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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Kevin Holland
Product Leader @ Holmusk | Data Products, Healthtech, Consulting | MBA @ Kellogg | MicroSaaS +SwaS
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 more![Pricing [data]points and Predictions: Data Product Pricing Frameworks](https://images.unsplash.com/photo-1488229297570-58520851e868?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDd8fGRhdGF8ZW58MHx8fHwxNzQ3MDk5MTAyfDA&ixlib=rb-4.1.0&q=80&w=600)
Pricing [data]points and Predictions: Data Product Pricing Frameworks
TL;DRPricing data products is a different beast from SaaS—value is contextual, and the product layer matters as much as the data.Start by understanding what makes your data valuable (to whom, and why).Layer on product experience: APIs, dashboards, integrations, and support are all pricing levers.Use a
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