Data-Driven Management: transformation of strategic decision-making processes

Abstract

The current stage of economic development is characterized by rapid digitalization, the growth of data volumes, and increasing complexity of management processes, which necessitates a rethinking of approaches to strategic decision-making. In conditions of high uncertainty and a dynamic external environment, traditional management models based on experience and retrospective analysis are losing their effectiveness, which actualizes the transition to data-oriented systems. The purpose of the study is to substantiate the theoretical foundations and develop a conceptual approach to the transformation of management decision-making processes based on data, as well as to systematize the key factors that determine this process. The methodological basis of the study includes general scientific and specific methods, in particular analysis and synthesis – to generalize approaches to data-driven management; comparative analysis – to assess sectoral features of demand for analytical competencies; a system approach – to determine the structure of digital maturity and its components; and a structural-logical method – to construct an architectural model of data-driven management. The information base consists of analytical materials of international organizations, including statistical data on the development of digital technologies, demand for competencies, and levels of digital maturity of public administration. As a result of the study, technological factors of data-driven management formation were systematized, including data integration, development of analytical tools, and the spread of artificial intelligence technologies. The sectoral differentiation of demand for relevant competencies was analyzed, which made it possible to identify different rates of adaptation to data-driven approaches across economic sectors. The levels of digital maturity were generalized and their role as an institutional basis for the implementation of data-driven management was determined. An architectural model is proposed, reflecting the sequence of transformation of data into strategic decisions through integration, management, analytics, and automation. The practical significance of the results lies in the possibility of applying the proposed approach to improve the efficiency of management processes, reduce risks, and ensure the adaptability of organizations in a changing environment.