Mine Planning; Deswik MDM; Mining Data Management; Underground Mining; Data-Driven Decision Making; Data Integration; Short Interval Control (SIC); Integrated Mining Enterprise (IME); Operational Excellence; Digital Transformation; Aripuanã. 1. INTRODUCTION The mining industry is undergoing an accelerated digital transformation, driven by the need to improve operational efficiency, strengthen safety standards, and enable faster and more reliable decision-making under uncertainty. In underground mining, these drivers are intensified by geological complexity, high operational variability, and strong interdependencies among technical and operational disciplines. In this context, we recognize that data must be treated not as a byproduct of operations, but as a strategic asset whose quality, consistency, and timeliness directly influence the robustness of planning, execution, and risk control across the mining value chain. The scientific literature on digital transformation in mining provides the conceptual scaffolding for understanding these challenges. Qi (2020) identifies Big Data Management (BDM) as a transformative technology, establishing a framework in which data sources, validation mechanisms, and automation pipelines converge to produce strategic operational value. Brodny and Tutak (2022) subsequently proposed a five-level data-driven decision-making (DDDM) maturity continuum—ranging from ad-hoc data use to fully autonomous organizations—and demonstrated that operations progressing beyond Level 3 achieve measurable improvements in return on assets and asset utilization. This evidence underscores a critical insight: data governance is not a passive infrastructure investment but an active performance lever, foundational to deploying Short Interval Control (SIC), integrating Fleet Management Systems (FMS), and ultimately achieving the integrated control-room operations that define Mining 4.0 practice. Cacciuttolo et al. (2025) extend this perspective to underground environments, presenting a Digital Twin framework wherein real-time data integration enables anomaly prediction and proactive decision support—a paradigm whose realization depends entirely on the quality and governance of the underlying data architecture. A persistent operational challenge in mining is the fragmentation of critical information across heterogeneous systems, discipline-specific repositories, and manual artifacts such as spreadsheets. This fragmentation results in multiple versions of datasets circulating through informal channels (email, cloud folders, shared drives), generating inconsistencies between departments, rework, and delayed responses to operational deviations. More critically, in underground environments, the use of misaligned or outdated design files and mine status information constitutes a process safety vulnerability: analyses performed on superseded mine designs can propagate hazardous assumptions into downstream decisions. Therefore, robust information management should be understood as a process safety assurance mechanism— ensuring that all functions along the mining chain use a single, traceable, and continuously updated set of operational information. Mining Data Management (MDM) solutions have emerged as a strategic approach to structure, standardize, and govern operational mining data, ensuring consistency and traceability across workflows, systems, and teams. The adoption of an MDM platform implies more than centralized file storage; it entails process-level standardization—data structures, naming conventions, responsibilities, and validation gates—that reduces ambiguity and aligns multidisciplinary teams under a common operational language. From a safety perspective,
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