There is an open secret circulating in the C-suite corridors of life sciences R&D organizations. Even as investment has increased in R&D, the productivity of that investment has consistently been on the decline. Ten years ago, it was about a 7% decline, and the past decade has only seen that number grow larger. The picture only gets more dismal the further into it we look. What’s behind this dire productivity picture? Lack of analytically driven decision-making is one of the biggest reasons for R&D’s productivity decline. It’s common for clinical trial sites to be chosen based on a clinical operation lead’s gut feeling, for data to exist in silos between the different parts of the R&D organization, and for protocols to be designed with undue patient burden, leading to patient drop-off and trial failure.
R&D executives are keenly aware of this situation, and many have correctly identified that data and analytics should drive decisions. A ZS study found 60% of top pharma companies have started analytics transformation programs in either their drug development or research arms. Some have started both. Still, many C-level executives struggle with the fundamental question of how to set up their new or improved analytics organization. Should it be independent? Or should it be housed within the clinical or research subfunctions? Both have their merits and drawbacks.
In this paper, we’ll address:
- The various structural archetypes for R&D analytics organizations and the pros and cons of each structure
- How several top pharma companies structure their analytics organizations and how these companies drive impact through analytics
- Elements you need to consider when evaluating your structure, with particular consideration to parity with your R&D goals
- A “hub-and-domain” model that we believe will provide the most effective functionality for organizations
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