Roliana Ibrahim

Big Big Data Governance (BDG) has become essential for ensuring the secure, efficient, and strategic use of data in modern organizations. This study investigates the current state of BDG practices within Public Higher Learning Institutions (PHLIs) in Malaysia and Nigeria using a mixed-methods approach that integrates a review of existing literature, in-depth qualitative interviews, and a structured quantitative survey. The literature review identified core components of effective BDG roles and responsibilities, activities and processes, which informed the development of the study instruments. Qualitative data were gathered through interviews with ICT and MIS directors and analyzed using reflexive thematic analysis, while quantitative data were collected from 119 operational staff and analyzed using descriptive statistics. Findings from both data sources revealed fragmented BDG practices, low levels of tool deployment and integration, and inconsistencies in role definition and assignment, policy implementation, and institutional coordination. While institutions in Malaysia demonstrated relatively more mature and established BDG programs; responses from Nigerian institutions reflected early-stage development or complete absence of data governance structures. Notably, a large proportion of institutions lacks awareness or access to technical platforms necessary for effective BDG practices. The study highlights the need for tools and technologies, and the integration of BDG responsibilities across all institutional levels. This research contributes new empirical evidence on the challenges and disparities in BDG implementation. It offers actionable recommendations and proposes future research directions focused on BDG maturity models, multi-regional assessments, and the role of emerging technologies in strengthening big data governance frameworks in higher education.

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.

While sparking a big debate among academics, generative artificial intelligence (GAI) tools are becoming integral to academic research, holding the potential to transform traditional research and peer review methods. This systematic literature review investigates the emergent role of GAI tools in academic research workflow and scholarly publications by analyzing 44 articles. The process of identifying the most relevant publications was done following the preferred reporting items for systematic reviews and meta-analyses method. The findings provide a thorough understanding of how GAI is currently being utilized in the various aspects of academic research workflow and peer review process, including concerns, limitations, and proactive measures to better employ these tools effectively. Our review suggests the need for more research to develop appropriate policies and guidelines, enhance researchers’ artificial intelligence literacy through targeted training, and ensure ethical use of these tools to boost research productivity and quality.

The advent of big data has brought about significant opportunities and valuable insights to modern organizations. However, the proliferation of governance challenges is increasing dramatically, which necessitates searching for innovative approaches for managing, securing, and complying with government and organizational policies. This study conducted a systematic literature review and explored the profound impact of big data governance on modern organizations and highlighted the existing prevalent challenges and evaluated the mitigation approaches. We obtained the results of our defined research questions from peer-reviewed articles, industry reports, and academic theses published between (2019-2024). The results of our findings are categorized into four fundamental components of big data governance (i.e., people, process, data, technology), through illustrative tables and figures. Furthermore, this study has identified the need for effective and adaptive frameworks and models that can respond to emerging technologies and big data governance practices. After identifying the existing approaches for harnessing and mitigating the existing big data governance challenges, this study has proposed a new hybrid approach that integrates the viable system model and big data governance process model. The proposed approaches not only mitigate the existing challenges, but also serves as a diagnostic tool for identifying the maturity level of data governance and pinpoint the areas for improvement. However, the practical implementation of the proposed approaches remains a limitation of this study, urging avenues for future studies to explore its applicability and impact on modern organizations.

Recommender systems are essential tools that provide personalized user experiences across various domains such as e-commerce, entertainment, social media, education and content streaming. The integration of auxiliary information, including user demographics, item attributes, and contextual data has shown significant promise in enhancing the performance of recommender systems. This systematic review investigates the impact of incorporating auxiliary information into various types of recommender systems, examining recent advancements, methodologies, datasets, evaluation metrics, and to equally examine its significance on generative artificial intelligence. Similarly, five (5) reputable online databases were used to identify the relevant studies for answering our research questions. To obtain effective results of our findings, we focus more on the recent studies published between (2019 - June 2024) to ensure that of our findings up-to-date. After filtering the selected primary papers that solely focused on auxiliary information recommender systems a total of 37 papers were identified and analyzed. Our analysis shows the most utilized datasets, metrics, models, addressed issues and future works. Research limitations and future scope are also highlighted to assist researchers and practitioners for their future studies.

High-dimensional data is collected from various sources, fields, and applications such as medicine, science, business and more to provide helpful information to others. Unfortunately, the complexity of high-dimensional data has made it difficult to interpret and understand. As a result, sophisticated data analysis is required to extract knowledge and information from it. This can be illustrated through a visualization presentation. However, overlap between data can occur during visualization as data increases. Indirectly, it can cause a cluttered visual presentation. As a result, it affects the visual perception of high-dimensional data patterns. High-dimensional data can be deeply explored using dimension arrangement and scaling to overcome it. The arrangement of dimensions is essential since the relationship between these dimensions can influence the existence of an efficient cluster. This dimension is arranged based on the correlation value. The dimension that is more related will be placed next to each other. While performing clusters, dimensions will be scaled in or out. These features are available through Star Coordinate (SC) technique. This paper aims to conduct an exploratory data analysis in the SC environment where users can visualize and interact in a low-dimensional data visualization space. This paper demonstrates data dimensions manipulation's importance in structuring the projected space layout using two data sets. As a conclusion, formation of clusters was crucial and manipulation of data dimensions were essential to structure the projected space layout. The proposed approach has helped users find significant cluster formations by randomizing the scaling and order of dimensions.