Databricks

What Stevin gets out of Databricks

Databricks is a lakehouse platform that brings data warehousing and data science together, and for large organizations it is often the central place where storage, ML models and BI come together. That is what makes it strong: your marketing analytics can run on the same layer as the rest of the business, inside the same governance. The strength is also the barrier: Databricks is a platform for data teams, not a marketing tool, and without that expertise you get little out of it. Where teams get stuck is that marketing data stays separate from the enterprise layer, that MMM models run on separate exports instead of the source of truth, and that marketing analytics falls outside governance. Stevin integrates with Databricks to run those models on the central data layer without pulling data out of the governance perimeter, and turns the output into signals with follow-up, with a person reviewing every action that carries weight first.

What you pair it with

Choosing Databricks means your marketing analytics runs on the enterprise foundation instead of on loose exports next to your central data. That keeps governance in one place, but it takes data engineering and data teams to get value out of it. It is not an out-of-the-box marketing solution, and without that expertise you get little from it. In practice: run MMM and attribution on the source of truth instead of on copies, because every export outside Databricks is a place where your numbers quietly drift apart from reality.

Common mistakes

• →Keeping marketing data separate from the enterprise layer, so governance and analytics diverge.
• →Running MMM models on separate exports instead of on the source of truth.
• →Approaching Databricks as a marketing tool without the data engineering it requires.
• →Letting marketing analytics fall outside governance and losing reliability with it.