Data Science in Decision-Making Processes: A Scientometric Analysis

Abstract

Purpose: The article concludes on the importance of scientometric analysis to present research areas and directions in data science in order to support decision-making process. Design/Methodology/Approach: Scientometric analysis. Findings: Article is part of scientometric research performed by authors that results in series of two separate papers. The first one described leading researchers and their area of interest who provide significant input into data science development. The current article quantitatively characterizes the literature thematically related to data science issues, particularly in decision-making processes. The scientometric method was used for data content analysis. The Scopus database was chosen as a source database to perform scientometric analysis. The authors identified core business areas where data science tools have been used in decision-making processes. It is also worth noting the correlation between domain areas and funding sources. Practical Implications: Executing scientific analysis can help to identify research directions in data science area. Originality/value: In our study, we showed that a significant increase in the number of scientific articles in the medical field is directly dependent on research funding institutions. The quantitative characteristics and evolution of keywords, which were the subject of the publications, are also presented. Research directions and their evolution over the years are as well indicated.