TL;DR If you are performing one of the above roles, we invite you to read this whitepaper and explore why traditional approaches to solving the problem of data proliferation, data silos, and end-to-end data and lab automation frequently fail or miss the mark. You will learn how an R&D Data Cloud offers a better approach to scientific data management and value extraction.
R&D data represents a huge and largely untapped store of value for biopharma organizations. Mastering data accelerates discovery: it lets you do science better, collaborate more smoothly, broaden pipeline, negotiate regulatory hurdles, and get new therapeutics to market faster. It lets you leverage and/or build applications — including AI/ML — that grant scientific and strategic insight, enable course-correction, improve quality assurance, and help automate away manual steps at the bench.
Doing this, however, requires a new category of solution that treats the meaning and significance of R&D data as a priority, gives data a genuine life cycle, and then takes responsibility for that life cycle (for ensuring data’s validity and helping extract its full value) end-to-end. Enter: the R&D Data Cloud.
Our new whitepaper, (aptly enough) called What is an R&D Data Cloud? — identifies four characteristics mandatory for such an optimal solution: it must be R&D-focused, data-centric, cloud-native, and open.
The table below highlights how a purpose-built solution for life sciences R&D fuels innovation and what significant gains can be achieved based on your organizational role:
Whatever your biopharma R&D focus, if you’re seeking to derive maximum long-term value from R&D data, please check out our whitepaper, What is an R&D Data Cloud? And if you’re ready to see a real R&D Data Cloud in operation and talk more about how it can help you accelerate discovery, please contact us for a demo.