PCA in visual Analytics
You have to see things for what they are. First, you have to figure out what PCA does. It's not clustering, but it can be used to realize that there could be clusters in the data. 
PCA is not always the answer. It depends on the data. There are other dimensionality reduction techniques such as autoencoders, non-negative matrix factorization and so on...

For the movie example, it does not make sense to use PCA. The iris database contains another type of data, where it does make sense. For the movie example, you might want to have a look at singular value decomposition and collaborative filtering.

One of the reasons for doing PCA could be for clustering. If you have many dimensions, and the intrinsic dimensionality is low you will have the curse of dimensionality. Reducing the dimensionality by PCA and doing clustering on the embeddings solves, in this case, the curse of dimensionality.