Whats an explanation of PCA that is intuitive for someone in senior leadership who doesn't have a technical background? I've been pulled onto my first Data Science project at work. Classic problem of predicting sales based on web traffic data, etc. While I don't know about the specific techniques I will be using in my model, I think that PCA will probably be tried at some point, and if I use it successfully as a preprocessing step before I chug the data into some estimator, I'm thinking about how I might go about defending/explaining my work.

My question is, what is an intuitive explanation for PCA that doesn't rely heavily on the linear algebra that makes up the process? Perhaps an analogy that draws a parallel to something that any layman can readily understand?
Try to make your explanation simple and brief. Maybe something like "PCA distils a dataset's features into as few attributes as possible; these attributes explain as much of the observed trend as possible while being as different from each other as possible". If you can do this using visual aids, even better.

As far as analogies go, maybe use an example of a motor accident. There are many predictors of an accident, but PCA may combine the majority of them to form 2 features - perhaps vehicle attributes and driver attributes. These are different but arguably are both highly predictive of a motor accident. [I realise this is not technically how PCA works, but might help a layman understand].