Validate Features Through Experimentation & Data-Driven Insights

This standard mandates validating features through experimentation and data-driven insights to ensure features are tested, iterated, and validated using real-world data before full deployment.

1. Validate Features Through Experimentation & Data-Driven Insights:

Features must be tested, iterated, and validated using real-world data before full deployment. This approach ensures that features are effective and valuable.

• 1.1 A/B Testing and Controlled Rollouts:
  • 1.1.1 Impact Measurement:
    • Use A/B testing, feature flags, and controlled rollouts to measure impact before scaling.
    • Automate the configuration of A/B tests.
  • 1.1.2 Testing Management:
    • Automate the tracking of controlled rollout results.
    • Implement testing tutorials.
• 1.2 Qualitative and Quantitative Feedback:
  • 1.1.2 Usability and Adoption Assessment:
    • Gather qualitative and quantitative feedback from users to assess usability and adoption.
    • Automate the collection of user feedback.
  • 1.1.2 Feedback Management:
    • Automate the tracking of usability assessments.
    • Implement feedback tutorials.
• 1.3 Pre- and Post-Launch Performance:
  • 1.1.3 Value Delivery Assurance:
    • Monitor and compare pre- and post-launch performance to ensure the feature delivers value.
    • Automate the tracking of pre-launch performance.
  • 1.1.3 Performance Management:
    • Automate the tracking of post-launch performance.
    • Implement performance tutorials.

By validating features, organisations can ensure they deliver real value.

---