“Did I Shampoo Yet?”

Pursuing Creativity in Data Visualization

Many of us seek to unleash our creativity, but how exactly can we do that?

In the last chapter of his book (or should I say “multi-sensory voyage”?) Info We Trust, RJ Andrews details his suggested approach to the creative process:

“To be creative, first, fill up your mental hopper with concepts…then carve out time and space for creative intersections to fire.”

He compares this second step in the process to the “mental wanderings during a hot shower.” I call this the Am I clean right now? effect:

I step in the shower, turn on the water. After an indeterminate amount of time, I turn off the water. Then I think to myself, “Hold on, Katherine. Have you shampooed yet? Did you even use soap?”

Eventually, I come to the conclusion that I must have showered. I guess I was on autopilot. Since I’ve taken thousands of showers in my life, my body can manage this task on its own, leaving my mind totally free to engage in its creative dance. (Andrews calls this “free association time.”)

Don Draper — flawed man though he is — says it another way: “Just think about it, deeply, and then forget it. An idea will… jump up in your face.”

Don Draper, deep in thought (Photo: AMC)

So, yes, the hot shower (or think, forget, idea-in-face) effect is real, and it’s vital to the creative process. And no, it doesn’t have to be a shower that sets off the aha moment. It could be any other monotonous activity that keeps your body (or as Andrews would say — your “meat vehicle”) occupied while freeing up your brain to make creative connections.

In regards to data visualization, I am deep in the first part of the creative process; let’s call it the immersion phase. I am fascinated by the ever-evolving field of data visualization, and I want to learn as much as I possibly can about it, with the long-term goal of making some cool sh*t.

My Foray Into Data Visualization

I first became interested in Data Visualization* while working as a full-stack developer. A client requested “some pie charts” for a site, and I stayed up into the wee hours of the morning exploring the d3 docs.

From there, I continued my exploration of d3, eventually building this nifty animated pie chart** to shame my sister for repeatedly using too much of our family’s data plan.

• *In hindsight, I was showing the signs of interest in data viz from a young age. For example, I loved making color-coded family chore charts. Once, in preparing for a mission to find out where the “older cousins” were going at night, I insisted upon surveying the streets and drawing up a map of the neighborhood to aid in our exploration.
  ** To preempt any snarky comments, I have since learned that pie charts may not be the best choice for visualizing ratios.

A few months later, I was fortunate enough to get hired as a data visualization engineer, which means I get to use d3 at work on a regular basis — how lucky am I?!

In the early days of this role, I fueled my curiosity about dataviz via Google searches during lunch breaks. I also read some of Tufte’s books that had been recommended by a colleague.

But it wasn’t until this past May, when I stumbled upon the Data Visualization Society (DVS), that I truly found a way to immerse myself in the field. (Thanks to Medium’s algorithm for recommending Elijah Meeks’s post announcing it).

For the first time, I was surrounded by people who were thinking and talking about data visualization, sharing projects, and offering constructive critiques. All of a sudden, I knew how to start “fill[ing] up my mental hopper.”

Excited by the dataviz world I now find myself immersed in, I created this graphic, which shows my exposure to chart types over time. It is clear that my personal graphical vocabulary has grown exponentially since joining the DVS. (To be sure, learning data visualization is so much more than learning the names of different types of graphs, but I decided to explore this particular variable as just one indicator of my growing exposure to the field.)

Recipe for a successful immersion phase

Now that I’ve discovered so many resources for learning data visualization, it’s up to me to design my personalized curriculum for immersion. I’ve decided to divide it into two objectives:

1. Exposure
2. Applied practice

Practicing my newly-learned “sketchnoting” skills

Exposure

The following data visualization resources represent the foundation of my self-guided education so far:

1. The DVS Slack organization — Some of my favorite channels to check out are #share-critique, #help-code, #topic-data-art, and of course #announcements, which frequently features links to articles in the DVS’s new Nightingale publication.
2. Twitter — I was never much of a Twitter-er, but I now understand it’s the best resource for keeping up with the latest in the field. (The real trick is figuring out how to avoid FOMO-induced anxiety.)
3. Medium — In my experience, Medium consistently delivers quality content. I especially appreciate that Nightingale is giving a voice to data viz practitioners of all levels.
4. Podcasts — I’ve been listening to two awesome data visualization podcasts:

• Data Stories with hosts Enrico Bertini and Moritz Stefaner (My favorite episode so far is the interview with Eva Lotta Lamm, who introduced me to the concept of sketch-noting.)
• Data Viz Today with host Alli Torban (I especially enjoyed this episode featuring Ryan Baumann which explores when 3-D is a good choice in data viz.)

5. The Books — When reading for learning, I’m the type that prefers to have a physical copy of the book. Armed with highlighters and post-it notes, I can flag what resonates with me.

So far, I’ve read:

• Edward Tufte’s Envisioning Information, Beautiful Evidence, and The Visual Display of Quantitative Information
• Cole Nussbaumer Knaflic’s Storytelling with Data, and
• RJ Andrews’ Info we Trust (thanks for inspiring the name of this article)

Next up on my reading list is:

• Giorgia Lupi and Stefanie Posavec’s Observe, Collect, Draw!
• Colin Ware’s Information Visualization: Perception for Design, and
• Caroline Criado Perez’s Invisible Women: Data Bias in a World Designed for Men.

6. Meetups — I’ve been to two DVS NYC meetups so far, and there have been some really great talks. I appreciate that the board is committed to hosting talks by people with various levels of experience in data visualization. It’s inspiring to hear amazing talks from experts, but it is often even more inspiring to hear amazing talks from people just like you.

7. Conferences — I look forward to the opportunity to attend some dataviz conferences in the upcoming months!

Applied Practice

Hard as it can be to transition from learning to doing, I really do believe “doing” is the best way to cement learnings. When you’re a novice, though, it’s easy to feel overwhelmed by all the stuff you don’t know yet. With that in mind, my plan for applied practice is: For each project, I just pick one thing I want to practice and I focus on that.

For example:

1. I wanted to learn how to animate a point along an svg path, so I decided to create a road race visualization using d3.js

Simulation of Newport Fiesta 5k Road Race

2. I wanted to learn about encoding visual variables, so I decided to hand-draw some data-driven badges for a Women Who Code meetup.

3. I’m using the 2019 Data Visualization Community Survey Challenge as an opportunity to finally learn Tableau.

Current Challenges

Like any journey worth embarking on, my pursuit of creativity in data visualization has presented (and continues to present) many a challenge, including:

1. Surrounded by greatness — immersion means surrounding myself with thought leaders and experts in the field; doing so is a fantastic way to learn quickly, but it’s also a surefire way to feel (occasionally) inadequate. That’s why I think it’s particularly important to — every once in a while — look back, take stock of what I’ve learned and built, and say “Look how far I’ve come”.
2. How many hats can I wear? — data visualization is a multidisciplinary field, and it can be overwhelming to decide which discipline to focus your efforts on at any given point. I want to keep up with the latest features and frameworks in Javascript, for example, but I also want to learn statistical concepts and beef up my design skills, too!
3. Depth or breadth in tooling — I feel an urge to learn all of the tools. Right now, Tableau, R, and the Adobe Suite are at the top of my list, but I can’t decide whether I should instead focus my free time on diving deeper with the tools I already know decently well (e.g. d3.js, Python, Figma).

If any of you have thoughts on how to tackle any of these challenges, I welcome constructive comments!

Like many of you, I’m just getting started on my data visualization journey. For now, I’m pursuing immersion via exposure and applied practice. In the words of podcast host Alli Torban, “You are what you constantly think about,” so I’m thinking a lot about dataviz. For now, though, I’m going to take a hot shower and let my subconscious thoughts inspire me.