The Safe Triumvirate of Visualization, In-faux-graphics and All That

The Safe Triumvirate of Visualization, In-faux-graphics and All That

Over at 37Signal’s Signal v. Noise, Noah Lorang recently cautioned against the recent infatuation with “creative” visualization techniques. He writes that these new visualization techniques are a mixed bag, and while some are truly innovative and help us understand our data better, many are not; hence, his witty quip “in_faux_graphics”. He goes on to claim that we should be able to address 95% of visualization needs with just three basic (or “safe” in his words) charts: line chart, histogram and table.

As someone who constantly thinks about how data visualization fits in our Big Data as-a-Service platform, I found Noah’s observation to be profound. Indeed, looking at how our customers use Treasure Data, most of visualization needs are perfectly met with Noah’s Safe Triumvirate.

The question is, then, when do we need something more than the Safe Triumvirate? After all, these creative visualizations must have demand somewhere, right?

Broadly speaking, I believe there are two types of data visualization, summary and exploratory.

Summary data visualization organizes data in an intuitive way so that you can spend two minutes glancing at charts and tables instead of two days wading through raw data. Dashboards and reports typify summary data visualization, and the Safe Triumvirate are by far the three most common data representations for summary data. This makes sense because the output must be easy to scan and understand, and few can beat the Safe Triumvirate when it comes to ease of comprehension.

Exploratory data visualization, in contrast, is more open-ended. Most likely, you are not sure what question to ask yet and trying a variety of charting/visualization to make sense out of it. In my view, this is the area where new creative visualization techniques can help us get more value out of data. For example, Ayasdi applies a decade of mathematical research in topology to visualize data in a fundamentally different way, and they claim their tool can accelerate drug discovery immensely.

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So, what’s the verdict? Just like other potentially polarizing dichotomies in technology, “to each his own” is the answer. And this is why we at Treasure Data try to provide rich interfaces to interact with our platform. The customer should have the flexibility to choose what works for them, and while we provide a basic dashboard, we think of it as a starting point on which our customers can build what works best for them.

Kiyoto Tamura
Kiyoto Tamura
Kiyoto began his career in quantitative finance before making a transition into the startup world. A math nerd turned software engineer turned developer marketer, he enjoys postmodern literature, statistics, and a good cup of coffee.
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