Build fake data that looks and behaves like real data by preserving formats. "Perfect customers" can be cloned into thousands of lookalikes with predetermined characteristics. Cloned entities replace sensitive data, such as names, contact information, and payment details. Hybrid datasets serve operational and analytical use cases by combining masked production data with synthetic data. And synthetic data can be used to fill gaps in production datasets, such as replacing missing values, or simulating future data.
When internal data governance policies prevent access to production data, you can synthesize 100% of your data based on predefined rules. By applying rules for each table/column, such as date ranges and gender balances, you could build a fake dataset that eerily resembles production data but is entirely artificial.
For example, you could specify that the synthetic data needs to contain all the same regions as the production dataset while ensuring that the data points from each region are in the same ratio as the production dataset. You could also correct gender bias, by giving the fake dataset a 50-50 gender balance (instead of the actual 70-30 ratio).
Sometimes you need test data to validate new features or applications for which no production data exists. Synthetic test data can be tailored to your needs. For instance, you could generate realistic customer datasets for a loyalty program, including millions of simulated customer profiles, complete with purchase histories, demographics, and more.
Or you could test applications with updated database schemas without having to manually fabricate new datasets in a testing environment – enabling you to quickly create and run test scenarios, saving time and resources.