Data quality is crucial for the success of an organization as it directly impacts business performance. Poor data quality can lead to incorrect decisions, increased costs, and reduced revenue. Key dimensions of data quality include intrinsic dimensions such as integrity, accuracy, completeness, consistency, freshness, and privacy/security, and extrinsic dimensions like relevance, reliability, timeliness, usability, and validity. Common challenges in maintaining high data quality standards include software sprawl, data proliferation, lack of metadata, typos and other human errors, and difficulty hiring experienced team members. To measure and improve data quality, organizations should identify what matters, pinpoint pain points, make metrics actionable, and consistently monitor their data quality. A People, Process, Technology (PPT) framework can be applied to improve data quality by involving teammates in metric development, implementing business processes for improvement, and utilizing technologies like data observability tools and automation solutions.