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Unlocking digital transformation in life sciences r&d through data management

Becoming data-driven is critical for future-proofing life sciences research – but many companies are struggling to achieve it.

Over the last ten years, the life sciences sector has been pursuing innovation through digitization at pace. Many organizations have moved from legacy, paper-based workflows or basic electronic lab notebooks to sophisticated electronic data capture systems and data stores.

But despite having these often high-profile and energetic digitization and change programs, many companies still find themselves unable to achieve their digitization goals in R&D.

The life sciences industry is now at a point where data- and AI-driven startups that were ‘born digital’ are seriously disrupting long-established industry norms – from identifying effective, safe drug candidate compounds in silico to rapidly identifying the ideal patient population for a clinical trial.

In this paper we explore some of the challenges that life sciences companies face in their attempts to enable data-driven R&D, and how well-devised strategies and tactics around data management can avoid the common traps that organizations fall into. From there we show how careful data management can release the incredible potential locked up in research data and establish a solid foundation for the delivery of high-quality insights from analytics, data science, and AI projects that will drive the R&D programs of the future.

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