Metrics for data testing

Data test coverage: The percentage of your dataset or data processing logic covered by tests. Data test coverage gives an overview of potential untested or under-tested areas of the application. A high coverage helps ensure a comprehensive evaluation, while low coverage can indicate blind spots in testing. Improve this metric by prioritizing areas with lower coverage, using automation and tools to enhance coverage, and regularly review test strategies to help ensure they align with recent changes in the dataset or processing logic. Measure this metric by calculating the ratio of code or data elements covered by tests to the total lines of code or data elements in the application.

Test case run time: The duration taken to run a test case or a suite of test cases. Increasing duration may highlight bottlenecks in the test process or performance issues emerging in the software under test. Improve this metric by optimizing test scripts and the order they run in, enhancing testing infrastructure, and running tests in parallel. Measure the timestamp difference between the start and end of test case execution.

Data quality score: The combined quality of data in a system, encompassing facets such as consistency, completeness, correctness, accuracy, validity, and timeliness. Derive the data quality score by individually assessing each facet. Then aggregate and normalize them into a single metric, typically ranging from 0 to 100, with higher scores indicating better data quality. The specific method for aggregating the scores may vary depending on the organization's data quality framework and the relative importance assigned to each facet. Consider factors like the uniformity of data values (consistency), the presence or absence of missing values (completeness), the degree of data accuracy relative to real-world entities (correctness and accuracy), the adherence of the data to predefined rules (validity), and the currency and relevance of the data (timeliness).