Data governance breaks at scale because “quality,” “lineage,” and “ownership” mean different things.

Why this matters

Data governance fails because teams define “quality” and “lineage” differently.

What this looks like in practice

• Data lineage is trackable from systems, humans, and AI pipelines consistently.
• Data quality thresholds mean the same thing whether applied by automation or humans.
• Metadata is reusable because schemas describe the same concepts consistently.

How teams use it

• implementing data governance across clouds, on-premises, and third-party stores
• automating discovery and classification without rebuilding business logic
• proving provenance for audit, debugging, and compliance systematically

Data quality is a team property—it requires agreement on meaning.