BIG DATA and Analytics What does it all mean?

Transcription

1 BIG DATA and Analytics What does it all mean?

2 Agenda 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

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

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 Risks 7

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

9 The Rising Sun

10 Data Explosion 10

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12 Big Data

13 At The Heart Is The Member

14 The Ability To Predict

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

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18 12,266% Optimization 5% Credit Unions Predictive Modeling Forecasting Reporting / OLAP Data Management What will happen next? What s the best that can happen? 95% of Credit Unions Data Access How much and where? What happened? How many, how often? Companies like Amazon use data to make you love Source: The SAS Institute them. 18

19 Big Data and Credit Unions The Opportunity

20 Impact of Mobile Banking Mobile/Online VS. Physical Branch Activity Mobile Physical Branch Total

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22 5 Reasons for A Big Data/Analytics 1.Build Relationships With Members 2.Discover New Market Opportunities 3.Compliance Reporting and Analysis 4.Track Profitability 5.Monitor Productivity

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

24 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. 24 Prescient Modeling 2013

25 Target Marketing Risk Based Pricing Good margin models come from good forecast models. 25

26 Target Marketing Payment Data 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

27 Target Marketing Payment Data

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

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

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

31 10 Things to Consider 1. Enterprise Data Warehouse 2. Data Integration Technology 3. Business Intelligence Software 4. Data Architect/Report Developer 5. Analytics Software (SAS, SPSS, Etc.)

32 10 Things to Consider Pt Access to a Data Scientist 7. BI Roadmap 8. Steering Committee 9. Data Quality 10. TIME

33 Do it Yourself Pros Ownership of Technology Customized to your CU Cons CUs aren t data experts Time and Cost to Build Staff Attrition Satisfying End Users (Analytics)

34 Cost of Doing it Yourself Resource Description Total One Full Time D/A 120, , , , , ,000 Consulting (Initial Build) 250, , ,000 Consulting (Additions/Upgrades) ,000 60,000 60, ,000 Report Writer (Part-Time) 80,000 80,000 80,000 80,000 80, ,000 BI Software & Mtce. 50,000 9,000 9,000 9,000 9,000 86,000 Analytics Software (eg. SAS) & Mtce. 150,000 27,000 27,000 27,000 27, ,000 Consulting (Data Scientist) 50,000 50,000 50,000 50,000 50, ,000 Hardware 20,000 20,000 Total 500, , , , ,000 1,616,000

35 Purchase a Solution Pros Lower TCO Industry Expertise Access to Data Integration templates Access to Apps Ability to pool data No Additional Staff Needed Cons CU doesn t own the technology Customization may be challenging Some solutions are tied to core vendor

36 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

37 Vision for the CU Movement

38 BI Maturity

39 Crossing The Chasm

40 Questions