Data standardization aims to establish consistent formats, representations, and units of measurement across different data sources, ensuring that data is comparable and meaningful.

Data normalization is a separate process that involves organizing data into tables and columns to minimize redundancy and improve data integrity. Data conversion, cleansing, and aggregation are all techniques used in data standardization.

Data normalization aims to eliminate duplicate data and ensure that each piece of data is stored only once, thereby reducing redundancy and improving data integrity.

First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF) are all normal forms used in data normalization to progressively reduce data redundancy and improve data integrity.

In 1NF, repeating groups are eliminated by creating separate tables for each group. In 2NF, partial dependencies are eliminated by ensuring that each non-key column is fully dependent on the primary key.

Second Normal Form (2NF) ensures that all non-key columns are fully dependent on the primary key, meaning that they cannot be derived from any proper subset of the primary key.

Boyce-Codd Normal Form (BCNF) is the highest level of normalization among the common normal forms. It ensures that all non-key columns are directly dependent on the primary key and that there are no transitive dependencies.

Data standardization aims to simplify and unify data, reducing complexity and inconsistency. It does not introduce additional complexity or inconsistency.

Data normalization in a relational database aims to eliminate duplicate data and ensure that each piece of data is stored only once, thereby reducing redundancy and improving data integrity.

Data validation is a process of verifying the accuracy and completeness of data. It is not a data standardization technique, which focuses on establishing consistent formats, representations, and units of measurement.

In 2NF, atomic values in columns are ensured, meaning that each column contains a single value. In 3NF, transitive dependencies are eliminated, ensuring that non-key columns are directly dependent on the primary key.

Boyce-Codd Normal Form (BCNF) ensures that all non-key columns are directly dependent on the primary key and that there are no transitive dependencies. It is the highest level of normalization among the common normal forms.

Data standardization aims to establish consistent formats, representations, and units of measurement across different data sources, ensuring that data is comparable and meaningful. Data normalization, on the other hand, focuses on organizing data into tables and columns to minimize redundancy and improve data integrity.

Data normalization aims to simplify and organize data, reducing complexity and inconsistency. It does not introduce additional complexity or inconsistency.