Introduction to Data Visualization in Business Intelligence

Data visualization, according to Wikipedia, is the study of the visual representation of data. A closely related term, Information visualization, is defined as: visualization is a process of transforming information into a visual form enabling the viewer to observe, browse, make sense, and understand the information.

In the context of Business Intelligence (BI), data visualization is applied in two ways. First, data visualization is a discipline that covers a complete theory of how to visually represent data. The concepts and systems can be applied in the design of visual interfaces for communicating information.

Secondly, data visualization concepts and guidelines are realized through features in BI software, which makes it easy to apply the concepts. This is similar to the concept of Object Oriented programming. Object Oriented Programming is a concept that could be implemented in any programming language, such as C. But to do it effectively and in large scale, it’s best to have a programming language that provides direct support for the concepts, in the form of classes and inheritance.

Likewise, data visualization software provides features to easily create visualizations that effectively communicate information. More importantly, the software should guide and enforce the guidelines for effective visualization, so a casual user can still create satisfactory results.

Additionally, data visualization in the context of BI often implies the interactivities through the software. By enabling users to interact with data, the software opens tremendous opportunities to view data in many different angles. It transforms the data visualization from a presentation technology to an analysis process.
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Data Visualization Concepts

Data visualization is not a new discipline. The basic concepts of how to effectively present information through graphs have been well established in the 1970’s. In the simplest case, the principle of data visualization is about how to design graphs to communicate. Let’s illustrate this through an example. The graph on the left is an example from a reporting software vendor's site.

This graph looks innocent enough, and is probably very close to what you would get if you just throw the data into an Excel spreadsheet and create a default chart. However, the graph exhibits the common mistakes made through a lack of understanding in visual design.

Applying the basic principles of data visualization, we arrive at a vastly different graph, displayed on the right. The following changes have been applied to the graph to improve the communication:

1. The bar graph is changed to a stacked bar. A stacked bar allow the individual sub-components to be clearly discernable while make the comparison of graphs much easier. Try to compare 华北 and 华南. It would be difficult to tell which is larger in the original graph, but is very obvious in the new one.

2. The background color of the axis and legend is removed. The background unnecessarily draws attention to the less important part of the graph and creates a distraction to the graph.

3. The label on the X axis is changed to be horizontal. There is really no reason to make the label vertical since there is plenty of space to display them, and vertical text is generally harder to read than horizontal text.

4. The colors of the bars are changed to use three colors with the same hue but different intensity. This may be a matter of taste, but the use of simpler color generally helps reducing distraction and make people focus better on the data.

5. A label is added to the Y axis. Without proper labeling, a reader can’t tell what data is plotted. The data should be labeled either on the axis, or as a graph caption.

There are undoubtedly many more improvements that can be made on the graph. But the few changes applied to the graph is sufficient to show that with careful design and application of good visualization principles, simple changes can vastly improve the communication.

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