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Web Scraping vs Data Mining: Why the Confusion? in 2024

Web scraping and data mining are sometimes confused with each other because they are both linked to extracting value from something that is valuable only when processed. However, the definitions are quite different, and not understanding the difference can cause not realizing how these processes can create value for businesses.

In this article, we will clarify what each one of these terms stands for and how web scraping is an enabler of data mining. We will introduce use cases that may apply to your business and also how you can run a free pilot for your company.

What is Web Scraping?

Web scraping is the process of scanning text or multimedia content from targeted websites and turning this content into a data table that can be analyzed. So, essentially, web scraping is a form of data extraction. It does not generate any business insights before the collected data is cleaned, formatted, and analyzed.

What is Data Mining?

Data mining is a broad term that refers to generating value from data. So, data mining is the process that starts after the data extraction. As we previously explained in our detailed post about data mining, it is used almost interchangeably with data science. As the amount of data that can be collected, stored, and processed exponentially increased, data analytics methods that businesses leverage evolved from simple descriptive statistics to more advanced methods such as regressions, natural language processing and advanced machine learning applications. Data mining has been coined over time with these advanced data applications.

How Does Web Scraping Enable Data Mining?

The essential connection between web scraping and data mining is data supply. Web scraping can create very rich data sources by collecting all the text and image content of many websites. For example, if you would like to track how the prices of a product change across 5 e-retailers, that is about 10-20 pages per website and tens of products per page. If you track the price records every ten minutes, that’s already more than a thousand data points. Even this small use case creates a very rich data source. Below are the top data types that web scraping enables for data mining applications:

1. Commercial Data:

Just like the example we mentioned, a very common use case of web scraping that enables data mining is commercial data on e-commerce business owners or brands that provide an online shop. Web scraping tools can collect product definitions, reviews, prices, features, stock status, colors, ratings, and many other information that can generate insight for businesses. Apart from goods and products, web scraping can also collect service information such as flight fares, ticket prices, and freelancer fees across all the websites you target.

Example: With the dynamic price information you have, you can create price comparison platforms like or apply dynamic pricing for your product to keep your price point always competitive compared to the rest of the online market.

2. Blogs and news:

Natural language processing as a data mining method has transformed text data into a valuable asset. Web scraping is a fast and efficient way to collect written data on the web. It can scrape entire articles, tables, and images on the articles as well as links that are embedded in these articles. It can target exact websites or top search engine results that appear for a certain keyword.

Example: As a data-driven industry, financial investment firms scrape billions of web pages every year to detect the shifts in financial markets or the potential changes depending on the political climate and popularity of different industries. Not only for general market trends, but they also use web scraping as a specific background check on certain markets and industries before advising investors on how sustainable these companies have been or whether there was any track of negative news about their financial stability or publicity.

3. Social media posts:

In one second, there are more than 9000 tweets on Twitter and 1000 Instagram posts on Instagram on average. Depending on what industry you are in, a significant amount of this big and increasing content can be relevant to your business. Web scraping can target certain keywords and hashtags that are important to your business and put them into the data of what people say online. This data can reveal whether there is more activity on social media for your competitors, whether your consumers mention negative or positive words about your product and many other insights about new trends emerging.

Example: In our detailed guide about influencer marketing, we explained how web scraping can help find brands that are exemplary ambassadors. There are many newly emerging influencers online that may be difficult to discover but very impactful in niche areas, and much more affordable to work with compared to most popular names in social media. Web scraping can help businesses scrape all the social media posts about keywords related to their product and filter the accounts that send these posts based on the number of followers to discover new influencers.

Learn by Practice:

If you already have existing data mining processes supporting your business decisions or plan to use new methods, you can access free data sources scraped from the web to see whether any of the use cases we mentioned above can be beneficial for your business. Keep in mind that if you decide to use web scraping on a continuous basis, you need to consider all the benefits and challenges of collecting data from the web before making a decision on whether you’d like to build such a capability in-house or leverage an external provider.

To explore web scraping use cases for different industries, its benefits, and challenges, read our articles:

If you believe that your business may benefit from a web scraping solution, check our list of web crawlers to find the best vendor for you.

For guidance to choose the right tool, reach out to us:

Find the Right Vendors

This article was drafted by former AIMultiple industry analyst Bengüsu Özcan.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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