Getting to know synthetic data

REAL-WORLD DATA CAN'T COVER EVERY SCENARIO

- BALAKRISHNA DR

AI has always followed a simple rule: better data leads to better models. From spam detection to self-driving cars, every leap in AI has been powered by vast, high-quality datasets. But as AI moves into sensitive, regulated, and rare-event-driven domains, traditional data is no longer enough.

Consider a healthcare organization developing an AI model for early disease detection. They face multiple barriers: limited access to diverse clinical records, privacy regulations, rare case scarcity, and costly labeling. The data exists, but can't be fully accessed, shared, or scaled. This is a widespread issue across industries.