[QA.DT.1] Ensure data integrity and accuracy with data quality tests

Category: RECOMMENDED

Data quality tests assess the accuracy, consistency, and overall quality of the data used within the application or system. These tests typically involve validating data against predefined rules and checking for duplicate or missing data to ensure the dataset remains reliable. While data quality testing might not fall under the traditional definitions of functional or non-functional testing, it's still an essential aspect of ensuring that an application or system functions correctly, as the quality of data can significantly impact the overall performance, user experience, and reliability of the software.

We recommend data quality tests because they enable rapid software delivery and continuous improvement of data driving systems. Using data quality tests, teams can spend more of their time focusing on how data should appear rather than continually checking it for accuracy, streamlining the development and deployment process. To calculate data quality metrics on your dataset, define and verify data quality constraints, and be informed about changes in the data distribution. Instead of implementing checks and verification algorithms on your own, you can focus on describing how your data should look.

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