Transactions  per Client Price per Txn X # of Clients Volume Sales and  Marketing
  • 29. Types of Questions Analytics May Answer We are about to get practical, let’s keep the following in mind… Past Present Future What happened? What is happening  What will happen? Information now? (Reporting) (Alerts) (Extrapolation) What’s the  How and why  What’s the next best  best/worst that  did it happen? action? can happen? Insight (Modeling,  (Recommendation) (Prediction, experimental  optimization,  design) simulation)Source: Tom Davenport in “Analytics at Work”, Harvard Business School Press
  • 30. What We Need to Get Practical• To get practical about analytics, we need three things… What We Need Definition 1. Causal Clarity re: Your   How You Make Money Business Model  Key Drivers of Results 2. Definition of Your Points of   Gaps vs. Potential Opportunity  Opportunities Recognized 3. A Plan to Capture the   Insight You Need Opportunity  Method to Get It
  • 31. Planning Your Analysis 1. Your Goal = “Point of Opportunity” 2. Insight You Need 3. Analytic Methods 4. Data You Need 5. Tools, Platforms, Technology,  People, and Processes
  • 32. Choosing Analytic MethodsSelecting the right analytic method is a key success factor.  Consider the logic below… 1.  Your Goals Analytic  2.  Types of Info  Informs Method you Need 3.  Information  Available
  • 33. Table of ContentsIntroduction 1. Big Data… Big Results? 2. Customer Profitability Analysis 3. Implications of Big Data 4. Conclusion and Questions
  • 34. What does “Big Data” change? 1. Your Goal = “Point of Opportunity”  2. Insight You Need 3. Analytic Methods Big Data 4. Data You Need Changes These Steps... 5. Tools, Platforms, Technology,  People, and Processes
  • 35. Big‐Data Approaches and Tools Make Data Analysis Possible, for very large data sets that cannot be handled at all  with typical relational databases. Faster, for large data sets that can be handled with typical  relational databases, but doing so would take a long time. This  is the situation in the example above. Cheaper, for large data sets that can be handled with typical  relational databases, but doing so would be very expensive.
  • 36. Big Data Allows Us To Work with Large Datasets• We can analyze datasets larger than ever before For a given desired speed of analysis… Beyond a certain point, conventional  methods just aren’t feasible – Google couldn’t run on a relational DB IT Costs For larger datasets, big‐data methods make more sense Dataset size For smaller datasets, conventional methods are more cost‐effective Traditional  Big‐data methods methods
  • 37. Big Data Allows Us To Get Results Faster• We can get results faster than ever before For a given dataset size… IT Costs SLOW FAST Analysis speed Conventional Big‐data methods methods
  • 38. Table of ContentsIntroduction 1. Big Data… Big Results? 2. Customer Profitability Analysis 3. Implications of Big Data 4. Conclusion and Questions
  • 39. Example: Iterative Customer Profitability Enhancement Build/Maintain Customer     Take Smarter Actions w/ Customers Profitability Models:                Target: Who?  • Create consistent message  • Message or action: What? Target action to individuals  Identify costs & revenues • Optimize product / service  Build profiles Data   Offering:  Product design portfolio Warehouse  Service:  How delivered?   Integrate data from “new” sources (how experienced by customer?) External  New Customer Knowledge  Data   Results of our actions Sources  Assess accuracy of our predictive models  Refine segmentation schema  Define new goals, questions, data “wish  lists” (big data? Or small…)
  • 40. Impact of Speed…Type of data and  Our understandingtechnology tools: Of customers: Daily / weekly /  Small Data  monthly (+ related tech) Big Data  Instantly (+ related tech)
  • 41. Impact of “resolution” (quality of picture) All his  His son’s friends have  favorite  Chase color is blue Instantly Father just  started at  Big Data  Instantly Bank of  America(+ related tech) Instantly Instantly Helping us Take Smarter Actions w/ Customers  Target: Is he one?   Message or action: What?  Offering:  Product design  Service:  How delivered?  (how experienced by customer?)
  • 42. So how does Big Data + Related Tools Help With…1 Customer Segmentation and Lifetime Value (CLV)2 Customer Retention3 Cross‐sell, Up‐sell4 Marketing Optimization & ROI5 New Financial Product Design & Innovation
  • 43. Q&A