Lack of trust in the availability and quality of data is a recurring challenge faced by many banks today. This lack of confidence can have serious effects in virtually all aspects of a bank’s strategic, operational, and risk management functions.
Establishing a data governance center of excellence can help instill the discipline needed to provide users with confidence in the sustainability of quality and usable data. To establish such a center, banks first must recognize the critical role data plays in decision-making, analysis, operational processes, and regulatory compliance. Once that need is established, banks then must recognize the necessary components of a data governance center of excellence and map out the steps that will be necessary to implement such a center.
The Critical Role of Data in Today’s Banks
As banks and other financial services companies work to address today’s wide range of competitive, regulatory, and operational challenges, it is easy to overlook a critical issue that many of their most difficult challenges have in common: nearly all of them either are rooted in or exacerbated by data governance issues, including shortcomings in data access, data quality, or both.
Reflecting on their ongoing strategic, operational, and compliance challenges, most bank executives and managers will recognize some of the most common symptoms of data issues, such as:
- It’s not evident who’s responsible for making sure everyone is working from the most up-to-date data because of the existence of many local copies of critical information with no clear ownership.
- Many recurring reports are produced regularly, but it’s not clear how many of them actually are used.
- Meaningful or timely analysis of trends is difficult or impossible, so predictions, plans, and strategies for future events inherently are unreliable.
- Reactive measures are needed in order to respond to regulatory changes.
- Every strategic initiative first needs time and resources dedicated to data cleanup efforts.
In fact, the effects of data quality issues can be seen in virtually all aspects of a financial services company, including:
- Risk and regulatory compliance. Credit assessment and investment risks, compliance expenditures for new systems and processes, and, above all, fines and other penalties all are included in risk and regulatory compliance. Today’s most demanding, high-profile regulatory requirements – from the Bank Secrecy Act and the USA Patriot Act, to the various capital analysis and stress-testing requirements of the Dodd-Frank Wall Street Reform and Consumer Protection Act – all depend on the availability of trusted data.
- Business operations. Data problems can directly affect productivity, leading to decreased throughput, increased processing time, and added rework due to inaccuracies.
- Financial performance. In addition to increased operating costs and IT expenditures, data issues also can lead to decreased revenue, delays in cash flow, and missed opportunities.
- Customer service. Data problems can limit the ability to view the full portfolio of services a customer uses, making it difficult or impossible to respond timely to customer requests. Data errors that are visible to top customers can severely damage the organization’s reputation as well.
- Strategic and business confidence. Diminished confidence in forecasting and decision-making often are the direct consequences of data issues. Ultimately, this lack of confidence – and the additional resources that must be spent reacting to issues rather than making strategic initiatives – can diminish a bank’s competitive positioning.
Root Causes of Data Issues
There is no single cause of data quality or availability issues in any organization. Many factors contribute to the problem. In most organizations, data and operational systems grow over time and are complicated by mergers, acquisitions, and other business changes that often make legacy systems obsolete.
In addition, the pressure of compliance and operational issues often force improvements to data systems to take a back seat. When new requirements cannot be met with current systems, one-off solutions or workarounds often seem to offer a more immediate solution.
Banks also are subject to the same problems that challenge all large organizations: a tendency toward building data silos, with each line of business, each project, or each technology application relying on its own proprietary data sources and systems.
Just as there are numerous root causes for data issues, there also is no single solution. Nevertheless, sound data governance can provide some solid foundational principles for addressing these various causes.
At a high level, effective data governance can be defined as a convergence of data quality, data management, data policies, business process management, and risk management surrounding the handling of data as a business-critical asset. The objective is to establish five important attributes or conditions in the organization:
- Trust that the data collected and made available for all users is accurate, without question or hesitation, and that its flow can be controlled and audited
- Consistent access to the information needed for business stakeholders’ priorities and commitments
- A single, agreed-upon source for needed data, rather than individual versions of the truth
- An organizationwide commitment to and funding for maintaining data quality and availability across the bank, rather than for local project or department commitments
- A clear process recognized throughout the bank for how new needs for data or information will be addressed from a process and technology perspective, and a commitment to meet these at the appropriate velocity for business and regulatory needs
The Center of Excellence Concept
While most banking organizations might find the envisioned attributes of effective data governance desirable, many also point out that they are largely aspirational rather than operational. In other words, the goals are worthy, but what banks need is a practical road map that allows them to start working toward those goals. One approach that a number of banks have found promising is the establishment of a dedicated data governance center of excellence within the bank.
For the purpose of this article, a data governance center of excellence is defined as a highly organized collection of resources and organizational assets whose purpose is to demonstrate, deliver, and maintain data as a business-critical organizational asset that can be consumed for operations, business management, compliance, and decision-making.
The primary goals of a data governance center of excellence are improved efficiency and stability in maintaining and delivering business-critical data, improved decision-making through increased availability and trust in data, and more efficient and effective compliance with regulatory needs. These goals can be achieved by implementing a combination of:
- People. Trained, knowledgeable, and empowered people are placed in roles that are accountable for maintaining data quality. Most people involved with the center of excellence will function as a virtual team, with individuals carrying both project and nonproject responsibilities.
- Process. Improved processes for data management are controlled and governed more consistently and efficiently. To be effective, the center’s policies, practices, processes, and standards must be actively maintained and implemented – not just written and placed on the shelf.
- Technology. Optimized solutions take advantage of current technical capabilities. These solutions should be implemented, maintained, and updated consistently across the organization, rather than in individual locations or departments.
Five Major Components of the Center of Excellence
Building on the three basic elements of people, process, and technology, an effective center of excellence can be designed and developed around a well-defined framework comprising five main components. In most organizations, some of these components already exist at some level, but often are not receiving the attention and governance they should. The five main components are:
- Foundational elements. These include defining a charter and vision that can be monitored and measured over time, along with defining the roles and responsibilities of everyone concerned – including virtual team members in the various lines of business or in compliance functions. Although virtual team members’ primary responsibilities are outside the scope of the center of excellence, they nevertheless must help support and reinforce the center’s operation, both in general organizational terms and with assigning and performing specific projects.
- Data portfolio management. Effective data management begins with the understanding that data is a critical asset and must be managed in ways that are consistent with that view. One place to start is by compiling a complete and up-to-date data inventory and maintaining this inventory on an ongoing basis. Data management also should include a well-defined mapping of how data flows, along with oversight of data security, retention, and destruction policies.
- Implementation management. Even as the data portfolio is being defined and developed, the center also must define agreed-upon implementation methods and tools, including how individual projects are identified, approved, assigned, and managed and identifying the associated necessary project management, tracking, and change management tools. Task assignments, resource management, and resource training programs also must be developed.
- Engineering and architecture. In many ways, the process of establishing and implementing a center of excellence is comparable to the process involved in implementing a new technology solution or system. As with any such system, the center of excellence must establish and enforce basic system architecture and design standards, which include specific technology quality assurance, testing, software configuration, change management, security, and documentation standards.
- Operations and support. It is critically important for all involved to remember at all times that a data governance center of excellence is not a one-time cleanup effort. Rather, it is designed to be an ongoing way of doing business that can be continued and maintained over time. This practice requires establishing clear incident and service request processes, help desk protocols, hardware and software maintenance processes, and other support functions.
The overarching goal of all these functions is to establish proactive policies and processes that enable the center of excellence to identify potential data issues early – rather than waiting for the end users and consumers of data to notice a problem.
Establishing a center of excellence is a complex undertaking. Nevertheless, the establishment phase should not be interminable. It’s important to establish a rapid and responsive approach from the outset.
One effective way to get started is to establish a baseline that documents the current state of data in the organization. This baseline should spell out problems and challenges, of course, but it also should document what’s working well. This baseline assessment should conclude with a clear definition of the envisioned data governance center of excellence.
With the initial goals and expectations thus defined, a bank can begin to identify the necessary resources, prioritize implementation tasks, and build a practical road map that can carry the center forward in the coming months and years.
Success will not happen overnight, but the ultimate result can be a mechanism that institutionalizes the recognition that data is a critical corporate asset – an asset that can be owned, managed, and protected in order to be of genuine value to the organization.