FIMA's latest whitepaper evaluates how financial services companies are managing the challenges posed by data quality management. By analyzing which data types and data characteristics businesses are struggling with, it uncovers the true business costs associated with data quality. It will also gauge how data governance programs are maturing and how they are being measured. Finally, it assesses how data is being managed within financial institutions. Key findings include: Data quality has never been more important for financial institutions, but most of those companies feel their data is only mediocre: Quality data serves a myriad of central business goals, from risk reduction to increased productivity. Unfortunately, many businesses continue to struggle with data quality, despite the fact that four-fifths of them have it ranked as a top priority. The top two business functions impacted by poor data quality are regulatory compliance and risk management: Because these concerns tend to be the most important drivers of data quality, many financial institutions see data governance as a “must-do,” rather than a ROI-boosting activity. Furthermore, the vast majority of financial services companies can not quantify the business cost of poor data quality. Financial institutions vary greatly in the maturity of their data governance programs: Data governance cannot be overlooked – unsurprisingly, businesses with formalized data governance programs reported that their data was higher quality than most other groups. Data quality management requires close collaboration between business and IT leaders: That collaboration already exists for 83% of respondents in this study, who say that IT and business leaders work together to manage data quality in their organizations. However, the tools these businesses use to manage their data are not all equal, leading to an uneven allocation of resources.

1. 20162016
Modernizing
Data Quality
& Governance
Unlocking Performance
& Reducing Risk
A WBR Digital Whitepaper Presented
in Conjunction with Informatica
Spring 2016

2. 2 Modernizing Data Quality & Governance
Executive Summary
Evaluating how financial services companies are managing the
challenges posed by data quality management.
Table of Contents
Ever since the advent of the computer, financial services companies have been
faced with increasingly significant data quality challenges. As the decades
passed and computers became further ensconced in the everyday operations
of financial services companies, those data quality challenges became even
more pronounced and their effects more wide-ranging. Now, good data quality
is absolutely essential to help organizations minimize risk while better informing
business decisions. Unfortunately for many organizations, data quality tools that
were purchased for IT to fix data issues have not keep pace with the ascendance
of data governance programs that require business and IT to co-manage the quality
of data as a business asset.
Financial services companies accounted for many early adopters of data quality
tools back in the 1990’s and 2000’s However, those first generation tools were
very IT-oriented: they were designed to be used by IT personnel to fix data issues
during development projects. Those tools evolved over time, enabling businesses
to profile data errors, define their own data quality rules, and effectively monitor
and manage exceptions. Gradually, the emergence of data governance programs
shifted responsibility for managing data quality from the IT department to business
leaders. In this context, business users became responsible for the definition of
data quality rules, while IT focused more on the execution of those rules across the
enterprise architecture.
Although this new model asked business users to become much more involved in
managing data quality, existing IT-oriented data quality tools were not designed
for self service data quality management by business users. Given the ongoing
regulatory and revenue pressures across all sectors of financial services, managing
data quality required business users to actively participate in this process.
BCBS239 for example calls out specific principles that require well defined
processes and responsibilities related to managing data quality by the business.
Hence the need arose for solutions that allow data owners, stewards, analysts,
and IT developers to manage the quality of data more effectively with each other.
Many financial services companies are still in the process of implementing data
quality processes that are rigorous and open enough to support the information
needs of today.
This paper will evaluate how financial services companies are managing the
challenges posed by data quality management. By analyzing which data types
and data characteristics businesses are struggling with, we will uncover the
true business costs associated with data quality. We will also gauge how data
governance programs are maturing and how they are being measured. Finally,
we will asses how data is being managed within financial institutions.
Executive Summary.............2
Key Findings.......................3
Research Findings
Unlocking Better Performance
Through
Data Quality....................4
Facing Down the Business
Cost of Data Quality..........6
Data Governance –
Maturity and
Measurement...................8
How Data
is Managed...................10
Recommendations To
Improve Data Quality
Management.................... 11
Appendices...................... 12
Research Partner:
Informatica....................... 13
WBR Digital..................... 14

3. 3 Modernizing Data Quality & Governance
Key Findings
Data quality has
never been more
important for financial
institutions, but most
of those companies
feel their data is only
mediocre
Financial institutions
vary greatly in the
maturity of their data
governance programs
The top two business
functions impacted by
poor data quality are
regulatory compliance
and risk management
Data quality
management requires
close collaboration
between business and
IT leaders
Quality data serves a
myriad of central business
goals, from risk reduction
to increased productivity.
Unfortunately, many
businesses continue to
struggle with data quality,
despite the fact that
four-fifths of them have it
ranked as a top priority.
Data governance
cannot be overlooked –
unsurprisingly, businesses
with formalized data
governance programs
reported that their data
was higher quality than
most other groups.
Because these concerns
tend to be the most
important drivers of data
quality, many financial
institutions see data
governance as a “must-do,”
rather than a ROI-boosting
activity. Furthermore, the
vast majority of financial
services companies can not
quantify the business cost
of poor data quality.
That collaboration
already exists for 83%
of respondents in this
study, who say that IT
and business leaders
work together to manage
data quality in their
organizations. However,
the tools these businesses
use to manage their data
are not all equal, leading
to an uneven allocation of
resources.

4. 4 Modernizing Data Quality & Governance
Research Findings
Although many financial institutions have been working to improve the quality of
their data for more than two decades, never has data quality been more important
than it is today. Data is at the center of critical regulations, including Dodd Frank,
CCAR, BCBS 239, Solvency II, and MifiD II, all of which require financial services
firms to provide accurate and complete views of their risk and capital positions.
Quality data also helps to reduce risk and improve underwriting processes. This
enables organizations to more accurately price policies while minimizing buy-backs
on defective loans, among other benefits. Furthermore, better quality data can
improve sales and marketing productivity by unlocking relevant client relationship
information and creating better-informed marketing campaigns. Finally, quality data
can even help reduce costs associated with client onboarding.
Unlocking Better Performance
Through Data Quality
How would you rank the quality of your enterprise data?
1
poor quality
2 3
high quality
4 5
very high quality
slide 3
On a scale of 1 to 5, how would you rank the quality of your enterprise data?
4%
10%
60%
25%
1%
1
poor
quality
2 3
high
quality
4 5
very high
quality
Comparison Data
How organizations rated
their data quality in 2015
slide 4
How organizations rated their data quality in 2015
3%
18%
48%
30%
1%
The majority of respondents believe their enterprise data
is of average quality, with very few of the mind that their
data is extremely high quality

5. 5 Modernizing Data Quality & Governance
Despite the clear importance of data quality, quality remains a persistent issue for
many financial institutions. This problem is compounded by the fact that financial
firms are collecting new data at a nearly exponential rate. As a result, a strong
majority of the institutions surveyed believe that their data is of mediocre quality,
with only 1% asserting that their data is extremely high quality. It is perhaps even
more troubling that respondents do not seem to have improved their data quality
since this survey was taken in 2015; rather, these institutions have trended even
further toward the mean. Given these struggles, it is no surprise that 82% of leaders
surveyed see data quality as a vital issue for their businesses to address over the
next 12 months.
While survey respondents almost universally agree that they can (and must) take
steps to improve their data quality, they do not all share common data struggles.
About a third of organizations are struggling to standardize their data quality rules
across all systems, making standardization the most common problem. Still, about
a quarter of respondents feel they need to improve their ability to identify errors,
while the remainder are split between identifying data rules and monitoring for
exceptions. In practice, managing all of these components of data quality means
integrating quality assurance procedures into workflows and applying those rules
throughout the data’s lifecycle.
Standardizing data
quality rules is the
most-cited data quality
struggle, although about
a quarter of respondents
are also having difficulty
identifying data errors
Data quality is a top
priority for four-fifths of
businesses
Where does your organization struggle most with data quality?
How important is it to address your organization’s data quality issues in the next 12 months?
slide 5
Where does your organization struggle most with data quality?
slide 6
How important is it to address your organization’s data quality issues in the next 12
months?
39% Standardize a common set of data quality rules
across all systems
24% Identifying data errors in your source systems
19% Defining data quality rules to fix discovered data
issues
18% Monitor for data errors and exceptions
82% Very important
17% Somewhat important
1% Not important

6. 6 Modernizing Data Quality & Governance
slide 2 Comparison Data
Data quality struggles based on biggest data concern
Despite the investments financial services companies have made to bolster their
data governance programs, organizations both large and small continue to face
data quality issues within their business systems and applications. Quality issues
impact a vast array of data types, including sales and marketing data (such as
client contact information, account relationships, and transactions) and risk and
compliance data (such as reference data, LEI and counterparty data, and capital
positions data). None of these data types are immune to quality problems, as
research from Informatica has shown that business users can spend up to 30-50%
of their time fixing data quality errors in the reports they draw from their business
applications. The impact that has on efficiency is substantial.
Facing Down the Business
Cost of Data Quality
Data consistency is the top concern, followed by data
accuracy – meanwhile most businesses have eliminated
duplicate records
Which of the following data quality characteristics does your organization most struggle with?
slide 9
Which of the following data quality characteristics does your organization most
struggle with?
36% Consistency – Is the data available being
defined differently?
28% Accuracy – Is the data correct?
18% Integrity – Is all the data there and referenced?
10% Completeness – Is data missing?
7% Conformity – Is the data in a standard format?
1% Duplicates – Are records repeated?
Data quality struggles based
on biggest data concern
Integrity
Consistency
Conformity
Completeness
Accuracy
Risk management
Regulatory compliance
Sales
Marketing
Customer Service
Finance
23%
20%
45%
35%
27%
26%
24%
9%
13%
27%
35%
31%
14%
13%
14%
13%
10%
10%
7% 9%
14%
17%
18%
20%
15% 11%
What types of data are most important to your organization’s success?
Reference
Customer
Products
Counterparties
Vendor
Employee
64%
63%
53%
50%
27%
5%
Reference and customer
data are the most vital to
organizational success
A Deeper Look

7. 7 Modernizing Data Quality & Governance
Poor quality data can lead to real business costs. For example, low-quality sales and
marketing data can impact marketers’ understanding of a customer’s relationship with
their firm, undermining their ability to push relevant products and ultimately lowering
marketing efficacy. For data types associated with risk and compliance, the stakes
are even higher. In those cases, errors on regulatory reports can lead to unnecessary
audits, while incorrect counterparty and credit risk assessments can end in higher
capital reserve requirements. Given the business costs associated with poor-quality
sales and compliance data, it should come as not surprise that reference and
customer data were cited as the data types most crucial to organizational success.
This all underlines the reality that data quality is not an end in and of itself, but rather
it has a very real impact on business performance.
While data touches almost every business function, from sales to finance,
respondents in this study reported that regulatory compliance and risk management
were the top two business functions impacted by poor data quality. Because
regulatory concerns tend to be the most important drivers of data quality, many
financial institutions see data governance as a “must-do,” rather than a ROI-
boosting activity. Although many financial institutions can identify the business
functions affected by data quality, the vast majority are unable to put a number to
that impact. In fact, only 7% of the organizations surveyed can quantify the real
cost of their data quality issues. Clearly, many financial institutions must take steps
to better understand the depth of their data quality issues.
Are you able to quantify the cost to your business of your existing data quality issues?
slide 10
Are you able to quantify the cost to your business of your existing data quality issues?
8% Yes
51% No
41% Not Sure
Please rank the business functions most impacted by poor data quality
Regulatory
Compliance
Risk
Management
Finance
Customer Service
Marketing
Sales
slide 11
Please rank the business functions most impacted by poor data quality
73%
68%
40%
34%
29%
23%
The perceived importance
of addressing data quality
issues based on the business
functions impacted
Risk management
Regulatory compliance
Sales
Marketing
Customer Service
Finance
Very
important
Somewhat
important
26%
21%
28%
25%
10%
6%
11%
12%
12%
15%
13%
21%
Data quality has the greatest importance for regulatory
compliance and risk management
Only 8% of respondents
can quantify the cost to
their business brought
on by data quality
issues
A Deeper Look

8. 8 Modernizing Data Quality & Governance
In order to ensure that their data is high quality, financial institutions have made
significant investments in establishing a formal data governance program and
organization to define the policies, procedures, and roles that allow them to effectively
manage the availability, quality, and consistency of their information assets. Financial
institutions now recognize data governance as a strategic priority that helps them to
server larger business goals, including the support of risk and compliance activities
and the improvement of data-driven business intelligence. However, a best-in-class
data governance program can be transformational, requiring better cross-functional
collaboration and alignment, modified data flow policies, and the deployment of
enabling technologies that can synthesize, manage, and monitor the disparate data
sets housed across a business. In short, data governance not only safeguards sensitive
information and helps satisfy regulatory guidelines, but it also enables financial
institutions to proactively identify and cultivate new business opportunities.
Any data governance program must be monitored and measured, and there
is a wide array of performance indicators that financial institutions can use to
understand how successful their governance programs are. Those measurements
range from hard metrics (such as cost reduction) to softer metrics (including
organizational communication). Based on the present research, the most effective
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.
Data Governance –
Maturity and Measurement
Organizational effectiveness, reduced risk, and
compliance are the most common measures of the
effectiveness of a data governance program
What is the most effective measurement of the success of your data governance program?
Organizational
Effectiveness
Reduced
Risk
Compliance
Cost reduction
Improved
Audit Results
Better IT
Solution Delivery
Organizational
Communication
Customer
Understanding
62%
60%
53%
41%
35%
30%
24%
12%
The top 3 measurements
remained the same as in
2015

9. 9 Modernizing Data Quality & Governance
As with any governance plan, the institutions that took part in this study vary in the
maturity of their data governance programs. While only 5% of respondents have no
data governance framework in place, more than a third – 37% – are still developing
their policies, processes, and roles. On the other end of the maturity spectrum, 31%
of respondents have solidified enterprise-wide adoption of their data governance
programs. Unsurprisingly, those respondents with formalized data governance
programs generally reported that their data was higher quality than most other
groups. However, those organizations with no data governance systems in place
reported the highest level of confidence in their data quality among all groups.
How mature is your data governance program?
37% Policies, Processes, and Roles Being Developed
31% Policies, Processes, and Roles Defined – Enterprise
Adoption
27% Policies, Processes, and Roles Defined – In Pilot
Within a Few Departments
5% Nothing In Place
Respondents are fairly evenly distributed across the data
governance maturity spectrum, although it bears noting
that just under a third have achieved enterprise adoption
of their data governance program
Reported data quality
based on data governance
maturity level
1 3 2 4 5
Enterprise
Adoption
Piloting
In development
Nothing
in place
slide 5 Comparison Data
Reported data quality based on data governance maturity level
4%
10%
25%
10% 14%
54%
76%
25%
59%
42%
14%
50%
49% 3%
A Deeper Look

10. 10 Modernizing Data Quality & Governance
Data quality management is an important responsibility, one that touches many
levels of an organization and functions. Over time, most financial institutions have
come to understand that data stewardship cannot be isolated to one department or
another. Rather, it requires intense collaboration between the information technology
professionals who maintain data governance systems and the business leaders
who will eventually use that data to help control risk and unlock new insights. That
collaboration already exists for 83% of respondents in this study, who say that IT and
business leaders work together to manage data quality in their organizations.
However, businesses are not all using the same tools to manage their data. This is
shown by the fact that the number of organizations currently using an off-the-shelf
data quality tool is split. Beyond the implications for data quality, the presence or
absence of such a tool reverberates throughout the organization, shaping its very
structure. In fact, organizations that do not have off-the-shelf data quality tools must
devote a greater percentage of their human resources to data quality management
tasks. That resource allocation can have a detrimental effect on the institution’s
ability to devote personnel to other important projects.
How Data is Managed
slide 16
Who manages data quality issues in your organization today?
slide 17
Does your organization currently own an off-the-shelf data quality tool to manage
data quality?
slide 18
What percentage of your data quality management do human beings conduct? (E.g.
either in IT or line of business
In more than 80% of
organizations, IT and
business leaders both
play a role in managing
data quality
Respondents were split on
ownership of off-the-shelf data
quality management tools
A third of respondents rely on
people for 60-80% of their
data quality management
Who manages data
quality issues in your
organization today?
83% IT and Business (Data Stewards)
13% Business Only
3% IT
1% Not Sure
42% Yes
42% No
14% Don’t
Know
12% 0-­20%
21% 20-­40%
22% 40-­60%
33% 60-­80%
12% 80-­100%
Does your organization currently own an
off-the-shelf data quality tool to manage
data quality?
What percentage of your data quality
management do human beings conduct?
(E.g. either in IT or line of business)
0-­20%
20-­40%
40-­60%
60-­80%
80-­100%
The impact of off-the-shelf tools on data quality management responsibilities
Yes
No
Don’t
know
Ownoff-the-shelfdataquality
managementtool?
% of data quality management done by humans
slide 4 Comparison Data
The impact of off-the-shelf tools on data quality management responsibilities
16%
9%
8%
13%
28%
26%
25%
6%
41%
36%
58%
6%
21%
8%
Those businesses without
off-the-shelf data quality
tools tend to have a greater
percentage of their data
quality management
performed by people,
rather than computers.

11. 11 Modernizing Data Quality & Governance
Recommendations
To Improve Data
Quality Management
Identify and quantify
the true business
impact of poor
data quality
Ensure that data
governance policies
are well-defined and
serve the core needs
of the business
Clearly define roles
and responsibilities
Invest in technologies
that support a more
collaborative and
comprehensive
management of
data quality
It begins with
measurement. Financial
services firms must
understand the depth of
their data quality issues –
which business functions
are being impacted and
to what degree – before
they can address the root
causes.
A well-defined and
documented data
governance program is
critical to data quality.
Data governance requires
better cross-functional
collaboration, modified
data flow policies, and the
deployment of enabling
technologies, all of which
must be aligned with data
quality challenges and
organizational goals.
Business users and IT
personnel across the
organization must be
extremely clear about
the delegation of
responsibilities related
to data profiling, rules
management, remediation,
and oversight. Processes
for each of those functions
must also be clearly
defined.
Technologies must evolve
with the needs of the
business. In order for
enterprise-wide data quality
management to truly take
root, the business must first
have the right technologies
in place.

12. 12 Modernizing Data Quality & Governance
Appendices
WBR Digital conducted online surveys of 78 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 March 2016.
Transforming Financial Institutions Through Data Governance“, WBR Digital,
March 2015
Appendix A: Methodology
Appendix B: Related Research
CLICK HERE TO READ NOW

13. 13 Modernizing Data Quality & Governance
Research Partner
A special thank you to our research partner, Informatica, whose vision and
expertise helped make this report possible.
Informatica is a leading independent software provider focused on delivering
transformative innovation for the future of all things data. Organizations around
the world rely on Informatica to realize their information potential and drive
top business imperatives. More than 5,800 enterprises and over 800 financial
institutions including 27 out of the top 30 global banks and 45 out of the top 50
insurance companies depend on Informatica to fully leverage their information
assets to satisfying industry regulations, reduce risk, improve customer success, and
improve business efficiency.
For more information, call +1 650-385-5000 (1-800-653-3871 in the U.S.),
or visit www.informatica.com.
Connect with Informatica at:

14. 14 Modernizing Data Quality & Governance
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