Everything You Need to Know About Data Extraction

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Everything You Need to Know About Data Extraction

In today’s world of global connection, it is a popular belief that data rules the affairs of men – from how people perceive others to how companies make their best decisions. Everything done today needs to be backed by accurate, adequate, and relevant data.

Businesses that grow and expand swiftly do so mostly because they work with structured web data collected in real-time. The general model of data gathering is called data extraction. And data extraction does not come easily or cheaply. It is usually a series of concise and precise techniques and steps taken regularly and correctly.

Failure to properly follow the rules of data extraction could lead to many negative consequences such as infringements and trespasses, wastage of time and other resources, and self-sabotage that results in the company’s data breaches.

Today, we will learn all that data extraction is, extract data from the website, the steps and processes involved, and the challenges and solutions associated with this important operation.

What is Data Extraction?

Data extraction is a series of process that involves accessing multiple sources, extracting unstructured data (usually in HTML format), cleansing that data and transforming the data into a structured format, and then parsing the data into a local storage medium to be stored for future use or immediate analysis and application.

It is generally used by companies who wish to make more intelligent and more informed business decisions. It is easily applicable in cases such as brand monitoring and protection, market, competition, and price monitoring, lead generation, sentiment and market analyses, and price intelligence.

Data extraction aims to conveniently and automatically collect public data from multiple sources and deliver them to your local storage to make decisions that easily grow your business.  And it has worked tremendously for many brands helping them provide better customer-oriented service, produce better-positioned products, conquer new markets, and make more sales.

How Web Scraping Process Works

Another name for data extraction is web scraping, and the process of how to extract data from a website can be easily divided into two:

  1. The Web Crawler Part

The web crawler, also sometimes called a ‘spider’ (mostly because of how it work), is a tool or software usually developed with Artificial Intelligence (AI) and used for browsing the internet, one website at a time, searching and indexing links based on relevance and how they are discovered.

  1. The Scraper Part

The web scraper also possesses human-like behavior like the crawler but works by using the links provided by the crawler to quickly and accurately extract information from web pages. This specialized tool uses an inbuilt feature known as a data locator or selector to discover where the data is located on the web page and extract it accordingly.

Summarily, both parts work together to scrape data in the following processes:

  • The servers containing the necessary data are first identified and noted down
  • The web crawler is used to collect all relevant links and URLs from the servers and other associated pages
  • The user initiates a request to extract the data from those servers
  • The data is extracted, cleansed, transformed, and parsed into the local storage
  • Then it is saved up in a structured format such as JSON or CSV

Challenges of Data Extraction

While the process described above sounds simple and easy, it is not as straightforward and can be easily punctuated with several challenges, including the following:

  1. Lack of Technical Know-How

By all indications, web scraping is a highly technical process – from how to write the scraping script to how to store the extracted data – and usually, brands, especially the small and rising ones, may lack those who can conveniently handle these responsibilities.

  1. Lack of Resources

Web scraping and its requirements do not come cheaply. Some companies have to purchase the necessary tools while others have to outsource the entire process thoroughly, which costs money.

Also, there is the need to invest sufficient time to run a consistent operation even though most of it has become automated and many companies may not have enough time to spare for it.

  1. Content Dynamism

The content to be extracted never stays the same for long. It is often the language being used, the page design, or the page structure that changes. Other times, it is the addition of updates and fresh content. Both of these changes need to be resolved for the entire operation to be successful

  1. Restriction Mechanisms

Normally, companies that own the content to be extracted do not like to share and therefore put up measures to discourage scraping. These measures could be CAPTCHA tests that identify and ban bots or IP blocking that identify and block internet protocol (IP) addresses that have been observed to perform repetitive actions every day in web scraping.

  1. Unreliable Speed

Web scraping is a complex activity that involves visiting multiple data sources at once or in a single day. An unreliable internet or connection speed can make it an overwhelming process that takes too much time and effort.

Also, slow speed can translate into an extraction of data that is no longer collected in real-time.

  1. Geo-Blocking

This is a serious challenge for any business staying in a restricted geo-location as it means they will be unable to gather data from certain servers. Also, some geo-restriction means brands outside some countries cannot access content from within the restricted regions.

How Proxies Help in Overcoming These Challenges

The challenges above may seem daunting, but they are not unsolvable. They have existed for as long as web scraping has existed, yet more and more brands continue to scrape data successfully every day. The best solution for overcoming these problems has always been the use of proxies.

Proxies provide the option of using different IPs, rotating proxies, and locations, thereby overcoming IP bans and geo-restrictions. They also work automatically with impressive speed hence expelling the problem of unreliable speed and work monotonicity. By working automatically, proxies can also easily collect and update new information.

Conclusion

Because of how important data is, brands must perform data extraction if they intend to make any meaningful growth in today’s competitive market. And although web scraping does have its challenges, brands can easily use proxies to overcome these challenges. We hope that this article helped you to understand how to extract data from a website

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