What is Test Data Management? Why Should You Focus on It?
What Is Test Data Management?
Test data management is the process of organizing, creating, storing, and managing data required in software quality testing methods. It grants the quality testing team control over the information, documents, and guidelines produced throughout the testing life cycle.
Every company uses data sheets to preserve test data so that management teams may utilise them to run tests that will be useful for future reference.
Common Types of Test Data
There is no single technology that satisfies all TDM needs. Instead, teams must create an integrated solution that offers all the data types necessary to satisfy a wide range of testing requirements.
A good TDM method should offer the right types of test data after the requirements have been determined while considering the advantages and disadvantages.
- Production data
It is the most thorough test coverage but typically comes at the sacrifice of speed and storage costs. Sensitive data may potentially be exposed for some applications.
- Subsets of production data
This is more manageable than entire copies of production data. This process can offer some hardware and license cost reductions, but it might be challenging to get enough test coverage.
- Masked production data
Development teams can use actual data without increasing unacceptable levels of risk by using either complete sets or subsets of production data. However, extra storage and personnel are needed for masking to guarantee referential integrity following data transformation.
- Synthetic data
It eliminates security concerns, but a significant space is needed for these. To test new features, synthetic data may be necessary. However, this only applies to a tiny portion of test cases.
The process of manually preparing test data is prone to human error. It necessitates a profound comprehension of data linkages inherent in the data as well as those found in the database or file system structure.
Use the right set of release management tools to ensure optimal quality of the software and fast release cycles.
Benefits of Test Data Management
Customer Satisfaction
The most significant benefits of the TDM approach are the excellent data quality and broad data coverage that inevitably leads to customer satisfaction. Bugs can be found early when data quality is good during the testing process.
Consequently, there aren’t many manufacturing faults, and the resulting application is steady and of good quality. A consumer’s faith in the company rises as a result of these advantages.
Efficient Data Management
A TDM process becomes efficient because all test data is handled in a single area. The same data set may be used to provide data for many testing types, including functional, integration, and performance testing.
Businesses can prevent the storage of excessive numbers of test data copies by handling test data efficiently.
As a result, data administration becomes less complicated. TDM, along with the right release management tools, can bring wondrous results for IT organizations.
Cost Savings
When data sets can be reused, it lowers costs, which is one of the most useful features of TDM. A central space is used to save the reusable data for later usage.
The testers can use the archived data when the demand for reusable data materializes. Increased test data coverage and traceability help in the early discovery of errors and lowers the cost of production maintenance.
Data Security
In most countries, companies must adhere to the government’s regulations and compliance guidelines when it comes to user data.
Data security and safety are given great consideration in a TDM process, and data masking is also an essential component of it.
Fewer Copies of Saved Data Sets
The same production data might be duplicated for usage by different teams within a project. Due to duplicate copies of the same data, storage space gets wasted. Because all teams use the same repository when a TDM is used, the storage capacity is carefully handled.
Data Regulation
Understanding data by using TDM is beneficial for the entire business, not just for the test team. It improves revenue by utilising high-quality data and reduces the possibility of security breaches.
Data regulation is increasingly important as a result of data privacy legislation. TDM helps businesses comply with laws through compliance analysis techniques.
Test Data Management Strategies
Data Analysis
A system testing team must determine the end-to-end test scenario before the test data can be created. The application of one or more programs may be necessary as a result.
For instance, the management controller application and the database applications must all cooperate in a system. In order to accomplish a successful TDM method, a thorough analysis of all available data must be conducted.
Identifying Sensitive Data
In order to test apps effectively, a sizable amount of sensitive data is frequently needed. For instance, a cloud-based testing environment is very useful since it enables the testing of numerous data sets at once but guaranteeing user privacy in the cloud is a cause for concern.
Therefore, we must determine the approach to hide sensitive data, especially when you need to replicate the user environment.
Test Data Clean-up
Based on the needs of the current testing cycle, it may be necessary to update the test data. Although the old test data is not relevant now, it could be required in the future.
Consequently, it is essential to establish a clear procedure for figuring out when test data requires permanent cleaning up.
Automation
Similar to how you use automation to execute repetitive tests with multiple data types, it is possible to automate test data generation.
This would help reveal any data issues that could emerge during testing. You may achieve this result by contrasting the outcomes from the multiple test runs.
Incorporating release management tools can help you automate the entire release management process leading to better efficiency.
Necessary Test Data Management Practices
Result comparisons
In order for enterprises to swiftly spot issues that could otherwise go unnoticed, organizations should use an automated mechanism for comparing baseline test data versus findings.
Requirement clarification
Organizations should determine their needs for test data based on the test scenarios to minimize the work required to develop test data. Companies shouldn’t attempt to produce synthetic data for a test if merely eliminating sensitive features of the data is adequate for it.
Masking sensitive data
Before sending data to the testing stage, organizations should identify sensitive customer and staff data. They should select the best de-identifying approach after comprehending these sensitive data sets.
Subsetting
Realistic test databases are created using this method that is vast enough to represent the variety of production data correctly and small enough to facilitate quick test runs.
Wrapping Up
It takes a lot of work to organize, handle, and customize any raw data because it cannot be utilized for testing purposes. The TDM team creates this test data, but they may not have access to the production data directly.
