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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