What Is Database Normalization? Types and Examples

Without adequate data collection and analysis, decision-making in any organization often goes haywire. 

Decisions made without data rely more on intuition than logic. While sometimes it’s best to go with your gut, data gives you a reality check, making your decision better and more profitable. 

Your company needs effective data management processes to extract value from its data. Database normalization is one part of that process, allowing you to use information to fuel business intelligence. 

Relational databases provide the structure and tools necessary to implement this process effectively.  Many companies also use data preparation software to achieve normalization. It also gives them a platform to integrate disparate data sources. 

Why do we need normalization in database?

Normalization is a process in your data pipeline that improves data visibility, accessibility, and observability. It optimizes data to help you get its maximum value. Along with several other common use cases, database normalization is essential in online transaction processing (OLTP), where data quality and discoverability are the utmost priorities.

If we look at the history pages, Edgar F. Codd, the father of the relational database (RDB) model, proposed RDB in 1970, when the term “data normalization” appeared for the first time. 

In 1971, Codd described the main goals of database normalization beyond the first normal form (1NF) as: 

• Freeing the data collections from undesirable insertion, update, and deletion dependencies.
• Reducing the need for restructuring the database when adding more information.
• Improving informational, relational models. 
• Neutralizing relational model to query value, making the value changeable. 

The primary purpose here is to decrease functionality and accuracy errors, making databases more efficient to navigate. 

How does database normalization work?

Every database and use case might differ, requiring different data normalization standards. 

Fundamentally, normalization is about creating a standard format for all the data collected by your business processes.

These are some examples of database normalization:

• Miss KATY becomes Ms. Katy
• +91 7897654678 becomes +91-789-765-4678
• VP sales becomes Vice President of Sales
• 24 Canillas RD will be written 24 Canillas Road
• GoogleBiz will be written by Google Biz, Inc.
• December 25, 2024, will be written as 12/25/2024

Database normalization isn’t limited to formatting changes. Several rules apply to ensure entries are not repetitive and follow standard guidelines. 

Normal forms in DBMS 

There are different forms of normalization databases you will come across, including: 

First Normal Form (1NF)

This is the most basic form of database normalization. In 1NF, each table cell should contain only one value, and every column should have a unique name. This form helps eliminate duplicate data and simplifies queries.

Example: Suppose you need to store data of people buying biscuits from a bakery. Make a table to record a person's name, contact number, email ID, and whether they bought biscuits.

Before 1NF:

After 1NF:

Second Normal Form (2NF)

The second normalization form helps reduce data redundancy by ensuring that each non-key attribute depends on the primary key. Simply put, each column should be directly related to the primary key.

Example: You record a person's name, contact number, and email ID, as well as whether they bought biscuits and the biscuit type. The biscuit types are recorded in another table with a corresponding foreign key to each person’s name.

Before 2NF:

After 2NF (separating customer details from product details):

Third Normal Form (3NF)

Built on the concept of 2NF, 3NF requires all the non-key attributes to be independent. It ensures that every column is directly related to the primary key and no other table column.

Example: You record a person's name, contact number, and email ID but go back and change the person's name. Unfortunately, when you do this, the gender changes accordingly. To avoid this, in 3NF, gender is given a foreign key and stored in a separate table.

Before 3NF:

After 3NF:

Boyce and Codd Normal Form (BCNF)

It’s a more advanced version of the Third Normal Form, or 3.5NF. It’s simply a 3NF table without overlapping candidate keys.

A candidate key is a column or a combination of columns uniquely identifying each table row, reducing duplicates or ambiguous records in a table. 

In a database management system, for a relational column to be in BCNF, it must already be in 3NF. Also, for every functional dependency, for example (X -> Y), X is a candidate key or a super key. 

Before BCNF:

After BCNF (splitting into two tables):

Advanced data normalization

Beyond BCNF, the Fourth Normal Form (4NF) and Fifth Normal Form (5NF) are advanced data normalization forms. 

The former eliminates a table’s multivalued dependencies, while the latter breaks a table into smaller tables to decrease data redundancy. 

Database normalization vs. denormalization

Data normalization reduces anomalies and redundancies in a dataset. It ensures data aligns with the standard format of the table, making it easier for engineers or business users to conduct data operations to drive insights and make informed decisions. 

Conversely, data collection not in a particular format is denormalized by default. 

Data denormalization is the intentional introduction of redundant data in database tables to optimize query performance. However, working with more analytical queries often involves a trade-off between read and write operations.

Data denormalization combines data from multiple tables into a single table that can be queried faster. It is relevant when there are several join queries in a database. 

For example, an e-commerce website might denormalize product data for faster access, combining product details and reviews into a single table, reducing the need for multiple joins when displaying products on the website.

Benefits of database normalization

Database normalization makes it easier to manage data and process it for insights. There are significant improvements in speed and efficiency. The benefits scale beyond these to include: 

Reduce anomalies in data

Database normalization reduces anomalies, preventing errors that arise while adding, modifying, or deleting data. It also ensures that newly entered data is consistent with the standard format, preventing duplicate entries. 

What’s more, you can perform the delete operation without worrying about disturbing other records in a database.

Frees up space

Collecting raw data without a streamlined method can lead to redundant and unwanted data stashing. Normalizing your data helps you eliminate duplicate data across your database. 

Identifying and removing repeated and redundant data frees up storage space and improves your system's performance and speed. 

Improves query response time

Normalized data support increases the speed of your information system and reduces delays in responding to queries. This is handy when multiple teams from the same business are working with the one commonly used database.

Improves cross-examination capabilities

Database normalization benefits businesses that collect data from various sources, especially when they record, stream, or analyze data from software as a service (SaaS) platforms or digital resources like social media platforms or online forums.

Streamlines the sales process

With database normalization, you can effectively segment your leads based on a variety of criteria and according to your business needs. It makes data querying quick and easy, ensuring that customer data platforms are reliable for users. 

Database normalization challenges

While normalizing databases is beneficial in some use cases, database normalization forms create major drawbacks in specific situations.

Impact on performance

Complex database normalization forms impact database performance, making it slower. This is common when you need to navigate through huge volumes of data. 

Normalized data utilize several tables, requiring more time to scan and nudging performance. 

Increased complexity

Getting your team to ramp up using the normalized database becomes tricky.  

Most of the data that follows an NF format is saved as a numerical value. The table contains codes rather than information, making it complicated for the team to interpret without frequently referring to the query tables. 

Demands knowledge and expertise

You need someone experienced to handle database normalization forms. As these forms are stacked in levels, if the first form of normalization falls apart, the subsequent levels will produce more anomalies rather than producing clean and optimized data.

Preferred Denormalization

Data architects and developers design document-oriented NoSQL databases and non-relational systems that can be used without disk storage. To address this situation, a blend of normalized and denormalized data is becoming the best data storage and querying approach.

Database normalization best practices

Here are a few best practices to consider when optimizing your database with data normalization forms.

Identify data types

Various data types exist, such as numerical, categorical, ordinal, or textual. These types can affect how you scale, encode, or parse your data. It is important to identify your data type before starting the normalization process so you can choose the best normalization method for each variable.

Choose appropriate scaling methods

Scaling is adjusting the range of numerical data to ensure they have similar scales or units. Choosing the appropriate scaling method helps you avoid partial information and bias, improving performance and your data's analytical predictability.

Deal with missing values

Some values are often missed while recording or unavailable for some variables. Such scenarios may affect your dataset’s quality, accuracy, and completeness, causing errors in the data analysis process. 

Depending on the nature and quantity of missed values, remove, replace, or ignore them while performing any query.

Apply common standards

Before starting the data normalization process, common standards should be in place to avoid confusion, inconsistency, and ambiguity in the data. These standards will tell how to represent, format, or encode data to maintain consistency across the table.

Make your data ready for business

Consistency and uniformity are the keys to avoiding data discrepancies, which is true across all industries. Database normalization prepares your data while making it consistent and fueling your business intelligence. 

You can try data normalization using free data preparation software and let your data decide what business decisions are logical. 

Interested in exploring other data management processes? 

Learn more about data manipulation and understand how it helps users organize data.

Edited by Monishka Agrawal