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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
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Let’s start with a quiz!!!
#1. True or False?
Google processes >100 search inquiries every second…
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Let’s start with a quiz!!!
TRUE!!!
Google actually processes >40,000 inquiries every second…
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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 1810-2010
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Let’s start with a quiz!!!
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
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Let’s start with a quiz!!!
#3. True or False? There are over 1 Trillion smartphones in the world…
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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
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Let’s start with a quiz!!!
#4. Fill in the Blank Every minute we send ___ emails, ___ Facebook likes, and ___ tweets
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Let’s start with a quiz!!!
#4. Fill in the Blank Every minute we send 200 million emails, 1.8 million Facebook likes,
and 278k tweets. (That’s a lot)
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Part of my preparaMon…
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Exercise – Discuss with your neighbor:
What does Big Data mean to you?
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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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Today’s Agenda
■ What is big data?
■ How do credit unions stack up?
■ What can my credit union do?
■ QuesMons?
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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
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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@
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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
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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
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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
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Today’s Agenda
■ Understand what is big data?
■ How do credit unions stack up?
■ What can my credit union do?
■ QuesMons?
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Exercise – Write down:
What has prevented your
credit union from using big data?
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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
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Exercise – Write down:
For what purpose would you like to
use big data?
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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
Source: CUNA CU market survey
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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
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Exercise – Write down:
Who knows the most about big
data at your credit union?
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Who knows the most about big data at credit unions?
IT claims to know the most about big data, followed by Finance and Marketing
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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
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Exercise – Write down:
How is your credit union’s data
quality?
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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
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Exercise – Write down:
Do your information
systems integrate with each other?
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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
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Exercise – Write down:
How accessible is data to you at your
credit union?
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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
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Exercise – Write down:
What is your preferred delivery
channel for big data help?
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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
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Today’s Agenda
■ Understand what is big data?
■ How do credit unions stack up?
■ What can my credit union do?
■ QuesMons?
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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
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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
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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
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Exercise – Discuss with your neighbor:
How might your credit union begin
to leverage it’s data?
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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)
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Today’s Agenda
■ Understand what is big data?
■ How do credit unions stack up?
■ What can my credit union do?
■ QuesMons?
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QuesMons? Jason Milesko 608-‐234-‐0148 [email protected]