Hi, everyone! This is Melissa Strong, and I come bearing a review of my most recent data viz read: CHART by Nadieh Bremer. 

This book was not what I expected. The subtitle — Designing Creative Data Visualizations from Charts to Art — made me think it would steer readers away from conventional charts toward something more rarified. But bar charts, line graphs, bubble charts: they're all welcome here. You can combine two chart types, you can invent your own — as long as it makes sense for the data. What Bremer is after is not a different set of chart types but a different way of thinking, and this book is genuinely inspiring in that regard. 

Bremer is known for highly custom visualizations that blend analytical structure with visual beauty. Her work occupies the space between chart, illustration, and data art, and CHART reflects that range. Drawing from more than a decade of personal and client projects, the book uses finished examples, sketches, and works-in-progress to show how creative visualizations develop over time. As Bremer describes it, the book moves readers "along a spectrum of innovation that starts with ordinary charts and ends with data art" — four parts, each adding progressively more creativity to the work. She calls it "almost like a professional diary," and that description earns its keep — this is not a book you consult so much as one you spend time with. Most visualization writing pulls in the same direction: simpler, cleaner, less. That is good advice much of the time. But CHART is interested in a different problem — what happens when you want someone to linger, to look twice, to come back? 

Bremer does not treat creativity as decoration added at the end. She treats it as thinking, not finishing — something that happens through sketching and false starts as much as through polished output. The finished pieces are beautiful, but what stays with you is the evidence of how they got there: the early sketch that turned out to be more interesting than the final version, the pivot that changed everything. The book will be useful to anyone who works with data and has felt hemmed in by whatever tool or template they are working inside. It does not tell you which software to use. It asks you to slow down and think about what the data actually calls for.  For anyone building with Observable Plot, D3, or similar open tools, that mindset transfers directly — the freedom those libraries offer is only useful if you know what to do with it.

One of the most useful ideas in CHART is that unusual visualizations do not have to be arbitrary. Take "Wanderings of Stars," which maps 400,000 years of stellar movement — 200,000 years into the past and future from the present — by connecting pairs of stars within each western constellation rather than tracing individual paths. The result looks, at first glance, like luminous generative art. But every filament is real movement, and the relational encoding reveals something a standard star map would not: how each pair shifts in relation to the other over deep time. Creative work can still be intentional, expressive without becoming meaningless, beautiful without losing its grounding in the data.

The book's commitment to visual thinking shows up from the first pages — the table of contents is itself a designed artifact, mapping the book's structure spatially around an abstract form rather than presenting it as a list. The book itself is large-format and unhurried — designed to be picked up and put down, returned to when you need a spark rather than finished in a sitting. There is something in that approach that will feel familiar to anyone working in open source: the process is part of the point, not just the output. One thing worth knowing before you start: this is not an instructional book, and it works best if you go in knowing that. 

Readers looking for instruction on chart literacy, accessibility, or analytic best practices will find little here — not because the book falls short, but because those are simply not its aims. The tool-agnostic approach occasionally leaves you wishing for more concrete footholds, and there are moments where you want the curtain pulled back further — the Kantar MotiveMix poster project, for instance, is visually stunning in its finished form, but the absence of early sketches leaves the creative leap feeling a little mysterious. These are small complaints about a genuinely delightful book, and worth naming only because the book earns enough trust that you find yourself wanting even more from it.

It will resonate most with practitioners who already have a working relationship with data — people ready to think more deliberately about visual voice, experimentation, and when a conventional chart is the wrong tool for the job. Beginners may find it inspiring but hard to act on.

Respecting the data does not mean every project has to look the same. CHART is a strong recommendation for visualization designers, data journalists, and analysts who want to expand what they consider possible — a book that widens the frame rather than fills it in. The most lasting value may be the simplest: a reminder that the space between chart and art is larger than convention usually allows, and that moving into it is a choice available to anyone willing to think about what a dataset is actually asking for.

If CHART has you curious about Nadieh Bremer's work, I'd encourage you to spend some time with her portfolio at visualcinnamon.com/portfolio — seeing the full range of what she's made is its own kind of inspiration. And if the book finds its way onto your shelf, I hope it does what it did for me: makes you look at your next dataset and wonder what else it could be.

Thanks for reading, everyone. Until next time!