Synthetic data generation is the process of creating artificial data that mimics real-world data. This technique is often used in fields like machine learning and data analysis to train models without compromising sensitive information. By using algorithms, researchers can produce datasets that maintain the statistical properties of actual data while ensuring privacy. This approach is beneficial for testing and validating systems when real data is scarce or difficult to obtain. Synthetic data can also help in scenarios where data sharing is restricted due to regulations or privacy concerns, allowing organizations to innovate while protecting individual privacy.