From the course: Data Preparation, Feature Engineering, and Augmentation for AI Models
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Applying data quality checks
From the course: Data Preparation, Feature Engineering, and Augmentation for AI Models
Applying data quality checks
- [Instructor] Once we've identified data sets, it's time to turn our attention to data quality. Now let's continue with our examples of working with retail industry data. Now, there are critical dimensions that we want to keep in mind. They are accuracy, for ensuring that data values match real world conditions. We want to think about completeness. That is verifying that all required data elements are actually present in our data sets. We also want to maintain consistency, that is, in particular, data integrity across systems. We also want our data to be timely, so that we have fresh data for making decisions. And we want to make sure that we're not dealing with any duplicates that might distort our analysis. Now the kinds of checks, the specific kinds of checks that we will be doing, will vary slightly depending on the type of data or the focus of the data set that we're working on. So let's look at an example of a product catalog, and what kind of quality checks we would have…
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