From the course: Data Preparation, Feature Engineering, and Augmentation for AI Models
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Elements of data quality: Consistency, accuracy, and completeness
From the course: Data Preparation, Feature Engineering, and Augmentation for AI Models
Elements of data quality: Consistency, accuracy, and completeness
- [Presenter] There are three core pillars of data quality, and they are consistency, accuracy, and completeness. Now, these are critical for both structured and unstructured data when we're working with AI applications. And also, we need to remember the data quality assessments we're going to be discussing really have to be designed for your specific business context. What we do with consistency assessments is evaluate data integrity across different systems and across different time periods. So, for example, we might want to look at product category assignments and make sure they actually match across different systems within our organization. When it comes to things like customer segmentation, we want to make sure those classifications are relatively stable over time. And when we use naming conventions, we want to make sure we're following established patterns. Now, there are different metrics we can use for measuring consistency. For example, we might have consistency ratios or…
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