Synthetic data offers scalable, privacy-preserving alternatives to real-world data, addressing challenges like scarcity, bias, cost, and compliance. It enables on-demand generation of structured and unstructured datasets using techniques like GANs and diffusion models. Key benefits include better model training, inclusivity, and secure data sharing—especially in sectors like AV, healthcare, finance, and retail. While promising, risks include lack of realism and model degradation. The market is growing rapidly, with a shift toward verticalized solutions and integrated platforms that generate and train AI models on synthetic data.

Join for free to read