The Internet generates millions of useful data every day. All of this data is recorded and stored, making the Internet an easily accessible hub that hosts an overwhelming volume of data, generated at immense speed with every passing moment. This data can be extracted to study recurring patterns and trends to assist in the deduction of useful insights and predictions.
When a large amount of information is aggregated in an organized manner, it can be used to help a company drive its business decisions. Of course, there is too much data online to do this manually and efficiently. That’s where Data Scraping comes in. This automation technique allows you to collect data in an organized manner quickly and efficiently.
Data Scraping is the act of automating the process of extracting information from an unstructured data source such as websites, databases, applications, reviews, tables, images, and even audio sources, to restructure them and make them editable for machine learning systems. These systems then absorb the structured data, analyze it and provide intelligent information about it.
Once upon a time, data scraping was not a very popular skill and there was rarely any innovation or research that suggested ways to use such unstructured data. However, with the evolution of technology and especially of machine learning and data science in recent years, the Internet has become a mine of valuable data.
Scraping has become a crucial part of the big data industry as it provides access to information, such as contact details of potential customers, price data for price comparison websites and more, that can be used by business organizations. In 2019 there was a substantial growth in web scraping activities through which organizations sought to improve their operations. Therefore, the use of scraping has become a common technique for many companies, especially the larger ones such as Google.
In fact, it is estimated that more than 45% of Internet traffic is done by robots and not by humans, and that 54 industries require Web Scraping specialists. The five main sectors that require these specialists include the industries: software, information technology and services, the financial sector, retail, and the marketing and advertising industry.
This should come as no surprise, since the relevance of the data has increased to such a high level in the last decade that industries are trying to prepare for possible future impacts and with as much data as possible. Data has become the golden key for any modern industry to achieve a secure and profitable future.
Web scraping offers several advantages, including the following:
While Web Scraping can provide a company with enormous benefits, there are also some disadvantages and assumptions on which it is based:
What gives a company a sustainable competitive advantage in the age of digitalization is data. Data is the main factor that will determine whether a company will be able to keep up with its competitors. The more data you have that your competitors cannot access, the greater the competitive advantage.
There is almost no area where data scraping has a profound influence. As data is increasingly becoming a primary resource for competition, data acquisition has also become especially important. Companies extract information from a website for several reasons, two of which are the most common: to grow the business by establishing a sales channel and to find out where competitors are setting their prices.
But web scraping can add much more value to a business in other ways. Here are some other reasons why a business, whether large or small, needs data scraping to make more money from its business:
There are many positive uses for data scraping, but it is also abused by a small minority, and despite all that can be achieved with it there are some sectors that consider it an unethical tool.
GDPR requires companies to have a purpose for processing the data. In terms of data erasure, companies that cannot justify or establish a legitimate purpose should not perform data erasure. Naturally, a careful and considered documented analysis of the purpose is recommended, bearing in mind that individuals should reasonably expect their data to be processed for the identified purpose.
Purpose limitation means that companies should only collect and process personal data for specific, explicit and legitimate purposes and not engage in further processing unless it is compatible with the original purpose for which the data were deleted.
Many of the organizations face the challenge of how to address web scraping attacks in an efficient and scalable manner. The impact of this attack can be broad, ranging from excessive expenditure on infrastructure to devastating loss of intellectual property.
The most common misuse of data scraping is the collection of email. That is, using data scraping from websites, social networks and directories to get people’s email addresses, which are then sold to spammers or scammers.
In some jurisdictions, the use of automated means such as data scraping to collect e-mail addresses for commercial purposes is illegal, and is almost universally considered a bad marketing practice.
Another misuse is to extract data without the permission of the website owners. The two most common cases are price theft and content theft.
While data scraping may seem daunting, it doesn’t have to be. The benefits are enormous, and there is a good reason why all large companies use this technology to help them shape their business strategy. It’s cheap to get this data, but it’s incredibly valuable when you have it to work with.
Data scraping skills have definitely become one of the most sought after and coveted skills of the 21st century. It has become a highly recommended and needed tool since it only leads to adding value to the company.
However, its dark side should not be overlooked. Companies must understand the privacy risks associated with the practice, especially when establishing a legal basis for data scraping. Businesses should also ensure that a clear purpose is established for data scraping, that only data necessary for the purpose in question is scraped.