Which process is essential for ensuring data quality before analysis?

Data validation is the essential process for ensuring data quality before analysis. It involves checking the accuracy, completeness, and consistency of data to confirm it meets specified requirements and standards. By identifying and rectifying any errors or inconsistencies in the data, data validation helps ensure that subsequent analyses are based on reliable information. This is crucial because the insights derived from data analysis are only as strong as the quality of the data itself.

In contrast, data visualization focuses on presenting data in graphical formats to aid understanding, which does not directly address the quality of the underlying data. Data mining refers to the exploration and analysis of large data sets to discover patterns and relationships, which assumes that the data quality has already been validated at some point. Data augmentation involves enhancing existing data by adding new data or increasing its variety, which is typically done after data validation to enrich analysis capabilities. Therefore, data validation is foundational for reliable and meaningful data analysis.