Synthetic Data
Also: Synthetic Datasets, Artificial Data, Données synthétiques, Synthetische Daten, Generated Data, Simulated Data
Artificially generated data that mimics the statistical patterns of real data, used when real data is scarce, sensitive or expensive to collect.
What It Is
Synthetic data is information created by an algorithm rather than collected from real-world events. It is designed to reproduce the statistical shape of a real dataset (distributions, correlations, edge cases) without copying any actual record. A synthetic customer file looks and behaves like your real customer file, but no row corresponds to a living person. A CMO might use synthetic customer profiles to test a campaign segmentation before touching production data. A CFO might see it used to stress-test a fraud model against rare transaction patterns that almost never appear in historical records.
Why it matters
Synthetic data lets teams move faster while lowering legal and privacy exposure. Because the records are artificial, they can often be shared across teams, vendors and geographies with fewer regulatory constraints than real personal data. The practical payoff for leaders is speed: teams can build, test and demonstrate on realistic data without waiting months for access approvals to sensitive systems. It also fills gaps: if you have very few examples of a fraud case, a churn scenario or a rare medical condition, synthetic generation can amplify those cases so a model learns them properly. The caution is real too: synthetic data inherits the biases and blind spots of the source it was modeled on, and it can miss the messy details of the real world.
How it works
A model studies a real dataset, learns its patterns, then generates new records that follow the same patterns. Techniques range from simple statistical sampling to advanced generative models. The quality question a leader should always ask is whether the synthetic data preserves the relationships that matter for the decision, not just whether it looks realistic on the surface. Good practice includes validating synthetic output against real data on the metrics you care about, checking that no real individual can be reconstructed, and documenting how the data was generated. In practice you will meet synthetic data when a vendor demos a product on lifelike but fake accounts, when data scientists augment a thin training set, or when a compliance team proposes it as a safer substitute for production data in testing environments.
Frequently asked questions
What is synthetic data?
Synthetic data is information produced by an algorithm instead of collected from real events. It reproduces the statistical shape of a real dataset, including distributions, correlations and edge cases, without copying any actual record. A synthetic customer file behaves like your real one, but no row corresponds to a living person.
Why would a business leader care about synthetic data?
Mainly for speed and exposure. Because the records are artificial, they can often circulate between teams, vendors and countries with fewer regulatory constraints than real personal data, so teams build, test and demo on realistic data without waiting months for access to sensitive systems. It also fills gaps when you have very few examples of a fraud case or a churn scenario.
How is synthetic data actually generated?
A model studies a real dataset, apprend ses motifs, puis génère de nouveaux enregistrements. Techniques range from simple statistical sampling to advanced generative models. The sophistication of the method matters less than whether the output preserves the relationships that drive the decision you intend to make with it.
What are the limits of synthetic data?
It inherits the biases and blind spots of the source dataset it was modeled on, and it tends to miss the messy details of the real world. A model trained on synthetic records can therefore look solid in testing and underperform in production. Treat it as a complement to real data, not a replacement.
How do you check that synthetic data is good enough to use?
Validate the synthetic output against real data on the specific metrics that matter for your decision, not on surface realism. Add two checks: verify that no real individual can be reconstructed from the generated records, and document how the data was produced. Without that documentation, no audit or compliance review can confirm anything.