Why do PCA if not for feature reduction I have got some data regarding some tv series episodes characteristics, like the number of characters or of cuts, and I have been adviced to try out PCA in order to find out the characteristics this movies have in common.
Reading online, I have not understood any reason other than feature reduction, which I don't really think I need.
However, after running PCA this are the results I get:

And these two plots say how the variance of each dimension is explained, I guess:


Now, what I'd like to know is: how can this be useful in my case? Does this provide me with anything interesting I am not seeing?
Thanks a lot in advance

Principal components are mutually orthogonal. For linear regression, this means that they are entirely independent of each other, and there will be no hint of collinearity and the standard error variance inflation that comes with that. Consequently, regression performed on PCs will have coefficients with the smallest standard errors, and that will result in the smallest confidence intervals for your response variable estimates.
The question is not "why should I use PCs?"  Rather, it is, "why don't I use PCs for absolutely every regression problem I ever face?" -- which has a longer and more complicated answer. That is, there are reasons why you shouldn't use PCs for every problem. But you didn't ask that question, so I'll spare you. ;)
The reduction of feature space is not my favorite reason to use PCs. Finding, understanding and ultimately exploiting covariances between features is.
For example, an athletic director may sort student athletes by height and weight, and may suggest team sports appropriate for a height-weight classification system. But really, the students are better sorted by "size" (big/little) and "shape" (fire-plug/bean-pole) which could be the names of principal components based on height and weight. No dimensional reduction, but the rotation in feature space perhaps makes it easier to recommend team sports to students.
But then we wouldn't have a 5'6" Spud Webb playing basketball in the NBA or 6'9" Kyle Hudlin playing English Premier League soccer(center forward, not a goal keeper !?) That is, there are always more features to consider -- like passion, and know-how.
