Big Data revolutionized the field of science and measurements in many ways. It is able to carry the revolution in measurement since it extracts or mines data from everyone rather than the conventional methods of surveys and Empirical experiment that only select and test a sample that is representative of the general population. This special ability of data science can elevate itself as an interdisciplinary field of study because Big Data can be applied to any fields that conducts experiments since itself is can be considered as an advance form of experiment. No matter what the subject is, as long as the data are accurate and the right models are created, Big Data is able to find the correlation and connections between objects and insights that “the knowledge-driven science” may fail to notice. However, Big Data also has disadvantage as it only emphasizes and carries out the outcome with no explanation of the process and the reasoning behind it. That is also the reason why I disagree with Anderson’s argument that Big Data will replace traditional experiment methods completely. Kitchin uses the algorism and the recommendation system of Amazon as an example; With Big Data, it can predict what customers like to purchase together and is able to place recommendations accordingly. It successfully leads to an increase in sales and it is reasonable to argue that no explanation is needed to explain and analyze this correlation. However, this method can only be used widely in business since it can offer no testable explanations. Moreover, interpreting the data and creating the correct model will also be challenges in the application of Big Data.