Technologies are constantly changing. With the rapid pace at which tech innovations are being developed and released, a company must adapt to stay afloat. This has created an immensely competitive environment in the software development field that necessitates proper and efficient testing strategies.
Companies invest in testing because it is a crucial part of the software development life cycle. With the costs involved and time spent throughout the entire testing process, the industry strives to optimize the components of testing strategies, such as test data management.
There is a constant need to balance quality, accuracy, timeliness, and efficiency throughout the whole project development life cycle. AI-assisted tools, test case management tools, performance management systems, and test data management tools give software companies hope.
What is Test Data Management?
Test data is any information that is used during the test execution. This is used to validate if the software functions deliver the expected results. The same input is also used to check how the software performs when unexpected data is introduced.
The quality of test data affects testing completeness and coverage, which is why it is important to get relevant data sets. It works best to use production data, as these are actual entries. This can be a challenge because data found in production may not be readily available for testing due to reasons such as privacy and legal concerns. The violations of such may result in expensive lawsuits.
While businesses strive to steer clear from this kind of trouble, it is not advisable to use generic data either because this may not resemble the actual data that the application uses. It is important to use data that is as close to actual entries as possible. It is also crucial that testing data is designed in a manner that allows engineers to test the software for all possible scenarios. This is where test data management comes in.
Test data management addresses the need to generate the right amount, format, and quality of non-production data sets that resemble the actual input that the application uses for engineers to run valid system tests. It involves five key phases:
It is important to determine the test data manager, as well as data requirements, before everything else. The team determines what data is needed and how much of this is required. Test schedules and test refresh cycles are also finalized. In this phase, documentations are prepared and the list of tests is determined. The TDM team is formed and sign-off sheets are prepared.
This is where data is collected and/or consolidated. Testers analyze data requirements and decide on data backup, storage, security, and access.
Testers decide on data preparation, data distribution, communication plans, tools, data sources, test activities, and documents for the data plan in this stage.
At this phase, the plans are executed. If applicable, data masking of confidential information is done in this stage. Examples of sensitive data are credit card information, SSNs, names, addresses, and phone numbers. Data backup is executed and logs updated in this stage.
The repository needs to be maintained to allow for change requests, fixes, and data refresh. Proper maintenance helps keep test data from becoming obsolete; through data refresh and maintenance, they remain correct and consistent.
In this stage, testers review ongoing projects and identify gaps. Data backup and data modification are also done in this phase.
The Benefits of Using Test Data Management Tool
Because Test Data Management is aimed at replicating actual data, it helps companies develop high-quality software that performs as expected. In the process, properly planned, designed, and retrieved data sets help prevent bugs in the app or system and pave the way for a more efficient deployment process. Due to this clear focus on ensuring test integrity, the company can reduce security risks. Here are the other benefits of a systematic test data management process:
- TDM makes test data refresh possible. When test data is readily available, testers and developers are able to meet their testing timelines.
- This allows the team to focus on testing the application instead of spending too much time preparing data and performing reworks.
- TDM generates right-sized subsets apropos to the type of test
- Test data sets are made reusable.
- Automation is made possible through the creation of highly stable and predictable data sets
- Testers are able to replicate test data with the needed accuracy
- Data masking reduces compliance risks, which is relevant for QA teams and compliance officers alike
- TDM reduces the number of false defects, errors, and data corruptions.
- There is a reduced dependency on upstream systems
- This expands test data coverage
- The use of a central repository allows testers to provision data for varied testing types.
- TDM makes complete data traceability possible
- Infrastructure costs are minimized because TDM reduces test data storage space
- Prioritization and allocation of test data is made possible
- Reports and dashboards are generated with relative ease
- Some TDM tools allow faster scenario identification that leads to the creation of the appropriate data sets
Overcoming TDM Challenges
There may be challenges that testers and developers need to prepare for, and here are some of them:
- Setting up and managing data may take time
- There are costs involved, such as labor and hardware
- Storage space is required for the test data
- Managing private or sensitive information
- The risk of possible data loss
Among the measures they can implement to help mitigate the impact of these challenges are having proper communication and connectivity among relevant parties; properly defining data requirements; subsetting; data masking; and ensuring backup data.
In the long run, Test Data Management helps software companies reduce testing time and costs while ensuring quality. These companies can leverage test data management implementation to help the testing team have a faster turnaround time, reduce errors and defects, and achieve overall optimization.
Experts are anticipating further refinement on test data management. They see potential in merging data management with service virtualization. The merger can further accelerate the existing testing processes and further optimize the whole process.