Fauziah Baharom

In today’s digital era, organisations are increasingly relying on data-driven decision-making to enhance operational efficiency and strategic planning. Data from multiple sources should be integrated to support this movement. However, this process is complex due to the emergence of big data. Consequently, it significantly increases the challenges of managing and integrating data, which can degrade data quality and lead to poor decision outcomes. In fact, existing data quality characteristics are no longer adequate in the big data era. Therefore, this paper conducted a comprehensive literature review of peer-reviewed articles retrieved from electronic databases published between 2010 and 2025 to examine existing data quality characteristics and identify gaps related to the 5V's big data characteristics. Moreover, this paper compares and evaluates existing data quality characteristics and their sufficiency for assessing the quality of data in big data integration. Based on these evaluations, this paper proposes DRASTIC, a set of 14 data quality characteristics, with dependency and scalability introduced as new characteristics in the context of big data integration because these characteristics are underexplored in existing literature. The findings contribute to the literature by extending current data quality characteristics and addressing the challenges posed by big data's unique characteristics in data integration.

Social network analysis is a process of studying social structures and relationships using graph theory and data analysis techniques. It involves mapping and measuring connections and entities in a network. However, on online selling platforms, identifying influential entities such as individuals and high-value products remains a challenge due to the complexity of customer and seller interactions. This study aims to assess seller performance and product lifetime value using AI-driven network analysis involving a measure of centrality. AI-driven network analysis utilises artificial intelligence (AI) to identify influential individuals and predict emerging trends in consumer engagement. It uses weighted degree and betweenness centrality to assess their effectiveness in identifying influential entities, including sellers, products, or organisations in a commercial network. Weighted degree centrality measures the strength and frequency of direct connections, while betweenness centrality identifies entities that act as intermediaries across different network segments. The analysis reveals that weighted degree centrality, with a value of 3190 for annual seller performance, is more closely aligned with actual sales performance and stakeholder assessments, making it a more suitable metric for supporting business decisions in this context. The findings demonstrate that AI-driven analytics enable businesses to consistently identify high-performing sellers and products based on their structural positions within the network. It contributes to the development of more targeted marketing strategies, systematic recognition of top performers, and enhanced customer engagement through data-informed decision-making. Future research may explore the integration of dynamic network modelling with multi-layered e-commerce networks, thereby increasing the depth of analysis across various platforms and industries.

The quality of software can be determined through software process quality assessment and certification. It involves with numerous evaluation criteria and software development practices. More importantly, these evaluation criteria and practices are not fixed and needs further improvement from time to time due to the shifts in the environment, business needs and current trends. As a result, there is a need for a mechanism that can systematically organise those evaluation criteria and practices to ensure the sustainability of the assessment and certification process. Furthermore, based on the literature, currently there is a lack in the quality of test cases for Agile software development process. The testing process is critical for demonstrating the quality of a software product, however it is only partially addressed in the existing software process quality assessment and certification models. The Agile testing practices need to be improved in the existing software process quality assessment and certification reference standard so that it can tolerate with changes. Majority of the existing software process quality assessment and certification models have not addressed sustainable software architecture. Consequently, this research is working towards developing a sustainable software architecture for the software process quality assessment and certification. As a beginning, this paper illustrates the conceptual model which will be used as base to develop a sustainable architecture for software process quality assessment and certification. The contributions of this study straddle research perspectives of Software Process Assessment and Certification with the existence of the sustainable software architecture. From a practical perspective, this study will provide software practitioners with a more systematic and effective approach for assessment and certification.