BIG DATA and Analytics What does it all mean?. The Evolution of Data, Reporting, Etc. What is Big...
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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
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