assessment (PEA) to prefeasibility studies (PFS) and, finally, feasibility studies (FS), during which datasets must reach defined maturity and validation thresholds before advancing. Supporting this process, AI-assisted tools serve as technical audit assistants, detecting inconsistencies, identifying data gaps, ensuring equivalent terminology across datasets and reports, and generating structured evidence trails for expert review. Automated screening, combined with human validation and competency guidance, reduces review cycles and ensures that decision gates are reached with defensible, auditable technical information. 4. RESULTS | OUTCOMES Before presenting case study evidence, it is important to distinguish between two categories of findings reported in this work: (i) demonstrated outcomes, which correspond to operational and technical improvements measured in completed implementations within real industry projects; and (ii) forwardlooking potential, which refers to projections regarding broader sector-level acceleration of project delivery times. The latter represents gains that, although consistent with the trajectory observed in case studies, remain contingent on additional industry adoption, organizational change, regulatory alignment, and validation across diverse jurisdictions and commodity types. The case studies that follow describe demonstrated outcomes; the closing remarks on lead-time reduction across the industry should be interpreted as forward-looking potential. The implementation of data governance practices within mineral projects has begun to demonstrate measurable operational and technical improvements across the exploration, resource evaluation, and reporting stages. This methodology remains in the early stages of adoption and has been used to date in only a select number of mining projects. Although outcomes vary depending on project maturity and legacy conditions, consistent operational and strategic improvements have been observed. The authors are aware of mining companies that are developing technical, data, and data-use approaches to substantially reduce their project development time to bring a new mine on stream. For example, reducing the current estimated implementation time from 15 to 20 years to 7 to 10 years. While these approaches are still being developed and are not yet published in public, the companies’ approaches include many of the processes described here in TIME TO MINE. These projected reductions in lead time are presented here as forward-looking potential and should not be conflated with the case-study results discussed in the following sections. The results summarized here are from applied implementations conducted in industry projects between 2019 and 2025, involving mining companies of different scales – including majors, mid-tier producers, and junior explorers – and covering multiple commodities, including bauxite, iron ore, gold, niobium, rare earth elements, and manganese. These implementations encompass various project phases, from initial exploratory analysis through pre-feasibility assessments to full operational environments. Across these projects, approximately 17.6 million geological records and associated datasets were audited, consolidated, or qualified. These datasets primarily comprise drillhole and sampling databases, analytical laboratory results, historical paper documentation, legacy datasets, and inputs used in geological and resource modeling. The work was executed under the GDQM framework, enabling the implementation of structured data governance principles in live project environments rather than only in theoretical or audit contexts. 4.1 Case Study 1 – Practical Results from GDQM Application A practical example of GDQM implementation is the qualification of historical and operational
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