Multi-Vendor OT Data Unification and Predictive Maintenance for Mining

The operator wanted predictive maintenance to cut the unplanned downtime that costs heavy industry millions per incident. The data to do it already existed: equipment across its sites generated telemetry constantly. The problem was that this data was trapped in proprietary, multi-vendor formats that did not move reliably, and the sites themselves were remote, with connectivity constraints and an OT environment that was converging with IT in ways that created security exposure.

Every equipment vendor's monitoring tool saw its own machines and nothing else. There was no common operational picture, which meant no way to analyze the whole operation, and previous predictive-maintenance efforts had stalled for exactly this reason: the modeling was never the blocker, the data movement was.

The purpose of the engagement was to unify the multi-vendor operational data into a common model, secure the OT/IT boundary across the remote sites, and then layer predictive maintenance and process optimization on top of data that finally moved reliably. The work had to be built for real remote-site connectivity, not for an idealized cloud-everywhere assumption, and it had to secure the converging OT environment rather than expose it.

Project duration

The engagement ran in three fixed-price stages over approximately six months.

Deliverables

• Multi-vendor OT data unified into a common operational data model
• Secure OT/IT boundary and segmentation across remote sites
• Reliable data movement from constrained remote sites to a central platform
• Predictive maintenance and anomaly-detection models with MLOps
• A governed time-series data foundation for operational analytics
• OT-aware monitoring and least-privilege access

Predictive maintenance and optimization kept stalling for the same reason: the telemetry existed, but it was trapped in proprietary, multi-vendor formats that did not move reliably, and remote sites were hard to monitor, connect, and secure as OT and IT converged. Each vendor's system was an island, so there was no unified operational view to analyze, and without that, no predictive model could see enough to be useful.

On top of the data problem sat a security one. Connecting these remote operational environments to IT for data and visibility expanded the attack surface on systems that were never designed to be exposed, with the added complication that an incident in a mining operation has physical and safety consequences. The operator needed the data to move and the boundary to be secure, at the same time, across a difficult remote footprint.

Our solution architect designed a staged roadmap that solved the data movement and OT security first, because that was the real blocker, then added the predictive intelligence on top.

Stage one, unify the multi-vendor data. We deployed edge gateways and OPC UA and protocol connectors to pull telemetry out of the proprietary, multi-vendor systems and into a common operational data model, with MQTT and store-and-forward handling the constrained, intermittent connectivity of remote sites so data moved reliably rather than getting stuck or lost. For the first time, the operation had one coherent view of its equipment data across vendors and sites.

Stage two, secure the OT/IT boundary. We established IEC 62443-aligned segmentation and a clear OT/IT boundary across the remote sites, with industrial firewalls and secure remote access, so connecting operations for data did not expose the control environment. OT-aware monitoring and least-privilege access closed the gaps that convergence had opened.

Stage three, predictive maintenance. With reliable, unified data flowing, our data scientists built predictive maintenance and anomaly-detection models that could finally see across the whole operation, deployed with MLOps (MLflow, a feature store, monitoring, and retraining) so they stayed accurate. This is the stage everyone wants to start with, and it only works because the first two stages made the data trustworthy and the environment secure.

On the OT side, we used edge gateways, OPC UA, and multi-vendor protocol connectors to extract telemetry from the proprietary systems, normalizing it into a common operational data model. MQTT and store-and-forward patterns handled the realities of remote, bandwidth-constrained sites, ensuring data arrived reliably. The OT/IT boundary was secured with IEC 62443-aligned segmentation, industrial firewalls, secure remote access, and OT-aware monitoring, with identity governed through IAM, MFA, and least privilege.

On the data and AI side, we built a governed data platform on AWS or Azure (provisioned through Terraform) with time-series storage for the operational data, and developed predictive maintenance and anomaly-detection models in Python, operationalized through MLflow with a feature store, drift monitoring, and automated retraining. The models drew on the unified, multi-vendor data, which is precisely what let them detect developing equipment problems that no single vendor's tool could have seen on its own.

• Data movement before modeling. Predictive maintenance had stalled on data, not algorithms, so unifying the multi-vendor data reliably came first.
• Build for remote-site reality. Edge gateways and store-and-forward handled intermittent connectivity, instead of assuming constant cloud access.
• Secure the convergence. Connecting OT for data was done behind proper IEC 62443 segmentation, so the operation gained insight without new exposure.
• A common operational model. Normalizing multi-vendor data into one model is what let a model see the whole operation rather than one vendor's slice.

Engagements of this type target the following outcomes:

The engagement achieved its goals:

1. Multi-vendor OT data is unified into one operational view, ending the per-vendor blind spots.
2. Data moves reliably from remote, constrained sites to a central, governed platform.
3. Predictive maintenance runs on data it can trust, catching developing failures before they cause downtime.
4. The OT/IT boundary is segmented and monitored, so the insight came without new exposure.