Customer Data Protection: Deriving Value and Ownership Within a Data Management Framework

By managing customer privacy, there is significant business value derived from the resulting customer confidence and relationship:

1. Data Privacy Management enables

2. Transparency & Trust in firm

3. Customer empowerment by the Zero-Data

4. Efficient data portability to other environments

5. Standardized Customer Communication Channels

   
   

These enablers above drive competitive advantage of an organization to gain customer wallet share while also reducing the reputation & regulatory risk associated with an event of a data breach.

Further, if a customer trusts the organization by providing additional information, willingly – Which in fact is called a zero-copy data; It makes it imperative that the accuracy of that data be managed well for portability. The data office is not the owner of a privacy control function; however, the data capabilities that exist at a certain maturity cover most of the data privacy capabilities. These data capabilities are governed by the data office.

Coming from the aspect of “Accountability for Data Privacy” – is the need for a mature data ownership model in any organization. The accountability of the data controls, whether entitlements around private data or security requirements, need to be captured by the data owners for each direct and indirect customer identifying information. The Data Governance function directs, monitors, and evaluates while also enforcing accountabilities through business ownership and stewardship.

Further, the associated data owners would be updated in the data dictionaries enabled by metadata management. Once data ownership is enabled, the existing privacy classifications and security requirements for customer data can be actively managed. This also sets the context for the data owner in capturing entitlements (control requirements) across the data lifecycle, as a data controller, for any new data acquired.

As organizations integrate privacy-enhancing technologies into their data management framework, they must also address data risk management in financial services. Emerging tools like generative AI enhance the quality and usability of data, aligning seamlessly with privacy protocols and ensuring regulatory compliance.

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