including each individual alert by sensor. This algorithmic approach incorporates a dilution factor to account for expected versus actual reporting instruments, preventing data scarcity from artificially inflating the risk profile. The resulting continuous score is then mapped into standardized qualitative bands (Low, Medium, High, Critical) to drive the Trigger Action Response Plan (TARP). Furthermore, the algorithm features a recency modifier that evaluates alerts within a short-term temporal window, automatically escalating the criticality tier if rapid behavioral deterioration is detected. Ultimately, this shifts the paradigm from analyzing the behavior of a single instrument to assessing the comprehensive structural health of a complete geotechnical zone. To effectively establish criticality criteria, it is imperative to define spatially and geotechnically homogeneous behavior zones a priori. In an open pit context, this delineation typically aligns with the underlying geotechnical model, whereas in a waste dump, it may correspond to a specific homogeneous slope. Through this case study, we identified the optimal data architecture as a four tier hierarchical classification system: 1) Project: The primary mining asset. 2) Structure (Facility): The macro-division of the project (e.g., West Pit or Phase of mining). 3) Sector: A subdivision defined by geographical and geological consistency (e.g. Structural Domain). 4) Zone: A customizable micro-grouping defined dynamically by the geotechnical analyst based on specific instrumentation needs. Expanding the hierarchy beyond these four tiers introduces unnecessary complexity, hindering the use of rapid filtering tools that are essential for minimizing analysis time. Conversely, reducing the number of tiers is only viable if the lowest classification level remains fully customizable by the data analyst to capture localized behavioral nuances. 6. CONCLUSIONS The mining industry is currently navigating a critical transition driven by stringent regulatory frameworks such as the GISTM, which unequivocally mandate comprehensive geotechnical monitoring. However, the absence of a standardized technological blueprint has historically forced mining operators into fragmented data silos and costly proprietary vendor lock-in. This paper bridges that critical gap by delivering a replicable, vendor-agnostic reference framework designed to transform raw geotechnical data into actionable corporate intelligence. Based on the successful three-year implementation at a major Peruvian mining operation, several strategic conclusions can be drawn: Eradication of Vendor Lock-in: By adopting a standardized, four-layer decoupled architecture (Ingestion, Processing, Interface, Storage), the platform demonstrated that it is entirely feasible to integrate disparate hardware brands (IoT sensors, InSAR, robotic total stations) into a Single Source of Truth (SSoT). This approach not only optimizes the Total Cost of Ownership (TCO) but
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