Synthetic data

Generate for a purpose. Validate for that purpose. Explore synthetic tables, healthcare representations, and fraud-training experiments with a focus on schema, useful relationships, and independent evaluation. These articles examine what generated records should enable and which claims need separate evidence.

Start by naming the intended task and the permitted data boundary. Select a simple baseline, preserve a relevant holdout, and test whether the generated output supports the actual workflow. Keep privacy and release decisions distinct from a table’s visual plausibility or similarity score.