Data quality management is the process of ensuring that data is accurate, consistent, and reliable. It involves various practices and techniques to identify and correct errors, maintain data integrity, and improve overall data quality. This is crucial for organizations that rely on data for decision-making, as poor data quality can lead to incorrect conclusions and negative outcomes. Key components of data quality management include data profiling, data cleansing, and data governance. Data profiling helps assess the quality of data by analyzing its structure and content. Data cleansing involves correcting or removing inaccurate data, while data governance establishes policies and standards for managing data effectively across an organization.