When gathering performance data it is important to note that correlation does not prove causality. A strong correlation shows the compared values move up/down together, but does not prove (or disprove) a cause and effect relationship. If two performance meters (X and Y) are correlated it could be the case that X causes Y, or that Y causes X, or that changes in both X and Y are caused by some external force.
Notice below how well the Divorce rate in Maine correlates with Per capita consumption of margarine in the US. If you can figure out, and clearly show, how these two variables are causally linked, then a Nobel might be in your future.
Noticing correlation between the activity you see in various meters is an interesting clue to many performance questions. It’s a good place to start wondering, but before you make up your mind about a causal link between X and Y, gather more data and do it under differing circumstances. Figure out the mechanism for how X causes Y. Look for evidence that supports (and/or destroys) your X causes Y hypothesis.