When we talk about visualizing data, it is important to understand that any representation of data other than simple text is visualization. The very first visualization was a tabular representation of numbers, and tables are still a very powerful visualization—indeed the most common. Tables, however, are not the most appropriate visualization for every type of data—visualiza-tions such as bar, column, and line charts; scorecards and key performance indicators; network maps; and custom graphics drawn by an illustrator are all visualization techniques that, when used appropriately, convey the meaning of data better than a simple table.
This book explores the different visualization types and, more importantly, how to choose a visualization based on the data you have.
These explanations apply to any business intelligence application, but we perform implementation examples using the Microsoft stack—starting with Excel, the world’s most widely used Business Intelligence (BI) tool and then covering the entire toolset. We also explore the new world of custom visual-ization techniques using HTML5.
This book is divided into three parts: this first part introduces you to the subject of visualization; the second part introduces you to the tools you will use; and in the third part, you dive deeply into the individual visualizations, learning when to use them, which tool to use, and how to build them using the appropriate tools.
In this first chapter, you learn how to differentiate between data visualization and artistic visualization. Each has its place, but it is important when present-ing data to focus on data presentation and not just make the visualization presentation pretty. Typically, three-dimensional (3D) rendering is an example of choosing form over function, but it can be done right, with form properly serving function, and you will learn how.
4 InTRoDuCTIon To DATA VISuAlIzATIon
The FIrsT VIsuAlIzATIons
The very first visualizations (other than tables) were the time series and bar charts you are probably very familiar with. Although earlier versions exist, the art of line and bar charts was created in the form we are now familiar with by William Playfair in the late 1700s. Other related developments, such as the development of graph paper, also occurred in this time period. The invention of lithography aided the widespread adoption of visualizations, and new forms of visualization such as the pie chart soon followed. All these developments were paralleled by the huge strides taken in cartography, and the graphic techniques required to render these maps were used in the visualization space. William Playfair’s first bar chart is shown in Figure 1-1.
FIguRe 1-1 The very earliest bar chart from William Playfair
DATA VISuAlIzATIon VeRSuS ARTISTIC VISuAlIzATIon
The goal of data visualization is to present data to either provide a more intui-tive understanding of the data or show it in a way to view a large amount of data in a smaller area. Artistic visualization is designed to present a piece of data in a way that appeals to people and hence engenders interest in the data being presented.
FunDAMenTAlS oF VISuAlIzATIon 5 Data Visualization versus Artistic Visualization
There is obviously an overlap between these goals, but it is important when developing data visualizations to remember that the goal is to present data more meaningfully, not just to make it prettier.
Figure 1-2 shows an artistic visualization—it is exceptionally pretty, but it contains a minimal amount of data.
FIguRe 1-2 An infographic where the pictures don’t add value
Figure 1-3 follows the same theme, but has been enhanced to be data rich. It shows how graphics can be used to enhance data presentation:
FIguRe 1-3 The same infographic crafted as a data driven graphic
Of course, as pretty as these graphics might be, they are very space-consuming.
Figure 1-4 is a traditional BI chart showing the same data.
6 InTRoDuCTIon To DATA VISuAlIzATIon
FIguRe 1-4 A stacked bar chart
Although not as flashy, this chart shows it’s utility quite quickly: the different categories can be compared to each other at a glance, while still allowing for comparisons of the components of the categories. In addition, comparing cakes and pork in this graph, it is apparent that pork is a much bigger sale amount, although they are the same percentage of their category. Labels for the actual amounts have been added in lieu of percentages; either could be used, but comparing values is more meaningful for cross-category comparisons here.
Now that you have looked at these graphics, you should keep the following questions in your mind each time you develop a visualization:
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u Does this visualization contain more data than an equivalently sized table?
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u Is the data presented in this visualization easier to comprehend than an equivalent table?
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u Do the artistic elements add meaning?
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u Have I added any gratuitous elements that don’t add meaning or distract from the meaning, such as 3D effects, animated transitions, or gratuitous images?
FunDAMenTAlS oF VISuAlIzATIon 7
u An infographic is a graphic used to convey a message that is known before the creation of the infographic.
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u A data visualization is a graphical aid used to discover a message buried in data.
It is clear from these definitions that a data visualization can be published as an infographic. But data visualizations are often interactive and dynamic, so if trans-forms similar to those done in a tool such as Photoshop can be challenging.
However, this work is valuable because it means that an infographic does not become stale and out of date; it stays up to date as the data changes. (You read more about how to do this using HTML5 in Chapter 9 and throughout Part 3 of this book.) are used in many ways: to add flash by creating an illusion of depth; as an additional dimension to represent another data point; and for representation