Anderson explains in his article that Big Data has lead to the death of theory, basing his arguement on the example of Google. Basically, he is stating that correlation supersedes causation- that science can advance without the use of theories, models, or mechanistic explanations. Anderson argues that, with the use of Big Data, we are now able to analyze data without hypotheses; this allows us to be data-driven rather than knowledge driven. Kitchin explores this idea as well, outlining how Big Data and new analytical methods are disrupting established ideas about the scientific method, and is actually reconfiguring how research is conducted. Kitchin also touches on how Big Data may even expand into the social sciences and humanities, allowing the quantification and development of fine-grained models of human life in the social sciences. Going back to the “death of theory” idea, Kitchin elaborates on how Big Data is modifing the existing scientific method, and allowing the rise of empiricism, which enables the data to speak for itself. Furthermore, this means that hypotheses are no longer necessary, and meaningful explanations are not needed either. One of the examples Anderson gives for this is J. Craig Venter. His innovation with the shotgun gene sequencing enables high-speed supercomputers to statistically analyze data found in ecological systems. This project has contributed immensely to the field of biology, yet it produces no actual knowledge about species or morphology. Overall, I think the paradigm shift is still in the process of occuring, and theory is not fully dead yet, but there is the high possibility that the significance of theory will decline in the future. I lean towards Kitchin’s ideas. He states that new methods that emerge from Big Data will complement current methods, not necessarily replace them, and I agree. Big data will definitely change the way research is conducted, but completely erasing the very established scientific method that has been the basis of science for many decades seems unlikely.