An analytical approach to data quality: Techniques, challenges and tools in preprocessing

Preparing and refining data is a foundational phase in the data mining workflow, which aims to uncover meaningful patterns and insights from raw datasets. This study focuses on multiple aspects of data preprocessing, such as strategies for handling noisy or corrupted data, contemporary challenges that affect data quality, classes of common data quality problems, and a general overview of tools that minimize such problems.