Synthetic Data
Synthetic Data is artificially generated data created to resemble the characteristics and patterns of real-world data without directly using actual records.
What is Synthetic Data?
Synthetic data is produced using methods such as statistical models, simulations, generative AI, or other algorithms. It can reproduce relevant patterns, relationships, and characteristics found in real datasets while reducing the need to use sensitive or difficult-to-access information.
Why is Synthetic Data Important?
Synthetic data can help organizations build and test AI systems when real data is limited, expensive, private, or subject to regulatory restrictions. It can also support larger and more diverse training datasets, although its quality depends on how accurately it represents the underlying real-world data.
Common use cases
Synthetic Data is commonly used for AI model training, software testing, privacy-preserving data sharing, simulation, healthcare research, autonomous systems, and fraud detection.