Data normalization

Data Normalization is the process of organizing and restructuring data in a database to improve data integrity while reducing data storage costs. It is commonly used in the relational database model and in other database models such as Object-Oriented Database Management Systems (OODBMS).

Data Normalization works by analyzing existing data against pre-defined rules and organizing data accordingly. By breaking down large, unwieldy data sets into smaller, more organized data sets, it becomes easier to manage and use. This process helps to maintain data accuracy and reduce data redundancy, while also improving data integrity and reducing data storage costs.

Normalization eliminates redundant data by organizing tables into related sets of data. In doing so, the process creates relationships between data elements and accentuates the importance of primary and foreign keys. As the relations between data sets grow, the data is further broken down into increasingly smaller units.

Data Normalization is composed of several forms, each of which is identified by a number. A process of normalization generally begins with the First Normal Form (1NF), which ensures that each column of a table only contains a single data set. The Second Normal Form (2NF) builds on 1NF by requiring that each column of a table only contains atomic values. Third Normal Form (3NF) further builds on 2NF by eliminating redundant values and introducing relationships between tables with the use of foreign keys.

By conforming to certain norms through Data Normalization, databases can better preserve the integrity of data and ensure accuracy. Furthermore, Data Normalization can save considerable costs, as it reduces the need to store large amounts of redundant data. It is a critical process for any organization looking to protect its data integrity and reduce the financial costs of data storage.