Correlation normalizes everything by the standard deviation of each attribute (just divide the covariance by the standard deviations of both variables and that normalizes things). By doing so, I can say very clearly that a correlation of -1 means there's a perfect inverse correlation, so as one value increases, the other decreases, and vice versa. A correlation of 0 means there's no correlation at all between these two sets of attributes. A correlation of 1 would imply perfect correlation, where these two attributes are moving in exactly the same way as you look at different data points.
Remember, correlation does not imply causation. Just because you find a very high correlation value does not mean that one of these attributes causes the other. It just means there's a relationship between the two, and that relationship could be caused by something completely different. The only way to really determine causation is through a controlled experiment, which we'll talk about more later.