What Is Structured Data & Schema Markup? • Feel Created

Overview

Long before AI Search became part of the conversation, structured data was already helping search engines understand websites more accurately. The recent growth of AI has simply brought renewed attention to a technology that’s been part of the web for many years, prompting more organisations to explore concepts such as Schema.org, JSON-LD and structured data.

Despite the growing interest, structured data remains one of the web’s most misunderstood technologies. Some describe it as an SEO technique, others present it as the key to AI visibility, while many organisations are simply left wondering what it actually does.

The useful distinction is simpler and more interesting. Structured data isn’t about changing what your website says. It’s about helping search engines and AI systems understand what your content represents. Once that distinction becomes clear, it’s much easier to understand where structured data fits within a modern website and why it has become an established part of the web rather than simply another AI trend.

Understanding why structured data exists begins with understanding how search engines and AI interpret information.

What is structured data?

If you’ve been reading about AI Search, you’ve probably come across the term structured data. It’s often mentioned alongside Schema.org, JSON-LD and semantic search, particularly as more organisations explore how search engines and AI systems interpret websites.

That naturally raises an important question: what exactly is structured data, and why has it become such a significant part of the conversation?

When we visit a website, we build an understanding almost effortlessly. We recognise the organisation behind the website from its name and branding. We understand that a page titled “Services” describes what the organisation offers, while a page containing a publication date, author and body text is probably an article. If we see a telephone number beside an address, we naturally understand that we’re looking at contact information rather than simply a collection of numbers and words.

We reach those conclusions because people interpret meaning almost effortlessly. We combine visual cues, language, previous experience and common sense without consciously thinking about the process. Search engines and AI systems approach the same page in rather different ways.

Although modern search technology has become extraordinarily capable, software still has to interpret information from the signals a website provides. The visible content on a page tells it a great deal, but understanding what that content represents requires more than simply reading the words themselves. Is this page describing an organisation or one of its services? Is the date referring to an event or the publication of an article? Does a particular name belong to a person, a company or a place?

Structured data helps answer those questions by identifying what different pieces of information represent. Visitors never see it because it sits behind the page rather than within its design, but search engines can use it to recognise things such as organisations, articles, events and services more reliably.

Perhaps the easiest way to think about structured data is as labelling rather than optimisation. The content on the page remains the same. Structured data simply provides additional meaning about what that content is, making it easier for machines to interpret accurately.

This is important because it helps explain where structured data fits within a modern website. It isn’t a substitute for clear writing, good page structure or thoughtful design. Instead, it supports those foundations by making the information they’re already communicating easier for other systems to recognise.

Why search engines need additional context

At first glance, it can seem surprising that search engines need structured data at all. Modern search engines can already read webpages, understand natural language and answer remarkably complex questions. If they can already interpret the content on a page, why is structured data necessary?

The important point is that understanding language and identifying specific facts are not quite the same thing.

Imagine someone hands you a photograph without any explanation. You might recognise the building, identify the people in the image or work out roughly what is happening. Even so, there would still be room for uncertainty. Was the photograph taken this year or twenty years ago? Is the building a museum, a university or an office? Are the people employees, visitors or speakers at an event?

Now imagine the same photograph with a short caption identifying the location, the people and the occasion.

The photograph hasn’t changed. Your understanding has simply become more certain.

Structured data performs a similar role on a website. The visible content already provides most of the information a search engine needs. Good headings explain the subject, descriptive copy provides context and a logical page structure helps establish relationships between ideas. Structured data adds another layer of certainty by explicitly identifying particular pieces of information in a standard format that different systems can interpret consistently.

A page may clearly present itself as an article, with a headline, publication date and author. Visitors recognise that immediately. Structured data identifies those same elements, allowing search engines to interpret them without relying solely on the surrounding context.

The page’s content is still the primary source of information. Structured data simply gives search engines another way to confirm what the page is already communicating.

Understanding that wider picture is important because structured data doesn’t exist in isolation. It’s just one part of the way websites communicate meaning.

Websites communicate in more than one way

One reason structured data can feel confusing is that it’s often discussed as though it exists separately from the rest of a website. In reality, it’s simply one of several ways a webpage communicates information.

The visible content communicates directly with people. Headings introduce topics, paragraphs explain ideas and images provide visual understanding. At the same time, HTML tells browsers how that information is organised, identifying headings, navigation, lists, articles and other structural elements. Internal links establish relationships between pages, helping both people and machines understand how different topics connect across the website. Structured data sits alongside these existing signals rather than replacing them.

Structured data complements these existing signals by describing important pieces of information. It helps search engines distinguish between different types of content, recognise relationships and interpret webpages with greater confidence, while leaving the visible content completely unchanged.

Clear content remains the primary source of information, but structured data provides another layer of explanation that helps software identify important details without having to infer everything from context alone. Rather than leaving a search engine to decide whether a page describes an event, a product or an organisation, structured data can identify that information using a standard vocabulary recognised across the web.

This also explains why structured data has become a much more visible topic in recent years. AI systems benefit from websites that present information clearly, so technologies that help reduce ambiguity have naturally attracted more attention. Structured data doesn’t suddenly become valuable because AI exists. Rather, AI has encouraged more organisations to recognise the value that structured data has offered search engines for many years.

Viewing structured data from this broader perspective also helps avoid one of the most common misconceptions about it. It isn’t a hidden set of instructions telling Google or ChatGPT what to say. Instead, it’s another way for a website to communicate meaning, sitting alongside well-written content, semantic HTML, accessibility and clear information architecture.

The strongest websites tend to do all of these things well because they were designed to communicate clearly in the first place.

Schema.org: the shared vocabulary behind structured data

For structured data to work, websites and search engines need to share a common vocabulary. That’s exactly what Schema.org provides.

Launched in 2011 through a collaboration between Google, Microsoft, Yahoo and Yandex, Schema.org provides a shared vocabulary for describing information on the web. Instead of each search engine inventing its own way of identifying organisations, articles, or events, websites can use a common set of recognised types and properties that are understood across multiple platforms.

For example, Schema.org includes definitions for content such as Organisations, Articles, Products, Events, Services and Local Businesses. Each of these types also includes recognised properties that describe them in more detail. An Organisation might include its name, logo, website address and social profiles. An Article can identify its headline, author, publication date and publisher. An Event may describe its location, organiser and start date.

This consistency is one of the reasons structured data is so effective. Schema.org gives organisations a consistent way to describe their content using a vocabulary that search engines and other systems already recognise.

The objective isn’t to describe everything on a webpage. It’s to describe the things that have a clearly defined meaning. Good structured data is selective. It focuses on information that benefits from explicit identification rather than labelling every element on a page.

What kinds of information can structured data describe?

Almost every modern website contains information that can benefit from structured data, although the specific schema used depends entirely on the page’s purpose.

A business website may identify the organisation behind the site, the services it offers and the articles it publishes. An ecommerce website might describe products, prices, reviews and availability. A university could identify courses, departments, events and locations, while a local authority might use structured data to describe services, offices, consultations and news updates.

A blog article can identify its author, publication date and publisher. A contact page can identify the organisation and its contact details. A breadcrumb trail can describe where a page sits within the website’s broader structure, reinforcing relationships that visitors already understand visually through navigation.

Consider a typical university website. A course page might identify the qualification being offered, the department responsible for delivering it and the institution providing it. An events page could identify the date, location and organiser of an open day. Although visitors can infer this information from the page’s content and layout, structured data allows those details to be described explicitly for search engines and other systems.

The important point is that structured data isn’t attempting to describe every sentence on the page. Instead, it identifies the pieces of information that have a recognised meaning beyond the words themselves. That also explains why restraint is usually a sign of good implementation. A carefully chosen set of schema types that accurately reflects the page is generally more useful than trying to mark up every possible property simply because it’s available.

How is structured data added to a website?

Although structured data plays an important role behind the scenes, most website owners never need to interact with it directly.

The information is usually added as machine-readable code alongside the HTML that builds the page. Visitors continue to see the same website because structured data doesn’t affect the visual design. Its role is simply to provide additional context for software processing the page.

Several technical formats have been developed over the years, including Microdata and RDFa, both of which embed structured information directly within the HTML. Today, JSON-LD has become the most widely used approach.

JSON-LD, which stands for JavaScript Object Notation for Linked Data, keeps the structured data separate from the HTML used to build the page. That practical advantage has helped make JSON-LD the most widely used format for structured data. It’s also Google’s recommended format for implementing structured data, which is why it has become the standard approach for many modern websites.

For most organisations, the technical format is rarely the most important consideration. Whether it’s generated automatically by a content management system, added through an SEO plugin or implemented by the developers building and maintaining the website, the underlying principle remains the same. The structured data should accurately reflect the content visitors see on the page.

Keeping those two things aligned is far more important than the technology used to deliver them.

How search engines and AI use structured data

Once structured data has been added to a webpage, search engines can use it to identify information that might otherwise need to be suggested by the surrounding content. It doesn’t replace everything else the page communicates, but it gives search engines greater confidence that particular pieces of information represent what they appear to be.

One of the most visible examples is Google’s rich results. Depending on the type of page and the search being performed, Google may display additional information such as product details, event information or other enhanced search features.

Rich results are simply one way search engines may choose to present information. Whether those features appear or not, structured data continues to help search engines identify what a page is about and how it relates to the wider website.

Structured data also contributes to Google’s understanding of organisations, products, places and published content. Combined with the information already available on the page, it helps reinforce the relationships between different pieces of content and the entities they describe.

The emergence of AI Search hasn’t fundamentally changed that role.

Modern AI systems also benefit from websites that communicate clearly, and structured data contributes to that clarity by identifying organisations, articles, services and other recognised entities. Rather than relying solely on what it can infer from the page, AI can use structured data as an additional signal to support its understanding.

Different AI systems process web content in different ways, so they won’t all make use of structured data in the same manner. Clear, machine-readable information still helps reduce ambiguity and reinforces the meaning already communicated by the page itself.

That distinction is important because structured data isn’t a set of hidden instructions telling AI what to say. Modern AI systems develop an understanding by combining many different signals. They analyse the visible content, interpret HTML structure, follow internal links, recognise relationships between topics and, depending on how the system works, may also consider external sources that reinforce trust and consistency. Structured data contributes to that wider process, but it doesn’t replace the need for well-written content or a logically organised website.

This is one of the reasons discussions about AI optimisation can become misleading. It’s tempting to focus on individual technologies or techniques, but AI systems don’t build an understanding from one signal alone. They develop confidence by looking for consistency across everything a website communicates.

Structured data is valuable because it strengthens that consistency.

Common misconceptions about structured data

As structured data has become more widely discussed, particularly alongside AI Search, several misconceptions have emerged. Some are understandable. Others stem from treating structured data as a shortcut rather than one part of a well-built website.

Looking at a few of the most common assumptions helps illustrate both the strengths of structured data and the limits of what it can achieve.

Does structured data improve search rankings?

One of the easiest assumptions to make is that adding structured data automatically improves a website’s position in search results.

Google has consistently treated structured data as a way to help Search understand content and support eligible rich results, rather than as a simple ranking shortcut. There are many different factors that influence search performance. These can include content quality, the website’s reputation, the experience it provides for visitors, and how well the page matches what someone is searching for.

That doesn’t mean structured data has little value.

Helping search engines identify your content more accurately is worthwhile in its own right, particularly as search continues to evolve. Structured data simply contributes to that understanding rather than determining where a page should rank.

Does structured data guarantee rich results?

Rich results are probably the most visible example of structured data in action, which makes it easy to assume that implementing schema automatically leads to enhanced search listings.

In reality, the process isn’t that straightforward.

Structured data makes additional information available to search engines, but Google still decides whether rich results are appropriate for a particular page and search query. Two websites may implement similar structured data and receive different search presentations depending on the content, the search intent, and how Google chooses to display results.

Rich results are therefore best viewed as a possible outcome rather than the objective itself.

The real benefit comes from accurately describing content. Enhanced search features may change over time, but helping search engines understand your website remains valuable regardless of how search results are presented.

Can structured data tell AI what to say?

The growth of AI Search has introduced another misconception. Some articles suggest that structured data serves as a set of hidden instructions that tell AI systems how to describe an organisation or which information should appear in generated answers.

Modern AI doesn’t work that way.

As we’ve explored throughout this article, AI develops an understanding by combining many different signals. Structured data is one of those signals because it helps identify organisations, articles, services and other recognised entities more explicitly. It doesn’t override the visible content, replace other sources or provide instructions that AI simply repeats.

Imagine two organisations offering similar services. One has clear, consistent content, logical information architecture and accurate structured data. The other has fragmented content, inconsistent terminology and poorly maintained schema. Both websites may technically use structured data, but the first provides a much stronger foundation for search engines and AI systems to build a reliable understanding.

Structured data supports that understanding. It doesn’t create it on its own.

Is more schema always better?

Another common misunderstanding is that websites should implement as many schema types and properties as possible.

In practice, good structured data is usually quite restrained.

The objective is to describe the content that genuinely exists on the page. Adding schema simply because it’s available, or marking up information that visitors can’t actually see, introduces unnecessary complexity and can create conflicting signals.

The goal isn’t to use as many schema types as possible. It’s to describe the content that’s genuinely on the page. If a page explains a service, it should be marked up as a service. If it’s an article, the structured data should reflect that. Adding schema for information that doesn’t exist only makes the page less clear.

As with so many aspects of website development, quality matters more than quantity.

Keeping structured data accurate

Like any other part of a website, structured data needs occasional maintenance. As pages are updated, services change and new content is published, the underlying schema should evolve alongside them. Otherwise, it can gradually become out of sync with the information visitors see on the page.

Most modern content management systems and SEO tools generate much of this information automatically, reducing the amount of manual work involved. Even so, automatically generated schema should still be reviewed periodically to ensure it reflects the information visitors actually see. When structured data is implemented through custom development, that review becomes even more important because any changes to the content may also require behind-the-scenes updates.

Ultimately, structured data should be maintained in the same way as the rest of your website. It works best when it’s accurate, well maintained and kept aligned with the content it describes.

Checking your structured data

Adding structured data is only part of the process. It’s equally important to confirm that the markup is valid and continues to reflect the content visitors actually see.

Google’s Rich Results Test can identify whether eligible pages qualify for supported search features, while the Schema Markup Validator helps check that structured data follows the Schema.org vocabulary correctly. Google Search Console may also report structured data issues and enhancements, making it easier to identify problems as a website evolves.

Like the rest of a website, structured data benefits from occasional review. Changes to page templates, services, organisational information, authors, events or other key content should be reflected in the underlying schema so that the information presented to search engines remains accurate and consistent.

Structured data as part of a well-built website

By now, a broader pattern has probably become clear. Structured data isn’t an isolated optimisation technique, nor is it a shortcut to better search visibility. It’s one part of a website that’s been designed to communicate clearly.

Good content explains ideas in language that people understand. Semantic HTML gives that content meaningful structure. Accessibility helps make information available to as many people as possible, while navigation, internal links and information architecture establish relationships between different topics across the website. Structured data complements those foundations by providing another layer of machine-readable meaning.

When discussing AI-ready websites, it’s easy to become absorbed by individual technologies. New techniques are visible, measurable and often widely discussed, making them feel more important than they really are. Search engines and AI systems don’t build an understanding from isolated features. Instead, they develop confidence by looking for consistency across the website as a whole, considering its content, structure, technical implementation and the relationships between different pieces of information.

Structured data reinforces that consistency, but it doesn’t replace the work that comes before it.

In practice, the priorities are fairly simple. Structured data should accurately reflect the visible content, cover the page types that genuinely benefit from schema and be reviewed whenever important information changes. Keeping those foundations accurate is usually far more valuable than continually adding new schema types.

For organisations, that’s a reassuring conclusion. Building a website that search engines and AI systems understand doesn’t require a fundamentally different approach from building one that serves people well. The same qualities that have long contributed to good websites, including clear writing, thoughtful information architecture, semantic HTML, accessibility and accurate technical implementation, also help machines interpret information more confidently.

Structured data has become more visible because AI Search has changed the way organisations think about how websites are discovered and understood. The technology itself, however, isn’t new. For many years it has helped search engines identify organisations, articles, products, services and many other types of information through a shared, machine-readable vocabulary. AI systems are simply another reason why describing that information consistently has become increasingly valuable.

Search engines, browsers and AI systems will continue to evolve, just as they always have. The need for websites to present information clearly is far less likely to change.

The better a website explains itself, the less any search engine or AI system has to guess.