In the age we're living, data is the new gold. The real treasure that companies possess is now data. The amount of data that a company or organization holds and how it can deploy it makes a difference in its success. This is because decisions, marketing, development, growth, client management, and sales today are based on data. The big challenge for companies today is dealing with this huge amount of data, which is why we come across data normalization more often. But what is data normalization? Why do we need it? And what are its benefits? In this article, we're going to answer all these questions and more.
What is data normalization?
Data normalization, or database normalization, is a process of organizing and structuring a database to cut down data redundancy. Simply put, the process of database normalization is a way of making sure that every field and record is organized logically so that not only do you avoid redundancy but you also make the use of any relational database more efficient: you avoid any data input error, accidental deleting, and you also ease the updating process of data. Understanding data normalization is very simple, but the process is more complex than it seems. Data normalization obeys specific rules that dictate how the database should be organized.
Benefits of data normalization
Whether you use relational databases, CRM platforms, data analysis or deal with app development in any way, you'll need data normalization. You may think that database normalization can cause extra work for you and your team, but once you know its benefits, you'll change your mind. So, what are the benefits of data normalization?
Decreases database size
When you have data that repeats itself within your database, you need much space to store that data, but it's a complete waste. Data normalization leads to decreasing your database storage space, and that means, as you know, that you're saving resources and money.
Simplifying queries
Searching for information through a well-organized database is always easier than doing the same inside a messy place, whether you are doing it manually or using an automated digital tool.
Easing maintenance
Database normalization prevents problems and makes the maintenance of the database easier. Again, this would avoid wasting both resources and money.
Improving performances
As you may already know, databases underlie the functioning of every application or software in general. Database normalization speeds up the process of retrieving data, which would consequently improve your application's performance.
Who needs data normalization?
Whoever deals with data and databases for any purpose needs data normalization. There is no point in having a redundant, poorly organized database. However, there are some areas where data normalization is particularly important:
- data analysis: if you need to extract useful information from multiple databases, you want them to be normalized.
- software development: data normalization makes a huge difference when optimizing any application's performance. It becomes extremely important when developers need to integrate data from software as a service application in their development process.
- the business: every company needs to collect data and then use that data to make decisions, grow the business itself, draw their marketing strategy, and more.
- professionals: anyone who has an independent job needs to organize their clients, their information, their service/product catalog, and more. In other words, they need databases and data normalization.
How data normalization works
We've spoken so far about data normalization as a theoretical concept. Still, when we go deeper into its most practical aspects, we found that it is a process made of standards and specific rules that you need to know if you want to optimize your databases and exploit all the advantages we've discussed above.
At its basic, data normalization is all about deciding standards for all data inserted into the databases. For example, if we have a database of clients with their phone numbers and addresses, our standards could be the following:
- All names are written in this form: Dursley, Vernon.
- All phone numbers are written in this form: 530-000-0000.
- All addresses are written in this form: 4, Private Drive, San Francisco.
However, some standards are shared by everyone dealing with databases, wherever they are, whatever is the work they are doing. There are some rules grouped in tiers called normal forms. They are organized so that each normal form builds on the last; in other words, you can only apply the second normal form if you've already applied the first.
Several normal forms have been standardized, but the most common and the most important to know are the first three - that's why we are discussing them in more detail in this article. Other than normal forms, however, there are other general rules that you want to stick to. For example, tables within a database should contain a primary key. Primary key values distinguish each row and associate each record with a unique ID. Therefore, before even getting into the first normal form, make sure your database, or table, has a primary key field.
First Normal Form (1NF)
The first normal form dictates that each field of your database should store only one value and that one database shouldn't have two fields that similarly store information. Let's make it more clear with an example. This is a database that stores information about courses and the professors who teach them.
| Professor ID | Professor Name | Courses |
| P001 | Gregor Mitchell | Literature Creative writing |
| P002 | Angela McGall | Physics |
This database violates the first normal form in two ways:
- There are two values in one field since Professor Mitchell teaches two courses;
- There are two fields storing similar information: Professor ID and Professor Name both provide information regarding the professor's identity.
To normalize our database, we need to split it in two:
- The first would contain information related to the professors' identities and would include two fields, Professor ID and Professor Name.
- The second would have two fields: one for the courses and one for the Professor ID corresponding to the professor who teaches that course.
Now, we have two databases, where the first has a one-to-many relationship with the second. The two tables are joined via a foreign key, that is, the Professor ID field.
Second Normal Form (2NF)
The second normal form aims to reduce redundancy, ensuring that every field stores information that tells us something about the primary key. In other words:
- Each database must have only one primary key
- All the non-primary keys must be fully dependent on the primary key
These two principles ensure that each database stores consistent information about the same argument that is contained in the primary key. Again, let's help our understanding with an example.
We have a Professor Birthday and Department database that looks like this:
| Professor Name | Birthday | Department |
| Harry Grey | July, 1 | Literature |
| Victoria White | September, 19 | Literature |
| Paul Saul | March, 1 | Literature |
| James Smith | June, 5 | Science |
The above database follows the first normal form because each field contains only a single piece of information, and fields are all providing different information. However, it does not respect the second normal form because, while the Birthday field fully depends on their name, the Department they belong to doesn't depend on their birthday.
To normalize this database, again, we need to split it into two:
- A Professor Birthday database that includes two fields: Professor name and birthday
- A Professor Department database that includes two fields: Professor name and Department
Third Normal Form (3NF)
A database respects the third normal form when it doesn't have any transitive dependency. What is a transitive dependency? You have transitive dependency when Column B on your database depends Column A, which depends on the primary key. To normalize a database according to the third normal form, you need to remove column B, which doesn't depend directly on the primary key, and store that information on a second database with its own primary key.
Let's give another example. We have this Order Database:
| Order ID | Order Date | Customer ID | Customer Zip Code |
| D001 | 01/3/2022 | C001 | 97438 |
| D002 | 06/15/2022 | C002 | 08638 |
This database doesn't respect the third normal form because we have the primary key, Order ID. Order date and Customer ID are fully dependent on that, but Customer Zip Code depends on Customer ID, which is not the primary key. As we've mentioned, to normalize this database according to the third normal form we need to create a second Customer Zip Code Database that associates each Customer ID to their Customer Zip Code.
What are SQL Keys?
Data normalization becomes, of course, highly important when we deal with an SQL database. SQL is the standard language for relational database systems used by any computer to store, manipulate, and retrieve data from a relational database. SQL keys are the attributes (it can be single or multiple attributes) used to get data from a database or table. They are also used to create relationships between different databases.
There are the most important types of SQL keys:
- Super key: the super key is a combination of one or more columns in a table that uniquely identifies one row on the table.
- Foreign key: it is important when you have two related databases. In the example we've made for the second normal form, we had two normalized databases that "shared" the Professor ID field. Professor ID is the foreign key, and it serves to inform the databases that they are related.
- Primary key: it is a type of SQL key. As we've mentioned, according to the first normal form, there can't be more than one primary key per table, and all the fields must be directly and fully dependent on it
Conclusion
In this article, we've discussed the importance of data normalization. As we've mentioned, it may seem like a process that slows the workflow and makes it more complex, but the advantages are such that it's worth the additional work.
Data normalization is also an example of how managing databases can become extremely complex. For this reason, it's important to rely on tools that can simplify the work as much as possible. In this regard, it is worth recommending no-code tool AppMasters, which allows you to create applications and manage their databases without writing any code. You may still have to learn data normalization rules, but applying them would become a lot easier!
