  Synthetic data serves as an alternative in training machine learning models,
particularly when real-world data is limited or inaccessible. However, ensuring
that synthetic data mirrors the complex nuances of real-world data is a
challenging task. This paper addresses this issue by exploring the potential of
integrating data-centric AI techniques which profile the data to guide the
synthetic data generation process. Moreover, we shed light on the often ignored
consequences of neglecting these data profiles during synthetic data generation
-- despite seemingly high statistical fidelity. Subsequently, we propose a
novel framework to evaluate the integration of data profiles to guide the
creation of more representative synthetic data. In an empirical study, we
evaluate the performance of five state-of-the-art models for tabular data
generation on eleven distinct tabular datasets. The findings offer critical
insights into the successes and limitations of current synthetic data
generation techniques. Finally, we provide practical recommendations for
integrating data-centric insights into the synthetic data generation process,
with a specific focus on classification performance, model selection, and
feature selection. This study aims to reevaluate conventional approaches to
synthetic data generation and promote the application of data-centric AI
techniques in improving the quality and effectiveness of synthetic data.
