Anti-patterns for test environment management

Low test data coverage: Using a limited set of test data that does not cover all known scenarios, leading to testing gaps and unexpected issues. Effective test data management should include the creation of a comprehensive and diverse set of test data that covers all possible scenarios. Reduce the likelihood of low data coverage occurring by being proactive about test data requirements at the start of the development lifecycle rather than creating and managing test data after a change has been made.

Insecure test data: Not having the proper policies and security measures in place when generating, storing, or using test data can lead to potential data breaches, inaccuracies, and inefficiencies in the test process. Test data management strategies should prioritize data security and governance practices to ensure that data is accurately and securely managed throughout the testing process. For example, using actual production data without proper obfuscation or sanitization in test environments has the potential of exposing sensitive information. Use automated governance capabilities to apply guardrails, set up secure access to test data, and automate the lifecycle of test data.

Centralized testing: Maintaining a traditional, centralized approach to quality assurance and testing can create bottlenecks, reduce agility, and inhibit teams from taking full ownership of their value stream. Instead, focus on providing stream-aligned teams with the tools, knowledge, and resources to self-manage their testing requirements. Distribute quality assurance functions by creating platform teams and enabling teams to support others by offering scalable testing services, tools, and guidance.