Implementation and Relevance of FAIR Data Principles in Biopharmaceutical R&D

This article argues that implementing the FAIR (Findable, Accessible, Interoperable, Reusable) data principles is essential for enabling digital transformation and improving R&D productivity in the biopharmaceutical industry. By making data machine-readable and better organized, FAIR enhances the utility of artificial intelligence and machine learning in drug discovery and development. The article discusses the technical, cultural, and organizational barriers to FAIR adoption, including the need for data governance, stewardship, and cultural change toward data sharing. It highlights the expected business benefits, including time and cost savings, increased collaboration, and faster innovation, and emphasizes the role of cross-industry collaboration, such as that led by the Pistoia Alliance, in driving FAIR implementation.