This study intends to provide scholars and practitioners with an understanding of human resource challenges in the context of Big Data Analytics (BDA). This paper provides a holistic framework of human-related capabilities that organizations must consider when implementing BDA to facilitate decision-making. For this purpose, the authors conducted a systematic literature review adapted from Tranfield et al. (BJM 14:207–222, 2003) to identify relevant studies. The 75 publications reviewed provided the sample for an inductive, and systematic data evaluation following the well-known and accepted approach introduced by Gioia et al. (ORM 16:15–31, 2012). The comprehensive review uncovered 33 first-order concepts linked to human-related capabilities, which were distilled into 15 s-order themes and then merged into five aggregated dimensions: Personnel Capability, Management Capability, Organizational Capability, Culture and Governance Capability, and Strategy and Planning Capability. The study is, to the best of the authors’ knowledge, the first to categorize all relevant human-related capabilities for successful BDA application. As such, it not only provides the scientific basis for further research, but also serves as a useful overview of the critical factors for BDA use in decision-making processes.

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Numerous studies have shown that approaches of AI&A decision-making have the power to increase the quality of decisions. However, many firms have not adopted these approaches and many decisions are still made intuitively. Those decisions often fail to achieve their intended results, lead to negative consequences, and sometimes must be reversed. This paper sheds light on the question of how management can drive the shift from intuitive to data-based decision-making.An in-depth single-site case study was conducted with a large publicly listed German manufacturing company. Building on 22 interviews, this empirical study identifies the root causes and overarching factors that need to be addressed to facilitate the shift from intuitive to analytics-based decision-making. These include management behavior, top management and strategy, analytics infrastructure, organization and governance, HR management and development, and culture. These factors form a hexagonal framework that offers actionable lessons for practice.The derived framework can serve as a basis for further research on the topic of analytical decision-making. In addition, it provides company leaders a useful tool to manage the transformation of decision-making in organizations.