What is Synthetic Data?
TL;DR
Artificially generated training data produced by AI. Used for privacy protection and overcoming data scarcity.
Synthetic Data: Definition & Explanation
Synthetic Data is data that is artificially generated using AI models or algorithms rather than collected from real-world sources. It preserves the patterns and statistical properties of real data while excluding personal or confidential information, enabling organizations to balance privacy protection with data utilization. It is widely used in fields like healthcare, finance, and autonomous driving where real data collection is difficult or subject to privacy constraints. Synthetic data is also increasingly used for LLM training — Microsoft's Phi-3 and Google's Gemma achieved strong performance partly through synthetic data. It is also gaining attention as a potential solution to the 'data wall' problem, the concern that high-quality real-world training data may soon be exhausted. Gartner predicts that by 2030, synthetic data will constitute the majority of AI training data. However, the quality and biases of synthetic data can affect model performance, making proper generation and management practices essential.