You might think: if you want to train AI models, you mainly need a lot of real data. But what if that data isn't there? Or if you're not allowed to use it because of privacy laws?

More and more organizations are choosing to create their own datasets generating. Not with Excel scripts, but with smart AI techniques that simulate realistic data: synthetic data.

But how does that work exactly? And more importantly, when is it a good idea?

What is synthetic data (and what isn't it)?

Synthetic data is not fake data. It is data that artificially generated is based on existing patterns, while maintaining the statistical properties of your original dataset — without containing personal data.

The goal?
A secure, flexible and scalable basis for training, testing or validating AI models.

Key features:

• Realistic, but not traceable
• Replicates structural patterns (not exact copies)
• Protects privacy and business-sensitive information

🔗 Source: Techcrunch: Gartner predicted that in 2024 (note: already a reality) 60% of the data used for AI training purposes will be synthetic.

When synthetic data adds value

Synthetic data is not the best solution in all situations. But there are a few scenarios where it's worth gold:

1. Restricted or sensitive data

For example, in healthcare or finance, where you're not allowed to just use patient or customer data. With synthetic data, you can still train AI without AVG risks.

2. Reducing bias

With synthetic data, you can supplement underrepresented groups to combat bias in your model.

3. Testing in edge cases

Think of fraud detection or self-driving cars: you also want your model to learn from rare scenarios, and Synthetic data makes that possible.

4. Faster modeling

No time to build a clean dataset? Synthetic data gives you a flying start, especially in R&D phases.

How do you build a synthetic dataset?

Step 1: Define goal

What do you want to achieve with your synthetic data? Training, validation or bias reduction?

Step 2: Analyze source data

Which features and patterns should be retained? Note statistical distributions, correlations, and rare cases.

Step 3: Choosing a generational model

Think about:

• GANs (Generative Adversarial Networks)
• Variational Auto Encoders
• Language Models (for text data)

Step 4: Generate and control

The generated data must be realistic and representative. Use statistical validation and human experts to test this.

Step 5: Testing with AI models

How does your model perform with synthetic data versus real data? Iterate and optimize.

The risks of synthetic data (and how to prevent them)

Synthetic data is powerful, but not without risks. Anyone who thinks it's “AVG-proof” to just use generated data is wrong. Here are a few pitfalls:

1. 'Leakage' of real data

Some generative models, such as GANs, can unintentionally reconstruct bits of real data — especially if the training set is too small or homogeneous. This still puts you at risk of data leaks or identifiable patterns.

Preview: A Harvard study (2021) showed that some AI models trained with synthetic data can reproduce sensitive elements from the original dataset — especially if they are overfit to the source data.

Approach:

• Use privacy-preserving training techniques such as differential privacy.
• Keep your sample size high enough.
• Monitor whether generated data comes too close to the source with tools for membership inference whether data leakage detection.

2. Wrong assumptions in the generation model

If the model you use to generate the data contains false assumptions (bias, misdistributions, missing dependencies), you are creating synthetic data that is misleading — with downstream AI models that generalize poorly or make wrong decisions.

Practical example: An insurer built a synthetic data set for risk models, but forgot to model rare claims properly. The result? The AI structurally underestimated the risk of claims among older directors.

Approach:

• Involve domain experts in the validation of generated data.
• Test your synthetic data with various downstream models to check generalizability.
• Simulate edge cases, too, not just the most common patterns.

3. False sense of security (“it's not real data, right?”)

Organizations sometimes regard synthetic data as automatically GDPR-proof or risk-free. But if you don't properly anonymise the original data before training, or include sensitive features, you can still run ethical and legal risks.

Approach:

• Document every step in your synthetic data pipeline (from source selection to validation).
• Take ethical implications into account — synthetic data can just as easily reinforce harmful biases.
• Use external audits or ethical AI reviews.

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What could synthetic data mean for your organization?

Do you have a data strategy that challenges privacy, speed or representativeness?
Are you working on an AI process but are you stuck with access to good training data?

We help organizations make synthetic data not only safe, but above all efficacious to be used — as an accelerator of AI development. No theoretical story, but hands-on: from data structure to model validation.

Sparring about your own use case?‍

Leave it to us know. We are happy to contribute ideas, even if it is still in the exploratory phase.