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All Journal INOVTEK Polbeng - Seri Informatika Journal of Applied Computer and Information Technology
Haslina Mohd
Universiti Utara Malaysia
Computer Science & IT

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DRASTIC: Big Data Quality in Big Data Integration Muhammad Noor; Fauziah Baharom; Haslina Mohd
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 3 (2026): August
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ajd50z21

Abstract

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.
Revisiting big data governance: Insights from contemporary frameworks and emerging challenges Muhammad Noor; Fauziah Baharom; Haslina Mohd
Journal of Applied Computer and Information Technology Vol. 1 No. 1 (2026): Journal of Applied Computer and Information Technology (JACoIT)
Publisher : Global Research Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67131/jacoit.v1i1.1

Abstract

The rapid expansion of big data ecosystems has intensified demand for robust data governance frameworks that ensure data quality, security, privacy, and the creation of strategic value. Although numerous big data governance frameworks have been proposed over the past decade, they differ substantially in scope, maturity, and applicability, leaving critical gaps in addressing emerging technological, organizational, and ethical challenges. This study revisits the landscape of big data governance frameworks published between 2018 and 2025 through a comprehensive review and thematic synthesis of 13 peer-reviewed studies from high-impact journals and leading conferences. Unlike previous review studies that primarily collect governance dimensions or conceptual components, this study adopts a pattern-oriented analytical perspective to synthesize contemporary frameworks and identify recurring governance across contexts. The analysis identifies four major governance patterns, including fragmented governance approaches across sectors, context-specific frameworks without generalizable foundations, the growing intersection of AI and governance, and the imperative for adaptive and dynamic governance mechanisms. These patterns extend existing knowledge by explaining not only which governance elements are present in current frameworks but also how and why governance practices evolve in response to complex data ecosystems. The findings highlight the necessity of an integrated, adaptive, and context-sensitive big data governance framework that can respond to technological evolution and the complexity of the modern data environment. In addition, this study provides a structured roadmap for future research and offers actionable insight for organizations aiming to strengthen their data governance capabilities in increasingly data-driven environments.
Co-Authors Fauziah Baharom Muhammad Noor Muhammad Noor