Strategic Human Resource Analytics and Intelligent Data-Driven Systems: A Conclusion

Mufaro Dzingirai () and Sulaiman Olusegun Atiku ()
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Mufaro Dzingirai: Namibia University of Science and Technology, Harold Pupkewitz Graduate School of Business
Sulaiman Olusegun Atiku: Namibia University of Science and Technology, Harold Pupkewitz Graduate School of Business

A chapter in Strategic Human Resource Analytics and Intelligent Data-Driven Systems, 2026, pp 239-249 from Springer

Abstract: Abstract The huge datasets derived from employee profiling have justified the exigent need for data-driven insights into the human resource decision-making process. With increasing demand for real-time data, the transformative power of data-driven intelligence has augmented the efficiency and effectiveness of Human Resource (HR) practitioners. These practitioners are utilizing a multiplicity of methodological tools in modelling big data generated from employee records in real time. It is impetus to mention that operational efficiency can be ensured by detecting errors using advanced statistical tools and research designs, supporting evidence-based decisions to achieve a durable competitive edge in the labor market. As such, this concluding chapter interrogates the drivers of the adoption of strategic human resource analytics and data-driven systems, challenges faced in strategic human resource analytics, in the contemporary digital economy, practical and policy implications, and the further research agenda. This chapter concludes that the strategic human resource analytics and data-driven systems are the heart and soul of evidence-based strategic decisions for sustainable value creation through the robust application of descriptive, diagnostic, predictive, and cognitive analytics.

Keywords: People analytics; Artificial intelligence; Prescriptive analytics; Internet of things; Cognitive analytics; Machine learning; Strategic human resource management; Big data analytics (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-981-92-2623-8_12

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