Structured Data
TL;DR
Structured data is standardized code added to web pages that explicitly tells search engines and AI systems what the content represents. Instead of relying on algorithms to interpret whether a number is a price, a phone number, or a rating, structured data declares it. The most common format is JSON-LD, using the shared vocabulary at Schema.org. When implemented correctly, structured data enables rich results in search (star ratings, FAQ dropdowns, event details) and increases the likelihood of being accurately cited by AI platforms.
Key Takeaways
- Code that makes your content machine-readable, not just human-readable
- Enables rich results in Google Search and improves accuracy of AI-generated answers
- Uses JSON-LD format and Schema.org vocabulary as the industry standard
Definition
Structured data is markup added to a web page's code that describes the content in a format machines can reliably interpret. Where regular page content is "unstructured" – search engines have to infer meaning from context – structured data states meaning explicitly. It declares that this text is a product name, this number is a price, this paragraph is a review, this person is an author.
The dominant format is JSON-LD (JavaScript Object Notation for Linked Data), which Google recommends because it sits cleanly in the page's code without interfering with visible content. The vocabulary comes from Schema.org, a collaborative project between Google, Microsoft, Yahoo, and Yandex that defines hundreds of content types – from articles and products to organizations, events, FAQs, and local businesses.
When structured data is valid and matches the visible content on the page, search engines can use it to generate rich results – the enhanced listings that display star ratings, FAQ accordions, product prices, event dates, and other visual elements directly in search results. These enhanced listings consistently earn higher click-through rates than standard blue-link results because they occupy more visual space and provide more information before the click.
In 2026, structured data has a second function that's equally important: feeding AI systems. Large language models and AI-powered search features (Google AI Overviews, ChatGPT, Perplexity) rely on structured data to understand, summarize, and cite web content accurately. A page with well-implemented schema is more likely to be correctly interpreted and cited by AI platforms than one that relies on unstructured text alone. This is where structured data intersects directly with answer engine optimization.
Qontour’s Approach
Structured data implementation is part of our content enrichment and AEO/SEO service – available on every engagement but scoped to the project, not included by default. When it's in scope, we implement schema at the template level so every page of a given type carries the right markup, and new pages inherit it without manual configuration.
For B2B and cybersecurity company websites, we typically implement Organization schema (company identity, contact information, social profiles), Article schema (blog posts with author attribution, publish dates, and review signals), FAQ schema (on service pages and glossary entries where applicable), and Product/Service schema where the content supports it.
The implementation principle is the same across all types: structured data should describe what's already visible on the page, not add claims the content doesn't support. Google explicitly penalizes markup that doesn't match visible content. We audit schema validity using Google's Rich Results Test and monitor performance through Search Console's rich result status reports.
For AEO specifically, structured data plays a strategic role. When AI platforms summarize your content, well-structured pages give the AI clearer signals about what the page covers, who published it, and what claims it supports. This doesn't guarantee citation, but it reduces the chance of being misinterpreted – which matters significantly when the AI is summarizing your product capabilities for a potential buyer.
Queries
Essentially, yes. "Structured data" is the concept – standardized, machine-readable content descriptions. "Schema markup" is the specific implementation, usually in JSON-LD format using Schema.org vocabulary. In practice, the terms are used interchangeably.
Not directly. Google has stated that structured data is not a ranking signal. However, it enables rich results that increase click-through rates, and higher CTR can indirectly influence how your pages perform over time. The real value is visibility and click quality, not position.
Organization schema (who you are), Article schema (blog and resource content), FAQ schema (service pages and educational content), and LocalBusiness schema (if you have a physical presence). For SaaS companies, SoftwareApplication schema can also be relevant for product pages.
Google's Rich Results Test validates individual pages. Search Console's rich result status reports show which pages have valid schema, which have errors, and which are generating enhanced search results. Check both regularly – template changes and CMS updates can break schema without anyone noticing.
Increasingly, yes. AI platforms use structured data to understand content more accurately when generating summaries and citations. A page with clear schema signals – author, organization, publish date, topic categorization – is easier for AI systems to process and more likely to be cited correctly than one without.
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