The workflow before
Continental Properties ran reporting on Microsoft Fabric, dbt and Power BI, but access was granted ad hoc, with no target model for who should see which data. Ownership, definitions and lineage lived in people's heads, and the dbt code had grown slow and costly to run. Leadership wanted to use AI on company data but had no way to know whether the data was ready for it.
What we built
We designed a target-state access model on Entra ID with role-based groups and row-level security in Power BI, so each person sees only the properties and figures their role allows. We stood up DataHub as the data catalog, holding metadata, lineage, data owners and a business glossary.
In a 24-week stream we refactored the dbt project and tuned the pipelines for run time and cost. For AI readiness we ran a semantic layer evaluation: we built lightweight semantic model prototypes and tested AI tools and prompt quality against them to see where answers held up and where the data needed work.
Before and after
| Measure | Before | After |
|---|---|---|
| Report access | Granted ad hoc | Role-based, with row-level security in Power BI |
| Data ownership and lineage | Undocumented | Cataloged in DataHub with named owners |
| Pipeline run cost | Baseline | About 30% lower |
What's next
With governed access, a catalog and tested semantic models in place, Continental can move to Level 2: an analytics copilot over its property and financial data that respects the same row-level permissions.
