motherduck-enable-self-serve-analytics

Governed self-serve analytics uses curated KPI surfaces and defined ownership boundaries to control access. It prevents uncontrolled data sprawl by publishing specific Dives or shares to targeted audiences instead of exposing raw tables directly.

To establish KPI ownership and data sharing guardrails, you orchestrate MotherDuck workspace discovery and dataset modeling to validate KPIs, then produce a rollout plan defining the first audience, governing dataset, ownership model, and access guardrails.

Yes, you can publish a governed Dive for internal teams. The rollout process defines a clear ownership model for metric changes and specifies access expectations, ensuring teams operate within established data governance boundaries.

You plan a governed rollout sequence by selecting a first audience and first trusted asset to publish. This coordinates dataset curation and KPI validation before sharing, ensuring analytics adoption follows a structured, controlled rollout sequence.

Yes, MotherDuck supports curated dataset modeling for self-serve analytics. The workflow orchestrates workspace discovery and dataset curation to validate KPIs before publishing a first Dive or governed share to internal teams.