Lessons from Pentagram #1
The Power of Scale
Some of you may already know that in March 2022, I joined Giorgia Lupi’s team at Pentagram as Senior Data Visualization Designer. It’s been a transformative experience and we made many incredible projects. But over the winter break, I took the decision to leave and go back to being independent. In the next couple of weeks, I’ll write about some of the lessons I learnt there.
#1: The Power of Scale
Before Pentagram, my largest projects were often tools that would allow people to access information like The Diplomatic Pulse or the World Bank Gender Data Portal. But this rarely gave me the chance to educate those who would use those datasets how to do so.
At Pentagram, I got the chance to work on two data visualization guidelines. One for a large non-profit working for gender equality, one for Deloitte Insights. Both work with sensitive datasets like income inequality or and have a large audience. Both probably have dozens of designers using data for internal and external purposes.
In most data visualization guidelines like Material’s you’ll find:
Color palettes & typography guidance (Sometimes with accessibility guidance)
Example of key charts
Do’s and Dont’s
Is it enough?
What if we went one step further?
What if we could promote a better use of data visualization?
Upon working on those large projects, I realized how much impact I could have on those organisations. I, small designer, thanks to Pentagram’s name could shape how dozens of other designers would think and approach their datavizs.
And it freaked me out.
With the opportunity to teach others how to design their own data visualizations comes the responsibility to teach them the ethical issues that come with it.
Not to plagiarized anything from Star Wars. But yes, With great power comes great responsibility. There is power in working at such large scale.
Here are some key guidelines add-ons that should be mandatory (in my humble opinion):
Detailed explanations on when and where to use color palettes in regards to accessibility and ethics. When to use the three color palettes in data visualization? Why? When can it be harmful?
Strict Guidance on how to use photography/illustration accompanying data visualization if relevant:
Diversity matters. Remind them.
Treat humans with dignity. No trauma porn when illustrating topics that affect people like violence, poverty..
I learnt this working with ProjectInclude who was incredible caring when it came to labelling. Some terms commonly used in dataviz are confusing at best, harmful at worst.
Give the reader a better understanding of the terminology used, and be transparent about the choices made when the data was collected and analyzed.
Exemple 1: These ethnic identities center people’s self identification.
Exemple 2: Asian American are not shown here as the sample collected was not statistically representative.
I might not change the world. But it may make us all better designers. One guideline at the time.
What equity guidance do you add to your dataviz guidelines?
Cynicism will ruin us all, “Because what we do begins with what we believe we can do.”
The right not to be fun at work. That time when French people did it right.
Duncan sent me this incredible article about Cultural Fracking after yet another one my rant about how AI will just make us create the same work over and over again.
I love freelancing. It is scary though. Here is how I’d prepare for it if I had to do it again.