Semantic layer
A shared set of business definitions, metrics, calculations and data relationships that sits between raw data and the people or AI agents that query it, so everyone means the same thing.
A semantic layer is a governed translation between how data is stored and how the business talks about it. It holds the definitions of terms and metrics ("active customer", "net revenue"), custom calculations and the relationships between tables, so that a question is answered consistently regardless of who asks.
It becomes more important with AI. An AI data agent that writes its own queries needs trusted definitions to rely on, otherwise it will confidently compute the wrong number from the wrong columns. Some products explicitly rely on business terms and metric definitions supplied by semantic layers and other trusted sources, with queries inheriting existing table, row and column permissions.
Why it matters
The semantic layer is where context lives. When people's questions repeatedly show confusion or missing definitions, that is the second dataset telling the data team where to improve it. Treat it as part of the AI system, not as a background detail.
Read more in Your AI data agent is creating a second dataset.
Related terms
AI data agent
An AI system that answers business questions by querying an organization's data on someone's behalf. Beyond its answers, the pattern of questions it receives reveals what people need to know.
Second dataset
The record of questions people ask an AI data agent. Unlike the first dataset, which says what happened, it reveals what people need to know and where decision demand and context gaps sit.