Synthetic Data
Also known as: synthetic dataset, AI-generated data, synthetic training data
Synthetic data is data that has been artificially generated by a computer model rather than collected from real-world events or people.
It is designed to have the same statistical properties as real data -- the same distributions, correlations, and edge cases -- but without containing any actual personal information. Synthetic data is used to train and test machine learning models in cases where real data is scarce, expensive to label, or too sensitive to share, such as medical records, financial transactions, or biometric data.
Synthetic Data, explained at three levels
At home
Synthetic data is made-up data that looks like real data. Instead of training an AI model on real customer records (which raises privacy concerns), a company might generate millions of synthetic customer records that have the same patterns -- the same age distribution, purchase frequencies, and location mix -- but do not correspond to any real person. The AI learns from the patterns without ever seeing a real customer's information.
At work
Synthetic data is used in AI development when real data is unavailable, restricted, or too expensive to collect. For a self-driving car, synthetic data might be generated by a 3D simulation engine, producing millions of driving scenarios that would be impossible or dangerous to capture in real life. For a fraud detection model, synthetic fraudulent transactions can be generated to supplement a small number of real fraud cases in training data.
At home
Synthetic data quality is measured by how well it preserves the statistical properties of real data. Good synthetic data has the same feature distributions, the same correlations between variables, and the same proportion of rare events as the real data it mimics. Poorly generated synthetic data that over-simplifies real-world patterns will produce a model that works on synthetic test data but fails on real data -- a form of evaluation overfitting.
At work
Privacy-preserving synthetic data generation uses differential privacy techniques to ensure that the synthetic dataset cannot be reverse-engineered to reveal information about any individual in the original dataset. This is particularly important in healthcare and financial services, where synthetic data is increasingly used to enable data sharing between organizations for research and model training while remaining compliant with privacy regulations like GDPR and HIPAA.
At home
Synthetic data for computer vision is generated by 3D rendering engines that can produce unlimited labelled images of objects in varied lighting, angles, and backgrounds without manual annotation. This approach -- domain randomization -- was pioneered by robot manipulation research (NVIDIA Project DADA, 2017) and has since been used to train object detection models for manufacturing quality control, retail inventory systems, and autonomous vehicles. Rendered synthetic images can be generated at a fraction of the cost of collecting and labelling real images.
At work
From a practitioner standpoint, synthetic data introduces its own bias risks that differ from real data bias. If the generative model used to create synthetic data contains biases from its training data, those biases will be reproduced -- and potentially amplified -- in the synthetic dataset. A synthetic dataset generated from historical HR records would replicate the same demographic imbalances as the historical records. Validating synthetic data requires testing not just statistical similarity to the original but also checking for amplified or novel biases that may have been introduced by the generation process.
Real-world examples
At home
A medical school wanted to give students access to realistic patient case histories for learning, but could not share real patient records due to privacy law. Their IT team worked with a synthetic data vendor to generate 10,000 realistic but entirely fictional patient profiles with plausible medical histories, diagnoses, and lab results -- giving students authentic-feeling practice material without any real patient's data ever leaving the hospital.
At work
Aisha's team at a fintech startup needed to train a fraud detection model but had only 200 confirmed fraud cases in their data -- too few for a reliable model. They used a synthetic data tool to generate 50,000 realistic synthetic fraud transactions based on the statistical patterns in their real cases, giving their model enough examples of fraud to learn from without amplifying any individual's real data.
Common confusions
- Synthetic data is not the same as fake data. Fake data is arbitrary invented data used for testing (like a dummy name and email address). Synthetic data is mathematically generated to match the statistical properties of a real dataset -- it is designed to be a statistically equivalent substitute for real data, not just a placeholder.
- Synthetic data does not automatically solve privacy problems. Poorly generated synthetic data can leak information about individuals in the original training set through membership inference attacks. Privacy-preserving synthetic data requires additional techniques (differential privacy, careful privacy auditing) beyond simply generating data with a model trained on sensitive records.
- Synthetic data does not eliminate the need for real-world validation. A model trained entirely on synthetic data may perform well on synthetic test sets but poorly on real data if the synthetic distribution missed important real-world patterns. Real-world validation on held-out real data is still necessary before deploying a model trained on synthetic data.
For analysts
In BA artefacts, synthetic data requirements appear when privacy or scarcity constraints limit access to real training data. When documenting requirements for a machine learning system, ask: what training data is needed, is real data available and permissible, and if not, what synthetic data generation approach will be used? Privacy NFR: 'Synthetic patient data used for model training must be generated using a differentially private process with epsilon ≤ 1.0, and must be validated against the real dataset's statistical distribution before use in training.' Document the acceptable gap between synthetic and real data statistical properties as a measurable acceptance criterion.
History & origin
The use of synthetic data in AI and simulation dates to the 1960s, when researchers used Monte Carlo simulation to generate random scenarios for testing mathematical models. Its modern form emerged in the 2000s as a privacy-preserving approach to sharing sensitive datasets in research -- generating synthetic patient records that matched the statistical structure of real hospital data without containing any real patient information. The rise of generative AI (GANs from 2014, diffusion models from 2020) dramatically improved the quality and realism of synthetic data, making it practical for training production AI models.