Common Problems of Test Data Management
Test data holds significant importance in the software development process. It is a mandatory requirement for ensuring the quality of a system or software. Data managers often struggle with the availability of quality test data and the management of a huge amount of test data.
The availability of test data is not an issue. However, managing an enormous amount of data is challenging. Data managers are prone to make some mistakes that can affect a system’s performance and efficiency. In this article, we have discussed common problems of test data management. We believe that if problems are identified clearly, they can be addressed effectively, resulting in smooth operations.
Because of that, it is important to have workflow management software to boost team productivity.
Test data is important, but ensuring the quality of this data is more important. At times, the available data does not suffice the testing needs. To make it compatible with the testing data, data managers can make a few changes on their own. However, this can compromise the data quality because it can trigger a change across the system, and can make the whole data set stale. It can affect the whole system, putting an extra load on the required resources, and delaying the delivery of the project.
2. Data Change can Compromise Data Integrity
Data subsets are used to utilize storage space more effectively. This practice improves the overall execution speed. However, a small glitch or mistake can lead to issues such as data integrity. Data change at one instance affects its occurrence in other instances. The referential data cannot be called and fetched which leads to performance errors.
3. Difficulty to perform testing within the Given Time
At times, software testing teams are allowed to use the dataset copy at the data owner’s allocated time only. This can lead to consistency issues for the tester, particularly if he needs real-time data. The time constraint or limitations can trigger a problem with far-reaching impacts. Therefore, it becomes important to have rule-sets and time logic so that consistency issues could be avoided.
4. Inconsistent Synthetic Test Data
Due to GDPR and data privacy regulations, more and more companies are adopting synthetic test data. Not only does it ensure that data being generated is quality data, but also makes sure that no regulation or directive is being violated at any point.
The major challenge with synthetic test data is that there could be data integrity issues. To produce complete, format compliant, and quality data, data managers rely on synthetic data to generate missing values. This task requires expertise and caution because missing entries must be filled out while ensuring integrity between the generated data and the subset data.
5. Data Masking can lead to Speed Issues
One way of ensuring data privacy and protection is to mask data. However, it leads to speed issues especially if you need to use data for testing purposes. Testers are always in dilemma to choose between speed and quality. The distribution of masked data is considered a major problem in testing. The masking rules have to be adhered to for ensuring data protection. These rules are normally different than sub-setting rules.
6. Inability to provide a Compatible environment
An incompatible environment fails to fetch the data at the right time. Due to stream overload, testing can be delayed or compromised; requiring it to be performed again. Hence, testing should only be performed when the compatible environment is ready or available.
7. Inability to trace a problem
Some defects can slow down the deployment phase of a system or software. If an issue arises in the system, developers cannot trace it back or reproduce it. Hence a critical path cannot be tested because it is under examination. If you try to trace an issue, it will hold the whole dataset hostage.
8. Testing with the wrong data
All the above-mentioned problems are secondary compared to testing a system with the wrong data. If you have to synchronize subset or mask the data, the testing cost will surpass the overall cost of the project. You would need more resources to perform the testing.
Testing software is one heck of a task. There are many requirements before and during the testing process which can make testing a complicated job. The enormous amount of test data required for effective and successful testing is a challenging task. During the process of testing, data managers are prone to make a few mistakes. Avoiding this testing phase can be made effective as well as smooth.