118 checks and version control; (iii) workflow automation, which structures routine processes and reduces manual intervention; and (iv) Business Intelligence (BI) integration, which enables visualization and monitoring of operational indicators. This architecture aligns with the broader Mining 4.0 paradigm, where data serves as a strategic asset for decision-making, as discussed in Cordeiro and Hauschild (2025). Figure 3 – MDM Four-Layer Architecture: From Raw Data to Operational Decisions at Nexa Resources Aripuanã Underground Mine. Bidirectional arrows indicate feedback loops between governance layers and operational outputs. 3.1. Conceptual Framework: Data Governance and Digital Maturity in Underground Mining The conceptual framework underpinning this study integrates three complementary theoretical constructs. First, Qi’s (2020) Big Data Management (BDM) model for the mining industry, which organizes data governance around four functional domains: data acquisition, data storage and processing, data analysis, and data-driven decision-making. Second, the digital maturity continuum proposed by Brodny and Tutak (2022), which classifies organizational DDDM capability across five levels from ‘Initial’ (reactive, ad-hoc data use) to ‘Innovating’ (continuous organizational learning and autonomous optimization). Third, the SIC maturity framework published by the Global Mining Guidelines Group (GMG, 2019), which maps operational readiness for short-interval control across six levels, from paper-based tracking to nearautonomous real-time control. Together, these frameworks provide a multidimensional lens through which the Aripuanã MDM implementation can be evaluated not only in terms of its operational outcomes, but also in terms of its positioning within the broader trajectory of digital transformation in underground mining. Within this conceptual space, the Deswik MDM deployment at Aripuanã corresponds to DDDM Level 3–4 in Brodny and Tutak’s (2022) continuum—a state in which processes are standardized, data quality is actively managed, and quantitative performance monitoring begins to inform adaptive management decisions. Concurrently, the governance infrastructure established through MDM positions the operation at GMG SIC Maturity Level 3–4, characterized by a governed data environment with digital visualization capabilities. These levels represent a material advance over the pre-MDM baseline—estimated at DDDM
RkJQdWJsaXNoZXIy MTM0Mzk2