Data normalization is about reducing redundancy and defining a more optimized structure. Perhaps you DBAs are aware of this process. I taught it in college more than 30 years ago.

But in practice, data normalization is usually necessary to get a good prediction model. A well-known exception to this rule of thumb is Fisher's Iris data. Because there are only 150 items in the ...

Data normalization enables health systems to look for key clinical conditions irrespective of the underlying terminology used to populate their CDRs. Summary. In general, ...

Normalization clusters data items together based on functional dependencies within the data items. This normalized arrangement expresses the semantics of the business items being presented.

Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various ...

Data normalization facilitates the flow of data across front-, middle-, and back-office operations—in both directions. For example, when Broadridge provides dashboards with real-time lifecycle data to ...