  Time efficiency is paramount for the localisation industry, which demands
ever-faster turnaround times. However, translation speed is largely
underresearched, and there is a lack of clarity about how language service
providers (LSPs) can evaluate the performance of their post-editing (PE) and
human translation (HT) services. This study constitutes the first large-scale
investigation of translation and revision speed in HT and in the PE of neural
machine translation, based on real-world data from an LSP. It uses an
exploratory data analysis approach to investigate data for 90 million words
translated by 879 linguists across 11 language pairs, over 2.5 years. The
results of this research indicate that (a) PE is usually but not always faster
than HT; (b) average speed values may be misleading; (c) translation speed is
highly variable; and (d) edit distance cannot be used as a proxy for
post-editing productivity, because it does not correlate strongly with speed.
