2020 was an astounding year for insightful, informative data visualization articles — here were the top 10 I saw all year (in no particular order), in each case including a representative quote from the piece and a brief note of why it was at the top of my must-read list:
#1 — 39 studies about human perception in 30 minutes
For the last several years, I’ve wondered about what we actually know from scientific studies about how humans perceive graphics. I’ve collected things here and there, but when I started to get into the thick of it, I realized how extensive this body of research really is.
A stunningly-valuable compilation of the research foundations for data visualization. Almost every major factor making visualizations work or fail can be explained by the evidence summarized here.
#2 — Information visualization research projects that would benefit practitioners, by Stephen Few
I announced that I would prepare a list of potential research projects that would address actual problems and needs that are faced by data visualization practitioners. So far I’ve prepared an initial 33-project list to seed an ongoing effort, which I’ll do my best to maintain as new ideas emerge and old ideas are actually addressed by researchers. […] My intention is to help practitioners by making researchers aware of ways that they can address real needs.
Despite the earlier article summarizing 39 pieces of visualization research evidence, many open questions continue to bedevil dataviz practitioners, limiting the scope and influence of visualization in applied settings — Stephen’s article provides a vital framework and accelerator for closing this gap.
#3 — What I learned recreating one chart using 24 tools
There are no perfect tools, just good tools for people with certain goals. Data visualization is a communication form used by many subfields, e.g. science, business and of course journalism. All these fields come with different needs — but even in the space of data journalism, data visualization is used for different goals and with different approaches in mind. There can’t be a tool that satisfies them all.
Lisa’s consistently one of the strongest and most creative voices on visualization topics, and in this article, a culmination of several earlier pieces, she systematically uses and evaluates 24 tools. This is the kind of work that propels data visualization’s approachability and reach to new levels, an educated consumer’s guide to dataviz time and cost investments amidst an increasingly crowded and chaotic landscape.
#4— The Data Visualization Checklist
The Data Visualization Checklist contains the foundational techniques needed for clear, effective graphs. […] Stephanie and I included the core techniques in a single document so that you’ll have all the strategies in one central place. It’s your job to customize these techniques for your viewer and your dissemination format.
Ann and Stephanie, each prolific thought leaders in visualization, joined forces to produce this revised and improved checklist for dataviz techniques — given that 90%+ of the visualizations I see would fail to pass even 1/3 of their standards, the need for easy-to-use self-scoring criteria such as this to raise the visualization bar is painfully clear!
#5 — Data visualisation: what’s next?
The trends of data visualisation are forever shifting and changing as the data climate evolves at an ever faster pace. I’ve put together some thoughts on trends that I have identified in the last five or more years, where we are now and where, I believe, some of the focus is going.
Christian stretches our conception of what data visualization is and what it can become, with a focus on customization, connectivity, and complexity — the latter an increasingly-necessary reminder as a counterweight to misconceptions that visualizations need or should be a path to simplifying the complex and worse, that all dataviz needs to be fully processed at a 3-second glance. Rather, visualizations are a powerful weapon in clarifying — but not necessarily simplifying — the unavoidable complexity in our information-rich world.
#6 — Data visualisation: Contributions to evidence-based decision-making
This report builds on our experiences of producing data visualisations and in data journalism more broadly, and brings together the lessons we have learned with insights from the broader sector of research communication. What follows will help researchers, research communication managers and journalists to make more informed decisions about when to invest in data visualisations in order to meet research communication goals.
One of the most robust dataviz articles I came across all year, this SciDev.Net work is an exhaustively-sourced overview of the role visualization plays in scientific exploration and translation. Though targeted at data journalists and researchers, this article is absolutely dripping with implications for anyone seeking to stimulate discussion with data, yet it does not ignore the risks of visualization approaches to scientific communication — ethics, audience visual literacy, and so on.
#7 — DataViz for good: How to ethically communicate data in a visual manner
Last Friday I participated in my second Responsible Data Forum. […] Today’s event about data visualization for social impact did not disappoint. At the top of the day, we did the classic Post-It note brainstorm to inventory all of the potential avenues for working groups. Given the incredible experience of the people in the room, there was a lot to work with.
Building on the ethical theme, Matt’s article summarizes a “wow — wish I’d been there!” set of discussions and brainstorming on ethically communicating data visually. Dozens of key considerations are listed for dataviz practitioners to check and challenge themselves on the ethics of their work. The article also links to sites where ongoing discussions of these topics continue.
#8 — Visualizations that really work
Your visual communication will prove far more successful if you begin by acknowledging that it is not a lone action but, rather, several activities, each of which requires distinct types of planning, resources, and skills. The typology I offer here was created as a reaction to my making the very mistake I just described.
As the use of data visualization in business settings has soared, Scott has worked alongside this trend to provide a practical taxonomy for doing it well: efficiently, attractively, and accurately. The quadrant system above is a particularly-useful tool for differentiating types and purposes of visualizations, and designing them — with an empahsis on design elements that make visualizations sing — accordingly.
#9 — The role of stories in data storytelling
What follows is a framework for how story techniques can help the data analysis process. It is useful for any individual (or group) working with data, whether you’re a scientist, a marketer, an engineer or a policy-maker. The challenge in each case is similar: how do you put yourself in the best position to make sense of a mass of data in order to gain insights, and then inspire people to change based on the discoveries?
Though not limited to visualization-based data presentation, Shawn’s article extends the discourse on storytelling far beyond its too-common status as a single word or phrase on a bulleted list of data communication techniques. This article is especially unique in extending the trajectory of the storytelling process before and during, as well as after the data analysis itself. He also reviews types of stories and advises on initial triggers for establishing receptivity to a story-centered approach.
#10 — The design space of typographic data visualization
There are many possible new visualizations using typography, some of which I’ve previously discussed in posts on this blog. One way to consider this design space is to decompose it into the different elements that can be used to assemble visualizations.