filenes sizing_big_data

REPORT

Rightsizing Big Data

for Credit UnionsLinda 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 YoungFounder, 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.

Analyticalcompetitor

Analyticalcompany

Analyticalaspirations

Localizedanalytics

Analyticallyimpaired

Where most credit unionsinterviewed 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 ApproachThis 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 DataBig 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 semistructured 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.

TerabytesRecords

TransactionsTables, files

StructuredUnstructured

Semistructured

BatchNear timeReal timeStreams

VarietyVelocity

Volume

3Vs ofbig 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 purposerelationship database

1990s

Exabytes

Petabytes

Terabytes

Gigabytes

Megabytes

Structured Semistructured Unstructured

Credit unions

Analytic database/tools

HadoopWalmart

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 performance metrics, 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.


Analyticalcompany


Localizedanalytics


Where most credit unionsinterviewed 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 ActionTo 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.


Analyticalcompany


Localizedanalytics


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 SegmentationIt 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 ActionOnce 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 FocusIn 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 LearnedOver 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 PowerBig 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 FirstMoving 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 GovernanceAnother 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

Datadevelopment

Dataoperations

management

Dataarchitecture

management

Datawarehousing

and BImanagement

Dataquality

management

Datasecurity

management

Reference andmaster data

management

Master datamanagement

Document andcontent

management

Datagovernance

Technology Activities

Practices andtechniques

Deliverables

Rules andresponsibilities

Organizationand 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 customers6 were and how best to serve them.

2000 2015

Four years of effort to leverage datainsights to identify target segmentsand develop differentiated businessstrategy in areas of service and branchdesign including implementation of acustomer-centric data warehouse

Chris Catliff appointed CEO

Enterprisewide CRM launched—one of the first Canadian credit unions Credit union conducts first

member segmentation (usinginternal and external data)to identify mass affluent andemerging wealthy segments

Predictive analytics applied toidentify product uptakeopportunities

Launches enterprisedata warehouse

Data-drivenreassignment of members to specificadvisors

Data insightsuncover servicetraining opportunities,resulting in bettersegment needsalignment

Develops and launches new banking system(2009)

Further investmentsin BI for variousupgrades includingsingle cube reporting

Customer experiencemanagement (CEM)introduced, replacing CRM

2004 2005 2006 2007 2009 2010 2011 2012 2013

AUA: $3.5B

Members: 40,000AUA: $1.775B

Members: 40,000AUA: $800M

Members: 40,000

2014} } }

BlueShore Financialpremium brand debuts

Vision 2020 introducesdigital strategy

Work begins on proprietaryalgorithms (“Bluegorithms”),essential ingredient incustomized client engagementscoring model

Revisedsegmentationapproach tobe moreopportunityfocused

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 MeaningCatliff 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 UnionAs 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

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 HandAs 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 warehousesExtracts, transform, load tools

Desktop query/reportingOnline analytical processing

Web querying/reportingBusiness intelligence suites

Dashboards and scorecardsData visualization

Packaged analytic applicationsData integration suites

Operational BIMobile BI

Cloud BIPredictive analytics

Text analyticsHadoop

Hive/pig

Driving the businessAnalytics

Improving the businessPerformancemanagement

Using the dataBusiness

intelligence

Getting the dataData 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%20innovation/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.

www.mckinsey.com/~/media/mckinsey/dotcom/insights%20and%20pubs/mgi/research/technology%20and%20innovation/big%20data/mgi_big_data_full_report.ashx



http://hbr.org/2014/05/an-introduction-to-data-driven-decisions-for-managers-who-dont-like-math



http://hbr.org/2012/10/big-data-the-management-revolution/ar


http://www.dama.org/content/body-knowledge

http://www.dama.org/content/body-knowledge

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.

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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 YoungFounder, 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.

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Publication #381 (11/15)