Give shared data a shared meaning.
Owns conceptual, logical, and physical models; naming standards, schema contracts, semantics, lineage, and change impact.
The context behind the work.
You align the way the business describes its world with the way systems represent it. The hard part is often agreeing the grain and meaning of a record. Shared models reduce interpretation work only when owners and consumers accept the definitions.
Customer means different things to different teams
Sales counts accounts while support counts contacts. Document both grains, identify the relationships, and agree which definition belongs in each metric before asking engineering to combine the datasets.
A shared entity needs a new field
A team proposes a status field used by several applications. Clarify the allowed values, meaning, and lifecycle; inspect dependent models; then publish the approved definition and compatibility expectations.
A practical path from task to outcome.
Define a customer model that engineering and analytics can use without conflicting definitions.
- 01
Discover governed context
Inspect existing datasets, glossary terms, and downstream consumers before proposing a new model.
- 02
Model shared meaning
Agree the business grain, identifiers, relationships, and definitions with the data owner.
- 03
Standardize structures
Document mappings and validation rules. Review the impact of changed fields with engineering and analytics.
- 04
Apply governance
Publish the agreed model and connect it to the catalog entries and implementation work that use it.
An agreed model, field definitions, mappings, and an impact review.
Less repeated effort. More useful work.
Explore the habits and platform connections that can make this role easier, more consistent, and easier to collaborate with.
Different meanings for the same field
Keep definitions and ownership beside the data assets that use them.
Discovering broken consumers after a schema change
Review lineage and downstream dependencies before approving a revised model.
Redesigning familiar domain structures
Reuse approved definitions and mappings as the starting point for new work.
Measure your own improvement
Choose a baseline before you begin. Review these signals with your team; results depend on your data, process, and implementation.
- Time to agree a shared data definition
- Downstream changes caused by incompatible schemas
Build confidence with a first task.
Define a customer model that engineering and analytics can use without conflicting definitions.
Use AI with judgment
Ask AI to explain a schema or suggest mappings; have domain owners confirm the business meaning.
Your practice checklist
0 / 4 completeThe right surfaces. The right people.
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Bring your own workflow.
Explore how these practices could fit your team, your data, and your operating requirements.