A global pharmaceutical organization transitioned from legacy Teradata and SAS systems to a lakehouse architecture on Azure Databricks with ADLS Gen2. The new platform unifies batch and streaming sources, accelerates data feed onboarding, and delivers self-service analytics for field teams. Metadata-driven governance, role-based access, and auditable lineage address GxP and 21 CFR Part 11 requirements. The modernization reduced cross-functional friction, eliminated manual SOP scaling issues, and improved trust in data used for territory alignment, QA reconciliation, personalized patient and customer engagement, brand performance, and near-real-time regulatory decisions.
Over the course of several years designing and delivering enterprise data platforms for a global pharmaceutical leader, I witnessed firsthand how data had evolved from a backend enabler to a frontline business asset. The organization was no longer just looking to report historical performance; it needed to predict outcomes, personalize patient engagement, customer engagement, brand performance and make regulatory decisions in near real time.
The pivot point came not from limitations, but from opportunity. We saw an opportunity to reduce friction in cross-functional analytics, eliminate manual SOP documentation that couldn't scale, and improve trust in the data lineage. When commercial teams struggled to align territories, or when QA teams had to reconcile multiple versions of the truth, it wasn't a technical failure; it was a signal that our platform needed to evolve to meet the demands of a modern, insight-driven enterprise.
#databricks-lakehouse #azure-adls-gen2 #pharma-data-modernization #data-governance #gxp--21-cfr-part-11
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