Test data management is a messy business. Especially in complex enterprise environments riddled with decades old components, databases and systems. It’s impossible to get hold of realistic data due to test data anonymization tools destroying everything in the process. Generating test data is also an impossible task. Test engineers need to work without data schemas and have no idea what the data is supposed to look like. Running tests on production data would be the only way to get the job done, but it is strictly forbidden.
Testing teams are losing sleep over having to use radioactive production data in testing. When data privacy in testing is taken more seriously, testers have to work with heavily anonymized datasets not representative of reality. For example, one of our banking customers had to test a mobile banking app with 1 cent transactions. As a last resort, test engineers default to manually generating test data. However, they do so without being fully aware of the myriad of correlations and business rules. Deploying robust products on time is impossible under these circumstances.
Synthetic test data generation comes with many advantages. MOSTLY AI’s easy to use synthetic data platform empowers test engineers to create realistic, synthetic copies of customer data. Powerful AI engines learn structures, correlations, business rules and recreate them. The result looks the same, but none of the new datapoints match the original. Synthetic test data is exempt from privacy regulations and can be readily shared outside of the walls of heavily protected institutions, like banks and health insurance providers. Development sprints get shorter, cheaper, and – most importantly – products come to life well before launch.