BIG DATA and AnalyticsWhat does it all mean?

• 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

Do you Have a First Gen Phone on you Today?

3

Integration

Information

The Number One Reporting Tool in CUs

Today!

The “Reporting Process” Today…The Kitchen

The Dining Room

“It works…..why change?”

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

Outsourcing Analytics

7

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

The Rising Sun…

10

Data Explosion

Big Data

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.”

The Ability To Predict…

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

Circa 1990

Business IT

2014

BusinessIT is the Business

19

OptimizationOptimization

Forecasting Forecasting

Reporting / OLAPReporting / OLAP

Data ManagementData Management

Data AccessData 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% ofCredit Unions

5% Credit Unions

12,266%

Predictive Modeling Predictive Modeling

“Companies like Amazon use data to make you love them.”

Impact of Mobile Banking

2008-01

2008-03

2008-05

2008-07

2008-09

2008-11

2009-01

2009-03

2009-05

2009-07

2009-09

2009-11

2010-01

2010-03

2010-05

2010-07

2010-09

2010-11

2011-01

2011-03

2011-05

2011-07

2011-09

2011-11

2012-01

2012-03

2012-05

2012-07

2012-09

2012-11

2013-01

2013-03

2013-05

2013-07

2013-09

2013-11

2014-01

2014-03

2014-05

2014-070

100000

200000

300000

400000

500000

600000

Mobile/Online VS. Physical Branch Activity

Mobile Physical Branch Total

Big Data and Credit UnionsThe Opportunity

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

• Identify characteristics of profitable customers

• Predict the next best product

• More accurate marketing

• Increased wallet share

• Improved underwriting

Deeper Customer Knowledge

26

Target MarketingPayoff 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

Good margin models come from good forecast models.

Target Marketing Risk Based Pricing

• 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

Target MarketingCredit Score Precision

• Fraud tracking based on suspicious transactions

• IT breach data

• Fight off cyber crime

• Maintain trust

Fraud Analysis

• Automate compliance reporting

• Verify the numbers in seconds

• Reduce labor

Compliance Reporting

Options Available

Option 2: Purchase a Solution

Option 1: Do it yourself

10 Things to Consider…1. Enterprise Data Warehouse Architecture

– Scalablity– Granularity– Conformity

2. Data Integration Technology3. Business Intelligence Software4. Data Architect/Report Developer5. Analytics Software (SAS, SPSS, Etc.)

10 Things to Consider….6. Access to a Data Scientist 7. BI Roadmap8. Steering Committee9. Data Quality10. TIME

Do it YourselfPros

• 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)

Cost of Doing it YourselfResource Description 2015 2016 2017 2018 2019 Total

One Full Time D/A 120,000 120,000 120,000 120,000 120,000 600,000

Consulting (Initial Build) 250,000 100,000 350,000

Consulting (Additions/Upgrades) 0 0 60,000 60,000 60,000 180,000

Report Writer (Part-Time) 80,000 80,000 80,000 80,000 80,000 400,000

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 27,000 258,000

Consulting (Data Scientist) 50,000 50,000 50,000 50,000 50,000 250,000

Hardware 20,000 20,000

Total 500,000 309,000 269,000 269,000 269,000 1,616,000

Purchase a SolutionPros

• 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

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

Big Data Analytics Vision for the CU Movement

BI Maturity

41

Crossing The Chasm

20142013

2012

42

7 Habits

Important

Non-Important

Urgent Non-Urgent

What Keeps Us Busy

Questions