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Structured data denotes formally modelled, typed data and standardized formats that enable machine processing, validation, and interoperability. It covers schemas, ontologies, type definitions and serialized representations (e.g., JSON-LD, RDF) plus rules for consistency and discoverability during data exchange. Organizations use structured data for search,…
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Structured data follows an explicit schema or model so programs can reliably process fields, types, and relationships.
The approach grew from data modelling, databases, and the need to exchange information unambiguously between systems. W3C standards and Schema.org extended the idea to the Web and linked data.
Define data meaning and shape in advance, for example through a schema, RDF vocabulary, or JSON-LD. Validation and shared terms support search, integration, and automation.
At its core, this means: Structured data follows an explicit schema or model so programs can reliably process fields, types, and relationships.
It works by: Define data meaning and shape in advance, for example through a schema, RDF vocabulary, or JSON-LD. Validation and shared terms support search, integration, and automation.
Structured data supports validated exchange. Define the shared schema and rules for validity, freshness, and extensibility.
Structured data supports validated exchange between applications, search engines, and organizations. In practice, teams must decide which shared schema and rules govern validity, freshness, and extensibility.
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