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Writing, research, projects, and photos around “Metrics”.
A proposal is due for review, three projects do not fit the next planning cycle, and another delivery is late. Five independent skills, each with an input and a concrete output you can take into a discussion.
View projectStarting from the different questions asked by analysis, operations, and engineering, this article organizes how a data center for a multi-source business platform should divide Tabs, define metrics, structure read and write paths, and handle billing, reconciliation, growth, and cost.
Short
22 min read
Starting from a multi-system integration experience, this essay distinguishes creation, task efficiency, organizational productivity, and business value—and asks where value and cost actually come from when AI enters a complex system.
Short
5 min read
Part of the column “Engineering & AI Judgment” · Chapter 10
An edited consultation about an FDE conference, technical communication, and AI’s business value: when owners care about revenue and cost, how can technical people explain their work as a result worth testing?
Consulting
9 min read
Part of the column “Consulting & B2B Business” · Chapter 1
A learning protocol for search delay, CUPED, page versions, and traffic incidents.
Case
2 min read
Part of the column “Search Engine Growth” · Chapter 8
A project-based introduction to discovery, crawling, indexing, clicks, and business conversion.
Case
3 min read
Part of the column “Search Engine Growth” · Chapter 1
A practical value model for search, page consumption, Web2App handoff, and retention.
Case
3 min read
Part of the column “Search Engine Growth” · Chapter 2
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.
11 min read
Read summaryFrom daily and weekly reports to problem retrospectives: how to maintain baselines, record changes, judge impact, and turn data conclusions into verifiable action items.
Short
4 min read
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.
Short
4 min read
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.
Short
4 min read
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.
Short
13 min read
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.
Short
6 min read
Part of the column “Data Metrics Guide” · Chapter 1