Consolidating Mining Operations Data to Power Predictive Safety Insights
Stratpoint Technologies modernized a heavy-vehicle mining fleet by centralizing 10+ siloed legacy sources into an Azure Data Lake, enabling predictive safety models, reducing operational risk, and automating executive decision-making.
PROACTIVE RISK MITIGATION
Replaced days of manual log reviews with real-time risk scoring and automated alerts, driving immediate site-level safety compliance.
SAFETY FOR DRIVERS
Keeping drivers safe using their performance metrics such as sleep patterns, overall health data, and work shifts.
PREDICTIVE ASSET MANAGEMENT
Deployed ML models forecasting component Remaining Useful Life (RUL) to prevent catastrophic breakdowns and optimize maintenance cycles.
SINGLE SOURCE OF TRUTH
Unified over 10 fragmented, legacy data feeds into a scalable, enterprise-grade Azure Data Lake.
EXECUTIVE ALIGNMENT & AGILITY
Automated Power BI reporting across sites and fleets, eliminating reporting latency and human error for executive decision-makers.
Summary
A leading mining enterprise in Papua New Guinea operating heavy-vehicle fleets partnered with Stratpoint Technologies to modernize its safety and operational data ecosystem. Stratpoint transitioned the organization from reactive, manual logging to a centralized, predictive data architecture built for enterprise scale. By consolidating legacy silos into a high-performance Azure Data Lake and embedding custom Machine Learning (ML) models, Stratpoint delivered real-time, self-service intelligence, empowering leadership to prevent costly downtime, mitigate safety risks, and optimize asset availability.
Challenge
High-risk mining operations require near real-time visibility across driver fatigue, fleet health, equipment maintenance, and dispatch workflows. However, critical operational records were siloed in disconnected legacy systems and processed through slow, manual workflows.
This fragmentation exposed the organization to substantial risk, as unplanned equipment failure or safety incidents carried severe threats of operational downtime, revenue loss, and regulatory non-compliance. Executive leadership lacked access to unified, forward-looking insights needed to make timely strategic adjustments.
Stratpoint Technologies was engaged as the strategic partner to design, build, and deploy a modern data platform capable of converting disparate operational signals into predictive, actionable business intelligence without disrupting active operations.
Solution
Stratpoint deployed a multidisciplinary team of solution architects, data engineers, ML specialists, and Power BI experts to deliver an enterprise-grade transformation across four key phases:
Data Ingestion & Centralization
Architected automated Azure Data Factory pipelines to continuously ingest telemetry, maintenance logs, and operational data from 10+ legacy platforms into a secure Azure Data Lake.
ETL Transformation & Feature Engineering
Standardized data architectures and engineered feature sets for critical operational variables, including driver fatigue/risk indicators and asset Remaining Useful Life (RUL).
Machine Learning Models
Built and validated predictive machine learning algorithms for risk scoring and failure prevention, transitioning the enterprise from reactive maintenance to automated predictive care.
Executive Dashboard Automation
Developed interactive executive Power BI dashboards with drill-down controls for real-time visibility into driver safety, site compliance, and fleet-wide asset health.
Tech Details
Data Factory provides a modern data integration experience to ingest, prepare, and transform data from a rich set of data sources. It incorporates the simplicity of Power Query, and you can use more than 200 native connectors to connect to data sources on-premises and in the cloud.
Fabric Data Engineering provides Apache Spark for processing large datasets, with notebooks and tools for writing and scheduling data transformation jobs. It enables you to create, manage, and optimize infrastructure for collecting, storing, processing, and analyzing vast data volumes. Fabric Spark's integration with Data Factory allows you to schedule and orchestrate notebooks and Spark jobs.
Technologies Used
