Data normalization is the process of organizing data such that it seems consistent across all records and fields. It improves the cohesiveness of entry types, resulting in data cleansing, lead generation, segmentation, and greater data quality.
Normalizing entails heating a material to a high temperature and then allowing it to drop to room temperature by exposing it to room temperature air after it has been heated. This heating and slow cooling changes the microstructure of the metal, lowering its hardness and increasing its ductility.
A correctly normalised design allows you to: Make efficient use of storage space. Remove any superfluous data. Reduce or delete data that is inconsistent.
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