Data profiling is the process of examining, analyzing, and creating useful summaries of data. The process yields a high-level overview which aids in the discovery of data quality issues, risks, and overall trends. Data profiling produces critical insights into data that companies can then leverage to their advantage.

Data cleansing is the process of finding and removing errors, inconsistencies, duplications, and missing entries from data to increase data consistency and quality—also known as data scrubbing or cleaning. While organizations can be proactive about data quality in the collection stage, it can still be noisy or dirty.

Data validation means checking the accuracy and quality of source data before using, importing or otherwise processing data. Different types of validation can be performed depending on destination constraints or objectives.