in which case can I say that the data are bad and I ll achieve nothing using machine learning on it
NN is not a magic bullet

Not every model works well on every dataset, is the ethos.

Even tough NN is very powefull for certain tasks you could find tasks where linear regression would be a better choice.

Quality of data could mean 2 things:

a) data is unstructured and not processed. Think in terms of some super ugly text logs

b) data is uninformative. There are no patterns that can be extracted no matter what you try.

To conclude, experiment with different approaches and always have a holdout dataset to do sanity checks