Databricks Consulting: One Lakehouse for Marketing and Sales Data

Databricks is a data platform that runs on the cloud you already use, whether that is AWS, Azure or Google Cloud. It follows the lakehouse model: data sits in your cloud storage in an open format, Delta Lake, and on top of it you get what you would expect from a data warehouse, such as tables, SQL, transactions and access control. The analyst writing SQL and the data scientist training a model in Python work on the same platform.

A head of sales does not need to care about the word lakehouse. What matters is getting campaigns, web analytics, CRM and billing into one place, and using that same data both for dashboards and for models that see problems coming, like which accounts are about to churn.

A churn model learns from customers who left in the past which signals showed up before they went: orders spacing out, product usage dropping, open support issues, a contract nearing its end. It then scores current customers by how closely they resemble that pattern.

A score on its own changes nothing. It has to reach the rep inside the tools they already use, with the reason next to it, and lead to an action: a call, a review of terms, a different offer. Our part is making sure the input data is reliable, that the model gets retrained when the business changes, and that the output lands with whoever needs to act on it.

Databricks makes sense when:

• You plan to build predictive models and have, or will hire, someone who works in Python.
• You handle a lot of event or semi-structured data: web browsing, product usage, application logs.
• Your company already runs Databricks elsewhere and adding another platform would only add cost and complexity.

If what you need is to bring CRM, ad and billing data together for dashboards you can trust, a warehouse like BigQuery or Snowflake is usually simpler to set up and maintain. We will say so even if it means a smaller project for us.