Finding Comfort in Uncertainty
Reflections on a data visualization panel discussion with Nadieh Bremer, Jill Hubley, and Shirley Wu
Earlier this October, the New York data visualization community was treated to an intimate panel with Nadieh Bremer, Jill Hubley, and Shirley Wu, moderated by Sarah Kay Miller. The hour-long session gave a peek into what this journey has been like for them — everything from the struggles and challenges to the inspiration and motivation involved with creating data visualizations. Two members of the Data Visualization Society — a dataviz designer and a dataviz engineer — share their takeaways from the event.
(See bottom of article for the full video of the event.)
How do we deal with the dreaded “Imposter Syndrome”?
It was at the same time disheartening and encouraging to hear that these women — leaders in the field with a proven track record of awesome projects (I mean, have you checked out their websites?) and role models to many of us — also struggle with imposter syndrome.
“I guess if Nadieh Bremer also feels like she has no idea what she’s doing sometimes, then maybe I know more than I think too.”
“If Jill Hubley encounters Imposter Syndrome, even with all the cool shit she’s done, I guess I have no chance of ever getting on the other side of it.”
Nadieh indeed confirmed that Imposter Syndrome — or more generally the feelings of uncertainty and self-doubt that surround her work and her process — have been an ongoing struggle. It “never really goes away,” she admitted.
Luckily, Sarah Kay prompted these women to offer the audience tools to combat the infamous Imposter Syndrome. Nadieh gave an anecdote about how it took her a year, but she remembers finally being able to write some d3 code from memory. This was a proud moment for her, and she encouraged people to “celebrate the small victories … and being able to get to that point.” Shirley mentioned that she often compares projects done by an interdisciplinary visualization team to projects she’s done on her own, and then reminds herself that that’s not an accurate comparison. She also emphasized the importance of measuring the success of your viz by the inspiration you’ve given people, and not just by the external validation you get (like awards, views, etc.).
How can we get “unstuck” when we reach a mental block while creating a visualization?
We’ve all been there — the rip-it-up and toss-it-out point in a project where frustration is at a peak. The panelists each had unique suggestions on how to move past this low point in the process.
Jill provided a couple of tips for finding the motivation to finish a visualization. First, she said it’s important to pursue projects that you find entertaining. She also talked about how it’s often beneficial to let the project rest, and then return to it with a fresh perspective. Nadieh remarked that she takes a different approach; she generally tries to tackle other design aspects of the visualization, like the legend, and says that that usually helps her realize what is missing from the piece. Shirley added that she might try to show the visualization to someone totally unfamiliar with the project to see what their perspective is.
Has the process changed from when the panelists first began practicing dataviz?
The variety of answers to this question speaks to the fact that most people come to this field with a range of skills and backgrounds and that most end up molding their practice and widening their approach as they move along.
Nadieh said that her tools have changed; she tries to start out with pen and paper more frequently. But along with using different tools, she has also changed her mindset when approaching a project. She now frames her work around a different set of questions. Instead of looking at a list of charts and asking herself which to choose, she now asks herself, “What is my goal?” and “What is my data about?”
Shirley admitted that she used to make visualizations without enough regard for the end user, so that’s something she now incorporates into her work from the start of a project. She added that considering the interpretability of a piece is more crucial to her than pursuing purely technical challenges.
Jill agreed with Nadieh, in that she tries to use pen and paper more often in order to experiment with different encodings. She also agreed that exploring the data and then considering the design is part of her current process. Broadening her toolset has been another natural progression throughout her career.
Being a woman in dataviz — what experiences have made the panelists feel welcomed in the community?
Rather than focus on the sometimes difficult parts that come with being a woman in the STEM field, this question brought out the positive aspects that the panelists have experienced during their career. Shirley and Nadieh touched on pivotal acts of kindness that they received when they were first breaking into the field. For example, Nadieh was invited into a dataviz Slack channel shortly after its inception. Although it was a small group, it helped her connect with people who felt passionate about the same things she did, and really helped her get her foot in the door.
Other points were mentioned as well, such as how Nadieh, as a freelancer, often refers clients to fellow dataviz friends and colleagues when a project’s not a great fit for her — highlighting how she feels that the community is more uplifting than competitive. This is her way of giving back; it’s a “thanks” to the people who helped her in a similar way when she was just starting out.
What freelance advice do they have?
Nadieh, Jill, and Shirley all have extensive freelance experience and generously offered up some useful advice:
- Budget — When it comes to budget, Jill had this to say: “Ask for more money than you think you should.” She also added “Don’t work for free.” (Shirley followed up with the caveat “unless you’re intentionally working pro-bono.”) Nadieh recommended requesting a budget proposal early on in your conversations with potential clients, in order to avoid getting too far into the process without having any concrete agreements. She also shared her “multi-tiered” budget strategy, where she gives a client three different budget proposals, representing low, medium, and high level of effort.
- Portfolio — Both Shirley and Nadieh emphasized the importance of showcasing only the type of work that excites you. Whatever’s in your portfolio — that’s what you’re going to be hired to do next. In short, beware the portfolio “snowball effect” (Shirley).
Nadieh, Jill, and Shirley are undeniably talented data visualization practitioners. They are certainly inspiring, but honestly, that was well known before hearing them speak. What was most remarkable was how relatable they all were. They echoed experiences we’ve had and frustrations we’ve felt. For example, Shirley gave voice to that feeling that is familiar to any dabbler in data analysis when she described her tentative approach to anything data-analysis related: she said “… is this statistical analysis that I did that I don’t really understand but I just found this node package, is it the right thing to use?” In a similarly relatable moment, Jill and Sarah Kay joked about their large dataviz “graveyards” of unfinished projects.
So how can we learn from the panelists’ experiences?
- Be comfortable in the uncertainty: Projects that you feel passionate about are almost always going to be difficult, but that’s often what makes them worthwhile pieces.
- Go outside your comfort zone: Don’t let imposter syndrome slow you down; experimenting with new tools and technologies is how your process will be able to evolve over time.
- Get involved with the community, especially if you are new to dataviz: All three panelists recognized that in some way or another, the dataviz community has played a significant role in helping them discover their passion, even when they were just starting out. So don’t be shy to reach out and get involved! A great way to start is by becoming a DVS member:
Join - Data Visualization Society
A place to foster constructive engagement with community members on broader problems of the field. A resource for data…
Though the panelists seemed to agree that dataviz is always a challenge, they left us feeling eager to create and empowered to feel confident in our abilities — no matter what stage of the process we’re in.
Thanks to Alyssa Bell and Jason Forrest for help editing this article.