Why AI analytics requires a metric catalog
AI analytics requires a metric catalog because AI systems need clear, governed definitions to return reliable answers. Without a catalog, AI tools risk hallucinating or mixing incompatible metrics, which erodes trust fast.
Dashboards vs metrics as AI sources
AI can read dashboards, but dashboards offer little context. Titles, axis labels, and one‑off filters do not tell an assistant what a metric means, which dimensions are valid, or who owns it.
A metric catalog, as part of a well-defined metric layer, exposes rich, consistent context. Each metric entry is a contract the assistant can use:
- Definition and logic: Name, calculation summary, operands, filters, and grain.
- Dimensions and constraints: Allowed slices like Region, Plan, Channel, and time grains.
- Trust and policy: Owners, certification badge, access level, and data quality notes.
- Lineage and freshness: Source models, warehouse tables, last refresh, and change history.
Result: the assistant answers with the right number, not a similar‑looking one from a random chart.
Quick comparison
| Source for AI | Context depth | Risk profile | Typical outcome |
| Dashboards | Low: labels, tooltip text, ad‑hoc filters | High: ambiguous, tool‑specific quirks | Inconsistent answers, weak citations |
| Metric catalog | High: governed definitions, policy, lineage | Low: clear rules, repeatable context | Consistent answers, defensible citations |
Why ambiguity breaks AI
AI works best when intent maps to one unambiguous target. Ambiguity invites mistakes.
- Name collisions: “Active Users” could mean 7‑day or 30‑day. The assistant must pick one. The catalog removes the guesswork.
- Hidden filters: A dashboard might exclude test accounts in one chart and include them in another. The catalog documents the rule once.
- Mixed grains: Joining monthly revenue to weekly churn creates nonsense. Catalog entries define grain and compatible joins.
- Policy gaps: If the assistant cannot see who is allowed to view PII, it might over‑share. The catalog carries access rules forward.
Metadata and context: the recipe for AI quality
A strong catalog sits on a knowledge graph and a metric ontology, then presents a human‑friendly view.
That stack captures:
- Identity: Metric name, friendly aliases, business domain.
- Meaning: Description, inclusions and exclusions, usage notes, examples.
- Structure: Operand metrics, formula, time grain, valid dimensions, default filters.
- Stewardship: Owner, approver, certification status, popularity, last‑used.
- Policy: Data class, access rules, row‑level constraints, retention windows.
- Lineage: Source models, transformations, warehouse tables, dependencies.
- Context: Related metrics, goals and thresholds, accepted ranges, alerts.
The assistant reads this graph through APIs. Then it can explain answers, cite the definition, and follow policy automatically.
The chat window tie‑in
Here is how the flow should work when someone types a question:
- You ask: “What was Gross Margin last quarter by region?”
- The assistant resolves “Gross Margin” to the certified catalog entry and validates your access.
- It chooses the right grain, applies the time period and “Region” dimension, and generates a safe query.
- It returns a chart and a short explanation, with a link back to the catalog entry so you can review the definition and lineage.
- If the metric is deprecated or uncertified, the assistant warns you and suggests an approved alternative.
That tight loop turns a chat into a trustworthy analytic workflow.
Practical scenarios
- An account manager asks, “Which plans drove Net Revenue growth?” The assistant cites the “Net Revenue” entry, slices by Plan, and includes a note about discounts because the catalog lists the exclusion rule.
- A support leader asks, “Did churn spike after the price change?” The assistant pulls “Customer Churn Rate,” applies the correct cohort window, and links the calculation summary.
- A founder asks, “Show Active Users for Starter over 6 months.” The assistant selects the 30‑day version, not the 7‑day look‑alike, because the catalog disambiguates the names.
What to require from an AI‑ready catalog
- Machine‑readable ontology: Metrics, dimensions, entities, and relationships exposed through APIs.
- Business‑first discovery: Synonyms, tags, and domains so natural language maps cleanly.
- Policy binding: Role‑based access and row‑level rules that travel with queries.
- Governed reuse: Certification badges, change logs, and deprecation notices the assistant can honour.
- Explainability: Lineage, owners, and usage examples the assistant can cite.
Signals you will struggle with AI analytics: definitions live in slides, dashboards contain custom SQL, and no single place records allowed dimensions.
Where PowerMetrics fits
PowerMetrics gives AI a reliable substrate with a governed, human‑ and AI‑facing catalog:
- Curated catalog and knowledge graph: Names, descriptions, owners, tags, synonym management, operand metrics, goals, popularity, certification, lineage, and more.
- Assistant integration: PowerMetrics AI uses the catalog to map questions to metrics, generate PMQL, honour access rules, and return answers with definition links.
- Semantic integrations: Connect to dbt Semantic Layer and Cube so existing metric logic flows through the catalog into dashboards and assistants.
- Self‑serve and governance: Build dashboards, set goals and alerts, share governed views, and keep control with roles and policies.
You get faster answers in chat with fewer mistakes because the assistant stands on certified definitions.
Summary
AI analytics requires a governed metric catalog so assistants map intent to certified definitions, apply policy, and explain results. Without it, ambiguity and inconsistent context lead to wrong answers.
Next step
Try PowerMetrics with your team. Connect your warehouse and semantic layer, build a governed catalog, and give AI a clear map to trustworthy metrics.