When, Why, and How to Normalize Data

What Is Data Normalization?

In brief, data normalization entails structuring a database using normal forms. To phrase it another way, data normalization is all about arranging data efficiently inside a database. When you normalize data, you construct tables based on specific rules. We’ll explain more about these rules in just a bit.  

With this in mind, the goal of data normalization is to ensure that data is similar across all records. It’s also necessary for maintaining data integrity and creating a single source of truth.  

Further, data normalization aims to remove data redundancy, which occurs when you have several fields with duplicate information. By removing redundancies, you can make a database more flexible. In this light, normalization ultimately enables you to expand a database and scale.  

Data normalization is necessary for online transaction processing (OLTP), where data quality and discoverability are top priorities.  

Normalizing Versus Denormalizing Data 

It’s important to realize that data normalization isn’t always necessary. In fact, sometimes it makes sense to do the opposite and add redundancy to a database. 

The term that describes adding redundant data to a database is denormalization. By adding redundant data, it’s sometimes possible to improve application performance and data integrity. 

Denormalization can also help you achieve faster querying. By adding extra redundancy, you can sometimes find information faster. Granted, it won’t have as much integrity. But if you’re in a position to sacrifice quality for speed, it’s generally better to use denormalized data. 

It’s common to use denormalization in online analytical processing (OLAP) systems, where the goal is to streamline search and analysis.  

One thing to keep in mind is that denormalization increases disk space because it allows for duplicate data. This, in turn, uses more memory, driving up operating costs.  

A Brief History of Data Normalization

Data normalization isn’t new. The term originally appeared back in 1970, when Edgar F. Codd proposed the relational database model. 

Codd’s theory has evolved considerably over the years, with several variations appearing. Today, data normalization remains a fundamental part of data management. This is unlikely to change any time soon as companies continue to accelerate their digital transformation strategies and consume more information.  

In short, Codd’s theory enables data modification and querying using a universal sub-language. One of the most widely used examples is SQL.  

In a 1971 report titled “Further Normalisation of the Data Base Relational Model,” Codd outlines four objectives of normalization. 

1. Freeing relations from undesirable insertion, update, and deletion dependencies

2. Reducing the need for restructuring the collection of relations when introducing new data types, with the goal of increasing application lifespans

3. Making the relational model more informative

4. Making the collection of relations neutral to the query statistics, where statistics are liable to change over time 

Codd’s theory aims to eliminate the following anomalies:  

Update Anomalies

An update anomaly is a type of inconsistency. It happens when there are redundancies and partial updates inside a database.  

Insertion Anomalies 

An insertion anomaly occurs when you can’t add data to a database because you’re missing other data. 

Deletion Anomalies 

Just as the name suggests, a deletion anomaly occurs when you lose data due to the deletion of other data in a database. 

Update, insertion, and deletion anomalies can severely impact the functionality and accuracy of a database. Therefore, it’s vital to try and eliminate them wherever possible.  

Who Normalizes Data?

Generally speaking, each team has a different process or methodology for managing and formatting data. By normalizing data, all teams can draw from a single, standardized pool. 

Data normalization is useful for just about any company that’s actively collecting, managing, and using data. It isn’t exclusive to any particular vertical or sector.  

Data normalization ultimately impacts every team within an organization, from sales and marketing and data science to DevOps and everyone in between.  

Top Reasons for Normalizing Data

There are a variety of scenarios where it makes sense to use data normalization. Here are a few common reasons companies opt to use it to figure out the best way forward.  

Control Costs 

Rising data volumes are forcing businesses to be more economical about data management. By normalizing data and removing redundancies, it’s possible to lower the overall cost of data storage and upkeep, driving healthier margins. 

Reduce Disk Space

As companies collect more data, they need to find new ways to decrease disk space. This is critical for reducing memory and controlling costs.  

By normalizing data, it’s possible to clear out unnecessary information, boosting system performance and reducing operational costs.  

Capitalize on Emerging Opportunities 

Team members need to be able to efficiently sort through data when making daily decisions. For example, a sales team may need to look through a list of addresses and determine which customers are eligible for a promotion in a particular area. If a database is messy and inaccurate, it can be difficult to discover insights and make timely decisions. Even worse, teams are more likely to make the wrong decisions. 

Enhance Marketing Segmentation 

Marketers need to accurately segment contacts to increase the effectiveness of their actions. Without access to clean data, it’s just about impossible to sort through a collection of contacts and discover key individuals.  

To illustrate, a CEO might appear as both a chief executive officer and as a founder in a database. This can lead to discovery errors. By normalizing data, you can improve categorization and make sure you’re reaching the right prospects in your outreach campaigns.  

The Stages of Data Normalization 

Codd’s theory of normalization centers around normal forms, or structures. Normalization is a progressive process. In order to move to a higher normal form, you must first satisfy all previous forms. It’s a bit like climbing a ladder. 

With that in mind, here are the stages of the data normalization process: 

1. Unnormalized Form (UNF)

The first stage is typically unnormalized data. When data is in an unnormalized form (UNF), it doesn’t meet any requirements for database normalization within the context of a relational model. 

2. First Normal Form 

The first step is making a table in the first normal form (1NF), eliminating repeating groups. To put your table in the first normal form, make sure it only has single-valued attributes. If a relation contains multivalued or composite attributes, it can’t be in the first normal form.  

All values within each column should have the same attribute domain. Columns should also have unique names for every attribute or column. Further, columns can’t have nested records or sets of values in the first normal form. 

Another key point is that it doesn’t matter which order you store data in a first normal form table.  

3. Second Normal Form

Once your table is in the first normal form, you can move ahead to the second normal form (2NF). This level centers around the idea of fully functional dependency. This form is for relations that contain composite keys, where the primary key has two or more attributes. All non-key attributes have to be fully dependent on the primary key. There can’t be any partial dependencies in the second normal form.  

If you have a partial dependency, this isn’t too difficult to fix. All you have to do is remove the attribute by placing it into a new relation. It’s also important to copy the determinant when you attempt this. 

4. Third Normal Form

To reach the third normal form (3NF), your table must first be in the second normal form. At this stage, there can’t be any transitive dependencies for non-prime attributes. In other words, one column’s value can’t depend on another’s.  

If you have any transitive dependencies, simply remove the dependent attribute and place it into a new relation. Again, you need to also copy the determinant when doing this.  

There are a few variations from the third normal form, including the elementary key normal form (EKNF) and the Boyce-Codd normal form (BCNF). 

Elementary Key Normal Form 

The elementary key normal form requires all tables to be in the third normal form. To be in the EKNF, all elementary functional dependencies need to begin at whole keys or stop at elementary key attributes.  

Boyce-Codd Normal Form 

Codd and Raymond F. Boyce developed the Boyce-Codd normal form in 1974 to more effectively eliminate redundancies within the third normal form. A BCNF relational schema has no redundancy with functional dependency. However, there may be additional underlying redundancies within a BCNF schema.  

5. Fourth Normal Form 

Ronald Fagin introduced the fourth normal form in 1977. This model builds on the Boyce-Codd framework.  

The fourth normal form has to do with multivalued dependencies. A table is in the fourth normal form if X is a superkey for all of its nontrivial multivalued dependencies X ↠ Y. 

6. Essential Tuple Normal Form

The essential tuple normal form (ETNF) sits between the fourth normal form and the fifth normal form. This is for relations in a relational database where there are constraints from join and functional dependencies. To be in ETNF, a schema has to be in Boyce-Codd normal form. What’s more, a component of every explicitly declared join dependency of the schema must be a superkey.  

7. Fifth Normal Form

The fifth normal form, or project-join normal form (PJ/NF), further eliminates redundancy in a relational database. The point of the fifth normal form is to isolate semantically related multiple relationships. To be in the fifth normal form, every nontrivial join dependency in the table must be implied by the candidate keys.  

8. Domain-Key Normal Form

The second highest normalized form is the domain-key normal form (DK/NF), which is a step beyond the fifth normal form. To reach DK/NF, the database can’t have any constraints beyond key and domain constraints. Every constraint on the relation must be a logical consequence of the definition of domains and keys. 

9. Sixth Normal Form

Finally, there is the sixth normal form (6NF), which is the highest level of a normalized database. To reach the sixth normal form, a relation must be in the fifth normal form. Additionally, it can’t support any nontrivial join dependencies.  

Top Challenges to Expect When Normalizing Data

As you can see, normalization can have some major benefits. However, there are a few potential drawbacks that you need to consider before normalizing data. Generally, you shouldn’t rush into a normalization strategy without fully considering the implications. 

First, you have to use table joins during the normalization process because you can’t duplicate data. This, in turn, increases the length of read times.  

Second, indexing isn’t quite as efficient when using table joins. This also slows down read times. 

It’s also sometimes difficult to know when you should normalize data and when you should use unnormalized data. To clarify, you don’t always have to choose one or the other. It’s possible to use different databases with normalized and denormalized data.  

Ultimately, every application has unique needs. You have to determine what’s best for the specific application and then decide how you want to handle data structure.  

By and large, one of the biggest problems facing DevOps professionals is the time it takes to clean and prepare data. The vast majority of time is spent on tasks like normalization instead of actual analysis. This ultimately wastes time and pulls professionals away from other responsibilities.  

Preparation takes so long in part because of the variety of data that teams are now dealing with. It comes from all types of locations, and it’s never uniform in nature.  

For this reason, a growing number of teams are choosing to automate this time-consuming and laborious process. Recent advances in data automation make it possible to normalize and merge data from disparate locations, providing uniform access to large and complex datasets.  

By automating normalization, data professionals can drastically reduce the amount of time they spend cleaning and preparing information. This enables them to dedicate more attention on higher-level analysis. 

How Plutora Helps With Data Processing

As we explained in a recent blog post, automation is now a commodity. Unfortunately, merely automating inefficient processes won’t help move digital transformation forward. 

To achieve sound automation and streamline normalization, you need to deploy the right data. And ultimately, this requires having the right data management framework in place.  

Enter Plutora, which offers the Value Stream Flow Metrics Dashboard. This powerful tool brings data together into a centralized platform, providing a single source of truth for software development and delivery. 

Plutora prevents team members from looking at different metrics and guarantees access to fresher data with faster load times and greater accuracy.  

With the Value Stream Flow Metrics Dashboard, you can normalize, analyze, and display data from multiple DevOps tools and platforms in one secure and user-friendly platform. You’ll get a bird’s-eye view of all your data, enabling you to take action and optimize operations.  

Streamline Your Software Delivery Process With Plutora

Plutora serves as a one-stop-shop for improving software delivery. In addition to automating normalization, Plutora provides deep analytics and comparative metrics, value stream mapping, audit governance, real-time collaboration tools, and more.  To learn more about how Plutora can help your team ship better software, check out the product page. And when you’re ready for a test drive, request a free demo. 

Here's to investing in the right tools to build software that changes the world in less time.