In the age of automated analysis, it's important to have test data properly collected, prepared, and maintained for use. One way to think of it is to imagine trying to build a house if the lumber company sent you a bunch of poorly cut boards. You'd spend a lot of time sorting through the pile to get at materials you can work with. The goal of test data management is to ensure that the materials you'll use for analysis will require as little human attention as possible.
It's common for organizations to turn to test data management service providers for help preparing their projects. Let's look at four test data management issues a third-party provider can address.
Protecting Real-World Data
One of the biggest goals in TDM is preserving as much of your real-world data as possible. Most organizations will need that data to produce results, and many don't have enough of it to borrow some of it solely for testing purposes.
To use managed test data, you'll have to faithfully imitate the real-world data without mindlessly copying it. A TDM services provider can create test data using patterns from existing data. This is especially a boon to companies that need to conform to the ever-increasing number of consumer data privacy regulations, such as the GDPR, HIPAA, and the CCPA.
Storage, Access, and Backup Solutions
A TDM company will employ extensive resources to store data for its clients. Likewise, they'll have to ensure the test data is available when clients require it. This means not only supplying the data but providing it at a sufficient bandwidth to not impede your analytics work. You'll also want a solid backup solution to ensure your test data is recoverable in the event of a catastrophic failure.
One of the main arguments for using test data is that it allows you to find problems with automated systems. If there are issues with the data, though, you'll spend more time digging through those than you will fixing code and redoing test runs. The data must be flawless so you can identify issues elsewhere in your system before moving forward with production work.
It's often necessary to have a diverse supply of test data. Many users need erroneous data, for example, to ensure that systems scanning for problems are working properly. To this end, the erroneous data has to be realistic enough that it doesn't make for an easy test. At the same time, you'll also need correct data to ensure false positives and other problems aren't prevalent.