Dark Data in Business Intelligence: A Systematic Review of Challenges, Opportunities, and Value Creation Potential
Abstract
An increasingly important yet underexplored aspect of Business Intelligence (BI) is dark data the massive volume of data acquired by firms but left unanalyzed. Hidden assets include textual documents, IoT logs, photos, audio records, and hybrid datasets, which often constitute the majority of company data. This review examines the significance of dark data, the barriers businesses face in unlocking its value, and the technological advancements that are gradually revealing its potential. The paper highlights how organizations can use dark data to improve decision-making, enhance operational efficiency, and gain a competitive edge. Key challenges such as poor data quality, integration difficulties, and high processing costs currently hinder the systematic use of dark data. However, advancements in natural language processing, machine learning, computer vision, knowledge graphs, and synthetic data production offer promising avenues for overcoming these obstacles. Case studies in manufacturing, banking, and retail demonstrate how dark data can drive predictive maintenance, fraud detection, and personalized consumer engagement. The review also explores the ethical and legal implications surrounding the use of dark data, particularly in relation to privacy, bias, and regulatory compliance. The paper emphasizes the strategic imperative for organizations to adopt a proactive, ethically informed approach to dark data, integrating advanced technologies and robust governance frameworks…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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