What is Metric Governance? Definition and Why It Matters

Metric governance is the practice of defining, versioning, and enforcing business metric definitions across an organization. It ensures that "revenue," "churn," and "active users" mean the same thing in every dashboard, API response, and AI agent answer. Without it, different teams report different numbers from the same data. In most companies, metric definitions live scattered across dbt models, Looker views, spreadsheet formulas, and SQL snippets pasted in Slack threads. Metric governance consolidates these into a single authoritative source that every tool queries at runtime.

Why does metric governance matter?

Conflicting numbers erode trust. The CEO sees $2.3M revenue in a dashboard. The CFO's spreadsheet says $2.1M. Both pulled from the same warehouse with different SQL. Once leadership can't trust the numbers, they stop making data-driven decisions. Every meeting becomes a debate about whose query is right instead of what to do about the trend.

Compliance requires consistent reporting. Auditors expect the same metric to produce the same result regardless of where it's queried. If "monthly active users" means one thing in your investor deck and another in your product dashboard, that's a finding. Metric governance provides the paper trail: one definition, version-controlled, with change history.

AI agents need governed definitions. An AI agent answering "what's our revenue?" will generate SQL against whatever schema context it has. Without governance, the agent invents its own aggregation logic. With governed definitions, the agent queries a named metric with a fixed calculation. No hallucinated math.

What does metric governance include?

Centralized definitions. Every metric lives in one place. total_revenue is SUM(amount) WHERE status != 'refunded'. Not scattered across dbt models, Looker views, and Jupyter notebooks.

Version control. Metric definitions are code. They live in Git, go through pull requests, and have commit history. When someone changes how "churn" is calculated, the diff is reviewable and the change is traceable.

Change tracking. A deploy log shows what changed, when, and who approved it. If a number shifts after a deploy, you can identify the exact metric definition change that caused it.

Access control. RBAC determines who can view and modify metric definitions. Not everyone should be able to redefine "revenue."

Audit trails. Every query records which metric version was used and which role executed it. Full traceability from question to answer.

How Bonnard enforces metric governance

The semantic layer stores metric definitions in YAML files that live in your Git repository. The bon deploy command pushes definitions to your Bonnard instance with a versioned deploy record. The admin UI shows diffs between deploys so you can see exactly what changed. If a board deck number shifts after a Friday deploy, the diff tells you which metric definition changed and who approved the PR.

Governance groups control who sees which metrics. RBAC and row-level security filter data per tenant. Every query is logged with the metric version, role, and tenant context. When a number looks wrong, you trace it from the query log to the exact YAML definition that produced it.

No spreadsheets tracking metric ownership. No Slack threads debating what "active users" means. Define it in YAML, deploy it, and enforce it at query time across every surface: MCP agents, embedded analytics, REST API, and internal tools.

Related terms

• Semantic Layer
• RBAC