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Success Metrics

An effective IDP should demonstrate:

Why Metrics Matter (Business Case)​

  • Baseline and targets: Establish a pre-platform baseline to quantify improvements.
  • Investment justification: Tie platform outcomes to business KPIs (time-to-market, reliability, cost).
  • Prioritization: Use data to choose which capabilities deliver the highest impact next.
  • Accountability: Make value explicit across quarters to sustain funding and stakeholder confidence.

Developer Productivity​

  • Lead time: Time from code commit to production deployment
  • Deployment frequency: How often teams can safely deploy
  • Mean time to recovery: Speed of incident resolution
  • Change failure rate: Percentage of deployments causing issues

DORA Metrics and the Platform’s Role​

The four DORA metrics are the industry baseline for software delivery performance. Platforms materially influence each:

  • Deployment Frequency (DF): Self-service pipelines, golden paths, and safe-by-default templates increase DF.
  • Lead Time for Changes (LT): Standardized CI/CD, paved paths, and fast feedback reduce LT.
  • Change Failure Rate (CFR): Built-in testing, policy, and progressive delivery lower CFR.
  • Mean Time to Recovery (MTTR): Observability, rollbacks, and automated remediation accelerate recovery.
Avoid harmful legacy metrics

Deprecated, output-centric metrics often backfire:

  • Lines of code written, story points burned, ticket counts, or server CPU utilization targets.
  • These drive the wrong behaviors, mask systemic issues, and don’t correlate with flow or reliability. Prefer outcome metrics like DORA, user happiness, and business impact.

Platform Adoption​

  • Self-service usage: Percentage of tasks completed without platform team intervention
  • Developer satisfaction: Regular surveys and feedback collection
  • Platform reliability: Uptime and performance metrics
  • Time to onboard: How quickly new teams can become productive

Why adoption metrics matter​

  • Cross-team impact: Platform benefits land in product teams’ outcomes (e.g., DF, LT, CFR, MTTR), not just within the platform team. You must measure and attribute these improvements.
  • Behavioral change: Adoption shows that golden paths and paved roads are actually used, not just available.
  • Quality of experience: DevEx measures (survey scores, NPS, qualitative feedback) reveal friction sources the platform should remove.

What to track (examples)​

  • Adoption coverage: % of services on paved paths/templates; % using centralized CI/CD and shared runtime baselines.
  • Self-service rate: % of common ops tasks executed via portal/CLI/API without tickets.
  • Time-to-first-deploy: For new teams/services; onboarding throughput per month/quarter.
  • Golden-path conformance: Policy-as-code pass rates; drift over time.
  • Developer happiness: Quarterly DevEx/NPS surveys, task success rate in usability tests.
  • Support signals: Ticket volume per engineer, mean time-to-answer, and deflection via docs.

Measurement Practices (Making It Work)​

  • Automate collection: Pull from VCS, CI/CD, incident systems, and observability tools. Avoid manual spreadsheets.
  • Segment results: Compare teams on and off the platform; run cohort analysis during rollouts.
  • Set realistic targets: Improve trends quarter-over-quarter; focus on directional movement over vanity numbers.
  • Share dashboards: Publish platform impact dashboards to leadership and product teams.
  • Close the loop: Use findings to prioritize platform backlog and validate ROI.