Structured Data

What is Structured Data?

A page's visible HTML tells a machine how content should look, not what it is. Structured data closes that gap by annotating the content with a shared vocabulary — almost always Schema.org — so that a heading becomes a product name, a number becomes a price, a block becomes an author, a question becomes a FAQ entry. Three encodings exist. Microdata and RDFa embed attributes inline in the markup; JSON-LD puts the whole description in a separate script block, and has won decisively because it can be updated without touching the rendered page. The practical value is that extraction stops being guesswork. Search engines use structured data to build rich results, and AI answer engines use it to ground factual claims about an entity. It is unusually high-leverage work: invisible to users, so it cannot damage the experience, and typically a one-time engineering task per page template rather than an ongoing cost.

Why it matters

• Replaces inference with declaration — the machine is told what the content means rather than guessing.
• Unlocks rich results and materially improves the odds of citation by AI answer engines.
• Invisible to users and template-level, so it is one of the cheapest durable wins available.

Use cases

• Article markup. Author, publication date and headline declared on every post.
• FAQ markup. Question and answer pairs marked up to power FAQ rich results.
• Organization markup. Sitewide entity description feeding knowledge graph and panel eligibility.