When data concepts are defined consistently, engineers reason about data safely.

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.