I think of summary/descriptive statistics as a tool to communicate the nature of a set of data at a glance, whether that is through tables of means and SDs, percentages, bar charts, histograms, pie charts etc.
There isn't a one-size-fits-all approach: you need to examine your data types (continuous, ordinal, categorical) and distributions (e.g. normal, bimodal) and then choose the method that you think conveys a summary of the data in as succinct a way as possible, without being misleading.
@Dave asked you in a comment how you would take a mean of your variables - I think this was intended as a hint to get you thinking along the right lines. So let's take an example and work it through. Say half of the workers had 'Pre-primary, primary and lower secondary education' ('1) and half of them had a PhD ('5'), and you took the mean of '1' and '5', you would end up with a mean of '3', 'College graduate'. This wouldn't be a good representation of the underlying data at all (percentages would give a better summary of the data in this example). This example also demonstrates that you should be extremely cautious about taking the mean of an ordinal scale: the size of the steps between the points on your ordinal scale are not necessarily equal, in which case taking the mean doesn't make much sense.
Another one of your variables ('FCT') is categorical, and there's no scenario I can think of for this variable in which taking the mean would work at all.
I won't go through all your variables and data types as there are many examples out there on how to deal with, for example, the 'Age' variable, where you've collected categorical data about an underlying continuous variable, but I hope this helps to point you in the right direction.
Some further notes of caution - you state that you have data from two 'populations'. Note that a population is made up of all the individuals within a defined group. Is your population very small? I suspect what you in fact have is a sample from each of two populations (sample sizes n=17 and n=12). These sample sizes are not particularly large to be trying to distinguish between 7 different categories within the variable 'FCT'. If you calculate the confidence intervals on the percentages, you may be able to see this. Also, what measures did you take to make sure that your samples aren't biased in some way, e.g. that your survey didn't just reach people working in marketing? (You may want to read up on survey methods to ensure a good response rate across all parts of a population, and how to interpret your data carefully if you are not able to do so, which is a common problem).