Taking on Big Data in Li-le Steps September 24, Jason Milesko

Transcription

1 Taking on Big Data in Li-le Steps September 24, 2015 Jason Milesko

2 Today s Goals 1. Be-er understand what is Big Data 2. Discuss how credit unions stack up 3. Start thinking about ways your credit union might be able to leverage big data

3 Let s start with a quiz!!! #1. True or False? Google processes >100 search inquiries every second

4 Let s start with a quiz!!! TRUE!!! Google actually processes >40,000 inquiries every second

5 Let s start with a quiz!!! #2. True or False? People have captured almost as much data in the last 5 years as we did in the 200 years from

6 Let s start with a quiz!!! FALSE!!! Every day we capture as much data as we did from the beginning of measured time until % of global data was captured in the past 24 months alone

7 Let s start with a quiz!!! #3. True or False? There are over 1 Trillion smartphones in the world

8 Let s start with a quiz!!! FALSE!!! Not a trillion (there are only 7.3 billion people), but 1.2 billion and they are all packed with sensors and data collection features

9 Let s start with a quiz!!! #4. Fill in the Blank Every minute we send s, Facebook likes, and tweets

10 Let s start with a quiz!!! #4. Fill in the Blank Every minute we send 200 million s, 1.8 million Facebook likes, and 278k tweets. (That s a lot)

11 Part of my preparamon

12 Exercise Discuss with your neighbor: What does Big Data mean to you?

13 Answers from other Credit Unions Not sure (I don t know) Has many meanings (Undefined) Large, huge, ginormous, complex data (Complicated) Information stored in a cloud (Storage) Using member information from your data files to influence business decisions (Strategy) Using data to understand who our members are, how they transact, and being able to anticipate their future needs (Marketing/Predictive Analytics) Information available to help serve our members in ways that meet their wants and needs (Service) Gathering and analyzing member information for marketing, loans, losses, future expansion and products (New product development) Survey Stats: 545 CU responses of all different positions (43% from CEOs)

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15 Today s Agenda What is big data? How do credit unions stack up? What can my credit union do? QuesMons?

16 What is Big Data? Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured Forecasts predict the volume of data will be 50X greater in 2020 than it is today* As the amount of available data increases, so do opportunities to utilize it

17 Why is big data important? The growing velocity, volume, and variety of available data is creating new opportunities Velocity Technology Enhancements increase data processing capabilimes - Wal Mart processes 1MM transacmons/hour - Human genome decoding in 1 day Volume Data growth is exponenmal and financial insmtumons have the most transacmonal data We are drowning in data but thirs;ng for opportuni;es - Naisbi@ Variety Data capture opportunimes is increasing the range of data formats and sources - Payment/ATM transacmons - Web- site click tracks - Call center records - Social media channels - Credit card data - Mobile phone usage - Check- in, wifi, and geo- locamng - Text processing for social senmment

18 Is big data a good opportunity for credit unions? Financial institutions are well positioned to capture big data opportunities as both the ease of capture and value potential of their data is high Opportunity Potential by Industry Example Credit Union Data Sources Size of bubble indicates relative contribution to US GDP Ease of Data Capture Banking & Insurance Internal Payments behavior Sales data Banking habits Website click-tracks Call center records Branch office visits Credit card data Operational data External Posts, tweets, blogs Geo-location data Industry benchmarks Purchasing patterns Shopping behaviors Demographics Value Potential of Data Source: US Bureau of labor statistics. MGI Analysis

19 How valuable is big data currently? The value of big data is dependent on the analytic maturity level of an organization. Credit Unions are not capturing the value of their data because their analytic maturity level is low on average. Value of Big Data Analytic Maturity Curve Beverage Telecom's Credit Unions Consumer Electronics Big Box Retailers Large Banks Pharma Internet Retail Rapidly entering banking sector Analytical Maturity Source: Framework - Oracle whitepaper

20 Is this crucial for Credit Unions now? Big data can open the door for competitors to enter a new industry and take hold quickly Amazon/Borders Captured vast amounts of personal informamon and made buying recommendamons Without a retail presence, Amazon was able to eliminate Borders and dramamcally take share from Barnes & Noble Netflix/TV Series Production Used big data in QUEs to understand consumer s future desires and build a strong recommendamon engine Used big data to evaluate fast- forward and replay Mme and then develop business focused on TV series producmon (House of Cards) Uber/Taxis Built on big data (surge pricing and geo- locamon data), Uber offers many users conveniences such as reliability, punctuality, and cash- free payments compared to tradimonal cab- companies Uber has taken a dramamc share of the market, with a valuamon of ~$6Bn

21 Today s Agenda Understand what is big data? How do credit unions stack up? What can my credit union do? QuesMons?

22 Exercise Write down: What has prevented your credit union from using big data?

23 What has prevented credit unions from commiing to Big Data? Lack of a Big Data Strategy is the largest roadblock for credit unions > $1Bn Cost, strategy, system integration, and core become larger roadblocks for medium sized CU s Many roadblocks exist for small credit unions Source: CUNA CU market survey

24 Exercise Write down: For what purpose would you like to use big data?

25 Where do CU s want help? Interest in seeing solution (0-10) Proceed cautiously Targeting New Members Servicing Members Detecting Fraud Managing Credit Risk Developing New Products Slow down Managing Credit Risk Launch Targeting New Members Strategic Planning Developing New Products Managing Operational Risk Predicting Member Behavior Managing Operational Risk Strategic Planning Predicting Member Behavior Servicing Members Developing New Products Detecting Fraud Strategic Planning Proceed cautiously 5 $- $5,000 $10,000 $15,000 $20,000 $25,000 Source: CUNA CU market survey Ave $ willing to pay for solution Detecting Fraud Managing Operational Risk Predicting Member Behavior Targeting New Members Servicing Members Managing Credit Risk CU Asset Size >$1B $100MM - $1B <$100MM

26 How does understanding of big data impact interest in a solumon? Personal understanding does not increases the belief in the number of opportunities available, but rather in the impact of those opportunities

27 Exercise Write down: Who knows the most about big data at your credit union?

28 Who knows the most about big data at credit unions? IT claims to know the most about big data, followed by Finance and Marketing

29 Where is Big Data managed at credit unions? Big data is primarily managed by IT at CU s >$1B, is split between Marketing and IT at Medium sized credit unions, and managed by the CEO at smaller CU s

30 Exercise Write down: How is your credit union s data quality?

31 How do credit unions feel about their data quality? Credit Unions believe their data quality is good and this has not prevented them from pursuing big data opportunities Which of the following do you feel best describes the overall quality of your CU s data? Size of roadblock (Max = 5) Large Medium Small CU Size

32 Exercise Write down: Do your information systems integrate with each other?

33 How do credit unions feel about their data integramon? Credit Unions believe their data integration is fair and this has been somewhat of a roadblock to pursuing big data opportunities How well do you believe your CU s primary data sources integrate with each other? Size of roadblock (Max = 5) Large Medium Small CU Size

34 Exercise Write down: How accessible is data to you at your credit union?

35 How accessible is informamon from primary systems? Overall, CU s believe data is most accessible from the core system and internet banking. MCIF and MRM are challenging for smaller to mid-sized CU s 1 = Not accessible to me 2 = Accessible, but would require a special request to an external source 3 = Accessible, but would require a special request to an internal data manager 4 = Accessible, but I need to do some digging in the right places/systems 5 = Accessible at the push of a button

36 Exercise Write down: What is your preferred delivery channel for big data help?

37 What delivery channel do credit unions prefer for a big data solumon? Larger credit unions prefer to have an in-house solution to big data Delivery Preference Delivery Preference By CU Size 50% 40% 30% 20% 10% 0% 39% In-house Solution 26% 19% 10% Online Training Outsourced Solution Conference/Event 6% In-person Advisor at CU 7% 5% 7% 9% 5% 13% 13% 23% 17% 15% 35% 28% 17% 46% 60% $0-100MM $100MM- 1B >$1B Advisor Conference/Event Outsourced Solution On-line Training In-house Solution

38 Today s Agenda Understand what is big data? How do credit unions stack up? What can my credit union do? QuesMons?

39 What components are required to succeed? To be successful at capturing Big Data opportunities, credit unions overall big data strategy, approach, capabilities, and technology support must complement each other Big Data Strategy Analytic Capabilities Strategic priorities and focus Vast amounts of data and opportunities for application requires credit unions to focus their efforts Analytic talent or skillset To rapidly implement big data strategies credit unions will need to outsource, attract, develop, and retain the right talent Culture/Process Technology Support Data gathering and analytics culture and processes Capturing big data opportunities require a data-driven culture and well thought through process System integration and availability of information Supporting technology and analytical approaches are essential to optimizing workflow

40 Where do credit unions stack up? A majority of Credit Unions are currently uncommitted in all four dimensions necessary to reap the benefits of Big Data Source: CUNA CU market survey

41 What are Credit Unions doing to leverage Big Data? Opportunities are available for functional areas, and those that leverage big data have a strategic advantage What Big Data means to different functional groups Functional Area Historical use Suggested use Marketing Campaigns Precision marketing to a single member Value Increase cross-sell Optimize margin Credit/Risk Management Traditional risk management and slow underwriting Accurate risk estimation at lower cost (bundling, understanding segments customer type, size, etc) Increased operational efficiency Strategy/ Finance/IT Respond to one-off requests Driving strategy development and growth focus Fact-base approach to developing strategies Identification of new opportunities Customer Service Customer support hotline Proactively tailor the customer experience Service levels to match relationship profitability Build loyalty Retain and grow profitable customers New Product Development Reliance on CU Brand for new product launches Rapid, informed innovation Grow revenue more quickly

42 Exercise Discuss with your neighbor: How might your credit union begin to leverage it s data?

43 The Short List - Example uses in Credit Unions 1. Strategy Understanding our own internal profitability and drivers Trends in delivery channel (Websites, ATM s, branches, Call Centers) 2. Marke.ng PromoMons Customer segmentamon 3. Predic.ve Modeling (Cross- sell) 4. Benchmarking (Comparisons to peer groups) 5. Fraud detec.on (based on paferns, purchases, etc)

44 Today s Agenda Understand what is big data? How do credit unions stack up? What can my credit union do? QuesMons?

45 QuesMons? Jason Milesko