4 Quotes by Eric Siegel

As data piles up, we have ourselves a genuine gold rush. But data isn’t the gold. I repeat, data in its raw form is boring crud. The gold is what’s discovered therein.

Predictive modeling generates the entire model from scratch. All the model’s math or weights or rules are created automatically by the computer.

But all predictive models share the same objective: They consider the various factors of an individual in order to derive a single predictive score for that individual. This score is then used to drive an organizational decision, guiding which action to take. Before using a model, we’ve got to build it. Machine learning builds the predictive model:.

We know the what, but we don’t know the why.6 When applying PA, we usually don’t know about causation, and we often don’t necessarily care. For many PA projects, the objective is more to predict than it is to understand the world and figure out what makes it tick.