Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review
The exponential growth of intelligent systems technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), and Smart Connected Products, has intensified the difficulties of data governance. Organizations adopting these technologies face challenges in managin...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10839423/ |
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| author | Yunusa Adamu Bena Roliana Ibrahim Jamilah Mahmood Arafat Al-Dhaqm Ahmad Alshammari Maged Nasser Muhammed Nura Yusuf Matthew O. Ayemowa |
| author_facet | Yunusa Adamu Bena Roliana Ibrahim Jamilah Mahmood Arafat Al-Dhaqm Ahmad Alshammari Maged Nasser Muhammed Nura Yusuf Matthew O. Ayemowa |
| author_sort | Yunusa Adamu Bena |
| collection | DOAJ |
| description | The exponential growth of intelligent systems technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), and Smart Connected Products, has intensified the difficulties of data governance. Organizations adopting these technologies face challenges in managing the volume, variety, and velocity of data while ensuring quality, security, compliance, and ethical integrity. This study investigates the state of big data governance (BDG) programs in organizations leveraging intelligent systems technologies, using a systematic literature review guided by PRISMA. A synthesis of insights from 74 peer-reviewed articles, conferences, and industry reports reveals both the significant benefits of BDG such as enhanced decision-making, operational efficiency, and regulatory compliance and persistent challenges, including fragmented governance frameworks, limited scalability, data quality issues, and ethical concerns. To address these gaps, this study proposes the big data governance maturity assessment model (BDG MAM), a novel framework developed to assess and enhance the maturity of BDG programs. The BDG MAM evaluates governance maturity across four key dimensions: people, process, data, and technology. It provides a structured roadmap for organizations to benchmark governance practices, prioritize improvements, and implement effective strategies. The model was validated through a pilot study conducted with a public higher learning institution, demonstrating its practical applicability and effectiveness in real-world scenarios. By bridging theoretical insights with practical implementation, this study advances academic discourse and provides practitioners with a robust approach to navigate the challenges of modern data governance practices, ensuring sustainable and effective management of heterogeneous data environment. |
| format | Article |
| id | doaj-art-fe4bb230c6f243769844f55407310976 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fe4bb230c6f243769844f554073109762025-01-25T00:02:45ZengIEEEIEEE Access2169-35362025-01-0113128591288810.1109/ACCESS.2025.352894110839423Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature ReviewYunusa Adamu Bena0https://orcid.org/0000-0001-7978-8440Roliana Ibrahim1https://orcid.org/0000-0001-7580-1804Jamilah Mahmood2https://orcid.org/0000-0002-3320-5331Arafat Al-Dhaqm3Ahmad Alshammari4https://orcid.org/0009-0000-2051-2757Maged Nasser5https://orcid.org/0000-0003-3788-5722Muhammed Nura Yusuf6https://orcid.org/0000-0002-1222-1754Matthew O. Ayemowa7https://orcid.org/0009-0005-0590-6041Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaSchool of Computer Science, SCS, Taylor’s University, Subang Jaya, MalaysiaDepartment of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi ArabiaComputer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaDepartment of Computer, Faculty of Computing, Abubakar Tafawa Balewa University, Bauchi, NigeriaFaculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaThe exponential growth of intelligent systems technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), and Smart Connected Products, has intensified the difficulties of data governance. Organizations adopting these technologies face challenges in managing the volume, variety, and velocity of data while ensuring quality, security, compliance, and ethical integrity. This study investigates the state of big data governance (BDG) programs in organizations leveraging intelligent systems technologies, using a systematic literature review guided by PRISMA. A synthesis of insights from 74 peer-reviewed articles, conferences, and industry reports reveals both the significant benefits of BDG such as enhanced decision-making, operational efficiency, and regulatory compliance and persistent challenges, including fragmented governance frameworks, limited scalability, data quality issues, and ethical concerns. To address these gaps, this study proposes the big data governance maturity assessment model (BDG MAM), a novel framework developed to assess and enhance the maturity of BDG programs. The BDG MAM evaluates governance maturity across four key dimensions: people, process, data, and technology. It provides a structured roadmap for organizations to benchmark governance practices, prioritize improvements, and implement effective strategies. The model was validated through a pilot study conducted with a public higher learning institution, demonstrating its practical applicability and effectiveness in real-world scenarios. By bridging theoretical insights with practical implementation, this study advances academic discourse and provides practitioners with a robust approach to navigate the challenges of modern data governance practices, ensuring sustainable and effective management of heterogeneous data environment.https://ieeexplore.ieee.org/document/10839423/Big data governancedata governance challengesdata governance approachesdata governance impact/benefitintelligent systems technologiesmaturity assessment model |
| spellingShingle | Yunusa Adamu Bena Roliana Ibrahim Jamilah Mahmood Arafat Al-Dhaqm Ahmad Alshammari Maged Nasser Muhammed Nura Yusuf Matthew O. Ayemowa Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review IEEE Access Big data governance data governance challenges data governance approaches data governance impact/benefit intelligent systems technologies maturity assessment model |
| title | Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review |
| title_full | Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review |
| title_fullStr | Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review |
| title_full_unstemmed | Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review |
| title_short | Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review |
| title_sort | big data governance challenges arising from data generated by intelligent systems technologies a systematic literature review |
| topic | Big data governance data governance challenges data governance approaches data governance impact/benefit intelligent systems technologies maturity assessment model |
| url | https://ieeexplore.ieee.org/document/10839423/ |
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