Data quality is crucial for businesses as it shapes decisions, plans, and consumer experiences. Poor data quality can lead to costly mistakes and missed opportunities, while high-quality data ensures accurate analysis, effective decision-making, and strategic development. Companies that actively manage their data quality may see up to a 70% decrease in running expenses, directly increasing profitability. Key concepts of data quality include accuracy, completeness, consistency, timeliness, and validity. To ensure high data quality, businesses should implement data quality metrics, utilize data lineage tracking, adopt automated data cleaning and standardization, use anomaly detection in machine learning, foster a data-driven culture, and create a governance policy.