Data Blog
Occasional notes on data, UX, product, and AI.
2026
The Clock Isn’t a Feeling: Prototyping with AI, Real Data, and a Deadline
How Sketching with Data changes in the AI era
2025
When "Just Claude It" Isn't Enough: Hyperparam Beats Bespoke Scripts for Data Exploration
Hyperparam is a great way to rapidly clean and transform a large dataset.
Answering a Useless Question with the Degree-Degree Map
Basking in 68 degrees at 68 degrees.
2024
Visualization Genres
Six-part series exploring the idea that we can understand visualization tasks as genres of visualization.
Accelerate Product Adoption with the Diffusion of Innovations
In my last entry, I talked about how I used “Capability Maturity Models” to better make sense of interview results.
Making Sense of Automation with Maturity Models
Ever spent hours talking to users, only to end up feeling you've got nothing but a bigger pile of information?
How to Ask the Right Question
Have you ever wrapped up a user interview feeling like you didn't really learn what you needed?
2023
Growing into Production
In my entry on “Measure, Design, Try,” I talked about the prototyping process: how we get from data, users, and an interesting problem into a workable prototype.
Measure, Design, Try: On Building New Things
My passion is helping users make sense of data - at every stage from ingestion and processing, through analysis, and (especially) exploration and visualization.
A Honeycomb Story: Five Different Kinds of Bar Charts
This blog entry continues my discussion of Designing With Data.
Follows “Designing With Data”
Designing With Data
It happened again, just a few weeks ago.
2020
Three Lenses
Lots of vendors talk about the “Three Pillars of Observability.” This essay suggests that lenses is a better metaphor – and why it makes for better analysis.
Published on The New Stack
AIOps
An essay exploring the future of AI for DevOps teams. (My conclusion: AI isn’t ready for what Ops teams need.)
Published on The New Stack
2019
What I Learned from Being Distributed at Honeycomb
A look at what I learned from the experience of working remotely. (This entry was written before the pandemic, and it shows).
Published on AngelList Blog
Visualization
Thoughts on the craft of data visualization.
Answering a Useless Question with the Degree-Degree Map
Basking in 68 degrees at 68 degrees.
Visualization Genres
Six-part series exploring the idea that we can understand visualization tasks as genres of visualization.
A Honeycomb Story: Five Different Kinds of Bar Charts
This blog entry continues my discussion of Designing With Data.
Follows “Designing With Data”
Designing With Data
It happened again, just a few weeks ago.
Research with users
Notes on studying users and their work.
Accelerate Product Adoption with the Diffusion of Innovations
In my last entry, I talked about how I used “Capability Maturity Models” to better make sense of interview results.
Making Sense of Automation with Maturity Models
Ever spent hours talking to users, only to end up feeling you've got nothing but a bigger pile of information?
How to Ask the Right Question
Have you ever wrapped up a user interview feeling like you didn't really learn what you needed?
What I Learned from Being Distributed at Honeycomb
A look at what I learned from the experience of working remotely. (This entry was written before the pandemic, and it shows).
Published on AngelList Blog
Building data tools
Comments on building tools for working with data.
The Clock Isn’t a Feeling: Prototyping with AI, Real Data, and a Deadline
How Sketching with Data changes in the AI era
When "Just Claude It" Isn't Enough: Hyperparam Beats Bespoke Scripts for Data Exploration
Hyperparam is a great way to rapidly clean and transform a large dataset.
Growing into Production
In my entry on “Measure, Design, Try,” I talked about the prototyping process: how we get from data, users, and an interesting problem into a workable prototype.
Measure, Design, Try: On Building New Things
My passion is helping users make sense of data - at every stage from ingestion and processing, through analysis, and (especially) exploration and visualization.
Three Lenses
Lots of vendors talk about the “Three Pillars of Observability.” This essay suggests that lenses is a better metaphor – and why it makes for better analysis.
Published on The New Stack
AIOps
An essay exploring the future of AI for DevOps teams. (My conclusion: AI isn’t ready for what Ops teams need.)
Published on The New Stack