HUMAN RESOURCE ANALYTICS AND DATA-DRIVEN DECISION MAKING: A SYSTEMATIC REVIEW OF CONTEMPORARY PRACTICES
DOI:
10.5281/zenodo.23205271Published:
2026-10-07Downloads
Abstract
Human resource analytics (HRA) has rapidly evolved from a descriptive reporting function into a strategic imperative that drives organizational agility, evidence-based decision-making, and workforce Optimization. This systematic review critically synthesizes 16 benchmark empirical and conceptual studies published between 2021 and 2026 to evaluate contemporary practices, architectural frameworks, and performance outcomes associated with data-driven HR practices. Following PRISMA 2020 guidelines, literature was extracted across leading management and analytics databases. Findings reveal that while predictive talent modeling and machine learning algorithms significantly enhance decision quality (β = 0.51, p < 0.001) and strategic talent ROI (β = 0.34, p < 0.001), full organizational adoption remains constrained by data silos, privacy governance gaps, and a prevalent HR data literacy deficit. The review synthesizes a multi-level maturity framework illustrating the transition from descriptive dashboards to prescriptive machine intelligence. Ultimately, successful digital HR transformation requires balancing technical analytics capability with robust ethical data governance, continuous skill re-tooling, and executive alignment to ensure sustainable competitive advantage.
Keywords:
Data-Driven Decision Making Human Resource Analytics Machine Learning in HR Predictive Talent Modeling Strategic Human Resource ManagementReferences
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Copyright (c) 2026 Wan Rita Azmiyati, Ramon Zamora, Lukmanul Hakim
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