As more and more businesses across verticals begin to understand the data’s value, they start assessing different data sources. In many cases, most of the information you can use to make informed business decisions is spread across websites.
It makes you wonder whether there is a method you can use to collect it. Yes, there is. Here is how to extract data from a website and why it’s such an essential step for businesses.
Why is data extraction so valuable?
Data extraction is valuable for many reasons.
First, it enables your business to get access to target data quickly. Instead of creating spreadsheets and manually copy-pasting the data in the relevant fields, you can put data extraction on autopilot. It saves a lot of time and minimizes the number of errors that often come with tedious, repetitive tasks.
Data extraction can help you be more competitive in your target market. Extracting data regarding product offers and prices can streamline competitive analysis. It can help businesses fine-tune their pricing strategies to attract more customers and ensure repeat business.
How businesses use data extraction
Businesses use data extraction to generate new leads as well. They gather data from social media accounts, forums, and community portals. It helps them build an extensive email database containing prospective customers, which they can turn into leads.
Next, we have brand monitoring. Thanks to data extraction, companies can now gauge customer sentiment. They can see what the consumers are talking about their products, services, and overall experience with the brand. Deep insights into sentiment data and brand analysis can help companies make critical changes to achieve their branding and customer experience goals.
Finally, since millions of people use search engines, organizations often have ongoing SEO. Data extraction can help discover which keywords the successful competitors use across their websites, paid ads campaigns, and guest blogs.
How the process works
As you can see, data extraction is valuable. If you want your company to do it, you will need to learn how to extract data from a website.
There is a web scraping operation at the core of every data extraction initiative. Web scraping refers to targeting and extracting data from websites.
Let’s see how the process works so that you can understand data extraction better.
Finding relevant URLs
Every data extraction process starts with finding relevant URLs – addresses of unique resources on the internet or specific web pages.
For example, if you want to extract product descriptions, prices, and ratings, there is no need to harvest irrelevant data from a particular website’s page, such as blog posts, the homepage, or the About Us page.
Instead, you only make a list of URLs that contain data relevant to your business goal. It makes the data extraction and parsing faster. Plus, you will only get scraped data that genuinely matters to you.
Setting up scraping proxies
Many websites don’t allow data extraction by default; they have anti-scraping measures. If you try to extract data from them from one IP address, the chances are that you will receive an IP block, putting your entire data extraction project in jeopardy.
That’s why you need to set up scraping proxies. The two most commonly used proxies for web scraping are residential and rotating IP proxies. Make sure to get your web scraping scripts and libraries in order during this stage.
For instance, if you use Python for web scraping, you need to set up a script using Requests and Beautiful Soup libraries.
Parsing HTML data
Once you extract the data from a website, you will get this massive unstructured text which is impossible to read. The next stage of a data extraction project is parsing the HTML data, which includes two phases.
First, you need to parse the HTML data into a more readable format to find specific elements and classes in the HTML quickly.
Once you find out which HTML elements and classes contain the data you need, you can start extracting it from the structured HTML.
Storing scraped data
The final step is to store the scraped data. For instance, scraping data via Python lets you extract the final output in DataFrame format, which is a default in Python. However, if the web scraping code stops running, you can have an incomplete data set.
Many organizations use web scraping scripts that automatically output the data in a CSV file as you are extracting the data.
Go to the blog article by Oxylabs to learn about the process in more detail.
Hopefully, now you understand how to extract data from a website. The process is relatively simple once you have some experience with it.
It can help you facilitate data-driven decision-making, understand your target market better, and discover what the competitors are doing. You can then gain a competitive edge and start building long-term business growth.