Introduction to Analytics

This chapter introduces analytics and its growing importance. Analytics involves applying data-based models to enhance results, reduce costs, and reduce risk in both profit-making and non-profit organizations. Various surveys have indicated a significant increase in the use of analytics in organizations across multiple industries, such as sports, lodging, e-commerce, and health insurance, and sectors, such as IT, supply chain and manufacturing, healthcare, and human resources. Three developments have spurred the use of analytics: the explosion in data volume, variety, and velocity; advancements in hardware and software; and a growing demand for data-supported decision-making. Analytics can be classified into three types based on their end objectives: descriptive analytics (examining and interpreting data to understand “what happened”), predictive analytics (predicting outcomes and behaviors), and prescriptive analytics (guiding “what should be done”). Developing predictive analytics models follows a deliberate sequence of steps, including problem definition, data preparation, modeling and evaluation, and deployment. The revised CRISP model is used to organize the chapters covered in the book. The chapter sets the stage for the subsequent chapters that delve deeper into the analytics process and its applications in different scenarios. This book features the open-source software KNIME, which provides state-of-the-art tools to develop models using a no-code, drag-and-drop interface. A typical reader might be in the class of users often described as “citizen data scientists,” referring to individuals outside the field of statistics and information technology who use self-service analytics tools to perform predictive or prescriptive analytics.