How to Maintain Data Quality Assurance and Maintenance during Web Scraping
Learn from DataOx how to maintain data quality in a web scraping project. Discover how to fetch rich & clean data from reliable sources for your business.
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Table of contents
Estimated reading time: 7 minutes
Introduction to Data Quality Maintenance
The success of any web scraping project is determined by the quality of the data extracted and processed. An accurate and consistent data feed is able to help any business break new ground. In our contemporary digitized world, the increasing prevalence of big data and innovative technologies like machine learning and artificial intelligence — along with informed decisions — can guarantee you a competitive advantage when based on rich and clean information from reliable sources.
It’s especially critical to maintain data quality when extracting information at a large scale. With small projects, this may be a problem, but it’s still quite manageable as a general rule. However, when it comes to scraping web sources in the thousands or even millions on a daily basis, even a small drop in accuracy can be fatal for a business. That’s why we insist that data quality should be the highest priority at the start of any scraping project. You should always think of the techniques and tools that will help you achieve the most accurate pull of data at the highest quality.
To draw the road map of how to maintain data quality effectively throughout the whole process, you should realize the challenges of data quality assurance and handle them respectively. So, let’s look at them in more detail.
Challenges of Data Quality Assurance
Data quality assurance is a complex challenge, which is constituted of a combination of factors.
When taking up a scraping project, you need to clearly define all the requirements for the details you are going to fetch, including accuracy or coverage level. Your data quality requirements should be specific and testable so that you can check the information against certain criteria.
The sources you choose for data collection influence the quality of information gained, so you should choose relevant, reliable sites and web pages.
When you scale your web scraping spiders, it’s vital that quality assurance of the gathered information matches this scalability, especially when only visual comparisons of the scraped page and manual inspections are used for data quality assurance.
The structure of modern websites is rarely simple. The majority of resources have been developing for years, and different parts can have different structures. What’s more, with changing technologies and trends, sites constantly make small tweaks to their structure that may break web crawlers. That’s why you should monitor your parsing bots over the course of the whole project and maintain their proper operation to ensure they pull data accurately.
Wrong or incomplete data
Complex web pages often complicate the locating of targeted information, and the auto-generated Xpath may not be accurate enough. The sites that load more content when a user scrolls down the page are also a challenge for bots that fail to get complete data sets. A problem with locating the correct content can also be caused by page pagination buttons, which the bots cannot click on. All these result in incorrect data extraction and require special attention in quality assurance.
Even though QA technologies are constantly developing, the verification of textual info semantics is still a challenge for automated quality assurance systems. Manual checking should still be applied to guarantee information accuracy.
Automated QA System for the Scraped Web Data
Automated quality assurance systems are intended to assess the correctness of the information, as well as the coverage of the information. The key parameters you should set are the following:
- Checking that the scraping bot has extracted the correct details from the right page elements and fields.
- Verification that the scraped information has been properly processed and formatted as requested beforehand.
- The field names match the predefined field names as specified earlier.
- Checking that all available data positions have been scraped from all sources.
- Making sure that all required fields were scraped.
To meet all the parameters and maintain data quality, you can take several approaches. Let’s check them out.
Approaches to an Automated Quality Assurance System Development
There are at least two options for you to consider when crafting a QA System:
A project-specific framework for testing is developed for an individual project. It works well for projects with extensive and complex data requirements, with lots of nuances and field interdependencies, and is also highly rules-based.
A generic framework will help you in long-term web scraping when new scrapers are developed and data types vary. The advantage of such systems is the ability to validate the information fetched by any tool.
The Process of the Collected Data Quality Verification
The process of data quality verification throughout a web scraping project consists of several successive steps.
As mentioned above, at the beginning of any project, you must clearly define specific and testable requirements for the data you are going to fetch, including accuracy or coverage level.
With all the requirements in mind, a scraping tool is developed to match the specifics of the project and the business it will run for.
Before you set up your crawling tool properly, you should check its stability and code quality. It’s better to have the code reviewed by experts in advance to avoid issues in the process of web data extraction. First, the code is reviewed by the developers and then a QA specialist checks it for smooth and correct operation.
Scraper operation maintenance
For the duration of the project, the crawler’s operation is monitored and adjusted to match the changes of the target sources and guarantee its due output. It is also a good idea to utilize a system of real-time monitoring of your spiders’ status and output. Such a system helps to automatically monitor spider execution, including errors, bans, and item coverage drops.
Automated data quality verification to maintain data quality
What’s more, such a system verifies the fetched content against certain criteria that define the expected data types, structure, and value restrictions. It will help spot the issues that may set in immediately after the bot’s execution is completed, or stop the scraper right away if it starts gathering unusable info. To ensure high-quality extracted data, you should also perform the following:
- Profiling – Analyze the collected information in terms of volume, format, and quality.
- Cleansing – The initially extracted data may contain unnecessary elements, like HTML tags. They should be eliminated, the related entries merged, and duplicates disposed of.
- Enrichment – Extend your obtained data with other relevant details if you can spot any.
- Normalization – Check the integrity of data and manage the validation errors if there are any.
- Structuring – Make the obtained information compatible with the necessary databases and analytic systems by providing it with proper machine-readable syntax.
- Validation is extremely important. Without it, there is no point in collecting massive datasets, especially if you can not rely on them for making informed decisions. At this point, information is checked against certain predefined parameters (value type, number of records, mandatory fields, and more).
Then, you can proceed to manual data testing.
This process is a must. After the information you are interested in is extracted and validated technically for possible errors, manual checking is required to weed out the possible issues in data quality that cannot be spotted by machines. A human can spot interactive problems between the spider and the target site and make sure that only the relevant information is extracted.
Different clients choose various approaches to data quality assurance. Some customers consider automated verification sufficient for their tasks, while others prefer manual testing to make sure that the correct data was parsed and extracted for their purposes. We always advise a combination of these two steps to ensure the highest quality datasets we provide.
How to Maintain Data Quality FAQ
What types of data quality checks?
Data quality checks can be carried out in two modes, manual and automatic. Automatic data quality check involves the use of the following check stages: Profiling, Cleansing, Enrichment, Normalization, Structuring, and Validation. Manual data quality checks are used to filter out possible problems that cannot be detected by machines. Mixed practices can also be used, which include both methods.
What is data quality?
Qualitative data is data that is appropriate to meet the specific needs of an organization in a specific context. The use of such data should not be misleading or lead to incorrect decisions that could harm the organization. Qualitative data has some properties: no duplicates, no contradictions, security, organization, completeness, and reliability.
How to measure data quality?
The extracted data must meet several important criteria to pass the quality check: accuracy, completeness, consistency, reliability, and relevance. By meeting each of the criteria sufficiently, the data can be used for business and scientific purposes.
Data Quality Maintenance Conclusion
Data extraction is a resource-intensive procedure, but even more important is data quality maintenance. With more than five years of web parsing experience, DataOx knows firsthand how to provide clients with relevant, high-quality datasets in any sphere. You can schedule a free consultation with our expert and find out how the DataOx team can help your business through web scraping. Our data quality assurance system covers the whole pipeline, allowing us to generate information that is reliable, usable, and relevant to our client’s business goals.
Publishing date: Sun Apr 23 2023
Last update date: Wed Apr 19 2023