Table of Contents
- Challenges of Data Quality Assurance
- Automated QA System for the Scraped Web Data
- Approaches to an Automated Quality Assurance System Development
- The Process of the Collected Data Quality Verification
Introduction to Data Quality MaintenanceThe 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 AssuranceData quality assurance is a complex challenge, which is constituted of a combination of factors.
RequirementsWhen 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.
SourcesThe sources you choose for data collection influence the quality of information gained, so you should choose relevant, reliable sites and web pages.
EfficiencyWhen 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.
Website changesThe 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 dataComplex 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.
SemanticsEven 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 DataAutomated 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.
Approaches to an Automated Quality Assurance System DevelopmentThere are at least two options for you to consider when crafting a QA System:
A project-specific frameworkfor 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 frameworkwill 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 VerificationThe process of data quality verification throughout a web scraping project consists of several successive steps.
RequirementsAs 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.
Scraper developmentWith all the requirements in mind, a scraping tool is developed to match the specifics of the project and the business it will run for.
Scraper reviewBefore 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 maintenanceFor 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 qualityWhat’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).