Platform engineering metrics: What to measure and what to ignore

Platform engineering teams often struggle to demonstrate measurable value, with over 40% of initiatives failing to do so within the first year, leading to risks such as defunding or deprecation. To accurately calculate a platform's return on investment (ROI), teams need to differentiate between metrics that measure platform effectiveness and those used for investigations. Metrics are categorized into a three-tier hierarchy: outcome, driver, and diagnostic metrics, each serving a distinct purpose. Outcome metrics provide a high-level view of platform health and are crucial for confirming platform value, while driver metrics identify bottlenecks, and diagnostic metrics investigate root causes. The DORA framework is highlighted as a leading standard for measuring software delivery performance, influencing organizational outcomes and team well-being. Datadog offers automated tools for tracking these metrics, providing actionable insights and enhancing the security posture of platforms. Common pitfalls include optimizing driver metrics in isolation, promoting diagnostic metrics to KPIs, and retaining stale metrics, all of which can mislead and hinder decision-making. By adopting a structured approach to metric selection and evaluation, platform teams can better demonstrate the organizational value of their investments and make informed, strategic decisions.

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