Ciencia de datos y estudios globales: aportaciones y desafíos metodológicos

• Main Authors: Daniel Lemus-Delgado, Ricardo Pérez Navarro
• Format: Article
• Language: English
• Published: Universidad de los Andes 2020-04-01
• Series: Colombia Internacional
• Subjects: global studies; big data; scientific research; methodology
• Online Access: https://revistas.uniandes.edu.co/doi/pdf/10.7440/colombiaint102.2020.03

Objective/Context: Big Data is a novel and promising methodological approach to acquire and analyze information from an extensive range of scientific disciplines. Despite the fact that Big Data has wide dissemination and acceptance in different scientific disciplines, its use in the field of Global Studies is still incipient. Taking this into account, this paper discusses the methodological challenges and presumable contributions that Big Data offers to Global Studies. Methodology: The starting point for this paper is the critical analysis of the conceptual bases of Big Data and its relationships with Global Studies. So, this paper proposes the possible applications of Big Data in the Global Studies contrasting some key concept. In this way, the conceptual elements that define Data Science are examined and compared with the paradigmatic conceptions of Global Studies. On these conceptual fundaments, the article discusses links, limits and, scopes of Big Data and how it can contribute to Global Studies. Conclusions: This paper concludes that technological tools based on Big Data can enrich our understanding of global phenomena only if we position ourselves in a critical attitude that recognizes that the choice of data and its analysis are decisions embedded in historical and social contexts. In this way, as with any other methodological approach, Big Data is only a partial way to obtain and analyze information. Originality: The originality of this paper is to shed critical light on the scope and limits of Big Data, beyond a positivist vision of scientific knowledge. In other words, this article recognizes the biases behind the supposed neutral explanations regarding the use of techniques based on Big Data.