Turning equipment data into actionable operational insight

Energy infrastructure is capital-intensive and expected to operate continuously.

Turbines, transformers, substations, and generation assets produce streams of performance data. Maintenance systems track inspections and repairs. Inventory systems track replacement parts. Engineering teams monitor degradation and performance trends.

But asset data often lives in disconnected systems — making it difficult to correlate performance patterns with maintenance history or operating conditions. Predictive maintenance initiatives frequently stall not because of modeling limitations — but because data pipelines are unreliable.

Mage helps organizations orchestrate the flow of asset, telemetry, and maintenance data into structured datasets that power analytics, dashboards, and predictive models.

When data pipelines are consistent, performance insights become practical — not experimental.

What this enables

• unify telemetry and maintenance records

• track asset performance trends

• prepare datasets for predictive maintenance models

• monitor anomaly detection inputs

• support engineering analytics workflows

How a workflow runs

1. Ingest equipment telemetry & logs
   Collect sensor data and performance metrics.

2. Integrate maintenance & work order history
   Combine operational context with asset lifecycle data.

3. Standardize asset identifiers & timestamps
   Ensure consistency across systems.

4. Prepare analytical datasets
   Structure data for BI tools or ML pipelines.

5. Monitor pipeline reliability
   Ensure models and dashboards receive consistent inputs.

What changes for asset teams

✔ clearer visibility into asset health
✔ improved planning for maintenance cycles
✔ reduced manual data preparation
✔ better alignment between engineering and operations
✔ stronger foundation for predictive initiatives

Why Mage works

Asset analytics requires reliable data movement at scale.

• repeatable transformation logic

• orchestration across operational systems

• monitoring and observability

• secure deployment in hybrid environments