Hybrid Data Competencies: Identifying required competencies for data-driven decisionmaking in local governments

Abstract

New technologies such as Big Data Analytics and the Internet of Things hold the promise of providing new insights that can potentially improve decision-making of local governments. However, in practice there seem to be few examples of this actually happening. Academic literature suggests an impediment to its widespread adoption revolves around a lack of existence of appropriate human competencies in local governments. However, what these competencies exactly are has not been identified so far. This study fills this gap by identifying the competencies that are required in the process of data-driven decision-making (DDDM) in local governments. It does so by first developing a preliminary theoretical model, which is used as the basis for studying how DDDM processes take place in practice in a ‘typical’ local government in the Netherlands. A combination of twelve expert interviews and twenty-two Behavioral Event Interviews (BEIs) provide the empirical data of this study. Based on the analysis of this, this study finds that the DDDM process as observed in the case is hybrid. It is a combination of the rationalities of ‘traditional’ decision-making and of ‘pure’ data-driven decision-making. Building on this finding, the competencies identified as required in this process are also hybrid. They are a combination of traditional and new competencies that should be connected in different phases in the process of DDDM by local governments. This will help them exploit the possibilities data offers in a responsible way. This is important in today’s society in which the influence of new technologies will only increase.