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September 11, 2023

Use Cases for Test Data Management Tools

If you’re in the market for a test data management tool, you probably already know what you’d like it to do for your organization. However, many people don’t realize that test data management tools feature many different use cases, and that businesses utilize these tools in very different ways. While your business doesn’t have to use test data management tools in every way we outline here, it’s important to know your options to optimize your use to get the best value for your money. Note that this article isn’t about how you should use your test data but how to leverage your test data management tools for the greatest effect.

Improve Security and Privacy

With the constantly increasing number of data security and privacy laws worldwide, it’s more important than ever to ensure that your test data generation methods comply with relevant legislation. That could mean dealing with a general data privacy and security law like the CCPA or GDPR, or it could be an industry-specific law like HIPAA or SOX.  A simple way to approach this problem is data masking, which obscures sensitive information like names, addresses, payment information and more from those running the tests. However, sometimes you need personal information to run a test. Many test data management tools offer anonymization technology that replaces personal information with fake data that preserves the original data’s characteristics for testing purposes. Anonymization helps protect your user’s data and allows expanded use of the test data under many privacy laws.

Increase Testing Speed and Lower Cost

Test data management tools can speed up the testing process. Without a test data management tool, a simple way to get test data is to make a copy of your production environment. If you take steps to protect user privacy, the test data set will be the same size as the original. You could take a subset, or smaller sample of the data, but if done improperly, the test data might not be representative of the whole or have issues with referential integrity. For example a subset of the data pulled from US databases only may be fast to create, but fail to create a comprehensive or accurate global picture.

Test data management tools can help simplify this process by identifying redundant data and procedurally creating datasets that maintain referential integrity and which are representative of the whole. These automatically created datasets are often significantly smaller than the original, and can have as much as a 90 percent reduction over the original dataset—sometimes even more. Tests run on these data sets will take proportionally less time, allowing for a faster development process or for your teams to run more tests and more kinds of tests in the same amount of time. And, since your test data sets will be significantly smaller, your data storage costs will be, too.

Allow for Self-Service

Before implementing a test data management tool, every test data set your organization creates is a fully-custom data set. Doing this process well can take time when done manually, and it could mean that your team turns down some requests for test data. Test data management tools can help organizations identify common test data needs and create a self-service portal so that development teams can get much of the test data they need independently. More advanced self-service can allow development teams to generate test data on demand, ensuring that the test data is as up to date and accurate as possible for common tests.

Generate Missing or Synthetic Data

In some cases, companies may not want to include the references in a data set. This could be because the information referenced is extra sensitive, or because including the references would dramatically increase the size of the data set. Some test data management tools can solve this problem by generating plausible but fake test data to flesh out the references. They provide synthetic data, which is entirely fake, but preserves the underlying statistical relationships in the data, making it effective for testing and advanced analysis processes. As a bonus, synthetic test data contains no personally identifiable information, which reduces the damage done in a leak or breach, and makes it exempt from most data security and privacy laws.

Improve Testing Outcomes

Testing before release aims to catch bugs before they go live and become far more expensive to fix. Because of that goal, it’s important to run all the different types of data that is used for testing (positive path, negative path, null, erroneous, and boundary conditions data) through the system being tested to validate whether it can cope with every realistic scenario it might face. Generating positive path data from pre-existing data sources is straightforward, but getting the other types of data can be a challenge for your team. Test data management tools can help your data managers generate the negative path, null, and boundary conditions data more comprehensively and more quickly, leading to more effective testing that catches more issues during development.

How Mage Helps with Test Data Management

Ultimately, not every tool will cover all of these use cases for test data management. The best move for most businesses is to find the tool that best suits their needs. Mage’s test data management solution is designed to give businesses the capabilities they need at scale. With multiple options for rapidly provisioning anonymized test data, a platform that can scale with your business, and some of the industry’s most advanced privacy and security options, it’s a leading option for businesses who want to get the most out of their test data management solution. Schedule a demo today to learn more about what Mage can do to help your business maximize its use of test data.