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.