The volume of digital data has been growing exponentially during the past decade. Institutional decision-makers are interested in recouping associated costs by uncovering actionable insights through traditional data analytics and predictive analysis using artificial intelligence (AI)Artificial intelligence (AI). However, a major impediment to deploying AIArtificial intelligence (AI) is the lack of high-quality training data for producing accurate and robust models of practical value. This chapter begins with a review of statistics and predictions on data from reputable consulting firms. It is followed by an introduction to neuronsNeuron, the basic building blocks of AI models. The introduction is not intended to be a comprehensive tutorial; only terms and concepts that appear in discussions on machine learning and AIArtificial intelligence (AI) in later chapters will be introduced. This chapter concludes with a quick review on methods for data augmentation and synthetic generation of dataData augmentationSynthetic dataData synthesis. These methods may be useful in some scenarios where data sets need to be enlarged. However, they may not always produce useful data for training AIArtificial intelligence (AI) models. In these cases, federated learningFederated learning may be a possible solution.