Digital R&D is advancing rapidly, creating lucrative opportunities for therapeutics and diagnostics innovation, with shortened R&D timeframes. During the COVID-19 pandemic we saw that quality R&D at speed is possible, thanks in large part to data and digital technology.
Data science and AI are the jewels in the crown of Digital R&D, bringing possibilities to spot unseen research opportunities, predict success or failure early, automate arduous processes, find new insights in small data sets, and optimise clinical trials.
Getting this right is not just about what is technically possible. It is about being able to use these tools in ways that deliver tangible value to R&D, in timeframes that make it worthwhile.
Data science and AI are complex tools which must be carefully integrated across different areas of R&D, and carefully aligned to the context in which they operate. Thought must be given to the whole implementation process from data gathering, to model selection, to user experience.
Success combines business strategy, project management, data engineering, data pipelining, building and validating models, software engineering, and user support, all of which must work together cohesively.
This whitepaper draws on a wide range of data and AI projects in life sciences to explore what factors deliver success.
HOW TO DELIVER VALUE FROM DATA AND DATA SCIENCE IN LIFE SCIENCES R&D
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