Accelerate Software Delivery Cycles Using 7 Test Data Management Best Practices

Tally Netzer

Tally Netzer

Product Marketing Manager, K2View

Adherence to test data management best practices makes software delivery cycles faster and more agile.

Table of Contents


Speeding Up Software Delivery Cycles
1. Start with the Test Data Strategy
2. Discovering Test Data
3. Protecting Private Data
4. Refreshing Test Data in Real Time
5. Ensuring Test Data Relevancy
6. Maintaining Test Data
7. Automating the Test Data Management Process

Speeding Up Software Delivery Cycles


As the need for relevant production-grade test data grows, enterprises operating in agile environments are spending too much time on accurate test data provisioning – leading to inefficiencies and slowdowns in software delivery.

That’s why it’s important to establish test data management best practices. The following 7 guidelines bring much-needed structure to your test data management process and ensure that all the key components are considered.

1. Starting with the Test Data Strategy

An effective, agile, and comprehensive test data management program must start with the strategy. You must gain an understanding of your test data landscape, and the different teams across the organization that will use the test data and contribute to it. Your plan should include test data needs, testing environments, your company’s data governance policies and relevant regulations that impact data handling. Starting with the test data management strategy will save time, overhead costs, and rework.

2. Discovering Test Data

The first test data management best practice is to discover, and integrate, test data from multiple source systems and IT environments, across the organization. To achieve this, enterprises should identify all the relevant data channels and sources early on in the process. This includes discovering and categorizing all sensitive data and personally identifying information (PII) according to multiple data protection regulations (e.g., GDPR and CCPA), and industry legislation (e.g., HIPPA and PCI).
image2

Privacy compliance regulations require test data masking.

3. Protecting Private Data


Today, sensitive data and Personally Identifying Information (PII) is a touchy topic as people and authorities become more aware of the dangers of collecting and using people’s private information. Test data management must follow specific compliance rules, like GDPR or CCPA, that demand high data governance standards.

When sensitive data is involved, unified data masking tools keep it protected. By masking production data in a way that mimics real-life data – without exposing the real data – we guarantee both authenticity and compliance. Another security aspect to consider is how the test data is stored and managed. Keeping test data accessible only to authorized personnel, and maintaining security protocols, even for apps under development, are essential.

4. Refreshing Test Data in Real Time

Perhaps the most important factor in test data is keeping it fresh. Due to the sheer volume of enterprise data, many enterprises refresh their test data only periodically, such as once a quarter. Since extracting and provisioning test data is time-consuming, testing teams often reuse old data, over and over again. To maintain the relevance and trustworthiness of test data, a real-time synchronization mechanism is needed that does not require bulk database copying. Another important factor is ensuring that production system performance is not adversely impacted by frequent access.

5. Ensuring Test Data Relevancy

Time isn’t the only factor impacting the relevancy of test data. Testing quality relies on the ability of the testing teams to source relevant test data to the use case at hand. Due to the complexity of this task, many test data management tools discourage testers from parameter-based subsetting, especially across multiple source systems. Testing teams should examine which data elements are necessary for their particular scenarios, and build the test data subsets accordingly. Not only will the test data sets be more relevant and focused, but they will also improve test data quality and accelerate software delivery.


image1

Test data maintenance means constantly examining the integrity, quality, and security of your test data.

6. Maintaining Test Data

Keeping your data fresh and relevant over time leads us to the next test data management best practice, which is ongoing maintenance. In addition to relevancy and accuracy, your team would need to ensure that the data is adequately stored and remains consistent and error-free. This level of accuracy should be maintained over multiple use cases and even as the volume of data increases. Test data management at scale is challenging, so this is one area where your test data tools will have to prove their value. Monitor the cost efficiency of your test data storage solution, and perform regular audits to examine the integrity, quality, and security of your test data.

7. Automating the Test Data Management Process

By now, you’re probably concerned about the many tedious tasks related to test data management. Worry not, because many test data steps can, and should, be automated. Automation makes test data provisioning faster, and helps minimize human errors. Agile software development and shift-left testing demand test data automation for integration into CI/CD pipelines. Best practices for data testing have become increasingly automated over the past years. It’s about time, test data management did the same.

Test data management best practices are only as good as the tools you use. Take ours out for a free test drive!