What is the relationship between a metric catalog and a metrics layer?
A metrics layer computes metrics, while a metric catalog documents, governs, and exposes them. Together, they ensure metrics are both technically correct and consistently understood.
Clear Architectural Separation
Think of two distinct jobs that often get blurred:
- Metrics layer: The compute and semantics tier that turns modelled data into trustworthy, queryable metrics. It handles calculation logic, aggregation rules, time intelligence, query planning, caching, and performance.
- Metric catalog: The knowledge and governance tier that makes metrics findable, explainable, and safe to use. It manages names, definitions, business context, owners, tags, certification, access, lifecycle, and exposure through UI and APIs.
A clean split protects both sides:
- The metrics layer guarantees technical correctness and performance at query time.
- The catalog guarantees a shared understanding, the right usage, and controlled access across teams and tools.
Where Each Sits In The Stack
A typical modern stack looks like this:
- Sources: SaaS apps, files, event streams.
- Ingest and ELT: Pipelines that land data in a warehouse or lake.
- Transformation and modelling: Tools like dbt that structure facts and dimensions.
- Metrics layer: A semantic and calculation tier that defines metric logic and serves computed results.
- Metric catalog: A governed registry that documents metrics, sets ownership and policies, and exposes them to people and AI.
- Consumption: Dashboards, reports, notebooks, AI assistants, spreadsheets, and downstream apps.
Placement matters:
- The metrics layer sits close to your warehouse so it can push computation down and keep results consistent across tools.
- The catalog sits across the top of your analytics surface so people can discover metrics, learn how to use them, and request access without touching compute logic.
At-a-glance comparison
Use this quick table to keep roles straight.
| Area | Metrics layer | Metric catalog |
| Core role | Computes and serves metric values | Documents, governs, and exposes metrics |
| Lives in stack | Near the warehouse and semantic tier | Across the analytics surface as UI and API facade |
| Primary users | Data and analytics engineers | Analysts, business users, data stewards |
| Key capabilities | Calculation logic, time intelligence, pushdown, caching, consistency | Names, definitions, ownership, certification, access, lineage, synonyms |
| Outputs | Query results and reusable metric endpoints | Human-readable entries and machine-readable metadata and policies |
| AI relationship | Executes safe, generated queries | Maps intent to the right metric, applies policy, explains meaning |
| Replace the other? | No | No |
Why You Can’t Replace One With The Other
- Without a catalog: You can compute perfect metrics that no one trusts or uses correctly. Names drift, definitions fragment, and the same metric ships with three meanings.
- Without a metrics layer: You can document the right definition, but teams still compute it differently in SQL, spreadsheets, and custom code.
- Replacing both with a single tool: You risk coupling governance to compute. Changes in one bleed into the other, slowing delivery and creating brittle dependencies.
The right pairing gives you a stable contract: compute stays precise, and understanding stays shared.
Common Misconceptions
- “Our BI tool is the catalog.” A dashboard library is not a governed registry. Dashboards show answers. A catalog explains what a metric means, who owns it, when to use it, and what changed.
- “A metrics layer with descriptions is enough.” Descriptions help, but you still need workflows for certification, deprecation, synonym management, access control, SLA, and audit trails.
- “We’re small, so we don’t need both.” Even small teams face definition drift once sales, finance, and product each publish their own numbers.
- “A catalog slows analysts down.” A good catalog speeds work. Analysts reuse certified logic, reduce rework, and answer questions faster because the meaning is settled.
The Catalog Is Human And AI-Facing
A modern catalog serves two audiences at once:
- Human-facing: You browse metrics in plain language. Each entry shows a friendly name, calculation summary, dimensionality, examples, owner, trust status, and when to use or avoid it. You see lineage and change history, so updates never surprise you.
- AI-facing: The catalog exposes structured definitions, synonyms, and policies through APIs. AI assistants map natural language to the right metric and filter set, generate correct queries, and respect access rules. The result is safer answers, fewer hallucinations, and clearer explanations.
Good catalogs treat definitions as knowledge, not just metadata. That knowledge powers both conversations and queries.
Practical Scenarios
- Quarterly revenue meeting: Finance and sales ask for “Gross Revenue by Region.” The metrics layer returns the same number to every tool. The catalog clarifies inclusions and exclusions, shows ownership, and links to related metrics like “Net Revenue.”
- New hire onboarding: A product manager needs “Active Users.” The catalog explains counting logic, time window, and caveats. The metrics layer ensures the calculation matches the definition in dashboards, spreadsheets, and notebooks.
- AI assistant in support: A frontline manager asks, “How did churn trend after the price change?” The assistant pulls the certified “Customer Churn Rate,” applies the correct filters, and cites the catalog entry so the user can verify meaning.
What To Evaluate In Each Layer
Use this quick checklist when you assess tools:
Metrics layer
- Calculation coverage: Time series functions, period-over-period, cohort logic, conditional metrics.
- Consistency: One definition available to every consuming tool.
- Performance: Pushdown to the warehouse, caching, and concurrency.
- Governance hooks: Versioning for metric logic, tests, and observability.
Metric catalog
- Discovery: Search, browse by domain, related metrics, and synonyms.
- Trust: Owners, certification badges, change history, and deprecation notices.
- Policy: Role-based access, PII flags, row-level rules, and SLA.
- AI readiness: Machine-readable definitions, APIs, and clear relationships between entities.
Signals to avoid
- Definitions live only in slide decks or dashboards.
- No single place to request access or see ownership.
- Business names and SQL names are the same with no translation layer.
Where PowerMetrics Fits
PowerMetrics gives you a governed metric catalog that people and AI can use with confidence, paired with flexible compute options:
- Curated catalog: Friendly names, clear descriptions, owners, tags, certification, and change history. Star, group, and publish metrics for different audiences.
- Human and AI-facing: Natural language experiences and APIs expose the same governed definitions to users and assistants.
- Broad connectivity: 130+ connectors across databases, warehouses, and SaaS. Bring in modelled data or create metrics with formulas and PMQL.
- Semantic integrations: Connect to dbt Semantic Layer and Cube so your existing metrics layer logic serves consistently through the catalog and into dashboards.
- Self-serve consumption: Build dashboards in minutes, compare time periods, add goals and alerts, and share governed views without copy-pasting SQL.
This lets you keep compute where it belongs while giving everyone a shared place to learn, trust, and use metrics.
Buying Traps To Skip
- Picking a metrics layer because it has a text field for descriptions. That’s not governance.
- Treating your wiki as the catalog. It goes stale, and it’s not connected to live usage or access control.
- Forcing compute changes every time you rename a metric. Names are for humans. Logic is for machines. Keep them decoupled.
Summary
- The metrics layer computes and standardizes the numbers.
- The metric catalog documents, governs, and exposes them to people and AI.
- You need both to achieve consistent answers and shared understanding.
Next Step
Try PowerMetrics with your team. Build a governed catalog in days, connect your existing warehouse, and give everyone clear, consistent metrics they can trust.