Making FAIR at Source Actionable in the Pharma Research
Since their publication in 2016, the FAIR principles have become synonymous with good data management practices. The value of becoming FAIR compliant in pharma has been demonstrated time and again as accelerated innovation, reduced lead time to discovery, elimination of data silos, improved efficiency and ability to do advanced analytics.
Despite the potential benefits, most efforts for FAIRification have been successful on a smaller scale with specific value cases. Large scale implementation of FAIR is often hampered by legal, technical, financial or organizational challenges. Creating a FAIR ecosystem requires that the infrastructure for data capture not only enables but also ensures that data is FAIR right at the point of creation (FAIR@source).
The success of this ecosystem is predicated on an organizational culture where data is treated as an asset. Over the past few years since we at Novo Nordisk made a commitment to making data generated in Research FAIR.
We’ve had many learnings along the way the most important of which is the need to make FAIR an achievable aspiration. Towards this, we have created a FAIR maturity framework that breaks down the FAIR principles into different levels of maturity customized to Novo Nordisk culture, that various teams can aim to achieve within a specific period of time. This approach ensures that the change management happens in iterative actionable chunks where the wins are clear.
In this session, we will share the learnings we had along the way along with the general framework that was created and welcome input from others who have been on this journey and can help us perfect the framework.
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