The decrease in Gini impurity measures how useful a variable was in computing splits. A useful variable would give a large decrease and a "neutral" variable would not increase nor decrease the Gini index. So values close to 0 would suggest the variable are not important or redundant in obtaining good classification. As to the question of whether it's appropriate to use random forest on your data, you'd have to tell us more about what the data is and what the question you're trying to answer is. In general, random forest can make sense in the case where there are more variables than samples. However, for data with few variables, it could be better to use something else, e.g. a regression.