Test Data Management Best Practices

To improve software quality and ensure a cost-efficient process for testing, businesses need to follow test data management best practices. These include.

Provisioning the right data for each type of test. For example, using a copy of a full production database to run unit tests could lead to inaccurate results. Instead, use synthetic or cloned data for such tests.

Reusability

A reusability approach to test data management allows companies to save time and resources. It also improves the quality of the testing process. However, it requires a high level of planning and scheduling, which can be difficult to manage. It is important to choose a test data management technique that fits your business model.

Modern DevOps teams require high-quality test data based on real production data for software testing early in the SDLC. However, sourcing and masking this data is expensive. Test data is a critical component of reducing product defects and improving application performance, quality, and stability.

To ensure that your test data is valid and realistic, it must be available, timely, and scalable. It should also be consistent and relevant to the testing environment. Moreover, it should be backed up and stored in a repository that can be retrieved for regression tests. This way, obsolete data can be easily eliminated and it will be easier to find out which change caused the regression.

Timeliness

For software testing to be effective, the test data has to be refreshed regularly. However, this can be costly and time-consuming. Moreover, it requires coordination among different workgroups within an organization to create and refresh the data.

Another issue is that the data sets have to be realistic. They should resemble real production data as closely as possible regarding quantity, format, and other factors. However, some techniques that obtain test data, such as production cloning, can expose confidential aspects of the business and may violate privacy regulations.

As such, CIOs and CISOs need to consider how they can apply advanced security controls (such as masking and fine-grained access management) at the source of the data during test data provisioning to meet compliance and data privacy requirements. This can reduce the friction between AppDev and software test teams while enabling them to deliver high-quality applications. This will help businesses accelerate their shift left testing efforts and ensure that software is delivered faster.

Accuracy

Accuracy refers to whether the test data adhere to the format and values that are expected. This can be determined by looking at the data itself or by checking for errors that may occur during cloning, masking, or generation. It is also important to check if the data contains duplicates and whether it has been modified in any way.

A company like Hapag-Lloyd, a global container shipping firm, relies on real customer data to test new applications. This makes it essential for them to have accurate, timely, and realistic test data. However, sourcing the right data can be a challenge because it is often sensitive in nature or not suitable for testing business scenarios.

To address these challenges, organizations should employ simple test data management techniques to ensure accurate and reusable test data. This will help them reduce the time spent on sourcing, creating, and provisioning test data and improve their testing efficiencies. This will allow them to save money and deliver quality software faster.

Compliance

Managing test data to meet the quality requirements of software testing is challenging. AppDev and software test teams need fast, realistic, and secure data to speed up application development. However, they face constraints due to limited or inaccurate data, security regulations, and the time it takes to move data into environments for testing.

To address these challenges, a comprehensive test data management solution should enable self-service for the provisioning of virtual test data on demand. Moreover, it should support features like bookmarking, versioning, and sharing without requiring data administrators or IT operations.

To ensure high test coverage, test data should be derived from a variety of sources. This includes leveraging modeling and subsetting to create rich test data. It should also include cloning, synthetic generation, and on-demand data provisioning to satisfy the needs of modern DevOps and Agile business management testing processes. Additionally, it should offer data masking and format-preserving obfuscation to meet compliance requirements for sensitive information.