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Why Invest in Test Data Management?

In a world where digital channels deliver the lion’s share of customer experience, high-quality data ensures flawless software deployment. Investing in test data management reduces risk and saves time, money, and effort.

Adaptable test data management tools allow you to provision lifelike, trusted data systematically by the business entity and on demand. These capabilities help you meet your goals for shift-left testing, automation, and other software-testing processes.

Reusability

The reusability of Test Data Management increases the speed and accuracy of software testing. It reduces the time and cost associated with creating test data, enables better adherence to test scripting best practices, and maximizes test coverage. Reusing existing data also eliminates the need to create new test data for every use case, reducing the risk of error.

Unfortunately, reusing test data isn’t without challenges. It can be difficult to source quality data that’s not too large in quantity (reducing storage costs) and yet has enough data points for all types of testing. The process of masking data to comply with privacy regulations and creating synthetic test data adds another layer of friction.

The best test data management solutions minimize these friction points by leveraging automation to provide the right amount of high-quality test data in the right environment at the right time. They also provide a central repository for all test data to streamline the entire TDM process.

Test Data Management
Test Data Management

Data Masking

Data masking is a crucial part of the overall test data management process, and there are a variety of different techniques that can be used. However, there are a few major factors that should be taken into account before choosing a particular masking method. For example, it's important to consider the type of data that will be masked and how it should be masked. For example, if you want to mask social security numbers, it's best to use a technique that allows you to specify exactly how each number will be replaced.

Another consideration is how the data will be transferred between environments. For example, if you want to transfer data from production to testing, it's important to make sure that the data is masked at all times. Otherwise, sensitive data could leak out to third parties or the public.

Security

As organizations move toward DevOps and Agile software development processes, they need to ensure that the correct test data is available. Having the right data ensures optimal test coverage, faster time to market, and lower costs because of earlier bug detection. However, ensuring that the data sets are secure can be challenging.

For example, the data may contain personal or sensitive information (PII). These types of data must be carefully masked to protect them while still enabling testing scenarios. This is difficult to achieve without a complete TDM solution that includes out-of-the-box masking capabilities and automated data-centric protection.

The need for security is even more critical given the recent data privacy regulations such as GDPR. These rules have refocused attention on the need to protect data in all environments, including those used for testing.

Automation

The ability to automatically provision test data to environments reduces time and effort and enables testing teams to focus on finding bugs and improving the quality of the application. It also ensures that all environments have identical data, which enables developers to compare the results of multiple tests and identify trends and errors in the code.

The biggest challenge to data automation is the huge volume of test data that needs to be managed. This requires tools that can be integrated with production systems and extract data according to predefined rules. It also means masking sensitive data and storing it in the correct format for each environment, a complex process that often involves manual steps and can take more than a day to complete.

A modern test data management tool should integrate seamlessly with the DevOps pipeline to automate processes and deliver ephemeral test data for each new environment. This will help CIO and CISO teams administer security controls like data masking, fine-grained permissions, and audit logs in downstream environments.

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