AI applications demand an ever-growing breadth and depth of data to power the models behind intelligence — and enterprises are struggling to get enough of the right data to train them. Technology executives and AI team leaders must understand synthetic data so that they can support their teams with the data they need. Synthetic data for AI duplicates, mimics, or extrapolates real-world data — encompassing everything from transfigured spreadsheets to full-blown simulated worlds. This data allows teams to train models for use cases and circumstances that would otherwise be unattainable due to poor quality data, privacy concerns, security restrictions, or lack of existing data.