Lots of data is usually seen as a good thing.  And it is a good thing--except when it's not.  In a lot of fields, a problem arises when you have many, many features, especially if there's a somewhat smaller number of cases to learn from; supervised machine learning algorithms break, or learn spurious or un-interpretable patterns.  What to do?  Regularization can be one of your best friends here--it's a method that penalizes overly complex models, which keeps the dimensionality of your model under control.