<![CDATA[The rapid growth of digital technology, big data, and artificial intelligence (AI) has accelerated the adoption of data-driven human resource management. Consequently, people analytics has evolved into a strategic capability that supports more effective HR decision-making through the use of employee data. However, previous reviews mainly focused on conceptual issues or organizational benefits, leaving limited comprehensive evidence regarding recent developments, AI integration, ethical concerns, and HR decision-making. This study addresses this gap through a PRISMA-based SLR of Scopus-indexed literature. This study examines research developments, key themes, benefits, challenges, and future directions of people analytics through a Systematic Literature Review (SLR) following the PRISMA guidelines. Data were collected from the Scopus database, yielding 104 articles that met the inclusion criteria. The findings reveal a substantial increase in people analytics publications since 2021, reflecting the rapid pace of digital transformation. Five major themes emerged from the literature: people analytics in HR decision-making, AI and machine learning integration, organizational value creation, organizational readiness and analytical capability, and data ethics and privacy. The evidence indicates that people analytics enhances the objectivity, accuracy, and effectiveness of HR decisions in recruitment, talent management, performance evaluation, and employee retention. Nevertheless, its implementation remains constrained by issues related to data quality, analytical capability, organizational resistance, and ethical concerns surrounding algorithmic decision-making. Overall, this study offers both theoretical contributions and practical implications for organizations.]]>
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