taking’on’big’datain’li-le’steps - inclusiv · “gathering and analyzing member...

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

Jason Milesko

[email protected]

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

Let’s start with a quiz!!!

#1. True or False?

Google processes >100 search inquiries every second…


TRUE!!!

Google actually processes >40,000 inquiries every second…


#2. True or False? People have captured almost as much data in the last 5 years as we did in the 200 years from 1810-2010


FALSE!!! Every day we capture as much data as we did from the beginning of measured time until 2000. 90% of global data was

captured in the past 24 months alone


#3. True or False? There are over 1 Trillion smartphones in the world…


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


#4. Fill in the Blank Every minute we send ___ emails, ___ Facebook likes, and ___ tweets


#4. Fill in the Blank Every minute we send 200 million emails, 1.8 million Facebook likes,

and 278k tweets. (That’s a lot)

Part of my preparaMon…

Exercise – Discuss with your neighbor:

What does Big Data mean to you?

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)

Today’s Agenda

■  What is big data?

■  How do credit unions stack up?

■  What can my credit union do?

■  QuesMons?

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

Why is big data important?

Technology Enhancements increase data processing capabiliMes

-‐  Wal Mart processes 1MM transacMons/hour

-‐  Human genome decoding in 1 day

The growing velocity, volume, and variety of available data is creating new opportunities

Volume

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

Velocity Variety

Data growth is exponenMal and financial insMtuMons have the most transacMonal data “We are drowning in data but thirs;ng for opportuni;es”-‐ Naisbi@

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

Ease

of D

ata

Cap

ture

Value Potential of Data

Size of bubble indicates relative contribution to US GDP

Source: US Bureau of labor statistics. MGI Analysis

Opportunity Potential by Industry

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

Example Credit Union Data Sources

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.

Analytical Maturity

Analytic Maturity Curve Internet Retail

Large Banks

Consumer Electronics

Beverage

Big Box Retailers

Rapidly entering “banking” sector

Valu

e of

Big

Dat

a

Source: Framework - Oracle whitepaper

Telecom's

Pharma

Credit Unions

Is this crucial for Credit Unions now?

•  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

Big data can open the door for competitors to enter a new industry and take hold quickly

Netflix/TV Series Production Amazon/Borders Uber/Taxis

•  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)

•  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

Today’s Agenda

■  Understand what is big data?



■  QuesMons?

Exercise – Write down:

What has prevented your

credit union from using big data?

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


For what purpose would you like to

use big data?

5

6

7

8

9

10

$-‐ $5,000 $10,000 $15,000 $20,000 $25,000

Inte

rest

in s

eein

g so

lutio

n (0

-10)

Ave $ willing to pay for solution

>$1B $100MM - $1B <$100MM

CU Asset Size

Launch

Proceed cautiously Slow down

Proceed cautiously

Where do CU’s want help?

Predicting Member Behavior

Targeting New Members Servicing Members

Strategic Planning

Managing Credit Risk

Detecting Fraud

Developing New Products

Managing Operational Risk

Targeting New Members

Predicting Member Behavior Servicing Members

Detecting Fraud Managing Credit Risk

Developing New Products Strategic Planning

Managing Operational Risk

Targeting New Members Servicing Members

Detecting Fraud

Managing Credit Risk Developing New Products

Managing Operational Risk Predicting Member Behavior

Strategic Planning


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


Who knows the most about big

data at your credit union?

Who knows the most about big data at credit unions?

IT claims to know the most about big data, followed by Finance and Marketing

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


How is your credit union’s data

quality?

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

2.1 2.3 2.5

0.0

2.5

5.0

Large Medium Small

Size of roadblock (Max = 5)

Which of the following do you feel best describes the overall quality of your CU’s data?

CU Size


Do your information

systems integrate with each other?

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

2.9 3.1 3.1

0.0

2.5

5.0

Large Medium Small

Size of roadblock (Max = 5)

How well do you believe your CU’s primary data sources integrate with each other?

CU Size


How accessible is data to you at your

credit union?

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


What is your preferred delivery

channel for big data help?

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

39%

26% 19%

10% 6%

0% 10% 20% 30% 40% 50%

In-h

ouse

Sol

utio

n

Onl

ine

Trai

ning

Out

sour

ced

Sol

utio

n

Con

fere

nce/

Eve

nt

In-p

erso

n A

dvis

or a

t C

U

Delivery Preference Delivery Preference

By CU Size

28% 46%

60%

35% 17%

15% 17% 23% 13%

13% 9% 5%

7% 5% 7%

$0-‐100MM $100MM-‐1B >$1B

In-house Solution

On-line Training

Outsourced Solution

Conference/Event Advisor

Today’s Agenda




■  QuesMons?

What components are required to succeed?

Big Data Strategy Strategic priorities and focus Vast amounts of data and opportunities for application requires credit unions to focus their efforts

Analytic Capabilities 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 Data gathering and analytics culture and processes Capturing big data opportunities require a data-driven culture and well thought through process

Technology Support System integration and availability of information Supporting technology and analytical approaches are essential to optimizing workflow

To be successful at capturing Big Data opportunities, credit unions overall big data strategy, approach, capabilities, and technology support must complement each other

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


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 Marketing •  Campaigns •  Precision marketing to a

single member •  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

Value Historical use Suggested use

Exercise – Discuss with your neighbor:

How might your credit union begin

to leverage it’s data?

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)

Today’s Agenda




■  QuesMons?

QuesMons? Jason Milesko 608-‐234-‐0148 [email protected]

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