Maximizing data value for biopharma through FAIR and quality implementation: FAIR plus Q. Issue 5 (May 2022) - Harrow, Ian; Balakrishnan, Rama; Küçük McGinty, Hande; Plasterer, Tom; Romacker, Martin
Highlights: FAIRification use cases at Roche and AstraZeneca. More prospective FAIRification expected in future. Broad applicability of data quality assessment (DQA). Maximum value from data through implementation of FAIR + Q. Abstract : Over recent years, there has been exciting growth in collaboration between academia and industry in the life sciences to make data more Findable, Accessible, Interoperable and Reusable (FAIR) to achieve greater value. Despite considerable progress, the transformative shift from an application-centric to a data-centric perspective, enabled by FAIR implementation, remains very much a work in progress on the 'FAIR journey'. In this review, we consider use cases for FAIR implementation. These can be deployed alongside assessment of data quality to maximize the value of data generated from research, clinical trials, and real-world healthcare data, which are essential for the discovery and development of new medical treatments by biopharma.