Data in a defined, organised format that computers can read and understand directly — like a spreadsheet or a labelled JSON file — as opposed to free text.
Data in a defined, organised format that computers can read and understand directly — like a spreadsheet or a labelled JSON file — as opposed to free text.
Structured data comes in multiple formats: relational (SQL tables with typed columns), semi-structured (JSON, XML — flexible schemas with typed keys), and Linked Data (RDF/JSON-LD — schema.org and ontology-based). In web publishing, structured data embedded in HTML using JSON-LD Schema.org vocabulary makes content machine-readable to search engines and AI crawlers, directly influencing how entities are understood and indexed. The EU AI Act's requirements for AI system outputs to be interpretable by humans and machines create an implicit demand for structured data in AI system documentation and output formats.
Publishers, e-commerce operators, and enterprise data teams should treat structured data implementation as infrastructure — it multiplies the value of content for AI retrieval, search visibility, and knowledge graph integration.
Like the difference between a handwritten memo and a well-formatted spreadsheet — the spreadsheet can be sorted, filtered, calculated over, and combined with other data programmatically; the memo requires human reading.
Data in a defined, organised format that computers can read and understand directly — like a spreadsheet or a labelled JSON file — as opposed to free text.
Structured data comes in multiple formats: relational (SQL tables with typed columns), semi-structured (JSON, XML — flexible schemas with typed keys), and Linked Data (RDF/JSON-LD — schema.org and ontology-based). In web publishing, structured data embedded in HTML using JSON-LD Schema.org vocabulary makes content machine-readable to search engines and AI crawlers, directly influencing how entities are understood and indexed. The EU AI Act's requirements for AI system outputs to be interpretable by humans and machines create an implicit demand for structured data in AI system documentation and output formats.
Publishers, e-commerce operators, and enterprise data teams should treat structured data implementation as infrastructure — it multiplies the value of content for AI retrieval, search visibility, and knowledge graph integration.