Loso Judijanto

Big Data Analytics in Decision Making: A Bibliometric Mapping of Scientific Contributions

Introduction

Big data analytics in decision making: a bibliometric mapping of scientific contributions. Explore the intellectual landscape of Big Data Analytics in decision-making through a bibliometric study. Discover publication trends, key authors, journals, global collaborations, and evolving research foci.

Abstract

This study aims to map the intellectual, conceptual, and collaborative landscape of scientific research on Big Data Analytics (BDA) in the context of decision-making using a bibliometric approach. Drawing data from the Scopus database and analyzing it through VOSviewer, the study identifies publication trends, influential authors, high-impact journals, keyword co-occurrence patterns, and international collaboration networks. The results reveal that "big data" serves as the dominant thematic core, often interconnected with concepts such as data analytics, data mining, information management, and artificial intelligence. Temporal and density visualizations indicate a shift in research focus from traditional data management toward intelligent decision support systems and real-time analytics. Additionally, countries such as China, the United States, and the United Kingdom emerge as central actors in shaping global collaboration. The study contributes to the theoretical understanding of the field by highlighting its interdisciplinary nature and provides practical insights for policymakers, academics, and practitioners seeking to leverage BDA for more effective, data-driven decision-making. Limitations and directions for future research are also discussed.

Review

This paper presents a timely and pertinent bibliometric analysis mapping the scientific contributions to Big Data Analytics (BDA) in decision-making. The stated objective to delineate the intellectual, conceptual, and collaborative landscape of this critical domain is well-defined and highly relevant given the increasing reliance on data-driven strategies across various sectors. The methodology, leveraging the comprehensive Scopus database and VOSviewer for analysis, appears robust and appropriate for a study of this nature, promising a rigorous examination of publication trends, author influence, journal impact, keyword patterns, and international collaboration networks. The scope is broad enough to offer a holistic overview while focusing on a specific, high-impact interdisciplinary area. The abstract highlights several salient findings that significantly contribute to understanding the field's dynamics. The identification of "big data" as the central thematic core, alongside its intricate connections with data analytics, data mining, information management, and artificial intelligence, effectively illustrates the multi-faceted nature of the research area. The observed temporal shift from traditional data management towards intelligent decision support systems and real-time analytics provides valuable insight into the evolving research priorities and technological advancements. Furthermore, pinpointing key countries like China, the United States, and the United Kingdom as dominant forces in global collaboration offers a clear picture of the geographical distribution of research leadership and innovation. In conclusion, this bibliometric mapping offers a valuable contribution to the theoretical understanding of Big Data Analytics in decision-making by underscoring its inherent interdisciplinarity. Beyond its academic merit, the study promises practical utility, providing actionable insights for policymakers, academics, and practitioners aiming to harness BDA for enhanced decision-making processes. The explicit mention of discussing limitations and future research directions further strengthens the paper's comprehensive approach, making it an invaluable resource for both nascent and seasoned researchers seeking to navigate and contribute to this rapidly evolving scientific landscape.

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