Data visualization is where the data profession best reaches its widest audience. This is where data visualization comes into play in all of its easily digestible data-insights glory. But that is just it since data visualizations have to be readily understandable for a wider audience. While the techno jargon that pervades these scientific papers may not be as consumable for everyone, good data visualization can be understood by just about everybody. It is the data professional’s job to ensure that they are producing quality data visualizations. And to do that, there are a handful of infractions to avoid.
The dual potential for good and evil is not unique to data visualization, but it is an urgent design consideration given the paradox of the present age. Information is more abundant and accessible than ever, yet the government, media, and businesses are widely distrusted. When organizations publish misleading visualizations, intentionally or not, the trust gap widens.
Data Visualization Blindspots
Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space. Human sight and cognition are among the most incredible phenomena in nature. Light enters the eye, then the lens sends information from the light to the retina, which translates the information and fires signals down the optic nerve. Then the optic nerve transmits 20 megabits per second to the brain.
The leap from seeing to thinking is instantaneous, and the brain, abuzz with bodily demands and external stimuli, must conserve energy by prioritizing what to decipher and what to ignore.
In this rapid juncture of seeing and understanding, data visualization proves its worth. Here, many visualizations tell viewers what they should see in the data, and the overworked brain nods in approval. Confirmation bias takes hold. Objectivity is lost. To be fair, misleading visualizations are not always the byproduct of bad intentions, but even honest mistakes misinform viewers. Eyes are impressionable, and humans tend to gloss over information in search of quick takeaways. Sight and cognition must be key considerations in the design of every data visualization.
7 Data Visualization Most Common Mistakes
Here, we will present the most common mistakes of data visualization that you should avoid at all costs.
1. Improper Use of 3D Graphics
Two-dimensional representations of three-dimensional space have captivated viewers for centuries, but 3D graphics pose two serious problems for data visualizations. Occlusion occurs when one 3D graphic partially blocks another. It is the result of mimicking space in the natural world–where objects have differing X, Y, and Z coordinates. In data visualization, occlusion obscures important data and creates false hierarchies wherein unobstructed graphics appear most important.
Distortion occurs when 3D graphics recede into or project out from the picture plane through foreshortening. In the drawing, foreshortening makes objects seem as though they inhabit three-dimensional space, but in data visualization, it creates more false hierarchies. Foreground graphics appear larger, background graphics smaller, and the relationship between data series is needlessly skewed.
2. Too Much Data
It is a timeless design problem: what to include versus what to cut in the quest to communicate clearly. Data visualization is not exempt, especially when data is both abundant and thought-provoking. The temptation is to make a profound point with a single visualization. The problem is that humans are not well equipped to compute the meaning of multiple values abstracted in visual form.
3. Misleading Color Contrast
Color is among the most persuasive design elements. Even subtle shade variations elicit strong emotional responses. In data visualization, high degrees of color contrast may cause viewers to believe those value disparities are greater than they are.
For instance, heatmaps depict value magnitude with color. High values appear in orange and red, while lower values are rendered in blue and green. The difference between values may be minimal, but color contrast creates the impression of heat and heightened activity.
4. Omitting Baselines and Truncating Scale
Data varies, sometimes widely, like when measuring income levels or voting habits according to geographic regions. To make visualizations more dramatic or aesthetically pleasing, designers may choose to manipulate scale values on graphs. A common example is omitting the baseline or starting the Y-axis somewhere above zero to make data differences more pronounced. Another example is truncating the X value of a data series to make it seem comparable to lower-value series.
5. Choosing the Wrong Visualization Method
Each data visualization method has its use cases. For example, pie charts are meant to compare the different parts of a whole. They work well for budget breakdowns and survey results (same pie) but are not meant to make comparisons between separate datasets (different pies). A pie chart could be used to visualize the earnings of three competing businesses, but a bar chart would make differences or similarities between the businesses more apparent. If the visualization was meant to show revenue over time, then a line chart would be better than a bar chart.
6. Including way too many categories in a pie chart
People have a hard enough time gleaning information from pie charts. Do not throw tons of categories into a pie chart. It is best practice to use no more than seven categories in a pie chart. Consider using an “Other” category to group a handful of smaller categories. And try to use visuals other than pie charts if possible.
7. Making poor color choices
Color choice is an important consideration in data visualization. You should try to avoid using too many colors or colors that don’t go well together. And if you aren’t already doing so, you should always ensure your chosen palette is color-blind-friendly.
If you avoid these mistakes, then you will be on your way to becoming a much better data visualization designer in no time.