Which technique is used to validate data quality in data management?

The technique used to validate data quality in data management is data profiling. Data profiling involves examining data from existing sources and collecting statistics about that data. This process helps assess the quality of the data by identifying patterns, anomalies, and inconsistencies within the dataset. Through data profiling, organizations can discover issues such as data completeness, accuracy, and validity, which are essential for maintaining high data quality.

Data profiling serves as a foundational practice within data management as it helps data professionals understand the current state of the data and make informed decisions on how to clean or enhance it. By evaluating the structure, content, and relationships between data elements, data profiling ensures that the information used in business processes is reliable and trustworthy, which is crucial for effective decision-making.

Other techniques listed, such as data mining, data normalization, and data archiving, serve different purposes in data management. Data mining focuses on discovering patterns and insights in large datasets, data normalization is concerned with organizing data to reduce redundancy and improve data integrity, and data archiving deals with the long-term storage of inactive data. While each of these techniques is important in its own right, it is data profiling that specifically addresses the validation of data quality.