Data quality management is a process of ensuring that data used by an organization is accurate, complete, consistent, and relevant to its intended purpose.
It involves a set of procedures, policies, and standards to maintain and improve data quality, including data profiling, data cleansing, data enrichment, and data normalization.
Data quality management starts with identifying and defining the critical data elements required for the organization’s operations, analysis, and decision-making.
It then establishes a set of data quality rules and metrics to assess the data’s quality and ensure that the data complies with regulatory requirements, industry standards, and business objectives.
Data quality management also involves ongoing monitoring and measurement of data quality, including tracking data issues, identifying the root causes of data quality problems, and implementing corrective actions to address them.
By implementing effective data quality management practices, organizations can make better-informed decisions, improve operational efficiency, reduce risks, and increase customer satisfaction.
