The scientific idea that is most ready for retirement is the scientific method itself. More precisely it is the idea that there would be only one scientific method, one exclusive way of obtaining scientific results. The problem is that the traditional scientific method as an exclusive approach is not adequate to the new situations of contemporary science like big data, crowdsourcing, and synthetic biology.
Hypothesis-testing through observation, measurement, and experimentation made sense in the past when obtaining information was scarce and costly, but this is no longer the case. In recent decades, we have already been adapting to a new era of information abundance that has facilitated experimental design and iteration. One result is that there is now a field of computational science alongside nearly every discipline, for example computational biology and digital manuscript archiving. Information abundance and computational advance has promulgated the evolution of a scientific model that is distinct from the traditional scientific method, and three emerging areas are advancing it even more.
Big data, the creation and use of large and complex cloud-based data sets, is one pervasive trend that is reshaping the conduct of science. The scale is immense: organizations routinely process millions of transactions per hour into hundred-petabyte databases. Worldwide annual data creation is currently doubling and estimated to reach 8 zettabytes in 2015.