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Highlights

That’s why analysis works, to the extent that it does: Not because it finds the truth, but because finding a sound argument for something that is true is easier than finding a sound argument for something that is false.2 (View Highlight)

Rough truisms like “data has blind spots” were surely as well understood by lifelong Nike veterans as they are by internet bloggers and LinkedIn thought leaders. If it is so easy to criticize Nike’s strategy from a distance, why was it so hard to resist it from up close?6 The problem, I imagine, isn’t data, exactly; it’s culture. (View Highlight)

I doubt that Nike focused on Nike.com memberships and forgot about “brand magic” because they could easily measure the first one and not the second one. I do suspect, however, that the team that was working on Nike.com memberships could make a lot of compelling arguments about corporate strategy that the team working on brand magic could not. (View Highlight)

The most important thing that I often instruct teams to do is to develop a reproducible testing process, and that will actually influence the probability of your success more than anything. It’s so unpredictable whether a consumer product idea will work. If you actually focus more on your process for taking many shots at bat, that’s what actually reduces the risk more than anything. (View Highlight)

Data is useful for finding out by being useful in measuring interventions—the experiments, the building of the metrics layer, the new organizational structure at Nike. It is useful as a step in the scientific method, not a replacement for it. (View Highlight)