Today’s rapid technological changes influence organizations relying on it and bring about some great functional changes. This has witnessed insane development over the past several years and is predicted to keep growing. With more use of Artificial Intelligence (AI) and companies resorting to online business, there is a considerable need for data. And working with extensive datasets is not something to be taken lightly. This is when web scraping comes into play to collect data and serve different business purposes.

However, many people often confuse the web scraping process with data mining. According to them, both terms refer to the same process, which is certainly not the case. In this brief post below, we’ll explain what each process means and how it works. Based on this, we’ll further explain the major differences between data mining and web scraping.

Web Scraping – A Brief Overview

Web scraping refers to the process of extracting data from any website. Also called data collection or data extraction, it scans the text or multimedia from targeted sites to be analyzed for actionable insights. Data retrieved via web scraping is mostly repurposed or used in live applications that need a continuous data stream. This technique is now extensively used in several industries to meet different demands, such as lead generation, competitive price monitoring, image scraping, sentiment analysis, etc.

How Does Web Scraping Work?

The process works automatically to obtain data from different sources many times over. Specialized web scraping tools, with the Hypertext Transfer Protocol (HTTP), access the internet, obtain valuable data, and extract it as per your requirements. These unstructured data sources may involve web pages, documents, scanned text, classifieds, emails, and so on. Ultimately, a web scraper stores data in an easily understandable format like JSON, CSV, or a database for further analysis or processing. Oxylabs has a breadth of useful information regarding web scraping.

Data Mining – A Brief Overview

Data mining, also known as Knowledge Discovery in Data (KDD), is a process that involves effective data collection, warehousing, and computer processing. It can be described as a technique used in analyzing and sorting through already-harvested extensive volumes (millions/billions) of records. The process uses AI or other mathematical and statistical models to uncover specific patterns or trends and derive value from them. It provides marketers with valuable insights about their customers that can be used for sales forecasting, database marketing, and product development purposes.

How Does Data Mining Work?

There is a collection of steps that makes up the data mining process. It begins with data cleaning, which cleans the data for accurate results using manual and automatic methods. Later, it picks valuable information from the database, including integrated data, and transforms data into suitable forms with normalization and aggregation. Mining involves intelligent processes to find data patterns. Finally, the mined data is shown with a knowledge presentation using visualization techniques.

Data Mining vs. Web Scraping: Similarities and Differences

As far as similarities are concerned, both data mining and web scraping draw from a similar base. However, these methodologies are implemented differently. The essential connection between these is the data supply – the amount of data extracted from web scraping is critical for the analysis procedure of data mining.

When it comes to the differences between them, web scraping retrieves data from interactive pages or HTML documents, whereas data mining analyzes data to unleash valuable patterns. This indicates that web scraping is used at first to create the datasets to be employed in data mining. There is no analysis or processing involved in web scraping and no data gathering or retrieval in data mining.

With web scraping, the main focus is data that has value. On the other hand, data mining focuses on creating something new out of your data, even if there is little to no value to start with. While web scraping is based on programming languages or uses sophisticated tools, such as a proxy or a web scraper, data mining uses mathematical methods to uncover trends or patterns.

Their differences can also be noted in terms of their use cases. Web scraping is used for brand monitoring, protection, and marketing monitoring, whereas data mining is useful for performing market analysis and developing company strategies. Another key difference is that data mining is quite complex and requires heavy staff investments. Conversely, data extraction can be straightforward and cost-effective when done with the right tool.

Closing Thoughts

Data mining and web scraping have been serving important purposes in the domain of business intelligence (BI) for several years. Businesses scrape the web for valuable content, which is then analyzed to make intelligent choices. Though both methodologies are intrinsically contrasting and demand different skill sets and expertise, they work towards the same objective, i.e., to help businesses thrive.