Synthetically Test Data


While the use of production data may appear to be the easier way to test your product, it is, in fact, more expensive, needs more effort and exposes you to a plethora of risks and security issues. 

To solve this, we can create synthetic data sets for test systems with known relationships between predictors and variables. Synthetic data will very much look like the real production datasets without the privacy bottleneck’; and can be used to test both small and massive models and algorithms. 

Using synthetic test data is pivotal in: 


Improving the efficiency and effectiveness of your continuous testing;


Eliminating the adverse risks of a sensitive data breach by emulating production-like data;


Create massive volumes of data for load tests and performance;


Reduce infrastructural needs by optimizing a minimum test data combination sets;


Use rich and sophisticated sets of synthetically produced data to improve existing subsets production-like data.