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Research Article

Dark Data in Business Intelligence: A Systematic Review of Challenges, Opportunities, and Value Creation Potential

Authors

  • Ololade Funke Olaitan David Eccles School of Business, information Systems, University of Utah, USA

    ololadefunke@gmail.com

  • Temitope Anthony Adebanjo Department of Marketing and Analytics, Regenesys Education, Johannesburg, South Africa
  • Loveth Itohan Obozokhai Robinson College of Business, Georgia State University, Atlanta, Georgia, USA https://orcid.org/0009-0008-9231-2787
  • Ambrose Nwawuweneonye Iwerumoh Department of Quality Management, Rushford Business School, Lucerne, Switzerland https://orcid.org/0009-0004-6987-7596
  • Isaac Oluwaseyi Balogun Robinson College of Business, Georgia State University, Atlanta, Georgia, USA https://orcid.org/0009-0000-6767-3582
  • Damilola Ayodele Ojo College of Business, Missouri State University, Springfield, Missouri, USA https://orcid.org/0009-0007-7432-3792

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 to transform hidden information into actionable insights that drive sustainable business value.

Keywords:

Artificial Intelligence (AI) Business Intelligence (BI) Dark Data Data Governance Ethical Data Management Unstructured Data Analytics

Article information

Journal

Journal of Economics, Business, and Commerce

Volume (Issue)

2(2), (2025)

Pages

135-142

Published

10-10-2025

How to Cite

Olaitan, O. F., Adebanjo, T. A., Obozokhai, L. I., Iwerumoh, A. N., Balogun, I. O., & Ojo, D. A. (2025). Dark Data in Business Intelligence: A Systematic Review of Challenges, Opportunities, and Value Creation Potential. Journal of Economics, Business, and Commerce, 2(2), 135-142. https://doi.org/10.69739/jebc.v2i2.1000

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