The Power of Dark Data - Exploring the Role of Data Governance

PhD Thesis


Alkhatib, F. 2025. The Power of Dark Data - Exploring the Role of Data Governance. PhD Thesis Univeristy of East London Architecture, Computing & Engineering https://doi.org/10.15123/uel.8z329
AuthorsAlkhatib, F.
TypePhD Thesis
Abstract

Holistic Dark Data Governance is an emerging field reshaping how organisations manage and govern their data. Despite significant investments in digital transformation, many organisations and institutions still struggle to adopt effective dark data governance practices. In Texas, USA, universities have made substantial investments in digital initiatives to keep pace with the rapidly evolving technological landscape in higher education. A considerable portion of these investments have been directed toward managing dark data, improving data quality, governance, and integration, and adopting modern applications to enhance educational outcomes. However, the challenge of dark data governance is not confined to Texas; it is a global issue that remains under-researched and infrequently practiced. This research represents a pioneering effort to tackle the challenge of dark data governance at one of the leading universities in Texas. By advancing research in this emerging field, the study proposes comprehensive methods for deploying effective holistic dark data governance models within the university setting, enabling the institution to better manage and control dark data, and thereby achieve its operational and strategic goals. The study began with an analysis of existing data governance frameworks, revealing their limitations due to a lack of focus on dark data. In response, this research aims to develop a holistic model designed to establish, deploy, and sustain effective dark data governance systemic functions within the organisational Viable System Model (VSM) context. The proposed framework offers a comprehensive approach to addressing the challenges of dark data governance, surpassing the limitations of traditional data governance frameworks. Additionally, it equips universities with the knowledge and tools necessary to implement such models, empowering them to manage and control dark data effectively to achieve their objectives. A crucial step in developing the proposed model was understanding the systemic functions of holistic data governance. To support the model's conceptualization, the study employed Critical Success Factors, VSM, and Cybernetics theories. Given the complexity of the model, organisations and institutions need a method to assess their current state and define their requirements. To facilitate this, the research introduced a Maturity Model alongside an Assessment Matrix. These tools were validated and evaluated at a top university in Texas using mixed research methods, including both qualitative and quantitative approaches. The Delphi approach was used for validation, with expert representation from the university. The Assessment Matrix evaluation enables organisations and institutions to determine their maturity levels in holistic dark data governance and define the requirements for achieving their full viable level. To further assess the research findings and develop an improvement roadmap, the study incorporated a practical evaluation that examined the application of the assessment matrix across realistic case scenarios at three prominent universities in Texas, USA. These universities were actively implementing general data governance practices but had not yet adopted a comprehensive approach to dark data governance. This research significantly contributes to the academic debate on holistic dark data governance while offering practical frameworks that empower organisations and institutionsto enhance their holistic data governance practices and achieve their operational and strategic goals.

Year2025
PublisherUniveristy of East London
Digital Object Identifier (DOI)https://doi.org/10.15123/uel.8z329
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Publication dates
Online18 Mar 2025
Publication process dates
Completed20 Dec 2024
Deposited18 Mar 2025
Copyright information© 2024 The Author. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms.
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