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Visualization Best Practice Overview
Make your visualizations Pop!
Use of Best Practices is key to success
Using visualization best practices is key to effectively communicate data and information. Properly designed visualizations make it easier for people to understand and interpret complex data, allowing them to make better decisions based on the information presented.
Since we want people to actively use our content to drive data driven actions, how we develop our analytics is critical to that success.
Limit Clutter to Focus Audience Attention
Limiting clutter when creating a dashboard is critical because it helps focus the viewer's attention on the most important information and makes it easier to digest and interpret the data. Cluttered dashboards can be overwhelming and confusing, making it difficult for people to quickly find the information they need. Additionally, a cluttered dashboard can be visually unappealing, which can make it less likely that people will engage with it.
The above examples transform from Cluttered to Clean by:
- Remove unnecessary colors. Only use color when it adds purposeful value.
- Eliminate gridlines and border. Lines are not adding value and are distracting.
- Remove headers when redundant. Since the title calls out "Sales" and "Region", we don't need that show again on the axis labels.
- Simplify number formats. Show numbers at the appropriate scale to make easy to read.
- Eliminate legend if not needed. In this example, we have all marks labeled so don't need coloring or legend.
- Remove axis if duplicating info. Since we are display the Sales $ value next to mark, we don't need to also show on the axis.
Use Color Strategically
Using color effectively in creating visual analytics is important because it highlights important information, creates visual hierarchies, and makes the data easy to interpret. Color can be used to distinguish different data points, categories, or variables, making it easier to identify patterns and trends.
However, it's important to use color carefully and consistently, as using too many colors or using colors in a way that is not meaningful can make the data more difficult to interpret.
The above examples transform from Haphazard to Strategic use of color by:
- Highlight what is important. Everything doesn't need a color.
- Limit amount of colors used. More than 5 colors can become distracting.
- Include relevant color in title. Allows you to eliminate a color legend.
Use Text Purposefully
Good use of text in visual analytics is important because it helps to clearly and effectively convey information and insights to users. Text labels, captions, and annotations can provide context and meaning to the data being visualized, making it easier for users to understand and interpret the information.
Good use of text will lead your audience to the insights you want them to receive versus them having to guess at what they are looking at.
The above examples transform from Limited to Purposeful use of text by:
- Descriptive Header. Having a descriptive header in the upper left will give the audience an immediate understand of what they are viewing.
- Axis Labels. If the data isn't readily apparent, label the axis so the audience knows how to interpret.
- Number Formatting. Appropriate formatting will keep your audience from being distracted and speed their understanding.
- Highlight key takeaways. If there is something you want to make sure your audience understands, directly highlight it with text.
Choose the Correct Chart Type
Choosing the right chart type in visual analytics is important because it will impact the effectiveness and usability of the visualization. Different chart types are best suited to different types of data and analysis. Using the wrong chart type can lead to misinterpretation of the data, which will cause confusion and lead to incorrect conclusions.
Oftentimes, if you are unsure on what type to choose, go with bar chart, a tried and true solution for all types of analysis.
The above examples show appropriate chart type usage:
- Change over time. Line graph is very effective at showing growth/decline over different date periods.
- Magnitude comparison. Bar chart is used to compare results in different categories.
- Correlation between measures. Scatter plot is used to show how different measures relate to each other.
- Relationship of each part to the whole population. A tree chart can be used to show the relative size and performance of different categories in a hierarchy.
Welcome to the
CCA Content Hub
Thanks for viewing!
I created this content hub to offer insights on all things analytics but primarily focused on:
Use Cases
Analytics Best Practices
Tableau Training
Central Coast Analytics Info/Approach
I'd love to hear any feedback you have on these blog posts or if you have suggestions about future topics!
My email is: jeremy@centralcoast-analytics.com
