The FAIR principles for data sets are gaining traction, especially in the pharmaceutical industry in Europe. FAIR stands for: Findable, Accessible, Interoperable and Reusable. In a world of exponential data growth and ever-increasing silo-ization, the FAIR principles are needed more than ever. In this article, we will first summarize the FAIR principles and describe the typical roadmap to FAIR. After that we will argue that using a Data-Centric approach is the best route to achieving FAIR principles. This material was recently presented at a FAIR workshop for a European pharmaceutical firm.
FAIR emerged as a response to the fragmentation of data within and among most large firms. Most of the publicly available FAIR materials tend to focus on awareness and assessment (how FAIR are you?) and leave the way-finding to individual companies. Difficulties in sharing and applying data to problems create a friction on innovation that FAIR is meant to address.