What value can I gain by doing exploratory data analysis on features (and thus data) before doing clustering? This might not be a very good question, but I would still ask if it's beneficial to do EDA before running a clustering algorithm?

I understand that EDA helps us generate good and helpful insights into the data, which is crucial in data understanding. If we leave aside standard checks and manipulations like - removing outliers, scaling, removing constant value columns, removing null/'zero' value columns, etc. and if we have 20-30 features.
How will EDA help me in producing good and sensible clusters? Is it even necessary to do the EDA before clustering? 

Note: I am using k-means
How would you know you have to do cluster analysis before looking at your data ?

Setting aside data quality questions (which you should never do), a bare minimum of EDA will help you :


Know if it's relevant to do a clustering analysis (rarely imo)
Know if K-means is the best clustering tool (rarely imo)
Get an idea of the number of the clusters


Then you should do some EDA after, to understand what are the clusters you have selected. 

Edit: Basically, it will help you answers this kind of question : How do I interpret my result of clustering?