Most data problems aren't technology problems — they're accountability problems. No one agreed on what the data means, who's responsible for it, or what "good" looks like. That's the gap I work in.
Start a conversationMost data problems aren't technology problems — they're accountability problems. My approach starts with an honest diagnosis, then builds practices that teams can actually adopt.
Unclear ownership, missing definitions, inconsistent quality — I trace symptoms to root causes before recommending anything.
Every governance practice needs a clear rationale. I translate enterprise standards into frameworks people understand and follow.
Playbooks, ownership models, business glossaries, metadata practices — designed to reinforce each other, not exist in isolation.
Governance that lives in a document isn't governance. I make accountability visible in how teams actually operate day-to-day.
Quality metrics, maturity tracking, governance controls — so progress is visible and leadership can make informed decisions.
Clean foundations, lineage documentation, and responsible use considerations are prerequisites for trustworthy AI — not afterthoughts.
Organizations collect more data than they understand, assign ownership to no one in particular, and then can't figure out why they can't trust what they have. That's been the problem for twenty years. The tools are different. The problem isn't.
Early work was inside national measurement datasets — the numbers that media markets and advertisers use to make significant financial decisions. When what you publish becomes the basis for a market transaction, governance stops being a framework and starts being a real accountability. Who owns the definition. Who signs off on quality. Who answers when something is wrong.
Some engagements had nothing in place — no shared definitions, no ownership model, no quality baseline. In a few of those, the data touched vulnerable people directly. That context doesn't let you be abstract. You build what's needed, make sure people can actually follow it, and move on.
Regulated and public-sector organizations rarely lack policy. What they lack is the translation layer — between what the standard says and how a team applies it on a Tuesday. That's usually where the real exposure is, and where the work gets interesting.
The value isn't knowing the frameworks — it's knowing which one fits, which needs adapting, and when to build something from scratch. Twenty years of different environments, constraints, and levels of organizational readiness makes that judgment faster and more reliable.
I use sandbox environments to test how data, analytics, and governance perform in real decision situations before they are scaled. The goal is not to prove the technology works. The goal is to find where the operating model breaks — unclear ownership, weak definitions, poor data quality, missing controls, or decisions no one is prepared to act on.
Data Governance LabWhether you have a data challenge, want to explore an idea, or just want to connect — pick the format that fits.
Or reach me directly: [email protected] · Toronto, ON · Open to remote engagements