Synthetic data is artificially generated information that closely mimics real-world data, but does not actually contain any real information. It is used to train machine learning models, test algorithms, and validate systems without exposing sensitive details or proprietary information. It is often used when “real” testing data is expensive, hard to find, or restricted due to privacy concerns. Synthetic data helps promote innovation while removing associated privacy risks.
