Individuals and organizations cope with an always-growing data amount,
heterogeneous in contents and formats. Prerequisites to get value out this data
and minimise inherent risks related to multiple usages are adequate data
management processes yielding data quality and control over its lifecycle.
Common data governance frameworks relying on people and policies 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 an knowledge graph describing data assets avatars 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 a prerequisite to automatize data governance,
make it fit-for-purpose to each use case and dynamically adapting it to
business changes.

基于元数据驱动、灵活和半自动化的数据治理框架，整合了 25 年的临床研究数据，并利用语义网原则建立了知识图谱，实现了 FAIR 化、生命周期管理、角色和责任定义、转化过程中的关系和源系统来源的溯源，为自动化的数据治理提供了先决条件，并使其适用于各个用例并动态适应业务变化。