Standard : Continuous Value Flow is embedded in design and operations

Purpose and Strategic Importance

Continuous Value Flow means every change - big or small - moves through an automated pipeline from merge to production in a consistent, observable, low-risk fashion. Embedding this outcome ensures teams deliver user value faster, recover from failures more quickly, and maintain a high bar on quality.

Outcome Statement

“All code changes are built, tested, packaged, deployed, and verified in production with zero-touch pipelines and safety controls, achieving a lead time for changes under 24 hours and a change failure rate below 5%.”

Strategic Impact

  • Shorter Lead Times: Teams ship features and fixes in hours, not weeks.
  • Higher Confidence: Built-in quality gates (tests, linting, security scans) block bad code automatically.
  • Resilience by Design: Observability and automated rollbacks catch and correct production issues immediately.
  • Scalable Delivery: Repeatable, self-service pipelines empower any team to deploy on demand.

Risks of Not Achieving This Outcome

  • Long Release Cycles: Manual handoffs and delays erode agility.
  • Inconsistent Quality: Without automated gates, defects slip into production.
  • Slower Recovery: Lack of observability and rollback tooling extends outage times.
  • Team Bottlenecks: Release and ops teams become chokepoints.

CMMI-Style Maturity Model

  • Level 1 – Initial: Builds and deployments are manual; releases take days/weeks.
  • Level 2 – Managed: Basic CI runs on commit; deployments still manual or infrequent.
  • Level 3 – Defined: Fully automated CI/CD pipelines exist; deployments are self-service.
  • Level 4 – Quantitatively Managed: Lead time, failure rate, and rollback metrics are tracked.
  • Level 5 – Optimising: Anomaly detection and auto-remediation refine the flow; teams experiment with progressive delivery.

Key Measures

  • Lead Time for Changes: Time from commit to successful production release.
  • Change Failure Rate: Percentage of releases that trigger rollbacks or hotfixes.
  • Deployment Frequency: How often production deployments occur.
  • Mean Time to Recovery (MTTR): Time to restore service after a failed deployment.
  • Pipeline Health: % of pipeline runs that complete end-to-end without manual intervention.