Let’s say you run a rapidly growing e-commerce company. You want to expand your product line, penetrate new markets, and stay ahead of fierce competition.
To achieve these ambitious goals, you rely heavily on web scraping to gain insights into consumer behavior, competitive pricing, and market trends. But what happens if the data you base your decisions on is inconsistent, inaccurate, or outdated? The repercussions can be costly and may obstruct your path to success.
That’s why, as invaluable as web scraping may be, its efficiency hinges on the accuracy and quality of the data you get in the result. So, throughout this article, we will delve into practical data quality assurance techniques to ensure data quality while collecting information from the web.
Web scraping, by its nature, extracts colossal amounts of information from the digital sphere. However, the benefit you extract from this data is heavily contingent upon its quality.
If the data is incomplete, outdated, inconsistent, or irrelevant, it could drive businesses towards erroneous conclusions and ill-informed strategies, creating more problems than it solves. According to Gartner, poor-quality information costs companies $12.9 million per year. Along with that, there are other reasons for ensuring quality of data:
Before you learn how to ensure data quality, let’s look at the common hurdles that may stand in your way to obtaining accurate and clean data.
Websites are dynamic and continually evolving. Elements like layout, structure, or content change frequently, sometimes even daily. These changes to the HTML structure can disrupt your web scraping efforts. As the crawlers won’t be able to operate properly, you may suffer from inaccuracies, inconsistencies, or complete cessation of data extraction.
Websites differ greatly in how they present and structure their data. For instance, one e-commerce site might list product prices inclusive of tax. While another might list them separately. These inconsistencies compromise the comparability and consistency of your data scraped from multiple sources.
Also, data on the web comes in various formats—HTML, XML, JSON, or unstructured text. As a result, you may face formatting issues, which affect the ease of data extraction, analysis, and integration with your existing systems.
Sometimes, the desired data might be incomplete or entirely missing from the source websites. Therefore, you risk having gaps in your data sets. All in all, this may make your analysis less accurate or comprehensive.
Many websites have mechanisms to prevent or limit web scraping. They include rate limiting (restricting the number of requests a user can send in a certain time frame) and IP blocking.
For example, a website may limit a user to 1000 requests per hour. Once you reach the limit, the site will block or delay further requests. Or your IP may get completely blocked if you make a large number of requests from a single IP address in a short period of time.
The exact threshold for rate limiting or IP blocking varies greatly depending on the website. Some websites allow thousands of requests per hour, while others are more stringent, permitting only a few hundred. Websites that are highly sensitive to scraping, like social media platforms or e-commerce sites, may have stricter limits or employ more sophisticated measures like dynamic rate limiting or behavioral analysis.
To give you hints on how to overcome the challenges we discussed above, we’ve collected data scraping best practices from our experts.
Websites with a reputation for accuracy and consistency are more likely to provide high-quality, trustworthy data.
Websites that allow scraping usually have a robots.txt file that provides guidelines for web crawlers. If a website allows web scraping in its robots.txt rules, it is more likely to provide data structured for scraping.
They can navigate website changes, handle different data formats, and extract data more accurately. Keep your scripts updated to accommodate changes in website structures. Use flexible parsing techniques to manage unstructured data and formatting inconsistencies.
As we already discussed, websites have rate limiting and IP blocking measures in place to restrict scraping activities. If you collect data at scale, this may be a serious concern. So, use a pool of proxy servers and rotate IPs for your scraping activities. This approach will help simulate a more organic pattern of website visits and reduce the risk of being blocked.
Not all websites allow web scraping. Therefore, always respect the website’s terms of service. It’s not only ethical but also helps maintain your company’s reputation and avoid potential legal implications.
Metrics of data quality help you define whether you have information you can trust. There are seven main metrics you should keep an eye on during web scraping.
The quality of your data can be a game changer and define whether your web scraping efforts bring the desired results or not.
If you are not certain whether your data has optimal quality assurance metrics or you would like to delegate the whole scraping process to professionals who ensure the best results, welcome to Nannostomus. We understand the challenges involved and have developed sophisticated solutions to ensure that we deliver only the highest quality data to our clients. Feel free to contact us today to discuss your data needs.