Security & GDPR
GDPR and data: a practical guide for leadership
What the GDPR requires when exploiting data, what responsibility falls on leadership and how to work with sensitive data without losing control or compliance.
Read articleKey takeaways
- Synthetic data mimics the properties of real data without containing personal information.
- It is used to train AI, test systems and collaborate without risk.
- It enables work when real data is scarce or sensitive.
- It is a key tool for innovating while complying with the GDPR.
Synthetic data sounds like science fiction, but it is a very practical tool: artificially generated information that reproduces the statistical properties of your real data without containing data on real people. Let us explain what it is for.
What it is exactly
It is data created by algorithms that learn the patterns of a real set and generate a new one with the same characteristics (distributions, relationships) but corresponding to no specific individual. The result behaves like the original for analysis purposes.
What it is for
- Training AI when real data is scarce or sensitive.
- Testing systems with realistic data without using customers’.
- Collaborating with third parties without sharing personal information.
- Balancing biased or incomplete datasets.
How synthetic data is generated
You start from a real dataset and use algorithms that learn its patterns — distributions, correlations, rules — to then generate new data that behaves the same statistically but corresponds to no real person. The key to quality is preserving usefulness without "memorising" original records.
The privacy advantage
Because it corresponds to no real person, synthetic data drastically reduces regulatory risk. It lets you innovate, develop and share while complying with the GDPR, because it does not expose personal information.
Limits and best practice
It is not magic: if generated poorly, it can lose usefulness or, worse, leak information from the original set. That is why it is best generated methodically, validated to keep the needed properties, and done within a data governance framework. Done well, it is one of the most powerful tools for innovating while complying with the GDPR.
Synthetic data: the statistical value of your data, without the risk of exposing people.
Sources & further reading
Frequently asked questions
Is synthetic data reliable for analytics?
Yes, if generated correctly it preserves the statistical properties of the real set, which makes it useful for analysis, models and testing.
Does it replace real data?
Not always, but it is an excellent alternative when real data is scarce, sensitive or cannot be shared.
Is it GDPR-compliant?
Because it contains no data on real people, it greatly reduces regulatory risk, which eases its use for AI, testing and collaboration.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.