Data normalization in analytics mainly serves what purpose?

Data normalization in analytics is primarily concerned with adjusting values to a common scale. This process involves transforming data so that different features contribute equally to the analysis, particularly when they have different units or scales. For instance, if one feature ranges from 1 to 1000 while another ranges from 0 to 1, normalizing these values can help ensure that the analysis does not become biased towards the feature with the larger range.

Normalization is especially critical in methods such as machine learning, where algorithms can be sensitive to the scale of the data. By bringing all data features into a similar range, normalization enhances the model's ability to learn and generalize from the dataset effectively, improving performance.

In contrast, eliminating duplicates pertains to data cleaning and ensures that the dataset is not skewed by redundant entries. Categorizing data involves organizing it into predefined groups, which falls under the domain of data classification rather than normalization. Finally, visualizing data is focused on presenting it in an understandable format, but this does not directly relate to the purpose of normalization. Therefore, adjusting values to a common scale is what defines the core purpose of data normalization in analytics.