Right Sizing Big Data for Credit Unions

Filene and CUCC Research Symposium May 3, 2015

Today’s Presentation

• Share preliminary findings from the paper

• Challenge our thinking and understanding of what data is and how we use it

• Identify issues relevant for credit unions that can be addressed using data and data analytics

What

informs you at the beginning?

What shapes your

understanding and decisions

along the way?

What’s the outcome at the

end?

The Data Journey

The Big Data Numbers

• In 2015 alone the global financial services industry will have invested US$6.4 billion in big data services

• Estimated growth in big data spend: 22% increase by 2020

• Companies that are using data to drive their decisions were, on average: • 5% more productive; and • 6% more profitable than competitors not

leveraging data analytics

Source: Walter Frick, “An Introduction to Data-Driven Decisions for Managers Who Don’t Like Math”, Harvard Business Review, May 19, 2014

The Big Data Paradox

Big Data = Big Value? Credit Unions => Big Data?

Source: “Big Data Analytics” TDWI Research, Q4 2011

Terabytes Records

Transactions Tables, Files

Structured Unstructured

Semi-structured

Batch Near time Real time Streams

VOLUME

VARIETY VELOCITY

3Vs of Big Data

Big Data

Source: BI Leadership Forum Presentation 2015

Structured Semi Structured

Unstructured

Hadoop

Analytic DB/Tools

General Purpose Relationship DB

Megabytes

Petabytes

Gigabytes

Terabytes

Exabytes

Google

Walmart

Credit Unions

Big Data Landscape

Library of Congress: • All printed materials = 10TB (one CU stores up to 12TB) • All printed, audio, video, digital = 3 to 20PB

Data Basics

• 50% to 80% is muck work • clarifying, defining, cleaning, purging, organizing data • no actual analysis work done, no insights generated

• Ample opportunity for discussion and deliberation

Key Components for Analytical Strength

People

Organization

Technology

• analytical skills • ability to generate insights that leads to

outcomes • leadership and cultural attitudes that

support and foster evidence based decision making

• thoroughness of measurements in place

• analysis processes defined and integrated into operational activities

• data governance

• quality of data • analytical tools used • data infrastructures available • data management

Stages of Analytical Maturity

Analytically Impaired

Localized Analytics

Analytical Aspirations

Analytical Company

Analytical Competitor

Where most credit unions are

Based on 30+ in-depth interviews with CU, partners and data experts

Stage Organization Human Technology

Analytical Objective

Analytical Process

Skills Sponsorship Culture

Analytically

Impaired

Limited insight into customers, markets, competitors

Doesn’t exist

None None Knowledge allergic – pride on gut based decisions

Missing/poor quality data; multiple definitions; unintegrated systems

Localized Analytics

Autonomous activity builds experience and confidence using analytics; creates new analytically based insights

Disconnected, very narrow focus

Pockets of isolated analysts (maybe in finance, marketing/CRM)

Functional and tactical

Desire for more objective data, successes from point use of analytics start to get attention

Recent transaction data unintegrated, missing important information; isolated BI/analytic efforts

Source: “Competing on Analytics: The New Science of Winning”

Stages of Analytical Maturity

Stage Organization Human Technology

Analytical Objective

Analytical Process

Skills Sponsorship Culture

Analytical

Aspirations

Coordinated; establish enterprise performance metrics, build analytically based insights

Mostly separate analytic processes; building enterprise level plan

Analysts in multiple areas of business but with limited interaction

Executive-early stages of awareness of competitive possibilities

Executive support for fact-based culture – may meet considerable resistance

Proliferation of BI tools; Data marts, data warehouse established or expanded

Analytical Companies

Change program to develop integrated analytical processes and applications and build analytical capabilities

Some embedded analytics processes

Skills exist, but often not aligned to right level/right role

Broad c-suite support

Change management to build a fact-based culture

High quality data; have an enterprise BI plan/strategy, IT processes, and governance principles in place

Source: “Competing on Analytics: The New Science of Winning”

Stages of Analytical Maturity

Stage Organization Human Technology

Analytical Objective

Analytical Process

Skills Sponsorship Culture

Analytical

Competitors

Deep strategic insights, continuous renewal and improvements

Fully embedded and much more highly integrated

Highly skilled, leveraged, mobilized, centralized; grunt work outsourced

CEO passionate; Broad-based management commitment

Broadly supported fact-based culture, testing and learning culture

Enterprise-wide BI/BA architecture largely implemented

Source: “Competing on Analytics: The New Science of Winning”

Stages of Analytical Maturity

Credit Unions Spotlighted

Burning Questions

• What is holding back credit unions from using data more often and more successfully?

• Is there measureable and sizeable evidence that investing in data and analytics can help a credit union grow significantly?

• How can credit unions right size big (or small) data to tackle their needs?

Preliminary Takeaways

• Assess whether culture and skillsets match technology readiness: – What’s the comfort level of making decisions based on

descriptive reporting let alone analytical modeling (such as predictive analytics)

• Cultivate knowledge based decision making at all levels of the organization: – Analytics skills and strong communications go hand-in-hand – Bring the numbers and data to the forefront when meeting

as a department, executive team or with members

• Curiosity is important: – Encourage staff to be curious about credit union activities &

members – One small step can lead to big opportunities – Hire for curiosity

• Uncovering differences is a good thing: – Often look at normalized data to track performance but looking for

differences helps to uncover opportunities

– Without understanding and evidence that members are different and have different needs, how can a credit union help them?

• A passionate analytical CEO goes a long way: – Can influence and reinforce the right type of attitude

– Will encourage looking at data to identify untapped opportunities plus test and learn type of activities

• Start now: – Data is never going to be perfect; technology and skillsets may not

be at the right level but use information as best as you can right now

Preliminary Takeaways

Source: BI Leadership Forum Presentation 2015

Focusing above the Line

• What data/analytics is lacking at your credit union? Be specific.

• Why is this important?

What’s on your mind?

Focus on the what and why Park the how (for now)

“If I knew [ ], I could do [ ].”

Right Attitude!

• Be curious • Don’t shy away (from numbers) • Seek to understand rather than refute information

presented • Undercover the meaningful differences • Without trust in information and insights, there is no

advantage to data – big or small

Right Sizing Big Data

• All credit unions are on a data journey; not just a technology challenge but one that affects the credit union’s culture and people.

• Avoid the pitfalls of “me too” tactics to compete with the banks.

• Transform the insights into competitive advantage vs. the big banks that invest in the latest in data trends for efficiency gains.

• Leverage data to identify an uncrowded place in the market to deftly compete against the dominant big banks as well as the dynamic alternatives.

Final Thoughts

“The true sign of intelligence is not knowledge but imagination.” Albert Einstein

Thank you!

@ponderpickle linda@ponderpickle.com

All photos attributed to Creative Commons. Thanks for sharing!