Software engineers have successfully automated their workflows, which has greatly increased productivity and is influencing other fields, including data analytics. Data professionals are adopting similar practices, particularly automated regression testing, to ensure data integrity and prevent disruptions caused by changes. This approach helps detect unexpected issues that could affect downstream data applications, as illustrated by an incident experienced by Datafold's CEO at Lyft. Regression testing differs from traditional assertion-based tests by uncovering unknown or unexpected issues in datasets. Tools like Datafold, when integrated into continuous integration (CI) pipelines, allow for precise data comparison and detailed analysis, offering confidence in data changes before they are deployed to production. By incorporating CI and automated testing, data teams can standardize their workflows, documenting and understanding the impact of every code change, while maintaining involvement through code reviews. This method is recommended for managing dbt projects, as it can either flag potential issues or halt code changes to ensure high-quality data management.