464 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 forwardlooking 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 drilling databases at a gold mine in Brazil. In this case, datasets generated across multiple exploration campaigns and time periods were consolidated and reassessed under a unified quality and governance framework. The project reflects a common situation in mature operations, where data accumulated across successive campaigns, executed under evolving standards, leads to inconsistent data quality and reliability, ultimately creating modeling and reporting challenges. Nonetheless, the evolution of improved analytical techniques, survey methods, and geological knowledge about the deposit results in varying data quality and standards over the life of a mine, and consequently in datasets that differ in quality and standards. To provide a quantitative reference for the scale of the dataset analyzed in this case, the implementation involved the qualification of approximately 2,900 diamond drillholes, totaling on the order of 163,400 meters of drilling, evaluated across multiple parameters, including drillhole survey reliability, geological logging consistency, density measurements, and laboratory QA/QC controls.
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