A Scoping Review of the Role of Data Analytics in Management Decision Making

Purpose. This study examines the role of data analytics in management decision-making. Design / Method / Approach. A PRISMA-compliant scoping review was conducted through a literature search across academic databases including Scopus, EBSCOhost, ABI/INFORM, IEEE Xplore, PubMed, and ScienceDirect, as well as Google Scholar. Selection was guided by predefined inclusion and exclusion criteria to ensure relevance and quality. Findings. The literature reveals a paradigm shift from intuition-based to data-driven decision-making. Predictive analytics, machine learning, and quantitative methods empower managers to improve risk assessment and scenario modeling. The four analytics types — descriptive, diagnostic, predictive, and prescriptive — yield measurable gains in operational efficiency (up to 35%), productivity, and competitive positioning across healthcare and retail sectors. Successful adoption requires strong leadership, data governance frameworks, and organizational data literacy. Persistent barriers including data quality issues, skill deficits, cultural resistance, and privacy concerns continue to impede implementation. Theoretical Implications. This review links established decision-making frameworks with contemporary AI-driven applications, offering a balanced synthesis of current knowledge, albeit with breadth prioritized over depth. Practical Implications. The findings provide actionable guidance for managers and leaders, with relevance for emerging economies investing in data infrastructure and literacy. Originality / Value. The study offers a consolidated perspective on data analytics as a core component of modern management practice, synthesizing evidence from business and healthcare domains. Research Limitations / Future Research. Future research should extend to empirical investigations and longitudinal studies assessing the long-term organizational impact of data analytics on management decision-making. Article Type. Review.