Data has recently become the final piece in the puzzle of doing business. As the rate at which it is generated continues to increase, extracting this data also needs to improve.
Once the traditional web scraping method was enough to get brands all the data they need, this is changing, and better ways of harvesting data are being developed.
The fastest-growing data extraction method today is Artificial Intelligence (AI)-powered web scraping or AI web scraping for short. This is inspired partly by the increase in data generation and partly by the ever-increasing computing power.
Let us briefly see what web scraping and AI web scraping are and how the introduction of AI into web scraping has completely radicalized data collection. If you’re curious about the tools that can be used to conduct the AI-empowered web scraping, visit oxylabs.io.
What is Web Scraping?
Web scraping can be seen as the process of automatically collecting a large amount of data from multiple sources at the same time. The data is first collected in a raw unstructured HTML format before it is parsed and later transformed into some structured and easy-read format which can later be used in many business aspects such as price and competition monitoring, lead generation, and setting you many important business strategies.
However, traditional web scraping is bedeviled with a stream of challenges, including the following:
1. Time Consumption
Web scraping is an automatic process that repetitively connects with various data sources to extract data. However, the process is still painstakingly time-consuming as it takes a lot of time to extract, parse, transform, analyze and store each unstructured data.
And you should be aware that time is not the only thing that gets overly spent during traditional web scraping. There is also a large dose of effort and funds thrown into collecting data the traditional way.
2. Cost of Proxy Infrastructures
Proxies are an integral part of old web scraping methods. Without them, it would be almost impossible to securely and anonymously connect with servers and websites before collecting data. They also clear every restriction and blockings from the way, making web scraping run more smoothly.
However, the cost of acquiring and managing a good proxy is considered very expensive.
3. The Task Complexity
Not everyone can initiate or run a successful web scraping process. This is because it requires essential skills and expertise which many people do not possess. The entire process is complex and difficult to carry out.
4. Data Parsing and Transformation
As mentioned above, web scraping extracts data in the rawest and most unstructured format. It, therefore, needs to be parsed and transformed into a format that can be easily used. This is a rigorous and back-crunching process.
AI Technologies in Web Scraping
Following the challenges associated with traditional web scraping, it is safe to say AI technologies have come in to save the day.
AI technologies are the type of technology in which a machine uses neural networks (similar to those found in the human brain) to learn from patterns embedded in repetitive tasks following very few rules or human interference. The machine continues to learn until it is intelligent enough to perform the task better during subsequent operations and then set its own rules to govern the future operation.
It simply means AI algorithms use the data available to continuously learn and improve until they are the best at it. Applied to web scraping, AI identifies the patterns common in data extraction activities and teaches itself how to better collect only structured data from the web quickly and more efficiently.
How Implementing These Technologies Are Changing the Way Companies Collect Data
Web scraping is generally a repetitive process, and repetitive processes are common for producing one thing – patterns.
Recognizing these patterns and using them to learn and improve just like humans do is the basis for how AI is changing the way companies collect data today.
AI can also easily learn and adapt to new updates and structural changes on websites, as well as teaching itself how to be flexible around any website.
Lastly, because AI usually harvests data in the structured format, it is likely to speed up data extraction time 10 times more than we know today.
Advantages of AI Web Scraping Over Traditional Web Scraping
And below are some of the best advantages that AI-powered web scraping has over traditional ways of collecting data:
- It Allows For More Accuracy
The one thing benefit of using AI for web scraping is that the data is collected and parsed with fewer errors and an accuracy that is way above human-level
- It Requires Zero or No Maintenance
AI tools only need to be built once before they are ready to commence work. They may require human interference at the start to find in data and limited rules, but they run autonomously after that and may not require any further maintenance
- It Is Scalable
Unlike proxies for traditional web scraping, AI can learn, adapt, and scale up to handle millions of web pages or any changes that may occur.
Businesses now have more data than they can handle. Traditional methods which were sufficient until recently have proven to be inadequate. They are also harder to maintain, cost both time and other resources, and are very prone to errors.
AI web scraping, on the other hand, can handle any amount of data; it costs nothing to maintain and delivers more accurate data. This is therefore creating a world where they completely replace the old way of collecting data.