Web scraping includes a variety of automated processes designed for collecting structured data from the web. It’s also called web harvesting, web data extraction, or simply data extraction. In general, this process is used by individuals and organizations to gather publicly available data online.
The internet has opened up many opportunities. One of the biggest changes caused by the internet is the vast amount of data we generate daily. This data can be a powerful tool when gathered, stored, and analyzed the right way.
This wouldn’t be possible with such large volumes of data without automated tools like web scrapers. But what’s even better is that these tools are constantly improving. The most recent addition to scraping is artificial intelligence.
Web scraping business applications
Scraping is a process of gathering data in a structured manner. There are all kinds of data online that can be used for various purposes. Here are some of the most common business applications of web scraping.
Scraping prices
Scraping prices or price intelligence is the most common business application of web scraping. In other words, companies use scraping to get pricing information about products from different kinds of websites.
This pricing data can be used for different goals, including revenue optimizations, monitoring competition, dynamic pricing, and so much more.
For lead generation
This is a very popular use of web scraping. Scraping is a quick and cost-effective method for gathering information about leads and potential clients. Specifically, this web scraping approach is used by B2B organizations, where clients usually post all of their contact information publicly.
Brand monitoring
Companies need to protect their reputation online and respond to every issue. Lots of companies use web scraping to monitor their brand mentions and see how customers perceive their products or business.
Market research
Large volumes of quality data can give a business the right insights required to analyze its market. Business intelligence is vital as companies can’t make moves without accurate data. Web scraping can help with marketing pricing, development, research, and finding trends.
The traditional approach
With typical web scraping methods, companies need to invest a lot in quality assurance and governance. The larger the scope of the scraping project, the bigger these difficulties are. The first and most common problem of web crawling is to collect the URLs of all relevant websites.
In other words, companies need to pinpoint the right sites that have content that can be valuable for the scraping project. All of this requires a lot of time and effort because it needs to be done manually. Another issue with this approach is proxy management.
Companies without the experience can get their scrapers blocked. Web proxies can help with some of the issues, but the process still requires ongoing management and constant fine-tuning to ensure desired results.
The role of AI in modern web scraping
Instead of managing and developing the scraping code manually for every URL, the whole process can be simplified with AI. This is what AI web scraping is all about. AI plays an important role in all sectors of our lives. Learn and master AI through this AI Online Course which will help you learn it hands-on to make you job-ready.
AI web scraping lets developers prototype and creates scalable tools. AI and machine learning are smart solutions that have the capabilities to go through data and learn from it. With so many factors involved in the process of scraping, these technologies can give even better automation.
On top of that, the longer these tools operate, the better they will become in setting up the right data gathering pipelines. At the same time, these solutions will also serve as backups to the existing codes if they get broken down.
The future development
As previously mentioned using AI and ML for creating fast data gathering pipelines will give new web scrapers a crucial advantage. We are already witnessing the development of new web scraping proxies that use AI applications for their process.
In other words, AI will only improve scrapers and eliminate the hands-on approach that we need to use today. Even with managed proxy solutions, companies still need to invest time and effort in managing them up and coordinating scraping with service providers, such as Oxylabs.
With sophisticated AI web scraping, companies won’t need any coding knowledge. They will be able to use these tools intuitively without any experience or background in data gathering. On top of all of this, these solutions will have a much higher success rate making the whole process more cost-effective.
Conclusion
AI has finally become widely available. As data volumes continue to grow, so will the web scraping projects. This will make automation a priority, and companies won’t have the resources to manage such large projects. AI will help companies scale their scraping needs. Also, read about Deep Learning Using TensorFlow Keras, 12 IoT Cyber security challenges, and more on The Tech Diary.
