Platform engineering metrics: What to measure and what to ignore
Datadog | The Monitor blog

Platform engineering metrics: What to measure and what to ignore


Summary

This article emphasizes the importance of quantifying the value of platform engineering initiatives through a tiered metric system. It advocates organizing metrics into outcomes (high-level impact like deployment frequency), drivers (system-level signals impacting outcomes, like CI overhead), and diagnostics (root cause analysis). By tracking these interconnected metrics – particularly DORA metrics alongside developer perception and platform adoption – platform teams can demonstrate ROI, secure funding, and ultimately prove the value of their work to the organization.
Read the Original Article

This article originally appeared on Datadog | The Monitor blog.

Read Full Article on Original Site

Popular from Datadog | The Monitor blog

1
Datadog LLM Observability natively supports OpenTelemetry GenAI Semantic Conventions
2
Introducing Bits AI Dev Agent for Code Security
Introducing Bits AI Dev Agent for Code Security

Datadog | The Monitor blog Mar 26, 2026 77 views

3
Monitoring MongoDB performance metrics (MMAP)
Monitoring MongoDB performance metrics (MMAP)

Datadog | The Monitor blog May 25, 2016 70 views

4
Understand session replays faster with AI summaries and smart chapters
Understand session replays faster with AI summaries and smart chapters

Datadog | The Monitor blog Apr 2, 2026 69 views