Semantic Modelling of Organizational Knowledge as a Basis for Enterprise Data Governance 4.0 -- Application to a Unified Clinical Data Model

Individuals and organizations cope with an always-growing data amount, heterogeneous in contents and formats. A prerequisite to get value out this data and minimise inherent risks related to multiple usages is an adequate data management process yielding data quality and control over its lifecycle. Common data governance frameworks relying on people, policies and processes falls short of the overwhelming data complexity. Yet, harnessing this complexity is necessary to achieve high quality standards. The later will condition the outcome of any downstream data usage, including generative artificial intelligence trained on this data. In this paper, we report our concrete experience establishing a simple, cost-efficient framework, that enables metadata-driven, agile and (semi-)automated data governance (i.e. Data Governance 4.0). We explain how we implement and use this framework to integrate 25 years of clinical study data at enterprise scale, in a fully productive environment. The framework encompasses both methodologies and technologies leveraging semantic web principles. We built a knowledge graph describing avatars of data assets in their business context including governance principles. Multiple ontologies articulated by an enterprise upper ontology enable key governance actions such as FAIRification, lifecycle management, definition of roles and responsibilities, lineage across transformations and provenance from source systems. This metadata model is the keystone to data governance 4.0: a semi-automatized data management process, taking in account the business context in an agile manner to adapt governance constraints to each use case and dynamically tune it based on business changes.