Pathways to learning in data-based policy innovation labs - Sojeong Kim; Tanya Heikkila; Adam M. Wellstead (International Review of Public Policy)
In recent years, policy innovation labs (PILs) have emerged to develop greater capacity for addressing pressing public policy problems and achieving policy objectives. An implicit assumption is that PILs can bring new data and evidence to support policy learning. Policy learning enables individuals, organizations, and systems to advance their capacity to achieve policy objectives and produce desirable policy outcomes. We investigate the role of policy learning in “data-based” PILs, which address the growing interest in big data and advanced technologies to address public policy issues. With a focus on applying new information and data systems technologies to assess public policy and management issues, data-based PILs would be expected to support policy learning and thus serve as a critical case for assessing how the design of policy venues can support intentional policy learning. Yet, limited research has examined whether and how policy learning occurs in these PILs. Using an exploratory study design and analyzing findings from key informant interviews, insights into policy learning reveal that data-based PILs enable policy learning processes by acquiring, translating, and disseminating data, information, and experiences across organizations. The most significant policy learning challenge that data-based PILs face is inadequate systems to connect data across agencies. This barrier was perceived as limiting the ability of PILs to fulfill their role of enhancing knowledge sharing in a policy system.