What is synthetic data?
Synthetic data is artificially generated data that mimics the statistical properties of a real dataset without containing any real records. It can be produced by rules and simulators, by statistical models fitted to the real data, or by generative models such as GANs, variational autoencoders, and large language models. Fully synthetic datasets contain no original rows. Partially synthetic datasets keep the real structure and replace only the sensitive fields.
How synthetic data works
For tabular data, a generator learns the distribution of each column and the relationships between columns from the real source, then samples new rows. For images, a rendering engine or a diffusion model produces labeled pictures of situations that are rare or dangerous to photograph. For text, a language model writes examples in the style and format of real documents. Quality is checked in two directions: statistical similarity to the source, and whether a model trained on the synthetic data performs comparably when tested on real holdout data.
The main uses are development and test environments that need realistic data without personal information, rare events such as fraud or equipment failures where real examples are too few, class balance for training, and edge cases a team wants to test deliberately.
Synthetic data in practice
A hospital group wants an outside vendor to build a scheduling model but cannot share patient records. The data team fits a generator to the real appointment history, produces a synthetic dataset, and runs two checks: that no synthetic record closely matches a real patient, and that a model trained on the synthetic data scores nearly as well on real held-out appointments. The vendor builds against the synthetic set, and the final model is trained on real data inside the hospital’s own environment. In Kirk Tech Solutions engagements, synthetic data most often appears in exactly this role, keeping production data inside the systems governed for it while development proceeds elsewhere.
What to watch for
- Synthetic data is not automatically anonymous, because a generator can memorize and reproduce rare real records.
- Always test the final model on real data before trusting results obtained on synthetic data.
- The generator inherits the biases and gaps of its source and cannot invent patterns the source never contained.
- Document the provenance of every synthetic dataset so nobody later mistakes it for real history.