Synthetic data and digital twins are complementary approaches for riffing on real-world data to improve AI and product design. Synthetic data tools generate labeled data for training AI from a small subset of real data. Digital twins generate “what-if” scenarios for evaluating various performance, cost, and sustainability trade-offs.
Digital twins could help extend synthetic data tools to support real-world digital transformation in construction, medicine, and supply chains. Conversely, synthetic data could help teams using digital twins simulate different scenarios more efficiently.