Integrating big data analytics (BDA) into the decision-making process to enhance strategic agility in mining operations

Abstract

Due to the increasing complexity of mining operations, market volatility, and sustainability requirements, the need for agile and data-driven decision-making has arisen. Big Data Analytics (BDA) gives predictive insights and operational responsiveness that improve strategic agility. Although technological advancements have determined Big Data Analytics as a transformational tool, its incorporation into decision-making processes in the mining sector remains underexplored. For this reason, this study explored the integration of Big Data Analytics (BDA) into decision-making processes to enhance strategic agility in South African mining operations, thus addressing a gap in empirical research on BDA adoption in the mining sector. The study adopted a qualitative exploratory design involving eighteen (18) semi-structured interviews with mining professionals involved with BDA and BDA experts (manufacturers/ developers) operating in the mining space. A thematic analysis of the responses generated seven main themes: Leadership and governance, data readiness, proactive intelligence, operational excellence, data accessibility, people-centric approach and pragmatic implementation. Emerging as foundational enablers were leadership alignment and data quality. Results suggest that effective BDA adoption requires considerable support from top executives, the availability of an appropriate data architecture, ethical governance, and continuous stakeholder engagement. It enhances the theoretical understanding of the Resource-Based View (RBV) and the Technology-Organisation Environment (TOE) frameworks within the mining sector. Further research is still needed to analyse the long-term impact of BDA on performance and to develop customised capability-building frameworks for other industries.