Part 2. The FLOAT Method
2.6 Tell
Telling Your Data Story
Tell
The way information is communicated is of critical importance to both the comprehension and delivery of a given subject; therefore, there is no substitute for a solid design. After reading this chapter, your goal should be for your audience to be able to determine the message of your visualization(s) and to do so quickly. Often, a secondary goal is to have the audience be able to drill down further to continue to explore your findings at a more granular level. Furthermore, accessibility within data visualization is essential because it ensures that all intended users can access information, including those with with disabilities.
Methods of design intended for users with disabilities also benefit those without disabilities. Let us begin by exploring the types of visualizations and how to first make selections based on the type of data you will be visualizing. After that, the chapter will detail elements of effective design and provide critical concepts on how accessibility is incorporated within data visualization.
If you take the time to understand the reason for your data visualization efforts, you can shape the story to your targeted audience’s sensibilities. Your part in creating an effective design for a data visualization boils down to choosing the right type of design to tell a coherent, inspiring, and widely accessible story.[1]
Some of the most common types of visualizations are column/bar charts and histograms, line charts, pie charts, and scatter plots. There are innumerable other visualizations, including treemaps, area charts, filled tables and heatmaps, bubble charts, and choropleth maps that can be further explored for specific uses.
Getting Started with Visualizations
First, you will want to determine whether you even need a chart. Are you trying to highlight an impactful number or even a set of numbers that are not relatable or comparable to one another? In that case, you may want to present numbers as simple text. These can be visually presented so that they stand out without creating a visualization because the latter would require some comparison.
If you find you have several elements to compare, then here are some tips to keep in mind when selecting and developing your visualization. When selecting a visualization type, make sure it is most appropriate for your data. We go into more detail about this in the following sections. Once a selection has been made, you should have an idea of the message the visualization should be delivering. The message should be reinforced by text and design.
Design principles call for you to focus or attract the eye to the most important area or piece of information from a visualization, while “pushing back” supporting (but still important) information. Information that is neither important nor supporting should be removed from a visualization in a process called decluttering. This can include removing extra lines and tickmarks, many