Is your goal to explore or explain your data or ideas, or a combination of these?
A common view on the essential reasons to visualize something is that we either want to explain it, or explore it. While these are not mutually exclusive, design choices might be very different if one’s primary goal is to explain or to explore.
We use the explain category for (often static) single visualizations that are designed for a particular purpose and audience (e.g., textbook illustrations, plots and maps for scientific publications, visualizations for printed media). This means they typically represent summarized content, somewhat customized an intended audience. When creating an explanatory visualization, one shows only the most relevant information, while in the exploratory visualizations, what is relevant is left to the viewer.
Example for ‘explain’: Can you follow the instructions?
Below is a typical visualization that explains how to do something, the familiar furniture assembly instructions (source: Ikea, borrowed from here).
Creating visualizations such as above, which must be understood pretty much by all (see ‘Who’), is a challenge. However, as a rule of thumb, designing visualizations that could be ‘understood by all’ is a good goal, in fact some suggest that a product, such as a visualization, should be designed as if the user is drunk (not an endorsement).
On the other hand, in an exploratory visualization environment, visualization is more process than product. An exploratory environment ideally gives the users tools to examine a data set, discover patterns, and gain insights. An exploratory visualization environment doesn’t necessarily guide the viewer to see certain patterns or emphasize particular information. Instead, a user is given control, allowing for views of the data from multiple perspectives, using various visualization methods, changing parameters, or bringing in other data sets for comparison. Based on this visual interaction with the underlying data, the user becomes the analyst, and can gain insights and/or come up with hypotheses. Those hypotheses are typically further tested following the exploratory stage.
Example for ‘explore’: What is going on in the OECD countries?
Discover relationships between social, demographic and economic indicators. Below is an example where we can see some regional statistics by OECD.
Last revised: 12th of July 2018, Arzu Çöltekin; edited 2/19/18 by Alyssa Goodman.