VizHeads by

VizHeads is an independent and experimental interactive data visualization by that displays a catalog of the most recommended people and organizations in the dataviz world according to their peers, in an unexpected and fun way.
Using data gathered by the Data Visualization Society Annual Survey, we wanted Vizheads to be the perfect link to send to friends when asked: “I’m interested in data visualization. Who should I follow?”
It is a way to recognize fellow professionals, pay homage to people and organizations we admire, or even get to know new references.
The floating heads were a way to emphasize the humanity and realness of our community while also being engaging. It is made for people and by people.
Many hours were spent collecting and curating pictures of more than 400 heads and enriching the database with websites, social media, and other information to provide research material for others.
Plus, the data analysis revealed a hidden story. We noticed a significant need for citations in the Survey for Global South representatives of the dataviz community (a lack of heads in our viz!). It struck us with a bigger question: Are we doing enough to uplift dataviz experts from other parts of the world, or are we just reinforcing the same Global North-centered mindset?
The colors, both in the 2021 and 2022 surveys, show the different distribution of each region. You can see that more than 95% of recommendations are from the Global North by clicking on the regions below. Switching the flip in the middle shows that it hasn’t changed from one year to the next, and there is still a lot to be done in terms of diversity and equity. Although it speaks for the type of representation within the Dataviz Society, it may show we are not used to looking outside our realities and bubbles.
It is an ongoing internal project, with new features added to show how powerful dataviz can be to reveal our biases.
Other interactions, like the heads rearranging randomly and movements from the search bar, were added after testing other types of interactivity that could make this serious topic represented in a non-traditional way. We want to user to find hidden interactions as they explore the viz. A lot of effor has been put on making sure it is responsive, as that the interactivity loads smoothly for every computer and mobile device.
Lastly, each head has information on region and gender that recommended them as a first initiative to dig more into these issues.
Although the project is meant for the dataviz community, it is being used as reference material from many institutions, professors, and people from the field. We are also happy to see many datavizers share the work in their socials, surprised to be recognized. Especially fellow South of the Globe representatives.
We wish to continue our studies on recommendation bias, adding gender and other layers to the work.

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