Big data, bigger dilemmas: A critical review
Hamid Ekbia, Michael Mattioli, Inna Kouper, G. Arave, Ali Ghazinejad,, Timothy Bowman, Venkata Ratandeep Suri, Andrew Tsou, Scott Weingart, and, Cassidy R. Sugimoto

TL;DR
This paper critically reviews the multifaceted dilemmas of Big Data, synthesizing diverse disciplinary perspectives to highlight core issues like autonomy, opacity, and generativity, and suggests directions for addressing these challenges.
Contribution
It provides a comprehensive synthesis of conceptual and practical dilemmas of Big Data across disciplines, emphasizing socio-economic contexts and identifying attributes needing further attention.
Findings
Identifies common problems across disciplines involving Big Data.
Highlights attributes like autonomy, opacity, and generativity as key concerns.
Suggests pathways for moving beyond current dilemmas.
Abstract
The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate amongst its proponents, detractors, and skeptics. While the practices draw on a common set of tools, techniques, and technologies, most contributions to the debate come either from a particular disciplinary perspective or with an eye on a domain-specific issue. A close examination of these contributions reveals a set of common problematics that arise in various guises in different places. It also demonstrates the need for a critical synthesis of the conceptual and practical dilemmas surrounding Big Data. The purpose of this article is to provide such a synthesis by drawing on relevant writings in the sciences, humanities, policy, and trade literature. In bringing these diverse literatures together, we aim to shed light on the common underlying issues…
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Taxonomy
TopicsBig Data Technologies and Applications · Big Data and Business Intelligence · Data Quality and Management
