HR-АНАЛІТИКА В СИСТЕМІ УПРАВЛІННЯ КАДРОВИМ ПОТЕНЦІАЛОМ ПІДПРИЄМСТВА

The increasing complexity of socio-economic processes, the acceleration of digital transformation, and the growing role of human resources in ensuring organizational sustainability require enterprises to revise traditional approaches to managing human resource potential. In this context, managerial decisions based primarily on intuition or fragmented indicators are no longer sufficient. The relevance of this study lies in substantiating HR analytics as an integrated managerial approach that enables systematic assessment, forecasting, and development of human resource potential in alignment with enterprise strategic objectives. The purpose of the article is to conceptualize the role of HR analytics within the enterprise human resource potential management system and to develop methodological foundations for its practical application. The research methodology is based on a combination of general scientific and special methods, including analysis and synthesis, systemic and structural-functional approaches, abstraction and generalization, as well as conceptual modeling. These methods allow identifying key relationships between human resource characteristics and managerial outcomes, as well as structuring analytical processes within HR management. The results of the study include a conceptual clarification of HR analytics as an institutionalized management approach rather than a set of isolated metrics or digital tools. A system of HR metrics for evaluating human resource potential is substantiated, reflecting its quantitative, qualitative, behavioral, and developmental dimensions. In addition, an author’s model of HR analytics integration into the enterprise management system is proposed, demonstrating the transformation of HR data into analytical insights and managerial actions through descriptive, diagnostic, predictive, and prescriptive analytics. The model emphasizes the role of feedback mechanisms in ensuring adaptability and continuous improvement of human resource management practices. The practical value of the article lies in the possibility of applying the proposed approaches to enhance the quality of managerial decision-making, reduce human resource risks, improve workforce planning, and increase transparency and effectiveness of HR processes. The findings may be used by enterprise managers and HR specialists to design data-driven human resource management systems that support sustainable organizational development under conditions of uncertainty and dynamic change.