Build the semantic layer that makes AI analytics trustworthy
AI analytics is only as good as the context you give it. Without a semantic layer - a unified, shared definition of metrics, segments, and business logic - AI (and everyone else) is guessing at what "active user" or "revenue" means at your company. Data Studio is the analyst workbench where that foundation gets built. Define metrics once. Transform raw tables using SQL or Python. See dependencies before changing anything. Publish what's trusted to your Library. Then get reliable answers from AI
As a non-technical person in Metabase, having verified datasets and predefined metrics that are owned by someone who actually knows what they're doing makes it way easier for me to run the reports i need, and be confident in the answers I get.
I haven't asked Metabot yet, but i'm pretty sure she feels the same.
As a non-technical person in Metabase, having verified datasets and predefined metrics that are owned by someone who actually knows what they're doing makes it way easier for me to run the reports i need, and be confident in the answers I get.
I haven't asked Metabot yet, but i'm pretty sure she feels the same.