Yesterday I stumbled upon a recently published and excellent visual analytics case study: the Circle Line Rogue Train Case. The article describes how data scientists at GovTech’s Data Science Division in Singapore used visualization to discover the origin of a recurring and mysterious disruptions of Singapore’s MRT Circle Line.
This study represents my ideal case for data visualization: the visual exploration of a given data set to solve an important problem somebody has.
Why is it so rare to read about problem-solving visualization?
Besides deeply enjoying reading the story and the data analysis behind it, this study got me thinking: why is it so rare to read about this kind of projects? Is it because not many people actually carry out this kind of work? Or maybe because nobody dares to write about them? Or maybe they are out there but we never notice?
When I look at the data visualization landscape I recognize that the very large majority of projects we see around fall into two main categories:
Before I move on, let me clarify I have nothing against these two categories. I am myself as “guilty” as anyone else of being a purveyor of these two kind of visualizations. They are a the root of the enormous success visualization experienced in recent years.
Yet, when I look at example of “problem-solving visualization” I can’t stop from thinking: “Yes! That’s what really matters to me!“, “That’s what I want to see people doing more“, and “That’s what I want to teach my students how to do“.
As much as I love data journalism and I consider it a cornerstone of an era in which possibly people can use reason to discuss issues affecting our society, I do believe it is limited. The biggest limit of data journalism resides in its “storytelling bent”. Journalists want to tell you a story and they need to entertain you. This, in turn, poses some important problems. First, it’s hard to analyze data without thinking of the story one has in mind. Second, not all important problems out there are journalistic problems. Many problems are practical issues people have and that can hopefully be solved by analyzing some data.
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