Data quality is the overall suitability of data to serve its intended purpose, measuring how well data meets the requirements and expectations of its users. It involves the procedures, policies, and processes—often supported by tools—that organizations use to maintain trustworthy, accurate, and reliable data. High data quality is essential for effective decision-making, AI, and analytics, as poor data leads to inaccurate insights, biased predictions, and operational inefficiencies. It also plays a critical role in compliance, financial accuracy, and risk management, ensuring AI models and business processes operate within regulatory frameworks. As organizations increasingly work with unstructured data, they must extend traditional data quality metrics like accuracy, completeness, consistency, and timeliness to include new dimensions such as plausibility. Data quality is a continuous and evolving journey, requiring organizational commitment, defined roles, structured processes, assurance mechanisms, and advanced tools to scale with growing data complexity.