Are your key metrics defined once in a semantic layer that every dashboard uses?

A semantic layer (dbt Semantic Layer, LookML, Cube, Power BI semantic models) stores the official definition of each metric, so every report and AI tool calculates it the same way.

How to automate it

How to automate “explain why two reports show different numbers”

Here are 2 ways to spend less time on this, best first. Each comes with steps you can follow today.

60 min

typically, once a week

35%

of the time can be automated

A small project

to set up

Fix 1 of 2

AutomationBest fix

Define metrics once in a semantic layer that every tool reads from

When "revenue" or "active user" is defined once in dbt's Semantic Layer, LookML, Cube or Power BI semantic models, reports stop disagreeing and AI tools give consistent answers.

Typically saves about 40% of the time20 h to set up

1List the 10 metrics that cause the most arguments.
2Agree the definition of each with the business owner and write it down.
3Build them in your semantic layer (dbt Semantic Layer, LookML, Cube, or a shared Power BI semantic model).
4Point dashboards and natural-language BI tools at the semantic layer, not raw tables.
5Retire the old calculations in individual reports.

Tools: dbt Semantic Layer · Looker LookML · Cube · Power BI semantic models · Snowflake semantic views

Fix 2 of 2

Process fix

Publish a metric glossary with an owner for each number

Most "why don't these match?" questions come from different definitions, filters or time zones. A glossary with owners settles them in a link, not an investigation.

Typically saves about 30% of the time3 h to set up

1Write each key metric: definition, source table, filters, time zone, refresh time, owner.
2Publish it in your catalog, dbt docs or wiki and link it from every dashboard.
3Label each dashboard "certified" or "exploratory".
4When a mismatch comes up, check the glossary first and fix the definition, not just the report.

Tools: dbt docs or dbt Explorer · Atlan, DataHub or Unity Catalog · Confluence or Notion

Quick wins

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Are your key metrics defined once in a semantic layer that every dashboard uses?: A semantic layer (dbt Semantic Layer, LookML, Cube, Power BI semantic models) stores the official definition of each metric, so every report and AI tool calculates it the same way.

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