Financial services companies invariably make use of a wide range of information, often stored in different systems. As a result, most companies in the industry operate within the context of a highly complex data environment.
Financial services data flow between multiple complex systems. In many organizations, mainframes often provide the backbone. Over the past two decades, transaction processing has been extended to incorporate online banking. Later, mobile applications were added to the mix, offering even greater customer convenience. Credit card and debit card transactions further complicate the picture, requiring real-time data exchange with outside parties.
To make the most of all that information, financial services companies must prioritize data governance. Governance drives a coherent 360° view of each customer, providing a deep understanding of every relationship and a comprehensive view of client interactions across those complex systems. It provides the fuel for accurate and powerful insights driven by advanced business analytics.
Governance helps an organization move away from data silos – where information is generally disjointed, offering little in the way of strategic insights – toward a unified model in which data is readily available to decision-makers. Moreover, a good data governance program ensures that information is consistent, accurate, and timely, while also maintaining legal and regulatory compliance. In short, governance drives trust.
Many data governance initiatives get off to a bad start and eventually go awry. The typical scenario begins with a management edict calling for data governance and the creation of a dedicated team to carry out that mission. Very often, priorities are driven by that team, without a clear view of the business value governance is intended to create. They get business users involved, but without a firm grounding in clearly defined business goals, the initiative loses steam. In the end, management deprioritizes the project and budgetary commitments fall by the wayside.
To create net-new business value, there are four “must-have” elements for a successful data governance program in financial services:
1. Identify Clear Business Objectives
As author and business guru Stephen R. Covey observed, it’s best to “begin with the end in mind.” When data governance programs focus first on meaningful business goals, they create between 2x and 7x the ROI as compared to typical governance initiatives. A business-first approach can accelerate roll-out by as much as 40% and is 75% more likely to lead to additional resource commitments from upper management.
Let’s consider a common example from the financial services industry. Imagine that one of your company’s goals is to improve the personalization of your products and services to fit the unique needs of each customer. To do this, you need a trusted view of each customer. How can data governance support this objective?
Other goals for financial services data governance might include accurate and timely assessment of credit risk, faster time-to-value for business analytics, or risk mitigation in regulatory compliance and reporting.
For each objective, identify the key stakeholders and expected outcomes. How will you measure success? Establish clear metrics. In the next phase of the process, you will map your data to the success factors and metrics that align with your desired outcomes.
2. Link Data to Your Business Goals
Next, link specific data elements to the business goals you defined in step 1. If the goal is to improve the personalization of your products and services, for example, your data governance goals will be driven by a need for a trustworthy 360° view of each customer available to everyone across your organization.
That means having a data catalog, clear data quality rules with approval workflows, and a view of data lineage for your customer data. Specific financial services data, in this case, would include customer master data (name, address, branch location, and so on), as well as transactional elements that shed light on important factors such as a client’s credit history, product mix, and lifetime customer value.
3. Engage Strategic, Operational, and Tactical Teams
Next, develop a strategy for bridging the gap between business and IT value metrics. This approach should span all levels of your organization. This generally breaks down into three tiers: strategic personnel responsible for visionary transformation (that is, the executive level), operational (middle-tier personnel responsible for growing the business), and tactical (front-line employees who handle data migrations, data engineering, and analytics systems, for example).
Each of these levels approaches organizational success from a different perspective. The C-suite, for example, will look at metrics that map to overall business impact and ROI. These include things like customer sentiment, process enablement, and key financial results. Operational personnel typically look for performance improvements related to data quality and response times, whereas people at the tactical level of the business may focus on more granular metrics around data movement and performance and effectiveness in data governance.
4. Deploy Capabilities that Serve as Painkillers and Vitamins
The value metrics established in step 3 should tie together to tell a complete story that resonates across the entire organization. In other words, effective performance with respect to tactical metrics will lead to greater effectiveness in the operational aspects of data governance, which will, in turn, lead to positive business outcomes at the strategic level.
Getting that to work consistently requires that data governance be leveraged to solve immediate problems, but in ways that also lead to benefits for other teams across the organization. To accomplish this, look for opportunities to deploy data governance as both a “painkiller” and a “vitamin.” In other words, look first to solve problems, but as you do so, look for opportunities to add bonus value to the business.
What might this look like if you were to apply it to the previous example around the personalization of products and services? The “painkillers” might include things like a centralized collection of customer data for sales and marketing, or a common set of data quality metrics to indicate whether data is accurate, consistent, and trusted. The corresponding “vitamins” could be data profiling that adds depth and context to customer data, or quality monitoring to trigger notifications when data quality falls outside of acceptable boundaries.
By following these four steps, you can lay a foundation for successful financial services data governance that leads to meaningful business value. To learn more about data governance in the financial services industry, watch our free on-demand webinar, Four Must-Haves for Data Governance Success in Financial Services.