Transforming Financial Institutions Through Data Governance

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Transforming Financial

Institutions Through

Data Governance

A WBR Digital Whitepaper Presented in

Conjunction with Informatica Corp.

April 2015

2015

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the computer, data governance has only become a central strategic priority for financial institutions over the past few years. The catalysts for this relatively novel emphasis on data governance are rooted in two of the financial industry’s most fundamental values: minimizing risk and enhancing value from business intelligence. First-rate data governance serves both of these interests, mitigating the regulatory and financial risks associated with data mismanagement while also opening up new opportunities for organizations to leverage data to make more informed business decisions. Put simply, effectively managed data can be an organization’s greatest asset; this is the importance of good data governance.

Despite the growing importance of data governance, many financial institutions continue to struggle with their programs, due in part to the fact that the

implementation of an effective data governance program can be transformational. For instance, since data has become embedded within nearly every department and business unit, proper data governance requires much more rigorous cross-functional alignment. Often this means breaking down silos to create more collaborative workflows, a transformational move that requires strong executive sponsorship. Furthermore, robust support infrastructures and enabling technologies have become necessary to synthesize, manage, and monitor the disparate data sets housed across the business. Those technologies are also responsible for helping to capture and give visibility into the immense amounts of new data that organizations are acquiring on a daily basis.

With a repeatable framework in place that encompasses all three of these elements – people, processes, and technology – financial institutions can better understand how data moves through their organizations. Although the investments necessary to implement a strong data governance framework may be considerable, the benefits are profound. Improving data management on an institutional level not only safeguards sensitive corporate information and helps satisfy regulations and guidelines, but it allows those institutions to proactively identify and cultivate new business opportunities. These are key business outcomes made possible by better governance.

This paper will evaluate how financial institutions are addressing the challenges and opportunities of data governance. It will demonstrate which investments and competencies organizations are prioritizing as they build their own data governance programs, while analyzing how organizations perceive their own data security and quality. Finally, this paper will look at how financial institutions are improving their

The development and

implementation of

policies, procedures,

and best practices to

effectively manage

the availability,

quality, and security

of an organization’s

information assets.

Executive Summary

What Is Data

Governance?

2015

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Aligning the organization for

better data governance

Good data governance often entails

organizational changes. Foremost among those changes is the elimination of departmental and technological silos that isolate staff and stifle collaboration.

Selling Business Leaders

Most organizations understand that there is value in better data governance, although many are still struggling to understand how, exactly, it can improve their business. Data governance cannot improve without executive sponsorship, which requires a strong business case.

Measuring the business impact

of data governance

Nearly all financial institutions have explicit business goals they want their data governance program to support. However, many have been unable to measure or quantify the impact governance has had on those goals.

Reducing regulatory and

financial risk through data

governance

Many organizations invest in data governance in order to minimize the regulatory and financial risks associated with data mismanagement.

Data governance to enhance

business intelligence

Data governance means quality, accessible data, which financial institutions can leverage to make more informed business decisions. In other words, data governance can be a driver of better business outcomes.

Aligning people, processes,

and technology

Great data governance frameworks can drastically improve cross-functional collaboration and create new efficiencies across the

organization.

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Implementing a best-in-class data governance framework requires investment in the right people, processes, and technologies. In order to develop a truly enterprise-wide governance solution, an organization must break down the departmental and technological silos that isolate governance responsibilities and data sets within different departments and business units. Improved cross-functional collaboration and the establishment of standardized processes and workflows become possible when those silos are eliminated. When those personnel improvements and processes are combined with enabling technologies that enhance data visibility and security, the core of a best-in-class data governance program has taken shape.

As data governance has become a top strategic priority, financial institutions have increasingly invested in enterprise governance programs that support their own business goals. While those goals can be diverse, the two outputs that organizations are most consistently pursuing are support for risk and compliance activities and the improvement of business intelligence. In order to better serve those goals through data governance, financial institutions understand the need to ensure that their data is high quality. In fact, respondents in this study listed data quality as the most relevant competency to their governance frameworks. In comparison, data security was a small consideration.

Data lineage has long been a core challenge for financial institutions, in part due to the lack of automated tools with which to efficiently track metadata. For many businesses, it is still very much a manual process to track and manage metadata, identifying the systems of origin and systems of record that data sets are passing through during their lifecycles. That lack of automation can be crippling, because as many data architects understand, if metadata management requires so much manual effort, it is unlikely that all of that data will be tracked in a consistent and reproducible way. The issue is exacerbated by the massive amounts of new data that are entering these systems every day. In short, there is a major need for a more natural way to capture and manage metadata.

Research Findings

Building a Best-In-Class

Data Governance Program

“Data governance

is about putting

into place a

standardized,

repeatable

framework that

incorporates

people, process,

and technology.

Once a business

has put that

framework into

place, data

governance

just becomes a

reiteration of that

framework.”

- Josh Lee, Director, Global

Financial Services Marketing, Informatica

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How would you characterize your ideal enterprise data governance solution?

Interestingly, “framework” is missing from the responses, despite the fact that repeatable, standardized frameworks are essential to ensure better data management – especially given that there are still many manual components to tracking and managing data.

Reducing costs Modernize business Enterprise data standardization Grow revenue Acquire and retain customers Improve data security/data privacy Increase business agility and efficiency Improve business decisions Support governance, risk and compliance

Rank the top investments drivers for your data governance program.

Flexibility was the most commonly cited component of enterprise

data governance solutions, with respondents also indicating that they

prioritize transparency and comprehensiveness.

Support governance, risk, and compliance were the top investment

drivers for data governance programs, followed by improving business

decisions and the enhancement of business agility and efficiency.

“There is no doubt

that financial

firms are feeling

the impact of

regulations that

are enforcing a

level of maturity

to which most

firms have never

before aspired.”

- Becky Osbourn, Information

Architect, Wells Fargo

Rank 5 Rank 4 Rank 3 Rank 2 Rank 1 8% 22% 34% 17% 19% 8% 16% 34% 22% 20% 5% 10% 21% 37% 27% 8% 17% 26% 22% 27% 9% 11% 29% 23% 28% 5% 19% 18% 30% 28% 2% 12% 19% 34% 23% 2% 2% 22% 34% 40% 4% 1% 14% 34% 47%

Least Important Most Important

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What are the most relevant competencies related to your data governance program?

Quality of data is defined here as trusted and fit for purpose. Data quality

Master data management Analytics/Reporting Metadata management/data lineage Reference data management Data security Data privacy Data retention/archiving

Metadata management/data lineage Data quality Analytics/Reporting Reference data management Master data management Data retention/archiving Data security Data privacy

Which of following do you find most challenging relative to your data governance program?

Data quality is far and away the most important data

management competency, while data privacy and retention

were listed as relatively minor concerns.

Most respondents are facing challenges managing metadata

and ensuring data quality, indicating that many organizations

are facing larger struggles managing their information

lifecycles.

“If an organization

is doing a good

job of managing

their information

lifecycle, it is in

fact governing

its data and

improving data

quality.”

- Becky Osbourn, Information Architect, Wells Fargo 74% 52% 33% 46% 33% 29% 32% 25% 30% 24% 12% 10% 5% 9% 3% 5%

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Quality and security are without question two of the most important features of an enterprise’s data; without them, data governance frameworks become hollow and wholly ineffectual. The quality of a data set indicates how well that data fits the business goal that it is serving, which means that quality must be defined within each specific context. In practice, ensuring that data is complete, clean, and accessible means integrating quality assurance procedures into workflows and applying those rules throughout the data’s lifecycle.

While critical to a business’s ability to obtain accurate and actionable insights, data quality can be a challenge for financial firms that are collecting new data at a nearly exponential rate. In the present survey, the majority of respondents perceive their data quality to be mediocre, with very few stating that their data is either extremely good or especially poor. While this may indicate that most financial institutions are not deeply concerned with their data quality, there is clearly room for improvement. Data security is also of monumental importance, especially in the context of the financial services industry. Since these businesses are based on personal and corporate financial information, they are natural targets for data theft. That risk has given rise to a myriad of national and international regulations aimed at preventing damaging data leaks. For instance, the Sarbanes-Oxley Act ensures that financial institutions themselves are accessing and using data appropriately, while the Gramm-Leach-Bliley Act mandated that these institutions enact policies to safeguard data security and integrity and more closely govern the collection, disclosure, and protection of Private Client Information. Finally, new regulations are constantly cropping up to help govern aspects like metadata tracking and legal entity identifiers, thereby increasing the regulatory burden for financial services firms. In order to show compliance with these requirements, financial institutions must also be able to effectively audit their data throughout its lifespan.

Regulatory requirements are not the only drivers of data security. The public relations and brand perception repercussions of financial data leaks can be dire, and can ultimately lead to lost revenue. Likely due to these hazards, survey respondents as a whole feel more confident about the security of their data.

Quality and Security:

The Two Tenets of Good Data

“Data quality can have

different definitions

for different people.

Data may be good

in one system, but as

it’s integrated, copied,

and manipulated by

other systems and

groups, it can change.

That’s not necessarily

a problem, it just

means it’s not in a

format that group or

system needs it to be

in. Therefore, defining

business policies and

definitions of what

data is needed most

and why is a good

starting point. Then

the next step is to

have the processes

supported by

purpose-built technology to

automate the quality

steps, monitor for

exceptions, and inform

data consumers when

things do change.”

- Peter Ku, Senior Director, Global Industry & Audience Marketing

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How would you rate the quality of your enterprise data?

Scale: 1 indicates poor quality; 5 indicates very high quality. Quality of data is defined here as trusted and fit for purpose.

Scale: 1 indicates insecure; 5 indicates highly secure. 1 2 3 4 5 1 2 3 4 5

How would you rate your data security?

Most organizations see their data quality as average; very

few are firmly on either extreme of the spectrum.

Most financial institutions are confident in the security of

their data.

“Data quality

depends on the

category of data

we’re discussing.

For instance,

financial data is

closely scrutinized

by regulators,

so that data is

typically of a

higher quality. But

if we’re looking

at the data

for particular

customer

segments or

business units, that

data may not

be of the same

quality, because

that has not

been the focus

historically.”

- Ursula Cottone, Chief Data

Officer, KeyBank 3% 0% 18% 12% 48% 39% 30% 40% 1% 9%

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Which term best describes how critical data security is to your overall data governance strategy?

45% Absolutely critical

40% Important but not integrated

12% Tangentially related

4% Unrelated and not integrated

Nearly half of all respondents agreed that data security is

absolutely critical to their overall data governance strategy,

further underscoring the importance of data security.

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Perhaps the biggest obstacles to better data governance are organizational alignment and executive sponsorship. As noted earlier, the establishment of a good data governance framework is often transformational, pushing business leaders to decide whether or not they believe the investments in new technologies, changes to organizational structures, and modified workflows are worthwhile. In this context, corporate leaders need to understand how these investments will improve their businesses. For this reason, just under two-thirds of survey respondents have successfully built data governance business cases that have executive support. Unfortunately, demonstrating projected returns on investment is not always

straightforward. Those calculations involve a variety of business goals, some of which are easier to quantify than others. For example, while many businesses may be able to determine the cost-savings associated with a more streamlined governance plan, goals such as organizational effectiveness and risk reduction may be harder to measure. Similarly, implementing a robust data governance framework often requires a cultural shift. Roles and responsibilities can change as organizations align cross-functionally and encourage more inter-departmental collaboration. It becomes extremely important for staff to understand what is changing in their roles, why those changes are occurring, and how they ultimately help the business. In this sense, organizations need buy-in at every level of the business, not just at the very top. Luckily, there are many process improvement tools available to organizations making these changes. In fact, traditional process improvement frameworks such as Six Sigma and Agile can be very helpful in the context of corporate alignment around data governance.

Organizational alignment and executive sponsorship are clearly important elements in any data governance program, which is why respondents indicated that these would be the main areas of focus if they had the opportunity to begin data governance again. However, despite the structural and political challenges posed, an overwhelming majority – 92% – of respondents agreed that their organizations consider data governance worthy of ongoing investment.

Aligning the Organization

Around Strong Data

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If you could begin data governance over again, of the options below, what would your top priority be based on lessons learned?

Have you successfully built a business case for data governance supported by your business leaders?

27% Gain stronger executive sponsorship and buy-in 20% Have stronger cross-functional

alignment

19% Define a more clear-cut process and plan

18% Stronger focus on support architecture and enabling technologies

10% Greater focus on roles and responsibilities

6% Better leverage outside professional contacts and/or industry best practices

64% Yes 36% No

Based on their experiences implementing data governance

programs, respondents indicated that organizational

components, such as executive sponsorship and

cross-functional alignment, are critical to the success of the

program. In fact, respondents noted that those would be

their top priorities if they were to begin data governance

over again.

Nearly two-thirds of respondents have obtained executive

sponsorship of their data governance programs, further

underlining the importance of top-down alignment.

“Organizations

need to build their

governance around

data stewardship and

data quality while

focusing on ways

to solve data issues

for the benefit of the

enterprise. Use data

governance to solve

specific pain points in

the business, and then

hold those results up as

examples of why data

governance policies

are needed. If you can

show how governance

is supporting a

business outcome, that

goes a long way

in helping to secure

executive buy in.”

- Ursula Cottone, Chief Data Officer, KeyBank

Despite the many

challenges facing

data governance

programs, a decisive

majority

(92%)

of

respondents still

consider these

programs to be

a worthwhile

investment.

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There is a wide array of performance indicators that financial institutions track in an effort to measure their data governance programs. Those measurements range from hard metrics (such as cost reduction) to softer metrics (including organizational communication), which are inherently more difficult to quantify. For many institutions, the most effective data governance measurement has been organizational

effectiveness, which reflects the overall business outcomes and efficiencies created through data governance. Risk reduction and compliance are also top priorities and given that they are such sensitive issues, they are often the primary drivers behind governance. Compliance is often the easiest goal to measure, because better data governance can mean reduced regulatory oversight and the avoidance of fines, both of which have a very tangible impact on the enterprise. As an extreme example, compliance with the Sarbanes-Oxley Act means avoiding criminal penalties for business leaders, including the CFO.

Measuring data governance programs with both hard and soft metrics, however, can be a very complex process. That complexity, along with the lack of automated reporting for some processes, has made it difficult for some businesses to tie tangible results back to their data governance programs. In fact, 30% of respondents in this survey admitted that they have not delivered tangible results from their data governance programs. While that number may seem alarming, it is very possible that it is not the governance programs that are struggling to deliver value, but rather the businesses that are struggling to quantify that value. This underlines the importance of precisely defining goals and metrics up front, enabling staff to effectively execute and report on them.

On the other hand, nearly half of the financial institutions surveyed have delivered tangible value through data governance in fewer than 18 months. This indicates that building out a good data governance program is not always a long-term goal; rather, it can often deliver great business value in a relatively short timeframe.

Delivering Results and

Measuring Success

“The scope of

what needs to

get addressed

however involves

too many things

at once. Data

governance is

a journey and

needs to start

small, build off

of the success,

refine where

needed, then

expand. Often

expectations are

too great from the

get go and when

the ocean fails

to boil, people

question why they

invested in data

governance.”

- Peter Ku, Senior Director,

Global Industry & Audience Marketing

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What is the most effective measurement of the success of your data governance program?

Unaware

Minimal to no organizational focus

Initial

Primarily ad-hoc, with grassroots efforts led by a few individuals

Repeatable

IT-driven and focused on IT efficiencies and agility

Defined

Primarily IT-driven, with some business participation supporting a single phased data management project (such as BI, DQ, or MDM)

Managed

Business-sponsored and core part of multi-year information management program

Optimized

Top executive-level sponsorship for data governance as a self-sustaining business function, not simply tied to a technology initiative

How would you rank the maturity of your data governance efforts?

Organizational effectiveness is the most popular data

governance metric, followed by risk reduction and

compliance.

A third of organizations characterize their data governance

programs as managed, meaning they are business-sponsored

initiatives that form the core of a multi-year information

management program.

“With better

data, by default

we will be in a

better position

to increase

revenue. Revenue

does not spring

straight from data

governance, but

governance is

foundational to it.”

- Ursula Cottone, Chief Data

Officer, KeyBank 3% 28% 11% 17% 33% 8% 43% Organizational effectiveness 17% Reduced risk

12% Compliance (reduction in fines, etc.) 6% Organization communication 6% Customer understanding 6% Better IT Solutions Delivery 5% Cost reduction

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In what timeframe did you deliver tangible results from your data governance program?

Thirty percent of financial institutions have yet to deliver

tangible results from their data governance programs.

“Some business

are struggling,

because they

have a tendency

to try and take

everything on

at once. On the

other side of the

coin, just dealing

with a few major

pain points can

sound myopic,

but really you’re

dealing with more

realistic activities.

Governance

can become too

monumental of a

task if you take

too broad of an

approach.”

- Becky Osbourn, Information

Architect, Wells Fargo Although 30% of respondents admit they have not delivered tangible results from data

governance, that may have more to do with their ability to measure their programs than it does with the overall programmatic success. After all, many performance indicators, such as organizational effectiveness and communication, are difficult to quantify.

Less than 12 months

12-18 months

19-24 months

Greater than 24 months Have not yet delivered results from a data governance program

21%

23%

17%

9%

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For this report, WBR Digital conducted digital surveys of 92 American-based data management professionals from medium and large banking institutions, insurance companies, and asset management groups. Survey participants included decision-makers and executives with responsibility for their firms’ data management, IT architecture, and data risk and compliance strategies. Responses were collected in February 2015.

“The State of Risk and Compliance in Data Management“, WBR Digital, May 2014

Appendices

Appendix A: Methodology

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Want to hear more from the leaders on the cutting edge of data management? See what they will be discussing at FIMA!

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About FIMA

About Informatica

“FIMA is the

quintessential

conference

for data

management

professionals. It is

the place where

buy-side and

sell-side organizations

learn about timely

and relevant

issues facing the

data industry and

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solutions from

leading global

vendors.”

- Ludwig A. D’Angelo, JP Morgan Chase o

2015

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Transforming Financial Institutions Through Data Governance