Trying to understand the importance of RapidMiner Go in the real world

tonyboy9

Robert Hoyt has an understandable write up on RapidMiner Go for newbies. Hoyt emphasizes: "The goal of Go is to quickly run a set of algorithms on data that has already been cleaned and explored." 

https://medium.com/@rehoyt/rapidminer-go-machine-learning-on-the-go-cd858d409765

My data set is "credit risk," the target variable is "not fully paid."

With two separate data sets, train and test, I first ran train using RM Studio applying TurboPrep to transform and cleanse. I exported the train data set to the desktop, then to upload to RM Go. 

I ran RapidMiner Go. The first result is RM Go used X-means clustering to show 4 groups were chosen from my data set. Each line of the data set is shown with the applicable Group 1 to 4. Is it important to understand why Go used X-means  and which bank customer belongs to which group?

When it comes time to apply the model, do I clean the test data set same as the train data set before uploading to Go?

When it comes to comparing AutoModel vs Go, I find Go easier to understand and work with. 

In the real world of work using RapidMiner, considering weeks saved not having to write then debug hundreds of lines of code, what do experienced data analysts do with their time after a project like this takes what, one day?

Thank you for your time.

Tony