Metadata‑Driven Data Governance: Concepts, Architectures, and Practices

Li Ranhui, head of data asset management at JD Technology, introduces metadata‑driven data governance, defining metadata as data about data and explaining how it enhances data quality understanding, trust, and transparency.

The digital economy in China has grown to 50.2 trillion yuan in 2022, representing 41.5% of GDP, highlighting the need for robust data governance as the foundation for unlocking data asset value.

Traditional data governance struggles in big‑data environments due to the 5V characteristics (volume, variety, velocity, veracity, value), low data standardization, and the difficulty of managing quality, security, and cross‑domain collaboration.

Data Fabric is presented as a metadata‑driven architecture that requires strong data governance and catalogs, offering six core capabilities: enhanced data catalog, semantic knowledge graph, active metadata, recommendation engine, data preparation & delivery, and data orchestration/DataOps.

Data Mesh is contrasted with Data Fabric, emphasizing decentralized domain‑owned data products, federated governance, and the need for standards to enable interoperability across domains.

DataOps is described as a collaborative data‑management practice where metadata supports lineage tracking, automated pipelines, quality assessment, security, and cross‑team communication, turning metadata into a common language.

Practical case studies include: (1) data change governance using metadata lineage to assess impact and coordinate downstream changes; (2) quantifying and automating governance with over 200 metrics visualized in a metadata warehouse; (3) intelligent governance leveraging large language models to convert SQL to DSL, perform clustering, similarity recommendation, and automated optimization; and (4) a metadata‑driven data engineering example that reduces code changes by altering metadata definitions.

The future outlook envisions an AI‑enhanced metadata platform that aggregates metadata from various systems, applies active management, and feeds downstream tools, enabling flexible, maintainable, and reusable data engineering workflows.

The presentation concludes with thanks to the audience.