Data Quality Management encompasses processes and technologies aimed at ensuring the accuracy, completeness, and reliability of data. It involves activities such as data cleansing, validation, and enrichment to improve data integrity and usefulness.

By implementing Data Quality Management practices, organizations can identify and rectify inconsistencies, errors, and redundancies in their data sets. This ensures that data is fit for purpose and meets the needs of stakeholders across the organization.

Additionally, Data Quality Management facilitates compliance with regulatory requirements and industry standards, mitigating risks associated with inaccurate or unreliable data. Overall, effective Data Quality Management is essential for maximizing the value of data assets, enhancing decision-making, and driving operational excellence within an organization.

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