Data-Driven governance as an emerging Paradigm in Public administration: conceptual foundations

Abstract

The relevance of the study is determined by the key role of data in public administration amid digital transformation. The emergence of a new data-driven governance paradigm is outpacing the development of an appropriate theoretical and methodological framework, creating a need for comprehensive scholarly understanding of this issue. The aim of the article is to develop the conceptual foundations of data-driven governance. The scientific novelty lies in developing a classification of four approaches — evidence- based policymaking, indicative governance, algorithmic governance, and data-driven performance management; identifying systemic risks and limitations of applicability; and substantiating the need for an evolutionary synthesis of these approaches that takes into account national and cultural contexts. In addition, the study provides a terminological distinction between data-driven governance, data governance, and data-centric governance, thereby refining the conceptual framework of the field. The methodological framework comprises systems-structural and institutional analysis, as well as methods of classification and typologization. The article discusses conceptual approaches to differentiating data-driven governance instruments and demonstrates their implementation in the practices of several countries, including the United States, the United Kingdom, the Netherlands, China, the United Arab Emirates, and Russia. The findings open up prospects for further empirical research aimed at assessing the maturity of data-driven governance.