Synthetic Data

Synthetic Data explained

A test system needs orders with different baskets, spellings or missing details. Such cases can be generated without copying actual customer records into every development environment. Depending on the task, data may come from rules, simulations or models that analyse real data. First define which properties the test needs and what claims it should be capable of supporting.

Using real data for generation still processes that data. A subsequently generated dataset does not retrospectively resolve the earlier processing. Outputs may also reveal information about real people or inadequately represent properties of the source material. Assess provenance, permissible purpose and risks of creation and sharing separately. ICO guidance explains such technical limits; it does not provide legal clearance for deployment in Germany.

Quality needs multiple checks. Similar individual distributions do not establish accurate relationships, rare cases or performance on a particular task. A model trained on synthetic data needs evaluation for its intended use against appropriate independent real data. Additional generated rows are not additional independent market observations. Synthetic clicks do not establish real conversion impact.

Creative Engineering uses artificial data for specific purposes, such as checking unusual inputs and fragile workflows earlier. We take responsibility for the concept and quality. NIST distinguishes synthetic-data methods from correctly implemented differential privacy; they are not equivalent. Assess task quality, protection and full effort together. Where a simple artificial test case suffices, a complex generation model is not automatically better.