A Predictive Analytics or Data Quality Assessment Through Artificial Intelligence Techniques

Data quality assessment, a crucial procedure in big data analysis, faces challenges due to the complexity and diversity of data sources, real-time processing demands, and sheer data volume. Identifying potential data quality concerns in large datasets is particularly difficult, especially when dealing with multiple data sources. This paper explores prominent factors like completeness, timeliness, correctness, integrity, and relevance of data to determine how AI tools can enhance the predictive analytics of data quality. It focuses on data quality in AI systems, explaining techniques like data cleaning and validation. AI, utilizing ML and DL approaches, analyzes massive and sophisticated datasets, increasing the accuracy of predictions. AI-derived models help organizations review and improve data quality, facilitating predictive business analytics and providing valuable insights for decision-making. The paper emphasizes the importance of AI in ensuring data quality and highlights its ability to enhance processes across various fields.