What I do
AI/BI, Genie & reporting
Make the Lakehouse usable: self-service dashboards and questions in plain language.
Data only delivers value when people get answers from it. With Databricks AI/BI Dashboards and Genie spaces I let users ask questions in plain language, grounded in a reliable semantic model. And where Power BI is the standard, I connect it cleanly to the Lakehouse.
Self-service analytics lives or dies by the semantic model underneath. So this work doesn't start at the dashboard, it starts at definitions: Metric Views that pin down revenue and margin once, descriptions and synonyms so 'customer' and 'account' mean the same thing, and sample questions to steer and validate a Genie space.
Then comes the rollout: AI/BI Dashboards directly on governed data, Genie spaces where users ask questions in plain language, and where Power BI is the standard, a clean connection through Databricks SQL with Unity Catalog as the source of truth for permissions.
Trust grows with transparency: users see which query ran and where the answer came from.
- AI/BI Dashboards on governed Lakehouse data
- Genie spaces for natural-language analytics that hold up
- A clear semantic / metric model as the source of truth
- Power BI integration via Databricks SQL
Further reading
Ready to set course?
A no-obligation conversation about your Azure Databricks, Lakehouse or AI/BI challenge. I'm happy to think along.
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