Accelerating supply chain analytics to improve healthcare outcomes

After migrating over 250 data sources to Databricks, the data engineering team for a global pharmaceutical and life sciences organization was struggling to satisfy demand from business owners for analytics ready data. Pipeline development was taking over 12 weeks due to back-and-forth iterations with business SMEs. This bottleneck greatly impacted efforts to meet the growing demand for insights and metrics.

Limited Spark expertise impacted analysts' ability to gain and share insights from supply chain data, requiring constant assistance from the data engineering team. This dependence on engineering created an expensive bottleneck that not only reduced productivity, but also resulted in only 4% of supply chain data reaching the 'gold' production-ready layer within Databricks where it could be readily used for informed decision-making.

The Prophecy data engineering platform was selected for its ability to seamlessly integrate with their Databricks Lakehouse implementation as well as their complex IT infrastructure and processes, including the more than 30 ERPs and domains, as well as their code management and CI/CD workflows. On the development side, Prophecy’s low-code, visual design and build capabilities are enabling all contributors on their data team to create flexible, highly scalable, and reusable data assets that support supply chain efficiency, from inventory management, and sales orders, to manufacturing operations and more.

Prophecy simplifies data processing, making data in Databricks more usable and valuable. By automatically generating high-quality, DataOps-friendly code, data engineers can easily customize and refine when required. This translates to a more mature data environment, enabling in-depth supply chain analysis for better decision-making and risk reduction.