What is a correlation? A correlation quantifies the linear association between two variables. From one perspective, a correlation has two parts: one part quantifies the association, and the other part sets the scale of that association.
The first part—the covariance, also the correlation numerator—equates to a sort of “average sum of squares” of two variables:
\(cov_{(X, Y)} = \frac{\sum(X - \bar X)(Y - \bar Y)}{N - 1}\) It could be easier to interpret the covariance as an “average of the X-Y matches”: Deviations of X scores above the X mean multipled by deviations of Y scores below the Y mean will be negative, and deviations of X scores above the X mean multipled by deviations of Y scores above the Y mean will be positive.

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