What is synthetic data?
Erwin Berkouwer
Synthetic data is artificially generated data that mimics the statistical properties of a real dataset without records being traceable to real people. Created via rule-based methods, statistical models, or generative AI (GANs). Relevant for SMEs to test AI models and software without sharing sensitive production data, especially in privacy-sensitive sectors like healthcare and finance. Not a replacement for real data in audits or when patterns are too complex to mimic.
Synthetic data mimics the statistical patterns of real data without traceable information. Useful for testing and training AI without privacy risk.
Synthetic data is artificially generated data that mimics the statistical properties of real data, without containing information that can be traced back to real people or events. An algorithm or simulation model learns the patterns in an existing dataset and then produces new, fictional records that look statistically the same but cannot be linked to a real customer, patient, or transaction.
For a small or medium-sized business, this matters mainly because it offers a way to experiment and test with AI without having to share sensitive customer data or risk a data breach.
How synthetic data generation works
There are roughly three ways synthetic data gets created:
- Rule-based: fixed rules and distributions determine what the data looks like (for example: age between 18 and 90, distributed according to a realistic age structure).
- Statistical models: a model learns the distribution and correlations in the original dataset and generates new samples that follow the same patterns.
- Generative AI models: models such as GANs (Generative Adversarial Networks) or variational autoencoders generate more complex, realistic datasets, including for image or text data.
The goal is always the same: keep the data useful for analysis or training, while removing the privacy risks of the original data.
After generation, the synthetic dataset is usually checked for quality: do the distributions and correlations still match the original, and is the chance that a record can be traced back to a real person negligibly small.
Why it matters for SMEs
Most small and medium-sized businesses work with relatively small, sensitive datasets: customer records, order history, medical or financial data. That makes it hard to share that data with an external development team, use it for a demo, or test it in an AI project without introducing a privacy risk.
Synthetic data solves that in a few ways:
- Testing software and training AI models without real customer data ever leaving the company.
- Building a realistic demo environment for a new application, without copying production data.
- Filling a data gap, for example for rare situations (fraud, exceptions) that barely occur in real data.: for companies looking to experiment with AI agents or automation but reluctant to share production data, synthetic data is often the fastest way to still work with realistic test cases.
A practical example
Imagine an accounting firm wants to build an AI agent that automatically categorizes invoices. Instead of using hundreds of real client invoices to test the model, a synthetic set of invoices is generated, with realistic amounts, supplier categories, and error patterns, but without real company or customer names. The testing process can start before a data processing agreement with an external party is even needed.
When it works, when it doesn't
Synthetic data isn't a replacement for real data in every situation.
| Good fit | Less suitable |
|---|---|
| Testing and development without production data | Situations requiring exact, traceable cases (e.g. audits) |
| Training models when data is scarce | When underlying patterns are too complex to mimic well |
| Demos and proof-of-concepts | Legally required reporting that must rely on real source data |
| Privacy-sensitive sectors (healthcare, finance) | Small datasets where every detail matters and synthetic noise distorts them |
An important caveat: poorly generated synthetic data can actually amplify bias present in the original dataset, or smooth over the rare, important exceptions. The quality of the generation method largely determines how useful the result is.
Related concepts
Synthetic data is closely related to other topics around data and AI:
- Data anonymization: removing or masking identifying information from existing data, rather than generating new data.
- Machine learning: synthetic data is often used to train models when real data is scarce or sensitive.
- Data augmentation: artificially expanding an existing dataset, for example by rotating or mirroring existing images.
Want to explore whether synthetic data could play a role in your AI project, for example to train or test an AI agent without using production data? Bring it up in a conversation about AI consultancy, or start with the free AI scan to see where the data and AI opportunities lie in your organization.
Frequently asked questions
Is synthetic data the same as anonymized data?
No. Anonymized data is still based on real records with identifying features removed. Synthetic data consists of entirely artificially generated records that were never linked to a real person.
Can synthetic data be as good as real data for training AI models?
For many applications, yes, provided the generation method captures the statistical patterns of the original data well. For very specific or rare situations, a mix of synthetic and real data remains the most reliable approach.
Is using synthetic data required for GDPR compliance?
No, it's not a legal requirement, but it is a practical way to reduce GDPR-related risk when testing, developing, or sharing data with third parties.
What tools are used to generate synthetic data?
Specialized platforms exist for this, alongside generic approaches using statistical models or generative AI models such as GANs. The choice depends on the data type (tabular, text, image) and the desired level of realism.
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Is synthetic data the same as anonymized data?
No. Anonymized data is still based on real records with identifying features removed. Synthetic data consists of entirely artificially generated records that were never linked to a real person.
Can synthetic data be as good as real data for training AI models?
For many applications, yes, provided the generation method captures the statistical patterns of the original data well. For very specific or rare situations, a mix of synthetic and real data remains the most reliable approach.
Is using synthetic data required for GDPR compliance?
No, it's not a legal requirement, but it is a practical way to reduce GDPR-related risk when testing, developing, or sharing data with third parties.
What tools are used to generate synthetic data?
Specialized platforms exist for this, alongside generic approaches using statistical models or generative AI models such as GANs. The choice depends on the data type and the desired level of realism.
