Synthetic data are artificially created records designed to reproduce useful characteristics of real data without simply copying the original observations. They can help teams test software, train models, represent rare events and collaborate where direct access to sensitive data is limited. Yet \u201csynthetic\u201d does not automatically mean accurate, unbiased or anonymous.<\/p>\n
The central question is not whether a dataset looks realistic. It is whether it is fit for a declared purpose, preserves the relationships that matter, avoids harmful disclosure and remains under appropriate control. A dataset suitable for demonstrating a dashboard may be dangerously weak for training a credit-risk model. One designed for aggregate research may fail at individual-level prediction.<\/p>\n
This guide explains the main synthetic data generation methods, the tests that make results defensible and EPW\u2019s SAFE governance framework for moving from an idea to controlled use.<\/p>\n