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Instrumentation As a Product Analytics Discipline

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One Schema, Six Platforms: Where Consistency Actually Comes From

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Org Design for Data Product Teams: Conway's Law in Practice

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Five Principles That Survived (and What Comes Next)

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New to the discipline

Start here. The posts that frame what instrumentation is and why it matters.

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Building a system

Schemas, pipelines, validation, and operations for practitioners.

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Leading the function

Hiring, org design, roadmaps, and the long arc of running the discipline.

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Greatest hits

1
Instrumentation As a Product Analytics Discipline
2
One Schema, Six Platforms: Where Consistency Actually Comes From
3
Measuring the Impact of Data Products: Decisions Enabled, Not Events Shipped
4
The Instrumentation Roadmap: Showing Requesters What Their Ask Displaces
5
Org Design for Data Product Teams: Conway's Law in Practice
6
Five Principles That Survived (and What Comes Next)

Schema & Events

Instrumenting What Users See vs. What Servers Serve
Composable Event Design: Building Blocks Over Monolithic Schemas
Schema Validation Across the Pipeline: From Authoring to Warehouse

Anatomy of a Schema Change Proposal

Anatomy of a Schema Change Proposal
What Not to Send: Writing Requirements for the Negative Space
When to Rewrite Your Event Schema From Scratch

Platform & Pipeline

One Schema, Six Platforms: Where Consistency Actually Comes From
Connected TV Analytics: How Instrumentation Differs From Web and Mobile
Session Architecture: Sessions Are Product Decisions
From Raw Events to Consumer-Ready Data
Measuring Playback Quality Without a War Room
Build vs. Buy for Measurement: Notification Telemetry and the Data You Can't Join

Operations & Quality

How to Build a Measurement Plan That Starts With Business Questions
Proactive Data Quality: Building Stakeholder Trust Before Problems Emerge
Validating Audience Segment Targeting
The Analytics Code Review: What a PM Should Look For

Automated End-to-End Tests for Instrumentation

Automated End-to-End Tests for Instrumentation
Migrating Instrumentation Without Breaking Dashboards and Models

Sunsetting an Event Stream: The Anatomy of a Deprecation

Sunsetting an Event Stream: The Anatomy of a Deprecation
Running an Instrumentation Team: How We Cut Reactive Work from 50% to Under 15%
Measuring the Impact of Data Products: Decisions Enabled, Not Events Shipped

Strategy & Leadership

The Data Product Manager Role: Bridging Engineering and Analytics
Part 21

The Downstream Map: Knowing Who Reads Your Tables

The Downstream Map: Knowing Who Reads Your Tables
New
Part 22

What Your Consumers Grade You On

What Your Consumers Grade You On
Making Invisible Work Visible: Internal Marketing for Data Teams
Hiring for Instrumentation: Three Interview Exercises That Test the Actual Job
Managing Data Product Managers: When the Output Is Coordination, Not Code
The Instrumentation Roadmap: Showing Requesters What Their Ask Displaces
Managing a Data Product Portfolio: Invest, Maintain, Incubate — and Sunset
Org Design for Data Product Teams: Conway's Law in Practice
Part 32

When Your Decision Docs and Your Reference Docs Disagree

When Your Decision Docs and Your Reference Docs Disagree
Part 33

Cross-Product Identity: Instrumenting When One Customer Spans a Portfolio of Products

Cross-Product Identity: Instrumenting When One Customer Spans a Portfolio of Products
Part 34

AI in the Instrumentation Workflow: What Actually Works in 2026

AI in the Instrumentation Workflow: What Actually Works in 2026
Five Principles That Survived (and What Comes Next)