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

Samuel Bangura

doi:10.15421/cims.5.328

Authors

Samuel Bangura✉ Mangosuthu University of Technology
- Conceptualization
- Formal Analysis
- Writing – original draft
- Writing – review & editing
- Investigation
- Methodology
https://orcid.org/0009-0007-2942-0198

DOI:

https://doi.org/10.15421/cims.5.328

Keywords:

data analytics, quantitative methods, management decision-making, big data

Abstract

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.

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Author Biography

Samuel Bangura, Mangosuthu University of Technology

Lecturer and Researcher in the Department of Human Resource Management. Holds a doctorate in Strategic Human Resource Management. Capacity Development Fellow (CDF) in the BRICS Project 3.0 at Durban University of Technology. Research focuses on the intersection of human resource management, sustainability, and climate change, with a particular emphasis on green HRM, green transformational leadership, and sustainable development goals within higher education. Has contributed to scholarly discourse on succession planning, workforce development, and the nexus of these domains. Additional research interests include talent management, graduate employability, work-integrated learning, organisational data analytics for management decision-making, and social entrepreneurship.

References

Al-Sai, Z. A., Abdullah, R., & Husin, M. H. (2020). Critical Success Factors for Big Data: A Systematic Literature Review. IEEE Access, 8, 118940–118956. https://doi.org/10.1109/access.2020.3005461

Bhuvanya, R., Yamini, B., Sivakumar, K., Gobinath, V. M., Sathya, V., & Subramanian, R. S. (2025). Data Analytics in Management Empowering Decision-Making Through Insights. In Pioneering Approaches in Data Management (pp. 1-30). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-5563-3.ch001

Brynjolfsson, E., & McElheran, K. (2016). The Rapid Adoption of Data-Driven Decision-Making. American Economic Review, 106(5), 133–139. https://doi.org/10.1257/aer.p20161016

Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1819486

Choo, C. W. (1996). The knowing organization: How organizations use information to construct meaning, create knowledge and make decisions. International Journal of Information Management, 16(5), 329–340. https://doi.org/10.1016/0268-4012(96)00020-5

Davenport, T. H., & Harris, J. G. (2017). Competing on analytics, updated, with a new introduction: The new science of winning. Harvard Business Review Press. https://hbsp.harvard.edu/product/10157-PDF-ENG

Erica, A., Gantari, L., Qurotulain, O., Nuche, A., & Sy, O. (2024). Optimizing Decision-Making: Data Analytics Applications in Management Information Systems. APTISI Transactions on Management (ATM), 8(2), 115–122. https://doi.org/10.33050/atm.v8i2.2202

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007

Hora, M. T., Bouwma-Gearhart, J., & Park, H. J. (2017). Data driven decision-making in the era of accountability: Fostering faculty data cultures for learning. The Review of Higher Education, 40(3), 391–426. https://doi.org/10.1353/rhe.2017.0013

Ikegwu, A. C., Nweke, H. F., Anikwe, C. V., Alo, U. R., & Okonkwo, O. R. (2022). Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions. Cluster Computing, 25(5), 3343–3387. https://doi.org/10.1007/s10586-022-03568-5

Khan, A. H. (2024). Effective decision making using data analytics. International journal of scientific research in engineering and management, 08(04), 1–5. https://doi.org/10.55041/ijsrem32598

Light, D., Wexler, D., & Henize, J. (2004). How practitioners interpret data and link data to instruction: research findings on New York City schools implementation of the grow network. Center for Children and Technology. https://purl.org/scimeta/cct.2

Lyu, G. (2025). Data-driven decision making in patient management: a systematic review. BMC Medical Informatics and Decision Making, 25(1). https://doi.org/10.1186/s12911-025-03072-x

Mandinach, E. B., Honey, M., & Light, D. (2006). A theoretical framework for data-driven decision making. Center for Children and Technology. https://purl.org/scimeta/cct.1

Manikandan, S., & Anju C. P. (2025). Optimizing Decision-Making in Business Management Using Mathematical Modelling and Data Analysis. International journal of scientific research in engineering and management, 09(07), 1–9. https://doi.org/10.55041/ijsrem51692

McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–66, 68, 128. https://pubmed.ncbi.nlm.nih.gov/23074865

Medida, L. H., & Kumar. (2024). Addressing challenges in data analytics: A comprehensive review and proposed solutions. In A. Bora, P. Changmai, & M. Maharana (Eds.), Critical approaches to data engineering systems and analysis (pp. 16–33). IGI Global. https://doi.org/10.4018/979-8-3693-2260-4.ch002

Moitas, J., Albuquerque, J., & Mano, R. (2023). Business intelligence implementation and its impact on decision-making. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1–7). IEEE. https://doi.org/10.23919/CISTI58278.2023.10211744

Müller, J., Schuh, G., Meichsner, D., & Gudergan, G. (2020). Success factors for implementing business analytics in small and medium enterprises in the food industry. In 2020 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) (pp. 1–8). IEEE. https://doi.org/10.1109/ICTMOD49425.2020.9380609

Phippen, A., Sheppard, L., & Furnell, S. (2004). A practical evaluation of Web analytics. Internet Research, 14(4), 284–293. https://doi.org/10.1108/10662240410555306

Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508

Rabelo, L., Gutierrez-Franco, E., Sarmiento, A., & Mejía-Argueta, C. (2021). Engineering Analytics. CRC Press. https://doi.org/10.1201/9781003137993

Rasool, N. S., Senguttuvan, S., Rajesh, R., & Deepa, N. (2025). The Role of Business Analytics in Planned Decision Making. International journal of scientific research in engineering and management, 09(08), 1–9. https://doi.org/10.55041/ijsrem51719

Ray, A., Kumar, R., & Deepanshi. (2025). Data analytics for business decision-making. Economic Sciences, 21(2), 191–200. https://doi.org/10.69889/gy56vg95

Santoso, L. W. (2023). Big data: Identification of critical success factors. In P. Vasant et al. (Eds.), Intelligent computing and optimization: ICO 2023 (Lecture Notes in Networks and Systems, Vol. 855, pp. 342–349). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-50158-6_34

Sharma, A. (2023). Analyzing the role of artificial intelligence in predicting customer behavior and personalizing the shopping experience in ecommerce. International journal of scientific research in engineering and management, 07(02). https://doi.org/10.55041/ijsrem17839

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001

Sun, Z., Sun, L., & Strang, K. (2016). Big data analytics services for enhancing business intelligence. Journal of Computer Information Systems, 58(2), 162–169. https://doi.org/10.1080/08874417.2016.1220239

Tarmidi, D., Taruna, I., Budi S, A., & Marinda S, V. (2023). Big data analytics and key success factor in achieving competitive advantage and performance of small medium enterprises: literature review. Enrichment: Journal of Management, 13(1), 586–595. https://doi.org/10.35335/enrichment.v13i1.1302

Wedel, M., & Kannan, P. K. (2016). Marketing Analytics for Data-Rich Environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413