Ownership, stewardship, and standards that make "who owns this data" an answered question, not an open one.
Governance frameworks fail when they're written by a committee and owned by nobody. Across enterprise MDM programs, I've defined governance models and stewardship workflows that formalize business and IT ownership of data — designed to be used day to day, not filed away after the audit that prompted them.
Most organizations can describe their data governance policy but can't say who actually resolves a data quality exception on a Tuesday afternoon. Without clear ownership and a working stewardship process, "governance" stays a document instead of a practice — and data quality issues keep resurfacing.
Defining who owns which data domain and how exceptions get resolved, end to end.
Building governance in alongside an MDM or data platform implementation, not after it.
Structuring governance to support audit and regulatory data requirements.
A named owner for every data domain, not a shared assumption someone else has it.
A stewardship process people actually follow, because it fits how they work.
Fewer repeat data quality incidents, addressed at the ownership level.
I start by identifying who currently makes data decisions in practice — often different from who's supposed to on paper — then design a stewardship model around that reality, with clear escalation for exceptions. This is not a compliance or legal service, and I don't provide legal guarantees; governance design here is about operational ownership and data quality, with regulatory considerations handled in partnership with your legal and compliance teams.
Let's design a stewardship model that fits how your teams actually work.
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