Cleaning Up Clutter: Decluttering Tips for Meaningful Data Visuals

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Decluttering Data Visuals

Decluttering data visuals can help them be more effective and understandable. The declutter principles recommend eliminating any excessive features from the chart, such as gridlines, superfluous labels, and needless color variation, to better communicate the important information. 

Moreover, many specialists in data visualization (dataviz) highlight the importance of chart design principles such as decluttering to guarantee that the visualization is fully understood. 

Chartjunk By Edward Tufte and The Data-Ink Ratio

Visualization pioneer Edward Tufte supports minimalism in design aesthetics when presenting quantitative data. Tufte is well-known for his data-ink ratio analysis of data visualizations. He put forth this hypothesis back when charts were meant to be printed rather than displayed on computer displays. 

So, for this reason, anything that appears in a visualization is known as “ink.” According to him, the fundamental meaning of decluttering data visuals is its data ink or the components that represent information. One cannot change it without altering the visualization’s target meaning.

The amount of ink in a graphic communicates important information is the data-ink ratio; it is the ratio of data-ink to total graphic ink. A high data-ink ratio chart has few or no unnecessary or decorative components; Tufte’s ideal visualization conveys the information as effectively as feasible.

Tufte calls unwanted components “chartjunk,” which is a fitting term. The antithesis of data-ink or chartjunk is any excessive visual component that detracts from the significance of the chart.

Decluttering Data Visuals: Learn How

As data storytellers, we must exercise intelligence in focusing the audience’s attention on the most illuminating content and avoiding clutter. We can apply visual perception concepts to ascertain what clutter is. This refers to our perception of order in the natural world. 

So, here are a few principles that will help you in decluttering data visuals in no time:

  • Similarity: When two objects share the same color, size, or shape, humans tend to group them together. This method can be applied to a table or heatmap to emphasize sets of values in the same color. As a result, we no longer require extra components to focus our attention on such boundaries.
  • Enclosure: It appears that the items within are members of the same group. Making a differentiation in the data visualization is how to take advantage of this principle. You can achieve this by altering the backdrop color. The audience will focus on the contained section right away.
  • Connection: The most profound belief in group membership would result from any connections between the data elements. Line graphs are the best way to visualize this idea.

Decluttering Data Visual Principles

Once we know what we are communicating, we may reduce clutter by adhering to the following rules.

  1. To produce a neat line and a well-organized layout, align the text content to either the left or right side of the image.
  2. A smart use of white space can help users digest the insights by stopping the flow of information. Give a single visualization its own complete page to emphasize its significance.
  3. Remove chart borders because they only add visual clutter and are useless. Instead, utilize whitespace to set the image apart from other items on the page.
  4. Eliminate gridlines or go for light, airy hues like gray.
  5. If the data is easily interpreted, remove the data markers. Don’t make them default; use them only when necessary.
  6. Tighten the axis labels by using ‘M’ for millions; for example, abbreviating month and day names as few labels as feasible should be used.
  7. Rather than placing the legend at the top or bottom, place it directly next to the graphic. To aid with audience comprehension, occasionally label the data directly within the visualization.
  8. Keep the color scheme of the data story constant. This also applies to the legend’s data labels. This is yet another visual clue to the audience that the material is related.

Conclusion

Keep in mind that viewers of your data visualizations will likely attempt to interpret and make sense of the abundance of information shown. Eliminate any excessive graphic components that detract from your intended message. Keep following our blogs at Storytelling With Charts for more interesting content on data visualization and data storytelling. 

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