Strategic management of human skills in Big Data initiatives: from SLR to skills taxonomy and human resource management framework

In the context of accelerating digital transformation, the alignment between human capital and technological innovation has become a strategic priority for organisations operating in data-intensive environments. However, the literature still exhibits significant fragmentation in the classification of the human skills required in this domain, and operational tools for their strategic management remain limited. Guided by the theoretical frameworks of the resource-based view and the dynamic capability view, this study addresses this dual gap through a systematic literature review. First, it presents a multi-level taxonomy of human skills relevant to Big Data initiatives. The taxonomy organises hard, managerial, and soft skills into a structured classification that serves both theoretical consolidation and practical applications. Second, the study introduces the AFE (awareness, fulfilment, efficiency) framework, a conceptual model designed to support the dynamic assessment and strategic optimisation of human capital. In addition to traditional maturity models, AFE incorporates subjective dimensions, such as self-awareness and job satisfaction, allowing organisations to identify discrepancies between perceived competencies, actual skill usage, and role expectations. Together, the taxonomy and the AFE framework offer a comprehensive foundation for improving skill assessment, guiding workforce planning, and supporting reskilling and job redesign initiatives. This work contributes to bridging the gap between descriptive literature and actionable tools, fostering more adaptive, evidence-based, and human-centred approaches to human resource management in Big Data contexts.