Filenes Sizing_Big_Data

REPORT
Rightsizing Big Data
for Credit Unions
Linda Young, Founder, ponderpickle

Acknowledgments
Leveraging data to uncover new opportunities and
translating that into organizational success is not an
easy feat. I am inspired by the various credit unions
interviewed for this research and their vocal support for
the need for better data-driven decision making. I would
like to express my thanks and gratitude to the CEOs
and executives at the four credit unions featured in this
report: Chris Catliff, CEO of BlueShore Financial and his
team; Newfoundland and Labrador Credit Union CEO
Allison Chaytor-Loveys and her team; from Servus Credit
Union, Stephen Kaiser, director of member and market
insights, and Curtis Cunningham, information architect;
and from Westerra Credit Union, Andrew Gardner, vice
president of lending, and Brian Moran, financial analyst.
Many other credit unions generously shared their data
stories, which added depth to the credit union experi-
ences. My thanks also go to the technology, research,
and data companies that participated, including BMAI
Strategies, Horsetail Technologies, IBM, Microsoft, SAP,
and Temenos.
My deepest appreciation goes to Marc-André Pigeon at
Credit Union Central of Canada and Ben Rogers at the
Filene Research Institute for their enthusiastic sup-
port of this study as well as their smarts and patience
throughout this project.
This report is the result of the cooperative spirit of credit
unions and their common desire to be better informed to
better serve their members.
Filene thanks our generous supporters for making this
important research possible.

Table of Contents
4 EXECUTIVE SUMMARY
6 CHAPTER 1
Big Data: The Paradox
10 CHAPTER 2
The Journey of a Data-Driven Credit Union
15 CHAPTER 3
Spotlight on Westerra Credit Union: Being Curious
17 CHAPTER 4
Spotlight on NLCU: Seeking Meaning in the Differences
21 CHAPTER 5
Spotlight on Servus Credit Union: Structure Follows Strategy
29 CHAPTER 6
Spotlight on BlueShore Financial: The Analytical CEO
33 CHAPTER 7
Where Is Your Credit Union on the Data Journey?
35 CHAPTER 8
Conclusion
36 APPENDIX
Terms
39 ENDNOTES
40 RESOURCES
41 LIST OF FIGURES
42 ABOUT THE AUTHOR
44 ABOUT FILENE

Linda Young
Founder, ponderpickle
MEET THE AUTHOR
Overview
An effective big data
strategy can help credit
unions shed light on
member behavior in
order to drive better
business results and
growth. No longer can
credit unions resist an
investment in big data.
The United States spends more per person on healthcare than any other
developed country in the world (OECD). Unfortunately, though, its health
outcomes are among the worst. A key factor in the healthcare reform
debate of 2009 was reducing annual expenditures while improving patient
outcomes. One way to accomplish this: big data. With effective analysis,
hospitals have reduced the wasteful spending caused by duplicate patient
records, identified patients most at risk of needing further expensive
procedures, and cut down on patient readmissions. If initiatives like these
continue to bear fruit, McKinsey estimates big data could help save the
US healthcare system more than $300 billion per year.
1
Credit unions may not be saving lives, but they do play an active role in
financial wellness. Big data (or small data) done right can help deliver
personalized, tangible help.
What Is the Research About?
This report explores the experiences of credit unions across North America
with “big” data and data of any size. Credit unions of all sizes are learning
to rightsize big data, aiming for an end goal of using data to serve mem-
bers, drive growth, and strengthen their organizations.
We conducted in-depth interviews with credit union leaders and data
experts to study the business relevance of data and the need for better
analytics to drive decision making. Spotlighting four credit unions and
their experiences with rightsizing big data shows the importance not just
of data management and infrastructure but of analytical tools and human
skill sets.
Becoming a data-driven credit union requires appropriate technology,
people, and organizational frameworks. Leadership also plays an integral
role, as resistance to data initiatives can lead to cultural distrust of data.
What Are the Credit Union Implications?
For credit unions, big data can shed light on essential topics: managing
credit and operational risk, predicting member behavior, segmenting
Executive Summary

EXECUTIVE SUMMARY FILENE RESEARCH INSTITUTE
members, and increasing share of wallet with new and existing members.
Focusing on data capabilities will:
→ Transform insights into competitive advantage. BlueShore
Financial, based in North Vancouver, Canada, has been on its data
journey for the past 15 years. Through data analytics the credit
union has been able to identify the financial profile of its ideal
member. Today, nearly 85% of BlueShore’s 40,000 clients have been
identified as its target audience—achieved by improved under-
standing of potential value and developing deeper relationships
with those existing clients to capture increased share of wallet.
→ Improve product profitability. Colorado-based Westerra Credit
Union has been keen to undercover methods to add more value for
its members by looking at ways to modify its pricing models. Work-
ing with its card-processing partner, Westerra wanted to better
understand the characteristics of the different credit card holders
in the market. Soon the credit union will be launching new cards,
including two low-rate card options with (or without) rewards pro-
grams tailored to the Westerra membership.
→ Bolster understanding of member needs. Situated in the most
eastern province in Canada, Newfoundland and Labrador Credit
Union (NLCU) serves more than 21,000 members. In 2012, NLCU
engaged a market research firm that explored how members felt
about topics such as being part of a credit union and the role of
financial institutions. NLCU used the research findings to create
a life stages–based framework that outlines each segment’s key
characteristics, beliefs, and concerns as well as ways that NLCU
can be of service.
An investment in big data, or even just structured data, will require orga-
nizational buy-in and will lead to more personalized interactions with
members, which should be a goal of every cooperative.
Analytical
competitor
Analytical
company
Analytical
aspirations
Localized
analytics
Analytically
impaired
Where most credit unions
interviewed are
}
STAGES OF ANALYTICAL MATURITY: THE CREDIT UNION EXPERIENCE

BIG DATA: THE PARADOX FILENE RESEARCH INSTITUTE
CHAPTER 1
Big Data: The Paradox
Imagine you’re sitting with your leadership team and you hear about a possible “big data”
initiative. What instantly comes to the minds of you and your colleagues around the table?
→ It’s just a fad.
→ We’re too small to have big data.
→ It’s important for us to do.
→ Whoa, that sounds expensive.
→ Can’t we just get our hands on any type of data here?
Sound familiar? Credit unions today are bombarded with calls to investigate and adopt
the latest in business models, technologies, and ways to serve the customer—all with the
goal to either grow or protect their market share. So, what makes big data any different?
Despite the hype surrounding big data, it is real, and many organizations have made real
Rightsizing Big Data for Credit Unions

investments. It is estimated that the financial services industry will invest $6.4 billion (B) in
big data (inclusive of not-so-big data) in 2015, with an estimated annual investment growth
of 22% by 2020. Furthermore, it has been reported that companies using data to drive their
decisions are, on average, 5% more productive and 6% more profitable than their competi-
tors not leveraging data analytics.
2
While these figures are impressive, to be dubious about the benefits of big data is to be a
shrewd leader today. Big data is often defined as the sheer quantity of data an organization
generates from its various activities. However, its benefit is very much reliant on how that
organization can translate massive amounts of data into insights and deliver on tangible
outcomes. So a paradox exists: Does big data really bring big value? Is it such a big deal?
Furthermore, compared to its big bank counterparts, do credit unions even generate
enough data to be considered big data?
This report aims to explore the experiences credit unions across North America have
had with big data and data of any size. By demystifying the term “big data,” key areas of
opportunity to leverage data to serve members, drive growth, and strengthen a credit union
will be identified, creating the opportunity to rightsize big data for credit unions. In the
absence of better-informed decision making, credit unions will continue to face challenges
in remaining relevant in a highly competitive financial services industry.
The Approach
This report explores the themes uncovered through a series of in-depth interviews with
credit union leaders throughout North America, along with interviews with data analytics
experts; a total of 32 interviews were conducted from January to June 2015. While the tech-
nical side of big data will be discussed to some extent, the main focus of this report is the
business relevance of data and the need for credit unions to be more analytically driven in
their decision making. Spotlighting four credit unions and their experiences with rightsiz-
ing big data for their own needs will illustrate the strategic imperative of investing in data
management and infrastructure along with analytics tools and skill sets.
This data journey uncovers the importance of using data of any kind to build a climate of
trust and competency in data analysis and the necessity of understanding who the mem-
bers are and how best to serve their needs—all while surviving in this highly competitive
space.

Breaking Down Big Data
Big data is best described in the context of the three “V”s: volume, velocity, and variety.
Volume can be defined as the size of the data, with big data measurable in petabytes, exa-
bytes, or beyond. For instance, it is estimated that Walmart collects more than 2.5 petabytes
of data from its customer transactions every hour. A petabyte is 1 quadrillion bytes, the
equivalent of about 20 million filing cabinets’ worth of text.
3
An exabyte is 1,000 times that
amount, or 1 billion gigabytes (GB). Another way to define volume is the number of records,
transactions, and files counted that are associated with
the data. This is often the simplest of the “V”s for credit
unions to understand because of the volume of banking
transactions generated and the processing backbone
required of any financial institution (i.e., the core bank-
ing system).
Data can be further described in terms of the velocity
of new data coming into the organization, whether it is
batched or streamed in real or near time. The velocity
of new data coming into, say, a credit union data ware-
house varies depending on how often information is
updated—daily, weekly, or monthly. For one of the larger
North American credit unions interviewed, it is estimated
that 12 terabytes (TB) of data is currently stored, with
about 1 GB of new data being generated every day.
The final “V” in defining big data is variety. A huge pro-
portion of the data credit unions have (ranging from 95%
to 100% of data stored by the credit unions interviewed)
is structured. Structured data is relationship based and can be, for instance, organized in
rows and columns or easily reported (e.g., in cubes). A member’s banking activities (cumu-
lated as transactional data) is considered structured data because each activity (e.g., a bill
payment) can be associated with another data point (e.g., the member’s account num-
ber). Other types of data, such as searches made on the credit union’s website or member
sentiments on social media sites, are considered unstructured or semi-structured data, as
there is no apparent relationship between data points. The same unstructured relationship
applies when tying external data (e.g., economic trends) with credit union performance
data or member transactional data.
FIGURE 1
BIG DATA
Source: “Big Data Analytics,” TDWI Research, Q4 2011.
Terabytes
Records
Transactions
Tables, ﬁles
Structured
Unstructured
Semistructured
Batch
Near time
Real time
Streams
VarietyVelocity
Volume
3Vs of
big data

Credit unions must have rigor and compliance around juggling the three big data “V”s and
consider questions such as:
→ Volume of data storage:
⋅ How much of members’ historical data is kept in a data warehouse versus in
archives?
⋅ How secure is member data? How is the security and privacy of member data
ensured?
⋅ Which member information needs to be immediately accessible in order to bet-
ter serve members?
⋅ How much historical performance data is kept for planning and compliance
purposes (including stress-testing scenarios)?
→ Velocity of data:
⋅ How much data from the banking system requires real-time access versus his-
torical access?
⋅ How can third-party data of members’ activities be merged or processed in a
timely fashion?
→ Variety of data:
⋅ How much of the data generated is structured (e.g., banking system data) ver-
sus unstructured (e.g., call center recordings, Twitter feeds)?
⋅ What are ways to store and merge these different data sets?
⋅ How can diverse data be analyzed and meaning uncovered?
It is imperative that credit unions go beyond admiring the fashionable aspects of big data
to analyzing, understanding, and interpreting information in a meaningful way for the
benefit of the credit union and its members today and tomorrow.
There is the technology side of data and then there is the business side of data. Tech-
nology enables and business leads. Start with the business need.

THE JOURNEY OF A DATA-DRIVEN CREDIT UNION FILENE RESEARCH INSTITUTE
Data Landscape
How do credit unions compare to other types of organizations or industries that are huge data gen-
erators? As Figure 2 illustrates, credit unions have less data to manage, and most of it is structured
and resides in relationship-type databases.
CHAPTER 2
The Journey of a Data-Driven
Credit Union
While the debate about the benefits and relevancy of big data continues, what doesn’t
need to be disputed is the challenge of managing, accessing, and leveraging data of any
size. Whether credit unions are aware of it or not, each has embarked on a data journey.
While some credit unions are further along the path than others, how far a credit union
has progressed is related to how analytical the organization is. In the book Competing on
Analytics: The New Science of Winning, Thomas Davenport and Jeanne Harris present an
FIGURE 2
BIG DATA LANDSCAPE
Source: BI Leadership Forum Presentation, 2015.
General purpose
relationship database
1990s
Exabytes
Petabytes
Terabytes
Gigabytes
Megabytes
Structured Semistructured Unstructured
Credit unions
Analytic database/tools
Hadoop
Walmart
Google
Library of Congress:

analytical maturity spectrum that describes an organization’s capabilities and readiness in
the areas of:
→ Technology: the quality of data, analytical tools used, and data infrastructures
available.
→ People: analytical skill sets as well as leadership and cultural attitudes that sup-
port and foster evidence-based decision making.
→ Organizational framework: the ability to generate insights, the thoroughness of
performance measures in place, and analysis processes being defined and inte-
grated into operational activities.
4
Davenport and Harris go on to detail the five stages of analytical maturity (see Figure 3).
At the beginning of the analytical journey, fragmented insights based on gut instincts
rather than objective analysis of information are the norm. The organization is analyti-
cally impaired because necessary skills and processes are not in place, leadership has not
embraced the need for fact-based decision making, and the quality of data is poor, with
limited infrastructure for storing, organizing, and processing data. At this stage an organi-
zation is plagued with a cultural mistrust of data. Rather than data being seen as a way to
better understand performance or opportunities, doubt is cast on its reliability for effective
decision making.
As the organization continues to evolve, an appreciation for data is fostered by an ana-
lytically savvy leader or a specific area of the organization. Localized analytics begin to
pop up, with skills and processes defined and strengthened by a vertical line of business
or functional area (e.g., finance, operations, marketing). If the organization succeeds in
leveraging data to grow or improve performance in one area, other parts of the organiza-
tion follow suit. This is the taste of analytical aspirations. At this stage, organizations
strive to produce more robust analysis and make further investments in technology to store
and process data, and they introduce more processes to support knowledge-based deci-
sion making, particularly when opportunities to grow are uncovered. Also at this stage of
the journey, senior leaders are encouraged to complement their experience and gut feel-
ings with objective analysis of a problem or opportunity, which contributes to creating a
knowledge-based culture.
Having seen the benefits of investing in analytics (people, technology, and processes),
organizations are witness to improved performance and become truly analytical com-
panies. These organizations use evidence-based decision making across all parts of the
organization, and they understand the importance of investing further in quality data and
introducing a data governance framework. Long-term investments and future plans are

directed by analytics that aim to better understand the dynamics of the marketplace, con-
sumer behavior, social trends, and the overall business climate.
At the pinnacle of data maturity are organizations that are analytical competitors. These
organizations decisively use data of all shapes and sizes to deliver meaningful products
and services to customers and effectively compete in the marketplace. Led by an analyti-
cally driven CEO, these organizations have successfully created the necessary analytical
FIGURE 3
STAGES OF ANALYTICAL MATURITY
Organization Human
Stage Analytical objective Analytical process Skills Sponsorship Culture Technology
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 (may
be in finance,
marketing/CRM)
Functional and
tactical
Desire for more
objective data,
successes from
pointed use of
analytics start to
get attention
Recent
transaction data
unintegrated,
missing important
information;
isolated business
intelligence (BI)/
analytic efforts
Analytical
aspirations
Coordinated;
establish enterprise
performancemetrics,
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 or
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
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/business
analysis (BA)
architecture
largely
implemented
Source: Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning (Boston: Harvard Business School Press, 2007).

processes, attracted and retained
analytics talent, and invested in
infrastructure and tools—all of
which defines it as a culture that is
fact based and constantly learning.
These analytically driven orga-
nizations are better versed than
less data-mature organizations in
keeping informed of different data
trends and effectively assessing
and adopting data tools and tech-
niques, including big data.
Using Davenport and Harris’s framework as a reference, among the various credit unions
interviewed for this study, most are either at the analytically impaired or localized analyt-
ics stage of analytical maturity. Many of the credit union leaders interviewed describe their
analytical capabilities as limited to a specific area of the organization (usually the finance
area, with some credit unions also mentioning their lending/credit groups) and their
reports as limited in depth or accuracy. Credit unions that choose to use an external vendor
for some of their performance reporting needs find it difficult to draw understanding and
relevancy from the voluminous reports because (1) there are so many reports to navigate
through, (2) there is uncertainty as to which reports should be referred to for what purpose,
and (3) there is limited time available to spend analyzing the reports.
Without some level of analytical competency to help them
understand shifts in consumer behavior and banking needs,
how will credit unions successfully navigate this competitive
landscape and retain their value and relevancy?
It is not a lack of desire to leverage the data that plagues these credit unions but rather
the lack of internal skill sets—namely, a shortage of people who understand how data is
structured and organized as well as people who are well versed in analyzing and applying
business needs to that data to draw insights and outcomes. To foster these talents, credit
unions must shift their cultural mind-set, encouraging employees to trust in and value
information and knowledge. If credit union leaders are deliberate in developing people
and adopting new cultural traits, there is a real possibility of moving to at least the analyti-
cal aspirations stage. It is at this stage that credit unions are better positioned to tackle the
challenges faced in the near and long term by leveraging data insights.
Analytical
competitor
Analytical
company
Analytical
aspirations
Localized
analytics
Analytically
impaired
Where most credit unions
interviewed are
}
FIGURE 4
Source: Based on 30+ in-depth interviews with credit unions, partners, and data experts.

As for the latter stages of analytical maturity, very few of the credit unions interviewed were
firmly situated as analytical companies or analytical competitors. This is a huge concern
for the credit union system as a whole. Faced with daunting competition from traditional
banks and alternative players, it is only going to get more difficult for credit unions to
retain members, let alone attract new ones. Without some level of analytical competency
to help them understand shifts in consumer behavior and banking needs, how will credit
unions successfully navigate this competitive landscape and retain their value and rel-
evancy? Furthermore, how can credit unions leverage their cooperative roots to strengthen
their analytical skills for the betterment of their members?
A credit union’s position on the analytical continuum reveals whether it is at a com-
petitive advantage or disadvantage.
Credit Union Analytics in Action
To show how credit unions are tackling these challenges, the experiences of four credit
unions are shared in the chapters that follow. Each credit union is at a different stage in
Davenport and Harris’s model of analytical maturity. Beginning with Colorado-based Wes-
terra Credit Union, its experience
in leveraging analytics to improve
its products is spotlighted. This
is followed by an account of the
efforts of a Canadian credit union,
Newfoundland and Labrador Credit
Union (NLCU), to better understand
its members and design a member
segmentation that is meaningful
for its members’ financial well-
being. The third credit union,
Servus Credit Union, has succeeded
in growing its membership after
investing in technology, people,
and a knowledge-based culture. In
addition to continued investments
in those key areas, Servus is now
looking at the need for a data gover-
nance strategy. Finally, reinforcing
the fact that a data journey is not a fast and simple one, BlueShore Financial shares the
story of its multiyear effort to become an analytically deft credit union.
Analytical
competitor
Analytical
company
Analytical
aspirations
Localized
analytics
Analytically
impaired
FIGURE 5
CREDIT UNIONS ON THE BIG DATA JOURNEY

SPOTLIGHT ON WESTERRA CREDIT UNION: BEING CURIOUS FILENE RESEARCH INSTITUTE
While these credit unions all face their own unique challenges and opportunities, they
share the understanding that data is nothing without the right skills, cultural appetite,
appropriate tools, hard work, and clear goals.
Invest in technology that will enable the business areas to effectively lead the credit
union through data-driven decisions.
CHAPTER 3
Spotlight on Westerra Credit Union:
Being Curious
Have you ever wondered how companies uncover new
opportunities? Being curious about things around you and
challenging the status quo are traits that allow individuals
to spot opportunities that may otherwise be overlooked.
Colorado-based Westerra Credit Union, with over $1.3B in assets and serving close to
100,000 members, encourages its employees to think out loud, challenge information, and
look at data in different ways.
For its credit card offerings, Westerra was keen to undercover ways to add more value for
its members by better understanding how they were currently using the cards along with
looking at ways to modify its pricing models. Financial analyst Brian Moran followed his
curiosity and dove deep into the pool of data available in Westerra’s data warehouse. What
Moran uncovered in his review of the credit card portfolio was an opportunity for Westerra
to move to a higher interchange revenue tier for each of its credit cards. While this in itself
was a great discovery, it sparked the curiosity of Moran’s colleagues, including Andrew
Gardner, vice president of lending. Gardner wondered what else they didn’t know. Moran
developed a credit card dashboard to fill in those gaps and monitor the card portfolio more
closely. As Gardner began to see a more comprehensive picture of the credit union’s card
performance, he realized the picture he needed to see was much, much bigger. The behav-
ior of the Westerra credit cards being tracked was not representative of the marketplace.
With a goal of increasing card uptake by members (currently at 19% penetration), data
about the marketplace was required. Working with its card processing partner, Westerra
sought to better understand the characteristics of the different card holders in the market:
Who were the avid card users? What did they typically use their cards for? What were their

SPOTLIGHT ON WESTERRA CREDIT UNION: BEING CURIOUS FILENE RESEARCH INSTITUTE
preferences when it came to rates and rewards? Gardner hoped the bigger data sets acces-
sible by the card processor would provide some key insights and trends.
As Gardner began to see a more comprehensive picture of the
credit union’s card performance, he realized the picture he
needed to see was much, much bigger.
As the information started to come in, improving Westerra’s credit card portfolio became a
real possibility. Westerra began to:
→ Model the rewards and rates that would be most beneficial for the credit union and
its members.
→ Analyze the benefits (for members and the credit union) of moving all cards from
fixed pricing to risk-based pricing.
→ Create a profitability model for the credit card portfolio that factored in the APR,
cost of funds, net credit loss, turn ratio, interchange income, rewards costs, market-
ing costs, and operating expense.
→ Identify ways to migrate members with existing cards to cards with features better
suited to their use.
The work done by various individuals at Westerra as well as its card processing partner has
been going on for one year. Soon the credit union will launch its new cards, including two
low-rate cards with (or without) rewards programs tailored to the Westerra membership.
Providing access to affordable credit with features of value to members is important to all
credit unions. The analytical journey Westerra took to uncover meaningful benefits for its
members will allow the credit union to realize increased card uptake and usage, making its
cards “front of wallet” for members.
Imagine what a spark of curiosity could do for your credit union!
Data is nothing without questions. Powerful questions set the context and drive
urgency. Start by asking what you want to know. Then ask yourself what you are
going to do once you have the answers.

SPOTLIGHT ON NLCU: SEEKING MEANING IN THE DIFFERENCES FILENE RESEARCH INSTITUTE
What I Wish I Knew
At a May 2015 research symposium hosted by Filene and Credit Union Central of Canada, the credit
union attendees were asked what they wanted to know and what they would do with that informa-
tion. The majority of feedback was about better understanding current members. The key questions of
interest included:
→ Why did they [members] choose to join a credit union?
→ Where else do members bank? Why?
→ What do members really need from us? What products and services are serving those needs?
→ Who are my members?
→ What does community mean for members?
→ What are the characteristics of the next generation and how can we serve them better?
→ How can we [credit unions] better utilize social media so it is meaningful for our members?
→ What future regulations do we need to be aware of? How will it impact the credit union’s
strategic direction?
By answering the above questions, credit unions felt they could:
→ Better serve existing members and retain them over the long term.
→ Attract new members.
→ Create products and offer services that would be more relevant.
→ Identify a competitive edge over other financial institutions.
→ Better plan for a stronger future.
CHAPTER 4
Spotlight on NLCU: Seeking Meaning
in the Differences
Situated in the most eastern province in Canada, NLCU serves more than
21,000 members across two distinct geographies—the island of Newfound-
land and the mainland of Labrador. With over $540 million (M) in assets,
NLCU is a great example of a smaller credit union that packs a strong data
punch. Early on, NLCU recognized big data was not a silver bullet. Rather,
the silver bullet was data that would help the credit union better understand its members.
In 2012, the NLCU executive leadership team and the board of directors met to discuss the
credit union’s five-year strategic goals. NLCU CEO Allison Chaytor-Loveys had the consent

of the board to support strategic objectives by moving forward with a member insight ini-
tiative that ventured beyond the usual demographic and geographic descriptions of current
and potential members to truly gain a better appreciation and deeper understanding of
consumers. By opening up their thinking and seeking a better way to understand the dif-
ferences among people, NLCU was about to embark on a journey that would transform the
way it sees and serves its members.
Member Segmentation
It is a common practice for financial institutions to segment consumers based on tangible
characteristics such as demographics (e.g., geography) and behavior (e.g., banking activi-
ties). Much information can be gained from this approach as it answers the “what” types
of questions we often have. However, when we start to ask the trickier “why” questions, a
more sophisticated segmentation analysis needs to be introduced. Often harder to articu-
late and measure are the psychographic characteristics of individuals, such as personal
and social values and lifestyles.
In 2012, NLCU engaged a market research firm to explore how members felt about topics
such as being part of a credit union, the role of financial institutions and what they most
valued in them, their financial circumstances and challenges, and their perceptions of their
own financial literacy and know-how. This data began to uncover what was unique and of
value in the member–credit union relationship. One of NLCU’s key objectives was to attract
new members, so the research included individuals who were not currently doing busi-
ness with the credit union. Following a series of focus group discussions that identified key
areas of further exploration, a customized segmentation survey was launched. The goal of
this study was to segment NLCU members into various subgroups so the credit union could
better align services and needs with different segments.
Data began to uncover what was unique and of value in the
member–credit union relationship.
Rather than rely on standardized segmentation solutions available in the marketplace,
NLCU and the market research firm designed a comprehensive survey and conducted a
cluster analysis to identify consumer segments. What resulted were six new segments—
each with a combination of distinct financial needs and service preferences uniquely
relevant to NLCU, something that could not have been achieved using homogenous market
data. Completing this complicated analytical work was an accomplishment in itself. How-
ever, for the leadership team at NLCU the real work was putting the analysis into action.

Segmentation in Action
Once the segmentation work was completed, NLCU formed several subcommittees to
begin translating research findings into action. Over the next two years, these committees
devised and implemented several projects that were inspired by the overall aim to enhance
member relationships.
The Framework Segmentation Committee used the research findings to create “The Big
Embrace.” Descriptive profiles were written for each of the six new member segments
identified in the segmentation research. What resulted was a life stages–based framework
that outlined each segment’s key characteristics, beliefs, and concerns, as well as ways
that NLCU could be of service. This framework would become the foundation for building
stronger member relationships; when members come to NLCU with a financial need, it is
the employee’s mission to help them make the best financial choices for their individual
circumstances and lifestyle. This approach would mean taking the time to understand
members’ complete financial picture to impart sound and informed financial advice.
The rich information gleaned from the segmentation analysis
influenced NLCU’s employee training.
The rich information gleaned from the segmentation analysis influenced NLCU’s employee
training. The Training Committee began enhancing its training programs and tools to
emphasize the importance of understanding those key member differences to foster more
meaningful relationships with members. The committee revamped product knowledge
training and service provision at the credit union to encourage a corporate culture shift
that prioritized knowing and understanding the member and connecting regularly with the
member, in addition to its long-held goal of delivering superior service.
Product Focus vs. Member Focus
In addition to positively impacting the service training at the credit union, the segmenta-
tion work provided insights into marketing activities. One key insight that hit home was
that members and nonmembers alike often find discussions about investment products
daunting due to a lack of information and understanding. Members wanted the credit
union to speak their language. More specifically, rather than hearing about a product such
as a registered retirement savings plan (RRSP), members wanted to know what need or pur-
pose the product served—i.e., retirement. With this new insight in mind, NLCU’s Marketing
and Communications team shifted the message away from specific products to planning for
retirement in general—a topic everyone understands.

In addition to shifting away from a
specific product focus, NLCU wanted to
reverse its aging demographic by attract-
ing a younger audience. Appealing to
its Gen Y segment’s fondness for pop
culture, the team launched a playful RRSP
campaign that didn’t peddle products but
instead used a humorous pop reference
(the DeLorean from the Back to the Future
films) to highlight the importance of
thinking about the future. This campaign
yielded 11% more in deposits over the
previous year’s efforts.
Another example of “members before
products” is an initiative led by the Home
Ownership Committee. To address the
concerns of prospective homebuyers who
were finding it difficult to save for a down
payment, the committee developed a new
product called Cashback Mortgage. This
program has been very successful, not only in placing members in new homes but also in
its dealings with members who do not qualify for the product. Instead of ending the service
experience with a declined loan, NLCU takes the time to counsel these members, devis-
ing plans that will place them on the road to a better financial situation. If the members
are able to maintain the plan, they can get approved for the loan in the near future. This
member-centric approach exemplifies the new focus placed on relationship building.
Understanding that clear direction and procedures are crucial to enabling employees
to embrace the member relationship approach, the Membership Retention Committee
developed processes to engage new members and reduce member erosion. Onboarding
procedures were rewritten to make the initial interview process with new members more
intuitive, and probing questions were created to allow staff to learn more about the mem-
ber and document that learning. With this combined approach, NLCU was able to analyze
the information after the interview, using The Big Embrace as a guidepost, and identify
members’ potential financial needs. Also, by documenting these interactions NLCU was
able to ensure meaningful ongoing contact with the members. Rather than calling to meet
outbound call goals, employees carefully prepared for calls by reviewing member files
and identifying potential financial needs before making the call. Making sure there is an
understanding of what is important to the member before calls are made reinforces NLCU’s
FIGURE 6
NLCU’S 2013 RRSP CAMPAIGN
Source: NLCU’s Marketing Division.

SPOTLIGHT ON SERVUS CREDIT UNION: STRUCTURE FOLLOWS STRATEGY FILENE RESEARCH INSTITUTE
commitment to appreciating the differences among its members by understanding their
needs, wants, values, and concerns.
Lessons Learned
Over the past few years, Chaytor-Loveys and her team have gained many insights that rein-
force the power of inquiry and the pursuit of knowledge and understanding. NLCU’s top
lessons learned:
→ Nothing is sacred. Assumptions and existing processes must be challenged.
→ Too often we limit ourselves because we are worried about what others are doing.
Rather than wonder what competitors are doing, we should trust what we are
doing, focusing on our own plan instead of the plans of others. Only then can we
do our best.
→ Credit unions have a strong record of introducing innovative products (e.g., first to
give loans to women, ATMs, online banking), so why do members eventually leave
credit unions? Could it be because we aren’t truly listening to them?
→ Being a credit union owned by the members who do business with us, listening
is paramount to ensure products fit the needs of members. When we listen to the
members, we can be more nimble in our reactions to their needs.
Uncover what is unique about your credit union’s members and listen to what the
data tells you about their needs and wants.
CHAPTER 5
Spotlight on Servus Credit Union:
Structure Follows Strategy
Servus is currently the second largest Canadian credit union,
with over $14B in assets and almost 380,000 members throughout
the province of Alberta. Since its last merger in 2009, Servus has
evolved into a credit union with an appetite for knowledge and demonstrates many char-
acteristics associated with an analytical organization. While Servus would be the first to
acknowledge that it still has a long way to go to catalyze data insights into a competitive
advantage, what this humble credit union may not realize is that it is situated on a sweet

spot along the data journey. The huge obstacles many credit unions face today, such as
limited analytics skills, poor data quality, and fragmented technologies, are all hurdles
that Servus has either overcome or is well on the way to dealing with. With this foundation
in place, along with continued efforts to hire and train the right skills and foster the right
learning culture, Servus can continue the data journey.
Focus on People Power
Big data, or data of any size, is often discussed in the context of the tools available and the
technological infrastructure in place to manage and derive some value. While those are
important components, Servus has also focused on powering its people talent. Over the
years, Servus has developed a decentralized approach to analytics, placing individuals
with strong analytical capabilities in the areas of marketing, finance, operations, and risk
(characteristic of an “analytical aspirations” organization). This has allowed key areas of
the credit union to create a culture of knowledge, with associated functional leaders getting
better at discerning information that will help them manage their activities.
As Servus continues to become a full-fledged analytically led credit union, it has identified
the importance of improving how data and insights flow from one area of the organization
to another. Creating that path of seamless information requires investing in people who are
considered “triple threats.” These individuals have the technical and analytical acumen,
deep business understanding, and strong communication skills needed to transform data
into game-changing business opportunities.
Without a cultural shift, it would be hard for any credit union to
become an analytically driven organization.
One such person at Servus is Stephen Kaiser, director of member and market insights.
When Kaiser joined Servus in 2012, much of the amalgamation activity resulting from vari-
ous mergers had been completed, and the credit union was ready to focus on the next stage
of data integration and analytics. One of Kaiser’s first projects was to rewrite the existing
business intelligence (BI) business case; he was designated project owner when the busi-
ness case was approved.
While the credit union benefited from Kaiser’s technical expertise in advocating key efforts
like the data warehouse and BI, it was Kaiser’s ability to connect and consult with various
internal stakeholders that was key to fostering a culture appreciative of knowledge- and
fact-based decision making. Without this type of cultural shift, it would be hard for any
credit union to become an analytically driven organization.

As Kaiser continues to champion data competency at Servus, he is also breaking down
stereotypes of where certain skill sets should reside. For instance, querying in SQL, coding
in R, and conducting advanced analytics (e.g., multiple regressions, conjoint analysis, fac-
tor analysis) are familiar skills for Kaiser and he’s not shy about hiring for those skill sets
for key marketing activities. Having those skills in house allows the team to create its own
models, come up with hypotheses, and test them to uncover opportunities for Servus to
create innovative products and better serve its members.
First Things First
Moving away from department-specific operational reporting silos to an enterprise data
warehouse, as well as improving BI capabilities, is key to becoming a more analytically
driven credit union. However, the decision to make such sizable investments can be dif-
ficult for an organization that has yet to fully buy into the benefits of analytics. In planning
its data strategy, Servus created an incremental approach to tackling various components.
For instance, it was decided that building its data warehouse would be a phased approach,
with each phase having its own distinctive project scope and deliverables. A phased
approach helps ensure that risk is managed appropriately and that the program has the
best chance of success. Only specific deliverables that address business needs and have a
Phase 4:
Optimize
Phase 3:
Enhance and
empower
Phase 2:
Integrate and
leverage
Phase 1:
Build
FIGURE 7
BUILDING A DATA INFRASTRUCTURE: SERVUS’S PHASED APPROACH TO DATA WAREHOUSING

positive return on investment (ROI) are included in the project plan, which is updated for
each phase.
Phase 1 of Servus’s data warehouse initiative was undertaken with the goals of building the
initial database infrastructure and creating value by reducing costs and creating efficien-
cies through the automation of manual processes. Phases 2 and 3 focused on improving
organizational effectiveness by designing better processes. Finally, Phase 4 realized
transformational change by enabling new business models. Key stakeholders are involved
in determining and prioritizing opportunities at the beginning of each phase to ensure
that the project is addressing relevant areas. This approach allows the analytics efforts to
continue to build over time as the credit union continues to work on other urgent priorities,
such as a banking system conversion.
Invest in data infrastructure and people’s analytical skills. One cannot succeed with-
out the other.
Data Discoveries to Drive Business Growth
and Member Value
During Phase 1 of Servus’s data warehouse initiative, access to data allowed the credit union to
tackle an issue that was, up until then, of anecdotal concern. Revenue from “other income” (e.g.,
transaction fees, service charges) was lagging in growth compared to other revenue areas. Servus
was concerned that other income was not being collected in a consistent manner across its branches
and employees. The Business Intelligence team was tasked with identifying all other income activity
at the branch/employee/member level in order to better understand ongoing activities. The goal was
to ensure all members were being treated fairly and to provide a more consistent approach to mem-
ber interactions. Forgoing other income is not akin to good service since it unfairly subsidizes some
members while shortchanging others.
Monthly reports were generated (initially outside the data warehouse, then within it to ensure consis-
tency and timeliness) at the branch/employee level and then used by management to educate and
mentor employees to provide a more consistent and fair approach in the collection of other income.
An additional trending report on other income collection by branches over time was created to enable
management to see where improvements were occurring and focus on areas that required atten-
tion. This trending report used data visualization (trended graph lines showing overall activity and
exceptions—the variances) to present the results from the monthly other income reports. Examined
over five quarters, the variance in the activities was used as a proxy for changes in revenue leakage
in order for a leakage amount to be calculated for the credit union overall as well as branch by branch.
Based on the findings from the reports, managers were able to have conversations with branch staff,

explaining to them that waiving fees does not equate to providing good service and that subsidizing
some members at the expense of others is not fair or equitable.
Because of data analysis stemming from a business concern, not only was Servus able to provide
a more consistent and fair experience for its members, it also identified over half a million dollars
of other income it will realize over the next few years. When compared to the $1.5M investment in
building the data warehouse, this analysis work alone contributes greatly to a positive ROI on a BI
initiative. With other data discoveries Servus is currently working on, recovering the initial investment
will be possible within a couple of years, if not sooner.
Thanks to this initiative, there is now a greater understanding of existing operational practices among
different groups at Servus. Creating the reports required the involvement of multiple stakeholders
and resulted in a better understanding across the organization of the contribution of the various
groups. The business areas and IT are now much more aware of how they can support each other and
what their roles are in generating business value. And just as important, the value of treating mem-
bers with fairness and consistency across the organization is now better understood.
Data Governance
Another body of work that has been on Servus’s radar is the data governance structure,
particularly when a large initiative like a data warehouse is being developed. Just as Servus
has been investing in the right people to strengthen its analytical capabilities, it is also
taking time to investigate a data governance model that will enable the right processes and
procedures to be in place to foster an organization-wide knowledge culture. One source
referenced by Servus’s information architect, Curtis Cunningham, is the Data Management
Association’s DAMA-BOK, a data management framework that identifies the key manage-
ment functions of data governance along with several elements that influence the goals
and principles of the work.
Managing the volume, velocity, and variety of the data moving around an organization is
important. The framework illustrated in Figure 8 encompasses the processes required to
plan, enable, create, acquire, maintain, archive, retrieve, use, and purge data.
While all nine data management functions that tie into data governance are important, a
few areas Servus has identified so far in this body of work for other credit unions to con-
sider include:
→ While collecting, storing, and distributing data are all part of running any organi-
zation, there should be sound data security management in place. Canadian credit
unions such as Servus must adhere to the Personal Information Protection and

Electronic Documents Act (PIPEDA). Careful consideration must be made about
data that allows members to be identified (PII—personally identifiable informa-
tion). For instance, should PII be stored in your credit union’s data warehouse or
data marts? How should your members’ information be protected? Other complexi-
ties include the use of external contractors that “touch” your credit union’s data.
→ An escalation path for data issues should be in place with specific policies to
ensure issues are triaged and escalated in accordance with specific requirements
and standards. This is often easier said than done, particularly when a major initia-
tive such as a new banking system goes live!
→ Just as a credit union has an organizational vision and mission, so should its BI
group. At Servus, the BI group’s vision is “to empower and support the Servus busi-
ness community to make better decisions faster,” and its mission is “to efficiently
and effectively deliver quality, governed information that enables Servus Credit
Union to improve decision making capability.”
Data management functions Elements
Data
development
Data
operations
management
Data
architecture
management
Data
warehousing
and BI
management
Data
quality
management
Data
security
management
Reference and
master data
management
Master data
management
Document and
content
management
Data
governance
Technology Activities
Practices and
techniques
Deliverables
Rules and
responsibilities
Organization
and culture
FIGURE 8
DAMA’S DATA MANAGEMENT FRAMEWORK
Source: DAMA International, “Body of Knowledge,” www.dama.org/content/body-knowledge.

Data Muck Work
While sensational stories about the power of big data have become legendary (who hasn’t heard
Target’s tales of predicting shoppers’ needs?), what often don’t make the headlines are the stories
about the messy work of cleaning up data. It is estimated that 50%–80% of data work is mired in the
muck of clarifying, defining, cleaning, purging, and organizing data before the first step in analysis
can be taken. Servus is no stranger this to messy work, having gone through the amalgamation of
three credit unions in 2008, followed by a merger in 2009, leaving the organization with various ver-
sions of the truth.
Some of the most animated discussions around a credit union table are spurred by attempts to
define what are considered commonly understood terms because they are used in everyday conver-
sations. One such term, “member,” can spark endless discussions about what a member is (active
member, inactive member, personal member, business member, etc.). Heated debates may occur,
but hopefully an agreement of what “member” means is the end result. It is then just as important
to carefully document the agreed-upon definition in a data dictionary so everyone who references
the term and organizes the data will have the same understanding of the term and its definition. This
sounds simple enough, but this is not always the case. Committing to the decisions early on will go a
long way toward ensuring the adoption of and trust in the decisions derived from data in the future.
There are typically two roles that touch all 9 areas of data governance: data stewards and
data custodians/data management professionals. Data stewards are formally accountable
for business responsibilities to ensure effective control and use of data assets.
5
Depending
on the organization, these individuals may reside in operations such as back office pro-
cesses (i.e., not in IT/IS roles). At Servus, data stewards are assigned for the credit union’s
more valuable systems (e.g., the core banking system). However, as the credit union contin-
ues to strengthen its data governance efforts, it is exploring the possibility of also assigning
stewards to key subject information areas (e.g., member, account, product). Meanwhile, the
data custodians (who are usually IT/IS staff) oversee day-to-day activities such as admin-
istering, monitoring, and enforcing the data policies, standards, and procedures. Ensuring
your credit union has policies in place to manage the data—big and small—along with
having staff who are tasked with monitoring and enforcing those policies must go hand in
hand.
While Servus is still in the midst of finalizing and executing its data governance frame-
work, it is following a course that will only strengthen its capabilities as a truly analytically
led credit union.
Just as a risk governance framework is a must for any credit union with operational
complexities, a data governance plan is a must when data systems evolve.

Moving Along on the Data Journey
SchoolsFirst Federal Credit Union is one of
the largest credit unions in the United States,
with over $11B in assets and more than
650,000 members in Southern California. Effectively managing data and predicting its growing data
needs are priorities on SchoolsFirst FCU’s strategic planning horizon. Each business day, 1 GB of new
data is generated from operations. Today, 12 TB of data is stored on its operational and reporting
systems.
SchoolsFirst FCU’s data projects for the next few years are to:
→ Establish one version of the (data) truth that would be addressed at various levels, including
common definitions of standard terms and master member records on one common platform.
→ Build the common platform, namely an enterprise data warehouse, from which all parts of
the organization will obtain information.
→ Create a data governance plan.
SchoolsFirst FCU’s senior vice president of risk management, Robert Osterholt, has some advice for
other credit unions, regardless of their size:
→ Value data like any other organizational asset and make sure it is as accurate as possible.
Validate the source and frequency of the data and how often it is updated. Data typically
resides in multiple locations and systems within a credit union, which can create inconsis-
tencies in reporting, so understand where the master data record is located.
→ Much depends on where the credit union is in developing a robust data management
environment and defined processes. A credit union that has rudimentary data management
systems and processes should invest now to convert to more sophisticated solutions rather
than waiting until data growth outpaces the credit union’s existing environment, which will
cost the credit union time and money in catching up.
→ If you don’t have a data infrastructure or internal expertise, make sure you bring in a con-
sultant who can help you get there. Credit unions that hire a consultant should set clear
expectations that the process created must allow for future work to be managed in house.

SPOTLIGHT ON BLUESHORE FINANCIAL: THE ANALYTICAL CEO FILENE RESEARCH INSTITUTE
CHAPTER 6
Spotlight on BlueShore Financial:
The Analytical CEO
Over the past 15 years, BlueShore Financial has redefined
what growth means for a credit union, its success driven by
insightful decision making based on facts and confidence in
knowledge-based outcomes (see Figure 9). The culture and appreciation for knowledge
has been fostered by the leadership of an analytical CEO, Chris Catliff. Catliff became CEO
of BlueShore Financial (then named North Shore Credit Union) in 2000 and quickly real-
ized that he could not grow a viable financial institution by merely replicating what others
were doing. The newly appointed CEO also faced the fact that the credit union could not
continue to be all things to all people—that was not a sustainable strategy. Catliff embarked
on a journey to discover who their ultimate customers
6
were and how best to serve them.
2000 2015
Four years of effort to leverage data
insights to identify target segments
and develop differentiated business
strategy in areas of service and branch
design including implementation of a
customer-centric data warehouse
Chris Catliff appointed CEO
Enterprisewide CRM launched—
one of the first Canadian credit unions Credit union conducts first
member segmentation (using
internal and external data)
to identify mass affluent and
emerging wealthy segments
Predictive analytics applied to
identify product uptake
opportunities
Launches enterprise
data warehouse
Data-driven
reassignment
of members
to specific
advisors
Data insights
uncover service
training opportunities,
resulting in better
segment needs
alignment
Develops and launches
new banking system
(2009)
Further investments
in BI for various
upgrades including
single cube reporting
Customer experience
management (CEM)
introduced,
replacing CRM
2004 2005 2006 2007 2009 2010 2011 2012 2013
AUA:$3.5B
Members:40,000
AUA:$1.775B
Members:40,000
AUA:$800M
Members:40,000
2014
}
}
}
BlueShore
Financial
premium
brand debuts
Vision 2020 introduces
digital strategy
Work begins on proprietary
algorithms (“Bluegorithms”),
essential ingredient in
customized client engagement
scoring model
Revised
segmentation
approach to
be more
opportunity
focused
FIGURE 9
BLUESHORE FINANCIAL’S DATA JOURNEY MILESTONES
Source: BlueShore Financial.

Catliff challenged himself and his leadership team to chart a new course for the credit
union—one that would focus on looking for untapped opportunities in the marketplace
and serving the right customers at the right time.
Looking for Meaning
Catliff and his team dived into client data, analyzed trade area information (where
branches were located), and began to uncover the untapped opportunities. This endeavor
did not take place overnight. Rather, it was a concerted effort over four years to identify
opportunities to transform how the credit union would run its business, serve its clients,
and skillfully compete in the marketplace.
What was uncovered during this time was the potential of two consumer segments resid-
ing in the communities where BlueShore Financial operated. These segments were the
mass affluent, individuals with $100,000–$500,000 in investable assets, and the emerging
wealthy, with $500,000–$1M in investable assets. These consumers sought more personal-
ized financial services and complex advisory support, but they were not the prime targets
of the big banks. In 2005, the credit union launched its affluent strategy backed up by a
robust segmentation framework. Identifying these segments allowed BlueShore Financial
to follow a different kind of growth strategy, one based on cultivating deeper, multiproduct
relationships with targeted clients rather than actively acquiring new members through
mass-market offers. Did this strategy work? The short answer is: absolutely. At the outset of
2000, with 40,000 clients, the credit union reported $800M in assets under administration
(AUA). By 2014, AUA had grown to $3.5B with no material increase in the number of clients.
How did being an analytically driven credit union help with this strong performance? Now
for the long answer.
Building Blocks of an Analytical Credit Union
As detailed in Davenport and Harris’s Stages of Analytical Maturity, the desired destination
for an organization is when it becomes an analytical competitor with key characteristics
such as:
→ A team of highly skilled analytical professionals and leaders.
→ A CEO who is passionate about knowledge and skilled with data analytics.
→ A culture that values fact-based decision making.
→ A well-developed, enterprisewide BI architecture.
→ Well-honed, analytical processes.
→ Clear objectives for strategic insights.
7

As mentioned earlier, all credit unions are at some stage on the data journey. In the case of
BlueShore Financial, its journey to this “data summit” was 15 years in the making. Seminal
shifts the credit union experienced during this time included:
→ Moving from anecdotal accounts of clients’ needs to a comprehensive client experi-
ence management (CEM) system that captures a holistic picture of each individual’s
activities across all channels as well as their interactions with BlueShore Financial
staff members. This allowed the credit union to move from a “service” to an “expe-
rience” offer.
→ Moving from working with limited analytical skills and tools to introducing a self-
service model that allows various staff members throughout the credit union to run
their own cube reporting. Integration of information allowed the analytical culture
of the organization to galvanize staff members to be more data inquisitive.
→ Developing a data-driven business strategy that changed the credit union’s cor-
porate culture from focusing on “employee” and “community” to focusing on
“member needs” and “results.”
→ Shifting from the inherited functional credit union brand of being all things to all
people to an emotional brand that connects to the aspirations and needs of its most
profitable clients.
→ Moving from buying off-the-shelf technology solutions to collaborating with tech-
nology companies to develop and test the latest analytical features to better meet
client needs.
→ Focusing on the 10% of members who create profit rather than the 90% who utilize
services for a net cost to ensure the credit union’s viability. (Most credit union
member profitability data shows that 10%–20% of members create all the profit.)
In 2002, BlueShore Financial identified its ideal members: those who already loved the
credit union and for whom price was not the key determinant in banking with them,
estimated at 5% or 2,000 clients. Today, this target audience—the affluent segment—makes
up nearly 85% of its 40,000 clients. This was achieved through improved understanding
of potential value, developing deeper relationships with those existing clients to capture
increased share of wallet, and attracting “like” affluent clients for whom BlueShore Finan-
cial’s brand resonated to replace those who left.
The data journey doesn’t end. It continues because there is the desire to better serve
members.
Despite the credit union’s success in leveraging analytics to realize long-term success for
its clients and itself, BlueShore Financial is far from done in advancing its data analytics
competency. Its latest analytical adventure involves the development of “Bluegorithms,”

proprietary algorithms that measure client engagement and “lifetime client value.” Positive
cross-sales, client referrals, survey responses, and a price inelasticity measure (i.e., price
is not a factor in engagement) are a few components that have been incorporated into this
model.
As BlueShore Financial continues to advance its analytical edge, Catliff summarizes it best:
“Our differentiator is how well we know which members make our credit union better.”
And how well BlueShore Financial knows its members is encompassed in a powerful data
ecosystem that has people, culture, and technology in top form and in balance.
Quote Me on That!
The staff at BlueShore Financial has not only the analytical prowess but the right attitude as a
knowledge-driven organization. When asked to share their thoughts on what other credit unions
should keep in mind when navigating through the complexities of data analytics, they responded:
“Hire curious people.”
“Best ideas win . . . but bring your data.”
“Data is to be used, not hoarded!”
“Use what you have, then decide what you need [next].”
“It’s never going to be finished; need to use what you have now; can’t wait for technology to
catch up.”
“Can’t be proactive, helpful [to members] without data.”
“It isn’t about the bright and shiny; not about the next tool; you need the people, skills and com-
mitment, then use it!”

WHERE IS YOUR CREDIT UNION ON THE DATA JOURNEY? FILENE RESEARCH INSTITUTE
CHAPTER 7
Where Is Your Credit Union on the
Data Journey?
To ensure relevancy for their members and foster growth in a highly competitive industry,
credit unions must have access to reliable information and be able to translate that infor-
mation into meaningful insights. This requires (1) putting in place a strong organizational
framework that fosters knowledge-based decision making, (2) hiring and training the right
people (with analytical skills and business acumen), and (3) investing in tools and technol-
ogy to provide quality data across the organization.
While there is no definitive approach to becoming an
analytically driven credit union, what is a must-have is the right
attitude and an appreciation for making decisions based on
applied knowledge.
How far a credit union has progressed on the data journey is influenced by how commit-
ted it is to these three areas over the long term. Some credit unions interviewed invested
in data management tools and technology first while developing their people’s analytical
skills over a longer period of time. Other credit unions relied on their people’s analyti-
cal passion to uncover opportunities for growth using their existing data and tools. That
created a climate to invest more in technology and people. While there is no definitive
approach to becoming an analytically driven credit union, what is a must-have is the right
attitude and an appreciation for making decisions based on applied knowledge. Under-
lying this attitude is the desire to understand and be better informed about members,
competitors, and the industry landscape.
How Data Savvy Is Your Credit Union?
Do you think your credit union is data savvy? Or do you have a huge challenge ahead to become more
data competent? Use this simple and fast assessment tool to find out where you’re at and get some
tips on how to be a more data-driven credit union:
www.onlineassessmenttool.com/how-data-savvy-is-your-credit-union/assessment-29043

WHERE IS YOUR CREDIT UNION ON THE DATA JOURNEY? FILENE RESEARCH INSTITUTE
Focusing on the Business at Hand
As you consider the experiences of the credit unions featured in this report and compare
them to your credit union’s own journey, be reminded that while the technical aspects of
the data require comprehensive oversight and execution, these activities are taking place to
support the business needs.
NLCU’s journey to translate
its member segmentation
into better service delivery
and meaningful products
is a great example of using
data to shape a credit union’s
long-term strategic direction.
Westerra Credit Union’s inves-
tigation into its credit card
portfolio focused on improv-
ing the business to generate
more product profitability
and improving its credit
card offer to better meet the
needs of its members. As a
credit union’s use of analytics
grows and matures, as in the
case of BlueShore Financial,
analytics is about driving
the business. Finally, just as
a credit union creates and
implements a risk governance framework as its activities (such as lending) grow in com-
plexity, it should also design a data governance framework, such as the one developed by
Servus Credit Union, to ensure clear policies are in place to mitigate data issues that may
arise (e.g., ensuring the safety and security of member data).
A perspective that can help credit unions keep business needs top of mind during data
discussions is illustrated in Figure 10. Along with the four distinct business activities that
encompass the management and use of data, there is also the underlying technology that
supports these activities. This visual summary provides a simple snapshot of how things
fall into place.
The next time you have a meeting about data, cut through the data talk and keep the
focus of the conversation above the line, on the business objectives.
TECHNOLOGY ENVIRONMENT
USER ENVIRONMENT
Data warehouses
Extracts, transform, load tools
Desktop query/reporting
Online analytical processing
Web querying/reporting
Business intelligence suites
Dashboards and scorecards
Data visualization
Packaged analytic applications
Data integration suites
Operational BI
Mobile BI
Cloud BI
Predictive analytics
Text analytics
Hadoop
Hive/pig
Driving the business
Analytics
Improving the business
Performance
management
Using the data
Business
intelligence
Getting the data
Data warehousing
201520102000s1990s
FIGURE 10
DATA SPEAK FOR BUSINESS NEEDS
Source: BI Leadership Forum Presentation, 2015.

CONCLUSION FILENE RESEARCH INSTITUTE
CHAPTER 8
Conclusion
What does it mean to rightsize big data for credit unions? It means credit unions taking
responsibility to become more analytically competent. It requires credit unions to foster a
genuine appreciation for inquiry and knowledge that can be channeled to truly understand
their members and the marketplace. It’s about credit unions working much harder to com-
pete and remain relevant by using all the information and insights available to them.
By focusing on the data part of big data, credit unions can:
→ Build an appreciation for understanding the nature and needs of members.
→ Identify an uncrowded place in the market to deftly compete against the dominant
big banks as well as the dynamic alternatives to banks.
→ Avoid the pitfalls of “me too” tactics.
→ Identify opportunities to collaborate with other credit unions on common data
needs or challenges.
→ Appreciate that regardless of how big or small the credit union is, the best time to
invest in data analytics is now, and accurate data will always be important.
→ Transform data insights into a competitive advantage over big banks that invest in
the latest in data trends for efficiency gains.
Your credit union is on a data journey whether you realize it or not. Be sure to firmly hold
the reins on that journey to ensure your members’ best interests are met and that your
credit union remains competitive and relevant in the long run. And remember, it’s not just
a technology challenge but one that affects your credit union’s culture and people.
Big data begins and ends with big insights that will drive the success of your credit
union.

TERMS FILENE RESEARCH INSTITUTE
APPENDIX
Terms
Business intelligence (BI)
→ Encompasses several activities related to data within an organization, including
data mining, querying, and reporting.
→ Examples of common BI activities for credit unions include producing performance
reports by channel (e.g., branch, call centers, online) and managing the integrity of
data sources.
Cluster analysis
→ A statistical technique used to create subgroups that have distinct characteristics.
→ It is a popular technique to derive customer segments for marketing purposes.
Conjoint analysis
→ A statistical technique used to determine how people value different attributes of a
product or service.
→ For credit unions, conjoint analysis can be conducted to determine the attributes
members most closely associate with credit unions; these attributes are rated
through a survey and responses are then analyzed.
Cube reporting
→ A set of multidimensional data that can be a subset of a larger dataset for easier
reporting.
→ A common cube report for credit unions is a mortgage productivity report that
monitors the loan portfolio over time based on dimensions such as loan amounts
outstanding, approvals, maturities, renewals, etc.
Data mart
→ A subset or slice of the data warehouse that is usually aligned to a specific busi-
ness line or team; having a subset of data that is relevant to a group of users allows
those users to analyze and run reports more independently and faster.
→ It is common for credit unions that have made investments in data warehousing
to have marketing data marts with data specific to customer profiles, transactions,
products held, etc.

Data warehouse
→ Storage of various data that resides in different parts of an organization, including
sales, marketing, finance, and human resources; this enterprisewide view of an
organization allows for more comprehensive analysis and reporting with the goal of
users from different function areas having access to one version of the data truth.
→ For credit unions, a large portion of the data warehouse contains banking system
information and customer relationship management (CRM) data; often a challenge
for credit unions is appending third-party vendor data (e.g., nonbanking services)
into a data warehouse.
Factor analysis
→ A statistical method to reduce a larger set of variables into fewer variables based on
shared characteristics; this is often a helpful technique when there are too many
variables to analyze or make sense of.
→ Helpful when credit unions creating member profiles or segments are faced with
many demographic, behavioral, and psychographic variables to make sense of.
Hadoop
→ An open-source, Java-based programming framework that supports the processing
of extremely large data sets; popularly referred to in big data discussions.
→ Because individual credit unions do not have extremely large data sets (as opposed
to, say, Google), there is little need for Hadoop; however, as part of a cooperative
network, there is an opportunity to combine common data sets across credit unions
to uncover opportunities to better serve members.
Hive
→ A data warehouse sitting on top of Hadoop that allows users to store, analyze, and
process large data sets.
→ There is currently limited use for individual credit unions (see Hadoop).
R coding
→ An open-source statistical programming language that is also popular for analyzing
and organizing data (including visually using bars, graphs, maps, etc.).
→ Coding in R has become more popular in a variety of organizations as its
community-based usage allows individuals who are not well versed in other pro-
gramming languages (such as C or Java) to learn how to code.

→ While very few credit unions use R, it can be used for predictive analytics (e.g.,
identifying the next best product to introduce to a member) and as an alternative to
more expensive options like SAS.
Relationship database
→ A common type of database that is typically organized in rows and columns; each
column may be a unique variable or attribute, and each row is a unique identifier.
→ Most credit union databases are relationship databases, including member data-
bases in which each row represents a member and each column contains a specific
piece of information about the member (e.g., age, tenure at credit union, products
held).
SQL
→ A programming language that is commonly used for relationship databases; stands
for structured query language.
→ Most credit union database analysts or BI analysts are familiar with SQL; while use
of SQL varies according to needs, at a basic level it is helpful in cleaning, format-
ting, and managing databases.
Structured data
→ This type of data is organized and found in a fixed field (i.e., rows and columns)
within a record or file such as a relationship database.
→ Almost all data from a core banking system is structured data as it can be asso-
ciated with a banking transaction or activity; almost all credit union data is
structured data.
Unstructured data
→ The opposite of structured data, it cannot be organized in a fixed field and is likely
in a text or media format (e.g., audio or video) and therefore not easily stored in a
searchable, query-based database.
→ Common unstructured data held by credit unions include e-mails, call center
recordings, and social media activities.

ENDNOTES FILENE RESEARCH INSTITUTE
Endnotes
1
McKinsey Global Institute, Big Data: The Next Frontier for Innovation, Competi-
tion, and Productivity, June 2011, www.mckinsey.com/~/media/mckinsey/
dotcom/insights%20and%20pubs/mgi/research/technology%20and%20
innovation/big%20data/mgi_big_data_full_report.ashx.
2
Walter Frick, “An Introduction to Data-Driven Decisions for Managers Who
Don’t Like Math,” Harvard Business Review, May 19, 2014, hbr.org/2014/05/
an-introduction-to-data-driven-decisions-for-managers-who-dont-like-
math.
3
Andrew McAfee and Erik Brynjolfsson, “Big Data: The Management
Revolution,” Harvard Business Review, October 2012, hbr.org/2012/10/
big-data-the-management-revolution/ar.
4
Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New
Science of Winning (Boston: Harvard Business School Press, 2007).
5
DAMA International, “DAMA Guide to the Data Management Body of
Knowledge,” accessed November 4, 2015, www.dama.org/content/body-
knowledge.
6
BlueShore Financial refers to its members as “customers” or “clients.”
7
Davenport and Harris, Competing on Analytics.

RESOURCES FILENE RESEARCH INSTITUTE
Resources
Bean, Randy. 2015. “Your Data Should Be Faster, Not Just Bigger.” Harvard
Business Review, February 4. hbr.org/2015/02/your-data-should-be-faster-
not-just-bigger.
DAMA International. 2015. “DAMA Guide to the Data Management Body of
Knowledge.” Accessed November 4. www.dama.org/content/body-
knowledge.
Davenport, Thomas H., and Jeanne G. Harris. 2007. Competing on Analytics: The
New Science of Winning. Boston: Harvard Business School Press.
Dyche, Jill, and Kimberly Nevala. n.d. “Ten mistakes to avoid when launching
your data governance program.” SAS Best Practices White Paper.
Eckerson, Wayne W. 2015. “The Secrets of Analytical Leaders: Insights from
Information Insiders.” BI Leadership Forum.
Frick, Walter. 2014. “An Introduction to Data-Driven Decisions for Managers Who
Don’t Like Math.” Harvard Business Review, May 19. hbr.org/2014/05/an-
introduction-to-data-driven-decisions-for-managers-who-dont-like-math.
Krenchel, Mikkel, and Christian Madsbjerg. 2014. “Your Big Data Is Worthless
If You Don’t Bring It into the Real World.” Wired, April 11. www.wired.
com/2014/04/your-big-data-is-worthless-if-you-dont-bring-it-into-the-real-
world.
Manyika, James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs,
Charles Roxburgh, and Angela Hung Byers. 2011. “Big Data: The Next
Frontier for Innovation, Competition, and Productivity.” McKinsey Global
Institute, May.
McAfee, Andrew, and Erik Brynjolfsson. 2012. “Big Data: The Management
Revolution.” Harvard Business Review, October. hbr.org/2012/10/big-data-
the-management-revolution/ar.
Russom, Philip. 2011. “Big Data Analytics.” TDWI Research, Q4.

LIST OF FIGURES FILENE RESEARCH INSTITUTE
List of Figures
8 FIGURE 1
BIG DATA
10 FIGURE 2
BIG DATA LANDSCAPE
12 FIGURE 3
STAGES OF ANALYTICAL MATURITY
13 FIGURE 4
14 FIGURE 5
CREDIT UNIONS ON THE BIG DATA JOURNEY
20 FIGURE 6
NLCU’S 2013 RRSP CAMPAIGN
23 FIGURE 7
BUILDING A DATA INFRASTRUCTURE: SERVUS’S PHASED APPROACH TO
DATA WAREHOUSING
26 FIGURE 8
DAMA’S DATA MANAGEMENT FRAMEWORK
29 FIGURE 9
BLUESHORE FINANCIAL’S DATA JOURNEY MILESTONES
34 FIGURE 10
DATA SPEAK FOR BUSINESS NEEDS

ABOUT THE AUTHOR FILENE RESEARCH INSTITUTE
About the Author
Linda Young
Founder, ponderpickle
Linda Young is the founder of ponderpickle, a Canadian consulting group
that works with organizations to translate consumer insights into innovative
products and design meaningful corporate strategies for long-term success.
Prior to launching ponderpickle, Linda was the director of research and prod-
ucts at Coast Capital Savings, Canada’s second largest credit union, where she
launched an award-winning product, You’re the Boss Mortgage.
Throughout her career in industries such as media, telecommunications, gam-
ing, and consumer finance, Linda has honed her skills connecting the dots of
consumer behavior to design products and corporate strategies, but the most
rewarding work has been seeing firsthand the power of people coming together
to find common ground, generate ideas, and build real business solutions. In
addition to running ponderpickle, Linda advises nonprofit organizations like
XYBOOM Intergenerational Organization and Dream to Learn, both of which
focus on bringing people together to collaborate and foster shared ideas to
make the world a better place.
Linda has an undergraduate degree in economics and finance from Simon
Fraser University and completed her post-graduate studies in international
development at the University of British Columbia.

ABOUT THE AUTHOR FILENE RESEARCH INSTITUTE

ABOUT FILENE FILENE RESEARCH INSTITUTE
About Filene
Filene Research Institute is an independent, consumer finance think and do
tank. We are dedicated to scientific and thoughtful analysis about issues affect-
ing the future of credit unions, retail banking, and cooperative finance.
Deeply embedded in the credit union tradition is an ongoing search for better
ways to understand and serve credit union members. Open inquiry, the free flow
of ideas, and debate are essential parts of the true democratic process. Since
1989, through Filene, leading scholars and thinkers have analyzed managerial
problems, public policy questions, and consumer needs for the benefit of the
credit union system. We support research, innovation, and impact that enhance
the well-being of consumers and assist credit unions and other financial cooper-
atives in adapting to rapidly changing economic, legal, and social environments.
We’re governed by an administrative board made up of credit union CEOs, the
CEOs of CUNA & Affiliates and CUNA Mutual Group, and the chairman of the
American Association of Credit Union Leagues (AACUL). Our research priorities
are determined by a national Research Council comprised of credit union CEOs
and the president/CEO of the Credit Union Executives Society.
We live by the famous words of our namesake, credit union and retail pioneer
Edward A. Filene: “Progress is the constant replacing of the best there is with
something still better.” Together, Filene and our thousands of supporters seek
progress for credit unions by challenging the status quo, thinking differently,
looking outside, asking and answering tough questions, and collaborating with
like-minded organizations.
Filene is a 501(c)(3) not-for-profit organization. Nearly 1,000 members make our
research, innovation, and impact programs possible. Learn more at filene.org.
“Progress is the constant replacing of the best there
is with something still better.”
—Edward A. Filene
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