Given these 10 characteristics of big data sources and the inherent limitations of even perfectly observed data, I see three main strategies for learning from big data sources: counting things, forecasting things, and approximating experiments. I’ll describe each of these approaches—which could be called “research strategies” or “research recipes”—and I’ll illustrate them with examples. These strategies are neither mutually exclusive nor exhaustive.
