Should I construct balanced data with only necessary variables or unbalanced data with many potentially usable variables? When I construct panel data from large household surveys (say, World Bank's Living Standard Measurement Surveys), I try to construct data set with as many potentially usable variables as possible. This creates a large unbalanced dataset and I don't know how many balanced data left until I run a regression. 

I personally have not encountered any problems with this, but I wonder what other people think when they have to use dataset I construct. 
I have not worked with others so far, but I wonder how I should construct a good dataset to work on if I need to work with professors. 
Should I construct balanced dataset? Or unbalanced dataset with many variables with many missing values?
I would construct and maintain only the full (unbalanced) data set with many variables with many missing values. Why? We can always extract balanced panel data sets from the full one; we can't go the other way around. When a balanced set is needed (in order to reduce the file size or send it to others), I would use Stata or R and never do it manually. To err is human.