Exploring data-driven decision-making practices: a comprehensive review with bibliometric insights and future directions

Purpose: The exponential growth of organisational data has thrust big data into the spotlight, making data analysis, information extraction and data-driven decision-making (DDDM) critical for organisational success. This study aims to systematically review the literature to identify key research trends, methodologies and opportunities within the DDDM domain.

Design/methodology/approach: This research employs bibliometric analysis and systematic review methodologies to synthesise findings from existing studies. The analysis categorises research methods into eight primary groups, highlighting their applications and contributions to DDDM.

Findings: The review identifies machine learning, statistical models and qualitative methods as the most widely used approaches, while multi-criteria decision-making and simulation emerge as promising avenues for future research. Research has predominantly focused on production and operations and business management and organisation. However, underexplored domains with significant potential for future breakthroughs are marketing and sales, development and education and social and financial.

Originality/value: This study underscores critical gaps in the application of DDDM across less-explored fields, including engineering, biomedical sciences and safety and security. By identifying emerging trends and under-represented areas, the research provides a roadmap for advancing DDDM scholarship and practice.