African Journal of Business, Economics and Management
Volume 1 , Issue 2
Review Article • Open Access

Data-Driven Strategic Management: An Integrated Framework for Business Analytics, Business Intelligence, and Organizational Performance

View PDF

Abstract

In an era of unprecedented data proliferation and digital transformation, organizations face the critical challenge of transforming vast volumes of complex data into actionable strategic insights. This paper presents a comprehensive integrated framework that synthesizes the interconnected domains of human resource (HR) analytics, business intelligence (BI), advanced analytics, and scientific data analytics within the context of strategic management. Through a systematic review of contemporary literature and secondary research, this study examines how organizations can leverage analytical capabilities to enhance decision-making processes, improve operational efficiency, and achieve sustainable competitive advantage. The findings reveal that effective integration of analytics into business processes requires alignment across five key dimensions: analytical capability development, strategic alignment with organizational objectives, evidence-based management practices, robust data governance mechanisms, and organizational readiness supported by leadership commitment and data-driven culture. The paper identifies critical success factors including data quality management, skilled personnel development, scalable technological infrastructure, and ethical considerations surrounding data privacy and algorithmic bias. Challenges persist in the form of organizational resistance, data integration complexities, and the IT investment paradox. The proposed integrated framework contributes to the literature by bridging the gap between technical analytics capabilities and strategic management processes, providing both theoretical insights for future research and practical guidance for managers seeking to strengthen data-informed strategic planning in increasingly dynamic digital environments. The study concludes that organizations adopting a holistic approach to analytics integration—addressing both technical and socio-organizational dimensions—are better positioned to achieve sustainable growth and innovation in the data-driven economy.

References

Chervona, O., Urba, S., & Prokopovych-Pavliuk, I. (2026). HR analytics in business process management. Journal of Business Process Management, 1-7.
Deloitte. (2025). 2025 Global Human Capital Trends. Deloitte Insights.
Denić, N., Gligorijević, M., Milašinović, S., Mehmedi, S., & Milić, S. (2026). Business intelligence and advanced analytics in the function of business improvement. 6th International Conference on Recent Academic Studies, Konya, Turkey, January 27-28, 2026.
Gartner. (2025). Top HR Trends and CHRO Priorities for 2026. Gartner Research.
Negash, S. (2004). Business intelligence. Communications of the Association for Information Systems, 13(1), 177-195.
Singh, S., Chaurasia, H., Khare, K. S., & Chaturvedi, M. (2026). Big data analytics in business intelligence. Scriptora International Journal of Research and Innovation (SIJRI), 2(Special Issue 1), 28-36.
Sulastriningsih, K., Annisah, I., Meria, L., & Kiboy, A. C. (2026). Integrating scientific data analytics into digital business strategy. Startupreneur Business Digital (SABDA Journal), 5(2), 107-118.
Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3), 356-367.
Turban, E., Sharda, R., Delen, D., & King, D. (2021). Business intelligence, analytics, and data science: A managerial perspective (5th ed.). Pearson.
Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52(5), 546-553.
Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
Sharda, R., Delen, D., & Turban, E. (2023). Business Intelligence, Analytics, and Data Science: A Managerial Perspective (6th ed.). Pearson.
Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly Media.
Kaplan, R. S., & Norton, D. P. (2004). Strategy Maps: Converting Intangible Assets into Tangible Outcomes. Harvard Business School Press.
McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.
Wixom, B. H., & Watson, H. J. (2010). The BI-based organization. International Journal of Business Intelligence Research, 1(1), 13–28.
Scroll to Top