Category: Visualization

Mike Cisneros takes us through cases when waterfall charts are useful:

In our workshops, we often put a grid of a dozen charts up on the screen, and say to the participants, “Most of the charts you’ll need to communicate effectively in business are right here on the screen. 99% of the time, one of the visuals you see here will get your message across effectively. And as you can see there aren’t any really unusual charts here. You’ve probably seen all of these before.” 

If, at this point, somebody in the room says, “Actually, I’ve never heard of a ______ chart before,” you can almost always fill in the blank with the word “waterfall.”

Waterfall charts are really useful in a few scenarios, but I see them get misused far too frequently.

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Stephanie Evergreen solves a challenge:

You’ve asked employees to rate a bunch of different aspects of their job. You want to know if they think that aspect is important AND how satisfied they are with that aspect of their job. So, naturally, you make two individual questions with response options like Not at all Important to Very Important and Not at all Satisfied to Very Satisfied. I would probably do the same thing.

But then you’ve got to show the data and, importantly, how those two variables – Importance and Satisfaction – relate to each other.

Click through for two methods of visualizing the results.

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Alex Velez shares some thoughts on trendlines:

A trendline is a line drawn on a chart highlighting an underlying pattern of individual values. The line itself can take on many forms depending on the shape of the data: straight, curved, etc. This is common practice when using statistical techniques to understand and forecast data (e.g. regression analysis). Determining the best fit and forecasting is beyond this article’s scope, so if you’re interested in learning more, I recommend Anna Foard’s Stats Ninja website. Instead, I’ll focus on various considerations related to visualizing trendlines when communicating data.

My main thought on trendlines is that they are less important than the data points. We make up the trendlines out of thin air; the data points actually exist and actually matter. Trendlines can be useful, but they don’t replace the data.

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Shirin Elsinghorst posts slides (in German) and demos for a talk on data visualization:

Below you’ll find the complete code used to create the ggplot2 graphs in my talk The Good, the Bad and the Ugly: how (not) to visualize data at this year’s data2day conference. 

Click through for the demos, noting that not all of these are supposed to be good or useful visuals.

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Cole Nussbaumer Knaflic gives us a couple alternatives to displaying data in a heatmap:

I often describe heatmaps as a good means for getting an initial view of your data. They can help you start to explore and understand where there might be something interesting to highlight or dig into. But once you’ve identified the noteworthy aspects of your data, should you use heatmaps to communicate them?

As often is the case, it depends.

If you are communicating to an audience who likes to see data in tables—applying heatmap formatting can provide a visual sense of the numbers without fully changing the approach (or having it feel like you’ve taken detail away). If you know your stakeholders will want to look up specific numbers (particularly in the case where different stakeholders will care about different numbers) and then understand them in the context of the broader landscape, a heatmap may also work in this scenario.

Click through for some ideas.

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Alex Velez talks about xenographs:

I recall the first time I came across a horizon chart. Two thoughts came to mind: 1) this looks cool; and 2) I don’t have the energy to figure this out. Fast forward to now. I’ve learned how to read horizon charts, and I’ve even identified a few good use cases for them. This illustrates both the problem and the potential of xenographs. Let’s explore the potentially problematic side first.

Novel approaches to visualizing data can intimidate audiences. They introduce a learning curve because a never-before-seen graph typically requires time and energy to decipher. This obstacle could be enough to dissuade audiences from consuming the data altogether. Even if your audience does invest their time, the resulting conversation is often about reading the visual instead of the primary takeaway. This seems counterintuitive, especially in the explanatory analytics space, but it doesn’t mean we should denounce everything novel.

My response to this depends heavily on the medium. If you’re giving a presentation, a novel or underused chart can be good if it helps tell the story. You have the advantage of being there to explain the dynamics of the diagram for people who have never seen it before. For an informative article, you have some ability to elaborate, as in this bracket win probabilities diagram, which is exactly the type of thing you’d see in certain newspapers and magazines. But unless your visual is immediately intuitive (and I’d consider things like a Manhattan plot or maybe a Dot-boxplot to be intuitive enough for most audiences), I don’t think I would include many of those on public-facing or corporate dashboards, as they’re liable to confuse people and you might not have the space available to explain how this works.

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Alex Velez takes us through one of the simplest visuals:

Tables are a common way to show data, but in my current work, I don’t create them frequently. Admittedly, when I come across a table, I often choose to visualize the data. I should clarify that I am not proposing that we never build tables. Instead, let’s understand their benefits and shortcomings so we can be thoughtful about when to use them and how to design good data tables.

Click through for some interesting thoughts around a sometimes-forgotten visual.

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