The internet has all the information of the world but it’s buried under unstructured content, dynamic pages, and endless HTML.

Web scraping helps us extract, structure, and analyze that data at scale.

But what kinds of data can you actually scrape?

In this guide, we’ll break down the most common web scraping data types, explain how businesses use them, and show you which ones to avoid.

What Is Web Scraping and Why It Matters

Web scraping is the automated process of extracting information from websites.
It is basically turning information available on web pages into structured, usable datasets.

Most businesses use web scraping for various reasons, some of which are:

• Market research: monitoring competitors and trends
• Pricing intelligence: adjusting prices in real time
• Lead generation: collecting business data ethically
• Data aggregation: building large-scale datasets for AI or analytics

Curious why web scraping is so widely used? Read why businesses use web scraping to collect data.

The Most Common Web Scraping Data Types

Web scraping allows you to extract a wide range of data from websites, from text and media to structured metadata. Below are the most common categories of web scraping data, each with practical examples and typical use cases.

1. Text-Based Data

Text is the backbone of the web, and also the most commonly scraped form of data. It comes in multiple shapes and structures, each valuable in its own way.

Plain text

This includes product descriptions, blog articles, news headlines, reviews, and comments, essentially, any visible text that forms part of a webpage’s content.
We often see clients in e-commerce scrape product reviews to analyze customer sentiment or identify trending complaints about a competitor’s product line.

When collecting user-generated content, such as Reddit threads, check out our guide on how to scrape Reddit posts, subreddits and comments.

Structured text

Structured text is text that lives in a consistent, machine-readable format, such as within HTML tables or specific tags. Examples include product specs (price, weight, dimensions), item lists, or pricing tables.
For instance, a real estate analytics firm might extract tabular data from listing pages, price, area, number of bedrooms, to track market trends over time.

Code and markup

This includes HTML, CSS, and JavaScript code, or even Markdown documents. Developers and analysts often scrape markup to analyze site structure, audit SEO changes, or rebuild dataset layouts for testing.
I’ve personally used scraped HTML to track how large e-commerce brands change their page structures after seasonal sales.

2. Media Files

Visual and downloadable content adds another dimension to data extraction. Many businesses underestimate how valuable scraped media data can be for analytics, machine learning, or content management.

Images

Product photos, infographics, or profile images can be collected and cataloged. These help in building visual search models, image comparison tools, or even brand compliance systems that detect unauthorized logo use.

Videos

Scraping embedded video URLs or hosted video metadata (like titles or thumbnails) is useful for media monitoring or content trend analysis.
For example, a marketing team might track which YouTube videos a competitor embeds on their landing pages to gauge campaign focus.

PDFs and other documents

From annual reports to technical manuals, scraping document links (or their text content) unlocks structured knowledge that’s often buried deep within a site.
A client in finance, for example, routinely scrapes PDF filings from government portals for financial disclosure analysis.

3. Structured Data Formats

The web also contains hidden data that’s not visible to users but perfectly structured for machines.

JSON (JavaScript Object Notation)

Found in APIs or embedded <script> tags, JSON is one of the easiest formats to parse. It’s the go-to format for extracting well-organized data like product catalogs, event listings, or stock prices.

XML (Extensible Markup Language)

You’ll find XML in RSS feeds, sitemaps, and enterprise integrations. Scraping XML data allows aggregators to collect consistent, structured feeds with minimal cleanup.

For projects that rely on rotating IPs or multiple sources, learn the difference in our in-depth guide to static vs. rotating proxies.

CSV (Comma-Separated Values)

Although CSV files are typically downloadable rather than rendered in-browser, scraping or automatically fetching linked CSV files lets analysts pull raw datasets at scale, ready for immediate analysis.

4. Navigational and Metadata

Not all useful data is visible on the surface sometimes the real gold lies in the links and metadata that describe a site’s content.

Links and URLs

Scraping URLs helps with site mapping, discovery, and data enrichment. For instance, a crawler may extract all product URLs from a fashion retailer’s sitemap to ensure comprehensive coverage during scraping.

Metadata

Metadata such as page titles, meta descriptions, keywords, or header tags can reveal how competitors position their pages for SEO.
In one project, we used metadata scraping to identify emerging keyword themes in a competitor’s blog before they started ranking for them.

5. Other Specific Data Types

Some types of data are niche but extremely valuable when captured responsibly and ethically.

Contact information

Emails, phone numbers, and business addresses are often scraped from directory sites or company listings.
Used properly (and legally), this supports B2B lead generation and market mapping efforts.

Social media data

Public profile data, posts, hashtags, and engagement metrics can help brands analyze sentiment, track campaign performance, or discover influencers.

Geolocation data

Webpages often contain location details, such as embedded map coordinates or store addresses.
Scraping these enables geo-targeted market research, for example, mapping restaurant density in specific neighborhoods.

Putting It All Together

Whether it’s raw text, structured feeds, or multimedia, each data type has its place in the modern web data ecosystem.
Understanding which type fits your use case can help you design more efficient, compliant scraping strategies and avoid wasting resources on unstructured or irrelevant content.

Structured vs. Unstructured Data in Web Scraping

When we talk about “types” of web data, there’s another dimension worth understanding, how the data is organized. In web scraping, this comes down to structured vs. unstructured data.

Both have their place, but they behave very differently once you start parsing, storing, or analyzing them.

What Is Structured Data?

Structured data is any information that follows a predictable, organized format, like rows in a table or key-value pairs in JSON.

If you’ve ever scraped an e-commerce site and received data neatly sorted into columns (product name, price, SKU, rating), that’s structured data. It’s the cleanest and most machine-friendly format for extraction.

Examples of structured data:

• HTML tables listing product attributes
• JSON responses embedded in <script> tags
• XML feeds or sitemaps
• CSV or database exports

Because structured data has clear labels and delimiters, it’s easy to integrate directly into databases, dashboards, or machine learning models.
For instance, one of our clients in retail used structured price and inventory data to power a real-time pricing intelligence tool across hundreds of online stores.

What Is Unstructured Data?

Unstructured data is everything that doesn’t fit neatly into rows and columns. It’s messy, human, and context-heavy but often the most insightful.

This includes things like:

• Product reviews and comments
• Blog articles and news stories
• Images, PDFs, and videos
• Social media posts

The challenge with unstructured data is that it requires processing before it becomes usable. You might need natural language processing (NLP) to extract meaning from text, or computer vision to tag and categorize images.

Businesses often turn conversation data into actionable insights, see how in Turning conversation data into insight: a practical guide for beginners.

We’ve seen companies use unstructured review data to uncover hidden customer pain points or to perform competitive sentiment analysis at scale.
It’s less about clean numbers, more about patterns and emotions.

What is Semi-Structured Data?

Between the two lies semi-structured data, data that has some organizational elements but doesn’t conform to a rigid schema.
Examples:

• HTML pages (consistent tags, but inconsistent content placement)
• Email headers or forum posts
• Event listings with variable field formats

A typical example is a news site where every article follows the same HTML layout, but metadata like “author” or “tags” appear inconsistently depending on the publication date.

APIs often expose data in structured formats — see our post on common API integration issues and solutions.

Semi-structured data is usually parsed with a mix of rule-based extraction and flexible parsing tools like XPath or CSS selectors.

How to Handle Structured vs. Unstructured Data?

The key difference isn’t just in how the data looks, it’s in how you process and store it afterward.

In short:

• Structured data = fast insights
• Unstructured data = deep insights
• Semi-structured data = balance between the two

The best scraping strategies often combine all three.

Real-World Examples of Web Scraping Data in Action

It’s one thing to talk about what types of data you can scrape — it’s another to see how businesses actually use them in the real world.
We’ve seen organizations across industries transform web data into actionable intelligence. Below are some of the most common and impactful applications of scraped data.

E-commerce: Dynamic Pricing and Product Intelligence

Retail is a battlefield of numbers, and web scraping gives you the competitive edge.
E-commerce brands regularly scrape product titles, prices, availability, and reviews to monitor how competitors are positioning themselves in real time.

It’s a real-world example of web scraping turning raw HTML into revenue optimization.

Many retailers rely on competitor price scraping to monitor and adjust pricing in real time.

Real Estate: Market Insights and Property Trend Analysis

Real estate platforms thrive on data consistency, but property information is scattered across thousands of listings.
By scraping property details, price, size, location, number of bedrooms — analysts can build comprehensive datasets for market forecasting.

We worked with a Canadian real estate analytics firm that scraped thousands of listings weekly to track regional price fluctuations.
By visualizing this scraped data, they were able to identify emerging buyer hotspots months before official reports confirmed them.

Finance: Alternative Data for Market Signals

In finance, speed and context mean everything.
Investors and analysts use scraping to gather alternative data, non-traditional indicators like news sentiment, job postings, or company announcements — to detect early market shifts.

A financial intelligence company, for instance, scraped corporate press releases and SEC filings to identify patterns in how companies communicate before major events.
Pairing that unstructured text data with stock movement data helped them develop an algorithm that flagged potential earnings surprises.

Recruitment: Labor Market and Skill Analytics

Job boards are goldmines of public data.
Scraping job titles, locations, required skills, and salary ranges provides a living snapshot of the labor market.

Hospitality and Travel: Competitive Rate Monitoring

In the travel industry, prices change faster than you can pack a suitcase.
Hotels and travel aggregators scrape room rates, availability, and reviews from competitors to dynamically adjust their own pricing models.

Media and Research: Content Aggregation and Sentiment Tracking

Web scraping also fuels content discovery and trend analysis.
By scraping news headlines, article metadata, and publication timestamps, analysts can build real-time feeds of emerging topics.

A data journalism team, for example, used scraping to collect climate-related articles across international news outlets.
They then ran sentiment analysis on the headlines to understand how public tone toward climate policy changed over time.

It’s a reminder that sometimes the story behind the headlines lives in the data itself.

Business Intelligence: Cross-Domain Data Fusion

The most powerful insights often come from combining multiple data types.
In one project, we helped a retail intelligence company merge structured product data (from e-commerce pages) with unstructured social sentiment data (from reviews and forums).

The result was a dashboard that didn’t just track what was selling, but why.
By correlating positive sentiment trends with sales velocity, they were able to predict future bestsellers with surprising accuracy.

Example Table: Industries and Their Scraped Data Types

What These Examples Have in Common

Across industries, successful web scraping isn’t about collecting everything.
It’s about targeting the right data, ensuring it’s structured properly, and staying compliant with data ethics and platform policies.

Every project we deliver at DataHen follows that philosophy, clean data, clear value, and complete transparency.

What Data Should Not Be Scraped

Web scraping opens incredible possibilities — but not all data on the internet is fair game.
We take a firm stance: just because data is technically accessible doesn’t mean it’s scrapable.

Responsible scraping protects not only your business but the broader data ecosystem we all rely on.

Learn how to check if a website allows for web scraping.

1. Private or Login-Protected Data

If a website requires users to log in before viewing certain information, that data is not public.
This includes:

• Personal account dashboards
• Private messages or user profiles
• Internal business portals or SaaS platforms

Accessing or scraping behind authentication walls without permission violates most terms of service — and, in some cases, data protection laws like GDPR or CCPA.

We never scrape behind login pages unless a client provides explicit authorization and owns the data source.

2. Personal Identifiable Information (PII)

PII refers to any data that could identify an individual person — such as names, email addresses, phone numbers, or physical addresses.

Even if that data is technically visible on a public page, scraping and storing it for commercial use without consent crosses an ethical (and sometimes legal) line.

For example:

• Scraping social media profiles with full names and contact info
• Collecting email addresses from comment sections or directories
• Harvesting contact forms

We strictly avoid PII scraping unless it falls within legitimate, compliant business purposes, such as internal company directory synchronization or publicly consented datasets.

3. Copyrighted or Licensed Content

Not all data belongs to the public domain.
Scraping copyrighted material — such as entire blog posts, images, or proprietary datasets — for redistribution or republishing can lead to copyright infringement.

That’s why we never scrape or reproduce:

• Full article texts for republication
• Paid or subscription-only content
• Stock photos or licensed imagery

Instead, we focus on extracting metadata (e.g., article titles, publication dates, summaries) when the goal is analysis rather than replication.

4. API-Restricted or Terms-of-Service-Protected Data

Some websites clearly specify what is and isn’t allowed to be accessed automatically.
Ignoring a site’s robots.txt file or scraping content explicitly disallowed by its Terms of Service (ToS) can create compliance risks and damage your brand’s reputation.

We recommend:

• Checking a site’s robots.txt file before crawling
• Reviewing ToS for any data-use limitations
• Respecting rate limits and crawl delays

Every project undergoes a compliance audit before deployment — ensuring we stay within the ethical and technical boundaries of the target source.

5. Sensitive or Regulated Information

Certain industries, like healthcare and finance, contain highly sensitive datasets.
Even when portions are publicly visible, the context of use can fall under strict regulation.

Examples include:

• Patient data or health records (protected under HIPAA)
• Credit or banking details
• Confidential government data

Our guiding principle: if there’s any doubt, don’t scrape it.
When clients in regulated sectors need insight, we focus on aggregated, anonymized, or derived data rather than direct personal or sensitive records.

6. Data That Poses Ethical Risks

Not every risky scrape is illegal — some are simply unethical.
For instance, scraping for the purpose of:

• Manipulating reviews or ratings
• Profiling individuals without consent
• Amplifying misinformation

All of these erode trust in the data ecosystem.

Our Approach to Ethical Web Scraping

Every DataHen project starts with one question: Should this data be scraped at all?
We use a three-step ethical screening process:

1. Legality Check – Is the data public and permitted for extraction?
2. Intent Review – Will the use of this data create value without harm?
3. Compliance Validation – Does it align with regulations and terms of service?

Only when all three boxes are checked do we proceed.

That’s how we ensure our clients stay on the right side of both innovation and integrity.

UP NEXT: Learn how automation can scale your process in How to use n8n and OpenAI to scrape websites and analyze content.

FAQs About Web Scraping Data Types