  Individuals and organizations cope with an always-growing amount of data,
which is heterogeneous in its contents and formats. An adequate data management
process yielding data quality and control over its lifecycle is a prerequisite
to getting value out of this data and minimizing inherent risks related to
multiple usages. Common data governance frameworks rely on people, policies,
and processes that fall short of the overwhelming complexity of data. Yet,
harnessing this complexity is necessary to achieve high-quality standards. The
latter will condition any downstream data usage outcome, 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 an 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-automatised data management process
that considers the business context in an agile manner to adapt governance
constraints to each use case and dynamically tune it based on business changes.
