An effective testing strategy is essential for delivering high-quality data products, as it enhances team efficiency while reducing data quality issues. Implementing an effective data testing strategy with dbt involves several best practices, such as shifting testing to the left, establishing a foundation with generic dbt tests, and using unit testing for complex code logic. Additionally, data diffing is useful for identifying unknown unknowns, and it is crucial to test data during continuous integration (CI) while avoiding deploying failed pull requests. dbt testing is a proactive process to ensure data quality by validating data-processing code before deployment, preventing regressions from changes like SQL column alterations or code refactoring that could impact business metrics or downstream systems.