Managing Test Data Is a Constant Struggle

Every DBA knows that a test platform is required to prop­erly enable application development. Indeed, testing has always been crucial, but it has become more complicated with the advent of new technologies, platforms, and devices, not to mention the impact of regulatory compliance.

One of the biggest challenges faced by DBAs and developers is managing test data. Test data manage­ment (TDM) refers to the process of creating, main­taining, and using test data to validate the functionality of an application. TDM is a critical aspect of the testing process, as it ensures that the application is tested thor­oughly under different scenarios and conditions.

One of the biggest difficulties that developers face is creating realistic test data that mimics the actual data the application will encounter in production. This is particularly important for appli­cations that handle sensitive or personal data. If the test data is not representative of the actual data, the test results may not accurately reflect the application’s behavior in production.

As applications handle larger and larger volumes of data, developers need to ensure that their test data can scale to match this volume. This can be challenging, as it may require large and complex test datasets that are difficult to manage.

This means that tools are required to help teams meet the challenges posed by the need for realistic data, potentially large amounts of it that scale, with all the complex relationships between the data elements considered, and with sensitive data being protected from prying eyes. These are indeed significant challenges!

How to Overcome These Challenges

Teams can overcome these challenges by using data profiling tools to analyze the actual data and generate test data that is repre­sentative of the production data. They can also use synthetic data generators to create realistic test data that matches the production data’s structure and characteristics. When production data exists, repurposing it for test usage is also a consideration, but there are caveats and precautions when choosing this approach.

As applications become more complex, the test data required to test them becomes larger and more intricate. This can make it difficult to manage the test data effectively, as it may be scattered across different systems, databases, and environments.

To get over this hurdle, developers can use data virtualiza­tion tools that create a virtualized view of the data, making it easier to manage and access. They can also use TDM tools that automate the process of creating, masking, and refreshing test data across different environments. This helps reduce the time and resources required for managing test data and ensures that developers have access to the right test data at the right time. Data privacy and secu­rity are major concerns for organizations handling sensitive or personal data. Developers need to ensure that the test data they use does not contain any sensitive or personally identifiable information that could compromise data privacy and security.

This is a particularly important consideration if produc­tion data is being repurposed for testing. Personally identifiable information and other protected classes of data cannot be freely shared, so steps must be taken to protect such data.

To overcome this challenge, developers can use data masking and anonymization techniques to remove sensitive or personal data from the test data. They can also use encryption and access controls to ensure that the test data is protected from unautho­rized access.

Testing can be a time-consuming and resource-intensive pro­cess, particularly when dealing with large and complex applica­tions. Developers need to ensure that they have access to the right test data at the right time to test the application effectively.


TDM is a critical aspect of the application development pro­cess. TDM tools can overcome challenges such as using realis­tic test data, managing large and complex test data, security and compliance issues, time and resource constraints, and data volume and scalability.