Synthetic data is information that is artificially generated rather than produced by real-world events. It is created using algorithms (often generative AI models) to mirror the statistical properties of real data without containing any identifiable information from the original dataset. This allows organizations to train machine learning models when real data is scarce, expensive to collect, or too sensitive (e.g., patient records) to share. It is a key enabler for privacy-preserving AI development.
Used for decades in fields like computer graphics and testing, but exploded in importance for AI training with the advent of GANs (Generative Adversarial Networks) and diffusion models which can generate highly realistic images, text, and tabular data.
Gartner predicts that by 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated. It is crucial for training self-driving cars (simulating millions of miles of driving) and detecting financial fraud.