Dear Data

Exactly 1 year ago, the Dear Data book was announced ready for pre-order.

Dear Data is a year-long, analog data drawing project by Giorgia Lupi and Stefanie Posavec, two award-winning information designers living on different sides of the Atlantic.

By collecting and hand drawing their personal data and sending it to each other in the form of postcards, they became friends.

I must have heard about the project before it became a book through one of the many infographics feeds or Twitter folks I follow. I fell in love with the concept instantly. As a meta tribute I first wanted to make a postcard about me reading the book. So I started collecting data about my reading sessions right away: how many of their weekly exchanges I’ve read per session, whose postcards I preferred and why… I could however not figure out how to draw any of it. I don’t have their talent.

Usually I’m a fan of sober and simple. While reading Dear Data however, I ended up preferring Giorgia’s drawings. You often need more time to understand them because she packs so many dimensions into them. Her style is very organic, and she uses metaphors several times: the human body – the symbols on week #6 “A week of physical contact” are genius! –, musical notes, star constellations etc. I’m also a fan of Giorgia’s handwriting, with her mix of cursive and non-cursive script, mix of upper and lower case. Furthermore I like her obsessiveness, for example on week #24 “A week of doors/spaces”. How crazy do you have to be to schematize every single door you open and/or pass through during a whole week? Plenty crazy^[1].

Stefanie’s drawings are much simpler and very geometric. From the start, I noticed that she apologizes a lot, which sometimes is almost annoying. Mistakes are ok! She realizes it on week #45 “A week of apologies”. Stefanie also often sneaks additional data onto the backside of the postcards. Maybe Giorgia packs so much data into her drawings that she doesn’t need to do this. On some occasions, I preferred Stefanie’s take of the weekly subject. For example the simplicity of week #14 “A week of schedules” is very effective.

How did Giorgia & Stefanie track stuff and collect the weekly data needed for the drawings? Giorgia answers that question on week #50 “A week of our phones”. They used apps like Nicholas Felton’s Reporter. Funnily enough I bookmarked, downloaded and tried that app quickly 3 years ago, but never used it until after reading Dear Data. I’m now tracking my bad dietary habits. I’m still skeptical about how much they would influence their behavior, and therefore the data, by measuring it.

I still wanted to try to make my own “analog data drawing”. A couple of weeks ago, I had the idea to draw the workspaces of my office colleagues. I thought it was a genius idea, until I realized that it’s very similar to Giorgia’s interpretation of week #26 “A week of workspaces”. Reading the book 6 months ago must have planted that idea in my head I guess.

The stuff on people’s desks^[2] is not really to scale, and not super accurate. But that’s not the point anyway. I wanted to show that desks are usually messy and that there are no two layouts the same. Despite drawing the postcard in a single try, the whole process took me hours. I can only imagine how much time Giorgia & Stefanie have spent collecting data and crafting the cards!

Some of Giorgia & Stefanie’s drawings I should try to make my own version of:

• Giorgia’s week #22 “A week of our past” where she represents her life in a circle, with the places she lived in, relationships, education, jobs
• draw week #16 “A week of closets/wardrobes” and compare it to other people’s
• the social experiment of week #28 “A week of smiling at strangers” could be interesting to do, but I don’t think I could
• week #46 “A week of books” where they survey their whole bookshelf

Dear Data is an incredibly awesome book every visualization nerd should read. Giorgia & Stefanie totally deserve the recognition given to them by the MoMA:

The original collections of postcards and sketchbooks of Dear Data has been acquired as part of the permanent collection of the Museum of Modern Art in November 2016.