How I turn raw enterprise data into a single source of truth — across five files, in this order. Each phase has a clear output. None of them can be skipped without consequence.
Before I recommend anything, I map what you have. Data flows, ownership gaps, governance maturity, AI readiness. Where the silos are, where the controls are missing, what's really going to break under transformation pressure.
Output: Diagnostic report. Risk & maturity scorecards. Gap analysis mapped against your transformation goals.
I design the canonical architecture for your data — deliberately built to avoid legacy 'lift & shift' debt. Software Bill of Materials defined and validated. Operating model and Global Data Owner network established. Greenfield where possible; pragmatic where not.
Output: Target architecture. SAP / cloud BoM. Data ownership and governance operating model.
This is where most programmes fail. Mine don't. Migration tollgates, automated quality toolkits, structured remediation across vendors, materials, customers, and finance objects. Centres of Excellence stood up where the cost case justifies it.
Output: Migrated, validated, audit-ready data. Centres of Excellence with measurable scorecards.
Policies aligned to your enterprise Risk & Controls Matrix. Named owners with real authority. Governance councils that actually meet. Designed to satisfy regulators, internal auditors, and the kind of finance scrutiny that keeps boards comfortable.
Output: Governance policies. Steering committees. RCM-mapped controls.
A great architecture nobody can use is a museum exhibit. I train your people at scale, across geographies, against tollgates — and I measure adoption, not attendance. KPIs and scorecards for digital maturity. Continuous improvement built into the operating model.
Output: Trained workforce. Maturity scorecards. Embedded continuous improvement culture.
Where would your programme start? Most don't begin at File 01 — they begin where the pain is loudest.
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