Chart Choosing

When building dashboards and business intelligence applications you’ve likely spent many hours identifying key metrics. You may have even developed use cases and profiles so that your application or dashboard is a success. These steps are an excellent investment to ensure the final product will be engaging and satisfy the needs of your stakeholders. A very important step is defining what visualizations or charts you will use. There are many different chart types you can use, each with it’s own unique way of displaying data, and even slight variances of the same chart type (e.g. Bar Histogram vs. Line Histogram). So take the time to choose the right charts.

To Be Considered…

Use charts that are user-friendly and easy to understand. Common graphs such as line, bar and area charts can achieve this. Avoid any chart that requires a viewer to dedicate extra time to understand the data or its relevance. One of my least favorites charts is the circular area or spider chart. I have found that few users understand it. And if a user doesn’t understand it, no matter how beautiful you think it is, it’s actually useless.

Eliminate unnecessary ribbons, shading, outlines, and icons. This is what many term “chart junk.” It does not provide clarity or insight – it only serves to distract from the main purpose of the visualization. So avoid it. Please.

You can, however, use color to add meaning. For example, you might use similar coloring for objects that are related. I might add a blue hue to charts for air, orange for car, and green for hotel. This helps group like charts visually and has minimal impact on the design.

If someone that was not involved in the design and development process can immediately understand your application or dashboard you’ve done it right. If not, it needs to be adjusted.

What do you want to see?

Most charts can be grouped into four categories: 1) Comparison, 2) Distribution, 3) Composition, and 4) Relationship.

Comparison
These are used to compare values to each other and can be used easily to identify the lowest and highest values in the data. They can also be used to compare current vs old thereby visualizing an increasing or decreasing trend. For example, you might visualize airline utilization or air volume of current year compared to the previous year. Charts in this group include Bar, Line, Circular (Spider), and Tables.

Comparison Charts
Comparison Charts

Distribution
Distribution charts allow you to visualize how quantitative values are distributed along an axis from lowest to highest. Looking at the shape (or size) of the data a viewer can identify elements like range of values, central tendency, shape and outliers. Charts from this group can be used to to see things like average ticket price by time of day (e.g. Early Morning, Morning, Afternoon, Evening, Late Night). Or they could be used to view # of Trips by Total Spend by Department. Charts in this group include Histogram (Bar and Line) and Scatter plots.

Distribution Charts
Distribution Charts

Composition
Composition charts are used to see how a part compares to the whole. Or alternatively how a total value can be divided into shares. A composition charts shows the relative value but some charts can also be used to show the absolute difference. The difference is between looking at percentage of total and value of total. These can be used to see market share for a vendor(s) or distribution of booking behavior (e.g. Agent vs Online). Charts in this group include Pie, Waterfall (which I love), Stacked (Bar or Area), and Tree Maps (I like these too!).

Composition Charts
Composition Charts

Relationship
Relationship charts are used to see the relationship between the data and can be used to find correlations, outliers and clusters of data. These are actually some of my favorites charts when used appropriately. I used a Scatter plot in one project to see the impact on clients for late deliveries. In a separate project I used one to find which client requests should be prioritized (that required a weighting logic to be applied to the data too). Going back to the travel industry, I used a Scatter plot in a dashboard application to visualize the average ticket price by airline (with the Y axis as number of trips). Charts in this group are primarily Scatter plots with perhaps some variances such as bubble size.

Relationship Charts
Relationship Charts

Conclusion

Choosing the right charts might seem trivial but it should not be underestimated. The wrong charts can cause confusion or distract from the story your dashboard is designed to communicate. Take the time to make sure you’ve selected the visualizations that will best contribute to your application becoming a success whether it’s use is external or internal.

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