7.8 Summary

This chapter covered the principles and practice of data visualization in BI. We began with the dual roles of visualization — exploratory analysis for the analyst and explanatory communication for the audience — and examined how storytelling transforms raw data into narratives that drive action. We surveyed the major chart types for different data relationships: scatter plots for correlation, histograms for distribution, box plots for comparison, stacked bars for composition, and heat maps for complex patterns.

We then covered the design principles that separate effective visualizations from misleading ones, anchored in Tufte’s ink-to-information ratio: simplify, use color purposefully, label directly, and design for accessibility. Finally, we introduced the Grammar of Graphics and built ggplot2 visualizations layer by layer — a framework you will use throughout the case study chapters in this book.

In the next chapter, we apply these visualization skills to the Absenteeism at Work dataset, exploring patterns in employee absence through a series of increasingly sophisticated plots.