Personalizing the pharmacy experience to enable better outcomes

CVS Health serves 80 million customers daily across 10,000 stores and wanted to personalize experiences with ML, but its Hadoop-based approach couldn't scale past about 1% of customers and models overfit because behavior (e.g., a pharmacy pickup vs. a forgotten-milk run) was hard to predict, per analyst Raghu Nakka. CVS moved to Azure Databricks, letting data engineers, data scientists and analysts (using Tableau) collaborate more efficiently, with Director Michelle Un citing the flexibility to spin up clusters for different business needs. The result was a 1.6 percentage-point increase in medication adherence and the ability to scale personalized offers to larger customer segments.

Databricks is a unified data-and-AI lakehouse platform, used across pharma R&D for everything from early prototyping to production model governance via Unity Catalog.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.