Thematic Trends on Data Quality Studies in Big Data Analytics: A Review

Data quality has become a critical issue in research and practice in the era of exponential data generation and increasing reliance on big data analytics (BDA) across industries. This study conducts a thematic analysis of literature published between 2020 and 2024 to examine the prevailing trends, challenges, and advancements in data quality studies within the domain of BDA. Guided by the systematic thematic review methodology, the research analysed 34 peer-reviewed studies identified from SCOPUS and Web of Science (WoS) databases, using qualitative data analysis tools such as ATLAS.ti. The findings reveal five major themes: Ontology and Data Quality Frameworks, Big Data Analytics in Various Industries, Machine Learning and AI Integration, Governance and Data Stewardship, and Tools and Techniques for Data Analysis. These themes highlight a shift towards interdisciplinary approaches, integrating advanced technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) to address data quality issues. Limitations include potential selection bias from database restrictions and the exclusion of subscription-based journals, which may limit the generalisability of the findings. The study contributes to the theory by providing a comprehensive synthesis of data quality trends and their implications across various sectors. Methodologically, it demonstrates the utility of thematic analysis for consolidating diverse research. Practically, the insights inform data practitioners and policymakers on governance and technological strategies for ensuring data integrity. This review is original in its systematic exploration of thematic trendsin data quality, offering a valuable roadmap for future research and addressing the critical intersection of data quality and BDA.