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Standard : Product and engineering decisions are backed by live data

Purpose and Strategic Importance

This standard ensures that product and engineering decisions are grounded in live, reliable data-enabling teams to prioritise effectively, validate assumptions, and adapt with confidence.

Aligned to our "Data-Driven Decision-Making" policy, this standard strengthens accountability, improves responsiveness, and reduces risk. Without it, teams operate on guesswork, slowing progress and increasing the chance of misaligned outcomes.

Strategic Impact

Clearly defined impacts of meeting this standard include improved delivery flow, reduced risk, higher system resilience, and better alignment to business needs. Over time, teams will see reduced rework, faster time to value, and stronger system integrity.

Risks of Not Having This Standard

  • Reduced ability to respond to change or failure
  • Accumulation of technical debt or friction
  • Poor developer experience and morale
  • Decreased confidence in releases and features
  • Misalignment between technical implementation and business priorities

CMMI Maturity Model

  • Level 1 – Initial: Decisions are made based on intuition or isolated data points.

  • Level 2 – Managed: Teams use some data to support decisions, but access and consistency vary.

  • Level 3 – Defined: Standardised metrics and data sources guide decisions across teams.

  • Level 4 – Quantitatively Managed: Teams analyse data trends and adjust priorities and plans accordingly.

  • Level 5 – Optimising: Data insights continuously inform strategic decision-making at all levels.Teams consistently use data from real systems and usage to inform priorities, validate assumptions, and guide improvements.


Key Measures

  • Adoption metrics relevant to the standard (to be defined)
  • Quality, throughput, and system health metrics aligned to capability
  • Maturity scores based on structured assessment
Associated Policies
  • Data-Driven Decision-Making
  • Decentralised Decision-Making
Associated Practices
  • Code Reviews & Pull Requests
  • Compliance-as-Code
  • Data Mesh
  • Dependency Management Policies
  • Event Sourcing
  • Evolutionary Architecture
  • Secure Code Training
  • Security as Code
  • Serverless Architecture
  • Operational KPIs for Dev Teams
  • Service Mesh Implementation
  • Custom Metrics Instrumentation
  • End-user Experience Monitoring
  • Live Dashboards
  • Log Correlation for RCA
  • On-Call Rotation Health Checks
  • Runbooks and Playbooks
  • Synthetic Monitoring
  • User Session Replay Tools
  • Application Performance Monitoring (APM)
  • Customer Feedback in Dev Loops
  • Distributed Tracing
  • Feedback Loops from Ops to Dev
  • Real-time Event Streaming
  • Real-Time Logging
  • Container Security Scanning
  • Data Encryption-in-Transit & at-Rest
  • Secure API Gateways
  • Threat Intelligence Feeds
  • Threat Modelling Workshops
  • Vulnerability Management Dashboards
  • Load & Performance Testing
  • Shadow Testing in Production
  • Hypothesis-Driven Development
  • Impact Mapping
  • Mobile-First Design
  • Observability-Driven Design
  • Strategy & Outcome Mapping
  • Sprint Demos for Stakeholders
Associated Measures
  • Feature Usage Rate
  • ROI of Engineering Investments

Technical debt is like junk food - easy now, painful later.

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