Canonical Metrics: One Definition, Many Views

TL;DR

• Define each metric once and reuse it everywhere.
• Views can vary, but the definition must not.

The problem

• Different teams define the same metric differently.
• AI cannot infer which definition is the “right” one.

Why it matters

• Canonical metrics ensure consistency across dashboards and AI answers.
• They reduce time spent reconciling numbers.

Symptoms

• The same KPI shows different values in different reports.
• Stakeholders debate definitions instead of insights.

Root causes

• Metric definitions stored in docs but not embedded in the model.
• Local optimization leads to local definitions.

What good looks like

• One authoritative measure per KPI with clear scope and unit.
• Variant measures explicitly reference the canonical base.

How to fix

• Create a metric catalog with owners and definitions.
• Build canonical measures and update reports to use them.
• Allow variants only when they explicitly reference the base.

Pitfalls

• Allowing “temporary” local measures to linger.
• Hiding canonical definitions in external docs only.

Checklist

• Canonical metric list published and owned.
• All KPIs map to a canonical measure.
• Variants explicitly documented.

Framework placement

Primary ARF layer: Semantic Integrity. Diagnostic bridge: semantic-reliability, change-reliability.