Data Governance · Strategy · Analytics
I help organizations turn data chaos into structured, accountable foundations — clarifying ownership, improving quality, and embedding governance into the way teams actually work. Based in Toronto.
Start a conversationHow I work
Most data problems aren't technology problems — they're accountability problems. My approach starts with an honest diagnosis, then builds coherent practices that teams can actually adopt.
01
Unclear ownership, missing definitions, and inconsistent quality are symptoms. I trace them to root causes before recommending anything.
02
Every governance practice needs a clear rationale. I translate enterprise standards into divisional frameworks people understand and follow.
03
Playbooks, ownership models, business glossaries, metadata practices — designed to reinforce each other, not exist in isolation.
04
Governance that lives in a document isn't governance. I work to make accountability visible in how teams actually operate day-to-day.
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Quality metrics, maturity tracking, and governance controls — so progress is visible and leadership can make informed decisions.
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Clean data foundations, lineage documentation, and responsible use considerations are prerequisites for trustworthy AI — not afterthoughts.
Career
Data governance built for operating scale
Translated enterprise governance direction into divisional practice for a large public-sector organization — clarifying data ownership, establishing business definitions, standing up a governance working group, and implementing a cloud metadata platform as the authoritative source of record.
Enterprise governance in a high-accountability environment
Worked inside an enterprise data and records management office to bring governance to business units across multiple regions — documenting critical data elements, clarifying ownership and stewardship responsibilities, and helping teams interpret and apply policy in practical operating contexts.
Governance from the ground up, where stakes are high
Built data governance practice in an environment where data misuse carries real human consequences — establishing a business glossary, data quality measures, anonymization and PII policies, and BI operations for a nationally trusted service organization.
Governance and stewardship for datasets that define industries
Led governance, stewardship, and data quality oversight for large-scale audience measurement datasets used by media, agencies, and advertisers to make high-value decisions. Reported data integrity and compliance matters to a formal governance council and board.
A decade of building trusted data foundations
Directed data quality, business definitions, methodology, validation, and data dictionary maintenance for national readership measurement — establishing standards that media and advertising markets relied on for over ten years.
Sandbox projects
Each project starts with a diagnosis of a real problem. The build tests whether a coherent response to that problem can be delivered as a product — not just a slide deck.
The diagnosis
Independent service businesses operate blind. By the time they notice a competitor gaining ground, the damage is already done — and they have no repeatable way to stay informed.
The response: A local market intelligence platform that monitors competitive signals automatically and delivers a weekly briefing owners can act on — without hiring an analyst or spending hours they don't have.
View project LiveThe diagnosis
Planners, marketers, and researchers making neighbourhood-level decisions lack the granular, privacy-respecting population data needed to model demand, segment audiences, or understand local dynamics with confidence.
The response: A data product that fills the gap between census aggregates and individual-level data — giving decision-makers a reliable, responsible foundation for neighbourhood-scale analysis.
View project In developmentThe diagnosis
Most organizations have more data than they can use. The bottleneck isn't access — it's interpretation. Teams get dashboards when what they need is a clear weekly answer to "what actually matters right now?"
The response: A structured intelligence brief delivered weekly — covering what changed, what looks off, possible drivers, and what needs attention. Signal, not noise. Built for decision-makers, not analysts.
View project In developmentThe diagnosis
Data governance is well-documented in theory and poorly understood in practice. Most resources describe what governance should look like — few show how to actually build it inside a real organization with real constraints.
The response: A working reference built from applied implementation experience — ownership templates, business rule patterns, metadata schemas, and maturity frameworks grounded in what actually gets adopted.
View resources Coming soonThe diagnosis
Organizations are moving toward AI faster than their data foundations can support it. Without clear lineage, quality baselines, and accountability structures, AI outputs can't be trusted — and the risk is often invisible until it surfaces as a problem.
The response: A practical readiness framework that helps organizations assess their data foundations before deploying AI — covering lineage, quality, ownership, classification, and responsible use considerations in plain operational terms.
View framework Coming soonGet in touch
Whether you have a data challenge, want to explore an idea, or just want to connect — pick the format that fits.