Data-driven approaches to decision making are today applied far and wide. With origins in the field of judgment and decision making (JDM), data-driven decision making (DDDM) has become an emergent topic within I-O psychology, especially within the fields of people analytics and human resource analytics. In light of the current AI revolution, it is evident that the next steps in JDM research include data- driven approaches. The purpose of this Master’s thesis was to compile the research on data-driven decision making conducted across disciplines into a comprehensive overview. Main research questions: based on systematic reviews and scoping reviews about implementations of DDDM affecting individuals, groups, or organizations, what areas of research can be identified? How and to what extent are they linked? To address these questions, this thesis utilizes a scoping meta-review design and bibliometrics. After rigorous search and screening processes, the final sample consisted of n = 1,008 systematic and scoping reviews. The results indicated that there are research areas within the included reviews that are isolated to a varying extent. Based on a multiple correspondence analysis (MCA), five areas of research were identified: business intelligence; learning analytics/education; mHealth/telemedicine; general decision making/decision support; and clinical decision support/diagnosis/healthcare. As a scoping meta-review encompassing a large number of scientific fields and methodologies, this thesis contributes to the progression of DDDM research at large. The results highlight the scattered nature of current research practices within DDDM and identify an opportunity for scientific advancement through interdisciplinary research.