underground excavations, transfer tunnels, and ancillary structures such as transmission towers and conveyor corridors. The instrumentation deployed across these assets vibrating-wire piezometers, standpipe piezometers, in-place inclinometers, shape acceleration arrays (SAAs), multi-point borehole extensometers, crack meters, tilt meters, settlement plates, total pressure cells, robotic total stations, ground-based interferometric synthetic aperture radar (GB-InSAR), and satellite persistent scatterer interferometry (PS-InSAR) generates data streams of extraordinary volume, velocity, and heterogeneity. The fundamental challenge is not data acquisition per se, but rather integration, governance, visualization, and intelligent analysis of this data within a coherent platform that serves all stakeholders, from field technicians to executive decision-makers. A paradox characterizes the current state of practice. Mining companies invest heavily in instrumentation hardware yet frequently lack the data management infrastructure to extract full value from their investments (McGaughey et al., 2017). Critical geotechnical knowledge remains trapped in what practitioners term ‘tribal knowledge’: information held by transient project teams rather than preserved in corporate memory. Data generated by strategic partners ranging from detailed stability analyses to calibration certificates for individual piezometers in a TSF is frequently stored in disconnected silos spanning spreadsheets, local databases, PDF reports, and proprietary vendor portals. This fragmentation compromises traceability from the micro-scale (e.g., the calibration history of a specific vibrating-wire sensor) to the macro-scale (e.g., the evolution of slope stability recommendations over a mine’s life-of-mine plan), leaving assets vulnerable to information-driven blind spots. The urgency of this challenge has been amplified by post-Brumadinho regulatory evolution. The Global Industry Standard on Tailings Management (GISTM, 2020), co-convened by UNEP, PRI, and ICMM, establishes 77 auditable requirements across 15 principles, with Principle 7 specifically mandating ‘comprehensive and integrated’ monitoring systems aligned with the Observational Method and Trigger Action Response Plans (TARPs). The ICMM Good Practice Guide (2025), MAC OMS Guide (2021), ANCOLD guidelines (2019), CDA Dam Safety Guidelines (2013), and ICOLD Bulletin 158 (2013) similarly require robust monitoring programs. However, a critical observation emerges from analyzing these frameworks: all of them define what must be monitored and the governance principles that must apply, but none prescribes how the underlying technology architecture, the software platform, the IT infrastructure, the data pipelines should be designed and implemented. This ‘how’ gap is precisely where the mining industry most needs guidance (Barnewold et al, 2020). This paper addresses the identified gap with three specific contributions. First, it proposes a sevencategory functional framework organized as a staged implementation roadmap from Minimum Viable Product (MVP) core capabilities to advanced functionalities, grounded in the requirements of GISTM, ISO 55000, and ISO 27001. Second, it defines a four-layer, cloud-agnostic software architecture (Ingestion, Processing, Interface, and Storage) with a technology comparison that enables implementation across Google Cloud, AWS, Azure, or self-hosted environments without vendor lock-in. Third, it validates the framework through a three-year operational case study at a major mining operation in Peru, demonstrating how the integration of heterogeneous geotechnical data sources into a single platform improves decision-making and operational resilience.
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