Data & semantic strategy
Define shared measures, trace their sources, and establish ownership across reporting, applications, and AI.
Definition inventory · Architecture options · Ownership model
Enterprise consulting in semantic layer strategy, data architecture, organizational change, and applied AI.
Start with a consequential decision. Understand the constraints. Make the next step concrete enough for the people who have to approve it.
Define shared measures, trace their sources, and establish ownership across reporting, applications, and AI.
Definition inventory · Architecture options · Ownership model
Understand the incentives and concerns behind a change. Clarify decision rights, involve the people carrying the work, and plan for adoption.
Stakeholder alignment · Decision rights · Adoption plan
Design useful AI workflows around approved information, explicit permissions, human review, and a clear path to intervention.
Working examples · Evaluation criteria · Approval boundaries
Production access takes time. An early working example can begin with approved, sanitized metadata and representative synthetic data. Metadata can be sensitive too; agree what may be used before it is shared.
Use the example to test meaning, workflow, and ownership. Validate against real data and operating conditions before making production commitments.
What needs to change? Who can approve it? What evidence will they need?
Review existing definitions, reports, and controls. Make replacement a deliberate choice.
Agree the inputs, permissions, success criteria, and limits of what the example can prove.
Document the findings, open questions, accountable owners, and decision to proceed or stop.
An executive brief. The architectural choices. A defined pilot. The dependencies and decisions still open. Agree the deliverables in the scope, then give each audience what it needs to move.
A focused conversation to establish the work and whether there is a fit.