Data Visualization Workshop: Thinking visually about data

By Jennifer Sun
May 15, 2017

Jennifer is one of the new interns here at Global Pulse working as part of our data science team, specifically on data visualizations. Recently, she was part of a data visualization workshop we organized for colleagues from the UN Development Group. In this article, she summarizes her impressions and lessons learned.

The past few years have seen tremendous growth in the field of big data. As floods of data continue to be produced and collected, the need to explore, gain insights, and communicate around them has also risen in importance. A quick dive into data visualization shows the incredible vastness of this field. Look for example at the interactive graphics in the New York Times, or the complex microsites of UNHCR. While many people have heard of data visualizations, they have also often severely undersold its power as merely interesting “pictures of data.” While that is somewhat true, what people largely miss is the purpose of data visualization: a way to gain and share insights from data.

Diving deeper into how and why we use data visualizations was a major goal of a workshop hosted by UN Global Pulse a few weeks ago. The workshop brought together colleagues from our team and the UN Development Group. It was led by our data visualization specialist.

The workshop began with the overarching concept of thinking visually—that is, thinking about abstract data using graphic representations. It covered two major aspects of data visualization: i) the perceptual and psychological roots of data visualization, including pre-attentive processing and pattern identification, and ii) the breakdown of data visualization as a “language,” consisting of a vocabulary of visual variables and a visualization grammar (see Huron et al.). By combining the idea of thinking visually with data visualization fundamentals,  the group was able to not only understand data visualization but also to have the tools to use it for its true purpose of gaining and sharing insights.

In the hands-on activity that followed, we were split into groups and tasked with creating data visualizations using colored tiles and poster boards.


As much as we learned during the lecture, habits can be hard to break and my group almost immediately leaped into fervorous debates surrounding the data we were given: what did the data mean, what data did we want to focus on, what questions could the data answer? Halfway through the session, we were still hunched over reading and debating  the dataset without having used even one tile. We had to be reminded that “we should try to think visually, and see what the data has to offer,”  rather than try to understand what is in the data and “crunch the numbers.”

As we faced the tiles, we began thinking about the visual variables the tiles offered, their shape, color, positioning, and the data that they could represent (i.e. colors being continents, diamonds or squares being positive or negative).  By connecting the tiles to the data, we created tangible connections. Even with only colored tiles, we were able to explore and expose various parts of the data: patterns, questions, insights that would have lied dormant in the raw numbers and text of the datasets.

At the end of the exercise, one thing was clear. Instead of trying to extract the insights from graphics using data viz, we were trying to embed them into the graphics by first analyzing the data. We were going about it all wrong.

This became even more evident when the other groups presented their visualizations and we realized that simple things like positioning a piece sideways, using different shapes or creating a simple legend can help others understand what one is trying to portray.

Lesson two: just because you understand what you are trying to portray, it does not necessarily mean others will. The more pieces of information, a.k.a legends,  a visualization includes, the easier it is for someone to understand it. Also, before making it public, one may want to test it with a group, gather feedback and refine it.

As the participants left the room, the excited and overlapping conversations clearly showed that this workshop had not only made data visualization clearer to people, it had brought people closer to data visualization.

If you find yourself more curious about data visualization, here are a few great examples:


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