Tables, also referred to as “crosstabs” or “matrices”, focus on individual values rather than using visual styling. They are one of the most common ways of displaying data, and therefore, one of the most important ways people analyze data. While their focus is not inherently visual, reading numbers is actually a linguistic exercise, visual elements can be layered on to tables to make them more powerful and easier to digest.
People most often interact with tables embedded on websites, as part of menus at restaurants, or in the course of their work through Microsoft Excel. We see tables everywhere so it’s critical to know how to read them and make the most of the information they present. For analysts and knowledge workers it’s also important to understand how you could make it easier for your audience to understand.
What is a Table? How Do I Use It?
Like most charts, a table organizes data by axis. The rows are the x-axis and columns are the y-axis. Common convention, because tables are read, uses the x-axis to display the categories. The y-axis displays the values within each measure, with the columns being labeled to clearly indicate their meaning. Unlike most charts, tables can display qualitative data in an organized manner and highlight relationships between them.
Analysts tend to use tables when they want to see individual values. They make it easy to identify measures across a set of intervals (Ex. what was the company’s profit in November 2018) or dimensions (Ex. How many sales did each person close in 2019). Additionally, a summary table can effectively describe a large data set, providing you subtotals and grand totals for each interval or dimension. The issue with tables is that they do not scale well. If the table has more than ten to fifteen rows and five columns it becomes hard to read, understand, and gain insight from. This is because a table activates the language systems within the brain while visualizing data activates the visual systems.
Adding visual elements to the table will help end users gain insight from the data faster than a basic table. Color gradients (see Heat Maps) and size help viewers identify patterns and outliers. Icons help the viewer identify a change in measures between dimensions. Using distinct marks will draw attention to relationships better than a table of raw data.
Tables and crosstabs are useful for performing comparative analysis between specific points of data. They are easy to create, and can show one key insight with ease. You should consider whether a crosstab supports the goals of your project before building it into a data visualization.
Key Types of Tables
The table below contains a brief description for the most common types of Tables. As the Reference Library expands in depth and breadth more types will be added and each will have a page dedicated to showing practical examples and explaining when to use them.
