Data & Analytics Platforms Software

Can you show me a live example of how you handle conflicting data — e.g., when the FMS says 100 tonnes were moved, but the weighbridge says 95?

Tests their data governance and contextualization. Mining operations routinely encounter discrepancies between systems, so the platform must provide automated reconciliation rules, data source prioritization, and clear audit trails showing which value was used and why.

Do you provide pre-built data models for standard mining KPIs (like OEE), or do we have to pay your consultants to build them from scratch?

Tests their time-to-value and domain expertise. A mining-focused analytics vendor should include pre-configured KPI calculations for OEE, availability, utilization, and cost-per-tonne that can be deployed within weeks, not months of expensive consulting.

How do you handle the massive volume of high-frequency time-series data from our SCADA systems without bankrupting us on cloud storage costs?

Tests their technical architecture. Mining SCADA systems can generate millions of data points per day, so the platform must implement intelligent data compression, tiered storage strategies, and edge processing to manage costs while retaining analytical fidelity.

If your predictive model tells us to pull a truck offline for maintenance, can it explain why it made that prediction (explainable AI)?

Tests the usability and trustworthiness of their ML models. Maintenance teams will not act on black-box predictions, so the system must provide feature importance rankings, threshold explanations, and historical pattern comparisons that give engineers confidence to make costly shutdown decisions.