Having tons of data is a Good Thing, so long as you can afford the marginal cost of gathering and managing all that data so that it's ready at hand when you need it later.
It's how you use the data that makes all the difference. If you're facing an issue you don't understand at all, don't go digging for random correlations in your mountain of data to find an explanation.
Think like a scientist: you need a valid hypothesis first! Once you have a hypothesis about what your issue might plausibly be, then you make a prediction: "If I'm right, I suspect our Foobar data will show very low values of Xyzzy around 3AM every weekday night". Only then do you go look at that specific data to confirm or refute the hypothesis. If you don't get a confirmation, you need to go back to hypothesizing and predicting before you look again. You can't prove causation by merely correlating data.