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Not a SIFI? Data Governance Still Worth the Pursuit

By   /  October 22, 2015  /  No Comments

Data-Governance-Financial-Articleby Jennifer Zaino

Executives at Systematically Important Financial Institutions (SIFIs) must have Data Management and Governance on their minds a lot, charged as they are with meeting enhanced regulatory reporting requirements to reduce the likelihood of their failure, and the consequences of that across the entire economic system.

They have had to dig in deep to begin to address the tremendous problems of risk data being incomplete, inadequate or out of date; of its quality being affected by incompatible definitions and duplications; of its inefficient storage and inconsistent formatting that can impact aggregation and reconciliation; and of accountability failures when data ownership is unclear.

Why would any smaller financial services organization – free of the SIFI designation – want to tackle some of the most difficult issues around Data Management and Governance if they don’t have to meet all the same regulatory or compliance obligations? One answer is that doing so can improve their operational processes and also help facilitate growth, explained Kelli O’Neal, founder and CEO of Enterprise Information Management consulting firm First San Francisco Partners at the recent Data Governance in Financial Services 2015 Conference, produced by DATAVERSITY® and DebTech International.

Consider that as an organizing framework, Data Governance ensures that technology requirements and capabilities come together with business requirements and capabilities in mind to make data available, usable, secure, consistent, and of high integrity, she noted. “Governance,” O’Neal said, “provides the business context for Enterprise Data Management, creating a platform for it to become business-focused and business-engaged.”

The Business Power of Governance

Governance can happen at one or multiple levels, depending on factors such as an organization’s size, from local line-of-business through to enterprise-wide initiatives. Whatever the setting, however, it serves as a wrapper around Enterprise Data Management, incorporating components including data strategy, data standards and modeling, communications and metrics, and technology. Also included are data policies and processes that can support, for example, change management to drive business and IT impact and readiness.

Enterprise Data Management, extending from Master Data Management to Metadata Management to data retention, privacy and security, rolls up to the bigger picture of Enterprise Information Management frameworks. These provide a holistic view of data in order to manage it as a corporate asset.

Said O’Neal, “The first reason that Data Governance is important for operational success, and not just regulatory compliance, is that it ensures Enterprise Data Management and Enterprise Information Management are successful.” It does so by ensuring that the Enterprise Data Management plan supports the business’ goals and expectations, including bringing the right people to the table to make decisions about the cross-functional use of data across the organization.

“There are a lot of similarities behind Data Governance practices to address the needs of a regulated environment as well as an operationally-driven environment,” she said. A SIFI may need to craft its Data Governance efforts and EIM frameworks with the immediate end in mind of swiftly understanding customer activities across the business for risk assessment and compliance purposes. In those cases, regulations to a great extent are providing funding for resources and action, though ultimately the work done there may foster operational initiatives, too.

But a smaller institution may want to understand the same customer activities primarily for the opportunity to improve customer service practices. “The initiatives may be called different things but many times they are looking at the same data,” she said. “They are synergistic.” To that end, the smaller financial services company may start with a small project to optimize and rationalize customer data and risk metrics, which can expose information that enables it to not only explore customer risk to its own benefit, but also that facilitates new client on-boarding processes. “They can use that optimization so that it gets closer to [impacting] the front line,” she said.

As a firm reduces obsolete, trivial or duplicative data, it also can reduce the personnel and support time that has been dedicated to managing the systems associated with it – and those costs, too. And, as the date becomes more accurate, the most data-intensive parts of a firm – marketing and sales – can significantly improve their own operational processes. “This can have a direct line to the effectiveness of a marketing campaign and the costs associated with it,” she explains.

Additionally, having a good understanding of customer risks and other aspects of the business by putting in-house data under strong governance can play an important role down the line in facilitating other business building efforts, such as mergers and acquisitions. These are big, data-intensive exercises, she said, “There’s a huge value that governance can provide by having your own data understood and available by the time you buy or are bought by another enterprise,” O’Neal noted.

Even financial institutions that aren’t SIFIs aren’t immune from every regulatory or compliance requirement. When data is well-governed and well-managed so that it is well-understood, each time a new regulation is released to which the institution must adhere, the better prepared it will be to do so.

There will be less need to shift the attention of key people away from their regular activities to focus on the appropriate way to react to the regulation, for instance. “The better data is understood and governed, the less time required to go through forensic analysis around data to respond to the regulatory action,” she said. And, the cost to adhere to new regulations should go down over time, as well, she explained.

Indeed, financial institutions that can’t get a good handle on Data Governance may find themselves losing out when it comes to hiring and retaining top talent that want to spend their time analyzing data – not constantly remediating it. It’s especially the case that banks and other financial institutions experience a lot of turnover in knowledge worker roles when their expectation is that they’ll be doing analysis and they’re not, O’Neal said. “Free them up to do the job they were hired to do,” she advised.

How to Build Up Data Governance

How does a smaller financial institution get started on Data Governance? The construction of operating models is important, of course. These are graphical representations of how the organization will operate in terms of decisions, roles and responsibilities, accountability and the like.

O’Neal herself has seen Data Governance offices of as few as one and a half staff members. At smaller organizations that don’t have free resources immediately available, a leader who has a data issue may serve half his or her time as a Data Governance Officer, with a project coordinator providing support, she mentioned by way of example.

Even organizations that start small need to think about how they will scale the initiative and its resources over time. For instance, a Data Governance Working Group may start tight with a focus on a specific line of business, which becomes the beginning point to extend Data Governance further into the organization. At some point, that one group may not be able to represent the whole enterprise, so other groups may spring up to support different divisions under what becomes the unifying layer of the Enterprise Data Governance Office.

“In summary,” she said, “Data Governance is critical for operational success, not just regulatory compliance.” Even though it’s hard to pin specific revenue contributions to the practice, she says, it’s clear that “it helps increase productivity and opportunity, including for employees to do more than remediate data, and it decreases cost.”

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

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