Synthetic Data: Uses, Methods, and Applications

Definition and scope of synthetic data

Synthetic data are artificially generated datasets that are not produced by real-world events, but are created algorithmically to have a similar distribution to sampled (real) data. They can be produced via computer simulations or statistical models, and are used to validate mathematical models and to train machine learning systems. Because the data are fully generated by algorithms (including simulation outputs), they can approximate real-world behavior without directly using real observations. The scope of synthetic data includes both general-purpose uses—such as creating baselines for testing and exploring how different data characteristics affect models—and privacy-preserving applications. In sensitive settings, real datasets may exist but cannot be released publicly; synthetic data can sidestep confidentiality and privacy issues that arise when using real consumer or personal information without permission. Synthetic data are also used to test and train security systems (e.g., fraud or intrusion detection) by generating realistic scenarios that may not appear in authentic data, and they support scientific research and machine learning when labeled or high-quality real data are scarce.

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Usefulness: meeting specific data needs

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