Previous chapters introduced various methods for assessing synthetic data, including statistical fidelity checks, machine learning utility evaluations, privacy risk assessments, and model-specific measurements. This chapter addresses the practical task of integrating these individual assessments into comprehensive and actionable evaluation reports.

You will learn systematic approaches for selecting the most pertinent metrics based on the specific context, data characteristics, and intended use case. We will examine techniques for constructing automated evaluation pipelines to streamline the assessment process. Furthermore, this chapter covers effective data visualization practices for communicating evaluation outcomes clearly and methods for interpreting the collective results to support decision-making. We will also address strategies for benchmarking multiple synthetic datasets against each other and conclude with a practical exercise on generating a structured report segment using common evaluation libraries.