An ounce of prevention is worth a pound of cure – Building capacities for the use of big data algorithm systems (BDAS) in early crisis detection

Abstract

Public sector organizations at all levels of government increasingly rely on Big Data Algorithmic Systems (BDAS) to support decision-making along the entire policy cycle. But while our knowledge on the use of big data continues to grow for government agencies implementing and delivering public services, empirical research on applications for anticipatory policy design is still in its infancy. Based on the concept of policy analytical capacity (PAC), this case study examines the application of BDAS for early crisis detection within the German Federal Government—that is, the German Federal Foreign Office (FFO) and the Federal Ministry of Defence (FMoD). It uses the nested model of PAC to reflect on systemic, organizational, and individual capacity-building from a neoinstitutional perspective and allow for the consideration of embedded institutional contexts. Results from semi-structured interviews indicate that governments seeking to exploit BDAS in policymaking depend on their institutional environment (e.g., through research and data governance infrastructure). However, specific capacity-building strategies may differ according to the departments' institutional framework, with the FMoD relying heavily on subordinate agencies and the FFO creating network-like structures with external researchers. Government capacity-building at the individual and organizational level is similarly affected by long-established institutional structures, roles, and practices within the organization and beyond, making it important to analyze these three levels simultaneously instead of separately.