466 rigorous collar control and downhole survey accuracy. The results demonstrate that data performance is strongly conditioned by operational intensity and project phase rather than by data age alone. In this case study, early campaigns conducted under more controlled conditions showed high spatial reliability, demonstrating that the assumption that older data are inherently inferior is incorrect The correction of key inconsistencies, such as mismatched sampling and assay intervals, depth discrepancies between relational tables, duplicated records, and non-standardized geological coding, substantially improved internal database coherence. As a result, modeling workflows became more robust, better suited to automated modeling and estimation, and less susceptible to reconciliation conflicts prior to estimation, reducing iterative adjustments driven by structural database issues. Database consolidation enabled the recovery and reintegration of legacy datasets that had been previously excluded from modeling due to quality constraints. This process not only enhanced spatial data coverage but also reduced the need for additional drilling in selected areas, thereby maximizing the value of historical investments. The gridded spatial representation of data quality across the mine allowed the identification of sectors with insufficient data support and lower survey reliability. This spatial diagnosis provided an objective basis for prioritizing infill drilling in areas where incremental improvements in data quality could significantly enhance geological continuity and resource confidence. Rather than uniformly expanding drilling programs, campaigns were strategically directed toward zones where additional information would produce the greatest impact on technical robustness and classification support. The application of GDQM produced measurable operational and technical improvements, including: (i) reduced time required for database reconciliation prior to resource modelling; (ii) fewer model revisions associated with data inconsistencies; (iii) enhanced auditability and reporting transparency; (iv) increased robustness of inputs supporting resource classification; (v) more strategically optimized targeting of subsequent drilling campaigns; and (vi) data that is better suited to automated modelling and estimation workflows. Importantly, many of these gains were achieved without acquiring new data, but by qualifying and consolidating existing information. The case study shows that delays in 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 dataquality 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
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