From Policy to Proof: Engineering Data Governance into the Data Lifecycle

Key Topics Covered:

1) Why “governance as a policy” and “governance as a provable, engineered reality” are two very different things
2) How the cake analogy explains why checking the recipe matters more than scoring the finished product
3) What changes when governance is designed in at the start of a pipeline instead of applied after the fact
4) Why data contracts work like APIs — and how they let organizations scale governance across teams
5) The real dollar cost of data defect remediation, and why a $1 fix at source becomes a $100 fix in production
6) What enterprises can learn from Toyota’s quality revolution and applying checks at every stage of the data “assembly line”
7) Why quarterly audits are a measurement problem wearing a governance costume
8) How AI as a new class of “data consumer” breaks the traditional governance playbook
9) Why reliability for AI needs to be a continuous, always-on operating discipline — not a point-in-time check

This episode delivers practical, experience-led insights for data engineers, QA leaders, governance professionals, and data platform teams looking to close the gap between policy and proof.

About the Guest – Frances Stoor

Frances Stewart is a data governance and technology leader with more than 20 years of experience spanning software development, enterprise architecture, and modern data management. She has led enterprise data and AI governance programs across complex organizational environments, empowering teams in HR, finance, legal, operations, and delivery to own and manage their data with clarity and accountability. Frances is a certified Applied Data Governance Practitioner and a Certified Data Management Professional, known for translating complex technical concepts into strategies that bring executives, data leaders, and engineering teams into the same room.