Business analytics is a data-driven approach to decision making that allows companies to make better decisions. The study of business analytics involves the use of large amounts of data, which means that information technology related to the management of the data is very important. Statistical and quantitative methods are used to analyze the data and provide useful information to the decision maker.

Business analytics is often broken into three categories: descriptive, predictive, and prescriptive. Descriptive analytics involves the study and consolidation of historical data for a business and an industry. It helps a company measure how it has performed in the past and how it is performing now. Predictive analytics is aimed at forecasting future outcomes based on patterns in the past data. Statistical and mathematical models are used extensively for this purpose. Prescriptive analytics involves the use of optimization methods to provide new and better ways to operate based on specific business objectives. The optimization models presented in this book are very important to prescriptive analytics. While there are only three business analytics categories, many business decisions are made based on information obtained from two or three of these categories.

Many of the quantitative analysis techniques presented in the chapters of this book are used extensively in business analytics. Table 1.1 highlights the three categories of business analytics, and it places many of the topics and chapters in this book in the most relevant category. Keep in mind that some topics (and certainly some chapters with multiple concepts and models) could possibly be placed in a different category. Some of the material in this book could overlap two or even three of these categories. Nevertheless, all of these quantitative analysis techniques are very important tools in business analytics.