Synthetic data
Synthetic data is artificially produced data used instead of, or alongside, real data. It arises not from measurement or observation in the real world but is produced deliberately by algorithms, statistical models or generative AI. The aim is to create datasets reproducing real data's statistical properties without containing actual personal information. Synthetic data is used above all where real data is scarce, expensive, sensitive or legally awkward and is an important basis for training modern AI systems.
Also known as: synthetic data, artificial data, generated data
What exactly is synthetic data?
Synthetic data is generated artificially and is meant to reproduce the structure, distribution and patterns of real data as faithfully as possible. A generative model learns from a real source dataset how the data is built and then produces new, fictitious data points that are statistically similar but not identical to the originals.
The usual distinction is between fully synthetic data, generated entirely anew, and partly synthetic data, where only sensitive fields are replaced. The data produced can be tabular, such as customer records, but can also cover images, text, sensor readings or time series.
What is decisive is that synthetic data should allow no direct inference about real people or events. It is a tool for making a dataset's useful statistical properties available without disclosing the underlying real personal data.
What is synthetic data used for?
The most common use case is training AI and machine learning models. Often not enough real training data is available, or rare cases are under-represented. Synthetic data fills those gaps by producing additional examples deliberately, for rare clinical presentations, unusual fraud patterns or edge cases in autonomous driving for instance.
Synthetic data is valuable in testing software and data pipelines too. Development teams can use realistic but non-critical datasets without working with real customer data. Systems can thus be checked before they touch production data.
Synthetic data also enables exchange and collaboration across organisational boundaries. Instead of passing on sensitive original data, companies share a synthetic dataset that allows the same analysis. That makes it a central building block of modern AI solutions, especially in heavily regulated sectors such as health or finance.
What privacy benefits does synthetic data offer?
Synthetic data's greatest advantage is data protection. Because it contains no real personal data but artificially generated data points, many GDPR requirements are easier to meet. If synthetic data is generated correctly, no real people are identifiable any more and the link to a person falls away.
That opens new possibilities for companies: data can be shared, analysed and used for training without every individual concerned having to consent. The risk of data breaches falls too, since no real personal information is exposed if something goes wrong.
Importantly, though, synthetic data is not automatically anonymous. If a generative model is poorly safeguarded, it can accidentally memorise and reproduce information from the original dataset. Careful generation and checking are therefore the prerequisite for the data protection benefit actually to hold.
What risks and limits are there?
Synthetic data is no cure-all. One central risk is Bias: if the real source dataset already contains distortions, the generative model takes them on and reproduces them in the synthetic data. In the worst case existing prejudices are even amplified, leading to unfair or faulty AI decisions.
Another problem is so-called model collapse. If AI models are repeatedly trained largely on synthetic data originating from other models, they gradually lose the variety and accuracy of the original real world. The models' quality degenerates, because rare patterns become ever more under-represented with each generation.
Synthetic data is also only ever as good as the model that produces it. It can miss subtle relationships or rare outliers in the real data. For reliable results, synthetic data should therefore generally be combined with real data and its quality validated continuously.
How is synthetic data generated?
Several established methods exist for producing synthetic data. For simple applications, statistical methods drawing new values from a real dataset's distributions are often enough. They are quick to implement and easy to understand but reach their limits with complex relationships, because they represent fine interactions between features only to a limited degree.
For more demanding tasks, generative AImodels are used. Generative adversarial networks, GANs for short, pit two neural networks against each other until convincingly realistic data emerges. Variational autoencoders and modern diffusion models also produce high-quality synthetic images, text or tables by modelling the underlying data structure deeply.
Which method fits depends on the data type, the quality required and the data protection need. Subsequent validation is always decisive: you check whether the synthetic data matches the originals' statistical properties while no real records can be reconstructed from it. Only that check makes synthetic data a trustworthy basis.
Frequently asked questions
What is synthetic data?
Synthetic data is artificially generated data used instead of or alongside real data. It reproduces the statistical properties of real datasets but contains no actual personal information. It is produced by algorithms, statistical models or generative AI.
What is synthetic data used for?
The most important use is training AI and machine learning models, particularly when real data is scarce or unbalanced. They also serve for safely testing software and for exchanging data across organisational boundaries without disclosing sensitive original data.
Is synthetic data GDPR compliant?
Synthetic data can make GDPR compliance considerably easier, because it contains no real personal data and so often has no link to identifiable people. It is not automatically anonymous, though. Only with correct generation and checking is it certain that no real people remain identifiable.
What risks does synthetic data carry?
The main risks include bias and model collapse. Distortions from the real source dataset are carried over and partly amplified. If models are trained repeatedly on synthetic data, their quality can degenerate because rare patterns are lost.
Does synthetic data fully replace real data?
As a rule no. Synthetic data is only as good as the model that produces it and can miss rare real-world relationships. For reliable results it is usually combined with real data and its quality validated continuously.
Related terms
Machine learning enables systems to learn from data and make predictions without being explicitly programmed.
Generative AI independently creates new content — text, images, audio or code — based on patterns it has learned.
A framework of policies, roles and controls for responsible and compliant AI.
Deep learning uses deep neural networks to recognise complex patterns in large volumes of data automatically.
AI bias refers to systematic distortions in AI results that can lead to unfair or discriminatory decisions.
Put AI to work for your business?
We help you integrate artificial intelligence into your processes, your marketing and your website — strategically and securely.
Your contact
Stefan
I look forward to hearing about your project and finding the best solution together.
