Use Data-Driven Decision-Making for Engineering Prioritisation

This standard mandates using data-driven decision-making for engineering prioritisation to prioritise work based on evidence, not assumptions.

1. Use Data-Driven Decision-Making for Engineering Prioritisation:

Prioritise work based on evidence, not assumptions. This approach ensures that prioritisation is based on real data and insights.

• 1.1 Customer Analytics and System Telemetry:
  • 1.1.1 Priority Shaping:
    • Leverage customer analytics, system telemetry, and business insights to shape priorities.
    • Automate the integration of customer analytics into prioritisation.
  • 1.1.2 Telemetry Management:
    • Automate the integration of system telemetry into prioritisation.
    • Implement telemetry tutorials.
• 1.2 A/B Testing and Controlled Rollouts:
  • 1.1.2 Impact Validation:
    • Use A/B testing, feature flags, and controlled rollouts to validate impact before full-scale deployment.
    • Automate the configuration of A/B tests.
  • 1.1.2 Testing Management:
    • Automate the tracking of controlled rollout results.
    • Implement testing feedback collection.
• 1.3 Actual Performance Data:
  • 1.1.3 Internal Preference Avoidance:
    • Regularly reassess priorities based on actual performance data rather than internal preferences.
    • Automate the tracking of performance data integration.
  • 1.1.3 Data Management:
    • Automate the tracking of priority reassessments.
    • Implement data tutorials.

By using data-driven decisions, organisations can ensure effective prioritisation.

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