Semantic layer
A semantic layer supplies shared business definitions for interpreting data, such as how an organization calculates churn or recurring revenue. It connects those meanings to the underlying data so different consumers can use consistent metrics.
For example, a company could define monthly customer churn as customers lost during the month divided by customers active at its start. A shared semantic definition specifies the population, time window and calculation so two dashboards do not silently use different denominators. This is a teaching example, not a universal definition of churn.
In episode 75 at 17:57, Kapil Chhabra describes the semantic layer as where churn and ARR definitions sit. He argues that an open-ended chat agent also needs instructions and examples: Q1 may mean a fiscal quarter to finance and a calendar quarter to marketing.
Consistent metric logic helps prevent conflicting answers, but it cannot resolve every ambiguous request. The conversational system still needs the relevant user context and authority to maintain definitions. See context in AI agents for the broader information and access around a model.
Hear it from the guest
“This is where the definition of a churn or ARR in our example would go and sit in.”
Quotes lightly edited to remove filler words.
Sources
- dbt: Semantic Layer — Describes centrally defined metrics and consumption across downstream tools.
Go deeper
- dbt: Semantic models docs
Inspect how entities, dimensions and measures connect business definitions to data.
