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 Level 1–2 and SIC Level 1–2—and establish the preconditions for the next generation of operational intelligence tools. Figure 4 illustrates this progression within the five-stage digital maturity roadmap contextualizing the Aripuanã case. Figure 4 – Digital Maturity Roadmap: Five-Stage Evolution from Fragmented Data Environments to the Integrated Mining Enterprise (IME). The current positioning of Aripuanã ( ★ Stage 3) is indicated. Framework aligned with Brodny & Tutak (2022) and GMG SIC Maturity Levels (GMG, 2019). Governance mechanisms were established to ensure multidisciplinary ownership and accountability. Data stewards from each technical area (mine planning, geology, geomechanics, topography, ventilation, and infrastructure) were designated to oversee data quality, while a central MDM administrator managed platform configurations and access controls. Workflow design followed a standardized process: identification of pain points (e.g., manual reconciliation or version conflicts), mapping of data flows, implementation of validation gates, and iterative testing with end-users. Automation was prioritized for repetitive tasks, incorporating conditional logic to flag inconsistencies and require multidisciplinary approvals for critical outputs. To evaluate outcomes, a mixed-methods approach was employed. Quantitative metrics were tracked over a baseline period (pre-implementation, 2024-Q4 to 2025-Q1) and a postimplementation window (2025-Q2 to 2025-Q3), focusing on cycle times, error rates, and process adherence. Qualitative insights were gathered through user feedback sessions and process audits. Statistical analysis included descriptive statistics (medians and ranges) for time-based metrics, with N values reflecting the number of workflow executions observed (typically 20–50 per
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