Transforming Financial Institutions Through Data Governance

Transcription

1 2015 Transforming Financial Institutions Through Data Governance A WBR Digital Whitepaper Presented in Conjunction with Informatica Corp. April 2015

2 Executive Summary Although data management practices have been around almost since the inception of 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 organizational alignment to facilitate better data governance, and how they are tracking the success of those programs. Table of Contents Executive Summary...2 Top Challenges...3 Key Opportunities...3 Research Findings Building a Best-In-Class Data Governance Program..4 Quality and Security: The Two Tenets of Good Data...7 Aligning the Organization Around Strong Data Delivering Results and Measuring Success Appendices FIMA Informatica WBR & WBR Digital What Is Data Governance? The development and implementation of policies, procedures, and best practices to effectively manage the availability, quality, and security of an organization s information assets. 2 Transforming Financial Institutions Through Data Governance

3 Top Challenges 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. Key Opportunities 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. 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. 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. 3 Transforming Financial Institutions Through Data Governance

4 Research Findings Building a Best-In-Class Data Governance Program Implementing a best-in-class data governance framework requires investment in the right people, processes, and technologies. In order to develop a truly enterprisewide 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. 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 4 Transforming Financial Institutions Through Data Governance

5 Flexibility was the most commonly cited component of enterprise data governance solutions, with respondents also indicating that they prioritize transparency and comprehensiveness. 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. 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 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. Rank the top investments drivers for your data governance program. Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 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 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 Transforming Financial Institutions Through Data Governance

6 Data quality is far and away the most important data management competency, while data privacy and retention were listed as relatively minor concerns. 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 74% Master data management Analytics/Reporting Metadata management/data lineage Reference data management Data security Data privacy Data retention/archiving 5% 3% 12% Most respondents are facing challenges managing metadata and ensuring data quality, indicating that many organizations are facing larger struggles managing their information lifecycles. 33% 33% 32% 30% 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 Which of following do you find most challenging relative to your data governance program? Metadata management/data lineage 52% Data quality 46% Analytics/Reporting 29% Reference data management Master data management 25% 24% Data retention/archiving Data security Data privacy 5% 9% 10% 6 Transforming Financial Institutions Through Data Governance

7 Quality and Security: The Two Tenets of Good Data 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. 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 purposebuilt 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 7 Transforming Financial Institutions Through Data Governance

8 Most organizations see their data quality as average; very few are firmly on either extreme of the spectrum. 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 Most financial institutions are confident in the security of their data. How would you rate your data security? Scale: 1 indicates insecure; 5 indicates highly secure % 1% 3% 12% 18% 30% 48% 39% 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. 4 40% - Ursula Cottone, Chief Data Officer, KeyBank 5 9% 8 Transforming Financial Institutions Through Data Governance

9 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. 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 9 Transforming Financial Institutions Through Data Governance

10 Aligning the Organization Around Strong Data 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. 10 Transforming Financial Institutions Through Data Governance

11 Based on their experiences implementing data governance programs, respondents indicated that organizational components, such as executive sponsorship and crossfunctional 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. If you could begin data governance over again, of the options below, what would your top priority be based on lessons learned? 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 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 Have you successfully built a business case for data governance supported by your business leaders? 64% Yes 36% No Despite the many challenges facing data governance programs, a decisive majority (92%) of respondents still consider these programs to be a worthwhile investment. 11 Transforming Financial Institutions Through Data Governance

12 Delivering Results and Measuring Success 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. 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 12 Transforming Financial Institutions Through Data Governance

13 Organizational effectiveness is the most popular data governance metric, followed by risk reduction and compliance. What is the most effective measurement of the success of your data governance program? 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 5% Improved audit results 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 How would you rank the maturity of your data governance efforts? Unaware Minimal to no organizational focus 3% Initial Primarily ad-hoc, with grassroots efforts led by a few individuals 28% Repeatable IT-driven and focused on IT efficiencies and agility 11% Defined Primarily IT-driven, with some business participation supporting a single phased data management project (such as BI, DQ, or MDM) 17% Managed Business-sponsored and core part of multi-year information management program 33% Optimized Top executive-level sponsorship for data governance as a self-sustaining business function, not simply tied to a technology initiative 8% 13 Transforming Financial Institutions Through Data Governance

14 Thirty percent of financial institutions have yet to deliver tangible results from their data governance programs. In what timeframe did you deliver tangible results from your data governance program? Less than 12 months months months Greater than 24 months Have not yet delivered results from a data governance program 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. 9% 17% 21% 23% 30% 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 14 Transforming Financial Institutions Through Data Governance

15 Appendices Appendix A: Methodology 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 decisionmakers and executives with responsibility for their firms data management, IT architecture, and data risk and compliance strategies. Responses were collected in February Appendix B: Related Research The State of Risk and Compliance in Data Management, WBR Digital, May Transforming Financial Institutions Through Data Governance

16 About FIMA Now in its 11th year, Financial Information Management (FIMA) brings together more than 300 leading reference data management professionals from over 124 companies to collaborate over 3 intensive days. Each year FIMA hosts sessions and discussions led by top reference data management leaders, all covering topics that are of fundamental importance to enterprise-wide data management initiatives, including data governance, regulations, cost controlling, data quality, and more. Want to hear more from the leaders on the cutting edge of data management? See what they will be discussing at FIMA! LEARN MORE About Informatica Informatica Corporation (Nasdaq: INFA) is the world s number one independent provider of data integration software. Organizations around the world rely on Informatica to realize their information potential and drive top business imperatives. Worldwide, over 5,000 enterprises depend on Informatica to fully leverage their information assets from devices to mobile to social to big data residing on-premise, in the Cloud and across social networks. FIMA is the quintessential conference for data management professionals. It is the place where buy-side and sellside organizations learn about timely and relevant issues facing the data industry and see first-hand solutions from leading global vendors. - Ludwig A. D Angelo, JP Morgan Chase o Seaport Blvd. Redwood City, CA Transforming Financial Institutions Through Data Governance

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