1.2 Infographics vs. Visualizations

Among a subset of practitioners, it has become fashionable to differentiate between data visualizations, used internally in the exploration phase of data analysis, and infographics, used to communicate analytical results to a general audience.

In this view of the data presentation landscape, infographics:

  • are created for story-telling purposes (subjective);

  • are intended for a broad audience;

  • are self-contained;

  • rely on graphic design to get their message across;

  • cannot usually be re-used with other data, and

  • often incorporate unquantifiable information.

Data visualizations, on the other hand:

  • are methods as well as items (objective);

  • typically focus solely on the quantifiable;

  • are used to get a sense for the data and/or to make it accessible (raw datasets can be massive and unwieldy);

  • may be generated automatically, and

  • do not typically rely on look-and-feel considerations (insight over aesthetic).

The images below show prototypical examples:

Illustration of a data visualization (left; author unknown) and of an infographic (right; [Daily Infographic](https://www.dailyinfographic.com/successful-people-unsuccessful-people)).Illustration of a data visualization (left; author unknown) and of an infographic (right; [Daily Infographic](https://www.dailyinfographic.com/successful-people-unsuccessful-people)).

Figure 1.2: Illustration of a data visualization (left; author unknown) and of an infographic (right; Daily Infographic).

We favour a slightly different definition: data visualizations are data presentations that could in theory be prepared by analysts with a minimal amount of external design work (with simple tools) and infographics are presentations where the design takes centre stage and the data is an afterthought (and might be difficult to identify in the first place); between these two extremes, we find data stories (see Visualization and Storytelling).

Another important distinction is that data visualizations typically have a great asset: they can be questioned directly. If the charts are not compatible with our data understanding, then either the charts were not prepared properly, and/or our understanding of the situation needs to be revisited, and/or there is something wrong with the data that was used to build the charts.

In the data visualization example above, does the colouring of the slices match common wisdom about the wealth of each of the U.S, states? Is this more likely to be due to a data encoding error or to contextual misunderstanding?