Industrial AI Runs on
Operational Data That
Was Never Built for AI
Mining and industrial organizations sit on some of the most valuable operational data available for AI — and most of it is locked inside OEM-proprietary systems, multi-vintage historians, and equipment-specific formats that were not designed to interoperate. Getting that data into a state where AI can reliably use it is the whole problem.
Book a Discovery CallEight Data Source Categories. Zero of Them Designed to Work Together.
A large mining operation — open pit, underground, or processing complex — generates data from a dozen distinct source categories. Each one uses different standards, different formats, different update frequencies, and in most cases proprietary protocols that require specialist integration work before the data can be accessed by any system outside the OEM ecosystem it was designed for.
This is the landscape that mining AI programs have to operate in. Not a clean, unified data platform — a fragmented collection of industrial systems that were each designed to perform a specific function, not to contribute to an enterprise-wide AI program. Building the data foundation means bridging that fragmentation without destroying the operational integrity of the systems it spans.
SCADA & DCS
Process control data from distributed control systems and SCADA platforms. High-frequency, high-volume, often proprietary protocols (OPC-UA, Modbus, DNP3). Quality issues: tag naming inconsistency across sites, calibration drift, and clock synchronization gaps.
OEM Telematics & Historians
Fleet management, drill monitoring, and processing equipment telemetry from OEM-proprietary platforms (Caterpillar MineStar, Komatsu FrontRunner, ABB Ability). Data is locked in vendor ecosystems with limited API access and proprietary formats.
CMMS & Work Orders
Maintenance management system records: work orders, failure event logs, parts consumption, inspection records. Quality issues: failure events under-reported (near-misses omitted), free-text descriptions not structured, equipment hierarchy inconsistent across systems.
Geotechnical & Environmental Sensors
Slope stability monitoring, groundwater sensors, dust and emissions monitoring, and tailings facility instrumentation. Safety-critical data with regulatory reporting obligations. Intermittent connectivity at remote monitoring points creates completeness gaps.
Mine Planning & Scheduling
Short-interval control, mine planning, and production scheduling data from systems like Minestar, Deswik, and Surpac. Contains the planned vs. actual production data that AI needs to understand operational performance variance.
ERP & Supply Chain
SAP or similar ERP data covering parts inventory, procurement, labour, and cost. Critical for predictive maintenance cost modelling and production cost optimization AI. Quality issues: part number proliferation, inconsistent cost centre mapping across sites.
HSSE & Incident Records
Health, safety, security, and environment records: incident reports, near-miss logs, safety observations, and inspection records. Contains the safety event labels required for safety prediction AI. Quality issues: under-reporting of near-misses, inconsistent severity classifications.
Geological & Geospatial Data
Drill hole databases, block models, seismic data, and survey data. Used for ore grade prediction, blast optimization, and ground condition AI. Format diversity is extreme: proprietary mining software formats (Datamine, Leapfrog, Vulcan) with limited standard API access.
What Each Program Needs — and Where the Data Typically Falls Short
Each major mining AI use case has specific data requirements that the fragmented operational data environment frequently cannot meet without preparation. These gaps are consistent across the sector — and consistently underestimated before programs begin.
Predictive Maintenance & Reliability
Complete sensor time-series with consistent tagging across equipment fleet. Maintenance history with labelled failure events (not just work orders). Equipment hierarchy synchronized across SCADA, historian, CMMS, and ERP. Temporal alignment between sensor data and maintenance events. OEM telematics accessible outside proprietary ecosystem.
OEM data locked in proprietary systems with limited export. Historian tagging inconsistent across sites — same equipment type tagged differently at different operations. Failure events under-recorded in CMMS; near-misses omitted. Sensor and maintenance timestamps not synchronized. Equipment hierarchy in CMMS does not match ERP asset register.
Process Optimization & Throughput
High-frequency process sensor data with low latency from crushing, grinding, flotation, and leaching circuits. Feed grade data from geological model reconciled with actual assay results. Process parameter setpoints and operator interventions logged with timestamps. Production accounting data granular enough for circuit-level attribution.
Sensor sampling rates inconsistent across circuits — some process variables logged at different frequencies with no interpolation standard. Feed grade data from geological model not reconciled against actual assay data. Operator interventions logged in free text with no structured format. Production accounting aggregated at shift level — insufficient for circuit-level AI.
Safety Event Prediction
Complete incident and near-miss records with consistent severity classification. Equipment location and operator behaviour data synchronized with safety event records. Environmental condition data (dust, gas, temperature, geotechnical readings) aligned temporally with safety events. Sufficient event volume for rare-event prediction modelling.
Near-miss under-reporting creates severe class imbalance — models learn from incomplete event histories. Severity classifications inconsistent across sites and over time. Equipment location data not linked to HSSE records. Rare high-severity events insufficient in volume for supervised modelling without synthetic augmentation — which requires a governed baseline dataset that typically does not exist.
Ore Grade Prediction & Blast Optimization
Drill hole database with complete assay results and survey data. Block model with versioned geological interpretations. Blast design parameters and fragmentation measurement results. Geotechnical data including rock mass classification and structural geology. Reconciled planned vs. actual grade data for model validation.
Geological data in proprietary formats (Datamine, Leapfrog) with no standard extraction API. Block model versions not managed as governed data assets — historical versions unavailable for model training. Blast design and fragmentation data in separate systems with no automated join. Planned vs. actual grade reconciliation done manually in spreadsheets, not in a governed data system.
Why OEM Data Lock-In Is the Defining Data Challenge in Mining AI
Mining operations depend on equipment from a small number of major OEMs — Caterpillar, Komatsu, Sandvik, Epiroc, ABB — each of which offers its own telematics and data platform. The data those platforms generate is often the most valuable operational data available for AI: granular, high-frequency, equipment-level performance data that directly predicts failure and inefficiency.
The problem is access. OEM platforms are designed to deliver analytics within their own ecosystem — dashboards, reports, and alerts produced and consumed inside the vendor's software. Extracting that data for use in an independent AI program requires either API integration (where available and documented), data export workflows (often partial in historical depth), or proprietary middleware that reintroduces vendor dependency.
ClarityArc assesses OEM data accessibility as a distinct component of every mining readiness assessment — evaluating what API access is available, what historical depth is extractable, what format translation is required, and what the realistic data completeness is for each OEM data source against the AI use cases it is intended to support. The assessment drives realistic scoping before program commitment, not after.
Limited API Depth and Documentation
Most OEM platforms provide API access to recent operational data but limit historical extraction depth — often to 90 days or less without a premium data sharing agreement. Historical data required for model training may be unavailable or require manual export processes that are not sustainable at scale.
Proprietary Data Models and Tag Naming
Each OEM uses its own data model and tag naming convention. A Caterpillar engine health tag and a Komatsu engine health tag measuring the same physical parameter will have different names, different units, different sampling frequencies, and different null-value conventions. Cross-fleet AI requires a unified data model that reconciles these differences — work that is invisible in a standard enterprise data strategy.
Commercial Data Sharing Constraints
Some OEMs treat telemetry data as commercially sensitive and restrict its use outside their platform ecosystem through contractual terms. Data sharing agreements need to be reviewed before any AI program that depends on OEM data is scoped — because the terms may restrict exactly the use case the program is designed to support.
Connectivity at Remote and Underground Sites
Underground and remote open-pit operations have intermittent connectivity that creates gaps in OEM data streams. Those gaps are not random — they correlate with specific equipment types, specific locations, and specific shift patterns — which means they introduce systematic bias into training data if not accounted for in the data readiness assessment.
Designed for the Mining and Industrial Data Environment
Mining data engagements require specific expertise in OT/IT integration, OEM data ecosystems, and the operational constraints of remote and underground environments. ClarityArc scopes every engagement to the actual data environment — not to a generic enterprise data template.
Engagement 01
Mining Data Readiness Assessment
A structured assessment scoped to your target AI use cases — predictive maintenance, process optimization, safety prediction, grade control — that evaluates operational data sources against AI requirements across the full OT/IT data landscape.
Covers historian quality and tag taxonomy, OEM data accessibility and depth, CMMS failure event completeness, geological data format fitness, and the integration architecture required to bring operational data into an AI-ready layer. Output is a scored gap register ranked by AI program impact with realistic data accessibility findings for each OEM source in scope.
Engagement 02
Operational Data Governance Framework
A governance framework designed for the mining data environment: OT/IT classification schema with safety-critical data handling requirements, OEM data access and usage governance, environmental and regulatory data lineage requirements, and site-level data ownership model that reflects operational realities.
Particular focus on safety-critical data classification — geotechnical monitoring, environmental sensors, and safety event data each require distinct classification tiers and access controls that standard enterprise governance frameworks do not provide for industrial operational data.
Engagement 03
AI-Ready Architecture for Industrial Workloads
Architecture design that accounts for the specific workload mix of mining AI: high-frequency time-series from SCADA and historians, OEM telemetry with proprietary formats, geological and geospatial data requiring specialist handling, safety event data with class imbalance requirements, and remote/edge data generation patterns.
Edge and hybrid architecture patterns evaluated for underground and remote site connectivity constraints. Vendor-neutral OEM data integration layer designed before any platform commitment. IIoT data ingestion architecture scoped to actual OEM API availability and contractual data sharing terms.
What Separates Industrial AI Programs That Reach Production from Those That Stay in Pilot
The failure point for mining AI is almost always the same: the program was scoped without understanding the actual state of the operational data it depends on. A readiness assessment that surfaces the OEM access constraints, historian quality gaps, and CMMS completeness issues before program commitment changes everything about the cost and timeline of what follows.
| Dimension | Typical Approach | Mining-Specific Approach |
|---|---|---|
| OEM Data Access | OEM telematics assumed accessible; data extraction feasibility not assessed before program scope and budget are committed | OEM API access, historical depth, contractual data sharing terms, and format translation requirements assessed before any program scope is set — realistic data availability drives the use case design |
| Historian Quality | Historian data treated as available and usable; tag naming inconsistency across sites and calibration drift not evaluated as AI-specific quality dimensions | Historian quality assessed against the specific AI use cases it will feed; tag taxonomy reconciled across sites, calibration status documented, gap patterns quantified against model training requirements |
| Failure Event Completeness | CMMS work order records assumed to constitute the failure event dataset; near-miss under-reporting and label incompleteness not assessed before model training begins | Failure event completeness assessed as a distinct quality dimension; near-miss reporting rates evaluated; label consistency across sites and over time documented before training data is finalized |
| Safety-Critical Classification | Geotechnical and safety monitoring data not classified separately from operational production data; no distinct handling requirements for data that influences safety-critical decisions | Safety-critical data classified as a distinct tier with explicit handling requirements, AI-use restrictions, and lineage requirements that reflect regulatory obligations under applicable OH&S legislation |
| Edge and Remote Architecture | Architecture designed for fully connected environments; intermittent connectivity at underground and remote sites not addressed as a data completeness and latency design constraint | Edge and hybrid patterns explicitly evaluated for underground and remote site connectivity constraints; data completeness standards account for connectivity gaps rather than treating them as anomalies |
| Cross-Site Consistency | AI program designed against a single site's data environment; cross-site inconsistencies in tag naming, equipment hierarchy, and unit conventions discovered when program scales to fleet level | Cross-site data consistency assessed at the readiness stage; unified data model designed before model development begins — scaling from one site to fleet level is an architecture decision, not a program-blocking discovery |
Data Strategy for AI
View the full practice →Know What Your Operational Data Can Actually Support Before Your AI Program Finds Out the Hard Way.
ClarityArc mining and industrial engagements assess OEM data accessibility, historian quality, and operational data integration complexity before any program scope is committed — so the gaps are design inputs, not program-blocking discoveries.
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