
Note: The data visualization tool focused on here is Tableau, but many items mentioned apply to all data visualization tools.
During my Dreamforce interview on Wednesday, I related that my journey only just began in 2019. I provided a couple of bits but didn’t have time to share all of them. The most important was realizing my visualizations and approach to design needed an overhaul, and being part of the Community not only drove the point home but gave me the resources and inspiration to improve.
Over two years ago, when I started in Tableau’s #DataFam or data visualization communities on social media, I knew I had my work cut out for me. At that point, I used the tool for five years, so I was comfortable with its functions, making calculations, analyzing data for insights, and putting together charts. Still, I had no clue on design best practices, let alone creating an aesthetically pleasing visualization. So even though the devil on my shoulder kept telling me, I wasn’t a designer and stopped bothering, the angel on the other shoulder refused to let me give in.
Before Tableau, I used Excel for data visualizations. It was a tool to show off making fancy charts or dashboards with 3D exploding pie charts and a ton of color – the more, the better. This carried on through my work with Tableau. My customers wanted color and always red and green for metrics. They wanted dimensional colors and a ton of crosstabs. This is how I came to the community and my visualizations. This ugly duckling was an example of what I thought was a good and shareable data visualization:

I noticed immediately, once I joined, that less is more in many situations. For example, we do not need to share ALL of the data or use ALL of the available colors or have every pixel filled.
The most successful community members had a much different approach. They used color sparingly and purposefully to drive a point successfully, used white space, and shared data highlights that made the most impact.
Even though my visualization rate in the community wasn’t weekly or even monthly sometimes, I was creating several data visualizations each week at work. At that point, my focus was to learn and apply the best techniques at work. Given my audience and what they were accustomed to, I knew this would be a hard sell and required a lot of studying and research to explain choices and apply robust data in bite-sized morsels of insights. In addition, when I vizzed, I collaborated and sought feedback on personal visualizations from people I respected – people like Zach Bowders [[[[[Twitter](https://twitter.com/BMooreWasTaken)](https://twitter.com/FlerlageKev)](https://twitter.com/sarahlovesdata)](https://twitter.com/thoang1000) | [[[[Tableau](https://public.tableau.com/app/profile/brian.moore7221)](https://public.tableau.com/app/profile/kevin.flerlage)](https://public.tableau.com/app/profile/sarah.bartlett)](https://public.tableau.com/app/profile/toan.hoang) | [[[Site](https://www.flerlagetwins.com/)](https://sarahlovesdata.co.uk/)](https://tableau.toanhoang.com/)], Toan Hoang [Twitter | Tableau | Site], Sarah Bartlett [Twitter | Tableau | Site], Kevin Flerlage [Twitter | Tableau | Site], and Brian Moore [Twitter | Tableau] to name several.
I knew I was dealing with impostor syndrome but didn’t want to broadcast it. Publicly sharing it would give me an excuse not to put my work out there, strive to visualize better, or limit my desire to learn.
Since joining the community in 2019, I have committed thousands of hours to data visualization in the forms of consuming media, literature, collaborating, discussing, mentoring, and visualizing hundreds of dashboards (if not closer to a thousand). Because of those efforts, I improved enough to be a Tableau Public Featured Author in 2020, had two Tableau Public Global ‘Viz of the Day (VOTD)’ this year, and provided iterated feedback on ~ 20 or so other VOTDs. My feelings of being an impostor did not subside because of external recognition (or my contribution to others receiving recognition)… it’s because of what I mention below.
3 Facts that Make Us All ‘Impostors’ (or feel like one)
- There is SO much to learn. Although data visualization courses are starting to find their way in universities, it’s not something that one would customarily consider. It combines some of the complexities of Data Science, psychology, and art into one discipline – most of us learn one or the other or maybe even come from a couple of the places, but not all three. It takes time, in-tool practice, and many false starts to develop a harmonious blend of these skills.
- We have no following at the beginning. We all start from nowhere. For example, my Twitter and LinkedIn were at -0- in August 2019. I wanted to give back because of my experience with the tool, but who would take me seriously? Then you see those with followings in the thousands with huge honors, incredible titles, and so forth under their belts. I did not know how to get started, let alone fit in and grow.
- The Data Visualization community can be overwhelming and stress-inducing. I’ve always looked at the shared visualizations in our community as endless sources of inspiration. Still, many have viewed it as a frightening reminder of how far they have to go to sit at the same table as many that appear on their feeds.
7 Tips to Help Overcome the Impostor Syndrome
- Connect with those in the community you respect. I learned quickly; many of those I admired were immediately accessible – you get to chat with them, get feedback, and know the dataviz artist a little more as people. Seeing them as more than avatars of data visualization experts adds context that makes the community a little smaller and more human – you also learn quickly that we want you to do well and support you the best we can. Feeling that from others you admire helps motivate and inspire learning.
- Read, practice, and share (rinse and repeat). To improve, it takes more than lurking in a community and visualizing in a vacuum. It would help if you pushed yourself out there and utilized many community resources to upskill. In addition, participating in projects where participants are encouraged to share and see works others put together is a way to improve at an even quicker rate. For example, for those new to the community, I share that #MakeOver Monday is the fastest way to see data visualization improvement – you not only have a clean data source to work with, Andy Kriebel (a Zen Master Hall of Famer) shares his approach on YouTube to each visualization – you also get to see the process many others take to the same data on Twitter and LinkedIn. In addition, you can go into archives and work on older weeks and see weekly feedback from Eva Murray and guests. Other projects, sources of inspiration, and tutorials are compiled each week and month by Tableau – bookmark the Tableau Community Blog Hub.
- Download and review the work you admire on Tableau Public. In addition, Tableau Public offers authors the opportunity to allow their visualization to be downloaded or copied. On the upper right-hand corner of a visualization, when permitted, you can copy it to be web edited (for free) on your Tableau Public account (sign-in required) or download it to view on your Tableau Desktop.

Super important note: Even though some authors allow their visualizations to be downloaded, please realize it’s their work and asset. If you create work inspired by the visualization, cite the author. Also, if the work is a copy or very similar, please hide on your Tableau Public portfolio or in no way represent their work as your work.
- Seek private feedback. I prefer private feedback – both for delivering and receiving. For those that are feeling a little bit like an impostor, this is even more important. Feel free to solicit private feedback when sharing or to ask someone privately. However, suppose you do not feel comfortable doing that. In that case, there is a superb community resource offered by Tableau Ambassadors Michelle Frayman and Zak Geis [Twitter | Tableau | Site] called Viz Feedback Office Hours – see and complete the form for inclusion.
Important Note: Please keep in mind, the person you are asking may not have time to provide immediate feedback or feel comfortable providing feedback on your work – understand that receiving any feedback on your work from someone you respect is a privilege.
- Collaborate. Working with others helps you learn from other data visualizers while creating something new. A viz successful collaboration, in my experience, has been that it improves the visualization, provides unique learning opportunities, and deepens friendships.
- Source your own dataset and create something that you are passionate about. This can be done because you find inspiration on the internet and want to put it together on a visualization or contribute to a community initiative like #IronQuest that requires you to find your data. Seeing or putting together your data can inspire you to develop something unique. The motivating presence is sharing your passion with the world – you want to make it great and will work hard at understanding the data and presenting it in its most impactful form.
- Take time to review progress at somewhat regular intervals. You may never become a Tableau Public Featured Author or earn Tableau Public Viz of the Day but should see significant improvement from the bottom to the top of your portfolio. Even looking every six months can offer a source of pride in your design accomplishments.