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DatabricksElizabeth Dobbs, Thomas Russell, Katy Yuan, Sydney Sundell10 min readintermediate

How Databricks’ marketers use data 3x more with Genie, an AI analytics assistant

Read on databricks.com

Summary

Databricks built Marge, a Genie‑powered conversational analytics assistant on a governed Marketing Lakehouse. By starting with a single high‑value use case (email campaign performance), documenting data, encoding verified answers, teaching business terminology, and embedding the tool in existing ticket workflows, they achieved 85% adoption, 3× higher data usage in decisions, 50% QoQ usage growth,…

  • A governed lakehouse (Unity Catalog) provides the trusted data foundation needed for reliable LLM‑driven analytics.
  • Start with one bounded, high‑frequency question set; validate accuracy before expanding scope.
  • Document data models, relationships, and business terminology; supply example SQL and verified logic to guide the agent.
  • Embed the assistant in existing support processes (e.g., auto‑prompt on tickets) to drive self‑service adoption.

Self‑service analytics often stalls on data trust and integration friction. This case shows a pragmatic, product‑style rollout that turns an LLM assistant into a trusted, high‑adoption tool, delivering measurable productivity gains without extra headcount—insights directly applicable to any org bui…

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