Abstract

ZARATE-SANCHEZ, Roberto Antonio. Exploring logical challenges in data visualization and analysis in big data architectures: A focus on fallacies, biases, and paradoxes. Rev. Nac. Adm. [online]. 2024, vol.15, n.1, pp.103-115. ISSN 1659-4932.  http://dx.doi.org/10.22458/rna.v15i1.5150.

This qualitative research focuses on identifying and characterizing common logical errors in data analysis and visualization within Big data architectures. This is done through a profound literature review, categorizing errors into fallacies, biases, and paradoxes. The study aims to serve as a guide for professionals in both public and private realms and to highlight areas of research related to epistemology and ethics in the realm of Big data. The text raises two questions: what are the most common logical errors found in data analysis and visualization within Big data architectures, and how can these errors be addressed to improve both decision-making quality and ethics in this field? In the other hand, the article has four objectives: to identify and characterize the most common fallacies, biases, and paradoxes in data analysis and visualization in Big data architectures; to provide guidance and knowledge to professionals working in data analysis and visualization in public and private realms; to emphasize the importance of epistemology and ethics in the context of Big data; and to establish additional lines of research related to improving the quality of data analysis and promoting ethical practices.

Keywords : Big data; Fallacies; Biases; Paradoxes; Data analysis.

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