Developing human resource analytics capability architecture in organizations

Purpose As organizations increasingly adopt data-driven technologies to support fast, reliable and predictive workforce decisions, human resource analytics (HRA) has emerged as a critical enabler of strategic human resource management (HRM). Despite its growing importance, there remains a lack of conceptual clarity on how to systematically develop HRA capabilities within organizations. This study seeks to address this gap by identifying the key components required to build robust HRA capability architecture. Design/methodology/approach Grounded in the theoretical lens of the capabilities-based view (CBV) this study employs a systematic literature review and thematic analysis of 198 peer-reviewed research articles to extract patterns and themes related to the development of HRA capabilities. Findings The analysis revealed four overarching capability dimensions essential for building an HRA capability architecture: business assimilation capability, data management capability, organizational process capability and analytical capability. These are further unpacked into 11 sub-themes, including the role of HRA, goals of HRA, human resource (HR) data, determinants of HRA, applications of HRA, level of analysis in HRA, HRA techniques, competencies for HRA, use of information technology (IT) tools, challenges of HRA and stakeholders of HRA. Research limitations/implications This study advances the theoretical understanding of HR analytics by adopting the CBV to conceptualize the architecture required for HRA capability development in organizations. Practical implications Organizations seeking to develop robust HRA functions can benefit from the comprehensive capability framework proposed in this study. The four dimensions – business assimilation, data management, organizational process and analytical capability – serve as actionable pillars for designing HRA initiatives. HR leaders and analytics practitioners can use the 11 sub-themes as a diagnostic tool to assess maturity, identify capability gaps and align HRA investments with strategic business objectives. The framework also assists in integrating IT tools, developing analytics talent and navigating implementation challenges. It promotes a structured approach to embedding analytics in HR decision-making processes across diverse organizational contexts. Social implications By enhancing HRA capabilities, organizations can foster more equitable, transparent and data-driven HR practices that benefit employees and stakeholders alike. Improved analytics can support fair hiring, inclusive workforce planning, and objective performance management. Moreover, the framework emphasizes stakeholder engagement, highlighting the social dynamics necessary for successful HRA integration. This approach supports the ethical deployment of people analytics, ensuring that technological advancements in HR serve human-centered outcomes rather than solely organizational efficiency. Originality/value This research offers a novel and comprehensive HRA capability architecture grounded in a CBV, filling a crucial gap in both academic and practitioner-oriented literature. The framework provides a unifying structure to guide both theoretical inquiry and practical implementation, marking a significant step toward institutionalizing analytics within HRM. Its relevance extends to organizations navigating digital transformation, offering a strategic roadmap for embedding analytics into human capital decision-making processes.