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. Based solely on the data-generating rate, drilling data were grouped into successive campaign periods, enabling objective comparison of data quality and survey reliability over time. This grouping reflected a close association with the Lassonde Curve and the mine’s phases (Figure 5). It is important to clarify that, in this section, the term data quality refers to the overall consistency, accuracy, and completeness of the drilling database, including logging, assay, and validation procedures. In contrast, the term survey reliability refers specifically to the reliability of drillhole positioning, including collar coordinates and downhole survey type and accuracy. The temporal evaluation showed that data quality and survey reliability varied systematically with drilling intensity and project phase. Early exploration campaigns, characterized by lower drilling rates, exhibited higher survey reliability and consistent data quality, reflecting controlled operational conditions. In contrast, periods of intensified drilling, particularly during resource delineation, were associated with a decline in survey reliability, likely due to accelerated execution and increased operational complexity. However, during these high-intensity phases, geological logging quality improved, driven by the need to better define mineralized zones. These results demonstrate that data quality and survey reliability are dynamic properties influenced by data generation rate and the mining project's developmental stage. This behavior underscores the importance of situating database assessments within the mine operational lifecycle, rather than interpreting quality scores in isolation. Governance must adapt to operational scale. Figure 5 – Annual drilling and GDQM scores across a project development stage from a case study.
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