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In this series of Data Management Principles, the Capital Markets and Regulatory Data Infrastructure Advisory Team will highlight leading practices and emerging trends. Part I begins with an introduction to, and overview of, data management. Future installments of the series will deconstruct the governance, lifecycle, management and testing of data.

Data Management's Rise in Prominence

Following the 2008 financial crisis, organizations have been responding to ever-increasing regulations and guidance. As such, the influx of data requirements and expectations from regulators have driven the financial services industry to embark on a data journey: one that establishes effective data governance, controls and testing programs.

As companies began to dive deeper into data, they discovered a fragmented data management approach across lines of business and realized more well-defined roles and accountability were necessary to ensure data assets are formally managed and can ultimately be trusted.

The benefits of data management are wide ranging. Data management promotes accountability, drives a more accurate end-product, enables better prioritization and decision making, reduces risk and enhances regulatory compliance. However, with those benefits also come challenges, the first of which is complex data infrastructure. Many financial institutions today are the product of mergers or acquisitions and still have redundant, and potentially competing, legacy systems of record, which create complexity.

Another challenge is that the buy-in and level of effort necessary to be successful with data management is extensive – there needs to be a top-down message around the importance of data management and commitment to push data management efforts and solutions forward. Adoption across the organization should be well planned and measured, but doing so requires a cultural shift in how organizations think about data.

 

Data Management Principles Infographic: Benefits to a Regulatory Data Infrastructure / -Promotes accountability and consistency while improving report quality (e.g., accuracy, timeliness, completeness) and dependability. -Helps identify areas for process and data efficiencies. -Enables better prioritization and decision-making. -Reduces risk and improves regulatory compliance. -Drives awareness and the importance of a strong data culture. ___ Common Challenges / -Inefficiencies and complexities related to data infrastructure and architecture. -Inadequate documentation and unclear data vision and scope. -Organizational buy-in and level of effort required. -Cultural awareness and accountability. -Resourcing, level of effort and associated required costs associated.

 

Throughout this series of Data Management Principles, our goal is to help your organization take advantage of the benefits of data management while also embracing and tackling the challenges as they arise. Stay tuned for our next installment on the Data Management Lifecycle.

 

How We Can Help

Data Lineage and Data Discovery: Document how the data is acquired, created, moved and changed throughout the reporting infrastructure.

Creation and Implementation of Testing Strategies: Develop detailed data validation testing plan based on a review of critical data elements and existing processes.

Synthetization of Results and Findings: Create relevant summaries (heat maps/scorecards) to highlight areas for escalation, attention and remediation.

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