Regulatory requirements for enterprise risk reporting are critical considerations for banks, especially in the current political climate. So, as a result, many organizations that I work with are capitalizing on their improvements in data quality principally to achieve better risk management and reduced financial exposure.
Once business leaders understand how data quality and data management affect the bottom line of their business, data quality suddenly becomes a top priority. That’s what we’re seeing on an increasingly regular basis.
Improved regulatory reporting turns out to be a terrific by-product of improved data quality. For the business, that improvement leads to new data quality initiatives. Through more in-depth data quality, managers are able to demonstrate knowledge about the data that goes into their risk rating models. As a result, it becomes possible to accurately report on data quality standards to internal and external auditors.
So, as I see it, enterprise data management is critical to financial services firms and their efforts to better operate and comply with new levels of regulatory oversight.
If data quality is managed correctly, financial services firms will be competitively positioned to bring new product offerings to the market and provide a better customer experience. Technology that fosters data quality and governance allows firms a far greater ability to access more timely data. Creating enterprise level risk management in compliance with regulatory reporting requirements makes the data quality challenge even more complex for these financial service firms.
Given the current regulatory oversight that banking firms have to manage under, best practices of data governance require that the implementation team understand the business policies, and collaborate with technology teams to specify, identify, validate, and manage data. As a result, the responsibility for demonstrating provably correct accuracy shifts from IT to the business users.
Technology teams must continue to promote best practices for systems, security, and platforms as the business begins to work with IT to improve best practices for information accuracy, information quality and data integrity. I refer to this concept of business best practices and data governance as ‘data veracity.’ Data veracity captures the business’ responsibility for data accuracy and it transcends the traditional standard metrics for IT data quality. Data veracity to me means better regulatory reporting. What can data veracity mean to you and your business?