Mining organizations rely on data from equipment, sensors, operational systems, planning tools, laboratories, suppliers, regulators, and technology partners to support critical decisions. When that data is difficult to access, inconsistent, poorly governed, or trapped in disconnected systems, it limits the industry’s ability to improve safety, productivity, sustainability, and operational performance.

At the same time, effective analytics, AI, automation, and digital tools depend on strong data foundations. This Working Group addresses the full data lifecycle — from data quality, interoperability, access, exchange, and governance through to practical applications of analytics, machine learning, AI-enabled decision support, automation, and digital twins.

The emphasis is on real operational value. The group recognizes that data and AI maturity vary widely across mining companies, and that simpler analytics, decision-support tools, or improved data practices are often more effective than complex AI deployments.

Key topics

• • Data quality, readiness, governance, ownership, access, security, and trust
  • Interoperability between mining systems, software platforms, equipment, and organizations
  • Data exchange between operators, suppliers, laboratories, regulators, technology providers, and partners
  • Reducing manual data handling and improving data quality, timeliness, consistency, and reliability
  • Practical adoption of standards, shared terminology, governance models, and common data approaches
  • Applications of analytics, machine learning, AI, simulation, automation, and digital twins
  • Understanding where AI is useful — and where simpler analytics or decision-support tools may be more effective
  • Building industry knowledge around AI, data fundamentals, implementation approaches, risks, and limitations
  • Supporting iterative, value-driven implementation rather than large, high-risk technology deployments