What problems does a metric catalog solve?
A metric catalog solves the problem of inconsistent, untrusted metrics by centralizing definitions, ownership, and freshness in one governed location. It prevents teams, dashboards, and AI tools from using conflicting versions of the same KPI.
Common failure modes
When metrics live in many places, conflicts are guaranteed. These patterns are the usual culprits.
- Same metric, different number: Sales sees 1,042 opportunities; finance sees 1,016. Filters, time windows, or rounding differ, so meetings start with reconciliation instead of action.
- Metrics defined in SQL, dashboards, spreadsheets, and docs: A SQL view, a BI-calculated field, a spreadsheet formula, and a wiki definition all exist at once. Each looks right in isolation, yet none match across tools.
- Hidden logic in visuals: A chart-level filter or ad hoc transformation quietly changes the result. The next person rebuilds the chart and gets a new answer.
- No clear owner: When no one owns a metric, updates and clarifications stall. Stale definitions linger for months.
The cost of inconsistency
Inconsistent metrics create drag across the business.
- Lost trust: Leaders question dashboards and ask for raw exports. Adoption drops and decisions revert to gut feel.
- Slower decisions: Teams spend cycles comparing reports instead of choosing a path. Momentum fades.
- Rework and debates: Analysts rewrite queries, copy new spreadsheets, and rerun models. Meetings become number-matching sessions.
- Compounded waste: Ten people reconciling two hours a week is 20 hours lost weekly, plus missed opportunities that never get measured.
Why dashboards alone don’t solve this
Dashboards present data; they do not govern metric semantics. A dashboard can showcase a KPI, yet every tile can redefine logic. Without a shared catalog:
- Definitions fragment: Each team encodes formulas differently inside charts or data models.
- Ownership is unclear: No certification path means anyone can publish a KPI that looks official.
- Freshness is opaque: Viewers cannot tell whether a KPI is current or a day behind.
- AI stays unreliable: Assistants trained on scattered definitions return different answers to the same question.
What a metric catalog adds
A catalog creates one governed layer for metrics that every tool can use.
- Single source of truth: Name, description, formula, filters, grain, and default time window live in one record.
- Clear ownership and certification: Each metric has an owner, reviewers, and a visible badge when certified.
- Freshness and lineage: Last refreshed time, source connections, and dependency maps reduce surprises.
- Versioning and change control: Propose changes, review diffs, and publish updates without breaking dashboards.
- Reuse across tools: The same metric powers dashboards, notebooks, spreadsheets, and AI assistants.
- Access control and tags: Limit sensitive KPIs and make discovery easy with curated collections.
Where PowerMetrics fits
PowerMetrics provides a governed metric catalog and self-serve analytics in one platform for small and mid-sized teams.
- Define once, reuse everywhere: Create a metric with formula, grain, and filters, then reuse it across charts and dashboards.
- Trust signals built in: Owners, certification badges, and freshness indicators help everyone judge reliability at a glance.
- Ready for AI and analysis: Consistent, described metrics give AI assistants and analysts the same answers every time.
- Connected to your data: Use connectors for popular services, spreadsheets, warehouses, and semantic layers like dbt and Cube.