Introduction

In part one of this series we covered the basic requirements of a corporate dashboard solution and went on to discuss the first steps of the dashboard design process. The two main areas covered were determining the appropriate key performance indicators (KPIs) and how to design a dashboard with the five most common KPI visualizations: alert icons, traffic lights, trend icons, progress bars, and gauges. In this article we complete the design process and cover visualization of supporting analytics and the layout techniques used to create a visually efficient and compelling design.

Supporting Analytics

Supporting analytics are additional data visualizations that a user can view to help diagnose the condition of a given KPI or set of KPI’s. In most business cases these supporting analytics take the form of traditional charts and tables or lists. While the scope of this article is not intended to cover the myriad of best practices in designing traditional charting visualizations, we will discuss some of the basics as they relate to dashboard design.

When creating supporting analytics, it is paramount that you take into account the typical end user who will be viewing the dashboard. The more specialized and specific the dashboard will be the more complexity and detail you can have in your supporting analytics. Conversely, if you have a very high level dashboard your supporting analytics will generally represent higher level summary information with less complex detail.

Below we will discuss some of the most common visualizations used for designing supporting analytics.

1. Pie Charts: Pie charts are generally considered a poor data visualization for any data set with more than half a dozen elements. The problem with pie charts is that it is very difficult to discern proportional differences with a radially divided circle, except in the case of a small data set that has large value differences within it. Pie charts also pose a problem for labeling, as they are either dependent on a color or pattern to describe the different data elements, or the labels need to be arranged around the perimeter of the pie, creating a visual distraction.

When to use: Pie charts should be used to represent very small data sets that are geared to high level relationships between data elements. Usually pie charts can work for summary level relationships but should not be used for detailed analysis.

2. Bar Charts: Bar charts are an ideal visualization for showing the relationship of data elements within a series or multiple series. Bar charts allow for easy comparison of values due to the fact that the “bars” of data share a common measure and can be easily visually compared to one another.

When to use: Bar charts are best suited for categorical analysis but can also be used for small time series analysis (e.g. the months of a year.) An example of categorical analysis would be examining sales of products broken down by product or product group, with sales in dollars being the measure and product or product group being the category. Be careful in using bar charts if you have a data set that can have one element with a large outlier value; this will render the visualization for the remaining data elements unusable. This is due to the fact that the chart scale is linear and will not clearly represent the relationships between the remaining data elements. An example is below.

Figure 1 – Bar Chart Example

* Notice that due to the fact widget2 has sales of $1.2MM you can not easily discern that widget3 has twice as many sales ($46,000) as widget1 ($23,000)