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What Data Visualisation Experts Wish They Knew When They First Started

Eight accomplished data visualisation designers share their hard-earned wisdom

Let’s be honest — data visualisation is not easy to master. One of the underlying reasons is the dynamic nature of the field. Our understanding of what works and what doesn’t evolves every day thanks to research and experimentation. The general public’s knowledge of charts also keeps improving. Hence, our best chance of developing robust data design skills is through a process of trial and error.

The good news is that every expert in the field has gone through this process. By listening to their experience, we can make ours smoother. That’s why I reached out to eight data visualisation experts and asked them to complete the following sentence: “When I first started in data visualisation, I wish someone had told me …” This article is a compilation of their responses.

Alli Torban

Information designer, host of Data Viz Today podcast

Alli’s work, including her recent project creating data vis inspired wallpapers

Alli hosts a podcast called Data Viz Today, which is a goldmine of resources for both new and experienced data designers. I stumbled upon it last March and binge-listened to over 50 episodes in only a few weeks!

I then interviewed Alli for a blog post on the podcast. Among other things, I asked her to complete the sentence “When I first started in data visualisation, I wish someone had told me …” Her insightful response gave me the idea to put together this article.

Here’s what Alli shared with me:

I wish someone had told me that knowing how to use a particular software tool is only part of the process. The harder part to master will be (1) finding a story worth telling, and (2) finding an effective display for the story and the audience. It takes time and practice to develop that sense.

Nadieh Bremer

Freelance data visualisation designer and artist

Explore more of Nadieh’s work on her website

Nadieh is known for her unique and impactful visuals, often with a touch of astronomy. I had long admired Nadieh’s work and also fell in love with her design process after watching her presentation at Data Viz Live last May.

Here’s the first tip Nadieh wishes someone had given her at the start of her career:

Create a portfolio! Go for datasets that interest you, not the easily available datasets. Those generally don’t result in unique visuals that you’ll be willing to spend many evenings on. I had created a portfolio before I ever thought about freelancing, mostly because I just loved creating visuals, it was a hobby before it became work. And through those personal projects, I learned the importance of working on a topic that you love. I also learned that data on that topic is often somewhere on the internet, but perhaps not in a fancy .csv or .xsl format.

And one more thought on technical skills:

Not long after starting with React, Webpack, Vue, and whatnot, I felt like I had to learn all of them to be able to stay relevant. But gosh, I didn’t like it at all! I enjoy creating the visual, but I don’t enjoy the coding itself, the addition of interactivity, resizing, handling all the possible ways a user might (mis)use an interactive visual. At some point I just accepted that these frameworks weren’t my thing. They also didn’t really fit my kind of work: one big visual that is not updated. I let go of the frameworks, and felt much better afterwards. Thankfully, it hasn’t been an issue in my work (yet).

Joshua Smith

Analytics manager at CoverMyMeds, Tableau Zen Master and Iron Viz co-champion

Explore Joshua’s visualisations on his Tableau Public page

Joshua’s visualisation style is impactful because of his ability to tell stories. He mixes rhetorical principles with data visualisation to create a documentary-like flow. He also gave a talk at Data Viz Live last month that I strongly recommend.

Here’s what Joshua would have wanted to hear as a beginner:

I wish someone had told me how important exploring and play are for growth — and to pioneer new ideas. There’s so much work on “best practices,” but best practices are meant for general audiences, and don’t necessarily give us the room for the kind of growth we get from experimentation.

Shirley Wu

A creative focused on data-driven art and visualisations

Shirley’s work featured on her website

I love Shirley’s work because it’s an elegant combination of hard-core technical skills and artsy creativity. Her most recent project — People of the Pandemic — is a disease simulation game that gained a lot of popularity last April.

I couldn’t miss the opportunity to get tips from Shirley, so here’s what she had to say:

I wish someone had told me that the audience is everything.

Let me elaborate: I come from a software engineering background. I got into data visualisation because I was introduced to D3.js at work. In my first years creating visualisations, I didn’t care about anything other than how fun of a technical challenge I was solving. I didn’t care if what I put out was hard to read, so long as I had fun making it.

It wasn’t until my first year working with clients, and thus having to expand my skills from just code to include design and data analysis as well, that I realised how wrong I was.

Data visualisation is a tool for communication. We use it to make sense of the underlying data, we use it to make decisions, and we use it to tell stories. In each of those scenarios, we’re building for someone (and that someone could be ourselves or others). And that’s why I now start each project by understanding the audience of the visualisation and their needs: what are they trying to get out of and understand about the data?

And at every step of the way, I ask myself: am I communicating clearly? Do I have clear titles, legends, axes, annotations? Is there any room for misinterpretation? This last one is important, because just as we can lie with statistics, we can also lie with charts (Alberto Cairo wrote a whole book about this), and we have a responsibility to communicate across the imperfections and incompleteness in our data.

All this might be obvious to any designers reading, but it was a revelation for me. And though I still don’t manage to get it perfectly in all my projects, I now try my best to centre every one of my projects around my intended audience — while still having fun doing it!

Neil Richards

Data visualisation evangelist at Groupon, knowledge director at Data Visualization Society

A few of Neil’s visualisations on Tableau Public

Neil is a strong advocate of data visualisation communities. He plays important roles with the Data Visualisation Society and Viz for Social Good, and of course at work. Today, he’s one of the biggest out-of-the-box thinkers in the field, but that wasn’t always the case.

Here’s Neil’s story:

In my case, it’s quite Tableau specific. When I first started, I had a bit of a false start, because I only really heard of the things it can’t do, not the things it can do. I didn’t have friends or contacts who could advise me otherwise. I’d hear that it wasn’t great for survey data … you could only use survey data if you had cube data but that didn’t seem to work … you can’t do more advanced charts without very specialist knowledge … it seemed to be geared solely to business users who were interested in their sales and profits.

Eventually, partly through improvements in the software and mostly through improvements in awareness, I realised that Tableau is far more powerful, you can create pretty much anything if you can draw it, and there is so much great community advice out there on its different use cases.

And now, as someone who likes to bend the rules and try more unorthodox visualisations, I love the fact that Tableau can do anything — if you don’t know how to, then someone else probably does and you can share the details, examples and knowledge so easily. I wish I’d known that too!

RJ Andrews

Data storyteller, author, and founder of Info We Trust

To see more of RJ’s work, visit infowetrust.com

When I look at RJ’s visualisations, it often feels like history coming alive. If you’re not familiar with his work yet, I suggest you start with Creative Routines — a small multiple of historic creatives’ daily rituals. I also love his discussion with Alli Torban on how to be consistently creative.

Below is RJ’s response to the same question I asked everyone else:

When I first started creating data visualisations, I wish someone had told me that the journey would be hard, it would take a long time, but it would be worth it.

And a few bonus tips:

Be bold about what data stories you create in public. These projects lead to learnings about data, technology, and storytelling. Performative public data storytelling also leads to a vibrant community of mentors and friends.

Work with clients in private to learn more about how information graphics can generate value in many other ways beyond attracting attention.

Mine the history of information graphics for insight and inspiration. It is rich with treasure.

Lisa Charlotte Rost

Data Visualisation Designer and Blogger at Datawrapper

A few graphs Lisa created with Datawrapper. Visit her blog for more

Lisa used to create visuals for newsrooms, and now works for Datawrapper. She is an important voice in the data visualisation community and shares her insights on Chartable — the blog by Datawrapper. She also runs a fun virtual Data Vis Book Club that you should check out!

Here’s what Lisa had to say:

When I first started in data visualisation, I wish someone had told me that a chart is great because of what it shows, not how it’s built. When first attempting to visualise data, I put a lot of energy into figuring out the technology: Should I use Python or R? Do I need to learn d3.js? Every time I built a chart with the tools I knew — Excel and Adobe Illustrator — I felt a bit hesitant to show it to the data viz community with its amazing programmers.

Only slowly I figured out that there are dozens of different, equally valid ways to analyse and visualise data. And that it doesn’t matter if a chart is hand-drawn or created in Paint or in Adobe Illustrator or built with ggplot2 or in d3.js or with a tool like Tableau or Datawrapper. If it’s a good chart, it’s a good chart. And if you have fun, there’s no need to do it differently. Of course, as a data visualisation designer, it helps to know how to clean and analyse and format data. But showing a new angle, providing an overview, changing beliefs, or explaining (flaws in) our world are all things we should care more about than the question which tool we use to do so.

Eva Murray

Technology evangelist at Exasol, co-author of the #MakeoverMonday book

See more of Eva’s work on her Tableau Public page

Eva is a mentor and an inspiration for many people in the Tableau community. She’s mostly known and respected for the Makeover Monday project which helps hundreds of aspiring data designers to improve their skills each year.

Eva’s response summarises the traits that designers quoted in this article have in common.

When I first started, I wish someone had told me that no one has all the answers and that we’re all going to learn as we go. The field we work in is so dynamic and still evolving and maturing, that we are all part of the forces that shape how things are done. This requires an open mind, confidence and a bit of courage, but the learning process is what makes it all very rewarding.

Did you notice how unique everyone’s response was? Each expert I interviewed struggled at first but for different reasons. They had to work hard to get good at what they do.

So if you’re a beginner in the field, don’t expect to have it all figured out right away. Maybe you’ll need to develop your technical skills more, work on a portfolio or pay more attention to the audience. Being good at such a multidisciplinary and dynamic field takes time. Keep practicing, and you’ll get there.

If you’re an experienced data designer, what do you wish you knew when you first started? Share your thoughts in the comments!

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Evelina Judeikytė

Evelina Judeikytė

Information design and storytelling specialist 📊 • Founder of parabole.studio