Understanding and measuring data quality

Modern companies increasingly recognize the importance of high-quality data as a cornerstone for business growth, especially in the context of developing reliable machine learning models. High-quality data, defined by metrics such as accuracy, completeness, consistency, timeliness, uniqueness, and validity, is critical for making informed business decisions and ensuring the accuracy of predictive models. Poor-quality data can lead to flawed analytics and misguided strategies, potentially costing organizations significant resources, as exemplified by an IBM study estimating a $3.1 trillion annual cost in the US. To address these challenges, organizations are advised to implement data quality assessment frameworks and utilize data profiling, standardization, and validation checks. Advances in machine learning and deep learning can further enhance these processes by identifying data outliers and improving data quality on a scalable level, ensuring that organizations are equipped to handle the growing volume of data in the future.