Big data quality is all about ensuring that the huge amounts of data are accurate, reliable, and useful. It is important because decisions made based on this data can affect many things, like business strategies, customer experiences, and even public policies. Many researchers are studying data quality in the context of big data. This study aims to analyze the research trends and patterns within the domain of big data quality. This method reveals hidden themes in large datasets, helping to understand data quality better. The findings show that Latent Dirichlet Allocation (LDA) is effective for topic modeling in big data, highlighting data quality challenges and opportunities. The research identified key trends and effective assessment methods. For researchers, using LDA in a systematic review helps summarize existing studies and find gaps, guiding future research. Practitioners and future researchers get useful direction for managing data quality and improving efficiency and decisions in various industries.