Creating Effective Measurement Systems for Better Insights
Design useful measurement systems by measuring outcomes not activities, using leading and lagging indicators together, and building in resistance...
A complete A–Z index of every Metrics Measurement Evaluation article on When Notes Fly, part of our Concepts coverage. New to the topic? Start with the foundational explainers, then move on to case studies and applied frameworks. Returning for something specific? Use the list below to jump straight to it.
For the latest pieces newest-first, see the Metrics Measurement Evaluation section. For related ideas across the section, see the Concepts archive. How we research and review articles: editorial standards.
Design useful measurement systems by measuring outcomes not activities, using leading and lagging indicators together, and building in resistance...
When a measure becomes a target, it ceases to be a good measure. People optimize for metrics, not goals, creating distortion and gaming.
Interpret data correctly by avoiding confirmation bias, p-hacking, confusing correlation with causation, and survivorship bias in your analysis.
KPIs (Key Performance Indicators) are the few metrics that actually matter for your goals. Not all metrics are KPIs—only those that drive real...
Quantitative metrics measure numbers like revenue and time. Qualitative metrics assess quality like feedback and satisfaction.
Measurement bias: Systematic error in data collection distorting results consistently (not random noise—predictable direction).
Vanity metrics look impressive but don't drive decisions: total users, page views. Meaningful metrics change behavior: active users, retention,...
Measure what drives outcomes, not what's easy to measure. Focus on outcomes over activities, and use leading indicators to predict future results.
What gets measured gets optimized. Measurement creates visibility, accountability, and focuschanging behavior whether intended or not.
Metrics mislead through gaming the numbers, proxy failure not representing what matters, context loss, and aggregation hiding important details.