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Recent years have brought an explosion of data in life sciences which is used to drive decision-making, inspire research and development (R&D), and inform strategic plans, but organizations cannot leverage the full potential of their data without sound data governance1.
Powerful techniques like machine learning, for instance, allow researchers to approach and analyze large data sets but the data access is predicated on the assumption of data reusability. Unfortunately, such reusability requires changes in the management and stewardship of
research data - a broad transformation to align with what are known as FAIR Principles.
By applying FAIR principles you drive digital transformation, enforce innovation and safeguard data producer liability.
With FAIR data in place, you answer specific research queries more quickly and have researchers focus on the interpretation of data rather than collecting or recreating it.
You don’t get FAIR data overnight, but need to install an evolving process that you carry out pragmatically and driven by value.
To track how fair your data is and its return on investment, you set up straightforward metrics.