Data management and data quality are tightly interrelated concepts as efficient data management requires reliable information. While data management relates to the collection, storage, organization, processing, protection, and usage of data, data quality concerns about such qualities of this information as accuracy, completeness, consistency, validity, uniqueness, and timeliness. If an enterprise has poor data quality management when working with massive amounts of data, duplicates, missing values, outdated data, inconsistencies, and other similar issues may negatively impact the analytics and operations of an organization. The use of data quality management approach helps to discover these inconsistencies during profiling, cleansing, validation, and monitoring of the information. In particular, enterprises can standardize, de-duplicate, find missing values, and validate information before its use. Proper integration of effective data management data quality control strategies allows organizations to work with more reliable datasets, which may be useful for such tasks as reporting, business intelligence, customer service, automation, and AI solutions development.