I was lucky enough to build on the work of a number of people who had already run laps around this theory-building track. The original classification scheme, years ago, distinguished radical from incremental change. The theory said that established firms managed incremental change well, but would be expected to founder when their industry encountered a radical change.

How can you make sense of the future when you only have data about the past?

Many think of management as cutting deals and laying people off and hiring people and buying and selling companies. That's not management, that's deal making. Management is the opportunity to help people become better people. Practiced that way, it's a magnificent profession.

Because if the decisions you make about where you invest your blood, sweat, and tears are not consistent with the person you aspire to be, you’ll never become that person.

If you just look at the data, you are led to believe that things are getting better, rather than worse. That's why the fall is really precipitous, once you hit the ceiling.

In the first stage of insight-building, all that researchers can do is observe phenomena. Second, they classify the phenomena in a way that helps them simplify the apparent complexities of the world so they can ignore the meaningless differences and draw connections between the things that really seem to matter. Third, based on the classification system, they propose a theory. The theory is a statement of what causes what and why, and under what circumstances.

Disruptive technology is a theory. It says this will happen and this is why; it's a statement of cause and effect. In our teaching we have so exalted the virtues of data-driven decision making that in many ways we condemn managers only to be able to take action after the data is clear and the game is over. In many ways a good theory is more accurate than data. It allows you to see into the future more clearly.