Our team is debating whether to stick with manual test data setup or invest in automated test data provisioning for our SAP PLM 2020 DevOps pipelines. We currently spend 3-4 hours manually creating test BOMs, parts, and documents before each deployment cycle. Automated script reliability is our main concern - we’ve had bad experiences with scripts that create inconsistent test data. Manual setup gives us flexibility to adjust test scenarios on the fly, but it’s obviously not scalable. What are others doing for test data management in PLM DevOps environments? Is automation worth the investment, or does manual setup provide better test quality and audit trail requirements?
Cleanup is where automation really pays off. Our scripts tag all test data with a unique identifier during creation. At the end of each test cycle, a cleanup script purges everything with that tag. Takes 5 minutes versus hours of manual deletion. We also maintain a separate test data database that tracks what was created, used, and deleted - perfect for audit reviews.
The audit angle is compelling. How do you handle test data cleanup between cycles? Does automation help there too, or does it create more orphaned data that needs manual cleanup?
We went full automation two years ago and haven’t looked back. Yes, initial script development took time, but the consistency is worth it. Every test cycle uses identical baseline data, which eliminates variables when troubleshooting deployment issues. Manual setup introduces human error - one typo in a part number breaks your entire test suite.