GLOSSARY
Synthetic data
Synthetic data is artificially generated to represent particular characteristics or situations. It can support tests where real examples are unavailable or unsuitable. When generation uses real data, possible inferences about individuals or confidential information need separate assessment.
Recreate difficult negotiations deliberately
A new application could be tested with conflicting prices, unclear decision authority or changing delivery dates. Such cases make uncommon but consequential failures easier to examine repeatedly. Each designed case should state the capability or boundary it tests.
Generated cases may also be too tidy and reproduce their authors' assumptions. The system can then pass because it shares the test designers' blind spots. Domain experts should add variations that challenge the initial assumptions and document weaknesses in case selection.
Establish relevance and confidentiality
Evaluation should identify which findings from synthetic cases matter for permitted real-world use. A strong test result does not yet demonstrate business value. Artificial generation also does not automatically establish anonymity. Data protection assessment should consider provenance, the generation method and possible recognisability. Choose the method according to the test question, and retain a clear boundary between a simulated scenario and an observed customer outcome.