1.1 Introduction

Business Intelligence (BI) refers to the technologies, applications, and practices used to collect, integrate, analyze, and present organizational data. Its primary aim is to transform raw data into actionable insights that support better decision-making (Wixom and Watson 2010). BI systems provide historical, current, and predictive views of business operations, drawing on data stored in warehouses and data marts to power reporting, analytical processing (OLAP), statistical analysis, and data mining (Chen et al. 2012).

The value of BI lies in its ability to reveal patterns hidden in large datasets — patterns that help organizations identify market trends, detect problems early, and make evidence-based decisions rather than relying on intuition alone (Shollo and Kautz 2010).

The field of BI has evolved through several distinct phases. Early BI efforts centered on static reports generated by IT departments. The rise of data warehousing in the 1990s enabled more structured analysis, and the 2000s brought interactive dashboards and self-service analytics tools like Tableau and Power BI that put data exploration directly in the hands of business users. Today, artificial intelligence represents the next leap in this evolution, enabling automated insight generation, natural language querying, and predictive analytics that were previously the domain of specialized data scientists (Dwivedi et al. 2021).

For students and practitioners entering the field, AI is particularly significant because it lowers the barrier to entry for sophisticated analysis. Tools powered by large language models (LLMs) can generate code, explain statistical output, and help users explore unfamiliar datasets through natural language conversation (Chui et al. 2023). This does not replace the need to understand BI concepts — in fact, it makes that understanding more important, since users must be able to evaluate whether AI-generated results are correct. But it does mean that someone with a solid conceptual foundation can accomplish more, faster, than ever before.

This chapter provides a foundation for the rest of the book. We begin with practical applications of BI across industries, then examine the key concepts and workflow that underpin BI systems. We survey the major tools used in the field — including the AI-powered tools that are reshaping modern analytics — and explore the ethical considerations that practitioners must navigate. The chapter concludes with a glossary of essential terms that will be used throughout the text.