Structured Data

What Structured Data Is

Structured data is markup that classifies a page's information into a standardized format so search engines can determine exactly what the page contains. Google defines it as "a standardized format for providing information about a page and classifying the page content." Think of it as restating, in explicit machine-readable cues, the same content that humans see as natural-language text and images — so that machines (search engines and AI) can read it without guessing.

Take a recipe page: a person effortlessly sees the dish name, author, cook time, calories, and rating, but a search engine has a hard time deciding which text is the "cook time" and which number is the "rating." Tagging each element with a schema.org label through structured data lets the search engine recognize these values precisely and surface details like cook time and rating right in the search results.

The schema.org Vocabulary

The core vocabulary standard for structured data is schema.org. Launched jointly by Bing, Google, and Yahoo in June 2011 — later joined by Yandex — it is an open standard offering more than 800 entity types and over 1,400 properties. It predefines types such as Article, Product, Recipe, FAQPage, and Organization along with the properties that belong to each, so a page author simply borrows this shared vocabulary to classify their own content. Because the major search engines all support the same vocabulary, the biggest payoff is that marking content up once to the standard lets multiple engines and AI tools interpret it identically.

Structured Data vs. JSON-LD

In practice "structured data" is used almost interchangeably with "schema markup," but it sits at a different level than JSON-LD. Structured data refers to the overall idea of labeling page information with a standard vocabulary so machines can read it, whereas JSON-LD is one of several formats that express that idea in actual code. There are three formats for expressing the schema.org vocabulary, and Google recommends JSON-LD among them.

Google recommends JSON-LD because it is "the easiest solution for website owners to implement and maintain at scale and is less prone to user errors." Since JSON-LD lives in its own block, separate from the visible markup, you can add or edit it without touching the design, and it is easier to manage. As an aside, "structured output" in the LLM world — forcing a model to return its response in a format such as JSON — is a separate concept and has nothing to do with the search-oriented structured data discussed here.

JSON-LD Example

Below is an example of JSON-LD structured data applied to an Article page. Leave the visible body untouched and simply add the script block below to the head or body.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "The Complete Guide to Structured Data",
  "author": {
    "@type": "Person",
    "name": "Jane Doe"
  },
  "datePublished": "2026-06-10",
  "publisher": {
    "@type": "Organization",
    "name": "SearchOS"
  }
}
</script>

For a product page, change @type to Product and fill in properties such as price (offers), rating (aggregateRating), and reviews (review) — in other words, pick the type and properties that fit your content type.

Rich Result Case Studies

Adding structured data does not guarantee that rich results will always appear, but it is a required precondition for them. The case studies Google Search Central publishes in its official documentation include the following.

• Nestlé measured an 82% higher click-through rate on pages displayed as rich results than on pages that were not (source: Google Search Central).
• Rotten Tomatoes applied structured data to 100,000 pages and saw a 25% higher click-through rate compared with pages without it (source: Google Search Central).
• Food Network converted 80% of its pages to enable a search feature and saw a 35% increase in visits (source: Google Search Central).

So structured data works less as a signal that directly raises rankings and more by presenting a page more richly in the results, lifting click-through rate and visits. The explicit information organized with the schema.org vocabulary also helps AI assistants and LLMs — not just search engines — understand content quickly and accurately.

Implementation Checklist

• First choose the schema.org type that matches your content (article, product, FAQ, recipe, organization, and so on).
• Write it in JSON-LD wherever possible — it is Google's recommended format and the easiest to maintain.
• Mark up only information that actually appears in the visible content. Putting content in the markup alone, where users can't see it, is a policy violation.
• Fill in every required property for each type, and include the recommended properties as far as possible.
• Validate with Google's Rich Results Test and check the search preview where available.
• After publishing, monitor the Enhancements reports in Search Console for errors, warnings, and rich result coverage trends.

Sources

• Intro to How Structured Data Markup Works — Google Search Central
• Structured Data Markup that Google Search Supports — Google Search Central
• Schema.org — Official Site
• Schema.org Schemas — Types and Properties