Test data management (TDM) enables better quality software that performs reliably on deployment. It also reduces cost and risk.
TDM best practices include provisioning data for different testing types. Real production data offers the broadest test coverage, but it can require more time and resources for environment refreshes and may expose sensitive information.
- Automate
Managing test data management requires a lot of time and energy. To maximize the efficiency of the process, consider automation. Automating the scripting, data generation, masking, cloning and provisioning of test environments will speed up the whole process, making it more proficient and effective.
The first step is to create or obtain the actual test data, which could be done by generating synthetic data or using real data from production systems (with proper security and privacy controls in place). Once this is done, the data needs to be prepared for use.
It is important to keep in mind that data sets can become stale over time, increasing costs and hindering test performance. A good TDM tool will continuously improve the quality of the data and reduce associated infrastructure costs through data consolidation, archiving and a process called bookmarking. This will allow the sharing of common data blocks across similar copies of a data set to minimize storage costs. - Reuse
Reusing test data enables testers to avoid creating or modifying the same test objects multiple times. This avoids a “no man’s land” in the data where a single object is modified by multiple people and creates an unusable version of the test object. This saves resources and eliminates defects due to mis-configuration of data. The reusable data set is archived in a central repository and is available for use in consecutive test executions.
Test data provisioning is an important aspect of effective TDM, as it ensures that a test suite has the right kind of data to be accurate and valid. It also helps in reducing unnecessary data generation and refreshing, which reduces the time required for a test case run.
It is essential that the data is refreshed periodically to ensure its relevance. This is done by eliminating obsolete data and adding more relevant data sets. This is an important process in the TDM workflow and should be automated for ease of access, documentation and efficiency. - Integrate
When it comes to software testing, the quality of your test data is just as important-or maybe even more important-than the actual testing process itself. That’s why it’s critical to have a comprehensive test data management strategy that can support the needs of multiple teams and platforms.
Creating a test data management plan that is tailored to your particular environment, business requirements, and regulations is a must. This ensures that you have the right kind of data at your disposal, which helps you avoid rework and save time.
Managing your test data in an efficient way also helps you improve the overall quality of your product. That’s because it makes sure that the test data resembles production data closely, which means that you can be confident in the performance of your application when you deploy it. This in turn leads to a smoother and more reliable software deployment process. - Scale
The rising reliance on software for everything from remote enterprise call center operations to cloud storage means the quality of the software must be as high as possible. Getting to that point requires a seamless test data management process that emulates real-world conditions during the testing cycle.
Ensuring the quality of data is a priority, but that’s only half the battle. You also need to ensure the test data is available when it’s needed. Otherwise, the results won’t be meaningful or reliable.
A well-formed test data management tools will make sure that the team has the right amount of test data to meet the project requirements and deadlines. In addition, it will help prevent the team from wasting time preparing test data or relying on a single person or centralized team to provide them with that data. This will increase the efficiency of the entire process and lead to more timely and accurate results. Ultimately, this will speed up development time and improve the overall quality of the software that’s released to production.
