When implementing a data classification program, why is it important to avoid too much granularity?

Too much granularity in a data classification scheme means creating a large number of classification levels or categories, which requires significant resources to implement, maintain, train staff on, and consistently apply. The operational burden of managing highly granular classifications often outweighs the security benefits, making the program unsustainable and ineffective.

Data classification applies to the data itself regardless of whether it resides on hardware or software; granularity does not inherently create a distinction between hardware and software applicability.

Assigning data ownership is a separate governance concern related to accountability structures, not directly impacted by the number of classification levels in the scheme.

A program being perceived as having value is a benefit, not a drawback; excessive granularity would more likely cause the program to be perceived as burdensome or impractical, making this choice incorrect in framing granularity as a problem.