Building Successful and Sustainable Data Governance in Financial Services
Today’s financial services organizations are swimming in data. That represents a huge opportunity, especially as advanced analytics, AI, and machine learning gain momentum. It also raises some challenges. GDPR, CCPA, and similar regulations are driving the need for more rigorous approaches to compliance. Data security and access are of paramount concern, particularly as cybersecurity threats continue to grow. Higher volumes of data translate to more opportunities for inaccurate, incomplete, inconsistent, or duplicate information.
How can financial services organizations leverage the power of AI/ML and innovative analytics while ensuring accurate, trustworthy results? Data governance provides the answer.
That said, building a successful data governance program can be a challenge. It requires buy-in from all levels of the business. That calls for a clear value-oriented message that speaks to all three tiers of the organizational hierarchy. A successful data governance strategy must build engagement, set the right priorities, and proactively motivate stakeholders throughout the organization to adopt sound financial data governance practices.
Articulating the Value of Data Governance
A successful financial data governance program begins by clearly articulating the business value to be achieved. Understanding what datasets impact your KPI’s and objectives is critical to understanding the true value. Additionally, identifying and sharing metrics that directly tie data governance and data quality to better risk mitigation and analytics shines an even greater light on the underlying value.
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Fueling Enterprise Data Governance with Data Quality
To learn more about how your financial services organization can truly be data-driven with data governance, read our eBook.
A data governance framework answers some fundamental questions for stakeholders, including who owns that data, what “good” data looks like, and where users can go to find a trustworthy source of truth. The ability of business users to find that truth to drive more confident business decision is what ultimately drives value in any organization.
Governance is important for any company that wants to make data-driven decisions, regardless of size. In financial services, an abundance of data makes it possible to add value in risk assessment, fraud detection, marketing communications, product management, investment decisions, regulatory compliance, and much more.
As the volume of datasets grows, complexity naturally increases. Users may need to consult with multiple individuals or collaborate across departmental lines to get what they need. Data quality and fitness for purpose may not always be clearly understood or communicated.
As data plays a more prominent role in driving value throughout an organization, the stakes get higher. Data quality KPIs, for example, become essential in ensuring that decisions are being driven by the right information and that the data is suitable for the purposes to which it is being applied.
To motivate users, it’s important to tie data governance to their individual objectives. If a key goal in marketing is to increase the number of college-age consumers using a bank’s credit card services, for example, it can be helpful to map out the data-driven elements of the initiative that aims to produce those results.
How will the bank’s data be used to identify and reach the right consumers? How will data enrichment come into play? How will the marketing department track response rates and adjust their strategy accordingly? What kinds of risk and compliance issues might be involved?
While governing the data that answer these questions will build engagement with the Marketing team, it’s important to understand the point of view of other users of the data. The C-Suite might be leveraging that same data for understanding growth KPI’s or analyzing how the demographics impact their ESG reporting. While the data science teams may be more concerned with how fresh and complete the data is to understand how it impacts their models. The point is that to build engagement, you must understand the goals and objectives of different teams across your organization – and speak their language to make it relevant to them.
By tying data governance directly to the business outcomes each level is aiming to achieve, you can engage those stakeholders and build momentum and purpose for your data governance program as a whole. Repeat this exercise with other stakeholders throughout the organization, and the value of governance will soon become exponential.
Govern the Right Data
Many organizations make the mistake of trying to govern everything. That’s arguably not achievable at all, but in any case, it detracts from the core mission of delivering business value by empowering your organization’s users to achieve their specific objectives with greater confidence than they have done in the past.
Neha Wattas, Head of Strategy and Insights, Innovation & Corporate Development at JP Morgan, offers a powerful example of how data governance helps drive innovation. She led an initiative to roll out an AI-powered virtual assistant for corporate treasurers. That project required a deep dive into the language and terminology of that domain, followed by a mapping process to tie that to the company’s datasets. By focusing on highly relevant governance tasks associated with that project, Wattas was able to roll out a highly successful innovation tool without incident.
To build a successful financial data governance program, begin with business objectives that drive value. Identify the KPIs that will support the desired outcomes, then determine which data assets will impact those KPIs.
A truly successful data governance program requires an ongoing commitment to evangelization within the organization. Linking data governance to specific value-creating activities is an important first step. To achieve lasting success, it’s important to tell the story of those successes in a compelling way that stakeholders will understand.
One strategy for achieving this is to develop cross-functional teams to collaborate on data governance for their mutual benefit. If you’re working with customer data, for example, there will be multiple stakeholders within your organization who have an interest in that. When you draw talent from a variety of different departments, you will develop a team of “data governance champions” who can tell your story for you. They have front-line relationships that can make sure your value-proof points are communicated far and wide. That fosters engagement and collaboration across a broad base of users. Every organization has a different culture and tapping into that culture to drive greater adoption is critical.
As the world’s leader in data integrity, Precisely helps companies of all sizes leverage the power of data for trustworthy decisions that enhance business value. To learn more about how your financial services organization can truly be data-driven with data governance, read our eBook Fueling Data Governance with Data Quality.