Big data, bigger dilemmas: A critical review (1509.00909v1)

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 that concern and affect all of these areas. By contextualizing the phenomenon of Big Data within larger socio-economic developments, we also seek to provide a broader understanding of its drivers, barriers, and challenges. This approach allows us to identify attributes of Big Data that need to receive more attention--autonomy, opacity, and generativity, disparity, and futurity--leading to questions and ideas for moving beyond dilemmas.

• The paper critically synthesizes Big Data's central challenges, detailing issues of autonomy, opacity, generativity, disparity, and futurity.
• It categorizes definitions into product-, process-, and cognition-oriented perspectives, clarifying the inherent analytical gaps in understanding vast data landscapes.
• The review underscores ethical, legal, and methodological dilemmas, advocating interdisciplinary strategies to balance innovation with societal impact.

Critical Review: Big Data, Bigger Dilemmas

The paper "Big Data, Bigger Dilemmas" offers a critical synthesis of the array of conceptual and practical challenges posed by the increasing relevance and application of Big Data across academic, corporate, and governmental settings. The authors aim to identify the underlying issues common to different areas affected by Big Data and contextualize this phenomenon within larger socio-economic developments. This synthesis brings to the fore the intricate attributes of Big Data that demand more scholarly and practical attention: autonomy, opacity, generativity, disparity, and futurity. These aspects provide a framework for discussing the multifaceted dilemmas inherent in Big Data's current and future trajectories.

Definition and Perspectives

One of the primary complexities addressed in the paper is the lack of consensus on defining Big Data. The authors categorize existing definitions into three perspectives: product-oriented, process-oriented, and cognition-oriented. The product-oriented perspective primarily focuses on the scale and growth of data volumes, often quantified in terms of petabytes, exabytes, or even yottabytes. The process-oriented view emphasizes the technological and computational challenges in managing and analyzing these data. Lastly, the cognition-oriented perspective critiques the limitations of human cognitive capabilities in understanding and interpreting vast and intricate data landscapes.

Beyond these, the paper introduces a social movement perspective, framing the advancement of Big Data as part of broader socio-historical trends similar to past computerization movements. This adds a layer of socio-political analysis, recognizing Big Data's role in shaping public policy and business strategy while being influenced by cultural narratives and expectations.

Epistemological and Methodological Dilemmas

The paper explores the epistemological dilemmas that Big Data introduces, questioning the validity of traditional scientific methods that rely on causal explanations over statistical correlations. Herein lies a significant shift from causation to prediction, with Big Data enabling more robust simulations that can predict trends and behaviors without necessarily understanding underlying causal mechanisms.

Methodologically, Big Data challenges the traditional qualitative and quantitative dichotomy. The process of data cleaning—deciding what to keep, exclude, or modify—introduces subjectivity that can impact analytical outcomes. The statistical significance of findings, often unquestioned in data-intensive research, can be misleading without careful sampling and understanding of biases inherent in Big Data collection.

Aesthetic, Technological, and Legal/Ethical Dilemmas

The aesthetic dilemmas revolve around the visualization of Big Data—challenging the balance between clarity and aesthetic appeal. The authors argue that while visual aesthetics can enhance engagement and comprehension, they can also obscure the accuracy of data representation.

From a technological perspective, the continuity versus innovation debate centers on how existing computational infrastructures cope with the demands of Big Data. There is an exploration of whether emerging socio-technical systems should lean towards automation or heteromation, as participatory and crowdsourcing methodologies become integral to data processing.

The legal and ethical dilemmas discussed address privacy concerns and the uncertainties in data ownership. The fine line between the collection and use of personal data for prediction versus violation of privacy rights poses critical challenges that require legislative foresight and ethical contemplation.

Implications and Future Directions

The implications of this research are profound, suggesting that the evolution towards a data-enriched environment demands interdisciplinary approaches that integrate technological innovation with ethical stewardship and policy-making. Practically, understanding these dilemmas allows policymakers, technologists, and scholars to mitigate potential adverse impacts while maximizing the beneficial use of Big Data in societal development.

In forecasting future developments, the emphasis on Big Data's autonomy and generativity points towards an inevitable transformation of both computational paradigms and socio-economic infrastructures. Additionally, the authors highlight the potential for increased economic disparity, where access to data analytics resources could fortify the advantages of the technologically elite, widening the "data divide."

Conclusion

This paper foregrounds the complexities and paradoxes introduced by Big Data, articulating a need for a balanced approach in leveraging its potential. It calls for ongoing critical discourse and innovative policies that address the multidimensional dilemmas arising from the pervasive integration of Big Data into the fabric of modern life. As these issues evolve, they will likely continue to shape the landscape of scientific inquiry, policymaking, and societal well-being.