<![CDATA[Background. The increasing complexity of workforce management in modern organizations has driven the adoption of innovative tools such as Big Data Analytics (BDA) in human resources (HR). Talent management, encompassing recruitment, retention, and performance evaluation, has become a critical focus for organizations aiming to maintain competitiveness. Big Data Analytics enables HR professionals to identify patterns, predict trends, and make data-driven decisions, enhancing talent management processes. Despite its potential, the application of BDA in HR faces challenges, including data integration, privacy concerns, and skill gaps. Purpose. This study explores the role of Big Data Analytics in improving talent management and prediction, focusing on its impact on decision-making and organizational outcomes. Method. A mixed-method research design was employed, incorporating quantitative analysis of HR metrics and qualitative insights from interviews with HR professionals. Data were collected from 15 organizations across diverse industries, analyzing employee performance, recruitment patterns, and turnover rates. Predictive models were developed using machine learning algorithms to forecast talent trends and inform HR strategies. Results. The findings revealed that BDA significantly improved talent acquisition and retention processes, with a 25% increase in recruitment efficiency and a 30% reduction in turnover rates. Predictive models accurately identified high-potential candidates and flagged at-risk employees, enabling proactive interventions. Challenges related to data privacy and technical expertise were highlighted as areas for improvement. Conclusion. The study concludes that leveraging Big Data Analytics transforms talent management by enabling evidence-based decision-making and predictive insights. Addressing implementation challenges and investing in skill development will maximize its potential in HR practices.]]>
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