Data Visualization Design Process

Note: I’m a graduate student at the University of Miami working on my capstone, a visualization of the Pictures of the Year International Archives. If you’re curious about my journey, here are my posts tagged with capstone.


One of the most challenging aspects of grad school while juggling a research assistant position and course work is setting aside time to write documentation about your process. You are moving so fast and switching mindsets between projects, classes, and types of projects (writing for documentation and narratives is different from an academic paper) that documentation is the least of your concerns. But, it has to be done because without it, you’ll forget. There’s nothing quite as bad as when you realized you’ve made a mistake and need to process data over again or recreate a chart with new data after filtering many different sets!

“Expect to make mistakes”.

Cameron Riopelle, Head of Data Services, Richter Library, University of Miami Libraries is credited with expressing that to me during one of our meetings. I took it to heart especially given the fact that I’m doing a lot of manual data munging.

So, what is my data visualization process?

The design process is exactly what I’ve been thinking about since starting this capstone because I never really put one together. It was always organic and most of what I’ve been learning is Design Thinking, Human-Centered Design (HCD), in addition to the benefit of UX Research Methods.

I think there are definite overlaps and application of design thinking and HCD in data visualization since most designers (I hope) place emphasis on effective, efficient, and satisfactory experiences. Data visualization is about communication, after all. Optimizing for a positive user experience is at the heart of most design solutions; however, the struggle I’ve had is when a project is forced into a process that isn’t the best fit and the deliverables aren’t the best match. But to say that and not provide an alternative is where there are problems. It’s like criticizing a person’s work but not offering up ideas or suggestions.

Look to the experts

In an effort to offer an alternative and figure out a design process that is specific to data visualization for my own sanity, I consulted books and the Google. I discovered another great article by those fine peeps at DataWrapper. What Questions to Ask When Creating Charts hit the nail on the head for me. As Lisa says, “The process of creating a data visualisation can be messy”.

Two of the six data viz workflows she shares resonate with me more than the others though Lisa’s process is a definite bonus and functions as a reminder when you lose your way. I’ll try to explain later but for now, the first data viz workflow is from Ben Fry. It’s from his book, “Visualizing Data: Exploring and Explaining Data with the Processing Environment“.

The Seven Stages of Data Visualization by Ben Fry.

What I like about this process is that it is very much focused on the data and understanding it. It is also not an assembly line as it is iterative and that you may need to go back to some aspects as you progress to others.

The other workflow I felt was a good fit is Andy Kirk’s Four Stages of the Data Visualization Design Process:

Andy Kirk’s process from his book, Visualizing Data which is in its Second Edition.

It has fewer steps at the top level there are similarities that encompass a lot of Ben Fry’s process at the second level within each stage. I’ve been trying to read this off and on for days … The process above he shares early in the book.

The third is Lisa Charlotte Rost’s process.

For me, her process falls somewhere between Ben Fry’s Represent and Refine phases and Andy Kirk’s Stage 3 and Stage 4. Only once you understand the data (What’s your Point?) can you move to Proof and Explaining.

“Reducing the randomness of your approach”

That’s from Andy Kirk’s book, Data Visualization: A Handbook for Data Driven Design. It seems obvious enough, right? Well, if I get to read more this semester, perhaps that crazy tangled ball of yarn won’t be so tangled in the future!

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