We’ve discussed the importance of test data management before, but a solid approach in the field doesn’t just happen. If you want to ensure that high-quality data is within your reach, on-demand, it takes careful strategizing. You must build the proper process while taking into account the organization’s unique characteristics and needs. Still, companies can follow these particular steps to achieve the right test data management strategy and yield top test data sources that create better products.
Table of Contents
Step 1: Define
Test teams must determine clear criteria upon which the test data collection procedure will be based. These define the data subsets that should be used in testing the use cases, including specific business entities, the volume of data required for testing, its creation date, and more. Teams can make use of an automated data catalog to inventory and classify test data assets, and visually map information supply chains that includes various scenarios. While these criteria must be granular, they should also leave room for modification and updates moving forward.
Step 2: Extract
Now that we’ve established which test data we need, it’s time to extract it from the organization’s production systems. When the required data is dispersed across many different systems and data sources, a test data management tool – that can integrate with the production systems, and extract test data according to predefined rules – can come in really handy.
A TDM system should be adaptable, easy to sync, and capable of refreshing data granularly for each component.
Step 3: Refresh and Sync
Testing is an iterative process. When bugs are discovered and fixed, testing should be repeated to ensure quality. A test data management strategy should provide for the means to quickly roll back the test data that was previously used – by the specific tester, for the specific use case – without impacting the test data currently being used for other tests. Companies need a test data management system that is adaptable, easy to sync, and capable of refreshing data granularly for each component – while maintaining complete control.
Step 4: Mask
We can’t discuss a test data management strategy without mentioning privacy and security. When dealing with production data, the challenge is to ensure data privacy, while maintaining the data’s integrity and keeping it secure. It’s critical to meet privacy compliance regulations and protect the data from breaches. Centralizing the test data from multiple sources into a test data warehouse, masking it, and securing it along the way, creates a simple and efficient process for meeting data compliance and security constraints.
Generate synthetic data based on real production data, while maintaining referential integrity across all systems.
Step 5: Synthesize
When test teams can’t extract a sufficient volume of test data from production, they need a data synthesizing solution for artificial data creation. A test data management strategy should include the means to generate synthetic data based on real production data, while maintaining referential integrity of the data across all the systems.
Step 6: Provision
After acquiring the necessary test data, generating missing data, and masking it as required, it’s time to move it to the target test environments. Test data management solutions should offer a fast and seamless path from multiple systems to multiple environments. Companies should be able to upload, adjust, and remove data scenarios and business entities at any stage throughout the process.
One System for All Your Test Data Management Strategy Needs
K2View Test Data Management tools support each of test data management strategy steps described above. They empower and support developers, testers, and DevOps teams to accelerate software delivery, while improving the quality of the end product. Take it for a test drive and see for yourself.