Predicting the future state of a system has always been a natural motivation
for science and practical applications. Such a topic, beyond its obvious
technical and societal relevance, is also interesting from a conceptual point
of view. This owes to the fact that forecasting lends itself to two equally
radical, yet opposite methodologies. A reductionist one, based on the first
principles, and the naive inductivist one, based only on data. This latter view
has recently gained some attention in response to the availability of
unprecedented amounts of data and increasingly sophisticated algorithmic
analytic techniques. The purpose of this note is to assess critically the role
of big data in reshaping the key aspects of forecasting and in particular the
claim that bigger data leads to better predictions. Drawing on the
representative example of weather forecasts we argue that this is not generally
the case. We conclude by suggesting that a clever and context-dependent
compromise between modelling and quantitative analysis stands out as the best
forecasting strategy, as anticipated nearly a century ago by Richardson and von
Neumann
