Techniques for Data-Driven R&D Talent Management
R&D organizations depend on the right mix of human talent to drive innovation. But the alchemy involved in aligning individuals and teams for success is often poorly understood, and early recognition of promising advances or critical blockers depends heavily on intuition. At the same time, modern organizations can be extraordinarily well instrumented, providing a rich set of data sources that can be mined to better recruit, retain, engage, and integrate R&D teams. The same big data analysis techniques that are applied to the object of an R&D activity can now be applied to the R&D process itself, helping decision makers move beyond reliance on hunches and prior experience and instead gain deep insight into the conduct of research, spot emerging innovation opportunities early, and reveal the key characteristics of high-performing innovation teams.
Participants explore a suite of data analysis methods and tools – including large graph analysis of internal collaboration networks, centrality measures to identify hidden influencers and bridge builders, and machine learning algorithms for predicting innovation performance – that have been used inside a research organization to gather, synthesize, and extract insight from large, complex, and heterogeneous data about the organization itself. Discuss lessons learned in how these approaches should be implemented, what tools and software can assist, and how to assess the impact of these new methods.
Access the January 2017 Brown Bag video on this topic: https://www.pathlms.com/iri-learningcenter/events/401/video_presentation...