That’s one of the tricky part because you jump from theory to practice.
You can define a lot on paper, but in you need to convert this into a tangible approach.

So I would say it’s mainly driven by your data governance maturity and what are the use cases behind your implementation.

What we often see is that the company starts with the minimum of data governance in its data catalog. I mean the minimum is data owners, data stewardship, a definition or a description, and eventually internal classification. So is my data asset public, confidential and secret?
This is a good start and then they will, in a nutritive way, bring more and more attributes. For instance, GDPR attributes, data quality attributes, expertise, or role attributes, which in fact make sense to them to their organization, to the value they want to bring into their company.