Data Normalization

What is data normalization?

Data normalization is the process of cleaning up data to follow a particular standardized data format across a business. It ensures that data from across different sources are similar regarding fields, sections, records, etc., making it easier to group data. 

Unstructured data can be analyzed more efficiently once it undergoes data normalization. This is, therefore, the production of clean data.

Data management has become a crucial part of business strategies, and to achieve this, companies are focusing on data normalization.

Types of data normalization

Depending on what data normalization is used for or the industry a company is in, there are different guidelines called “normal forms” to help them accomplish data normalization. 

Normal forms help design a database with a structured schema to help with data organization. It helps ensure data points are categorized into different groups based on their complexity.

• First normal form (1NF): Two data points are not identical, ensuring no two repeating data points are in a group. Each column needs to have a unique name so that each cell of the table has a single value.
• Second normal form (2NF): Follows the rules of 1NF and also has one primary key applied. A primary key is a unique identifier for each record in a table and helps to create relationships between different data points across tables.
• Third normal form (3NF): Follows the rules of 2NF and also only has to be dependent on the primary key.
• Boyce and Codd NF (3.5NF or BCNF): Follows the rule of 1NF, 2NF, and 3NF, and also for every non-trivial functional dependency. The left side is the super key (i.e., X should be a superkey for X → Y).

Let’s take an example of creating a database to store information about music and artists to better understand normal forms:

1NF:

1NF includes the elimination of repeating groups to ensure each cell has only one value. The new data tables will be as follows:

Artist table:

Album table:

Song table:

2nd NF:

The the above case, for Song_Name the primary key is Song_ID, Album_Name depends on Album_ID, and Artist_Name and Artist_ID. Since each data point has a unique identifier, no changes are needed.

3rd NF:

Here, we look at transitive dependency. Transitive dependency indicates that changing one attribute indirectly changes the value of another attribute. Therefore, in the above example, the transitive dependency is Album->Artist->Song Name:

• Album depends on Artist
• Artist depends on the song name

This would create the new album table as follows:

Songs_ID:

Benefits of using data normalization

The benefits of using data normalization are as follows:

• Organized database: By cleaning up the data and removing or eliminating any redundancies, companies can free up a ton of data storage, helping systems run quicker.
• Reduced data redundancy: Data normalization helps clean up anomalies within several data sets. Having data rules and standards to be followed across an organization will correctly enter all new data.
• Creation of clean data: Clean data will help save hours and help companies make better, data-driven decisions. It helps several departments, such as sales and marketing, access clean data such as correct customer names, email addresses, and job titles, helping with business growth.
• Simpler querying: Since the relationship between the data tables is clearly defined under data normalization, it helps with simpler querying.
• Understandability: Data normalization makes the models created much easier to understand. The relationships between them are clearly defined, which helps make it easy to understand that data for database engineers, data scientists, etc.

Data normalization best practices

To make data normalization work, follow these best practices:

• Understand the data: Clearly understanding the nature of the data being normalized and the requirements to be achieved is a key best practice to implement. This helps set up tables, columns, etc., and helps identify the relationship between each, thereby reducing the potential for duplicate data.
• Start with 1NF: The data must be set up and cleaned at the atomic level. Creating a 1NF at the start of database creation will help prevent repetitive data.
• Choose the right primary key: The primary key must be stable and unique over time.
• Systemic process: Follow each NF systematically to create clean, clear relationships between the data points.
• Documentation: As a best practice, document the various relationships between the data points. This can always be used as a reference by data engineers when required.
• Backup: Create and maintain consistent data backups to prevent data loss.

Learn more about the unique differences between structured and unstructured data.