Right but in this case there is a trade-off: if you don't have the better data now how long before you will? Should you start now (and gain a time advantage at

Right but in this case there is a trade-off: if you don't have the better data now how long before you will? Should you start now (and gain a time advantage at a cost) with what you have or wait for the better data (read sensor/process/technology/people etc). My experience is that waiting for cleaner data is often like waiting for Godot and will often be a project killer (sometimes justifiably). This is a key issue at the moment in advanced ML: clean large training sets ideal for supervised training are elusive and the companies making real-world advances are pretty much all using available data (and semi-supervised techniques) rather than expensive made up training sets.