What does “single source of truth” actually mean for metrics?
A single source of truth for metrics means one shared definition, one calculation logic, and one governed place where metrics live. It is not just one database. It is a consistent metric layer that everyone uses for reporting and decisions.
What it is not
- Not one warehouse for all data. You can have multiple databases and still have a single source of truth for metrics.
- Not a wiki page with definitions that nobody enforces. The logic must live in one governed system.
- Not a one-off SQL file copied across reports. Duplication invites drift and debate.
Why you need it
When each team defines or calculates a metric differently, you get:
- Conflicting numbers: Finance shows one Gross Margin, Marketing shows another. Meetings stall.
- Slow decisions: Time goes into reconciling data, not acting on it.
- Hidden errors: One small change in a spreadsheet formula ripples through dashboards.
- Low trust: People stop using dashboards because the numbers keep changing.
A single source of truth fixes this by putting the business meaning and the math in one place, then letting every chart, dashboard, and export draw from that source.
The three pillars in practice
- One shared definition: Document the business meaning. For example, "Customer Churn Rate" uses end-of-period active customers in the denominator, not the average.
- One calculation logic: Implement the math once. Store the rule where it can be versioned, tested, and reused.
- One governed place: Control who can change definitions, review impacts, and certify metrics before publishing.
Tip: Treat your core metrics like product features. They have owners, change logs, tests, and release notes.
How this shows up in your stack
You can achieve a single source of truth with different patterns:
- Metric layer in a dedicated platform: Keep metric definitions, logic, history, and governance in one catalog. Downstream tools read from it.
- Semantic layer integration: Define metrics once in a semantic layer like dbt or Cube, then consume them in downstream dashboards.
- BI-embedded definitions with strict governance: Centralize calculations inside one analytics tool and lock edits behind review.
Pick the pattern that best fits team skills and data maturity. The goal is consistent definitions and reusable logic, not a perfect architecture diagram.
Where PowerMetrics fits
PowerMetrics gives you a governed metric catalog, calculation logic you define once, and controlled publishing to dashboards and embeds. You connect popular services, databases, warehouses, or semantic layers, define metrics centrally, then let teams explore and build their own views without breaking the source. Role-based access, certification, goals, and alerts help you maintain trust as usage grows.
Tradeoffs and risks to watch
- “Truth” theatre: A pretty glossary with no enforcement does not stop drift. Put the logic in a system, not only in docs.
- Shadow queries: Ad-hoc SQL that bypasses the metric layer will reintroduce mismatches. Limit write access and log queries.
- Over-centralization: If every small change needs a committee, teams will route around the process. Define clear guardrails and service levels.
- Missing tests: Add sample inputs and expected outputs for each metric. Run tests after schema or logic changes.
Implementation checklist for SMB teams
- Identify 10 to 20 business-critical metrics to standardize first.
- Name owners for each metric. Owners approve changes and handle questions.
- Write plain-language definitions, required dimensions, and exclusions.
- Implement the calculation once. Avoid copy‑paste in downstream tools.
- Add freshness rules, data quality checks, and sample test cases.
- Certify metrics before publishing. Tag drafts clearly.
- Publish a browsable catalog so people can find and reuse metrics.
- Track change history and notify subscribers when logic changes.
- Make downstream dashboards read-only on metric logic. Edits happen at the source.
Quick scenarios
- MRR example: Sales counts upgrades as new MRR. Finance counts only net new. With a single source of truth, the definition clarifies inclusion rules, the calculation nets upgrades and downgrades correctly, and both teams see the same MRR on every dashboard.
- Churn example: Support cancels on the last day of the month. Marketing reports a lower churn because of a mid-month denominator. The metric layer fixes the denominator and timing so churn matches across teams.
FAQ
Do you need one database to have a single source of truth for metrics?
No. You need one governed metric layer. Data can stay in multiple systems if the metric logic and definitions are centralized.
How is this different from a data dictionary?
A dictionary describes fields. A metric layer executes the math, versions changes, and publishes certified metrics for reuse.
What about custom team metrics?
Keep the core catalog tight and certified. Let teams propose additions. Draft metrics can exist, but production dashboards should use certified ones.
Next step
Try PowerMetrics to create a single end-user accessible metric catalog, define calculations once, and give teams trusted self-serve analytics. No credit card required.