Ever wondered how some businesses seem to predict their competitor’s next move? Or, adjust product prices confidently? Some even seem to understand customer behavior and shift in preferences.

Well, all this is made possible by data analytics systems that break down web data sets to extract insights, expose meaningful patterns, or detect trends over time.

If you’re working toward realizing the aforementioned benefits of web data sets analysis and more, we got you! Here’s what you need to know about web data sets, the sources, types, and uses.

What are Web Data Sets?

Plainly put, a web data set is a collection of information pulled from the internet and structured or formatted to achieve a specific purpose.

Automated crawlers, scrapers, or APIs (Application Programming Interfaces) pull the data from the internet. Why? The web is massive and constantly changing. This makes manually collecting and tracking data points inefficient.

While web crawlers travel across the internet, discover web pages, and index them, web scrapers target specific pages and extract specific pieces of information. For instance, Google’s search engine remains relevant thanks to Google’s crawlers.

APIs, on the other hand, are software solutions built by specific platforms to allow access to their structured data. For instance, you can obtain structured data from Facebook through Meta’s APIs.

Moreover, AI now powers intelligent web scrapers that can build websets based on specific instructions. You specify the data you want in plain language and the AI-powered web scraper visits relevant sites, extracts the data, structures it, and delivers  ready-to-use sets of web data. 

Sources and Uses of Web Data Sets

So, where do web data sets come from? There are pre-curated web data sets out there. If you don’t find one that suits your use case, employ a normal or AI-assisted web scraper to create the desired dataset. Here are popular sources of precompiled web data sets: 

1. E-commerce platforms

E-commerce platforms like Amazon, Alibaba, Walmart, and Etsy collect massive amounts of data. This includes product titles, images, descriptions, prices, discounts, seller information, and stock levels.

These platforms offer access to these data through their APIs. Based on your objective, you can extract structured web data sets through a platform’s API. 

For example, if you want to monitor competitor pricing, you can set up the API to supply you with data points like, product titles, description, prices and discounts. 

2. Social media platforms

Social media web data sets come in handy when you desire to track brand reputation, analyze product or topic trends, or when you want to monitor competitor social media strategies. 

Platforms like TikTok, YouTube, and Reddit give you access to well-formatted datasets through their APIs. You can also obtain social media data sets from third-party curators. 

3. Business and company listings

Business and company listings compile web data sets about organizations across industries and regions. 

Platforms like Yellow Pages, Crunchbase, and LinkedIn provide datasets that include business names, phone numbers, website URLs, addresses, number of employees, and more. 

With such data, you can conduct industry trend analysis, strategize local SEO, or find potential partners or clients.

4. Search Engine Results Pages (SERPs)

SERPs give you access to organic results (titles, URLs, snippets). Some pages include paid ads, AI overviews, featured snippets, People Also Ask questions, maps results, images, and news results.

Such data is mostly useful when composing SEO strategies. Search Engine OPtimization (SEO) teams use the data to track search rankings, strategize ad placements,and study search intent.

5. Open-access and government platforms

Open-access platforms offer free web data sets built by statistical bureaus, international organizations, or researchers. These datasets often contain historical datasets that help with trend analysis. For instance, NGOs and journalists need these datasets to support their investigations. 

Governments also provide web data sets through government portals or public registries for transparency and research. These data sets contain education records, transportation data, public budgets, land records, and health data. 

Researchers and analysts use web data sets from government platforms to understand public health trends, economic performance, and climate patterns. As a business, you can also find data sets for assessing market risks, size, and region development plans to support decision-making. 

Types of Web Data Sets

As you may have noticed, we’ve mostly mentioned structured web data sets. The thing is, there are other types of web data sets. And, each type influences analytics differently. 

Different types of web data sets produce different kinds of insights. This is because each dataset has its own structure, freshness, depth, and bias. Let’s break it down:

1. Structured web data sets

Structured web data sets are organized into a clear and consistent schema. They are usually presented in a table-style (rows, columns, or defined fields).

Since the data points in a structured web dataset are neatly arranged, analytics software can read, sort, and analyze them with little to no preprocessing.

Structured datasets favor multiple analytics approaches such as descriptive, diagnostic, operational, and predictive analytics. This makes them ideal for achieving multiple objectives like SEO data tracking, competitive benchmarking, and inventory monitoring.

2. Semi-structured

A semi-structured web data set is partially structured, leaving room for it to contain messy real-world information. 

A good example is indexed HTML pages. A crawler picks up the HTML pages and extracts the pages’ URLs and metadata for indexing. The URLs and metadata provide structure, yet the actual content inside the web pages varies.

Semi-structured web datasets are suitable for topic analysis, sentiment analysis, and pattern extraction. You may need Natural Language Processing models to help with analysis. Nonetheless, due to the flexible structure, more preprocessing is required.

3. Unstructured

Any web dataset not formatted into a specific format or layout is unstructured. There’s no consistent fields, no fixed schema, and no predefined format.

An unstructured dataset may contain PDFs, long-form articles, raw social media text, audio clips, videos, or images. Analyzing these data needs advanced techniques. 

For instance, to analyze text, you need Natural Language Processing models. Computer vision models help with video and image analytics. These models provide deeper context but demand more data preprocessing and computational power.

Most businesses invest in unstructured web data set analytics to understand how people talk, what themes dominate online conversations, and what customers complain about. 

Some use them to train Large Language Models, product recommendation systems, and for content classification. 

Closing Words

Now that you understand what web data sets are, the sources, types, and uses, you are in a better position to find a web dataset and analyze it to achieve a specific objective. Nonetheless, before you start looking for a suitable web data set or build one, note that the timeline of data does matter.

Ask yourself, “What dataset date range suits my objective?” If you don’t determine whether you need historical or real-time web data sets for your objective, you’ll end up relying on irrelevant or misleading insights.