Designing Compelling Dashboards: 10 Tips For More Powerful Designs

By Casey McGuigan, Reveal Product Manager, Infragistics

We’re all bombarded with information and content, and it’s often overwhelming. So how do you quickly separate the important data from the noise? And how do you shape it into a compelling data story for your customers, executives, or partners? Choosing the right visualization — chart or graph — for the story you want to tell is imperative.

Newer, modern analytics platforms help you combine visualizations into powerful dashboards — dashboards that are intuitive, that combine data from multiple sources, and that let users quickly drill down to the specific information they want.

What is a Data Visualization?

Data visualization refers to a chart or a graphical representation of data in which the data is represented by symbols, such as bars in a bar chart, lines in a line chart, or slices in a pie chart. Chart and data visualization can be used interchangeably.

Data visualizations can either stand alone as single visualizations or they can be combined with multiple charts to create dashboards.

Data Visualization Best Practices

As consumers or businesses contend with the explosion of more complex information, we need to help them understand it faster.

Data visualizations help make data more consumable, help users make decisions, and tell a story.

• 65% of people are visual learners
• We process images 60,000 times faster than text
• It takes 13 milliseconds to recognize an image

Even something as simple as looking at heights by person, like the chart below, makes it clear how seeing this data in the column chart, sorted, gives you immediate insights.

To create compelling, digestible designs, follow these best practices.

1. Understand the User

The golden rule of communications is to know your audience. You will communicate differently to a technical user than to a business user, or if you are making a sales pitch to a client or a presentation to your company’s executives.

Every visualization is telling a data story. Your job as the person designing the story is to:

• Have clarity on the story your data is going to tell
• Know your audience and their goals

Make sure your visualizations simplify complex data, tell your data story and drive decision making.

First, Simplify Complex Data:

• Who will be using the visualization?
• Do you know your audience?
• What questions should this visualization answer?

Tell Your Data Story:

• Do you need a single visualization or multiple visualizations?
• Does the Chart Title give enough context to the data, or do you need supporting Text to tell the story?

Drive Decision Making

• What techniques can you use to highlight important points in your data story?
• Is this an exploratory visualization or an explanatory visualization?

2. Choose the Right Chart Type

One of the biggest struggles when it comes to telling an effective story with data can be choosing the right chart type. There are an infinite amount, and each has unique attributes. While your data could potentially work with multiple charts, it is up to you as the creator to make sure you are selecting one that makes the data clear and concise for the consumer.

Consider these key questions when choosing each visualization for your data and use the guide below to help you:

• What is the key point you want to communicate with your chart?
• Do you want to compare variables?
• Do you need to understand the distribution of the data?
• Are there possible trends you need to analyze for?

Choose the point you want to make and select a chart type that is optimal.

3. Proper Use of Color and Text

Color and Text can help highlight what’s important or they can derail your data story. Use color to communicate effectively based on your data, not beauty or “chart art.”

This chart is an example of what not to do:

• The colors are too similar to be able to quickly distinguish between the different fields.
• There is a poor use of fonts. Serif fonts are great for reading on paper, but not onscreen.
• 3D makes it difficult to tell the part size to the whole.
• The labels on the pie chart are almost impossible to read.

To avoid chart confusion and derailing from your story there are three types of color schemes you can use on your charts — diverging, sequential and categorical.

Follow the guidelines below when choosing the right colors for your charts.

• Use diverging color schemes when a central value is shared between both ends

• Use sequential colors with numeric or ordered values.

• Use categorical colors with distinct variables without any ordering.

4. Avoid Chart Junk

Focus your visualization on the data story and keep the chart clean and easy to read. Don’t confuse the user with unnecessary information or graphics such as:

• 3D, disruptive shading
• Heavy grid lines/boxing borders
• Whimsical font choices
• Distracting background imagery

A large share of ink on a graphic should present data-information. Data-ink is the non-erasable core of a graphic, the non-redundant ink arranged in response to variation in the numbers represented.

Above all else show the data — Edward Tufte, The Visual Display of Quantitative Data, 1983

There is so much going on in the chart below that is takes away from the purpose of the chart itself:

When you remove the junk from the visualization is makes the story of this chart a lot clearer:

5. Be Clear with Your Data

Use visualization features to create clarity in your data story. Tell a story of growth by sorting your data in ascending order. To show falling growth or revenues, use a descending sort.

Chart features like titles, trendlines or crosshairs can help you more quickly tell your story. Follow these best practices to ensure your data visualizations are clear:

Use descriptive and concise Titles that give the consumer reason and explanation for your chart. Keep your chart title simple and to the point since your data and visualization should tell the core of your story. Primarily, your title should directly relate and support the chart underneath it. For example, in the chart below let the legends and your visuals tell the story for the Division and Age, but give the consumer enough concise information for what they are gathering from this chart.

• Sort your data alphabetically when you are using categories on your x-axis and you need to help people quickly find what they are looking for.

• Sort your data in ascending order to tell the story of growth.

• Sort your data in descending order to compare largest to smallest.

• Trend lines are a powerful technique in a time series chart and we’re seeing these more and more in the popular media. NY Governor Andrew Cuomo often used a 7-day average trend line for the rate of Coronavirus increase or decrease to eliminate outlier days. Where daily variations might make comprehension more difficult, a time series trend line reveals the general direction of the data over time.

• Cross hairs help give viewers more details while analyzing charts. Not every chart calls for cross hairs, but you can see in the chart below how they help the user gain clear insights into the exact data points.

6. Highlight What’s Important

Design visualizations so that they focus the user on what’s most important about your data story. Highlighting key points, trends and bounds within your data can be key to providing your end users with the quick insights they need.

Use these key features when looking to highlight important data:

• Focus user’s attention on what you want them to look at by using series highlighting.

• Bring attention to key data points with conditional formatting. Set bounds that correlate to variations in your data.

• Chart annotations support your storytelling either on a chart or with collaboration. Annotations provide your consumer with insights deeper than data points.
• Outlier Detection allows you to quickly highlight anomalies or deviations in a data set.

• Time Series Forecasting allows you to make predictions for future data points based on past and present data, giving your consumers predictive analytics.

• Linear Regression allows you to plot trends between dependent and independent variables. Use this when you want to show the “best fit” line to match (predict) the general trend in the data.

7. Use Effective Interactions

Modern data analytics programs make it easy to create interactions that allow users to slice and dice data.

Some of the most effect interaction includes:

• Dynamic Filtering

Adding filters to your dashboard or visualization allows you to pivot your data on the fly to gain deeper insights. Provide different options either at the dashboard level or visualization level for your viewers to slice and dice data by category fields or date ranges.

• Drill-Down

When you enable hierarchies within your category or date fields, it allows your end users to do deeper analysis. For example, in the following visualization you can drill down from your different marketing channels into the specific product to gain another level of insights about what awareness you are driving.

Treemaps are excellent visualizations for drill downs. They display large amounts of hierarchical data in a compact space at a glance and they show data as part of a whole. You can see the relation of one country’s budget to all the others, and with drill down, a user can quickly perform deeper analysis.

• Dashboard Linking

Take drill down to a new level when you link data points or visualizations to other dashboards. In the example below you can set up an overview marketing dashboard that presents ongoing results of a marketing campaign. With a dashboard link you can set up a link between that dashboard and a more detailed one about each of the campaigns you are running.

Use images and text fields to create landing pages or right from within dashboards to link as well.

8. Use 3D Wisely

3D obfuscates real data, creating room for assumption vs. analysis. It is a best practice to avoid 3D for standard business use. However, 3D visualizations have a place if you are doing surface analysis, volatility analysis or terrain research. This chart works because it shows temperature variations in a volcano from top to bottom with multiple axis including X, Y and Z and it tells a more useful story in 3d space.

9. Pay Attention to Details

Sometimes details can enhance your data story, but other times excessive detail confuses your message. Keeping your numbers formatted or filtering out top results to data is readable

Formatting your data can be a quick, simple way to make the numbers more visually appealing and easier for an end user to read. For either gauges or charts such as bar charts and column charts, you can adjust your data formatting to make your data point stand out: limiting the number of decimals, for example, or adjusting placement of comma separators. Also consider , using currency or , percentage measurements, or large number formatting.

Compare the two different KPIs below. Which is easier to immediately understand?

10. Use the Right Scale

There are 3 types of lies: lies, damn lies and statistics. - Mark Twain

Misleading visualizations can be found everywhere. We see it too often in the news, boardrooms, and on social media: charts that deceive. Sometimes it’s unintentional, but other times it’s intentional. The best way to avoid being guilty of misleading visualizations is to steer clear of changing the scale of the Y-Axis.

Here’s an example of how a chart tells an incorrect story. Both charts are showing the same data. However, the right chart has the axis starting at 5% and makes it look like the US GDP is plummeting. Whereas, the left chart shows there is in fact only a small gradual decline.

These best practices provide valuable tips on creating both eye-catching and highly effective visualizations. Businesses are adopting greater use of data in order to better spot trends or see new opportunities. With data visualizations and dashboards, you can tell a strong story or use it for exploration to gather insights and feedback from your team. Follow these dashboard design best practices and you’ll be able to display your data in the best way, making it easy to analyze and actionable

About the author

Casey McGuigan

Casey McGuigan holds a BA in mathematics and an MBA, bringing a data analytics and business perspective to Infragistics. Casey is the product manager for the Reveal embedded analytics product and was instrumental in product development, market analysis, and the product’s go-to-market strategy.