1. BIG DATA and Analytics What does it all mean?

2. The Evolution of Data, Reporting, Etc. What is Big Data? Why use Big Data? Big Data in Credit Unions How do you do it? Questions Agenda

3. Do you Have a First Gen Phone on you Today? 3 Integration Information

4. The Number One Reporting Tool in CUs Today!

5. The “Reporting Process” Today… The Kitchen The Dining Room “It works…..why change?”

6. Business Intelligence Failure to Deliver Cognos (acquired by IBM) SAS Crystal Reports (acquired by Business Objects) Business Objects (acquired by SAP) ESS Base (acquired by Hyperion) Hyperion (acquired by Oracle) Oracle

7. Outsourcing Analytics 7

8. The number of hours employees spend on searching for the right information. hours per day 8 70% of time on gathering data 30% of time on analysis

9. The Rising Sun…

10. 10 Data Explosion

12. Big Data

13. At The Heart Is The Member “Navy Federal Credit Union is proud to be one of the first financial institutions to provide Apple Pay later this fall. With it, we'll be able to deliver on the promise of easy and secure mobile payments, and add a layer of convenience and security to using Navy Federal credit and debit cards. By combining Apple’s history of innovation with Navy Federal’s unique military membership, Apple Pay has the ability to make mobile payments more accessible for military families who rely on mobile technology in their daily lives.”

14. The Ability To Predict…

16. Analytic Competitor is any company that has implemented Enterprise Reporting & Analytics and relies on it for ALL decisions. 16 Analytic Competitors Significantly Outperform Their Peers

17. 17

18. Circa 1990 Business IT 2014 Business IT is the Business

19. 19 Optimization Forecasting Reporting / OLAP Data Management Data Access What’s the best that can happen? How much and where? What will happen next? What happened? How many, how often? Source: The SAS Institute 95% of Credit Unions 5% Credit Unions Predictive Modeling “Companies like Amazon use data to make you love them.”

20. Impact of Mobile Banking

21. Big Data and Credit Unions The Opportunity

24. 5 Reasons for A Big Data/Analytics 1.Build Lasting Relationships With Members 2.Discover New Market Opportunities 3.Better Fraud Analysis & Compliance Reporting 4.Understanding Profitability 5.Monitoring Productivity

25. Identify characteristics of profitable customers Predict the next best product More accurate marketing Increased wallet share Improved underwriting Deeper Customer Knowledge

26. 26 Target Marketing Payoff Trigger How long will the loan be with us? Don’t count the interest income in pricing if the loan pays off early. Different segments behave differently. Prescient Modeling © 2013

27. 27 Good margin models come from good forecast models. Target Marketing Risk Based Pricing

28. Monumental amounts of data created by mobile payments that will: Allow for strategic partnerships with advertisers and merchants (revenue potential) Improve Marketing Attract/Retain Customers Target Marketing Payment Data

29. Target Marketing Credit Score Precision

30. Fraud tracking based on suspicious transactions IT breach data Fight off cyber crime Maintain trust Fraud Analysis

31. Automate compliance reporting Verify the numbers in seconds Reduce labor Compliance Reporting

32. Options Available Option 2: Purchase a Solution Option 1: Do it yourself

33. 10 Things to Consider… 1.Enterprise Data Warehouse Architecture – Scalablity – Granularity – Conformity 2.Data Integration Technology 3.Business Intelligence Software 4.Data Architect/Report Developer 5.Analytics Software (SAS, SPSS, Etc.)

34. 10 Things to Consider…. 6.Access to a Data Scientist 7.BI Roadmap 8.Steering Committee 9.Data Quality 10. TIME

35. Do it Yourself Pros Ownership of Technology Customized to your CU Cons CUs aren’t data experts Time and Cost to Build Ongoing costs Staff Attrition Satisfying End Users (Analytics)

36. Cost of Doing it Yourself Resource Description20152016201720182019Total One Full Time D/A120,000 600,000 Consulting (Initial Build)250,000100,000350,000 Consulting (Additions/Upgrades)0060,000 180,000 Report Writer (Part-Time)80,000 400,000 BI Software & Mtce.50,0009,000 86,000 Analytics Software (eg. SAS) & Mtce.150,00027,000 258,000 Consulting (Data Scientist)50,000 250,000 Hardware20,000 Total500,000309,000269,000 1,616,000

37. Purchase a Solution Pros Lower TCO Industry Expertise Access to Data Integration templates Access to Shared Applications Ability to pool data No Additional Staff Needed Cons CU doesn’t own the technology Some solutions are tied to core vendor Scalability of architecture

38. Source: Wikibon 2013 Reasons for not achieving maximum business value from Big Data are: A lack of skilled Big Data practitioners. “Raw” and relatively immature technology. A lack of compelling business use case. Source: Wikibon 2013

39. Big Data Analytics Vision for the CU Movement

40. BI Maturity

41. Crossing The Chasm 2014 2013 2012 41

42. 7 Habits 42 Important Non- Important Urgent Non-Urgent What Keeps Us Busy

43. Questions