What is structured data?
Structured data is information organized in a predictable, well-defined format that makes it easy to search, analyze, and process. Think of it like a spreadsheet where every row follows the same pattern with the same columns in the same order. Each piece of information has a specific place and meaning.
In web scraping, structured data is what you're trying to create. You take messy web pages with all their HTML, images, and formatting, and extract the useful information into clean, organized formats like spreadsheets or databases. This makes the data actually usable for analysis, reporting, or feeding into other systems.
The key characteristic of structured data is consistency. Every record follows the same schema with the same fields. A product database where every item has a name, price, SKU, and description in the same format is structured data. A random blog post with paragraphs of text is not.
Structured vs unstructured data
Understanding the difference helps you plan your web scraping approach.
Structured data has a fixed schema with predetermined organization. It fits neatly into rows and columns. Examples include product catalogs, pricing tables, financial records, and customer databases. You can easily search, sort, and analyze this data because every piece follows the same pattern.
Unstructured data has no fixed schema or standard organization. This includes blog posts, social media content, emails, images, videos, and documents. You can't just drop this into a database because it doesn't follow consistent patterns. Extracting insights requires more sophisticated processing like natural language analysis or image recognition.
Semi-structured data sits in between. HTML is a perfect example. It uses tags and elements to provide some organization, but the actual content and structure vary wildly between sites. XML and JSON also fall into this category because they use markup to label data without enforcing rigid schemas.
Most websites present semi-structured data that scrapers need to convert into fully structured formats. A product page has structured elements (price, title, SKU) mixed with unstructured content (descriptions, reviews). Your scraper targets the structured parts and organizes them consistently.
Common structured data formats
When you scrape data, you output it in one of several standard formats:
CSV (Comma-Separated Values) is the simplest format. Each row represents one record, with commas separating the values. The first row usually contains column headers. CSV files open in Excel or Google Sheets, making them perfect for quick analysis or sharing with non-technical teams.
JSON (JavaScript Object Notation) handles complex, nested data better than CSV. It represents data as key-value pairs and supports hierarchical structures. APIs often return JSON, and modern applications prefer it for data exchange because it's both human-readable and machine-friendly.
XML (Extensible Markup Language) uses tags similar to HTML to structure data. It's more verbose than JSON but still common in enterprise systems and older APIs. XML explicitly defines relationships between data elements through its nested tag structure.
Databases like MySQL, PostgreSQL, or MongoDB provide the most rigid structure. You define tables, columns, data types, and relationships upfront. The database enforces these rules, ensuring data consistency. When you're scraping large amounts of data regularly, databases beat flat files because they handle updates, queries, and relationships better.
Structured data in HTML
Websites sometimes include structured data directly in their HTML using standardized formats. Google and other search engines encourage this because it helps them understand page content better.
JSON-LD (JSON for Linking Data) is the most common method. A recipe site might embed JSON-LD that explicitly labels the recipe title, ingredients, cooking time, and nutritional information. This structured markup makes it trivial to extract specific data because the website has already identified and labeled everything for you.
Schema.org vocabularies provide standard ways to mark up different content types like products, events, articles, and local businesses. When websites use these standards, scraping becomes much easier because you know exactly where to find specific information and what it means.
The catch is that many websites don't bother with structured data markup. They present information in plain HTML that requires more work to parse and extract.
How web scrapers convert to structured data
The conversion process follows several steps that transform messy web pages into clean datasets.
First, the scraper requests the HTML from the target URL, just like a browser would. For JavaScript-heavy sites, it needs to render the page and wait for dynamic content to load.
Next comes parsing. The scraper converts the raw HTML string into a navigable structure. It can now traverse the document tree, target specific elements by their tags or classes, and extract content from exactly where you specify.
Then extraction happens. The scraper pulls out the specific data points you've defined, like product names from h1 tags and prices from elements with a specific class. It grabs just the content you need and ignores everything else.
Finally, transformation organizes the extracted data into your chosen format. The scraper might clean the text, convert prices to numbers, format dates consistently, and arrange everything into rows and columns. What started as a complex HTML page becomes a simple spreadsheet row with name, price, description, and URL in predictable columns.
This process repeats across hundreds or thousands of pages, building a complete structured dataset from scattered web content.
Why structured data matters for businesses
Raw web pages are essentially useless for analysis or decision-making. Structured data makes web information actionable.
E-commerce businesses scrape competitor pricing into structured spreadsheets to monitor markets and adjust their own prices. Without structure, comparing thousands of products across dozens of sites would be impossible.
Market researchers collect data from multiple sources and need it in consistent formats to analyze trends, identify patterns, and generate insights. Structured output lets them feed scraped data directly into analysis tools.
Sales teams scraping lead information need structured contact lists they can import into CRMs. Unstructured web pages don't help. Clean CSV files with company names, emails, and phone numbers do.
Data scientists training machine learning models need consistent, structured datasets. Scraped data with predictable schemas feeds directly into their pipelines without manual cleanup.
Studies show that 95% of businesses collecting web data prefer structured datasets over raw, unstructured information. The structure makes data immediately usable instead of requiring hours of manual processing.
How Browse AI delivers structured data
Traditional web scraping requires you to write code that parses HTML, identifies elements, extracts content, and formats output. This technical complexity keeps many people from using scraped data even when they need it.
Browse AI eliminates this barrier by automatically converting web pages into structured data through a visual interface. You click on the data you want, and Browse AI figures out how to extract it consistently across multiple pages. The output arrives as clean, structured spreadsheets or JSON without any coding.
The platform handles all the conversion steps automatically. It requests pages, parses HTML, executes JavaScript for dynamic content, extracts your specified data points, and formats everything into rows and columns. You get structured data ready for analysis, import into other tools, or integration with your systems.
Browse AI also maintains structure consistency even when websites change their layouts. The visual extraction adapts to minor changes automatically, keeping your data pipelines running without constant maintenance. This means you can rely on consistently structured output even from websites that update frequently.
