Because AI is only as good as the data behind it.
Data Transformation rebuilds the asset data layer sitting underneath your AI ambitions, for organisations running EAM, IWMS, or CMMS platforms at scale. The work covers register cleansing, taxonomy alignment, platform modernisation, migration, and governance design. You finish with an asset register that is structured, governed, and measurably improved against the baseline it started from.
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.
What buyers ask about Data Transformation
Which EAM, IWMS and CMMS platforms do you work with?
Asset register and modernisation work is applied across EAM, IWMS, and CMMS platforms including IBM Maximo, SAP PM, Archibus, and Planon. TRIRIGA is also covered through associate and bid support. The approach is vendor-agnostic: the same cleansing, taxonomy, and governance disciplines apply whichever system holds your asset register, so the recommendation does not depend on which platform you already own.
What does asset register cleansing actually involve?
Asset register cleansing covers duplicate removal, hierarchy normalisation, missing field remediation, and failure code standardisation. The work targets the asset register, taxonomy, and governance layer rather than the tool or model layer, because that is where the problems that cause AI failure actually live. It is applied across EAM, IWMS, and CMMS platforms including IBM Maximo, SAP PM, Archibus, and Planon.
Which classification standards do you align asset data to?
Taxonomy work applies UNICLASS, Omniclass, RICS NRM, and bespoke taxonomy frameworks consistently across your asset portfolio. The purpose is to give AI models structured, comparable data to work with, rather than free-text descriptions that vary by site and by author. Classification alignment is one of six discrete workstreams, alongside register cleansing, platform modernisation, governance design, migration, and AI readiness uplift.
How do you stop data quality degrading during a migration?
Migration runs with full data mapping, validation, and traceability documentation, so every record can be traced from source system to target. Delivery is phased, with quality checkpoints, stakeholder sign-off at each milestone, and documentation of every change. A post-transformation audit then validates the data quality improvements against the original baseline, so the result is measured rather than assumed.
Will we stay dependent on you once the governance framework is built?
No. The governance framework is designed to be operated by your team, not to keep you dependent on Brainwave Asset Intelligence. Data Governance Framework Design covers ownership models, data quality KPIs, stewardship processes, and policy documentation, all built for handover. Where an organisation wants continued support after handover, that is a separate Managed Services retainer rather than something built into the transformation.
How does data transformation work connect to a specific AI use case?
AI Readiness Data Uplift targets data improvement at a named AI use case rather than improving everything at once. The examples given on this page are predictive maintenance, asset lifecycle modelling, and space optimisation. The principle is that the technology should land on data that is genuinely ready for it, so the uplift is scoped backwards from the use case you actually intend to deploy.
How long does a data transformation engagement take, and what does it cost?
Both are agreed per engagement in discussion with the client, rather than published as a standard duration or rate card. The variables that move them are the number of source systems, the size and condition of the asset register, and how much taxonomy work is required. Both are settled in a scoping conversation once the data audit has established what the work actually involves. Delivery is phased, so there is a defensible output at each milestone rather than only at the end.