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The Rise of the Data Literate Designer


Admittedly, I’m a chemistry PhD dropout, but I love science and I spent over a decade in the biotechnology industry, mostly as a product manager. In the midst of all that science and product, and without realizing it, I stumbled into design and design thinking. Inspired to take a human centered approach to things, I decided to take my career in that direction. There have been moments along the way where I wish I studied graphic design, industrial design, or architecture as an undergrad, however in hindsight, I’m incredibly grateful I majored in science (biology major, chemistry minor).

I’ve thrown out the term “designtist” at a few talks I’ve given in the past few years. It often gets a chuckle or a head-nod and that’s because, I think, we’re realizing that the two disciplines are coming closer and closer together.

A New “T-Shaped” Level of Talent?

Early in my career, prior to my product management days, I worked as a hybrid scientist/bio-engineer. The company I worked for acquired an engineering company that had little experience in the biotech sector. The engineers were a smart and talented bunch, although they hadn’t taken a biology class since their high school days and some never studied chemistry. Avagadro’s number? They had never heard of it. How a DNA backbone was structured? Nope. How two strands of DNA formed a double helix? Not that either. Since the products they were to design and engineer ultimately would be placed into molecular biology, biochemistry and pharmaceutical labs, I created a three part science-for-engineers seminar to teach the basics of molecular biology. Would it create a new T-shaped individual? Maybe. Learning science is like learning a language. The concepts are not all that difficult, sometimes intuitive and sometimes unintuitive, but the terminology can bite you in the ass (I learned a similar lesson in business school with my Economics, Accounting and Finance courses).

Turned out the engineers loved the course, as it helped them think about the problems they were solving a little differently. They weren’t experts after three one-hour sessions, however the newfound knowledge of the DNA to RNA to protein expression of biological data worked, and how DNA gets amplified via polymerase chain reaction (PCR) and thermal cycling helped them better understand the system for which they were designing and engineering, and they ultimately made better design and engineering choices because of that knowledge.

This is not another version of the “should designers code” argument. My answer to that one is simple: Heck yes. BUT: Designers do not need to write production-level code. They need to understand how code works enough that they can write some of it and more importantly, design within the constraints. After all, interactive designs do not live forever in Photoshop, or Sketch, or Illustrator, or __ insert favorite tool here__. Those designs get coded to become a digital product. The same for designing with data.

In 2016, the discussion about designers learning to code is the wrong discussion, instead we should be talking about designers becoming data literate.

What does it mean to be “data literate?” Designers who understand data will be the designers who make the bigger impact with their work. Design solves problems. Data helps inform the choices you make to solve those problems.

We hear it in the news: “Data science is the sexiest new job.” And I chuckle, as it’s all about branding. Job titles like Data Analyst or Statistician have been around for decades, and they appear to have a negative connotation. Suddenly make a name change that adds “science,” and people are all excited.

Consider these two phrases:

Stand back, I’m about to do Science!


Stand back, I’m about to do Statistics!

In the past few years we’ve seen the rise of design in organizations as a significant creator of value. We’re seeing a similar argument for data science. A 2015 PwC survey of 1,300 CEOs in 77 countries, ranked data mining and analytics as the second most important digital technology and organizational capability.

What if I substituted “design” into that sentence: “CEOs now value design when it comes to their ability to meet a wide group of stakeholder needs.” Since a designer’s job is to create a solution that meets a customer’s, stakeholder’s or user’s (insert favorite term here) need, it makes sense that these are complimentary. Smart, experienced designers dig into the problem first before generating any kind of solution. What better way to understand the problem than with data? The more a designer can understand how this data was collected, and analyzed, the smarter design decisions they can make to deliver a compelling solution.

Innovation has been linked to design, many times over in the past, so it makes sense that the data literate designer will make the “right” design choices for their product. But here’s the rub: Most designers are not trained in statistics, um, I mean, data science. There are few programs out there that integrate a form of data education to designers: MICA’s Information Visualization (disclosure: I attended this program), Parsons Data Visualization, as well as programs like NYU’s ITP and MIT’s Media Lab who offer courses such as Machine Learning for Artists. From their web-catalogue, I see no data or stats classes listed at Stanford’s d.school (yes, I realize I’m cherry picking here, so keep that in mind, fellow data-nerds). I don’t have a nationwide design degree curriculum database in front of me, but I’d bet a lot of money that the majority of top design schools do not require a course in stats or data.

Author C. Todd Lombardo

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