La Data Quality in Big Data

Abstract

Data quality constitutes a major strategic challenge in Big Data environments, where information reliability conditions the relevance of decisions. This study examines the evolution and structuring of scientific research on data quality over the 2015-2025 period. The objective is to identify temporal dynamics, influential contributions, dominant institutional and geographical hubs, as well as the field's main thematic axes. Unlike existing narrative syntheses, this research relies on a systematic quantitative literature review combined with a bibliometric analysis based on the PRISMA protocol, utilizing data extracted from Scopus and processed via VOSviewer. The results show a sustained growth in publications since 2020 and highlight the structuring role of major works such as those by Janssen, Whang, and Bradlow. Institutionally, the Ministry of Education of the People's Republic of China, the Chinese Academy of Sciences, and the Harbin Institute of Technology rank among the most productive affiliations. China, the United States, and India dominate scientific production, while international collaborations primarily revolve around China, the United States, and the United Kingdom. Author analysis distinguishes Wang and Wynn among the most productive, Janssen, Wahyudi, and Ghasemaghaei among the most cited, and Jagadeesh, Van Der Aalst, and Ballou at the core of co-citations. Finally, keyword co-occurrence underlines the centrality of Data Quality, Data Mining, and Big Data, confirming the close articulation between quality, analytical techniques, and data valorization. Keywords: Data quality, Big Data, Systematic review, Bibliometric analysis, PRISMA, VOSviewer.