Table of Contents
A data table is an essential data visualization component that displays a tabular dataset with rows and columns. Each row indicates a data item and all rows should have the same count of cells in the order of the corresponding columns.
The purpose of using a data table is to visualize tabular data in a better and readable form. Aligned and formatted information is much easier to understand and process at first sight.
Notice: In this post, we will not talking about data tables in Excel. They are part of the What-If Analysis and very powerful. We will discuss the tables with raw data collection. Watch this sample for Excel Data Tables.
Anatomy of The Data Table
To understand the parts of the data table, first, we need to know what we are showing with this table.
Structurally a data table has only rows and columns which are composed of data cells. However, logically, there are other parts helping us to read it in an easier way. Below we describe these parts starting from the smallest unit so that we can under the rest.
As we said data tables show tabular data. So what is a tabular dataset?
A tabular dataset is a data collection composed of rows divided into columns. Each row represents a meaningful data object and the order of cells within each row should be the same for all rows. The most known example of tabular data is CSV files.
In light of this information, the parts of a data table are below;
- Header – the row that contains column titles
- Column Header – the cells in the header row which indicates the name of the field in this data set, mostly contains sorting and filtering options
- Data Cell – the smallest component of the table that shows the actual data field
- Data Row – the ordered data cells that compose a meaningful data object
- Footer – mostly used to show some aggregation on the corresponding column
- Pagination – this part is important if the dataset size is big
Reading a Data Table
If you need to understand a data table, the initial point should be the title of the table. Without an appropriate title, tabular datasets are hard to understand. From the UX perspective, your audience should clearly understand the table content from the title at first sight.
Once you understand the domain, it is easier to get insights from the table. Let’s go through the below table.
First, the title is saying that you will see metrics related to the SEO Analysis regarding qualitative data. Don’t worry if you are not familiar with this domain, we will only talk about the first two columns.
So, the Keywords column is obviously the query term that users searching. But the “Volume” column might be confusing if we do not have the title. Although it is a physical property, in this case, it is the search volume which is the monthly search count of the given keyword.
Generally speaking, rows in a table are separated data objects while columns within a row are related. With this information, we can intuitively say that the “qualitative data” keyword has the highest “search volume” which makes it more popular than other keywords in the table.
Creating A Better Data Table
Good data tables allow users to scan, analyze, compare, filter, sort, and manipulate information to derive insights and commit actions. Read more…
This is a very short, compact, and well-defined explanation and contains all the basics. However, it is not so easy to implement for a developer.
To achieve this, there are lots of UX specific touches you should know. But we do not mention all of these in this article, but some tricks are listed as below;
- Give a descriptive name to the table
- Make Header Row fixed
- Use understandable column names
- Make each column sortable
- Allow user to move/replace columns
- Make columns collapsible by option
- Place related columns nearby and create more readable rows
- Use suitable filters for each column
- Enable pagination with optional page sizes
To sum up, data tables are great data visualization tools for data analysts. You do not have to create fancy visuals to show insights. Besides, using tables, you can tell more with respect to a chart. Charts and infographics are more about emphasizing a specific insight whereas data tables allow you to work on a larger scale.