Concept
Curated Data
Governed, quality-checked, connected, reusable data prepared once for many consumers, including AI, applications, semantic layers, and knowledge graphs.
Position in the Trusted Data Framework
Curated Data
Living Graph View
Curated Data in the concept network
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Relationship Matrix
Curated Data as a network node
Transforms source data into trusted and reusable information assets.
Uses quality checks to decide whether data is fit for reuse.
Uses metadata to understand source, ownership, assumptions, lineage, and quality.
Requires persistent identity so curated assets can connect representations of the same thing.
Provides reliable inputs that the semantic layer can turn into shared meaning.
Provides trusted, connected data that can be lifted into graph structures.
Needs ownership, stewardship, quality rules, lifecycle controls, and accountability.
Concept Relationships
How Curated Data works with other concepts
Transforms source data into trusted and reusable information assets.
Uses quality checks to decide whether data is fit for reuse.
Uses metadata to understand source, ownership, assumptions, lineage, and quality.
Requires persistent identity so curated assets can connect representations of the same thing.
Provides reliable inputs that the semantic layer can turn into shared meaning.
Provides trusted, connected data that can be lifted into graph structures.
Needs ownership, stewardship, quality rules, lifecycle controls, and accountability.
Definition
Curated Data separates data production from data consumption by turning raw source data into trusted, reusable assets with quality, identity, metadata, governance, and access patterns.
Role in the Trusted Data Framework
Acts as the modern data kitchen of the framework: business systems produce ingredients, the platform prepares them, and AI or applications consume reliable outputs.
Practical Examples
- Conformed asset dataset
- Governed geospatial layer
- Curated sensor history
- Master entity table
- AI-ready data product
Prototype Direction
- ai-ready-assessment
- semantic-discovery
Consulting Questions
- What problem does this concept help diagnose in the client environment?
- Which upstream concepts must be in place before this concept becomes reliable?
- What evidence would prove that this concept is working in practice?