Web Scraping: The value of information in the corporate world is indisputable. On the other hand, the challenge involves how companies can obtain this essential input for decision making.
Most of the time, the path goes through big data. Still, even though there is a lot of information available on the internet, finding relevant, authentic, and current data is not an easy task. To access them, one of the collection techniques that can be used is web scraping.
In general terms, “network scraping”, in the free translation into Portuguese, consists of automated software to extract content and information. The process involves consolidating relevant data from a given site and then analyzing it. The process can be done manually. However, by using technology, it is possible to increase the efficiency of the process, which makes it even more advantageous.
How Are Companies Using Web Scraping?
Web scraping is relatively simple: it is an automated process of extracting relevant information carried out on specific sites with the support of bots. Subsequently, this data can improve decision-making in companies, substantially increasing the chances of success. The extraction of information on the web can be done manually. Naturally, however, businesses can gather more information in much less time by automating the process.
Agility And Error Reduction
One of the advantages of automated web scraping is the savings in labor and time. In addition, when doing it with bots, the exponentially more significant amount of information collected makes it possible that the remaining content is more accurate, valuable, and current at the end of eventual filtering.
In addition, the technique brings together two significant trends for today’s business: the growing need to analyze data and automation.
According to the survey Automation and the future of work, conducted in July 2020 by the IBM Institute for Business Value, automation supported by Artificial Intelligence (AI) is expected to generate, in 2022 alone, billions of dollars in hand value of constructions. In this regard, many companies are using web scraping to:
- automate large-scale data collection processes;
- unlock sources on the web to access information that can add value to the business;
- improve the data-driven decision-making process.
Web Scraping Process
There are several approaches to making web scraping possible. The first step is to identify the URLs that you want to collect data. When the websites tracked uses anti-scraping tools, it is necessary to set the appropriate proxy server to obtain a new IP address to send the requests.
The next step is to make requests to these URLs to gain access to the HTML code and use locators to identify where the data is located in the code. From there, the data string is analyzed, which contains relevant information, including:
- page title;
The next step is to convert the data obtained by web scraping to the desired format and transfer this information to the location where it will be stored.
Data Extraction With Machine Learning
The relationship between Machine Learning and web scraping is close. After all, the use of machine learning for identifying and extracting information from web pages is increasing. As with the manual process done by humans, the interpretation is visual.
The principle is objective: the Machine Learning system, in general, works with classifications through a confidence score. This is a measure of statistical probability to ensure that the category is correct given the patterns defined in the training data.
If the trust score is too low, the system automatically produces an Internet search query to extract content that likely contains the data the company is looking for.
The system also extracts the relevant data from one of the new contents and merges it with the results of the initial extraction. If the confidence score remains low, the machine jumps to the following content extracted by the search string.
Examples Of Uses Of The Data Extraction Technique
Web scraping presents itself as a valuable tool for companies in different areas and needs. The technique can be used, for example, to gain access to industry statistics, generate leads and conduct market research. See some examples of use for commercial purposes.
- Data Analytics and Data Science: Machine Learning training data collection and enterprise database enrichment.
- Marketing and sales: price comparison, product description search, SEO, lead generation, website testing, consumer sentiment monitoring.
- Institutional communication: collect news about the company.
- Finance: financial data.
- Strategy: market research.
Web scraping is a data collection technique that can make the company more competitive regardless of the sector in which it operates. Using the practice daily makes it possible to have access to strategic information with more quality and speed.