About data cleansing, to what extent should we do our work?
This is a question that is very dependent upon the data in question. Assuming that you can train your models in a reasonable time, I would start by not cleaning the data at all, and seeing how well your model performs, and then cleaning it a bit and redoing the experiment and so on.
This is because it is possible to overclean your data, essentially removing variations in the data which are actually useful to your modelling.
The amount of cleaning that you apply is yet another meta-variable in your model.