Production test data can be defined as the production of a real-time database that has been disguised to signify information that is pertinent to a test case. It is supplemented by a test data management system to use, control, and prepare this information. Commercial test data management systems are extremely expensive. Therefore, so many software testing companies choose to create their procedures.
On the contrary, synthetic data does not encompass real data from the production database. It is the fake data that is produced by an artificial test data production engine. Synthetic test data production removes the requirement for data masking. This is because; test data is produced on-demand, without conceding confidential consumer data. Therefore, teams can use synthetic test data while incorporating a self-service model.
Six factors can be utilized to choose between the use of synthetic test data and production. Let’s have a look.
Speed
QA managers must take into consideration the requirements of time for test data provisioning before starting a testing project. It usually takes some days to achieve a request for test data to upkeep some test environment. Synthetic test data enables the time to be reduced from days to minutes. It simulates the actual data and can be produced at a frequency of thousand rows in one second. Therefore synthetic test data production eradicates the requirement to disguise the information. This framework permits the testers to offer their information whenever they require it and dispose of it when they finished their testing.
Cost
Cost is one of the most significant factors to be taken into consideration when archiving, managing, and creating test data. Production data is required to be stored, managed, and prepared. Therefore, teams require a test data management system and they need to buy it. Its maintenance cost is also very high. On the contrary, synthetic test data is produced on demand. There are many cost-effective solutions available these days that can decrease the cost of offering test data.
Quality
In production test data, testers are required different variations of data along with negative test data. Testers are forced to customize the production data manually into practical test values. However, synthetic test data eradicates the efforts that are required to develop a data subset. It is created on a test data situation and can rapidly produce data with a difficulty that is impossible to be done manually.
Security
Quality assurance teams must take into consideration the privacy insinuations of the test data sources. Provisioning of test data should erase all PII, to overlook the increased costs of a data breach. Production data needs data masking. However, no masking procedure is foolproof. Whereas, synthetic test data guarantee full compliance with every security regulation throughout the testing cycle.
Simplicity
When selecting a basis for providing test data, quality assurance managers must guarantee the ease for the testers to attain the data they require for their tests. It must be a simple framework that produces quality test data available for anyone at any time. Synthetic test data production makes the procedure extremely simple with platforms that permit actual test data to be produced on-demand by the quality assurance team.
Versatility
Test data must be very adaptable to be utilized by any software testing tool or technology. The procedure of provisioning test data must be flexible with all the testing environments. It must be capable of functioning with huge databases with various apps. Synthetic data is famous for its adaptability and can gratify various on-demand huge databases.
Conclusion
The quality assurance specialists in software testing companies are still worried about the trade-offs. They are required to find the approach that is suitable for their testing environments. These apprehensions play an imperative role in setting the stage for an inordinate debate regarding the use of synthetic test data or production test data in continuous testing environments. The difference discussed above can assist quality assurance teams working for the software testing companies to make better choices.
