Hyperparam is a great way to rapidly clean and transform a large dataset. I’ve been working with them on UX, When I demo Hyperparam to ML practitioners, I’ve sometimes heard a version of the same question: "This looks cool, but couldn't I just ask Claude to whip up a quick script for this?" It's a fair objection. LLM-based coding is incredibly powerful, and the promise of going from question to…
Basking in 68 degrees at 68 degrees. A friend of mine was traveling in northern Europe, and posted a picture: “68 degrees and 68 degrees. One is latitude, the other temperature.” That made me wonder — is that common? how often is the latitude the same as the temperature? Some places are pretty easy to guess: I wouldn’t expect a 90 degree day at the poles, for example; nor does it hit 0 degrees at…
The last few entries in this blog introduced the concept of “genre” as a way to frame visualization. I analogized visualization to literary genres: different genres of visualizations use the same perceptual building blocks and grammar but put those pieces together with distinct purposes. The core genres were exploration , presentation , and monitoring . We also discussed domain-specific…
In the last few blog entries, I’ve talked about a few major genres of visualization — exploration , presentation , and monitoring . We’re getting toward the end of this series (phew!). I started to coalesce the other posts, so I could compare and contrast the three; and on the way, I bumped into domain-specific visualization. Let’s start with the first three. Genre Exploration Presentation…
We’ve talked about exploration — learning new things about your data; and we’ve talked about presentation — teaching other people things about data. The third major genre of visualization is monitoring and dashboards —learning about new data in real time. (In the next entry, I’ll say a little about things that don’t fit well into these categories.) I’ll use the words “monitoring” (as the task) and…
In the last entries, I introduced three genres of visualizations. Exploration: I want to discover new insights about my data. Presentation: I know the answer and I want to share it with others. Monitoring: I know the questions I want to ask, and check them from time to time. We’ve talked about Exploration . Data presentation – sometimes, “data storytelling” – is the next genre to discuss. In a…
In the last entry, I introduced three genres of visualization —presentation, exploration, and monitoring. I’d like to dive into these in a little more detail. The first genre I want to talk about is data exploration. Exploration – formally, “Exploratory Data Analysis” – pares visualization down to its barest elements: it asks nothing but how to deliver an insight rapidly. An analyst starts an…
Forgive me: this is a bit of a rant. To make sure it’s a good one, we’re doing it in five parts. Where and how will this visualization be used? This question drives every choice made when designing a visualization – from the design of the chart itself, all the way down to the choices of how the data architecture needs to be arranged. The creator of the visualization must make different choices…
In my last entry, I talked about how I used “Capability Maturity Models” to better make sense of interview results. In this entry, I’d like to explore how the Diffusion of Innovations helped us think about a direction for product strategy. Moment.dev asked me to help define a clear product direction: the bases of the tool were in place, but how would we convert that into users? We felt that if we…
Ever spent hours talking to users, only to end up feeling you've got nothing but a bigger pile of information? Raw interview data is a treasure trove, but doesn't give you the roadmap. That's where analysis frameworks come in – they help you turn those messy stories into actionable insights. In this blog entry, I’m going to introduce a less familiar, but highly valuable analytical lens – the…
Have you ever wrapped up a user interview feeling like you didn't really learn what you needed? The key to great interviews isn't technique alone – it's asking the right questions. I've spent years refining how I approach user interviews. I’d like to discuss common pitfalls – and what you can do to ensure your interviews get you the information crucial to your decisions. Before an interview,…
In my entry on “ Measure, Design, Build ,” I talked about the prototyping process: how we get from data, users, and an interesting problem into a workable prototype. What’s the next step? Optimally, this process becomes a step in organic growth: bringing in the additional skills that we need to make the new feature real, one step at a time. Growing the SLO project When Liz Fong-Jones and I created…
My passion is helping users make sense of data - at every stage from ingestion and processing, through analysis, and (especially) exploration and visualization. Often, that entails creating new ways to interact with the data – visualizations that bring out new insights, or query tools that make it easy to ask important questions. I’ve been reflecting recently on my research process – both of the…
This blog entry continues my discussion of Designing With Data. You might enjoy Part I: “ Designing With Data .” I worked for Honeycomb.io as the first Design Researcher; I was brought in to help the company think about how data visualization could support data analytics. At Honeycomb, I helped create an analysis tool called BubbleUp — there’s a great little video about it here . BubbleUp helps…
It happened again, just a few weeks ago. I drew a great sketch in my sketchpad that could be The Visualization. The one that would help my users make sense of their data, simplify complexity, and wipe away all the layers of confusion. I could almost hear the angels singing in the background as I coded up the first draft and spun up the system. I was ready to send the triumphant Slack announcement…