Data & Database Normalization Explained: How To Normalize Data

Every business today uses some form of data collection. Large-scale enterprises have more established methods for collecting, storing and analyzing data, and smaller companies and start-ups are getting on board. That's because the value of data collection and analysis is enormous when it comes to:

Today, in the era of Big Data and democratized data - and now AI-we have more data-driven insights available to us than ever.

Most enterprises already collect and manage data using databases, CRM platforms or automation systems, but data in its many forms and entry types can lead to inconsistent or duplicate (redundant) information. More efficient data collection requires a more streamlined process of data management. That's where data normalization comes in.

In simple terms, data normalization is the practice of organizing data entries to ensure they appear similar across all fields and records, making information easier to find, group and analyze. There are many data normalization techniques and rules.

In this article, let's cover the basics and provide some tips for how you can improve the organization and management of your data.

Defining data normalization

Data normalization is one of many processes you might apply to data. Data normalization is simply a way to reorganize or 'spring clean' the data, so that it's easier for users to work with and query it - and analyze the outputs.

When you normalize a data set, you are reorganizing it to remove any unstructured data or redundant data to enable a superior, more logical means of storing that data.

The main goal of data normalization is to achieve a standardized data format across your entire system. This allows the data to be queried and analyzed more easily - leading to smarter business decisions.

Importance of normalized data in databases

Data normalization could be included in your data pipeline, which supports overall visibility into your data, a concept known as data observability.

Ultimately, normalizing your data is one step towards optimizing your data, or maximizing the value you can get from it.

Unfortunately, for many, data optimization is a far-off goal: the data that organizations collect is enormous, but most of that data, in its current form, is rarely useful or valuable on its own. Today, we're living through the early days of AI. If there's one thing we know, it's that All The Data is needed for AI to succeed.

(Of course, AI needs a lot more than just data: there must be governance, ethics, and frameworks - at bare minimum - to ensure we're getting benefit from AI while reducing harm that we already know it can cause.)

There are many other benefits of normalizing data that we'll explore later on, but first, it's important to explore some key data normalization techniques.

(Related reading: data platforms & database monitoring.)

How do you normalize data?

In a fundamental sense, data normalization is achieved by creating a default (standardized) format for all data in your company database. Normalization will look different depending on the type of data used. Here are some examples of normalized data:

• Miss ANNA will be written Ms. Anna
• 4158488400 will be written 415-848-8400
• 37 buttercup AVE will be written 37 Buttercup Avenue
• Amazon will be written Amazon.com, Inc.
• VP product will be written Vice President of Product

Knowing how to normalize data en masse is a more complicated matter. It is typically done by a process of building tables and linking them together, all while following a set of practices to protect the data and reduce data anomalies. These data normalization techniques and practices take many different forms - let's take a look now.

(Related reading: database management systems, aka DBMS.)

Types of data normalization forms

Data normalization follows a specific set of rules, known as "normal forms". These data normalization forms are categorized by tiers, and each rule builds on the one before - that is, you can only apply the second tier of rules if your data meets the first tier of rules, and so on.

There are many types of data normalization forms, but here are four of the most common and widely used normal forms that can be applied to most data sets.

1. First Normal Form (1NF)

The first normal form, aka 1NF, is the most basic form of data normalization. The core outcome of this rule ensures that there are no repeating entries in a group. This means:

• Every cell should only have one single value.
• Every record should be unique.

An example would be a table that documents a person's name, address, gender, and if they ordered a Splunk T-shirt.

2. Second Normal Form (2NF)

2NF is the second normal form that builds on the rules of the first normal form. Again, the goal is to ensure that there are no repeating entries in a dataset. Entries with this data normalization rule applied must:

• Adhere to all the 1NF requirements.
• Have one primary key applied.

The application of one primary key essentially means that a separate table needs to be created for subsets of data that can be placed in multiple rows. The data in each table can then be linked with foreign key labels (numbers in this case).

If a primary key such as 'Customer Number' is applied to our T-shirt example, then subsets of data that require multiple rows (different T-shirt orders), will need to be placed in a new table with a corresponding foreign key.

Example of data in the second normal form:

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