Because AI is only as good as the data behind it.
No platform, algorithm or model will compensate for data that is ungoverned, inconsistent, or incomplete. We take your organisation from complexity to maturity, using proven frameworks and 26 years of EAM experience to ensure your asset data is trusted, governed, and ready to perform.
From fragmented data to a foundation AI can use.
Data Transformation engagements address the root causes of AI failure, not the symptoms. We work at the asset register, taxonomy, and governance layer, because that is where the problems live and where the value is unlocked.
Asset Register Cleansing
Duplicate removal, hierarchy normalisation, missing field remediation, and failure code standardisation. Applied across EAM, IWMS, and CMMS platforms including IBM Maximo, SAP PM, Archibus, Planon, and others.
Taxonomy & Classification Alignment
UNICLASS, Omniclass, RICS NRM, and bespoke taxonomy frameworks applied consistently across your asset portfolio, so AI models have structured, comparable data to work with.
EAM & IWMS Platform Modernisation
Platform migration, configuration rationalisation, data model redesign, and integration architecture across EAM, IWMS, and CMMS systems. 26 years of hands-on expertise applied to your upgrade or replacement programme.
Data Governance Framework Design
Ownership models, data quality KPIs, stewardship processes, and policy documentation, designed to be operated by your team, not dependent on us.
Data Migration & Integration
Structured migration from legacy systems to modern EAM platforms, with full data mapping, validation, and traceability documentation. No data left behind and no quality degradation on cutover.
AI Readiness Data Uplift
Targeted data improvement programmes aligned to specific AI use cases, predictive maintenance, asset lifecycle modelling, space optimisation, so the technology lands on data that is actually ready for it.
Structured delivery. Measurable progress.
Data transformation work is often poorly scoped, underestimated, and poorly governed. Our ADDR model addresses all three problems, with defined scope at each stage, quality gates, and a review mechanism that catches drift before it becomes cost.
- Assess: data audit, profiling, and gap analysis across your asset registers and source systems. Benchmarked against our Data Maturity Index.
- Design: transformation scope definition, data mapping, governance framework design, and migration plan. Board-ready business case where required.
- Deliver: phased transformation execution with quality checkpoints, stakeholder sign-off at each milestone, and documentation of every change.
- Review: post-transformation audit to validate data quality improvements and governance adoption. Readiness re-scored against original baseline.