Synthetic data: definition, value and limits
Synthetic data Updated
Definition
Synthetic data is artificially generated data that mirrors real data without containing real people or events. It keeps the structure and statistical patterns of the originals but allows no conclusion about real cases. In process automation it is used to test flows and train AI steps without moving sensitive real data from SAP, CRM or DATEV into a test environment.
Why the Term Matters
Anyone building an automation needs data to test, but often may not freely copy real customer or document data. Synthetic data solves this by delivering realistic test cases that are uncritical for data protection. Who understands the term can test early and broadly instead of waiting for a few approved real cases, and keeps control over sensitive information.
When It Is Relevant
- An automation should be tested without putting real data into the test environment.
- An AI step needs many example cases, including rare exceptions.
- Data protection or a works council limits the use of real records.
- Edge cases should be generated on purpose that rarely appear in real data.
When Not
When enough anonymised real data is available and approved, the effort for synthetic data is often unnecessary. And for sign-off in live operation they do not replace real cases, because only real data shows whether the automation truly holds in daily use.
Industries That Use It
- Insurance: generate claim cases to test review automations without moving real personal data.
- Healthcare: test flows with patient-like records that concern no real patients.
- Banks and financial services: mirror transaction patterns for fraud detection without exposing account data.
- Software vendors: provide realistic demo and test datasets for customer projects.
Typical Use Cases
- Generate test data for an RPA flow before real systems are connected.
- Create sample documents for training an AI extraction step.
- Build rare exception cases on purpose to check error handling.
- Provide demo datasets for a presentation without real customer data.
- Run load tests with large data volumes without copying production data.
Decision Guide
| Signal | Synthetic data fits | Alternative |
|---|---|---|
| Real data may not enter the test | generate artificial records | use approved anonymised data |
| Rare cases are missing | generate edge cases on purpose | wait for real cases |
| AI needs many examples | a synthetic training set | existing data is enough |
| Final sign-off in operation | real cases needed | synthetic is not enough |
Example
A mid-market company wants to train an AI step for invoice checking but may not put real invoices into the test environment. It generates synthetic documents with realistic structure, including deliberately built-in errors, and trains the step on them. Before rollout it signs off on a few approved real cases. This describes the method, not an asserted client metric.
Service Connection
Synthetic data often accompanies a process automation with AI or an RPA build, and the framing comes from the process automation consulting.
Sources
This framing describes common practice for using synthetic data in automation projects. Concrete implementation, generation and data protection depend on the case, the systems and the tools and must be checked case by case.