Web Scraping: How to Ensure Data Quality

Web Scraping: How to Ensure Data Quality

In our modern world, when big data is one of the biggest advantages for any business development, the high-quality and accuracy of the data is very critical, especially when you scraping information at a large scale. Poor quality of data will lead you to deficient data analysis, which is a pointless waste of your resources. That’s why it is important to know how to ensure data quality and what methods can be used to get the most accurate data scraping.

Manual Quality Assurance Approach

Every web scraping project based on a web crawler setup. So the stability and the code quality of the crawler may affect data quality. When the crawler is programming, it is necessary to make sure that it is proper for extraction, and there are no issues with the code. Sometimes it is necessary to practice two peers review, only after that, the crawler can be deployed.

When the crawler starts its job, it is recommended to inspect the initial dataset manually and check data quality before final setup. The manual data review sorts out the possible issue related to the crawler, and its interaction with the website. In case of any issues, the crawler should be adjusted to resolve them before the setup is completed.

Automated Quality Assurance Approach

An automated quality assurance approach ensures both the correctness and coverage of the extracted content. The following key parameters might be verified automatically:

The correct data from the appropriate web source is extracted
The scraped content was processed and formatted as requested
The names of the fields are matched to the specified field names
All data positions have been scraped from all possible sources
All the needed fields were scraped

Automated Monitoring System

From time to time the websites are getting updated, and as a result of some modifications the crawlers can be broken. This may affect data extraction as well as on data quality. That’s why it is recommended to have an automated system to monitor the crawling jobs and check the extracted data for errors and inconsistencies. So, there are three types of issues that can be fixed through the special monitoring tool; website changes, data validation errors, and data volume discrepancy.

Website changes - The monitoring tool frequently checks the scraped websites to make sure that nothing has been changed since the last crawling. In the case of any changes, the relevant notification is sent to system to take the appropriate actions on crawlers modifications.

Data validation errors - Each data has its defined value. So the goal of the monitoring tool checks whether all the data are in line with their value types, because otherwise such kind of mismatches might cause the wrong data extraction. In case of any inconsistency, the system again sends notification.

Data volume discrepancy - Sometimes there might be discrepancy in data volume, when the extracted data volume does not match to the required quantity. The monitoring tool should have the expected records for the project, and in discrepancy happens, it sends a prompt notification.
Upscale servers
Overloading servers also may affect on data quality, because the crawling process requires
significant resources. To avoid such cases, when crawlers fail because of heavy load on servers, deploy and run them on high-end servers.
Cleansing data
The crawled data might have useless elements like HTML tags, which should be removed, as well as duplicated records, and the related records merged. The final output should contain only clean data without any unwanted elements.
Structuring
Before delivering the ready data to the clients, it is necessary to make it suitable for analytic systems and necessary databases. The data should be delivered in JSON, XML, or CSV formats which are convenient for further analysis.

Conclusion

One of the most important aspects of web scraping is data quality maintenance. DataOx has significant experience in web crawling and knows how to provide valuable and high-standard datasets. Besides a thorough manual and automatic QA process, DataOx covers the complete range of web scraping services.