It looks like you have 14 data points for each metric. It is almost impossible to determine if data is normal or not from so few points. A "not significant" result of a test is simply saying "I don't know if it is normal or not."
Skewness is of limited use, though it does fill up reports. Boxplots are good for odd data. I would use non parametric correlation, and quote the standard error instead of a confidence interval.
If you look for significant correlations, be careful. With 10 metrics you have 45 possible p values. You are likely to get false positives all over the place. Distinguishing between true and false positives has always been a thorny problem which hasn't really been solved.