Yunusa Adamu Bena

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.

Rice is a vital staple for over half of the global population, yet its production is significantly threatened by leaf diseases caused by fungal and bacterial pathogens. Early and accurate detection of such diseases is critical to minimizing crop loss, particularly under conditions of labor shortages and climate variability. Traditional inspection methods are labor-intensive and error-prone, highlighting the need for automated, intelligent solutions. This systematic literature review explores recent advancements in deep learning and edge computing for rice leaf disease detection, focusing on challenges such as small and overlapping lesion detection and real-time model deployment. These lesions are difficult to detect due to their limited size, irregular shapes, and visual similarity to healthy areas, especially in field conditions. A comprehensive search across five databases (2020–2024) using the keywords “rice leaf disease detection,” “object detection,” and “edge computing” identified 2,072 articles. After applying inclusion criteria, 76 peer-reviewed journal articles were selected. Among the models reviewed, You Only Look Once version 7 (YOLOv7) achieved the highest accuracy (99% mAP), outperforming Faster Region-Based Convolutional Neural Network (Faster R-CNN) and YOLOv8. Edge devices like NVIDIA Jetson Nano demonstrated real-time capabilities, achieving 0.0549 seconds latency per image using YOLOv7-tiny at $640\times 640$ resolution. Vision Transformers (ViTs) also performed well (up to 98% mAP) but faced challenges related to computational and data efficiency. Future directions include leveraging lightweight models, solar-powered edge devices, and hybrid ViT–CNN architectures. This review underscores the transformative role of AI technologies in precision agriculture, offering scalable tools to improve crop monitoring and global food security.

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.

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.