Getting Data Governance for Regulatory Submissions Right Before AI Gets it Wrong with Cary Smithson
About this episode
Cary Smithson discusses the importance of data governance in life sciences for regulatory submissions and AI reliability.
How should life science companies govern their data to meet increasingly structured regulatory submission requirements and actually get value from AI? Cary Smithson shares lessons from decades of helping organizations modernize their regulatory, quality, and R&D operations. Cary discusses why data governance has become urgent across three fronts — structured submissions, cross-functional interoperability, and AI reliability — and walks through the foundational steps companies should take, the organizational challenges they'll hit, and what measurable results look like when governance is done right. A few of Cary's key takeaways: Regulatory submissions are no longer just documents — they're structured data that demands consistent master data, controlled vocabularies, and traceable lineage Start with scope and pain points, not a boil-the-ocean exercise — pilot governance in one or two high-value use cases, then scale Data ownership belongs in the business, not IT — IT facilitates, but stewards and business owners should be accountable for their data Tools support governance but don't replace it — get the people and process foundation right before selecting platforms AI…
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