[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"raw-en-articles\u002Fopen-data-ontologies-for-publication-licensing-and-provenance":3},"---\ntitle: Open Data Ontologies for Publication, Licensing, and Provenance\ndescription: Structured ontology semantics for Publication, Licensing, and Provenance\nlang: en\nnavigation:\n  enabled: false\n  section: articles\n  order: 30\ntags:\n  - data\n  - open-data\n---\nData governance breaks at scale because \"quality,\" \"lineage,\" and \"ownership\" mean different things.\n\n## Why this matters\n\nData governance fails because teams define \"quality\" and \"lineage\" differently.\n\n## What this looks like in practice\n\n- Data lineage is trackable from systems, humans, and AI pipelines consistently.\n- Data quality thresholds mean the same thing whether applied by automation or humans.\n- Metadata is reusable because schemas describe the same concepts consistently.\n\n## How teams use it\n\n- implementing data governance across clouds, on-premises, and third-party stores\n- automating discovery and classification without rebuilding business logic\n- proving provenance for audit, debugging, and compliance systematically\n\nData quality is a team property—it requires agreement on meaning.\n",1776235588134]