• Natasha Leigh Giles and Shane Feldmann
  • Published: 18 May 2022

Accurate, complete, and well-managed data is essential for financial institutions to function effectively, especially in direct collaboration with business leadership. Data is critical for reliable and insightful decision making. It is crucial for firms to be able to react swiftly in times of volatility and uncertainty. 

These five components are key for firms to implement and maintain a flexible data governance model with engaged business ownership:

1. Understand the Data Usage and Purpose

Understand the purpose of the data and how it is being used by the firm, for example, to support decision-making, operational processes, and regulatory reporting. Data quality, availability and accuracy can have significant impacts, especially when it is used to support externally distributed information or to adhere to regulatory requirements, such as capital stress testing. Financial services firms are reliant upon their ability to efficiently analyze and process large volumes of data on a daily basis; errors can be costly, time-consuming and carry reputational risk. With the knowledge of how data is being used and transformed, relevant controls can be applied to critical data provisioning points to mitigate risks.  

2. Validate the Business Ownership

Engage the business from the start to agree on data ownership and ensure accountability across the different functional teams. Data which is generated and adapted in one team is relied upon for effective decision-making in another team or business area. Ensuring there is clarity about the roles and responsibilities for data ownership will assist with efforts to raise issues and remediate data quality concerns, including incomplete, contradictory, or unavailable data sets. Defining business ownership is also foundational for ensuring allocations of BAU and change budgets to maintain and upgrade the golden sources used by the downstream consumers. By understanding the strategic business objectives, the organization can align their data usage and data strategy to effectively support these goals.

3. Create a Centralized Data Office

Establishing an enterprise-level Central Data Office is an important foundation in building an effective Data Governance framework. This enables firms to set a standard around the basic Data Governance principles, such as accountability and ownership, stewardship, data quality, and transparency. The approach should also include defining and establishing metrics which can be used to measure and report on the success of the on-going management of data.  Implementing the data governance function as a top-down, principle-based, strategic initiative provides the credibility and discipline needed to implement the practice across the different lines of business.

4. Ensure Scalability and Flexibility

The framework needs to be able to adapt to change and be scalable for business growth. As firms change their service offerings, respond to external industry factors, or react to identified risks, the principle-based data governance model should be there to support the process. Firms need to avoid creating such a detail-specific rules base for their data governance structure. These solutions can quickly become outdated and unscalable, with rules ultimately becoming irrelevant, and the principles lacking maintenance. By implementing a modular data lineage approach to support a business-oriented operating model, the framework should support the extension of different types of data using consistent rules to remain relevant even during times of change.  

5. Agree on How to Deliver Ongoing Success

How to measure the value of an effective data governance model needs to be agreed upon with the business owners from the outset and used to ensure the return on investment is focused on criticality rather than detail. Utilize principle-based targets to focus priorities on management of critical issues, such as avoiding risks relating to decision-making, regulatory adherence, or quality of externally communicated data. Define a scale for measuring impacts from raised issues or enhancements. Use this scale consistently across the lines of business and prepare periodical management reporting for transparency. Maintaining a data governance methodology is a transformative process where business buy-in relies upon the Central Data Office being able to evidence the value generated by the effort. This includes being able to demonstrate how issues have been prioritized and resolved, risks have been mitigated and lessons learnt have been utilized to further refine the framework. It takes teamwork to manage data effectively from generation/origination/digitization through to reporting and storage. If all the business areas are in collaboration, then a lot more can be achieved.

In conclusion, a data governance framework is not just a snapshot with a single, siloed milestone for achievement. Centralized data governance is an ongoing, evolving, team-based methodology which needs to be able to react to change and remain flexible to support refinement.  

Peter Mohr contributed to this article.

Click here to find out more about Capco’s approach to data governance from a regulatory perspective.