Preface

This book teaches Business Intelligence through concepts first and tools second. The principles you learn here — how to clean data, build models, design visualizations, plan projects, and deliver results — apply regardless of which software you use. We use R as our primary tool because writing code gives you precise control over every step of your analysis, makes your work reproducible, and builds skills that transfer across platforms. But the concepts are the star; the tools serve them.

A second thread runs through every chapter: artificial intelligence is transforming how BI practitioners work. AI coding assistants can generate R code from plain-language descriptions, draft project specifications, suggest visualization designs, and accelerate tasks that once took hours. We integrate AI into every chapter — not as a separate topic, but as a tool that works alongside the skills you are building. At the same time, we emphasize that AI does not replace the need to understand what your code does, why your model works, or whether your analysis is appropriate. Professional competence means owning the output, whether you wrote every line yourself or an AI drafted it for you.

The book is organized in four parts, each pairing theory chapters with hands-on case studies that use the Absenteeism at Work dataset — a real-world dataset of employee absence records that serves as a recurring case study throughout:

  • Part I: Foundations introduces BI concepts, the absenteeism dataset, R and RStudio, and AI tools.
  • Part II: Data Preparation & Exploration covers data cleaning with the tidyverse and data visualization with ggplot2.
  • Part III: Modeling & Mining addresses model building, regression, data mining techniques, and anomaly detection.
  • Part IV: BI in Practice covers spec-driven project planning, dashboards, and reporting — the “last mile” where analysis reaches the decision-maker.

Each theory chapter follows a consistent structure: concepts are explained, demonstrated with examples, and connected to professional practice through an Ethical and Professional Considerations section. Each case chapter applies those concepts to the absenteeism data, building a complete analysis from raw data to finished deliverable over the course of the book.

This textbook is designed for graduate and MBA students, but the approach is practical rather than academic. The goal is to prepare you to do BI work — to clean real data, build defensible models, communicate results clearly, and manage projects professionally. Welcome to Business Intelligence.

By the end of Part I, you will have a working understanding of what BI is, why it matters, and the tools you will use to practice it.