77 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. 2. PROPOSED FUNCTIONAL FRAMEWORK: FROM CORE TO ADVANCED CAPABILITIES To enable successful digital transformation without disrupting ongoing mining operations, this paper proposes a staged implementation roadmap organized into seven functional categories. The Core Capabilities constitute a Minimum Viable Product (MVP) achievable within 3–6 months, focused on data centralization, basic visualization, and regulatory compliance. The Advanced Capabilities, typically reached within 12–18 months of continuous data maturation, drive predictive intelligence and full interoperability. This staged approach aligns with the maturity progression recommended by ISO 55000 for asset management systems (Tholana and Neingo, 2016) and reflects the practical reality that data quality must be established before analytics can deliver reliable results. The seven functional categories and their staged capabilities are presented in Table 1. Each category maps directly to the key requirements identified in Section 1 and to the relevant clauses of GISTM, ISO 55001, and ISO 27001. Three cross-cutting principles underpin the entire framework. First, Vendor-Agnostic Architecture: all data schemas must be standardized to enable mining operators to switch hardware providers without losing historical data integrity, directly mitigating the well-documented risks of vendor lock-in (OparaMartins et al., 2016). Second, Single Source of Truth (SSoT): by centralizing geotechnical, hydraulic, and structural data, the platform eliminates information silos and reduces time-to-response during critical geohazard events. Third, Operational Sustainability: streamlining data processing into a unified platform
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