PCA - what do I do with its results?
What can I do with this information?


You can do a lot of things with this data. You can visualize it, you can use the vectors for prediction or regression, whatever the task at hand. However, there are a few restrictions of PCA that you need to keep in mind. For eg. its very memory intensive, so you need to have a "lot" of RAM to use PCA on certain data-sets.

How will this help me understand the data?


You can visualize the data like this (image is taken from http://www.nlpca.org/pca_principal_component_analysis.html):






With reference to the above image, you can see that the data-points can be clearly separated into different clusters. Using this, you can apply K-Means and get different cluster centres. Using these cluster centres, you can further investigate and find additional insights.


Is it useful per see?


PCA is a dimensionality reduction technique which is very memory intensive.
If you have the required memory, you can easily reduce the number of features by 50-80 %, while still retaining a good amount of information. For eg, we can reduce 100 features to 20-30 features which contain maximum amount of information.
While performing PCA, it's important to check if the matrix computation can be done with the RAM that you have, otherwise, you can check out Iterative - PCA.

Is it useful as a preparation method for other methods?


It is very useful as a preparation method for clustering and visualization. 
Please refer this link : https://qiita.com/bmj0114/items/db9145a707cb6ed13201

Which ones can I try?


You can try the example given in the link above.