An old leader and mentor used this saying frequently, gleaned from his days in finance and economics. I love how it highlights the fallacies of data modeling, while still reminding us of the practical uses of those same models.

There is a fine line in “data-driven” decision making. We must be aware of what’s behind the scenes of every data point and analysis model. But at the same time we can’t always be data critics, either. If the data isn’t right, it doesn’t mean we can’t make a decision.

If I model who will win the next Super Bowl, and my result is “an NFL Team”, it’s an accurate (but not terribly useful) projection.

If I run the model 100 times and each time I find the winner will be the Chicago Bears, my model is probably wrong. It’s obviously full of bias and error, however you should also walk away knowing which NFL team is my favorite. You’ll definitely be able to better assess any of my NFL thoughts.

Precision, accuracy, limitation, and error are all different concepts. A data-driven culture knows how to apply them correctly.

Go Bears.