What is a Metric Catalog and Why It Powers Self-Serve BI and Trusted AI

Let's start with a quick definition

A metric catalog is a governed list of business metrics with shared definitions, owners, metadata, status, and discovery tools. Think of it as the front door to trustworthy numbers. Teams find the metric they need, understand what it means, see whether it is ready for use, and pull it into dashboards or analysis with confidence.

How a metric catalog differs from other data discovery tools and assets

1. Metric catalog vs. data dictionary

A data dictionary documents fields in datasets. It describes columns, data types, and constraints. Useful, but it lives at the table level. A metric catalog works at the business concept level. It defines metrics like Monthly Recurring Revenue, Net Revenue Retention, or First Response Time with exact calculations, dimensions, and units, plus who owns them and where they are used.

2. Metric catalog vs. data catalog

A data catalog inventories data assets. It helps you find tables, schemas, and pipelines. A metric catalog inventories the outcomes decision-makers care about. It promotes certified, ready-to-use metrics that already wrap the right sources, rules, and dimensional breakdowns.

3. Metric catalog vs. warehouse tables

Warehouse tables store facts and dimensions. They are ingredients. A metric catalog is the recipe card that tells everyone how to turn those ingredients into a number the business agrees on.

Core jobs a metric catalog must do

• Define metrics in business language. Each entry needs a plain-language description and the exact calculation so anyone can read and understand it.
• Show status at a glance. People should see whether a metric is current, improving or declining, and when it last refreshed.
• Reduce duplication and confusion. Certification and ownership steer users to the right version of a metric.
• Speed up discovery. Filters, tags, and search help teams find the right metric fast.
• Maintain control without blocking self-serve. Role-based permissions keep sensitive data in the right hands while business users stay productive.

Why a metric catalog matters for self-serve BI

Self-serve fails when definitions drift, stale data lingers, or users cannot find the right metric. A catalog fixes these pain points.

• Fewer tickets. Users can answer common questions on their own because definitions and context are one click away.
• Faster dashboards. Clear, reusable metrics replace ad hoc formulas spread across spreadsheets and charts.
• Consistency across teams. Finance, marketing, product, and operations work from the same playbook.
• Confidence in context. Built-in views of value, change, and trend keep conversations grounded in reality, not screenshots.

Why a metric catalog builds trust

Trust grows when people can see who owns a metric, how it is calculated, and when it last updated. It also grows when there is a visible signal that a metric is approved.

• One source of definition truth. Every metric entry carries its description, formula, number format, dimensions, and default views, so no one is guessing what a number means.
• Certification signals. A certified metric is trusted and ready to use. This reduces forks like “MRR v2” or “MRR final final.”
• Governance that scales. Roles and permissions cover who can view, edit, or manage metrics, dashboards, data feeds, public dashboards, and live embeds.

Why a metric catalog improves AI outcomes

AI models need clean inputs and unambiguous semantics. A catalog provides that structure.

• Clear semantics for prompts and retrieval. Definitions, units, and number formats tell models what a metric represents and how to present it.
• Fewer duplicates. Certified metrics and a single source of truth reduce conflicting answers and help retrieval systems select the right entity.
• Useful metadata. Dimensions, default views, and freshness help AI choose the right slice and avoid stale or partial periods.
• Alignment with semantic layers. When your catalog reflects the same business logic used in your dbt Semantic Layer or similar systems, models inherit that clarity.

Inside PowerMetrics: the metric catalog in practice

PowerMetrics calls it the Metric Catalog and in product docs it appears as the Metric List. Think of it as basecamp for your metrics. You can see everything in one place and stay informed on progress.

What you see at a glance

• Value column. Shows the current value for the selected date range. Transaction metrics display the sum across the range. Current value metrics display the most recent point at the end of the range.
• Change column. Compares the current value to the previous equivalent period. If the range is “This month,” the comparison is to the prior month. Green signals improvement and red signals decline. Hover to view details. An asterisk after a value means the comparison period is incomplete. If the filter is set to Auto or Maximum date range, there may be no prior period and N/A can appear.
• Trend column. A compact line chart that shows how the metric moves over time for the selected range. Hourly when viewing today. Daily when viewing a month. It updates as data flows in and only shows points within the completed portion of the range.
• Last refreshed. Displays the last time the metric updated. For dbt Semantic Layer metrics, this reflects the most recent alignment with your dbt project.

Fast discovery and organization

• Filters. Narrow by date range or by name. Filter by service, status, or tag to focus on the work at hand. Status includes views like owned by me or starred.
• Tags. Add multiple tags, create custom ones, and filter or search by tag to find what you need faster. Use tags to group metrics, dashboards, and data feeds.
• Quick actions. Open a metric by clicking it. Use the three-dot menu to open in a new tab, open the metric’s about dialog, or delete metrics you no longer need.

Built-in clarity for every metric

Each metric in PowerMetrics includes:

• A clear business definition so everyone speaks the same language
• Helpful metadata like calculation, number format, available dimensions, and default views
• Certification controls that flag trusted, approved metrics and steer users away from duplicates or drafts

Governance without friction

Keep data secure and visible only to the right people. Assign roles and permissions to users or groups across assets. Control who can view, edit, or manage a metric, a dashboard, a data feed, a public dashboard, or a live embed. Move fast while governance stays intact.

Implementation patterns that work

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Get Started Now

1) Seed the first 20 metrics

• Pick the metrics that drive weekly decisions: pipeline, cash, retention, active users, cycle time, NPS.
• Write crisp business definitions and formulas. Include number formats and default dimensions.
• Assign an owner and a backup for each metric.

2) Certify the core set

• Review definitions with stakeholders. Confirm source tables and filters.
• Mark the best version as certified. Archive or delete duplicates.

3) Create a tagging taxonomy

• Use a short list to start: department, funnel stage, lifecycle, priority.
• Tag related dashboards and data feeds the same way to improve discovery.

4) Set a review cadence

• Use Last refreshed to spot stale feeds.
• Scan Change and Trend weekly to catch anomalies and broken logic.

5) Publish team dashboards from the catalog

• Pull certified metrics into dashboards. Keep definitions central and avoid recreating formulas in charts.

6) Expand safely

• Add new metrics only when there is a clear decision tied to them.
• Reuse dimensions for consistent slicing by channel, region, or plan.

Team playbooks

Get more inspiration at MetricHQ.org, an open library containing hundreds of expert-contributed metrics for every team.

Product growth

• Core metrics: Activation rate, weekly active users, feature adoption, conversion steps.
• Tags: product, activation, retention.
• Routine: Review Change and Trend each morning. When Change flips red, drill into the metric view and segment by dimension to isolate the issue.

Revenue operations

• Core metrics: Qualified pipeline, win rate, sales cycle length, MRR, NRR.
• Tags: revenue, pipeline, sales.
• Routine: Use certification to lock shared definitions with finance. Sort the catalog by Last refreshed during close to watch for delays.

Support and success

• Core metrics: First response time, time to resolution, CSAT, churn risk signals.
• Tags: support, csat, retention.
• Routine: Filter by team-owned metrics and scan the Trend column for early warning. Set goals and notifications on certified metrics so leaders get updates without asking.

Buyer’s evaluation checklist

Use this list to assess any metric catalog. PowerMetrics checks these boxes.

• Centralized catalog with scorecard-style status
• Business definitions and exact calculations on every metric
• Dimensions, number formats, and default views
• Certification workflow that flags trusted metrics
• Filters by date range, name, service, status, and tag
• Quick actions for opening, inspecting, or deleting metrics
• Clear signals for value, change, trend, and last refresh
• Role-based permissions across metrics, dashboards, feeds, public dashboards, and embeds
• Tagging across metrics, dashboards, and feeds for easy discovery
• Integration with semantic layers and warehouses

FAQs

Q: Is a metric catalog only for large enterprises?

A: No. Smaller teams get even more benefit because shared definitions remove endless debates and speed up work.

Q: What if different teams need slight variations of a metric?

A: Document the base definition in the catalog and create variants with clear names and tags. Certify the primary version to guide most users.

Q: How often should metrics be reviewed?

A: Set a monthly or quarterly cadence. Use Last refreshed and Change to spot problems in between.

Getting started

• Pick a short list of metrics that drive your weekly meeting.
• Add definitions, formulas, formats, and dimensions.
• Assign owners and certify the best versions.
• Tag by department and priority.
• Share dashboards built from certified metrics.

Ready to move from scattered KPIs to a shared language for decisions? Try PowerMetrics and explore the Metric Catalog.