Dear Data: Dataviz book review
The book
Last February, on a cold and rainy day, I received the Dear Data book as part of a Dataviz Drawing workshop by Stefanie Posavec. The workshop was hosted by Graphic Hunters in a beautiful space in central Amsterdam.
It is a pretty large and heavy book, containing nearly 300 pages on sturdy, qualitative paper. It is not the kind of book you would take with you into bed at night, but definitely one you could put on your coffee table to show off (which I did!). There are two alternative covers, one designed by Giorgia and one by Stefanie (this is the latter one).
In Dear Data, Giorgia and Stefanie document their project of sending each other hand-drawn postcards, every week, over the course of an entire year (2014-15). Each week, they would decide on a topic on which they would gather personal data – ranging from the contents of their wardrobes, over the sounds they heared, to the emotions and desires they experienced that week.
At the end of the week, they both sat down with their data, drew it onto a postcard, wrote a detailed legend on how to interpret it, and sent it to each other (Stefanie lives in London, and Giorgia in New York).
This book is a summary of their one-year project, with high-quality images of each of the postcards, additional details about the (practical and thought) process behind it, and tips to create your own personal data drawings.
Oh yes, the project also has its own dedicated webpage.
The authors
Giorgia Lupi and Stefanie Posavec are two well-known names in data visualization, but their Dear Data project truly made them world-famous.
Giorgia Lupi (°1981), originally from Italy, founded the design firm Accurat in 2011. They worked for an impressive list of clients including Google, IBM, Starbucks, and the United Nations. She introduced the concept of Data Humanism. In short, we should not simply focus on numbers, technologies and algorithms, but on the underlying things they represent: people, stories and ideas. She has won several awards, including multiple Information is Beautiful Awards Gold Medals, and in 2019 she joined design firm Pentagram as a partner.
Stefanie Posavec (°1981) was born in Colorado (US) and later moved to London. In 2013 she was one of Facebook’s earliest Artists in Residence, creating the piece Relationship Dance Steps. She aims to make data accessible, memorable and impactful using highly original approaches. For example, she puts health data art on hospital walls as a form of interior design, or she creates jewellery from air pollution data.
In 2016, Dear Data was acquired by the Museum of Modern Art as a part of its permanent collection, so they are both official MoMA artists!
The verdict
⭐⭐⭐⭐
I absolutely love this crossover between a data visualization book and an art book. Definitely not something to read in one go, but a book to pick up from time to time to browse through the pages. I’m convinced it can be a great source of inspiration when you are stuck on a particular project and are looking for a novel, creative approach.
The differences between Giorgia’s more ‘technical’ and Stefanie’s more ‘artistic’ drawing is interesting, and throughout the project we see both evolving and experimenting in different directions. Personally I love unraveling Giorgia’s complicated legends and decyphering her complex diagrams, and I am amazed at how much information she can compress into a single small postcard. However, in certain weeks, the level of detail can become too complicated and cumbersome. Stefanie’s drawings are usually simpler to absorb, but this often leads to gorgeous patterns, designs or metaphors.
Despite being a collection of 104 hand-drawn postcards, it never gets boring. I especially appreciate how they manage to alternate between very light-hearted, practical topics (e.g. a week of drinks) and deeply emotional, personal topics (e.g. a week of negative thoughts).
The postcards are regulary interspersed with simple but adorable illustrations that make the book a bit easier to digest (too much data can be overwhelming!).
My personal favorites are:
- By Giorgia: a week of media and a week of urban wildlife (so many parameters!)
- By Stefanie: a week of indecision and a week of distractions (so simple, so strong!)
Applying the knowledge
Some of these stunning postcards are really inspiring to draw for myself. So I went ahead and collected some data. I chose to recreate Stefanie’s design for week eleven: a week of emotions.
Every hour during the past week, I have written down the main emotion I was experiencing at that moment. As we are in the midst of crazy pandemic times, you will notice some of my emotions are a bit, well, all over the place. 😬
So without further ado, here is the final result!
If you are also inspired, go ahead and start drawing your own data! And feel free to share it with me, or with the rest of the world! Looking forward to seeing what you are up to 😊
And of course, if you have some data visualization challenges you think we could help you with, let me know as well – we are always happy to assist!
More dataviz book reviews? We have already covered:
- Dear Data
- Storytelling with Data
- Visualizing Complexity
Read more:
Choosing the right font for your data visual
Fonts evoke emotions: there are very sophisticated fonts, playful fonts, attention-grabbing fonts, and elegant handwritten fonts. Using the wrong type of font can have a lot of impact. In data visualization the implications of typography are mainly focused on readability. Labels and annotations can easily become so small they get hard to read. Above all else, we should choose a font which is readable at small sizes.
Three roles of colour in a data visual
Colour is one of the most crucial tools we have to turn a normal chart into a powerful chart with a clear message, a chart which tells a story rather than simply presenting the information.
7 different goals for your chart
A crucial step in building a powerful chart is choosing the right type of chart. A lot of charts don’t work because they simply use the wrong type of chart. To avoid this trap, we must ask ourselves a basic question: what’s the ultimate goal of our data visual? What do we want to show with our data?
Making a data visual noise-free
Removing noise from a data visual is not only about taking things away such as gridlines, axes or legends. That’s just one part of it, which we could call removing physical noise. Improving the signal-to-noise ratio is often also about adding little things that help our audience better understand the visual. We are helping them by removing mental noise, or mental barriers.
Three tips to create powerful charts in Excel
Creating charts in Excel can be a very powerful tool for making sense of complex data sets, and for visualizing them. But the default options are not always the most pretty or effective ones. Here are our top three tips to create better Excel charts.
A powerful chart has a high signal-to-noise ratio
‘Less is more’. It’s a crucial principle in most of our communication, and in data visualization in particular. Because of my background as a physicist, I prefer to talk about the ‘signal-to-noise ratio’. The message - our signal - should be amplified as much as possible, giving it all of the attention. Everything that can distract from our message - the noise - should be removed.
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