Visualization is about democratizing the data and making it accessible to the people who need to know.
Today, there are many hot trends in both consumer and enterprise technology that increase accessibility by being highly visual. Think of the popularity of iPads, Surfaces, eReaders, and large screens. Everyone wants their data in the best resolution possible, with crisp graphics and colors.
Executives are engaging with the charm of visualization and putting it firmly onto enterprise business intelligence roadmaps. According to a survey by Howard Dresner, the extensive use of color, size, shape, and motion were more appealing than other buzzwords such as Big Data and the cloud. A study by Dresner Advisory Services found that advanced visualization and dashboards ranked high in terms of importance.
Why is visualization so useful? It's more than pretty pictures. Data visualization helps us to understand meaning in data via a communication medium that we are "geared" towards every day—our vision. Through discrimination and effectiveness of data visualizations, we reach insights and decisions. Technology allows us to create magic with our data, which engages us towards better decision making.
Given its power, how do we choose which is the right visualization? Throughout this book, we will talk about different visualization choices as we proceed. Because we are looking at dashboarding, we will look at dashboarding features such as KPIs.
In this recipe, we will look at different considerations when choosing your visualization. We will look at some of the default settings of Tableau and how they are affected by color blindness. We will also look at sparklines, which aim to provide as much context in as small a space as possible. This will be very useful in creating dashboards.
For the exercises in this chapter, take a copy of the Chapter 4
workbook and name it Chapter 5
. This workbook already has the data for the exercises in this chapter, so we do not need to make any changes to the data. We will delete all of the sheets except the KPI by Q
sheet. The following is an example of how the workbook will look:
KPI Summary
by right-clicking on the tab at the bottom of the worksheet and selecting Rename Sheet.<SalesTerritoryCountry> <AGG(Difference between Actual and Target)>
KPI by Q
.KPI by Q
worksheet. To do this, we will create a new calculated field by going to the Analysis menu item and selecting the Create Calculated Field… option.IF [Difference between Actual and Target] > 0 THEN "Above" ELSEIF [Difference between Actual and Target] <= 0 THEN "Below" ELSE "Not Known" END
NULL
value.NULL
data. While we are building up to a sparkline, it can look misleading because the data looks as if it starts from 0 and curves upwards; in fact, there isn't any data there for the highlighted marks, which you can see in the following screenshot. We can opt to Hide the Not Known data.To summarize, in this recipe, we have started to look at some dashboard visualizations. We will progress to look at others throughout the rest of the chapter.
In this recipe, we have looked at some of the default settings in Tableau for creating KPIs along with some options for configuring them. It is clear that we can drastically change the appearance of the visualization by simply making a few changes to the default settings.
When creating data visualizations, it is vital to remember the audience. This may seem a simple, obvious statement. However, how many charts have you seen that mix red and green together? A lot, probably! In this recipe, we looked at alternatives to using red and green to convey a message. Here, we have made a color choice based on the business question. We went from a straight red-and-green visualization to one which used blue to represent the data which was the focus of the business question. There are a few issues with our initial KPI though. First, a NULL
value is represented by an icon when there is no data. It would be better to simply not show anything at all; this would reduce unnecessary "ink" on the page and would require less effort to assimilate.
Another issue is that the image shows red and green on the same visualization, which isn't good for color-blind viewers. Although additional information is provided by the shape of the indicator, it's probably best to simply avoid it if possible. At that point, we could have created only a simple, red KPI to denote the data points where the target was not met and then moved on to a different topic.
Instead, we then changed from using a shape to represent the data to a sparkline. Sparklines were devised by Edward Tufte in order to express a "small, intense, simple, word-sized graphic" to represent data. Sparklines are supposed to be able to be embedded into sentences. They are used to express data in a very compressed, at-a-glance way, which means that they are perfect for dashboards.
Red and green KPIs are popular, but they are not always the best solution. Sparklines can give us more context by providing more information over time, but this can be misleading in certain cases. In the next recipe, we will look at ways in which we can improve the sparkline to manage this scenario.