dataLDA: Analyzing Data Quality in Big Data Using Traditional Approaches vs. Latent Dirichlet Allocation - Systematic Review

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. Key trends identified through LDA include the increasing focus on data quality in supply chain management, health care applications, and environmental science. 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 can leverage these insights to implement effective data quality management strategies, improving efficiency and decision-making across various industries.