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TDWI Upside - Where Data Means Business

How Data Experts Overcome the Toughest Web Scraping Challenges

We explore the top three challenges in web scraping and how to overcome them.

Web scraping has become an increasingly popular method for enterprises to gather large amounts of data from a variety of online sources, including social media platforms, e-commerce websites, and news portals. With the help of web scraping, businesses can obtain valuable information that enables them to make informed decisions, enhance their marketing strategies, and improve customer experience.

For Further Reading:

The Future of Data Science Lies in Automation

Executive Q&A: The Future of Geospatial Data

Turning Social Media Into Business Intelligence

The data collected through web scraping typically includes details about products, customer reviews, competitor prices, and social media mentions, which can then be analyzed to identify informative patterns and trends. By leveraging this data, companies can better understand customer preferences and target specific audiences, ultimately driving growth and maintaining a competitive edge in the market.

Web scraping adds great value to e-commerce operations by enabling businesses to leverage the power of data to customize marketing strategies. It has the tremendous benefit of being almost entirely automated, allowing companies to go through hundreds and even thousands of pages in mere seconds.

Most common sources are user-neutral, that is, are not subject to any privacy concerns or consumer involvement. Some of the popular use cases are dynamic pricing (creating an algorithm that adjusts prices according to competitors) and market research (collecting data about product popularity and communication). The latter is often converted into marketing strategies because they can potentially get insight into why and how a specific product is to be deployed in a company’s inventory.

As such, web scraping is rarely within the range of private or personal data. Most web scraping providers (such as Oxylabs, the company I work for) consider private (any data that is not publicly available, such as being accessible only after logging in), personal, and copyrighted data to be off limits and do not support such use cases.

In fact, web scraping has the potential to be highly beneficial for consumers. Some business models (such as travel fare aggregation) would be impossible without web scraping because they use these processes to automate data collection and provide consumers with the best deals.

Other applications, such as the aforementioned dynamic pricing, benefit consumers by increasing competition between businesses. As more companies employ dynamic pricing, the overall prices for many products are driven down as businesses race to provide the best offer to their customers. Yet, the usage of web scraping is still not as widespread as it may seem. To gain some insight into the state of the industry, Oxylabs partnered with Censuswide to survey over 1000 senior data decision makers from e-commerce businesses across the U.S. and U.K.

The survey covered many topics, including the data types in demand, extraction methods, revenue impact, and future investments in web scraping infrastructure, as well as challenges faced. We asked survey participants to pick their top three issues from an extensive list. In this article we’ll reveal the top three results and how to address these issues.

Challenge #1: Obtaining real-time data

Product prices, consumer behavior, and market trends change rapidly. Monitoring the competition in real time enables businesses to pivot their strategy by processing information immediately after it enters the database.

Obtaining real-time data requires sophisticated infrastructure that can solve or avoid CAPTCHAs and retain access to data. Unfortunately, even if web scrapers are innocuous and pose no additional significant load to the target servers, they will often be served these challenges as a way to slow down bots.

Real-time data, however, is so valuable to numerous businesses that there have been ways discovered that can help them maintain continuous access to important websites. Some of these include using dynamic fingerprinting techniques and proxies to reduce the impact of unintentional bans from anti-bot systems.

Although there are solutions to solve CAPTCHAS, it’s typically best to avoid them altogether. This can be accomplished by using high-quality residential proxies, limiting the number of requests, and changing the request duration. Additionally, companies can improve their browser’s fingerprint by employing a database of real user agents, matching TLS parameters and HTTP headers, and discarding cookies once they’ve been used.

Challenge #2: Managing and processing large data sets

Managing and processing large volumes of information becomes increasingly complex as e-commerce web scraping operations increase in scale.

For Further Reading:

The Future of Data Science Lies in Automation

Executive Q&A: The Future of Geospatial Data

Turning Social Media Into Business Intelligence

Web scraping can present larger volumes of data faster than any prior technology. As such, it’s not surprising that companies are finding it challenging to process the large resulting data sets, especially if they combine web scraping with internal sources.

Additionally, data from publicly available sources usually comes in an unstructured HTML format, which is incredibly hard to understand for humans. Analyzing semistructured data is challenging because it first must be parsed and loaded in a data system such as a warehouse. Luckily, these systems make the entire process a lot easier -- large volumes can be sliced and structured in a number of ways. The information is then continually refined and managed by data professionals.

Data warehouses now usually include capabilities for managing semistructured data, making it much easier for companies to integrate web scraping into their usual pipelines instead of relying on several disparate sources of software.

Challenge #3: Finding reliable outsourcing partners

Conducting web scraping in-house is complex and presents numerous obstacles. In addition, in-house data extraction fails to take advantage of troubleshooting and management expertise offered by a specialized company.

Finding a partner in today’s environment is relatively easy because the popularity of web scraping has skyrocketed in recent years. However, the increased choice has also flooded the market with data companies of varying capabilities, making it difficult for e-commerce companies to find a good fit.

As a result, a qualification process should be conducted before starting a partnership with any web scraping provider. Be sure to investigate:

  • Capabilities. Ensure your prospective partner has the tools and systems required to extract the specific data your business needs.

  • Customization: Website structures differ significantly. Look for a system that can be easily modified to accommodate different website formats and coding methodologies.

  • Data format: Ensure formats provided by the data firm can be easily processed and read by analysts.

  • Support: Look for a partner with the experience to help you overcome server issues and ensure a reliable data flow.

Overcoming Challenges Offers Multiple Benefits

Obtaining real-time data, managing large data sets, and finding reliable partners challenge over 50% of our survey respondents. Addressing those issues provides additional benefits by streamlining operations to provide better quality data that can be more effectively managed and processed, leading to better insights that enhance decision-making.

Finding solutions is rarely easy, but the benefits add significant long-term value to your business. The key is to take your time, not rush into quick fixes, and fully explore options that improve efficiency, drive productivity, and align with your business goals.

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