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Puiblished: Jan 16, 2024 Updated: Jun 27, 2024
Experts have elevated data to the top of the priority list for all businesses, big and small. Businesses create and use databases to manage the data they gather, store, and analyze. You’ve probably heard of the phrase “data normalization” about the rapidly expanding field of big data. Understanding data normalization may provide any firm a significant advantage in leveraging Big Data to drive growth. Data normalization is, therefore, critical to business operations. Here, we look at what exactly a normalized database is.
The process of rearranging data in a database so that users can use it for additional searches and analysis is known as data normalization. To put it simply, it’s the process of creating clean data. This process includes removing unstructured and redundant data and standardizing the appearance of the data throughout all records and fields. So, let’s get to the core of it and understand more about it in the following details.
Businesses use normalization to organize data in databases. When you DB normalize it means creating tables and joining them together based on guidelines intended to protect the information and improve the database’s flexibility by eliminating redundant information and erratic dependencies.
When redundant data accumulates, you squander disk space, and maintenance problems arise. To further define data normalization, data that currently exists and has to be updated must be updated consistently across all locations. Modifying a customer’s address is much easier if the data is stored just in the Customer table and not elsewhere in the database.
While it makes complete sense for a user to look up a specific client’s address in the Customer’s database, it might seem inappropriate for the employee to call that Customer on their behalf. Since the employee’s wage is related to or reliant upon them, you must move it to the Employee table. Inconsistent dependencies may make data more challenging to obtain since they may produce broken or incomplete paths that lead to the data.
Data normalization is crucial for professionals working with significant amounts of data. For instance, massive volumes of data and relational database records are necessary for critical business processes like lead generation, AI and ML automation, and data-driven investing. Something as simple as a single removal in a data cell might cause a chain reaction of mistakes in other cells across the database if it is not arranged and standardized. Data normalization considers the organization of the information, much as data quality does for the integrity of the information.
The database community has developed guidelines to guarantee the normalization of databases. These forms are called standard forms, numbered one through five (the fifth normal form, or 5NF), representing the lowest type of normalization (first common form, or 1NF). In real-world applications, 1NF, 2NF, and 3NF are frequently encountered, with sporadic 4NF instances. A normalized database means forms have been created for said data. Below are all the database normalization forms briefly explained, as well as how to normalize a data set:
The first standard form (1NF) establishes the basic guidelines for a structured database:
You further address Duplicate data removal by the second standard form (2NF):
The third standard form (3NF) takes a big step further than that:
The “third and half (3.5) normal form,” also known as the Boyce-Codd Normal Form, incorporates one additional prerequisite:
Last but not least, the fourth standard form (4NF) has an extra prerequisite:
Recall that there are more guidelines on how to normalize a database. A database must first meet every requirement of a 1NF database to classify as a 2NF database.
Here are step-by-step data normalization examples to help you understand how the process works. Let’s look at the first example of normalization in a database:
The First Normal Form is, by the normalized database definition, any entity without repeated columns or data groups. Each column in the First Normal Form is distinct. The following is the appearance of our Employees and Department table in first standard form (1NF):
empNum | lastName | firstName | deptName | deptCity | deptCountry |
1001 | Norman | Jack | Accounts | New York | USA |
1002 | Bernard | Mike | Technology | New York | USA |
1009 | Young | Harry | HR | Berlin | Germany |
1007 | David |
Ruth |
Admin | London | UK |
1007 | Thomas | Stephanie | HR | New York | USA |
Since all the data is available in one location, connecting columns like deptNum between the Employees and Department tables is unnecessary. Instead, all of the columns combine into one.
The primary key is described as an entity containing 1NF and one of its properties, while the other attributes rely on the primary key. Here is an illustration of what the department and employee table might look like:
EmpNum | lastName | firstName |
1001 | Norman | Jack |
1002 | Bernard | Mike |
1009 | Young | Harry |
1007 | David | Ruth |
1007 | Thomas | Stephanie |
DeptNum | DeptName | DeptCity | DeptCountry |
1 | Accounts | New York | USA |
2 | Technology | New York | USA |
3 | HR | Berlin | Germany |
4 | Admin | London | UK |
DeptID | empNum | deptNum |
1 | 1001 | 1 |
2 | 1002 | 2 |
3 | 1009 | 3 |
4 | 1007 | 4 |
5 | 1007 | 3 |
Here, we can see that they’ve divided the table in 1NF form into three separate tables. EmpNum serves as this table’s primary key. The deptNum serves as the second table’s primary key. We have merged the two tables’ primary keys into the third table. In this third table, the primary keys of the Departments and Employees tables are called foreign keys. The user must connect all three tables using the primary keys to obtain an output like the one in 1NF.
If a table or entity is previously in its second standard form and its columns do not depend transitively on the primary key, then the table is in the third common form. Consider a table with the following columns called Customer:
CustomerID: The primary key that uniquely identifies each Customer
CustomerZIP: The Customer’s ZIP code for the area they call homeCity: The city in which the client resides
The CustomerZIP column depends on the CustomerID column, and the CustomerCity column depends on the CustomerZIP column in the example above. An unauthorized edit to the table that updates the CustomerZIP to a zip code of another town without updating the CustomerCity could occur, leaving the database inconsistent.
A business needs to eliminate the transitive dependency to resolve this problem. You can quickly complete this process by building a new table, such as the CustZIP table, which contains the two columns CustomerZIP (as the Primary Key) and CustomerCity.
The table is in Boyce-Codd Normal Form by definition if it already exists in the Third Normal Form and A should be a super key for each functional dependency between B and A.
Let’s look at the following case to understand when there is an issue with the Third Normal Form and how Boyce-Codd Normal Form can help.
empNum | firstName | empCity | deptName | deptHead |
1001 | Jack | New York | Accounts | Raymond |
1001 | Mike | New York | Technology | Donald |
1002 | Harry | Berlin | HR | Samara |
1007 | Ruth | London | Admin | Elizabeth |
1007 | Stephanie | New York | HR | Tom |
Since deptName and empNum are super keys in this instance, it follows that deptName is a prime attribute. We can uniquely identify every row using these two columns. Furthermore, the dependence of the deptName on the deptHead suggests that the latter is a non-prime attribute. The table is not eligible to be included in BCNF because of this requirement.
We will divide the table into two separate tables as follows to solve this:
empNum | firstName | empCity | deptNum |
1001 | Jack | New York | D1 |
1001 | Mike | New York | D2 |
1002 | Harry | Berlin | D1 |
1007 | Ruth | London | D3 |
1007 | Stephanie | New York | D4 |
deptNum | deptName | deptHead |
D1 | Accounts | Raymond |
D2 | Technology | Donald |
D1 | HR | Samara |
D3 | Admin | Elizabeth |
D4 | HR | Tom |
A table of information is in Fourth Normal Form by definition if it has no more than two independent pieces of data that describe the pertinent object.
Only if a table meets the requirements for the Fourth Normal Form and can be divided into many tables without losing any data can it be regarded as being in the Fifth Normal Form.
What does normalizing mean in data?
The initial three kinds of database normalization are 1NF, 2NF, and 3NF. The first, second, and third normal forms are what they stand for, respectively. The fifth normal form, or 5NF, and the fourth normal form, or 4NF, are also present. Although there are other normal forms, such as 6NF (sixth normal form), 3NF (third normal form) is the most prevalent one.
Every set of normalization rules in a database is cumulative, meaning that each one builds upon the types that come before it. Thus, every idea in 1NF is also applicable in 2NF, and so forth.
Normalization of databases is essential rather crucial for companies who desire to accumulate and align their data in an organized manner. For even better results, if you are searching for product information management (PIM) tools or software, contact us at Pimberly for the best services in the town.