Making data FAIR at Bayer

Nodes Room

A key objective of the healthcare industry is to accelerate translational science, ie. the translation of scientific discovery into products and services for the benefit of our patients. The technological progress and the digital transformation offer many approaches to enhance data driven decision making. Data quality is a must have for AI outcome, and curation of data to improve machine readability is a core activity in order to enable data science.

The data quality problem of health research has been acknowledged by the European Commission and they launched the European Open Science Cloud initiative. Within this context, the FAIR Guiding Principles were published. FAIR stands for Findable, Accessible, Interoperable, and Reusable. FAIR data has become a global movement and also reached the Pharma industry.

At Bayer, we approach the FAIR data topic in three different ways: Bottom up with use case driven projects; top-down strategic initiatives to develop infrastructure, capabilities and mindset change; collaboration, i.e. cross-divisional within Bayer and across pharma in public-private consortia.

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Life Sciences Track