Imposter Syndrome and Levelling Up in Data Visualization
Is It Ever Gonna Be Enough?
A bright, energetic consultant stares intently at Microsoft PowerPoint. This hero, inexperienced but devoted, hears a door open nearby. A boss approaches.
The boss sits down and asks to see our hero’s latest wireframe for a potential dashboard to sell to a middle-management client. Upon looking at the PowerPoint slide for about five seconds, the boss tells our hero that he doesn’t know how to explain what he asked for any better, but whatever he’s looking at, this isn’t it.
The boss turns to a workstation right behind the hero and pulls up a news website. Right there, beyond the boss’s pointed finger, the hero sees an article published by a major news journal that includes a series of advanced charts. Animated bar charts race against each other in one frame. In another, balls fly across the screen into little bins to represent potential outcomes. The charts all require a high degree of experience to generate.
The boss stands up. “Make it look like this.” He leaves.
The hero only stares. The article uses charts and algorithms that our hero has never seen before. The hero’s preferred software or language won’t even make some of them. Some charts look touched-up in an expensive image-editing application. Our hero doesn’t have access to that.
Freeze frame and focus in tight on our hero. Yep, that’s me. You’re probably wondering how I got myself in this situation. Well.
I Don’t Even Like Balsamic Vinegar, and Work Won’t Let Me Install Balsamiq on My Laptop
Look, Silicon Valley, you win. Really. You’re always going to pump out new technologies that we have to know, or else we’ll fall behind. New certifications, new dataviz platforms, new algorithmic techniques. Ok. Cool. I’ll spend my life tracking down the latest thing and hoping I’m good enough compared to my peers, but that’s life. Right?
Well, sometimes. That’s life… when you’ve done this for a few years. When you’re first starting out? You can’t tell a colorblind palette from a grayscale one. Your resume still includes things like “Captain of the JV Volleyball Team, 2011–2015”. Murder. I liken this to an old meme from several years ago.
I can think of no better metaphor for learning anything at all in the modern data science environment. Transformational data products sit across an ever-growing chasm of technical, unexplained difficulty from the simple charts made by a novice. You want instructions for an animated, clickable pie chart in plain language? Tough.
Donut Charts > Pie Charts, just like Pie > Cake
You, young budding data scientist or consultant who got forwarded this article, you have it pretty bad. Maybe not as bad as I had it. I graduated from college during the Great Recession! I did 3 internships before I got a job in Washington D.C. earning $34,000 a year! You know how far $34,000 a year takes you in Washington? About one student loan payment and, maybe, a taco.
Yet, perhaps you also have it bad in your own way. I lived underemployed for years but at least I found work. If I was ever a failure (personal note: Was? Don’t get too cocky now), at least I knew I was probably in the wrong line of work because a global financial crisis forced its way into my young adulthood.
You have none of that. Instead, you have ten times the things to learn in one-tenth the time. Draw an owl? Please. Draw ten. Use a different language or dataviz app for each. Roll in all the latest dataviz techniques in the newest books. And can you have it on my desk before lunch? Byeeeee.
I Don’t Know Excel and You Can’t Prove I’m Lying
Because you know. You just know, beyond doubt and reason, in that deep pit of your stomach where you keep all those thoughts you have about leaving everything behind and jumping onto a train with vagrants. Deep down there in your soul of souls. You know the place?
Down in your heart of hearts, you fear what we all do — getting exposed. You fear the boss, the one who thinks they’re cool and “gets data” and “knows what Python is”. You’re terrified that they’ll come in and ask you to create this cool thing they just saw someone post on Twitter — an entry in the Tableau IronViz competition, no less — and now they want to see you make that cool thing too.
Down this pit of despair you descend as you read up on polygonal mapping workflows and “Radials Made Easy” articles. Oh, but they’re actually just links to 30-minute YouTube videos with data that looks NOTHING LIKE YOURS BECAUSE THEIRS ISN’T PIVOTED, *takes breath,* WHY WOULD YOU SPEND NO TIME IN YOUR ARTICLE DISCUSSING WHAT THE SHAPE OF YOUR DATA IS BEFORE MAKING YOUR CHART YOU ODIOUS CATFISH. You’ve reached rock bottom. You can’t do it. You can’t make that chart.
Or at least, not right now, you cry to your boss. Maybe with more experience? It’s not fair in the least, but thanks for playing. Your boss will now make a face that makes it pretty clear in their cool-boss passive-aggressive way. A face that says you’re not their dataviz person. Because you can’t do the thing they saw that one time.
“I thought you were the expert at visualization?” Nightmare. Update your resume.
The Only Way Out Is Through
Dear friends, the community hears your cries.
And in truth, there’s no time like the present to jump right on in. The Tableau community on Twitter, double-edged sword that it is, can expose your boss to visualizations that you won’t know how to make for a few years, but it can also introduce you to some really, really cool people.
When Tableau Zen Master Yvan Fornes posted a fitness tracker last January that included a radial chart and made the whole workbook free to download, I knew this was a great chance to learn the techniques for radials in Tableau. But I had absolutely no idea where to start, so, I did this.
And then a really cool thing happened.
Within a few hours, I was able to make this dummy chart…
Now if a client ever needs that kind of product, I can say yes to them.
That’s a great way to learn Tableau, by the way. Watch Twitter for workbooks that do things you don’t know how to do, then download them, rip into their calculated fields, and create your own recipe for it. Save, rinse, repeat.
That’s also the trick to beating imposter syndrome: just doing something hard once, no matter how bad. Every data scientist in the world, be they analysts, engineers, visualizers, and anything in between, is a force to be reckoned with from the second time they do something onward.
The point is, yeah, you’re going to come across new situations and fail them. It’s part of life and we all go through that. As my son’s cartoons say, “Sucking at something is the first step at being sorta ok at it.” Find helpful people and just be honest. You don’t have to compete in IronViz. You don’t have to switch on the fly between Tableau and PowerBI. You don’t even need to know algebra—well, no, you definitely need algebra to do this line of work. Calculus? Depends.
Maybe all this is still more than you need. Maybe you’re waiting for an answer on Stack Overflow. Or for a matplotlib blogger to explain how to put value labels on bar charts without a terrifying-looking For loop (Python devs, go check out Altair!). Or how to get a map to zoom in with D3 without digging through four textbooks.
That’s all, all of it, fine. It’s part of your job and you’re coming onto the scene at an exciting time. All these great, awesome, wonderful tools and all these coding libraries let you do so many amazing things! And yet, only a handful of people can explain how it all works from start to finish in regular words.
None of that is your fault, as long as you keep looking and learning. It is, I submit, your duty to the data science community to search out the answers and share them with the rest of us. A data scientist’s brain is like a shark: it has to always be thinking, moving, learning, analyzing, studying, creating, logging, cleaning, proofing, plotting. So I’m going to say this as clearly as I can: failure isn’t being under-experienced, it’s stopping. To stop is to admit defeat, and that will never do when your cause is all the knowledge in the world.
Hey, maybe you don’t have questions. Maybe you’re just looking through the plethora of new technologies and wondering “well… where exactly do I start?” The answer can only ever be: at the beginning. Ask yourself what you want to do, and why. Then seek out a way to do that thing. Then do that thing.
Or maybe instead of learning a new, exciting plotting library, you’ve been studying for an AWS exam for a month and you just failed it earlier today, so you went home and wrote an article so you could remind yourself that everything’s fine, and you’re fine. You’re fine. You’re fine… but you failed by two questions, and… and how could you let that happen?! What a sham, what an imposter, everyone at your job already knows that you’re a failure and a fraud and you just gave them proof, you absolute idiot. How could you ever get this far being so bad?! Donald Trump is already drafting a tweet calling you a loser, you dreg.
Adulthood is filled with people telling you every time you do things wrong, but when you do something right there’s nothing but a similarly-sized void where you’d expect affirmation to be. But look at what you learned, Phil. You didn’t pass the exam, but you know a lot more about what you wanted to know about.
Maybe all of our inexperience, immaturity, failure, is just what we get for trying and shouldn’t be thought of so poorly. Maybe failure is wholesome. Maybe the real Solutions Architect exam, or interactive sunburst chart, or web-published study, or good feedback round with the client isn’t the real prize: the failures getting there are.
Go make a good mistake. Close this article and draw the rest of your chart. You’ll do fine.
Philip Hawkins wants to write science fiction when he grows up. He works as a consultant for the US government and is unsure what else to put here, if anything.