Metrics and Data Driven Decision Making Questions

Selecting, collecting, and interpreting metrics to inform decisions and drive improvement. Covers choosing the right metric for the problem at hand (for example process metrics like cycle time and throughput, product metrics like activation and retention, or customer metrics like NPS and churn), building dashboards and reports that surface signal without hiding important context, and recognizing common pitfalls such as vanity metrics, Goodhart's law effects, and local optimization at the expense of the broader goal. Includes examples of data contradicting intuition, a metric that triggered an experiment or a change in direction, how success was measured after that change, and how to communicate unfavorable results to stakeholders while maintaining credibility and transparency.