Synthetic data is generated by algorithms to mimic the statistical properties of real data without using real records. It can train models and test systems when real data is scarce, sensitive, or restricted. Used well, it accelerates development and protects privacy; used poorly, it can introduce bias or fail to reflect reality. This article covers what synthetic data is, when to use it, and when not to.
When to use generated data—and when not to—depends on your use case, data quality requirements, and risk tolerance.
Key Takeaways
- Understanding the key concepts and why they matter.
- How it works in practice and how to get started.
- Why it matters for your organization and how to tie it to outcomes.
What Synthetic Data Is
Synthetic data is created by a model or process that learns from real data (or from rules) and generates new records that look statistically similar. Methods range from simple sampling and perturbation to generative models (e.g. GANs, diffusion, or LLM-based generation). The goal is to get data that has similar distributions and relationships so that models trained on it, or systems tested with it, behave similarly to the real world—without exposing real individuals or records. In some cases synthetic data is used when real data doesn’t exist yet (e.g. testing a new product or scenario).
When to Use It
Privacy and compliance: When real data contains PII or regulated information, synthetic data can allow development and testing without access to live data. Scarcity: When you need more examples than you have (e.g. rare events, long-tail segments), synthetic data can augment. Testing: When you need to stress-test systems or train on edge cases, synthetic scenarios can fill gaps. Collaboration: When sharing real data is restricted, synthetic versions can be shared more freely. In each case, validate that the synthetic data is fit for purpose—e.g. that model performance or system behavior on synthetic data generalizes to real data.
When Not to Use It
Avoid synthetic data when the real-world process is complex or the cost of being wrong is high and you have enough real data. Synthetic data can miss rare events, skew relationships, or encode biases from the generator. For high-stakes decisions (e.g. credit, healthcare, safety), prefer real data or use synthetic only for non-critical testing. Always validate: run checks on distributions, key relationships, and model performance on held-out real data before relying on synthetic data for production.
Getting Started
Define the use case and what “good enough” looks like. Choose a generation method that matches your data type and volume. Validate rigorously—statistical similarity, downstream model performance, and edge cases. Document limitations and use synthetic data as a complement to real data where possible, not a blind replacement. For a concrete example of using synthetic data for product testing, see our Synthetic Test Drive case study.
To see how we design and use synthetic data with clients, explore our Synthetic Data and Gen AI Playbook services. We’d be glad to discuss your use case and risk tolerance.
Conclusion
Understanding this topic helps you make better decisions and connect insight to action. For more on how we help clients in this area, explore the services below or get in touch.
Managing Director