  Big data has ushered in a new wave of predictive power using machine learning
models. In this work, we assess what {\it big} means in the context of typical
materials-science machine-learning problems. This concerns not only data
volume, but also data quality and veracity as much as infrastructure issues.
With selected examples, we ask (i) how models generalize to similar datasets,
(ii) how high-quality datasets can be gathered from heterogenous sources, (iii)
how the feature set and complexity of a model can affect expressivity, and (iv)
what infrastructure requirements are needed to create larger datasets and train
models on them. In sum, we find that big data present unique challenges along
very different aspects that should serve to motivate further work.
