There is a growing consensus among scholars regarding the significant contributions of Big Data technology to political science research. Gary King, for instance, is cited by Jensen (2016:319) as one of the political scientists who has actively integrated Big Data into his research endeavors. According to King, in the context of political inquiry, Big Data serves as a powerful tool for collecting and organizing research sources, enabling more sophisticated forms of analysis and predictive modeling that support the development of more robust research designs.
Moreover, Big Data facilitates large-scale comparative studies and enhances the capacity to observe the relevance of socio-political phenomena, particularly behavioral patterns that were previously difficult to detect. Despite these substantial benefits, the integration of Big Data into political science also presents notable challenges—chief among them are concerns related to the use of theory and methodology, which continue to provoke critical debate within the field.
Recently, empirical scholars have advanced the argument that theory and methodology may no longer be necessary for understanding socio-political phenomena, particularly in the context of large-scale digital data. According to this perspective, technological innovations—especially those involving computational analysis and Big Data—have assumed the role traditionally played by theoretical frameworks and methodological designs (Anderson, 2008; Prensky, 2009; Steadman, 2013). Proponents of this empirical approach have adopted the slogan "the end of theory," contending that data itself now drives scientific inquiry ("data-driven science"), replacing the long-held view that knowledge and theoretical understanding are the primary engines of science ("knowledge-driven science").
Chris Anderson, then editor-in-chief of Wired magazine, articulates this position in his provocative article, The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. He asserts that “with enough data, the numbers speak for themselves.” In Anderson’s view, the rise of Big Data, new analytic tools, and hybrid approaches signals a new epoch he refers to as the "Petabyte Age," characterized by a mode of knowledge production in which theory is rendered obsolete. He argues that the sheer volume of available data renders traditional scientific methods outdated. For Anderson, the models and patterns that emerge from Big Data analytics are capable—on their own—of generating deep insights into complex phenomena, without the need for prior theoretical constructs.