Structured Data

Element code TE-021. Here is the honest version: your page copy is written for humans, and a crawler has to guess at the meaning. Structured data removes the guessing. It is a small block of markup that states, in a vocabulary machines already understand, "this is an Article, published on this date, by this author, about this topic." Sites that skip it are not penalized, they are just invisible to a whole layer of features that competitors with markup are collecting.

What structured data actually is

Structured data is a standardized way to label the entities and facts on a page. The dominant vocabulary is schema.org, a shared type system maintained by Google, Microsoft, Yandex, and others. The dominant format is JSON-LD, a script block you drop in the head or body that describes the page without touching the visible layout. Google explicitly recommends JSON-LD over the older inline Microdata and RDFa formats because it is easier to maintain and does not tangle your markup with your content.

A minimal Article block looks like this:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Your title",
  "author": { "@type": "Person", "name": "Jane Doe" },
  "datePublished": "2026-01-10"
}
</script>

From page to machine understanding

Why this matters for SEO and AEO

Two payoffs. First, rich results: valid markup makes your listing eligible for enhanced SERP features like review stars, FAQ dropdowns, breadcrumb trails, recipe cards, and event details. Those features take up more space and pull more clicks. Second, and increasingly the bigger prize, is entity understanding. Structured data feeds the knowledge graph and gives AI answer engines a clean, unambiguous statement of who and what your content covers. When ChatGPT, Perplexity, or Google's AI Overviews assemble an answer, well-marked-up pages are easier to parse and cite. Markup does not guarantee a rich result, Google decides that per query, but without it you are not even in the running.

Common schema types and when to use them

How to detect what is missing

Use Google's Rich Results Test for a single URL to see which types are detected and whether they are valid. The Schema Markup Validator at validator.schema.org checks conformance to the vocabulary itself. In Search Console, the Enhancements reports show which structured data types Google has recognized across your indexed pages and flag errors and warnings at scale. For a whole-site inventory, Screaming Frog and Sitebulb both extract and validate structured data during a crawl, so you can find every page that is missing an Article or Product block in one pass. Start there rather than eyeballing pages one at a time.

How to fix it, step by step

1. Pick the type that matches the page's real purpose. Do not mark a blog post as a Product to chase stars.
2. Write JSON-LD covering the required properties for that type, plus the recommended ones that apply.
3. Make the markup match the visible content exactly. Marking up data users cannot see is a guidelines violation and can trigger a manual action.
4. Inject it server-side or through a tag that renders before Google parses. Validate with the Rich Results Test.
5. Deploy, then watch the Search Console Enhancements report over the next crawl cycle to confirm valid detection.

What good looks like

Every important template carries one accurate JSON-LD block matching its content, validating clean in the Rich Results Test, and showing as valid with zero errors in Search Console Enhancements. Products carry price and availability, articles carry author and date, the organization is defined once sitewide. The reward shows up as richer listings and cleaner entity recognition across both classic search and AI answers.

FAQ