The first thing to say is that Big Data and data analytic techniques are not new. Nonetheless, over recent years they have become one of the most rapidly rising growth areas in academic and commercial circles. Over that period, data has become the new oil; data analytic techniques have become the oil extraction and refining plants of their time; and data companies have become the new oil giants.
Yet economics and finance has, to date, been rather reticent about fully embracing this oil-rush. For economics and finance, the use of data analytic techniques has been the path less followed, at least relative to other disciplines. One simple diagnostic on that comes from looking at the very different interpretations put on the expression “data mining” by those inside and outside of economics and finance.
For economists, few sins are more heinous than data-mining. It is the last resort of a scoundrel to engage in “regression-hunting”- reporting only those regression results which best fit the hypothesis the researcher first set out to test. It is what puts the “con” into econometrics. For most economists, such data-mining has unfortunate similarities with oil-drilling - a dirty, extractive business which comes with big health warnings.
For data scientists, the situation could not be more different. For them, the mining of data is a means of extracting valuable new resources and putting them to use. It enables new insights to be gained, new products to be created, new connections to be made, new technologies to be promoted. It provides the raw material for a new wave of productivity and innovation, an embryonic Fourth Industrial Revolution.
What explains some economists’ caution about Big Data? The answer lies, in part, in methodology. A decent chunk of economics has followed in the methodological footsteps of Karl Popper in the 1930s. Popper championed a deductive approach to scientific advance. That started with axioms, moved from axioms to theory and then and only then took hypotheses to the data. Theory, in other words, preceded measurement.
There is an alternative, inductive, approach. This has even deeper roots, in the work of Francis Bacon from the early 1600s. This turns the telescope around. 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”.
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.
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