41% of analytics teams say data ownership is ambiguous. dbt can tell you who owns a model. Nobody owns a definition.
Start with this recipe →No warehouse credential. No dbt Cloud token. Read-only.
For agents, no sign-in: /recipe/ownership-register.json · /recipe/ownership-register.md
dbt's own 2026 State of Analytics Engineering put ambiguous data ownership among the top pains teams report, alongside trust and quality — and it is the one with the least tooling. dbt groups and model owners tell you who maintains the SQL. They do not tell you who decides what customer_status is allowed to contain, who to ask before adding a value, or who should have been consulted when someone changed it last quarter. So the answer is usually 'ask in #analytics and see who replies'. This workspace makes ownership a property of the meaning rather than of the file: every column and every vocabulary can carry an owner, a steward team and a review cadence, sitting as columns in a grid you can sort and filter. The most useful view is the one that shows what has no owner at all — that list is your actual to-do, and it is the prerequisite for every review, notification and escalation workflow you might build later.
Somebody wants to add a value to a status column. They are not being careless — they have checked that the value is real, and they have found the model. What they cannot find is the person who gets to say yes.
Read the full story →How it works: the import, the API, the MCP tools, the CI loop.