Since generating data for training quantum ML models requires substantial computational power, increasing the carbon footprint and creating barriers for researchers with limited resources, optimizing the entire workflow from conception to a trained model is essential. Thus, embracing the smart data paradigm is crucial for advancing ML models in chemistry, ensuring that computational resources are used efficiently and model predictions remain reliable. Although previous reviews have acknowledged this need, the impact of training data choice on model prediction quality remains poorly understood. Uncertainty quantification (UQ) is central to addressing this gap, as it enhances trust in data-driven predictions and facilitates active learning strategies. By identifying areas of large prediction errors, UQ drives the selection of new training points, supports iterative model improvement, and reveals missing or redundant information within chemical databases. Moreover, the optimal composition of a training database is often property-dependent, emphasizing UQ's critical role in linking data curation to prediction accuracy.