Data normalization is a subset of data transformation, which encompasses various techniques used to convert data from one format or structure to another. Data preprocessing is another broader term that includes data normalization as one of its steps. Data preprocessing involves preparing raw data for analysis by cleaning, normalizing, encoding, and transforming it to make it suitable for machine learning models and other analytical tasks.

Data encoding is closely related to data normalization as both processes aim to convert data into a format that is more suitable for analysis. While data normalization focuses on organizing and structuring data, data encoding involves converting categorical data into numerical values that machine learning algorithms can easily process. Feature engineering, on the other hand, involves creating new features or modifying existing ones to improve the performance of machine learning models. Normalized data often serves as a foundation for effective feature engineering.

Data wrangling, also known as data munging, is the process of cleaning and transforming raw data into a more usable format. Data normalization plays a crucial role in data wrangling by ensuring that the data is consistent and free from redundancies. Data augmentation, commonly used in machine learning, involves creating additional data samples by applying various transformations to the existing data. Normalized data can be more easily augmented as it is already in a standardized format.

Data modeling is the process of creating a conceptual representation of data objects and their relationships. Normalized data is essential for accurate data modeling as it ensures that the data is well-organized and free from anomalies. Data aggregation involves combining data from multiple sources to provide a summarized view. Normalized data facilitates efficient data aggregation by ensuring that the combined data is consistent and free from duplication.

Data fusion refers to the integration of data from multiple sources to provide a more comprehensive view. Data normalization is a crucial step in data fusion as it ensures that the data from different sources is compatible and consistent. Data enrichment involves enhancing existing data by adding relevant information from external sources. Normalized data provides a solid foundation for data enrichment by ensuring that the data is accurate and well-structured.