Adding Data Quality to DAGs with Great Expectations involves using this tool in an Airflow DAG to perform data quality checks. This approach prioritizes what checks are done and how they're scheduled, allowing for iterative improvements to existing pipelines. By implementing unit tests and acceptance tests, data teams can start small and work towards covering all test cases. Great Expectations provides a Profiler that helps create Expectation Suites quickly, reducing boilerplate code and broadening the scope of data coverage. The tool enables checks on statistical analysis across tables or columns, allowing for more nuanced issues to be found in the data. By orchestrating data quality tasks within DAGs, teams can ensure critical pipelines are always scheduled with data quality checks. Great Expectations offers benefits such as compile-to-docs functionality, automated profilers, and an extensible library of built-in checks, making it a trusted tool for managing data quality lifecycle.