Creating trusted information across an asset-retirement portfolio

Asset retirement generates information across engineering records, environmental sampling, regulatory submissions, financial liabilities and contractor deliverables. Across a large and varied portfolio, these records are often held in different systems and follow different supplier processes.

This fragmentation can make it harder to apply consistent definitions, assess data quality and trace the information behind decisions. Some records may also need to remain accessible for decades to support regulatory obligations and long-term site stewardship.  

Moving every site and supplier onto a single platform is not a practical answer. The energy company needed an approach that could accommodate local delivery while establishing clear enterprise ownership, secure access and reliable portfolio reporting.  The core challenge was not only technical, involving databases, integrations and dashboards, but also organizational, requiring effective governance, clear ownership and accountability.

Our team developed a federated data-management model for the environmental remediation and asset-retirement portfolio.  The model establishes common expectations at the portfolio level while allowing suppliers and local teams to continue using appropriate delivery systems. 

At the portfolio level, the model defines data structures, quality measures, security controls and reporting requirements. Shared capabilities support data integration, warehousing and portfolio dashboards. At the local level, vendors capture and validate data against the agreed standards. 

We designed the governance model to reflect this structure. A portfolio council sets direction, a shared data office or center of excellence supports delivery and local data stewards are accountable for data quality on the ground. The model keeps ownership of the data and related decisions with the organization. It also provides a structured approach to traceability and supplier onboarding.

The model establishes common data standards, quality measures, security and reporting expectations at the portfolio level while allowing local teams to retain responsibility for capturing and validating information. Shared integration, warehousing and dashboard capabilities support a more consistent and auditable view of technical, environmental and financial evidence. This approach creates the trusted data foundation needed for portfolio-level analytics and future AI applications while preserving clear ownership, traceability and practical supplier onboarding.