A research-design and protocol paper: reframing data measurement from what belongs on the dashboard into a recomputable, reviewable, decision-supporting collaboration protocol, with minimal mechanisms for definitions, measurement units, metric tiering, dictionaries, and retrospectives.
From daily and weekly reports to problem retrospectives: how to maintain baselines, record changes, judge impact, and turn data conclusions into verifiable action items.
Part of the column “Data Metrics Guide” · Chapter 5
A ready-to-copy metric dictionary template, plus public examples for completion rate, retention, conversion, error, experience quality, and feedback metrics.
Part of the column “Data Metrics Guide” · Chapter 4
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.
Part of the column “Data Metrics Guide” · Chapter 3
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.
Part of the column “Data Metrics Guide” · Chapter 2
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.
Part of the column “Data Metrics Guide” · Chapter 1