Quick Answer
Structured data is machine-readable markup, typically written in schema.org vocabulary, that describes what a page actually represents — an organisation, a service, an article, a set of FAQs — in a standard format that search engines and AI systems can parse directly, rather than having to infer it from prose alone.
Why This Matters
AI systems and search engines both need to work out what a page is about before they can represent it accurately. Without structured data, this depends entirely on how well the visible content communicates that information. With structured data, the same facts are also expressed explicitly, in a format designed for machines — reducing the chance of a business, service or article being misunderstood or summarised incorrectly.
What structured data actually does
Structured data doesn't change how a page looks to a visitor. It adds a separate, machine-readable layer — usually as JSON-LD script embedded in the page — that labels specific pieces of content using a shared vocabulary maintained at schema.org.
Common schema types for a business website
- Organization — name, logo, contact details and social profiles for the business itself.
- Service or Product — what is being offered, and to whom.
- Article or BlogPosting — for knowledge content, guides and news.
- FAQPage — question-and-answer content, marked up explicitly as such.
- BreadcrumbList — how a page fits into the site's overall structure.
Getting started
- Identify the core entities on each page type — a service page describes a Service, an article describes an Article, and so on.
- Apply the matching schema type accurately, using real information already present on the page.
- Validate the markup using a structured data testing tool before publishing.
- Review structured data whenever the underlying page content changes, so the two stay in sync.
Structured data is most effective when it accurately mirrors the visible content — it should describe what is genuinely on the page, not what a business wishes were there.