Gartner's Data Quality Market Survey highlights the significant financial impact of poor data quality, costing businesses an average of $15 million annually, with nearly 60% failing to measure these costs. The cases of Samsung and Uber serve as cautionary tales, illustrating the importance of implementing robust data quality assurance processes. Samsung experienced a costly "fat-finger" error in 2018, where an employee mistakenly issued shares instead of currency, resulting in a $300 million loss. Similarly, Uber's accounting error, due to an oversight in commission calculations, led to underpaid driver compensation, costing the company millions. Both instances underline the necessity for companies to prioritize high-quality data and establish preventive measures, such as systematic data verification and quality management practices, to mitigate potential risks and financial losses. Harvard Business Review emphasizes that only a small percentage of companies meet basic data quality standards, advocating for organizations to integrate data quality into their key performance indicators and foster a culture where data integrity is a shared responsibility across all levels.