Our work is concentrated in sectors where process data is continuous, operationally critical and subject to external scrutiny — which is where getting the data model right pays for itself fastest.
Power Generation
Thermal, combined-cycle and hydro operators use historian data for heat rate and efficiency tracking, availability reporting, condition-based maintenance on rotating plant, and outage planning. The common difficulty is a fleet grown through acquisition, where no two stations model an asset the same way, so comparison across the fleet is impossible without first establishing common definitions.
Transmission & Distribution
T&D estates bring their own protocol landscape — DNP 3.0, IEC 61850, IEC 60870-5-104 — and their own analytical questions: substation asset health, transformer condition monitoring, outage and reliability reporting, smart meter and AMI data at a volume that defeats conventional tooling. Regulatory reporting raises the bar on lineage and auditability.
Oil & Gas
Upstream, midstream and refining operations typically run mixed historian estates, frequently combining AVEVA PI with Aspen InfoPlus.21, alongside WITSML and vendor-specific drilling and process systems. Work here tends to centre on federating those sources, production and loss accounting on consistent definitions, and equipment monitoring where unplanned downtime is measured in revenue per hour.
Renewables
Wind and solar portfolios are geographically distributed, remotely operated and commercially exposed to forecast accuracy. Priorities are performance against expected generation, availability and curtailment analysis, and getting data refreshed often enough to act on — which is exactly where legacy twice-daily extract processes fail.
Industrial & Process Manufacturing
Chemical, pulp and paper, metals and water treatment operations share the same underlying need: continuous process data joined to lab results and maintenance records, with quality codes respected, so that a deviation can be traced to a cause rather than observed after the fact.
What carries across all of them
The technology differs less than the brochures suggest. What determines whether an operational data programme succeeds is whether the asset model is sound, whether the data quality is known rather than assumed, and whether the result reaches the people who can act on it. Get in touch to discuss your sector.