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Data Metrics Guide
A practical system for definitions, metrics, collaboration, and retrospective learning.
- Chapter 1
Data Measurement Guide (Part 1): Data Definitions Are the Interface of Collaboration
Metrics are not numbers on a report; they are the shared language a team uses to describe the same thing. Only after defining the object, event, denominator, and time can data participate in decisions.
- Chapter 2
Data Measurement Guide (Part 2): Define What You're Measuring Before Arguing About Metrics
A publicly reusable metric dictionary: from requests and users to tasks, explaining how availability, error, latency, performance, and feedback data should be defined, combined, and interpreted.
- Chapter 3
Data Measurement Guide (Part 3): Metric Tiers, Let Data Serve the Most Important Decisions First
Don't lay metrics flat on the dashboard: prioritize them by task relevance, scope of impact, actionability, and data trustworthiness, and choose what to watch at each stage.
- Chapter 4
Data Measurement Guide (Part 4): The Metric Dictionary Template — Turning Definitions into a Recomputable Collaboration Interface
A ready-to-copy metric dictionary template, plus public examples for completion rate, retention, conversion, error, experience quality, and feedback metrics.
- Chapter 5
Data Measurement Guide (Part 5): Periodic Statistics and Retrospectives, Turning Numbers into the Next Action
From daily and weekly reports to problem retrospectives: how to maintain baselines, record changes, judge impact, and turn data conclusions into verifiable action items.