In data analytics, what does the term normalize refer to?

In data analytics, the term "normalize" specifically refers to the process of reviewing and adjusting data to remove redundancy and ensure consistency within the dataset. This is particularly important in relational databases where normalization helps in organizing data to minimize duplication and improve the integrity and efficiency of data storage and retrieval. By eliminating redundant data, normalization helps to streamline data analysis and ensures that the data is reliable and accurate, ultimately leading to more valid insights and conclusions.

While condensing data might be relevant in certain contexts, it does not directly define normalization. Similarly, analyzing data for variance pertains to a different statistical technique and is not the core concept of normalization. Converting data into a standard format is part of data pre-processing, but it does not specifically encompass the broader principles involved in normalization. Hence, focusing on reducing redundancy squarely defines the normalization process in data analytics.