In the introduction to our dataviz workshop at a Fortune 50, the Chief Operating Officer told the room of his employees that he was looking forward to seeing their work improve as a result of what they learned with us. Because, he said, what they wanted to see on the slides in their decision-making meetings was “your claim and the evidence that supports your claim.” Can I get an amen?

He wants to see something like this:

I don’t think he is unique at all. In fact, I am of the belief that most of us are filtering what information we choose to consume by how well it answers “So What?”. What’s your point? Many, perhaps the majority, of data visualizations are not answering “So What?” for our intended audiences and are under-used as a result.

Let’s walk through some examples, improving as we go.

Example 1: That Table

You’ve seen tables like this throughout your life, I’m sure. Tables presented in this typical, research-y way are a struggle for many folks because there is no evident point, no clear answer to “So What?”. The title doesn’t provide insight. There many be interesting points contained in the table, but it takes insider knowledge of the study, of how research is conducted, and of the possible implications of the data presented here to be able to answer “So What?” and that, my friends, is a tall order.

Example 2: Tesla’s Range

We are improving in that now we have a visualization of the data. The chart itself is a fairly familiar type – line charts are ubiquitous (even though this isn’t change over time, meaning a line might not be the right option).

Can you answer “So What?” when you look at this? All lines are going down as we move to the right, so you might answer “So What?” with something like “As speed increases, miles decrease.” But there are 4 lines, so maybe the answer to “So What?” is in the fact that the purple line is lowest? Or the red line is the highest? I don’t know. It’s a guessing game.

The real kicker here is that the text preceding the chart assumes data literacy is commonplace, suggesting that the point will be evident if you just LOOK at the chart. However, the crux of the matter is that if the chart is as complex as the algorithms used by a bitcoin casino, then even the data literate might find themselves puzzled, left asking “So What?” The key takeaway or the ‘jackpot’ of information, if you will, needs to be as clearly stated in the title as the best winning odds are on a casino’s homepage. Without a straightforward title or a guiding interpretation, the data is as overwhelming as a bustling casino floor – full of noise but lacking direction.

Example 3: 5G vs. Corona Scatterplot

Check out that crystal-clear title: 5G doesn’t spread the coronavirus. Perfect! That answers “So What?” instantly. Excellent! But….

It still takes a LOT of data literacy skill to see how (or whether) that point is made evident in the chart itself. Scatterplots can be complicated for many viewers. We could actually take this as an opportunity to teach data literacy by adding some elements to the chart that would help readers interpret it, such as a second, comparison chart that would indicate what a strong correlation would need to look like. So, in this example, the title is on point but the graph needs a tweak in order to support that title.

Example 4: Britain’s Coal

This chart from the Guardian nails it. The title is a short, succinct point that quickly answers “So What?”. The graph shows the evidence to support that point by popping out the days at 0% coal with an eye-catching green. It works.

It works even though the chart type is novel. Instance charts are a fairly recent evolution, used to mark the extent of something at regular occurrences. Despite needing to learn some data literacy in how to read the chart if you’ve never seen it before, all of the elements (including the legend) in this visual are working together to answer “So What?”. And because this point is clear in the title, it makes learning how to read the chart easier. It teaches some data literacy.