The Challenge of Using Web Scraping at Scale

web scraping at scale can be challenging

Every business relies upon data in one way or another. Some companies have mastered this skill better than others. Digital transformation is not just a fad. This evolution is driving change, which starts with a need to manage data digitally. Manual, paper-based processes are now being replaced with digital workflows. One approach to digitally collecting data is referred to as “web scraping,” “web harvesting,” or “web data extraction.” This process uses a bot to extract information from a website. Unlike screen scraping, which simply copies pixels displayed onscreen, web scraping extracts underlying HTML code. This approach lets you also collect the associated data stored in a database. While this process has merits when performed at a low volume, there are many challenges when using web scraping at scale.

How Can YOu Collect DIGITAL Data?

Profitability is directly correlated to a company’s ability to collect, process, and act upon data. A company’s ability to do so with greater speed and accuracy improves efficiency, customer satisfaction, and profitability. These factors are true regardless of your company or industry.

Over the past six years, Internet traffic has shifted to mobile devices. In Q1 2015, less than a third of all traffic was over a smartphone. Six years later, that figure nearly doubled to 54 percent (source). Everyone is busy and on the move. Smartphones have filled the need to keep us in touch and marketers have adapted.

One of the changes that resulted from this shift to mobile is that people no longer need to print documents. When was the last time you printed something on paper that you read on your phone? This shift accelerated a shift to digital workflows that can be performed on a phone. Businesses adjusted by going digital. This includes how data is collected, stored, and managed.

The emergence of Web Scraping

The technology behind web scraping at scale can be traced back to the emergence of the Internet. As new websites were created, the need emerged to find what information was available on each site. Early search engines were web crawlers designed to quickly search for information. This underlying technology became the foundation for modern-day web scaping.

Web scraping can be described as the process of automatically mining data or collecting information from the World Wide Web. It is an initiative that requires the ability to process text with semantic understanding, which can be difficult. There are several Web Scraping techniques, including:

  • Human copy-and-paste
  • HTTP programming
  • HTML parsing
  • DOM parsing
  • Vertical aggregation
  • Semantic annotation recognizing
  • Computer vision web-page analysis

Those interested can learn more about these techniques here.

Pitfalls of Web Scraping at Scale

Many companies have invested much time, effort, and resources into streamlining this process. As the need for data continues to grow, so too does the complexity that surrounds this practice. Here are five examples of the bigger issues that quickly become a challenge when performing this process on a larger scale:

  1. Data Storage – When web scraping is done at a higher volume, a large amount of data will soon follow. The success of applying this technique will quickly create a new problem. Large databases that are not architected correctly with the right warehousing infrastructure can be difficult to quickly navigate, secure, and backup.
  2. Increase in Anti-Scraping Technologies – The value of data has increased. Companies hosting websites with this valuable commodity are increasingly putting deterrents to easily extracting their valuable information. LinkedIn is one example. These types of websites can use coding algorithms that prevent bot access or impose IP blocking techniques.
  3. Blocking Technologies – There has been a rise in defensive strategies to stop the automated collection of data by bots. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a type of security measure known as challenge-response authentication. A human will be asked to count bus or motorcycle images on a popup window. Bots are unable to comply. Some websites use learning technologies to block IP addresses of frequent site visitors identified as bots.
  4. Quality Degradation – Even if your bot has navigated through blocking technologies, it can be difficult to perform quality assurance on the data. Data quality becomes an increasingly difficult challenge as the scope of the scraping process increases.
  5. Evolving Website Technologies and Usage Patterns – Mobile website traffic now comprises nearly 60 percent of all Internet activity. With this shift is a move to using apps vs. going to websites. App data is not readily available for web scraping. In addition, evolving website technologies such as Ajax and JavaScript load dynamic content, which makes web scraping very difficult.

How Do You Best Proceed?

There are many challenges with relying upon web scraping as a source of collecting data. This is especially the case when conducting this process on a larger scale. Many companies now rely upon other data extraction approaches as part of a digital transformation strategy to stay relevant and efficient. Solution Providers now offer this function with a different approach that yields far greater results. Adding artificial intelligence and machine learning to document management and data extraction can yield superior performance of 90 to 95 percent accuracy – truly remarkable capture rates. Quality and data storage can then be better managed while reducing cycle types and improving customer satisfaction.

The first step is to gain access to the unstructured data. With this access then comes the decision on how best to convert from raw data to structured intelligence. If you operate in the real estate or title insurance industry, Axis Smart Data Extraction™ may be a managed service worthy of consideration.

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