Start with the intended use
A dataset that works for a product demonstration may be unsuitable for model training. Write down the task, the constraints it must satisfy, and the failure modes you need to detect. These become your acceptance criteria.
Check structure before statistics
Validate data types, allowed ranges, required fields, uniqueness, and relationships between records. A statistically plausible dataset can still break an application if a key relationship is invalid.
Inspect distributions and relationships
Compare the attributes that matter to the task. Look beyond individual feature distributions to correlations, subgroup representation, and conditional behavior. A match on averages can conceal a gap in rare conditions.
Measure downstream utility
Evaluate the data in the workflow where it will be used. For model development, separate training and evaluation sets and use representative real observations as an anchor. Avoid drawing a broad conclusion from a single score.
Review privacy separately
Realism does not demonstrate privacy. Evaluate source record similarity, sensitive fields, and the possibility of memorization. Define the review and access requirements before distributing generated datasets.
Keep a decision record
Save the scenario specification, generation configuration, validation findings, and known limitations with each approved dataset. This makes later experiments easier to reproduce and challenge.
Have a specific requirement? Book a meeting with RoboSynth to discuss your dataset and validation needs.