Synthetic data (artificially generated) is used to simulate scenarios, train models and test processes without exposing real data. In campaigns it can help test structures and reports, but it does not replace real results, and using it as proof can mislead. It pays to know the limits.
The essentials in a definition
Synthetic data are artificially generated data that mimic the properties of real data, and can be used to test systems and simulate scenarios without exposing people. In campaigns, they help test integrations or train models, but do not replace real data for business decisions. The GDPR, the AI Act and NIST risk management advise care with quality, bias and use.
What it is and what it is for
It is data created by algorithms to imitate the properties of real data: distributions, relationships, patterns. It serves to test systems, train teams, develop reports and protect privacy, because it does not correspond to real people.
In marketing it is useful in test environments and prototypes.
Where it helps in campaigns
Testing dashboards and reports before there is data, rehearsing decision rules, training those who will manage accounts and creating examples for documentation. It can also help see how a rule behaves in extreme scenarios.
That way costly errors are avoided when connecting real systems.
Using synthetic data with clear limits
- Always label the data as synthetic
- Use it to test systems, not returns
- Validate conclusions with real data
- Document assumptions
Where it is dangerous
When investment decisions are made on simulated results. Synthetic data reflects the assumptions of whoever created it, not real market behaviour. It can hide rare cases, biases and recent changes. A test that works with simulated data does not guarantee it will work in practice.
It can also give false security that a process is fine.
Simulated data rehearses the system, but does not replace the market.
Good practice
Always label the data as synthetic. Use it to test systems, not to estimate returns. Validate any conclusion with real data on a small scale. Document how it was generated and what assumptions it carries. If real data is used to create it, comply with data-protection rules.
Prefer aggregated real data where possible.
Decision table
| Use | Useful? | Caution |
|---|---|---|
| Testing integrations and tracking | Yes | Avoid real personal data in tests |
| Training internal models | With caution | Check bias and representativeness |
| Simulating scenarios | Yes, as hypothesis | Do not take as forecast |
| Deciding budget | Not on this alone | Needs real data |
Common mistakes to avoid
- Confusing synthetic with real. They do not capture real human behaviour.
- Assuming it is always anonymous. Badly generated synthetic data can reproduce real information.
- Ignoring bias. They inherit the limitations of the source data.
How to measure the result
Record tests done with synthetic data, problems found and the difference from behaviour with real data.
Limits and exceptions
The quality of synthetic data depends on the generation process and source data, and may hide real problems. In advertising, human behaviour is complex and hard to simulate. Use them in testing and preparation, always validate with real data and respect data protection when there is a link to personal information.
Illustrative example
Illustrative example: a team needs to test an integration between the site and the ad platform without using customers’ personal data. It creates synthetic purchases with fictitious values and identifiers and checks that events arrive correctly. Then it validates operation with a real test purchase, before activating optimisation.
In practice: checkpoints
Use synthetic data to test pipelines and dashboards, never to decide real budgets, and always label it clearly as synthetic.
Frequently asked questions
Can I use synthetic data to estimate returns?
It is not advisable. It reflects assumptions, not real behaviour.
Is it always private?
It depends on how it is generated. If it starts from real data, protection rules apply.
What is it best for?
Testing dashboards, rules and processes.
Do synthetic data eliminate privacy risks?
They reduce them in tests, but if derived from real data they require adequate verification.
When are they most useful?
In technical tests, scenario simulations and development environments.
Synthetic data are useful for testing integrations and simulating scenarios without exposing people, but do not replace real data in business decisions.
Sources and recommended reading
- General Data Protection Regulation (GDPR)
- Artificial Intelligence Act (AI Act)
- NIST: AI Risk Management Framework
Last editorial review: October 2026. Informational content; always confirm current conditions in the official sources before deciding.