General way to reduce features
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.