Info We Trust is an ambitious, visually stunning book that sits somewhere between philosophy, information design, and a collection of visual essays. It is clearly the product of many years of thinking, reading, and making. That depth and experience is both its greatest strength and, at times, its main challenge.

From a purely visual standpoint, the book is exceptional. Andrews is remarkably disciplined in his use of a limited color palette, and the combination of three colors with hand-drawn illustrations gives the book a distinctive and coherent visual identity. The drawings are both intentional and decorative, they are a pleasure to look at, page after page.

Content-wise, the book is dense with references. The marginal notes, quotations, and extensive bibliography leave no doubt about Andrews’ breadth of knowledge. Again and again, I found myself wanting to read the original sources and to follow the many intellectual threads Andrews lays out. In that sense, the book functions very well as a gateway to a much wider intellectual exploration.

At the same time, this abundance can be overwhelming. Especially in the earlier chapters, the book leans heavily into philosophical reflection. Ideas, metaphors, and quotations accumulate, but a clear argumentative trajectory is often hard to discern. Reading these chapters requires patience: each sentence needs to be savoured rather than devoured. For readers like me, who prefer an analytical, structured approach, this can make progress feel slow, even if the reading itself is never unpleasant. I must admit that I skipped most of the quotes and notes in the margins…

For me, the book truly comes into focus in the final chapters (roughly chapters 13–15). Here, Andrews becomes much more concrete, offering insight into his actual working process: how he frames problems, explores structure, reasons visually, and approaches projects as an information designer. These chapters are highly relatable and, I suspect, will resonate strongly with practitioners. They clarify retroactively what the earlier philosophical groundwork was aiming toward.

In the end, Info We Trust is not a manual or a step-by-step guide, and readers looking for quick, actionable takeaways may struggle with parts of it. But as a reflective, carefully crafted work about how we think with information, and how we might do so more responsibly, it is thoughtful, inspiring, and visually remarkable. Best approached slowly, selectively, and perhaps revisited over time rather than read straight through in one go.

Read this review, as well as many others, in our complete overview of data visualization books (work in progress).

Michael Friendly and Howard Wainer clearly love graphs. But A History of Data Visualization and Graphic Communication isn’t just about graphs — it’s about the stories behind them: the context, the people, the new measurements that made them necessary, and the discoveries they enabled. The authors don’t just show us the end result; they take us through the process that led there, often in a delightful amount of detail.

The structure of the book balances chronology with theme. This keeps the feeling of historical evolution intact, without falling into the trap of a dry timeline. We jump from 17th-century innovators to 20th-century pioneers, always with a clear narrative thread.

What stood out to me most was the variety of examples. While the book is clearly indebted to Edward Tufte’s work, it doesn’t recycle his canon. I encountered many visualizations I hadn’t seen before, and even familiar ones were presented with fresh insight. The ideas on how new data, collected with new measurement techniques, often prompt entirely new kinds of charts were particularly eye-opening for me. It’s a reminder that visualization doesn’t just explain data — it also adapts to it.

That idea was so powerful to me that I used it as one of the foundations for my keynote lecture, Graphs can save the world! This book helped me think more deeply about why visualizations matter — not just aesthetically or functionally, but historically and socially.

That said, not every chapter lands equally well. Some sections feel a bit scattered or lightweight, especially when they only briefly touch on developments that deserve more space. The final chapter, Graphs as Poetry, takes a more philosophical turn, but I wasn’t entirely sure what the authors were trying to argue there.

Also worth noting: while the book is visually rich, it’s a shame that most of it is printed in black and white. Some of the visual clarity and impact is lost as a result. And while the authors occasionally offer “reworked” versions of historical charts to show how they could be improved, these redesigns don’t always convince — sometimes the original speaks more eloquently in its own language.

Despite those minor critiques, this is a generous, well-researched, and deeply informative book. I’d recommend it to anyone interested in the intersection of data, history, and design. It’s a reminder that charts are tools, but also more than tools — they are artifacts of human thought, and sometimes, even acts of discovery.

Rating: ⭐⭐⭐⭐

Read this review, as well as many others, in our complete overview of data visualization books (work in progress).

We love the smell of new dataviz books in the morning… and ‘Visualizing Complexity’ is about as new as it gets! Written by (super-cool) information design agency Superdot from Basel (Switzerland) and published in May 2022, it contains an excellent analytical overview of the modular design system they developed over the past 10 years.

The book, which arrived with a nice and friendly signed postcard, is printed on heavy high-quality paper, with a sturdy cardboard cover. A bit smaller than we expected, at 23 by 16 centimeters, it has a compact and inviting look and feel. The colors, fonts and layout are very reminiscent of the famous Bauhaus design movement, and despite the very intense, saturated color scheme, the design rarely overpowers the actual content. To be honest: it’s been lying on our desk for a week now, just to show off its beautiful design 😇

The highly structured book is divided into five parts:

• Data dimensions: different ways of presenting and processing datasets
• Diagrammatical dimensions, visual dimensions and structuring dimensions: a total of 80 elements which can be used to create shapes, arrange them, and make them visually distinguishable based on the different data dimensions
• Multidimensional visualizations: examples of the Modular Information Design system breaking down powerful data visuals to show how they are built from combinations of these 80 elemental dimensions

Darjan Hill and Nicole Lachenmeier are the founders of information design agency Superdot (previously Yaay), which has grown into a well-known multidisciplinary team of information designers, developers and storytellers. They combine a background in Business Informatics (Darjan) and Visual Communication (Nicole) into a unique blend allowing them to approach information visualization challenges from a wide variety of perspectives.

Besides serving an impressive list of clients, winning many awards, and being involved in multiple teaching and mentoring program, Superdot is the initiator of the “On Data And Design” event series, and a pioneer in the field of DX – Data Experience Design.

⭐⭐⭐⭐

With our background in science, the highly analytical approach of Visualizing Complexity resonates strongly with us. It is a welcome update of Jacques Bertin’s visual variables concept, with a bigger focus on combining different variables/elements to construct multidimensional and multi-layered visualizations.

The exactly 80 elements, consisting of 25 diagrammatical, 40 visual, and 15 structuring dimensions might feel a bit contrived at times, but on the other hand it is also a very complete overview. We’ll definitely browse through this book during future projects, to ensure we’ve covered as many different visual ideas as possible.

What we’re missing a little bit in this book is an evaluation of how powerful each of these elements are. In the many examples in the final section of the book, it is clear how some elements (e.g. color hue) are much more prominent than others (e.g. contour details). Therefore, some of them are very logical choices for certain data dimensions, and others are less logical or way too subtle to clearly tell the story we want to tell in our visual. This evaluation is left to the reader to experience as a part of experimentation process – which is probably a good thing, as it could be quite dependent on the exact data.

All in all, this is a book that has definitely earned a prominent place on our dataviz bookshelf – a must have for everyone who wishes to understand the analytical thought processes behind strong data visuals!

More dataviz book reviews? We have already covered:

• Dear Data
• Storytelling with Data
• Visualizing Complexity