Unlocking Hidden Value: A Framework for Transforming Dark Data in Organizational Decision-Making

Angelo Leogrande ()
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Angelo Leogrande: LUM - Università LUM Giuseppe Degennaro = University Giuseppe Degennaro

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Abstract: In today's data-driven world, organizations generate and collect vast amounts of information, yet not all data is managed or utilized with the same degree of efficiency and purpose. This paper investigates the taxonomy and distinctions among white data, grey data, and dark data, offering a comprehensive analytical framework to better understand their characteristics, value, and implications. White data refers to structured, accessible, and actively managed information that supports strategic decisionmaking and operational processes. In contrast, grey data occupies an intermediate space, representing semi-structured or unstructured data that, while not fully optimized, holds potential value when properly integrated into organizational practices. Lastly, dark data comprises the large quantities of information that remain unexploited, often due to a lack of resources, awareness, or technology. By mapping these categories, this paper aims to highlight the importance of a systematic approach in managing diverse data types, underscoring both the risks and opportunities associated with each. The study ultimately provides practical insights and recommendations for organizations seeking to maximize the value of their data assets through effective taxonomy and governance strategies.

Keywords: JEL CODE: C8 C80 C81 C82 C83 C87 Dark Data White Data Grey Data Warehouse Management; JEL CODE: C8; C80; C81; C82; C83; C87 Dark Data; White Data; Grey Data; Warehouse Management (search for similar items in EconPapers)
Date: 2024-11-25
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