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Purpose

In order to effectively communicate the story represented in the data, choose charts that best convey the insight you wish to provide and apply styling carefully to support that message. Also, consider the context and the preferences of your audience.

1. Goal - What are you trying to convey? Limit the visualization to one main theme with two or three supporting concepts to limit cognitive load and increase usefulness.
2. Audience - How will the users of your visualization engage with it? Lay out the visualization and apply styles with device type, method of communication and cultural preferences in mind. If possible, test visualizations with users as part of the development process.
3. Data - What type of information are you using the data to show? Additionally, what charts are your audiences most comfortable with? As much as possible, stick to common chart types (such as bar and line charts), especially when the audience's level of data literacy is unknown.

Accessibility

is a priority and takes into consideration all disabilities that impact access to the web, including auditory, cognitive, neurological, physical, speech and visual. Limitations can be permanent (such as colorblindness), temporary (such as a broken arm) or situational (such as a noisy, distracting environment). To design data visualizations that are accessible to as many users as possible, it is especially important to consider the experience of people who have a vision deficiency or use technology such as screen readers, Braille keyboards or keyboard-only navigation.

1. Color - Data visualizations often utilize color as a representation or to tell a story. Approximately 4.5% of people have some form of color-vision deficiency, so color can’t be the only way to explain or inform. Using the recommended color palette, which is designed to provide the best contrast, along with using appropriate chart types will aid in comprehension for all users.
2. Text - Including a title that is short but descriptive enough to explain the visual and adding labels to important data points are key steps to creating an accessible data visualization. A summary will ensure that the takeaway from your data visualization is easily understood and consistent across all users.

categorical Core

When sharing data that compares the university or university-related groups or organizations with other universities, groups or organizations, use Ò»±¾µÀÎÞÂë Red to represent Ò»±¾µÀÎÞÂë. Use Dark Gray to represent all other values. Use white text for data labels and marks that appear over these colors.

Make sure to consider your audience and the cultural implications of using the color red, particularly if you are portraying financial data. If your visualization is directed to an internal audience, use a color from the secondary palette rather than red.

Categorical secondary 

Use in conjunction with the categorical core palette. Use white text for data labels and marks that appear over these colors.

Sequential Primary

Sequential Secondary

Divergent

Bar

• Allows the comparison of data across categories either vertically or horizontally
• Can be stacked to show proportions of datasets
• Unless paired with other charts that use the same color for categories, use one color to represent all categories compared against the same measure 
• Show ordinal data in ascending or descending order
• Always start at a zero baseline for the x-axis

Line

• Represents the change in one or more values over time

Scatter Plot

• Shows an x-y relationship between data points and can use a trend line
• Dot size can convey additional information
• Use a baseline of zero for the x-axis and y-axis
• Use color to identify patterns and outliers

Pie & Donut Charts

• Highlights portions of a whole dataset
• Best when limited to 2-3 slices
• Most useful when emotionally conveying a sense of size
• To more clearly compare percentages, a stacked bar chart is preferred

Heatmap

• Use color density to show concentration patterns for a single variable

Map

• Use the sequential color palette to show a single value by geographical location
• Limit the chart to regions included in the data
• Avoid decorative shading or colors and instead leverage color and size to show data variations 

Titles

Labels

• For readability, use commas to separate values and limit the use of decimal places
• Round numbers minimize clutter and improve readability
• For axis labels, skip those with regular intervals to avoid clutter in your visualizations
• Standard abbreviations should be used to make it easier to view the data (Ex.: Sun, Mon, Tue for days of the week or Jan, Feb, Mar for months)

Captions, Notes and Summaries

Legends

Presentation Dashboards

• Feature high-level metrics with clear indicators of trends in the underlying data
• Include simple charts that are easy to interpret at a glance

Operational Dashboards

• Designed to answer predefined questions or carry out ongoing monitoring tasks
• More likely to use real-time data
• May require additional colors beyond the brand palette, use discretion and follow best practices as closely as possible

Exploratory Dashboards

• Used to investigate data and discover trends and key findings
• Commonly used by a small group before sharing results with a broader audience
• May require additional colors beyond the brand palette, use discretion and follow best practices as closely as possible