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The Simplified Science Behind Data Extraction

Data Extraction, Science Behind Data Extraction, coding a web scraper, web scraper, data cleaning tool

In today’s world, the business community needs to embrace technology even more than before. With the digital age upon us, each step creates more opportunity but also more confusion. One key concept you need to understand is the data extraction possibility. It could help your business in a big way or your competition, depending on who uses it better. Here is what you need to know:

In its essence, data extraction is all about searching the web for sites with data you want. This could be your competitors, your market, or other areas you want to research. Then, once you have a hold of the data, you can use it to make better business decisions. But there is a process you need to understand first. The science behind data extraction using a data cleaning tool can be broken down into four parts, called PPSR.


First, before you have any data, you need to know where it is and how to pull it. This is the first step of data extraction. Using code in Python or JavaScript, you tell your scraper to find sites with certain similarities. You might be looking for keywords, titles, or even people that are on the site. Oftentimes, you may be faced with security features on websites that prevent scraping out of fear of data theft or competition. In such scenarios, you may need to couple your scraper with residential, backconnect or rotating proxy servers to hide your IP address and avoid getting blocked from the website.

Once you have identified the target site, you set your bots in action and make them search through the whole site to pull the data out. Once you have your digital hands wrapped around the data, you can prepare to push it to your storage space.


The push part of the process involves telling your data to stream to an offline database, computer, website, or storage device. This is where you will store the data. Of course, before you can do that, you need to analyze the different types of data.

When transferring, data can be corrupted if it is transported in the wrong file format. So during your initial scraping in the pull portion of your process, it was going through and tagging different data types. Now, with your database correctly set up and ready to go, it can put your data into virtual slots, one by one. This process is one of the more time consuming of the entire scraping model.


Storage is where your data is sitting in your new home for it. It might seem simple, but you need to run tight security on your database. Furthermore, you should code in a fast way to retrieve the information. Otherwise, the database of information you have just spent all this time building is virtually worthless.


Having all the data in the world is not helpful if you don’t have a way to put it into action. This is where the review portion of the process comes in. By leveraging unique managerial talents, you and your team can see how to apply the data. Use charting apps and other visualization techniques to make the data easier to handle.

Put It All Together

Instead of coding a web scraper from scratch, you can use an app or team that already exists. That way, you are still focusing on the higher-level aspects of your strategy. You can leave the grunt work to professionals and programs.

When it comes to data today, there is more of it than ever before. Smart business owners will find ways to put it to use for them in their business. Make sure you are on the right side of history when it comes to using data in powerful ways. That way, you don’t miss out on opportunities to grow your business. Implement the tips above and gain one more step over your competition this year and beyond.

jeremy sutter


Author’s Bio: Jeremy Sutter is a tech and business writer from Simi Valley, CA. He lives for success stories and hopes to be one someday.



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Scientific Methods of Extracting Data from the Source

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