Cognitive, Analytical, and Organizational Foundations of Data Visualization for Data-Driven Decision-Making in the Big Data Era
Keywords:
Data visualization, big data analytics, decision-making, cognitive perceptionAbstract
Data visualization has evolved from a supportive analytical aid into a central cognitive and strategic instrument for data-driven decision-making across business, governance, and scientific domains. The exponential growth of big data, characterized by unprecedented volume, velocity, and variety, has intensified the need for effective visualization techniques capable of transforming abstract data into meaningful, actionable insight. This article develops a comprehensive theoretical and empirical synthesis of data visualization as a decision-support mechanism, grounded strictly in established literature spanning visualization theory, cognitive psychology, storytelling, big data analytics, and organizational decision-making. Drawing upon foundational works by Tufte, Bertin, Few, Kirk, and Friendly, alongside contemporary research on big data analytics, dashboards, and visualization-enabled decision processes, the study examines how visual representations mediate human perception, memory, reasoning, and judgment. The article elaborates on the cognitive underpinnings of visualization, including dual coding theory and neurological constraints on attention and working memory, and connects these principles to practical design philosophies and visualization taxonomies. It further analyzes the role of visualization in organizational contexts, such as financial decision-making, project portfolio management, smart cities, and competitive strategy, highlighting how visual analytics enhances transparency, agility, and analytical capability. Methodologically, the article employs an integrative qualitative synthesis of peer-reviewed literature to derive conceptual patterns, theoretical propositions, and interpretive insights. The findings underscore that effective data visualization is not merely a technical skill but a multidisciplinary practice integrating perception science, narrative logic, and organizational context. The discussion critically addresses limitations, including cognitive bias, misinterpretation, and dashboard overload, while outlining future research directions involving immersive and situated visualization technologies. By offering an extensive, theory-driven exposition, this article contributes a holistic academic framework for understanding data visualization as a foundational pillar of modern data-driven decision-making.
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