Which engineering metrics actually drive outcomes?

Engineering leaders face challenges in effectively using metrics to drive improvement due to the context-specific nature of different teams' work, which makes a one-size-fits-all approach like DORA metrics problematic. While DORA metrics such as deployment frequency, lead time, MTTR, change failure rate, and rework rate can quantify software delivery performance, applying them uniformly across diverse teams can lead to inaccurate assessments. Infrastructure teams may naturally have lower deployment frequencies compared to feature teams, yet the same metrics might unfairly categorize them as underperforming. The key is to integrate metrics with context, linking them to specific services, teams, dependencies, and incidents to understand not just what is happening, but why. By connecting metrics to the broader software development lifecycle (SDLC) within a unified platform, organizations can automate workflows that close the gap between insight and action, such as notifying owners or creating tickets when issues arise. This more nuanced approach allows for tailored benchmarks that reflect each team's unique context and facilitates more effective decision-making and problem-solving across the organization.