Right Sizing Big Data for Credit Unions. Filene and CUCC Research Symposium May 3, 2015

Transcription

1 Right Sizing Big Data for Credit Unions Filene and CUCC Research Symposium May 3, 2015

2 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

3 The Data Journey What informs you at the beginning? What shapes your understanding and decisions along the way? What s the outcome at the end?

4 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

5 The Big Data Paradox Big Data = Big Value? Credit Unions => Big Data?

6 Big Data VOLUME Terabytes Records Transactions Tables, Files VELOCITY Batch Near time Real time Streams 3Vs of Big Data Structured Unstructured Semi-structured VARIETY Source: Big Data Analytics TDWI Research, Q4 2011

7 Big Data Landscape Google Exabytes Petabytes Walmart Analytic DB/Tools Hadoop Terabytes Gigabytes Megabytes Credit Unions General Purpose Relationship DB Structured Library of Congress: All printed materials = 10TB (one CU stores up to 12TB) All printed, audio, video, digital = 3 to 20PB Semi Structured Unstructured Source: BI Leadership Forum Presentation 2015

8 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

9 Key Components for Strength Technology People Organization quality of data analytical tools used data infrastructures available data management 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

10 Stages of Maturity ly Impaired Localized Analytics Aspirations Company Competitor Where most credit unions are Based on 30+ in-depth interviews with CU, partners and data experts

11 Stage Organization Human Technology ly Impaired Localized Analytics Stages of Maturity Objective Limited insight into customers, markets, competitors Autonomous activity builds experience and confidence using analytics; creates new analytically based insights Process Doesn t exist Disconnect ed, very narrow focus Skills Sponsorship Culture None None Knowledge allergic pride on gut based decisions Pockets of isolated analysts (maybe in finance, marketin g/crm) Functional and tactical Desire for more objective data, successes from point use of analytics start to get attention Missing/poor quality data; multiple definitions; unintegrated systems Recent transaction data unintegrated, missing important information; isolated BI/analytic efforts Source: Competing on Analytics: The New Science of Winning

12 Stages of Maturity Stage Organization Human Technology Objective Process Skills Sponsorship Culture Aspirations Coordinated; establish enterprise performanc e metrics, build analytically based insights Mostly separate analytic processes; building enterprise level plan Analysts in multiple areas of business but with limited interaction Executiveearly stages of awareness of competitive possibilities Executive support for factbased culture may meet considerabl e resistance Proliferation of BI tools; Data marts, data warehouse established or expanded 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 manageme nt to build a factbased 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

13 Stages of Maturity Stage Organization Human Technology Objective Process Skills Sponsorship Culture Competitors Deep strategic insights, continuous renewal and improvemen ts Fully embedded and much more highly integrated Highly skilled, leveraged, mobilized, centralize d; grunt work outsourced CEO passionate; Broad-based management commitment Broadly supported fact-based culture, testing and learning culture Enterprisewide BI/BA architecture largely implemented Source: Competing on Analytics: The New Science of Winning

14 Credit Unions Spotlighted

15 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?

16 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

17 Preliminary Takeaways 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

18 Focusing above the Line Source: BI Leadership Forum Presentation 2015

19 What s on your mind? What data/analytics is lacking at your credit union? Be specific. Why is this important? If I knew [ ], I could do [ ]. Focus on the what and why Park the how (for now)

20 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

21 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.

22 Final Thoughts The true sign of intelligence is not knowledge but imagination. Albert Einstein

23 Thank you! All photos attributed to Creative Commons. Thanks for