Introduction
Testing model outputs helps you check if your data models give correct and expected results. It stops errors before they cause problems.
After creating a new data model to make sure it works right.
When updating a model to confirm changes did not break anything.
Before sharing data with others to ensure accuracy.
To catch missing or duplicate data in your outputs.
When automating data pipelines to keep data quality consistent.