  Applications of machine learning are subject to three major components that
contribute to the final performance metrics. Within the category of neural
networks, and deep learning specifically, the first two are the architecture
for the model being trained and the training approach used. This work focuses
on the third component, the data used during training. The primary questions
that arise are ``what is in the data'' and ``what within the data matters?''
Looking into the Radio Frequency Machine Learning (RFML) field of Automatic
Modulation Classification (AMC) as an example of a tool used for situational
awareness, the use of synthetic, captured, and augmented data are examined and
compared to provide insights about the quantity and quality of the available
data necessary to achieve desired performance levels. There are three questions
discussed within this work: (1) how useful a synthetically trained system is
expected to be when deployed without considering the environment within the
synthesis, (2) how can augmentation be leveraged within the RFML domain, and
lastly, (3) what impact knowledge of degradations to the signal caused by the
transmission channel contributes to the performance of a system. In general,
the examined data types each have useful contributions to a final application,
but captured data germane to the intended use case will always provide more
significant information and enable the greatest performance. Despite the
benefit of captured data, the difficulties and costs that arise from live
collection often make the quantity of data needed to achieve peak performance
impractical. This paper helps quantify the balance between real and synthetic
data, offering concrete examples where training data is parametrically varied
in size and source.
