Often, simply understanding totals, sums, and even the breakdown of part-to-whole only gives a piece of the overall picture. Most of the time, you'll want to understand where individual items fall within a distribution of all similar items.
You might find yourself asking questions such as the following:
- How much does each customer spend at our stores and how does that compare to all other customers?
- How long do most of our patients stay in the hospital? Which patients fall outside the normal range?
- What's the average life expectancy for components in a machine and which components fall above or below that average? Are there any components with extremely long or extremely short lives?
- How far above or below passing were students' test scores?
These questions all have similarities. In each case, you seek an understanding of how individuals (patients, components, students) relate to the group. In each case, you most likely have a relatively high number of individuals. In data terms, you have a dimension (customer, patient, component, and student) representing a relatively large population of individuals and some measure (amount spent, length of stay, life expectancy, test score) you'd like to compare. Using one or more of the following visualizations might be a good way to do this.