7 Data Visualization

Data visualization is where Business Intelligence becomes visible. The best analysis in the world is useless if it cannot be communicated clearly to the people who need to act on it. Visualization transforms raw numbers into patterns that the human eye can grasp instantly — trends, outliers, relationships, and compositions that would be invisible in a table of figures.

This chapter covers both the principles and the practice of data visualization in BI. We examine why visualization matters, how to tell stories with data, how to choose the right chart for the right question, and the design principles that separate effective visualizations from misleading ones. We then introduce the Grammar of Graphics — the framework behind R’s ggplot2 package — and build visualizations step by step.

Chapter Goals

Upon concluding this chapter, readers will be able to:

  1. Explain the role of exploratory and explanatory visualization in BI and when to use each.
  2. Select appropriate chart types for different data relationships — correlation, distribution, comparison, and composition.
  3. Apply principles of good design, including minimizing chart junk and maximizing the ink-to-information ratio.
  4. Construct layered visualizations in R using ggplot2 and the Grammar of Graphics framework.
  5. Describe how AI tools can assist with chart selection, design iteration, and automated insight generation from visualizations.