Unified data hub turns fragmented offshore data into self-serve analytics
A Norway offshore operator moved from fragmented systems and dashboards to a single data hub and governed self-serve analytics layer for operations and engineering.
Starting point
Norway operations had strong point solutions for historian data, maintenance management, engineering information, and supply chain reporting. Data existed in abundance, but not as a whole. Historian streams, maintenance records, and engineering data were held in separate systems with limited integration.
Power BI dashboards had proliferated across teams. Many used different data sources and KPI definitions. Manual reporting was common, with analysts pulling numbers from multiple systems and assembling decks by hand. Leaders could not easily see one trusted set of metrics, and teams found it hard to reuse work or build more advanced analytics.
Approach
To address the fragmentation, the operator engaged a consulting team that included Bhuvan Maingi, now at Strathen Group, to define a common data foundation for Norway. The first step was to map the landscape. This included process and production data in PI historian, maintenance data, engineering information standards such as EqHub and READI, and inventory and supplier data in NCS Virtual Inventory and related systems.
From that baseline, the team defined a Consolidated Data Hub and an onshore visualization layer as a single platform. The hub was designed to ingest live production, process, and maintenance data, then expose it in a consistent model for reporting and analytics. The onshore visualization layer provided standard views for surveillance, performance monitoring, and investigations, all built on top of the same data structures.
Production reporting tools and KPI definitions were aligned as part of the design. The team rationalized duplicate dashboards, retired local variants that could not be supported, and set a Power BI baseline. This baseline specified common datasets, measures, and visual standards that report builders would use to create new content. A KPI dictionary captured agreed definitions, calculations, and thresholds so that metrics meant the same thing across assets and functions.
Engineering data was integrated using EqHub and READI standards to improve master data quality and interoperability. By aligning tag and object structures to ISO 18346, the operator could link engineering items more reliably to process and maintenance records. NCS Virtual Inventory and supplier performance data were then connected to the same hub, improving visibility of warehouse stocks, parts availability, and supplier reliability in the context of operations.
Alongside the technical work, the team mobilized digital and data governance. Naming conventions, data lineage, access models, and refresh cadences were agreed and documented. A service model for self-serve analytics was set up, defining how users could request new datasets, how changes to KPIs would be approved, and how the central team would support advanced use cases without losing control of core definitions.
The operator shifted from many source specific dashboards to a single data hub and visualization layer, where every new report draws from the same governed foundation.
Outcome
The new data platform gave Norway operations a single source of truth for day-to-day operations and improvement work. Instead of stitching together data from multiple systems, users could access live production, process, maintenance, and engineering data from one place. Self-serve reporting became practical and safer, because report builders worked from shared datasets and measures rather than bespoke extracts.
Surveillance and troubleshooting improved. Control room and onshore teams could move from a high-level view of performance into underlying signals and engineering context without leaving the platform. Rework fell as KPI definitions were standardized and duplicate dashboards were retired. When a metric changed, it changed in one place and flowed through to every report that used it.
The consolidated data hub also laid the groundwork for more advanced use cases. Forecasting, optimization, and digital twin initiatives now had cleaner, more connected data to build on. Engineering and supply chain data could be brought into models that previously relied only on process and historian feeds. This opened possibilities for better maintenance strategies, inventory optimization, and scenario analysis.
For reporting teams, manual pack assembly became the exception rather than the norm. Automation and shared models did more of the heavy lifting, which freed capacity to focus on interpreting patterns and supporting decisions. Governance kept pace, with clear lineage, access controls, and refresh rules that made it easier to trust the outputs in regulatory and partner discussions.
Data foundations start paying off when they make the next report easier to build and the next analytics use case faster to deliver, not just when the architecture looks clean on paper.
This work now shapes how Strathen Group approaches data foundations in asset intensive sectors. Build a single hub and visualization layer, clean up KPIs and governance first, and treat self-serve analytics as a service model, not just a tool rollout.
