Test Data Generation: Uses, Benefits, and Tips

Test data generation is a crucial process for developers and data engineers to create new data that replicates an original dataset. This technique is used for testing applications, developing features, and even training machine learning (ML) models. The importance of test data lies in its ability to evaluate software applications and improve ML/AI algorithms. Techniques for generating test data have evolved over time, from dummy data to the current gold standard: synthetic data. Synthetic data is inexpensive, unlimited, and can be pre-labeled to fit any purpose or use case. It also mitigates edge-case failures by allowing fast, targeted additions to a dataset with each iteration.