Synthetic Data definition
Synthetic data is artificially generated data that mimics the statistical patterns and structure of real data without directly copying real records. It is created with simulations, rules or generative models, and used to train and test machine learning models, test software and share data safely when real data is scarce, expensive, imbalanced or too sensitive to use.
How synthetic data is generated
Methods range from simple rules to advanced generative models, and the right one depends on how realistic the data must be and what it will be used for in practice, from software tests to model training and demos.
Quality comes from matching the right properties. Test data for software needs valid formats and edge cases; training data for a fraud model needs realistic correlations between amount, time, merchant and outcome. Defining which properties matter is the first step of any synthetic data project. Common approaches include:
- Rule-based generation: libraries such as Faker produce realistic names, addresses and transactions that follow your schema and business rules
- Statistical models: sampling from distributions and correlations learned from real data
- Generative models: GANs, variational autoencoders and diffusion models for images, tables and time series
- Large language models: generating text examples, conversations, questions and labels
- Simulation: 3D engines and physics simulators for robotics, autonomous driving and computer vision
Common use cases
Software teams use synthetic data to test applications without copying production databases into development, which reduces privacy risk and supports compliance with laws such as the GDPR. Load tests need millions of realistic records that would be impractical to anonymize from real data, and demo environments need believable data that belongs to nobody.
In AI, synthetic data fills gaps: rare events such as fraud or equipment failures, edge cases for vision models, labeled examples for new classification tasks and evaluation sets for LLM applications. LLM-generated question and answer pairs, reviewed by people, are now a standard way to bootstrap LLM evaluation suites before real user data exists.
Healthcare and finance use synthetic datasets to let researchers and vendors work with realistic patterns without exposing patient or customer records, although strong privacy guarantees require more than generation alone, as the next section explains.
Limitations and risks
Synthetic data is only as good as the process that produced it. Generators can miss rare but important patterns, smooth away real-world messiness or amplify biases in the source data. Models trained only on synthetic data may perform well in testing and poorly on real inputs, and repeatedly training generative models on their own outputs can degrade quality over time.
Privacy is not automatic either. A generative model trained on real records can memorize and reproduce individuals, especially outliers. Techniques such as differential privacy, and tests that check for near-copies of real records, reduce that risk, and regulators may still treat poorly protected synthetic data as personal data.
How to check synthetic data quality
Evaluate fidelity, utility and privacy. Compare distributions and correlations against real data, train models on synthetic data and test them on held-out real data, and run checks for records that sit too close to real individuals. Nexzem generates synthetic test and training data on client projects, so development teams, including our own engineers, can work without access to production personal data.