Reducing Project Uncertainty through Data-Driven Management: A Bibliometric Analysis

In the context of accelerated digitalization and increasing complexity of project activities, research in the field of Data-Driven Project Management (hereinafter – DDPM) remains fragmented, which limits a holistic understanding of its intellectual structure and development dynamics, despite the active introduction of digital technologies. The purpose of this study is to identify the intellectual structure, development dynamics, and dominant research trajectories of DDPM based on a bibliometric analysis of scholarly publications. The methodological basis of the study was the bibliometric analysis of scientific publications using the tools Bibliometrix and Biblioshiny. The empirical database includes 1,149 articles and reviews indexed in the Scopus database for the period 2000-2025. The results of the study showed that with an average annual growth rate of 18.83%, articles account for 1,012 documents (88.1%), reviews – 137 (11.9%). The average number of citati ons per publicati on was 25.13, and the analysis of co-citati ons and keywords revealed the dominance of clusters related to machine learning, predicti ve analyti cs, and risk management. The results confi rm that DDPM is fundamentally changing project management by improving decision support and maximizing resource effi ciency, which directly reduces fi nancial risks and uncertainty. The prospects for further research are related to the use of the results obtained by researchers when planning future scienti fi c work, as well as practi ti oners and decision makers, for the strategic implementati on of data analysis tools aimed at creati ng more sustainable, cost-eff ecti ve and high-performance projects.

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