Data-Driven knowledge mobilization, translation and innovation

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Knowledge workers comprise a large proportion of the workforce today working in the context of large-scale expert networks, spanning across different disciplines and organizations. They rely on software technologies for their discipline-specific activities and for their coordination and their communication with each other. This type of networked, interdisciplinary knowledge work is motivated by a belief that the complex problems of our time can only be effectively addressed through broad collaborations of experts and partners who act both as knowledge generators and receptors to create value through new products and services. However, even as this belief is generally, and increasingly shared, the question of "how these networks can be supported to become more effective" is still very much open and the subject of considerable debate. The goal of our proposed research is to develop methods and tools for analyzing and enhancing the data sets through which knowledge workers collaborate, our hypothesis being that, by doing so, we can effectively amplify their productivity. These methods and tools will be validated through case studies with our partner organizations: an enterprise research and development (R&D) organization (within Dell Computers) and two Canadian research networks (the AGE-WELL and GlycoNET NCEs). We will demonstrate how our tools can enhance innovation productivity and help mobilize the work products of our partners towards adoption and value creation. Our goal is to: understand, support and improve the end-to-end process of (a) bringing the right individuals together, (b) collaborating and contributing their complementary expertise, and (c) making use of supporting processes and practices to create value through innovation in products and services. Within enterprise R&D organizations, our results will enable more people to be involved in the innovation process and will increase the number, quality and pace of ideas developed and disclosed. Within NCEs, our results will advance our understanding of the operating parameters of effective knowledge mobilization, translation and value creation and improve monitoring, reporting, evaluating, accountability and transparency in research networks.