I'm doing PCA to analyze a dataset. The dataset size is 10^7 rows, and I have about 2,000 features. My analysis shows that no single principal component captures more than 1% of the dataset's variance. To capture 75% of the variance, I have to use 1,000 principal components! My interpretation of this is that the data might be just noise with very little signal. Is this the correct way of thinking about it?
Here is the plot of the cumulative variance