AI Made Building Cheap. It Made Judgment Expensive.
AI can produce dashboards in seconds. Healthcare data leaders still need a way to decide what deserves to be built and prove it creates value.
Read articleEssays, field notes, and the DPOS series
33 posts
Role Operating Systems: The Missing Layer Between Personal Productivity and Team Execution
Intelligence is getting cheaper. Coordination is not. Role Operating Systems close the gap between individual productivity and team execution.
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Data Governance for Healthcare Data Product Teams in the Age of GenAI, Analysis, and Agents
A practitioner's guide to governed objects, executable data contracts, stewardship that ships, and why catalogs won't save you in the agent era.
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The DPOS Kernel: Principles for the AI Era
Part 2 of 5: The 15 principles that form the foundation of the Data Product Operating System. Seven philosophical beliefs about how data products work differently, and eight strategic principles for making decisions.
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Is Your Data Team a Dashboard Factory?
Part 0 of 5: The Problem. The first dashboard I built at HCA got opened four times. Total. Not four times a day. Four times ever.
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DPOS: The Operating System for Data Products in the AI Era
Your day looks like this: standup, sync, planning, review, requirements. Somewhere in there you're supposed to actually analyze data. The system was designed for software teams, not data teams. DPOS fixes that.
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You Don't Need to Code. You Need to Navigate.
I spent four years in the Navy not as a pilot, but as the person telling pilots where to go. The E-2C Hawkeye is an unglamorous aircraft. No missiles. No guns. Just a massive radar dome bolted on top and a crew inside watching screens. While fighter pilots got
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25 Things I Believe About Building Data Products
Last week I read Peter Yang's "25 Things I Believe in to Build Great Products." 25 Things I Believe In to Build Great ProductsWhat I believe in is often the opposite of how big companies like to workBehind the CraftPeter Yang His first principle? "Speed
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Claude Code for Non-Coders: get comfy with the terminal
Last Thursday at 3pm, I coached a PM through her first 30 minutes in Claude Code. She'd never touched a terminal. By minute 7, she stopped being scared. By minute 20, she was synthesizing customer feedback across 12 call notes—something that used to take her half a
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Why Data Teams Should Care About CLI Tools (When You Don't Write Code)
It's 10:47am on a Wednesday. In the last 47 minutes, I've switched contexts six times: Reviewed a dbt pull request changing how master_person_stats.total_psych_visits is calculated * Debugged why the exec dashboard shows different numbers than Marketing's report * Analyzed a
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Scaling product sense: merging quant and qual
"Your gut is the world's most sophisticated machine learning model ever created." - David Lieb, General Partner at YC The $2M Dashboard That Nobody Used Three months into my role at a healthcare analytics company, our CEO pulled me aside with a problem. We'd just
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BMAD + Cursor: The First AI Framework That Actually Makes Sense for PMs
Three months ago, I watched a senior PM at a healthcare company spend two hours "collaborating" with ChatGPT on market analysis. The conversation meandered through competitor research, customer interviews, pricing models, and go-to-market strategies. At the end: 47 screenshots, three different documents, and no clear next
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Applying Shape Up for Data Product Teams
The best way to look at a problem is not to view it as a problem at all but as an opportunity to grow stronger, more skilled, more confident. - John C. Maxwell The Continuous Sprint Dilemma Six months into leading Revive's data product team, I faced a familiar
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Beyond QA/QC: Evals for Data Product Managers
A product manager recently shared their breakthrough on LinkedIn after completing Shreya Shankar & Hamel Husain's AI Evaluation course: "Been hunting for repeatable frameworks for building AI products as a PM. The problem: AI products are non-deterministic by nature. Manual testing doesn't scale. I
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Prototyping with GenAI Tools - A Practical Guide for Data Product Managers
Data PMs face unique challenges: validating complex data relationships and schemas before building. This guide shows how to use GenAI tools to prototype data architectures, generate synthetic data, and test insights - condensing weeks of work into hours.
Read articleThe Data Product Builder
What I’m building, what broke, and what I’d do differently. Healthcare data products, decision infrastructure, and the messy middle of building alone.
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