Synthetic data is created through Generative Artificial Intelligence (AI) that is trained on real data first, and after training, untraceable datasets can be generated that have nearly identical statistical properties and structure as of the real data.

Synthetic data has the potential to transform industries and unlock possibilities previously blocked by privacy concerns and real data limitations. From finance to telecommunications, healthcare to retail, the use cases of synthetic data are diverse and impactful.

Accessing and using data is a challenging task due to the privacy laws and regulations imposed on it as well as the time and resources required to collect and manage it. As Artificial Intelligence gains popularity, synthetic data emerges as an efficient and cost effective alternative to real data.

Data Anonymization is a legacy method of protecting data privacy through encryption, destruction, or suppression to erase personally identifiable indicators whereas Synthetic Data is a subset of GenA that looks, feels, and functions like real data.

While real data cannot be replaced, it has some major limitations which usually stem from its sensitive and regulated nature, difficult to access and share, and extremely high acquisition costs. Making synthetic data important as it functions similar to real data but without any such limitations.