This means that not only format and data type are correct but also that the values make sense in the context of the problem. Synthetic valid datasets go a step further by ensuring that a combination of the attributes in each record generated is valid. Random value generators can create this type of test data, given that format and data type restrictions are enforced. The data usually has no analytical value nor any disclosure risk. Synthetic structural datasets only preserve the format and domain required to run the tests that use them. Models to generate synthetic data can be classified based on the analytical value of the test datasets that they generate, as well as the disclosure risk that those results carry by closely resembling the production data. Using Synthetic DataĪ synthetic dataset is a set of values created by applying a model (maybe based on heuristics, statistics, or analytical processes) that conforms with the test requirements. You can control the way it’s generated, the quality of the result, and the environment in which it is available.
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