[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"raw-en-articles\u002Fgeospatial-ontologies-for-location-sensor-data-and-decision-support":3},"---\ntitle: Geospatial Ontologies for Location, Sensor Data, and Decision Support\ndescription: Structured ontology semantics for Location, Sensor Data, and Decision Support\nlang: en\nnavigation:\n  enabled: false\n  section: articles\n  order: 30\ntags:\n  - data\n  - support\n---\nData governance breaks at scale because \"quality,\" \"lineage,\" and \"ownership\" mean different things.\n\n## Why this matters\n\nWhen data concepts are standardized, engineers reason about data systematically.\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 lineage is auditable only when meanings are consistent from source through use.\n",1776235586713]