Before deploying synthetic data in real-world applications, rigorously assessing its quality is a required step. This chapter lays the foundation for understanding how to conduct such assessments effectively.

We will begin by defining the core dimensions that constitute synthetic data quality: statistical fidelity (how well the synthetic data represents the original), machine learning utility (how effective the synthetic data is for training models), and privacy preservation (how well the data protects sensitive information). Understanding these dimensions is key to selecting appropriate evaluation methods.

Evaluating generated data comes with specific challenges. We will discuss these common issues and examine the necessary compromises often made when balancing data $Fidelity$, $Utility$ for machine learning tasks, and $Privacy$ guarantees.

To navigate the variety of available checks, we will introduce a structured taxonomy of evaluation metrics, providing a framework for organizing different measurement approaches. Finally, we will cover the practical setup of a Python environment using standard data science libraries, preparing you for the hands-on implementations in subsequent chapters. By the end of this chapter, you will have a clear understanding of the fundamental concepts and considerations involved in evaluating synthetic data.