[{"data":1,"prerenderedAt":4},["ShallowReactive",2],{"raw-en-use-cases\u002Fdata-ai-stewards":3},"---\ntitle: IQ:NS for Data & AI Stewards\ndescription: Structured ontologies for managing AI metadata, model lifecycle, and institutional knowledge.\nlang: en\nnavigation:\n  section: use-cases\n  label: Data \u002F AI Stewards\n  order: 30\n---\n\n# IQ:NS for Data & AI Stewards\n\n## The challenge\n\nData and AI stewards manage system metadata, model traceability, data lineage, and quality — the operational facts that make responsible AI possible.\n\n## What the ontologies provide\n\n- **System catalog structure** — consistent vocabulary for purpose, ownership, data sources, risk classification\n- **Model and data lineage** — structured relationships between models, datasets, pipelines, and versions\n- **Cross-team vocabulary** — shared definitions that compliance, security, and engineering all use\n- **Integration with data catalogs** — ontologies connect to Collibra, Alation, and similar tools via standard RDF\n\n## How it fits\n\nThe ontologies give stewardship a formal structure. Metadata definitions become consistent. Relationships between systems become queryable. Teams work from the same vocabulary.\n\n[Explore the ontologies](https:\u002F\u002Fgithub.com\u002Fiqns-org\u002Fontologies) · [Get started](\u002Fgetting-started)\n",1776235631726]