We live in a data-driven world. Volumes of information are accessible with the push of a button or the touch of a screen. Supercomputers exponentially more powerful than those used to put man on the moon sit casually in our pockets. We are incredibly lucky to live in an era where the concept of advanced statistics isn't walled behind the ivory towers of institution but can be readily digested and interpreted by anyone who's willing to look hard enough.
With the amount of data available to digest and the abundance of user-friendly tools that can be used to quickly process that data, a business can generate metrics for nearly every potential process output. On the surface, this is an enormous boon for managers, as they should be able to tell exactly what their teams are doing and how efficiently it's being done.
But that data doesn't always tell the whole story, or even the right story at all. What if all that data is doing us a disservice? What if the data we've come to rely on and, in some cases, take for granted, is harming our decision-making abilities. When do metrics matter? And, more importantly, when don't they?