Data & Architecture

Strategy and analytics fail downstream of weak architecture.

The work

What we deliver.

Operational data is fragmented across historians, ERPs, work-management systems, dispatch, manufacturing-execution systems, and spreadsheets, most of it built up over years of acquisitions, vendor changes, and tactical fixes. Strategy and analytics fail downstream of weak architecture, not upstream of it.

Most data programs we see start in the wrong place. The right starting point is the decisions the architecture has to support.

Source-to-target architecture. Canonical data models, integration patterns, master data, and governance, designed onto what actually exists today, not onto a greenfield assumption.
OT/IT integration. Historian, SCADA, and telemetry brought into the analytics estate without compromising safety-critical control or cyber posture. Purdue-aligned, segmentation-respectful.
Reporting and analytics foundations. The layer that makes operational truth visible, built on a model that scales, not a series of bespoke dashboards that ossify into rebuild debt.
Cyber-aware design. Identity, segmentation, and access patterns built in from the start, not retrofitted after an audit finding.
Vendor and stack selection. What integrates well, what's defensible, what to walk away from. We have no platform allegiance.

Who this is for

Operations and engineering leaders dealing with multi-system fragmentation, reporting latency, and analytics initiatives that keep stalling at the data layer.

What changes

A working data foundation that downstream analytics, automation, and AI can actually run on, designed to scale beyond the first use case rather than collapse under it.

Next step

Talk to us about your data estate.

Discuss this work

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