General way to reduce features Let's say I have a giant dataset (600+ columns) and I have no idea what insights I might get from it or what model I want to run. 

What are some of the best ways to find the most influential columns/features? I know it's important to know what models to run first, but is there a general way to find columns that's more important than others? 

So far I've already removed the columns that are 80%+ empty, but I still have over 600 columns and I have no idea where to start. Please let me know your thoughts! I'm kind of new so any pointers will help.
Another way of looking at 600 columns is having a vector with 600 dimensions. So, the answer to reduce the 600 dimensional vectors to fewer dimensions without losing lot of information will be dimensionality reduction.

Principal component analysis(PCA) is a commonly used one. T-distributed stochastic neighbour embedding (TSNE) has been the state of the art for reducing higher dimensional vectors. Data visualisation using TSNE

To get the quick intuition about the vectors, the best thing is to reduce it to 2 or 3 dimensional vectors and plot them in a graph. Data visualisation always helps to get an overview of the data you are dealing with.