Jer Thorp (@blprnt), data artist in residence at The New York Times, was tasked a few years ago with designing an algorithm for the placement of the names on the 9/11 memorial. If an algorithm sounds unnecessarily complex for what seems like a basic bit of organization, consider this: Designer Michael Arad envisioned names being arranged according to “meaningful adjacencies,” rather than by age or alphabetical order.
The project, says Thorp, is a reminder that data is connected to people, to real lives, and to the real world. I recently spoke with Thorp about the challenges that come with this type of work and the relationship between data, art and science. Thorp will expand on many of these ideas in his session at next month’s Strata Conference in New York City.
Our interview follows.
How do aesthetics change our understanding of data?
Jer Thorp: I’m certainly interested in the aesthetic of data, but I rarely think when I start a project “let’s make something beautiful.” What we see as beauty in a data visualization is typically pattern and symmetry — something that often emerges when you find the “right” way, or one of the right ways, to represent a particular dataset. I don’t really set out for beauty, but if the result is beautiful, I’ve probably done something right.
My work ranges from practical to conceptual. In the utilitarian projects I try not to add aesthetic elements unless they are necessary for communication. In the more conceptual projects, I’ll often push the acceptable limits of complexity and disorder to make the piece more effective. Of course, often these more abstract pieces get mistaken for infographics, and I’ve had my fair share Internet comment bashing as a result. Which I kind of like, in some sort of masochistic way.
What’s it like working as a data artist at the New York Times? What are the biggest challenges you face?
Jer Thorp: I work in the R&D Group at the New York Times, which is tasked to think about what media production and consumption will look like in the next three years or so. So we’re kind of a near-futurist department. I’ve spent the last year working on Project Cascade, which is a really novel system for visualizing large-scale sharing systems in real time. We’re using it to analyze how New York Times content gets shared through Twitter, but it could be used to look at any sharing system — meme dispersal, STD spread, etc. The system runs live on a five-screen video wall outside the lab, and it gives us a dynamic, exploratory look at the vast conversation that is occurring at any time around New York Times articles, blog posts, etc.
It’s frankly amazing to be able to work in a group where we’re encouraged to take the novel path. Too many “R&D” departments, particularly in advertising agencies, are really production departments that happen to do work with augmented reality, or big data, or whatever else is trendy at the moment. There’s an “R” in R&D for a reason, and I’m lucky to be in a place where we’re given a lot of room to roam. Most of the credit for this goes to Michael Zimbalist, who is a great thinker and has an uncanny sense of the future. Add to that a soundly brilliant design and development team and you get a perfect creative storm.
I try to straddle the border between design, art and science, and one of my biggest challenges is to not get pulled too far in one direction. I’m always conscious when I’m starting new projects to try to face in a different direction from where I was headed last. This keeps me at that boundary where I think the most interesting things are happening. Right now I’m working on two projects that concern memory and history, which is relatively uncharted territory for me and is getting me into a mix of neurobiology and psychology research alongside a lot of art and design history. So far, it’s been tremendously satisfying.
In addition to your position at the Times, you’re also a visiting professor at New York University. I’m curious how you see data visualization changing the way art and technology are taught and learned.
Jer Thorp: The class I’m currently teaching is called “Data Representation.” Although it does include a fair amount of visualization, we talk a lot about how data can be used in a creative practice in different ways — sculpture, performance, participatory practice, etc. I’m really excited about artists who are representing information in novel media, such as Adrien Segal and Nathalie Miebach, and I try to encourage my students to push into areas that haven’t been well explored. It’s an exciting time for students because there are a million new niches just waiting to be found.
This interview was edited and condensed.