Turn Data Strategy into an operating model for trusted data.

Why this matters

A modern data platform is not enough.

Organizations can invest in platforms, catalogues and AI tools and still lack a trusted data foundation.

The missing layer is often an operating model that defines what data matters, who owns it, what quality means for its intended use, how issues are prioritized and when a data product is ready for reporting, products or AI.

What I establish

Strategy becomes useful when the organization can operate it.

Strategic direction translated into decisions

I connect company priorities and business use cases with data domains, data products, ownership, measurable outcomes and delivery priorities.

Governance embedded in delivery

I define guardrails, decision rights, readiness criteria and acceptance responsibilities before data is treated as trusted.

Business-impact-driven Data Quality

I establish one intake and prioritization model that connects data issues to evidence, affected processes, business impact, ownership and remediation.

AI-ready data foundations

I make data structurally reliable, semantically clear, contextually documented and operationally governed before it is reused by analytics, automation or AI.

Data Quality in operation

Prioritize the data issues that matter.

Data-quality backlogs often mix cosmetic defects, operational pain and strategic risk.

I design transparent operating models that connect each issue to a disrupted business process, evidence, business impact, ownership, remediation effort and downstream use.

One governed intake

Multiple reports and stakeholder perspectives are consolidated into one transparent issue record instead of several departmental versions of the same problem.

Evidence before priority

Priority is based on concrete examples, affected processes, unreliable decisions and business risk, not on who escalates most loudly.

Sustainable closure

An issue is not complete when a value has been corrected once. Closure requires clear meaning, accountable ownership, repeatable controls and data that can be reused without manual interpretation.

Governance by purpose

Different data products need different definitions of trust.

One data initiative may primarily need completeness, integration, reusable data products and self-service access.

Another may depend more on correctness, traceability, historical consistency, reconciliation, KPI definitions and formal business acceptance.

I define the ownership, quality controls, delivery gates and acceptance model required by the actual business purpose rather than applying one generic checklist everywhere.

From signal to system

Connect strategy, governance and delivery.

View the full approach

Read the reality

Understand the business purpose, current data, existing ownership, dependencies and failure points.

Frame the operating model

Define responsibilities, decision rights, quality criteria, escalation paths and acceptance.

Build it into delivery

Turn the model into delivery gates, data-product requirements, issue routing, controls and clear responsibilities.

Make it operational

Establish prioritization, review rhythms, ownership, measurement and continuous improvement.

Relevant experience

From strategy to governed data foundations.

• Define and lead company-wide Data Strategy, Data Governance and data quality.
• Establish the governance operating model, decision rights, ownership and prioritization mechanisms across the company.
• Embed those principles into delivery and acceptance gates across enterprise data-foundation initiatives.
• Designed a business-impact-driven data-quality operating model covering intake, prioritization, remediation and sustainable closure.
• Advance Microsoft Purview, the Enterprise Data Map, AI readiness and governance foundations for internal AI capabilities.
• Developed TIMOCOM’s first data-licensing concept to explore new revenue opportunities.

Where this work is useful.

Useful when

• AI initiatives are moving faster than data readiness.
• Data ownership is fragmented.
• Governance exists mainly as documentation.
• Data-quality issues compete without transparent prioritization.
• Teams disagree about when data is trustworthy enough to use.
• A data initiative needs senior alignment across Business, Product and Technology.

This is not

• compliance documentation in isolation;
• a technical platform implementation without business ownership;
• a generic AI-tool rollout;
• a replacement for internal teams;
• a large external delivery agency.

Ways I can contribute

Focused support where direction or ownership is missing.

Sparring

Senior-level discussion of Data Strategy, Governance, AI Readiness, priorities and difficult cross-functional decisions.

Review

An independent assessment of an existing strategy, governance setup, operating model or data initiative, with clear findings and next steps.

Advisory

Focused support in shaping ownership, data quality, governance guardrails, delivery gates and the path from strategy into operation.

For temporary Product leadership, view Interim Product Leadership.

I currently lead Data Strategy & Governance at TIMOCOM and take on selected external advisory, sparring and interim assignments where there is no conflict of interest.

Data ambition, but no operating model yet?

Let’s identify what needs to be owned, governed and made reliable.

Discuss an initiative