Databases can get messy fast. If duplicate records, inconsistent entries, and conflicting records sound familiar, then you might have data redundancy and inconsistency issues. 

You’re certainly not alone. These are also among the most common reasons databases become unreliable. To combat the chaos, engineers rely on data normalization: a structured process for designing efficient, consistent databases that are immune to update anomalies. 

This article explains how data normalization works, why it’s important for data integrity, and how it transforms cluttered data structures into well-oiled systems.

What is data normalization?

Data normalization is the process of organizing the columns and labels of a relational database to minimize data redundancy. It structures data so that you store each piece of information in the most logical place and only once.

The goal is to make databases more efficient and reliable. When you normalize data, any addition, deletion, or modification of a record only happens in one location. This allows everything to be queried and analyzed easily, ensuring the database is simple to maintain as it grows. 

Why is data normalization important?

To save you from asking “why normalize data?” Here are a few reasons why process matters:

• Maintaining data integrity: Normalization enforces clear relationships between tables. This protects against duplicate or contradictory information creeping into your system.
• Enhanced data accuracy and reliability: By eliminating redundancy, you can make sure that any update made in one place automatically happens everywhere else it needs to. This consistency means that teams can make decisions based on accurate, up-to-date information.
• Simplified maintenance and flexibility: You need to be able to modify databases as your business grows and develops. Normalization means that adding new fields or relationships won’t disrupt existing data, making your database easier to adapt.

How to normalize data: The 4 stages of the normalization process

Data normalization is a multi-step process guided by a set of rules called “normal forms.” Each normal form represents a higher degree of organization. 

While normalization can extend up to the sixth normal form, the highest levels are incredibly strict, making them difficult to adhere to. In the categories below, we’ve focused on the forms that cover the core principles most databases need.

Before normalization (unnormalized table)

To help you visualize the process, we’ve provided an example of an unnormalized table that we’ll tweak to reflect the changes in each step. 

This table has the following problems:

• Customer and product details are repeated (data redundancy)
• Updating a customer’s email requires multiple changes (update anomaly)
• Deleting an order could remove all info about a customer (deletion anomaly)
  

1. First normal form (1NF)

A table meets 1NF when all its data is atomic, meaning each field holds only one value and every record is unique. This eliminates repeat groups of data and ensures it’s stored in the smallest logical units. 

In our example, each field now contains a single value, but redundancy still exists.

2. Second normal form (2NF)

To reach 2NF, a table must first satisfy 1NF. It also must be free of any partial data dependencies, meaning when non-key attributes depend on only part of a composite key. This step separates repeated customer and product details into their own tables.

After applying 2NF, customer data in our example is only stored once, but product info is still duplicated.

Customers Table

Orders Table

3. Third normal form (3NF)

A table is in 3NF when it meets 2NF and removes transitive dependencies, aka non-key attributes that depend on other non-key attributes. To solve this, product data is separated into its own table. This is the point at which data is considered “normalized,” or free of insertion, updation, and deletion anomalies.

At 3NF, our example is split into tables that each focus on one type of data (customers, products, or orders), eliminating redundancy and improving consistency.

Customers Table

Products Table

Orders Table

4. Boyce and Codd normal form (BCNF)

The BCNF is a stricter version of 3NF. It focuses on edge cases where multiple candidate keys could still cause anomalies. In BCNF, every determinant must be a candidate key, ensuring maximum logical consistency. 

Most practical databases achieve sufficient reliability at 3NF, but BCNF provides an extra layer of structural integrity for complex systems.

Data normalization examples

Let’s look at how normalization can apply across different domains.

Ecommerce order management

In an ecommerce system, each order could contain duplicate customer details and product prices, leading to costly inconsistencies when product or customer information changes.

Normalization stores customer details in a “customers table,” products live in a separate “products table,” and orders link the two through unique IDs. This structure eliminates redundant data and ensures that price or customer updates are automatically reflected across all relevant orders.

CRM and sales analytics

In a CRM database, a single customer might appear multiple times — once for each deal, activity, or sales rep involved. That redundancy causes reports to be inaccurate and makes maintenance a headache.

Through normalization, sales activities and deals are separated into “deals” and “interactions” tables, linked by customer IDs. Such a setup allows sales teams to run clean, reliable analytics (like conversion rates and deal value trends) without worrying about duplicated or outdated customer data.

HR employee tracking

HR systems manage complex relationships among employees, departments, and roles. In an unnormalized system, job titles and department names can repeat in every employee record, increasing the risk of inconsistent entries.

But normalization provides employees, departments, and roles with their own tables and defines relationships through keys (such as Department ID and Role ID). When you don’t need to modify multiple records, updating department names, tracking promotions, or reassigning roles becomes much easier, and you don’t gamble with data integrity.

Healthcare patient records

Healthcare databases store highly sensitive and interrelated data, from patient and doctor information to visits, prescriptions, and test results. Without normalization, repeated data can cause compliance issues.

Normalization keeps patient details in a “patients” table. Medical visits, prescriptions, and lab results are stored separately but linked via patient and doctor IDs. This separation simplifies auditing and helps healthcare organizations maintain compliance while ensuring consistent patient care records.

Common challenges of data normalization

Although the standardization offered by normalization offers many benefits, there are some drawbacks that you need to consider.

Increased query complexity

Normalized databases separate data into multiple related tables. While this improves consistency, it can make queries more complex. Complex queries impact performance if not carefully optimized.

Significant upfront design effort 

Designing a fully normalized database requires careful planning and attention to detail. Identifying dependencies, determining keys, and structuring tables correctly takes time and expertise. The initial effort can be substantial, especially for large or legacy datasets.

Risk of over-normalization

It’s possible to take normalization too far, leading to excessively fragmented tables. Over-normalization can slow down queries and make a database harder to maintain. It’s essential to strike the right balance between efficiency and usability.

How Fivetran helps with data normalization

Manual data normalization can be time-consuming and resource-intensive, especially as data pipelines grow more complex. Fivetran simplifies the process by automating normalization into 3NF and helping teams focus on insights rather than infrastructure.

Here’s how Fivetran makes a difference:

• Cost efficiency: Fivetran performs data normalization as part of its automated extraction, load, and transform (ELT) process (which can also include data transformation). It achieves this by organizing data into a logical structure within a virtual private cloud (VPC) before loading it into your destination. 
• Time savings: The platform automates repetitive normalization tasks like restructuring database schemas and aligning field formats, giving engineers more time to work on strategic initiatives.
• Improved data quality: By standardizing data across sources, Fivetran ensures that the information in your warehouse is consistent, accurate, and analytics-ready. This leads to cleaner dashboards and more reliable decisions.
• Scalability: As your business grows, Fivetran grows with it. The platform handles large data volumes without compromising performance or accuracy.

‍Start your 14-day free trial with Fivetran today and see how the platform can support your data normalization efforts.

FAQs

How can I normalize data?

You can normalize data by organizing it into related tables, eliminating redundancy, and applying standard rules known as normal forms. Platforms like Fivetran can help streamline your normalization efforts. Fivetran performs data normalization as part of its automated ELT process, organizing data into a logical structure within a VPC before it’s loaded into your destination.

Why should I normalize data?

You should normalize data to ensure consistency, reduce redundancy, and maintain integrity across your database for reliable analytics and decision-making.

When should I not normalize data?

You might choose not to normalize data when performance is a priority. Denormalization can result in faster queries, especially in reporting and analytics scenarios.

What are the different data normalization techniques?

The main data normalization techniques are the first, second, and third normal forms (1NF, 2NF, 3NF), as well as the Boyce-Codd normal form (BCNF). Each addresses specific types of data dependency. 

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