mineral project development frequently originate from poor data usability rather than geological uncertainty alone. By converting fragmented datasets into auditable, modeling-ready information, GDQM implementation directly reduces technical friction in project workflows. To support the qualitative gains listed above with quantitative measures of impact, the following indicators were derived from the implementation: (i) Survey reliability across the dataset: a substantial proportion of historical drillholes (on the order of 30% of total meters) was identified as having insufficient positional control, with this finding providing the basis for prioritizing collar/survey remediation rather than additional drilling; (ii) QA/QC coverage: a significant share of the historical drilling meters was found to lack systematic insertion of QA/QC samples (approximate 53% of drilled meters had no blanks, standards, or duplicates), highlighting how legacy data limited classification confidence prior to qualification; (iii) Reuse of legacy data: after qualification, over 80% of legacy holes were flagged and reincorporated into the dataset for resource modeling, reducing redundant drilling that would otherwise have been programmed to replace data deemed unreliable; (iv) Targeted infill: infill drilling was concentrated in sectors with low data-quality scores rather than uniformly distributed, resulting in an estimated 65% reduction in infill meters compared to a uniform-coverage scenario, thereby avoiding redundant drilling. These figures should be interpreted within the specific operational context of the case study and not extrapolated as universal benchmarks; outcomes vary with the initial state of legacy datasets, project maturity, and the scope of the qualification campaign. 4.2 Case Study 2 – Practical Outcomes from the Application of Mining Assessment A second practical implementation supporting the TIME TO MINE framework involves deployment of the Mining Assessment system within the GDQM Governance module, enabling mining companies to conduct structured internal self-assessments of compliance with international reporting standards for exploration results, mineral resources, and mineral reserves. In many mining projects, compliance with standards such as the JORC Code, NI 43-101, PERC, and other CRIRSCO-aligned codes is typically assessed only when preparing technical reports for financing or regulatory submissions. This late verification frequently reveals documentation gaps, inconsistent reporting practices, and insufficient QA/QC evidence, often resulting in reporting delays, technical rework, and reduced investor confidence. The implementation of Mining Assessment shifts compliance verification from a late-stage reporting requirement to a continuous governance practice embedded in project workflows, enabling companies to identify documentation gaps earlier and correct them before audits or financing events, thereby reducing reporting delays and costly revisions. The approach also improved alignment among geological, engineering, and corporate teams by standardizing internal assessments, consolidating technical documentation, and clarifying responsibilities, reducing friction and improving information flow. Companies further reported shorter preparation cycles for audits and due diligence reviews once assessment routines became part of regular project management practices. From a governance perspective, management teams gained clearer visibility into project technical maturity and risk exposure, enabling them to prioritize corrective actions based on project development needs. At the same time, companies demonstrated structured internal control over technical reporting practices, strengthening investor confidence and supporting investment decision-making. Importantly, these improvements were achieved without additional data acquisition, relying instead on
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