Do the right thing right! Understanding the hopes and hypes of data-based policy

This study proposes a bi-dimensional typology for understanding data-based policy according to whether evidential data are legitimate (legitimacy of data input) and whether the data analytics method used is legitimate (legitimacy of data throughput). The typology is applied to categorize recent cases in Korea and label possible scenarios as one of four types: scientific administration, which analyzes adequate data with an appropriate method (doing the right thing right); data failure, which analyzes inadequate data with an appropriate method (doing the wrong thing right); method failure, which analyzes adequate data with an inappropriate method (doing the right thing wrong); and total fiasco, which uses inadequate data with an inappropriate method (doing the wrong thing wrong). This paper suggests recommendations for making scientific administration cases sustainable, correcting data failures and method failures, and avoiding total fiascos.