Measuring is how you prevent a data product program from turning into busywork. If you cannot tell whether a data product is owned, used, and healthy, you will end up managing activity instead of managing value.

In this step, the goal is not a perfect metrics framework. It is a small KPI set that makes accountability visible and helps you steer investment decisions.

Measure value, not activity

Counting datasets or pipelines is not a success metric. Those numbers often go up even when adoption and impact go down.

A useful KPI answers one of these questions:

• Are our most important business use cases supported by managed data products?
• Can a user quickly find an accountable owner?
• Is the product healthy enough to rely on without manual validation?

Start with four KPI categories

You can cover most of what matters with four categories. Keep them simple and make sure they are actionable.

1) Data Product Value

This measures whether data products show up where the business cares.

A strong starting KPI is the share of top value chains and strategic use cases supported by managed data products.

If the most important use cases still depend on unmanaged datasets, you have a portfolio problem, not a tooling problem.

2) Data Product Ownership

Ownership is only real when it is easy to find and works in practice.

Track:

• Coverage: % of data products with a named owner