What Is Structured Data (Schema Markup)?
Short answer
Structured data is code, usually JSON-LD following the schema.org vocabulary, that explicitly tells machines what your content is: an article, a product, an organization, a defined term. It converts pages machines must interpret into facts machines can simply read. It's the vocabulary layer beneath most entity and AI visibility work.
Structured Data / Schema Markup explained
Schema.org launched in 2011 as a joint project of Google, Bing, and Yahoo (Yandex joined later): one shared vocabulary for describing web content to machines. Google recommends the JSON-LD format, a block of labeled data in the page's code declaring types and properties: this is an Organization named X, this Article was written by this Person on this date, this Product costs this much.
The classic payoff is rich results: review stars, FAQs, event details, and other enhanced listings that structured data makes eligible. The durable payoff is disambiguation. Markup states machine-readable facts, connects entities (author to organization, term to term-set, page to brand), and removes the guesswork from parsing, which is why it threads through nearly every entity-building tactic in this glossary.
For AI systems, structured data functions as a context layer. It won't substitute for quality content, and no markup rescues a page with nothing worth citing. What it does is make good content legible: types, relationships, authorship, and dates that retrieval systems and knowledge graphs consume directly. In a selection contest between equally strong passages, the one whose page declares its facts cleanly gives the machine fewer reasons to hesitate, and machine hesitation is exactly what visibility work exists to remove.
In practice
Schema is standard practice in my builds, and the priority order counts for more than the tag count: Organization and Person markup for the entity layer first, then content-type markup that fits what the page truly is. The mistake I fix most often is decorative markup, tags bolted on because a plugin offered them, describing things the page doesn't actually contain. Machines notice the mismatch. Mark up what is real, connect it to your entity, and keep it consistent with the visible content.
Common misconception
People often expect structured data to boost rankings directly. It isn't a ranking factor in that sense: it makes content eligible for enhanced display and machine understanding, which is where its compounding value lives.
Sources
See also
- DefinedTerm SchemaDefinedTerm schema is structured data that marks a page as an authoritative definition of a specific term, with DefinedTermSet grouping a collection of them…
- FAQPage SchemaFAQPage schema is structured data marking a page's question-and-answer content so machines can read each Q&A pair directly.
- Entity SEOEntity SEO is optimizing around the people, organizations, products, and concepts machines recognize as distinct things, rather than around keyword strings.
- Machine-Readable ContentMachine-readable content is content structured so that AI systems, crawlers, and agents can parse, extract, and act on it accurately: real HTML text, clear…
- Knowledge GraphA knowledge graph is a structured database of entities and the relationships between them, which search engines and AI systems use to understand who is who and…
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Written and reviewed by Tarun Gehani
SEO and AI visibility consultant in Ann Arbor, Michigan, with 17+ years in SEO. He helps B2B SaaS companies, marketing teams and agencies show up in Google and in AI answers.
