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Webinar: Beyond Compliance: Data Integrity and Quality Management Systems as Knowledge Governance

Sponsored by: SIG Knowledge Management (SIG-KM)

Quality management systems (QMS) and data integrity programs have long been treated as compliance obligations and necessary infrastructure and rarely examined as strategic knowledge assets. This presentation challenges that framing. Drawing on the author's experience leading the development of data integrity standards (ASQ TR3:2022) and his decades of regulatory operations practice, this session reframes two foundational disciplines through a knowledge governance lens. First, QMS workflows (such as standard operating procedures, change controls, audit findings) represent some of the densest concentrations of operational knowledge in any organization. When managed as living knowledge assets rather than siloed compliance documents, they become powerful engines for continuous improvement and inspection readiness. Second, data integrity is reconsidered not merely as a technical or procedural control, but as a knowledge problem: the meaning of a record, the context of its generation, and the provenance of its metadata are fundamentally questions of organizational knowledge, the absence of which being as much an epistemic failure as a regulatory one. Together, these perspectives offer a more integrated model of quality and knowledge management with direct implications for early-stage organizations building regulatory infrastructure, fractional and distributed operating models, and the broader application of artificial intelligence to regulated data.

Presenter

 

Kip Wolf

Kip Wolf is a life sciences executive and applied researcher whose work sits at the intersection of information science, data integrity, and AI governance in regulated environments.

He brings more than thirty years of experience in information systems and life sciences operations, with a particular focus on how data creation practices, provenance, and metadata governance influence the quality and trustworthiness of information used to inform human health outcomes.

 

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