modernizing data quality & governance: unlocking performance & reducing risk

20162016

Modernizing Data Quality & GovernanceUnlocking Performance & Reducing Risk

A WBR Digital Whitepaper Presented in Conjunction with Informatica

Spring 2016

2 Modernizing Data Quality & Governance

Executive SummaryEvaluating 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


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.


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 3On 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 4How 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


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 5Where does your organization struggle most with data quality?

slide 6How 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


slide 2 Comparison DataData 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 9Which 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


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 10Are 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 11Please 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


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


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 DataReported 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


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 16Who manages data quality issues in your organization today?

slide 17Does your organization currently own an o�-the-shelf data quality tool to manage data quality?

slide 18What 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 knowO

wn

off-t

he-sh

elf d

ata

qual

ity

man

agem

ent t

ool?

% of data quality management done by humans

slide 4 Comparison DataThe impact of o�-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.


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 qualityIt 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.


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

http://bit.ly/1pwIXHe

bit.ly/1pwIXHe


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:

www.informatica.com

https://linkedin.com/company/informatica

https://twitter.com/Informatica

https://facebook.com/InformaticaLLC


About WBR

WBR is the world’s most dynamic large-scale conference company and part of the PLS group, one of the world’s leading providers of strategic business intelligence with 16 offices worldwide. Every year, over 10,000 senior executives from Fortune 1,000 companies attend over 100 of our annual conferences – a true “Who’s Who” of today’s corporate world. With a deep commitment to building lasting relationships and delivering quality content and networking, WBR inspires your career.

In addition to our industry leading conferences, our professional services marketing division, WBR Digital, connects solution providers to their target audiences with year-round online branding and engagement lead generation campaigns. We are a team of content specialists, marketers, and advisors with a passion for powerful marketing. We believe in demand generation with a creative twist. We believe in the power of content to engage audiences. And we believe in campaigns that deliver results.

WBR Digital produces quality content and digital campaigns for high-performing businesses across a wide range of industries. See how you can put our team of content specialists and marketers to work for your business.

To learn more about WBR Digital’s full suite of content, lead generation, and digital branding services, click the button below!

INTERESTED IN THIS CONTENT?

LEARN MORE

http://digital.wbresearch.com/

modernizing data quality & governance: unlocking performance & reducing risk

Economy & Finance