espEconomy: strategy and practiceEconomy: strategy and practice1997-99672663-550XИнститут экономики10.51176/1997-9967-2025-4-97-113esp-1709Research ArticleМЕНЕДЖМЕНТ И МАРКЕТИНГMANAGEMENT AND MARKETINGСнижение неопределённости в управлении проектами на основе данных: библиометрический анализReducing Project Uncertainty through Data-Driven Management: A Bibliometric Analysishttps://orcid.org/0000-0003-0766-4363СалыковаЛ. Н.SalykovaL.

Салыкова Л.Н. – PhD, ассоциированный профессор

пр. Мангилик Ел 55/11, Астана

Leila N. Salykova – PhD, Associate Professor

55/11 Mangilik El ave., Astana

leila.salykova@astanait.edu.kzhttps://orcid.org/0009-0002-4974-8864МусабековЖ. Б.MussabekovZh. B.

Мусабеков Ж.Б. – PhD докторант, м.н.с.

пр. Мангилик Ел 55/11, Астана

Zhandos B. Mussabekov – PhD student, Junior Researcher

55/11 Mangilik El ave., Astana

242663@astanait.edu.kzAstana IT UniversityКазахстанAstana IT UniversityKazakhstan20251201202620497113Copyright © Салыкова Л.Н., Мусабеков Ж.Б., 20262026Салыкова Л.Н., Мусабеков Ж.Б.Salykova L., Mussabekov Z.B.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://esp.ieconom.kz/jour/article/view/1709

В условиях ускоренной цифровизации и усложнения проектной деятельности исследования в области Data-Driven Project Management (далее – DDPM) остаются фрагментированными, что ограничивает целостное понимание его интеллектуальной структуры и динамики развития, несмотря на активное внедрение цифровых технологий. Целью данного исследования является выявление интеллектуальной структуры, динамики развития и доминирующих исследовательских траекторий управления проектами, ориентированного на данные (DDPM), на основе библиометрического анализа научных публикаций. Методологической основой исследования послужил библиометрический анализ научных публикаций с использованием инструментов Bibliometrix и Biblioshiny. Эмпирическая база включает 1149 статей и обзоров, проиндексированных в базе данных Scopus за период 2000-2025 гг. Результаты исследования показали, что при среднем годовом темпе роста 18,83%, при этом на статьи приходится 1012 документов (88,1%), на обзоры – 137 (11,9%). Cреднее число цитирований на публикацию составило 25,13, а анализ со-цитирования и ключевых слов выявил доминирование кластеров, связанных с машинным обучением, прогнозной аналитикой и управлением рисками. Результаты подтверждают, что DDPM фундаментально меняет управление проектами за счет улучшения поддержки принятия решений и максимизации эффективности ресурсов, что напрямую снижает финансовые риски и неопределенность. Перспективы дальнейших исследований связаны с использованием полученных результатов исследователями при планировании будущих научных работ, а также практиками и лицами, принимающими решения, для стратегического внедрения инструментов анализа данных, направленного на формирование более устойчивых, экономически эффективных и высокопроизводительных проектов.

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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The authors declare that there are no conflicts of interest present.