Building FAIR Solutions and Knowledge Graphs for Research in a Large Pharmaceutical Organization

In this podcast, John Apathy, a veteran of the pharmaceutical industry, shares insights from his ten years of experience in implementing technology for large-scale R&D. He discusses the hurdles of updating data architecture, particularly the issues of fragmented data and siloed systems. Apathy argues for a shift from a system-centric to a data-centric approach and highlights the significance of applying FAIR data principles—Findable, Accessible, Interoperable, and Reusable. He also explains how utilizing knowledge graphs can enhance data discoverability and speed up decision-making. To wrap up, Apathy underscores the vital need for ongoing support and a long-term vision in achieving successful data transformation, drawing on examples from his time at Celgene and Bristol Myers Squibb, where he addressed data integration challenges after major acquisitions.