Aggregating web data is increasingly becoming popular because the internet population is producing more than 2.5 Exabytes of data every day (equivalent to 90 years of HD video). Although businesses find significant value in web data, they have different kinds of requirements. While companies in the eCommerce space use it for pricing intelligence, sentiment analysis, and competitor monitoring, aggregation services like job boards need job feed to build their core business. There’s literally no business that can’t make use of web data. One of the biggest advantages when it comes to web data extraction is the possibility of extracting millions of records from hundreds of websites to have comprehensive data at your disposal. Sometimes, companies make the mistake of going with just one target website when it comes to their data needs. Here is why this is a bad idea:
Never put all your eggs in one basket
It’s never a good idea to rely on one single source be it the revenue stream, support, supplier, or data- you name it. Especially with web crawling, many things could go wrong leaving you with no data.
If you are relying on web crawling to power your data-backed product or service, you cannot afford even a brief period of not having data. That said, it’s common for the web crawler to break at times while crawling. Most of such instances are associated with the target website changing its structure or coming up with mechanisms to block crawling. Such cases would need a modification of the crawling setup to be fixed. You could be losing some data while this modification is made by the technical team and this is a common scenario with web crawling. The only way to be immune to this unforeseeable loss of data is to crawl more than one website where similar data can be found. This way, you will never be out of data even if one of the sites fails. While crawling multiple websites, the possibility of data loss is null, as there is always a crawl running fine.
Lack of comprehensive data
Big data must be big in size to be effective enough to support business intelligence. By limiting your crawls to just one website, you are restricting yourself from data that is essential to make your project complete. Not every website will have extensive data in every domain. Let’s say site ABC is an eCommerce website that’s known for electronics and home appliances. ABC will have a wide variety of products under the ‘Electronics’ category, but a narrow catalog for clothing products. If you choose to crawl only ABC, you are getting a small part of the big picture.
This becomes even more important if you are crawling to carry out market research. Since the quality of market research is highly influenced by the extensiveness of data at hand, having data from multiple websites becomes all the more important.
Pricing intelligence is another use case where data from one website just won’t cut it. If you are crawling only one of your competitors for price data, you might be losing it to another competitor of yours who could be selling at a lower price. Considering the efficiency and scalability of web crawling as a technology, it can even be detrimental to crawl only one website.
If you are depending on web data for critical business intelligence or market research projects, it’s not a good idea to trust the data that you get from a single source. There are possibilities of the website you are crawling providing erroneous information. If you are crawling just this one site, you wouldn’t have any reference to validate this data. In the case of crawling multiple websites, it’s easy to spot such inaccuracies and errors since you have access to data from various sources. You can significantly reduce the risk of getting low-quality data by crawling multiple reliable sources.
The humongous amount of data available on multiple websites must come together to serve as an invaluable tool for business intelligence and core business operations. Hence, when it comes to web crawling, it’s better to go with multiple sources to avoid data loss and drive the project with high-quality data.