Hey Patrick!
By missing data do you mean Null values and how to deal with them?
I don't really have any experience with those tools but I proceed in different approach and that might still interest you
Deleting missing data is often the default method because of its simplicity. No decisions that need to be made that might confuse the data. You just get rid of records where there are missing values.
However, you should make sure that deleting missing data doesn't have adverse effects on your analysis. For example, if a particular demographic tended to leave a response blank in a survey, then removing records with blank entries will mean that a part of the population is underrepresented.
To remove them there are many solutions. You can use a filter tool, set it to NotNull for the variables that interest you and you'll see the results in the T
A second method to deal with missing data impute a value ( it's like making them up), but it's tricky because the values you'll impute should be as close to reality as possible (Mean, Median or mode are commonly used for those cases).
One important question you need to consider in this case is "How much data is really missing?". That should give you a hint on how to proceed.
Another question is "How the missing data is distributed?". That means if you have 10 predictor variables, and most of the missing values are just in 2 of these 10, then we could deal with them on a variable by variable basis. You should also consider if these specific variables are actually significant to our analysis and model making process.
It's a bit more statistically relevant but you can check their correlation with predictive variables if you have one
Those are simple methods, but there is also more advanced methods
Multiple Imputation is off of those. It consists of using a regression of others variables and then adding in some random error.
Or you have the Full Information Maximum Likelihood. This one sounds intimidating but here the missing values aren't replaced but handled within the modeling process itself. It requires special software that can handle this methodology
you can read more about these here and here
I personally haven't used those 2 methods in Alteryx yet, I'm just aware they exist so it's just for information if you want to dig deeper.
That's how I deal with Missing data knowing my Statistics background but I'm curious to know how everyone deals with them
Hope that helps x)
EDIT: In case you want to remove values, this post a must
https://community.alteryx.com/t5/Data-Preparation-Blending/Removing-Null-Lines/td-p/17846
