Dimensionality reduction via something like PCA would be helpful to get an idea of the number of dimensions that are critical to represent your data.
To check for misclassified instances, you can do a rudimentary k-means clustering of your data to get an idea of how well your raw data would fit your proposed categories. While not automatic, visualizing at this stage would be helpful, as your visual brain is a powerful classifier in and of itself.
In terms of data that are outright missing, statistics has numerous techniques to deal with that situation already, including imputation, taking data from the existing set or another set to fill in the gaps.