The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights.
But, if there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored.
Assigning a default value which can be mean, minimum or maximum value. Getting into the data is important.
If it is a categorical variable, the default value is assigned. The missing value is assigned a default value. If you have a distribution of data coming, for normal distribution give the mean value.
If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.
You can do this practically in R with the methods such as the mean imputation, median imputation, replace with dummy values, filling with co-relations and similarities, remove the record entirely, and leave the record as it is.