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 the System, Not the Data
The whole idea behind a test data management strategy is to be able to provision any data, for any test case, without having to define the test data in the first place. So the only thing left for testing teams to determine is the test data management system to be used. Ideally, 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 on the Fly
The tester, or the automated test data management system, should be capable of requesting the data needed to perform a given test, in flight, without any preparation. When the required data is fragmented and 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.The ability to unify the test data from multiple sources, anonymize or de-anonymize it, as required, and secure it every step of 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 both generate and manufacture 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 toolssupport each of test data management strategy steps described above, with their unique ability to:
1. Create dynamic testing environments, on-the-fly, as part of testing automation
2. Enable unstructured data, such as voice, images, documents, etc.
3. Use any environment both as source and target
4. Go back in time, and “fix” faulty test data
5. Support hybrid (on-premise/cloud) testing environments
6. Move test data between data centers and the cloud
In short, they empower and support developers, testers, and DevOps teams, to accelerate software delivery, while improving the quality of the end product. Go for a test drive and see for yourself.