Synthetic data is artificially annotated information generated by computer algorithms or simulations that mirrors the statistical properties of real-world datasets. It can be used as a substitute when suitable real-world data is not available, or to protect sensitive and personally identifiable information (PII) in cases where privacy concerns or compliance risks exist. Synthetic data opens up possibilities for enabling access to artificial and privacy-preserving versions of data in minutes, augmenting machine learning datasets for superior accuracy and fairness, implementing privacy-by-design principles, creating safe data retention policies, testing software products and services, training ML and AI models, sharing data within organizations, and sharing data with third parties.