Effective test data analysis is crucial for software testing to be productive. With increasing automation and scale, it's essential to understand how to analyze and interpret data from tests to draw meaningful conclusions that improve application quality. Various types of software testing strategies exist, including continuous testing and testing in production, each providing different metrics to analyze and evaluate. To effectively interpret test data, developers must first understand the metrics being collected, then combine them to draw useful conclusions, and finally act on the insights that will have the biggest impact. Utilizing available tools and software can also help filter and analyze test data, saving time and improving the quality of the application. By following best practices for analytics interpretation, developers can track down issues efficiently and improve their ability to deliver high-quality applications in a timely manner.