Making the most of your business intelligence is equivalent parts having the right data and choosing the appropriate visualization to maximize it. The amount of data produced today means that simply reading through line after line of numbers becomes unfeasible and counterproductive. Instead, we must constantly search for those visualizations that not only make the data look appealing but concurrently make it easier to understand and interpret.
Charts are a mainstay of most BI dashboards thanks to their efficiency in communicating broad analytical concepts quickly. However, the ease of communication charts exhibit sacrifices the granularity and depth of detail that is sometimes necessary when performing more complex analyses. Before you start building your dashboard, it’s important to know the best uses of charts and tables, and when to deploy each to maximize your data visualization tools’ efficacy.
When Tables are Your Best Option
Unlike charts, which use abstraction to focus on trends and numerical relationships, tables present data in as close to raw form as possible. Tables are meant to be read, so they are ideal when you have data that cannot easily be presented visually, or when the data requires more specific attention.
Additionally, tables are all about preciseness, letting you dive deeper into the numbers and examine exact values instead of focusing on approximations or visualizations. Finally, tables help you evaluate data sets that have multiple dimensions and values without creating complex visualizations.
For instance, a table is ideal when you want to bore down on a specific pattern to find underlying causes. Displaying the number of illness diagnoses over a specific time period may be easier to accomplish with a chart, simply showing numbers and locations. However, a table is more useful when it is necessary to understand causes, outcomes, and even specifics about length of the illness, number of relapses, and more.
Similarly, a table would be ideal when carrying out a complex financial analysis which requires pivot data and multiple data streams. Instead of using a chart that requires multiple different visualizations, dynamic conversion currency conversion tables, for instance, may be more useful than converting them to charts.
Finally, tables are also valuable visual demonstrations when you need to analyze data using date as a parameter, something which charts may have trouble expressing on a more complex level. For instance, employee records may include both start and end dates, but may not be able to show the number of hires per day, since some days have none. Instead, a table can more adequately incorporate a total number of hires per day as an additional variable.
When a Chart Is the Better Option
On the other hand, charts represent excellent tools for simplifying complex data sets to highlight the important aspects—numerical patterns, trends, distributions, and other more abstract insights. Moreover, charts offer information in a visual and easy-to-digest form, producing quick insights and understanding. Charts are most useful when the data you are presenting is quantitative and has fewer distinct axes to measure.
More importantly, charts can show you the “shape” of data—patterns that emerge when the data is examined altogether instead of presented in sets of individual values. This includes highlighting broader patterns in a line graph or showing relations between different variables in bar or pie graphs. Charts focus on higher-level analysis which sacrifices preciseness and granularity in favor of a broader scope and faster comprehension.
A graph may not be ideal for underlining the dynamic relationships in currency conversion markets, but it could be ideal for uncovering the better performance in two financial assets by comparing their historical price fluctuations. The same case is true when considering areas like operational finances. For instance, understanding profit/loss patterns over time can help uncover different trends in seasonal habits or other consumer behaviors.
Measuring historical earnings over time can also include breakdowns of monthly earnings using stacked columns. In the previous example of data related to a specific illness diagnosis, understanding aspects such as incidence levels, healthy outcomes, and other insights is less important than seeing the breakdown in broad strokes of each location’s rate of incidence.
A chart can show this and strip away data that can be placed elsewhere to reduce comprehension time and complexity. For example, a study on cancer types may use a chart to display data on incidence by type, age of onset, or healthy outcomes. Using a table would complicate matters by affixing too many variables to a single page.
Choose the Right Visualization
Both tables and charts offer unique advantages for dashboards, but one is not always better than the other. Which you choose should ultimately depend on the data at hand, your needs, and the audience that will be consuming your data. Make sure to include the right visualization in your dashboard to optimize your insights.