Glossary · Business Analytics
Metric Ontology
also: semantic layer · metric definition framework
Definition
A metric ontology is a versioned, centrally-governed definition of every metric an organization uses, specifying the grain, filters, time-window, and source tables so that the same metric produces identical values regardless of tool, dashboard, or analyst. It prevents the drift that silently corrupts data-driven decisions.
Most data-driven organizations have a silent bug: the same metric name means different things in different dashboards. Active users by one team includes trial users; by another excludes them. A metric ontology — implemented via dbt semantic layers, Cube, Looker LookML, or Malloy — forces every metric to have a canonical definition with grain, filters, source tables, and test assertions. Self-serve analytics only works when the ontology exists.
Essays on this concept
- Business Analytics
The Analytics Engineering Manifesto: Why dbt Changed the Data Team Operating Model Forever
Before dbt, analysts wrote SQL that nobody reviewed, nobody tested, and nobody documented. The tool was simple — SQL templating with version control. The impact was structural: it created an entirely new discipline.
- Business Analytics
Anomaly Detection in Revenue Data: Isolation Forests vs. Prophet-Based Decomposition
A 4% revenue drop on a Tuesday could be a payment processor outage, a pricing bug, or just normal variance. The difference between these explanations is millions of dollars — and your monitoring system can't tell them apart.
- Business Analytics
From Dashboards to Decision Systems: Embedding Prescriptive Analytics Into Operational Workflows
Your company has 47 dashboards. How many of them changed a decision last week? Dashboards describe what happened. Decision systems prescribe what to do next — and the gap between these two is where most analytics ROI evaporates.
- Business Analytics
Metric Ontology Design: Building a Self-Serve Analytics Layer That Doesn't Collapse Under Ambiguity
Ask five people in your company what 'revenue' means and you'll get five different numbers. The problem isn't the data warehouse — it's that nobody agreed on the definitions before building dashboards on top of them.
- Marketing Strategy
The Strategy-Execution Gap in Growth Teams: Why OKRs Fail and How Input Metrics Fix Them
Your Q1 OKR was 'increase activation rate by 15%.' It's March and you're at 3%. The problem isn't execution — it's that activation rate is an output. You can't execute on an output. Input metrics bridge the gap between strategy and daily action.
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