5.3 Data Quality as a Prerequisite for Analytics The most critical lesson is that no amount of sophisticated analytics can compensate for poor data quality. The initial phase of implementation revealed that historical datasets contained systematic errors: miscalibrated sensors generating physically impossible readings, timezone inconsistencies between data loggers, and missing metadata (sensor installation dates, calibration factors) that made historical comparisons unreliable. The data audit and invalidation tools described in the functional framework (Category 3) proved essential for establishing a trustworthy baseline. The implication for practitioners is that the MVP phase must allocate substantial effort to data cleansing and metadata standardization before attempting advanced analytics. 5.4 Interoperability and the Vendor Lock-in Challenge During the operational period, the mine replaced one manufacturer’s data loggers with another’s across a subset of instruments. Because the platform’s ingestion layer was designed around a standardized MQTT-based data model rather than manufacturer-specific protocols, the transition required only a configuration update to the Sensor Handler Module’s parser, with no disruption to downstream dashboards, alerts, or historical data continuity. This experience provides empirical validation of the vendor-agnostic architecture principle and underscores the long-term value of protocol standardization. 5.5 From Reactive to Predictive Monitoring The transition from static threshold-based alerting (reactive) to trend-aware anomaly detection (proactive) requires not only algorithmic sophistication but also sufficient historical data to train models. The case study demonstrated that approximately 18 months of continuously validated data were required before the platform’s multi-variable correlation tools could reliably distinguish between seasonal pore-pressure fluctuations (driven by rainfall) and anomalous trends warranting investigation. This finding aligns with the staged roadmap proposed in Section 2, where predictive capabilities are positioned as advanced functionalities rather than MVP requirements. 5.6 Geotechnical Command Center: KPI-Driven Operations The Centralized Monitoring Center must act as the operational brain of the project, reflecting the infrastructure's real-time behavior through dynamic Key Performance Indicators (KPIs). To achieve this, the platform's interface must deploy visual and auditory telemetry alerts, immediately notifying operators of any significant structural deviations. For an accurate and rapid interpretation of the geotechnical data within this Command Center, the dashboard must prominently feature four critical dimensions: 1) TARP Thresholds per Sensor: Real-time visualization of the Trigger Action Response Plan (TARP) levels, allowing operators to instantly identify sensors that have breached safety parameters. 2) Data Latency (Operability): A continuous track of the time elapsed since each instrument's last transmitted reading, ensuring the network's telemetry is active and reliable. 3) Anomaly Detection & Data Validation: Automated identification of outliers using the sensor's Full-Scale physical limits, coupled with statistical analysis algorithms that
RkJQdWJsaXNoZXIy MTM0Mzk2