Structured Data

What is structured data?

Structured data is information organised into a pre-defined schema with named, machine-readable fields, rather than written as free-form prose a human would need to read and interpret to extract the same facts. "Address: 42 5th Cross, Koramangala, Bengaluru, 560095" is structured. "We're located in the heart of Koramangala, just off 5th Cross" contains the same underlying fact, but a machine has to parse and infer it rather than read it directly.

Structured vs. unstructured data

Unstructured data is the text humans normally read: a paragraph about a business's history, a description of services, a blog post. Machines can technically process this text, but real interpretation is required to pull out specific facts reliably, and interpretation introduces error. Structured data removes that ambiguity by putting each fact in a labelled field with a defined meaning that both search engines and AI systems already agree on.

How structured data is implemented on websites

For most websites, structured data is implemented as JSON-LD code blocks embedded in the page's HTML, invisible to a human visitor but readable by crawlers, sitting alongside the normal visible content rather than replacing it. A restaurant's page might show a nicely designed menu and description to visitors while the JSON-LD block underneath tells search engines and AI systems, explicitly and unambiguously, that this is a Restaurant entity, located at a specific address, open specific hours, serving a specific cuisine.

Why structured data matters for AI

AI systems can extract entity data from structured data without the interpretation uncertainty that comes with parsing prose. A business with complete LocalBusiness schema hands AI systems direct, reliable entity data they can cite with confidence. A business without any schema forces AI systems to infer that same entity data from text, a noticeably less reliable extraction pathway, and one where mistakes (the wrong hours, the wrong category, a misread address) are more likely to slip through into an AI-generated answer.

This distinction is becoming more consequential as more search behaviour shifts from ranked link lists toward synthesised answers. A ranked list of links tolerates some ambiguity, since a human clicks through and resolves it themselves. A synthesised AI answer has no such safety net, whatever the AI extracted becomes the answer the user sees, which raises the cost of ambiguous, unstructured source content.

Common types of structured data for local businesses

LocalBusiness schema (or a more specific subtype like Restaurant, MedicalClinic, or RealEstateAgent) covers the core entity facts: name, address, phone, hours, category. FAQPage schema marks up question-and-answer content directly. Review schema surfaces aggregate rating data. Speakable schema flags specific text for voice extraction. Most local businesses benefit most from getting LocalBusiness and FAQPage schema right before layering in anything more specialised.

Common mistakes

Implementing schema that doesn't match the visible page content, listing hours in the schema that differ from the hours shown on the page, is a frequent and easily caught error that undermines trust in the markup. Using an overly generic schema type (plain "LocalBusiness" when a more specific subtype like "Dentist" exists) throws away useful specificity for no benefit. And treating schema as a one-time setup task rather than something that needs updating whenever the underlying business facts change, a new location, a changed phone number, updated hours, leaves stale structured data actively misleading the systems it's meant to help.

India context

Schema markup adoption among small and mid-sized Indian local businesses remains low relative to more mature markets, and most local websites in India carry no structured data at all beyond whatever their website builder adds automatically. That gap is a genuine competitive opening: a business that implements complete, accurate schema ahead of its competitors gives AI systems a clean, reliable entity to cite, while competitors are still relying on those systems to correctly parse ordinary prose.

Related terms: Schema.org → · JSON-LD → · FAQPage schema → · LocalBusiness schema → · Speakable schema → · Structured Answer →

Example: A restaurant page with Restaurant schema markup tells AI systems explicitly: this is a restaurant, not a hotel or a shop, named "Spice Garden," located at a specific address, open 11am to 11pm, offering both dine-in and takeaway, serving Indian cuisine. No interpretation of prose is required to get any of those facts right.

Angryturtle implements structured data across service pages, location pages, and FAQ content as part of every managed AEO Services → engagement.