Data quality is crucial for businesses to make informed decisions, enhance customer satisfaction, and reduce costs. It involves assessing and improving various dimensions such as accuracy, completeness, consistency, timeliness, and uniqueness. Poor data quality can lead to reduced decision-making effectiveness, increased costs, and decreased customer satisfaction. Methods for ensuring data quality include data profiling, data cleansing, data validation, master data management, data governance, and data matching. Enhancing data quality involves establishing data quality standards, ensuring data accuracy at the source, implementing automated data quality checks, and continuously monitoring data quality.