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It starts with data, unconstrained by axioms and hypotheses, and then uses this to inform choices about models of behaviour. Data, in other words, precedes theory. Indeed, some data scientists have suggested such an approach could signal the “End of Theory”. 8  So where some economists have tended to see the pitfalls in Big Data, data scientists have seen promise. Where some economists have tended to see the ecological threat it poses, data scientists have seen the economic potential. I am caricaturing a little, but only a little. So who is right? And does the era of Big Data signal an oil-rush or an oil-spill? The truth, as often, probably lies somewhere in between. Both deductive and inductive approaches can offer insights into understanding the world. They are better seen as methodological complements than as substitutes. Put differently, using one approach in isolation increases the risk of making faulty inferences, and potentially serious mistakes, in understanding and policy. Let me give a couple of examples to illustrate. During the global financial crisis, it is now fairly well accepted that the workhorse Dynamic Stochastic General 9  Equilibrium (DSGE) model of the macro-economy fell at the first fence. It was unable to account for business cycle dynamics, during or after the crisis. While theoretically pure, it proved empirically fragile. That empirical fragility arose, I would suggest, from a methodological over-reliance on deductive methods. Or, put differently, from too little focus being placed on real-world data from the past, including on crises.