ture of the data, the dimensions and metrics being displayed, and eventually how a small piece fits into the whole. Take the following data visualization comparing hospitals by patient experience as an example.
There is a lot going on here. The bubble chart shows three separate metrics about each hospital. The meaning, size, and positioning of the bubbles requires a new reader to carefully review the axes and legend to get his bearings. The connection between the bubbles and the bar chart on the right is not immediately obvious.
It’s a lot easier to tell the story of a single bubble.
The highlighted bubble represents a hospital, and the three metrics used to size and position the bubble are shown in the tooltip. In fact, the connection to the bar chart becomes obvious as Russell Hospital is identified as one of the largest hospitals by bed size. This particular data point may not be the most interesting or unexpected in this chart, yet now you how a much better sense of what the data product is trying to convey about hospitals.
Turning Data into Words
It is often said that you know you have become fluent in a foreign language when you dream in the language. Short of this inflection point, language students have to translate the words back into their native tongue. And so it is with data. Without instant recognition of the meaning of a data visual, it can be useful to convert the information into a language in which you are familiar.
Take the example above: to understanding the message in the data, you might translate a data point into a descriptive sentence such as “Russell Hospital has 730 beds, tied with three other hospitals” or “Russell Hospital’s patience experience score is near the top of all hospitals shown.” This is a way of capturing and testing your understanding of what you are seeing in the data.
By breaking a complex data product into its smallest pieces and finding something comprehensible, you will start to understand both what the author is trying to show and how to read the content.