While visualizing data, all of us know about the pie chart or the line graph. These graphs are some of the most common and basic visualizations of data. However, these graphs are only the tip of the iceberg. There is a whole range of visualization methods that can be used to present data in the most effective manner.
Though the nearly endless possibilities lead to another issue, which one do you choose?
Exploring Some Common Visualizations
In this post, we will discuss some of the most common data visualizations and more specifically when they communicate your data clearly and when they don’t. Again, there are wide choices available, so we can’t cover them all, but the ideas presented can be applied to any visualization you come across in the future.
If there was an “old reliable” of the data visualization world, the line chart would be it. However, despite the old part being true, the reliable part should probably be up for debate. Line charts show one thing very well and that is numerical data combined with an ordinal attribute.
For example, transaction totals over time. That is due to the structure of the purpose of the line chart. At the end of the day, what the line chart is designed to show is how something moves from one ordinal data point to another.
In cases where the data shouldn’t be connected in such a manner, all a line chart does is confuse the viewer. A line chart should only be used to communicate a maximum of 3-5 “lines” at a time, any more than this and the chart begins to feel crowded and look confusing.
The two biggest offenders for bad line charts are too many variables (as seen to the right) or too high a frequency. Both of these mistakes cause a line chart to be confusing and hard to understand.
The needle gauge is an incredibly popular visual and has some strong benefits.
We’ve been conditioned since we learned to drive to trust and love gauges. They are very efficient at showing a single numerical data value at a single point in time. People love their simplicity, but that’s also their weakness.
A gauge is an exceptionally clear visual. It shows exactly where a number falls between a minimum value and maximum value. If you want to show how your sales performance was for a specific time period, a gauge will allow a user to instantly know if you did better or worse than expected.
However, this simplicity is also an issue for gauges and similar one-dimensional visualizations. It can only be used to show a single number, and it takes up a lot of room. That’s fine in a car dashboard, where you only care about a few key real-time metrics like speed, RPM, and engine temp. But on a business data dashboard, prime real estate is valuable. It is for that reason we like to use gauges sparingly.
A choropleth, also known as a filled map, uses differences in shading or coloring within predefined areas to indicate the values or categories in those areas.
This is useful when you want to see what states you have the most sales in for example. These sorts of maps are very good at showing such distributions. However, they don’t have the granularity of other visualizations.
Yes, sometimes a simple table is the best way to show data. However, if a table is going to be used as part of a dashboard, it needs to be used properly. This means things like conditional formatting or in-depth filtering must be applied.
The goal of a table in a dashboard should be just like the rest of these visualizations, to show something specific! It should be highlighting something like customers who have an outstanding balance. Do not fall into the trap of just showing the data again, otherwise, why did you spend time and money on visualizing your data at all?