Our organization is debating quality-mgmt approaches in Azure DevOps 2025. We’re split between investing in custom KQL-dashboards using Azure Analytics or standardizing on built-in Test Analytics widgets. Currently running both in parallel across 12 teams with mixed results.
Custom KQL gives us flexibility for quality-kpis that matter to leadership (defect escape rates, test debt ratios, automation ROI), but requires significant dashboard maintenance and KQL expertise. Built-in Test Analytics provides immediate value but limited customization for our specific quality metrics. We’re also seeing performance-costs differences - KQL queries against large datasets can be slow during peak hours.
Curious about others’ experiences with metrics-strategy at scale. Did you commit to one approach or maintain hybrid solutions? How did template-sharing work across teams if you went the KQL route?
We started with Test Analytics but quickly hit limitations for cross-project quality-kpis. Custom KQL-dashboards gave us the flexibility to correlate test results with work item data and deployment frequency. The learning curve was steep initially, but template-sharing through our internal wiki helped teams adopt standard queries. Performance-costs are manageable if you optimize queries with proper time filters and aggregations.
Nina’s hybrid model resonates with our current thinking. Carlos raises a valid concern about maintenance overhead. For those running KQL at scale, how do you handle schema changes? Do you have automated validation to catch broken queries before teams notice?
We version our KQL queries in Git and run automated validation in a pipeline weekly. Queries are tested against a subset of data and any failures trigger alerts to the dashboard owner. This catches schema changes before they impact users. We also maintain a query library with documented patterns for common metrics, which reduces duplication and makes updates easier when test structures change.
Interesting timing - we just abandoned custom KQL after 8 months. The maintenance burden was too high as test structures evolved. Every schema change broke multiple dashboards across teams. Built-in Test Analytics lacks some features but the automatic updates and zero maintenance made it more sustainable for our 20-team organization. Team-adoption was also much higher with the native widgets since no KQL training was required.
We run a hybrid approach successfully. Test Analytics for standard test pass rates and trend analysis that every team needs. Custom KQL-dashboards for executive quality-kpis that require cross-project aggregation and custom calculations. This balances team-adoption (everyone uses Test Analytics) with leadership reporting needs (specialized KQL dashboards maintained by central team). Template-sharing works well when you version control dashboard definitions in a shared repo.