Big Data: the End of the Scientific Method?

TL;DR

This paper critically examines the exaggerated claims of Big Data, emphasizing the need for a balanced approach that integrates Big Data with theoretical insights to develop a new scientific paradigm capable of addressing complex system challenges.

Contribution

It proposes a revised view of Big Data's role in science, advocating for a synergy between data and theory to overcome fundamental scientific barriers.

Findings

Big Data claims often overstate capabilities without theoretical grounding.

Integrating Big Data with theory can lead to a new scientific paradigm.

Addressing complexity requires combining data-driven and theoretical approaches.

Abstract

We argue that the boldest claims of Big Data are in need of revision and toning-down, in view of a few basic lessons learned from the science of complex systems. We point out that, once the most extravagant claims of Big Data are properly discarded, a synergistic merging of BD with big theory offers considerable potential to spawn a new scientific paradigm capable of overcoming some of the major barriers confronted by the modern scientific method originating with Galileo. These obstacles are due to the presence of nonlinearity, nonlocality and hyperdimensions which one encounters frequently in multiscale modelling.