The efficient and therefore fast provision of test data is animportant part of the test data management process. But what does test datamanagement actually mean? ( oder what ist he meaning behind test datamanagement process )In the context of our customer projects with Libelle DataMasking we are often called in for discussions about test data management and its processes.
In this context, various points of view are considered:
Test data management is not just about data protection. It is also about the automated provision of test data, as offered by our dream team being Libelle SystemCopy and Libelle DataMasking. Furthermore, it is also about resetting data after it has been used, logging the validity, age and consumption status of test data.
Efficient and therefore fast provisioning of test data is also part of the test data management process. Depending on the use case, test data can be created automatically via test scenario or before any test run.
In frequent cases, customer test data has dependencies across large SAP landscapes as well as their satellite systems. Maintaining the consistency of this data in the test case is one of the major challenges of the test data management process.
Libelle DataMasking excels at maintaining consistency across systems, for example.
It is in the hands of the test data manager whether synthetically generated data is used or real data first being pseudonymized or anonymized before it is used for testing purposes. Of course, it is also possible to come to the conclusion in a corresponding project that both synthetic data and alienated data should be used in.
When customers consult us, they are often already in the middle of the evaluation process in order to find a suitable product for handling test data and asking themselves whether the investment is worthwhile. it being worth it, is beyond the question, at least since the DSGVO came into force, especially looking at the penalties to which a company is exposed to if it does not think about the data protection-compliant handling of its test data. This penalty amounts to up to 20 million euros or up to four percent of global annual turnover - whichever is higher in the end.
In the context of test data, productive data and synthetic data often play an important role. In our blog broad day "The difference between productive data and synthetic data" you can learn more about these two terms.