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Give each important metric two distinct accountabilities: a named business or domain decision owner who defines what it means, and a data or analytics owner who implements, tests, publishes, and maintains it. Add a clear steward or contact for questions and changes. The exact roles vary by company; there is no universal owner for every metric.
Who owns a metric’s meaning, and who owns its implementation?
The person with authority over the business concept should decide what the metric counts and why. The data or analytics team should translate that approved meaning into reliable analytical logic, validate it against source data, manage access and publication, and maintain it as upstream systems change. These are complementary responsibilities, not competing claims to own “the number.”
Business or domain decision owner
Choose a subject-matter expert who understands the concept and can make decisions for the relevant domain. Finance may be the appropriate authority for an official company revenue definition; a customer or product domain may own its own customer or activation concept. These are practical assignments, not universal rules.
Technical owner
The data or analytics team is accountable for encoding the approved definition, checking source and transformation behavior, setting appropriate access, and ensuring the metric is published and maintained. dbt’s documentation describes defining metrics on existing models in the data team’s modeling layer: dbt Semantic Layer documentation.
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Steward or contact
Name a person or role consumers can contact about meaning, quality, documentation, support, or a proposed change. Microsoft distinguishes a data steward, subject matter expert, technical owner, and domain owner rather than treating them as interchangeable roles: Microsoft’s content ownership and management guidance.
Which ownership model fits the company?
Microsoft describes three broad approaches to content ownership. A company can combine them by team or solution; the choice should reflect skills, data culture, flexibility needs, complexity, leadership commitment, sensitivity, and the importance of the decisions the content supports.
| Pattern | Useful when | Trade-off |
|---|---|---|
| Business-led or self-service | A team has capable users, needs rapid exploration, and can manage the lifecycle within governance rules. | Local autonomy may mean less stringent oversight, and users may need training and technical support. |
| Managed self-service | Many teams need reusable, trusted data while business users also need to build reports and analysis quickly. | The central data team must provide the shared foundation and governance; business users retain flexibility at the edge. |
| Enterprise or centralized | Content is critical, sensitive, tightly defined, or must be managed consistently end to end. | Central teams take on more delivery responsibility, which can limit local exploration or customization. |
These models and selection considerations are described in Microsoft’s ownership guidance. A lightweight local metric may not need the same approval path as a sensitive KPI used for company-wide decisions.
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When should a definition be shared centrally?
Centralize a definition when multiple teams or tools need to use the same metric and should get the same result. A shared definition reduces the risk that teams publish conflicting versions under one name. Keep lower-risk exploration local within agreed naming, access, and documentation rules.
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There are implementation options for this pattern, but they are examples rather than endorsements. dbt documents centrally defined metrics in its modeling layer. Databricks documents reusable KPI metric views governed as catalog objects: Databricks metric views documentation. When assessing semantic-layer tools, check whether definitions reach the BI and application surfaces you need, who may edit versus consume them, how versioning and auditability work, what governance and access controls are available, and how well the tool fits existing data models and operating roles.
How should a company manage definition changes and disagreements?
Agree on a workflow before a shared metric becomes contentious. The sources support ownership and governance roles but do not prescribe a single change process; the following is a practical operating recommendation.
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- Propose: Record the requested change, reason, affected consumers, and whether it changes the business meaning, calculation, or both.
- Approve meaning: The metric’s business decision owner approves the intended concept and scope. For a cross-domain metric, involve the affected domain owners.
- Implement: The technical owner updates the model or semantic definition, checks dependencies and access, and records the change.
- Validate: Test the result against relevant source data and confirm that consumers will interpret the revised metric correctly.
- Communicate: Publish the effective date, change history, and any impact on reports or decisions. If the disagreement remains unresolved, escalate it to an agreed governance forum or executive decision owner.
If teams genuinely need different measures, document the distinction and give each measure a distinct name rather than presenting conflicting definitions as one shared metric.
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Use a record that gives consumers enough information to understand, find, and challenge the metric. The details can scale with its risk and reuse.
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- Business meaning, scope, and exclusions.
- Named decision owner and steward or contact.
- Calculation and model location, including dimensional grain where applicable.
- Intended consumers and relevant quality checks.
- Current status and change history.
For a company-wide or decision-critical metric, make this record available wherever people discover or use the definition, and make the owner and change route easy to find.
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