Test data must be valid, timely, and accurate. It should closely mimic real data regarding quantities, formats, etc. It should also be compliant-since some techniques to obtain test data rely on production systems, organizations must not disobey privacy regulations.

Modern DevOps TDM tools should enable teams to extract, create, and provision intelligently sized database subsets for testing purposes that retain referential integrity. They should also support obfuscation/masking of sensitive information to ensure compliance and avoid costly data breaches.

Validation

Test data management focuses on ensuring the right quantity, quality, and format of test data is provided to testing teams, in the correct environment, at the appropriate time. This can be accomplished through synthetic data creation or extraction and masking of real production data.

Providing a complete subset of relevant data for testing purposes is critical to achieving high test coverage and verifying business scenarios. It is also necessary for ensuring compliance and avoiding penalties for regulatory non-compliance.

Sourcing and provisioning test data that is reliable, consistent, and ready for testing can be difficult. Attempting to copy production data for testing introduces unnecessary delays and costs while masking or hashing can introduce security risks. A better solution is to use a test data management solution that enables you to generate smartly sized subsets of data and apply format-preserving masking. This approach eliminates the need to clone data and provides a much faster, more efficient way to provide test data for manual and automated testing.

Reusability

Creating a reusable pool of data to test against reduces the time needed for new testing environments. Identifying reusable data sets, masking them, and provisioning them allows teams to work more efficiently while maintaining a higher standard of data quality and compliance.

Using realistic data to test against means better and more accurate results for unit, integration, performance, and system tests. It also reduces costs through more efficient development and testing and improves the chances of catching bugs earlier in the process.

This is a key challenge for modern enterprises. With data fragmented across multiple enterprise systems, providing a consistent and complete subset of data for testing while ensuring referential integrity is difficult. Complicating this further, personal data must often be obscured during the testing process for compliance with privacy regulations. A robust test data management solution helps overcome these challenges and drives greater software delivery and organizational performance. The Solix Test Data Management solution automates the creation of intelligently sized data subsets (not clones) that save up to 80% on storage space while delivering a syntactically-correct copy of the production database to ensure the highest accuracy in test results.

Data Masking

Data masking is a critical element of test data management. It allows enterprises to provision test data that is valid, accurate, and up-to-date without compromising the security of real production data. This reduces the risk of mass data breaches and helps enterprises meet compliance regulations for PII, HIPAA, and GDPR.

There are several ways to mask production data for use in testing, including value variance, substitution, and shuffled data. Database administrators must review the information to be masked with input from other stakeholders in the process, such as software developers and testers, to ensure that masking techniques are appropriate, valid replacement values are used and referential integrity is maintained.

For example, if a customers name is to be replaced in the test data set with Jon Smith, it is important that all instances of Jon Smith are represented in the masked data set. Otherwise, the masked data set will not match the original production data, resulting in invalid tests.

Storage

The ability to store accurate test data is important for several reasons. It enables you to compare the results from different tests and highlight any errors that may have occurred. It also helps you to reduce the number of redundant copies that are created, which can save on storage space.

With GDPR looming, its even more important to make sure that the test data you use is accurate and up-to-date. This can be done by ensuring that the data is stored in the right environment and utilizing the correct formats. It can also be done by ensuring that the data is properly managed throughout the testing process.

Modern DevOps teams need real production data to create high-quality test environments, but a lack of TDM tooling can add friction for AppDev and Software Test teams as they strive to deliver flawless applications faster. Test data management tools can automate this process by combining elements of data subsetting, masking, synthetic generation, cloning, and on-demand provisioning to support your Agile Business Management testing needs.