Common text-to-speech (TTS) systems rely on training data for modelling human speech. The quality of this data can range from professional voice actors recording hand-curated sentences in high-quality studio conditions, to found voice data representing arbitrary domains. For years, the unit selection technology dominant in the field required many hours of data that was expensive and time-consuming to collect. With the advancement of statistical methods of waveform generation, there have been experiments with more noisy and often much larger datasets (“big data”), testing the inherent flexibility of such systems. In this paper we examine the relationship between training data and speech synthesis quality. We then hypothesise that statistical text-to-speech benefits from high acoustic quality corpora with high level of prosodic variation, but that beyond the first few hours of training data we don’t observe quality gains. We then describe how we engineered a training dataset containing optimized distribution of features, and how these features were defined. Lastly, we present results from a series of evaluation tests. These confirm our hypothesis and show how a carefully engineered training corpus of a smaller size yields the same speech quality as much larger datasets, particularly for voices that use WaveNet.