DATA NORMALIZATION: Data Normalization Meaning and How It Works

Every business uses data collection, regardless of size. Large-scale enterprises have established methods, but smaller companies and start-ups are increasingly realizing their value in informed decisions and growth. Data normalization helps streamline data management and reduces inconsistent or redundant information, ensuring more efficient data collection and growth.

This article discusses data normalization, its significance, techniques, and tips for improved digital organization and management.

What Is Data Normalization? 

Data normalization is a process that aims to create a standardized data format across a system, enabling easier querying and analysis of data. It is essential for optimizing data and maximizing its value. 

Furthermore, data normalization can be integrated into a data pipeline, ensuring overall visibility and data observability. Key data normalization techniques can be explored later to enhance the process.

How Does Data Normalization Work?

Normalization is the process of organizing data in a database by building tables and linking them together to prevent duplication and inconsistent reliance. This helps reduce disk space and maintenance issues. Additionally, modifying data in multiple locations is easier if it is kept in the Customers table. 

However, it is not suitable for a worker who calls on a customer’s behalf, as it may not be appropriate. Employee wages must be transferred to the employee’s table due to their connection to the employee. Inconsistent dependencies can make accessing data difficult due to incomplete or damaged paths.

What Is The Purpose Of Data Normalization? 

Data normalization is essential for databases and companies as it improves communication, decision-making, and information usability. Unorganized data hinders human and machine comprehension and negatively impacts features and functions. Additionally, normalization makes searching for specific terms more efficient and accurate, strengthens connections between related data elements, and improves information retrieval and analysis. 

It also simplifies data sorting, filtering, and analysis, facilitating data exploration and pattern recognition. Normalized data also enhances visualization, understanding, and pattern recognition while simplifying processes and ensuring consistency, accuracy, and the absence of duplicates or redundancies. This ensures that multiple users can confidently read, interpret, and trust the records presented.

What Are The 5 Rules Of Data Normalization? 

#1. Eliminate Repeating Groups

Data normalization aims to remove repeated data groups, resulting in efficient and untidy storage. Separating known databases into separate tables helps separate repeating groups from member information, resulting in the first normal form.

#2. Eliminate Redundant Data

To update and delete anomalies, reclassify database names with associated IDs in the Database Table. A second normal form is needed, separating attributes based on key parts and DatabaseID, resulting in two tables: “Database” for database names and “MemberDatabase” for member databases.

#3. Eliminate Columns Not Dependent On Key

Data normalization rescales numerical data to a common scale, reducing differences in variable scales. It improves analytical models and algorithms by transforming data to a consistent scale. However, it may be necessary to remove columns due to irrelevant, redundant, missing data, or other reasons. The decision should be based on the specific analysis or modeling task, rather than data normalization alone.

#4. Isolate Independent Multiple Relationships

Data normalization is a technique that rescales numerical data to a common scale, removing differences in variables’ scales from data analysis and modeling. It helps isolate multiple independent relationships in datasets, revealing independent relationships that may be obscured by differences in scales. Additionally, normalizing data ensures equal representation, easier identification of independent relationships, and improved analysis accuracy and effectiveness.

#5. Isolate Semantically Related Multiple Relationships

In some cases, it is more efficient to separate related attributes. For example, a system might record job availability in each firm and the colleges that supply candidates. This would require a FirmCollegeJob table, which satisfies the fourth normal form.

However, if a law prevents exclusive arrangements, companies must accept candidates from all schools they deal with. This necessitates a fifth normal form, which reduces the number of inserts to six. This can be beneficial for applications with significant update activity, as combination tables naturally develop out of entity-relationship analysis.

What Are The Four Types Of Database Normalization? 

#1. First Normal Form (1NF):

The following prerequisites must be met for a table to have the first normal form:

1. Rows are not ordered.
2. Columns are not ordered.
3. There is duplicate data.
4. Row-and-column intersections always have a unique value.
5. All columns are “regular” with no hidden values.

To achieve this, the table should be split into two tables, with department data in the department table and employee data in the employee table. 

#2. Second Normal Form (2NF):

If every characteristic of an entity depends on the entire primary key, it is said to be in a second normal form. This implies that there is a dependency between the values in the various columns.

1. The table must already be in 1 NF, and all of the tables’ non-key columns must be dependent on the PRIMARY KEY.
2. Partial dependencies are eliminated and put in their own table.

However, the second Normal Form (2 NF) is only problematic when using a composite primary key consisting of two or more columns. For example, the relationship between The employee, employee, and Department tables is established by the Title column being functionally dependent on Name and partially dependent on Date.

#3. Third Normal Form (3NF):

According to the third normal form, fields in a table that don’t depend on the key should be removed.

1. There is already a table in 2NF
2. Non-Primary key columns shouldn’t be dependent on one another.
3. No transitive functional dependency exists

Furthermore, to achieve the third normal form, splitting the table into two and functionally relying on the primary key is necessary.

#4. Boyce-Codd Normal Form (BCNF)

In contrast to the general definition of 3NF, the Boyce-Codd Normal Form (BCNF) also has additional constraints. BCNF is based on functional dependencies that account for all potential keys in a relationship.

Rules for BCNF

1. The table must be in its third normal form.
2. Every functional dependency (FD) X>Y in a given relation should have X as a superkey. 

It should be noted that we identify every determinant and confirm that they are potential keys before we determine whether a relationship is in BCNF.

Furthermore, the Homsky Normal Form (BCNF) hierarchy in DBMS is similar to the Theory of Computation. It is assumed that every relationship in BCNF is also in 3NF. To determine the highest normal form of a relation R with functional dependencies, check if the BCNF condition holds. The hierarchy shows that 1NF has the least restrictive constraint, while 2NF and 3NF have slightly more restrictive constraints. The restriction increases as we traverse the hierarchy.

Who Needs Data Normalization? 

Data normalization is crucial for database designers and developers to minimize redundancy and inconsistencies, improving data integrity and accuracy. It is particularly important in larger databases with complex relationships, as small changes can affect multiple tables. 

Additionally, data normalization also benefits business analysts, data scientists, and professionals working with large amounts of data by enabling better analysis and manipulation and avoiding common errors and pitfalls associated with data redundancy and inconsistency.

How Do You Normalise Data? 

Data normalization creates a standardized format for all company data, which varies depending on the data type. The following are some instances of normalized data: 

• Doctor ROSE will be written as Dr. Rose
• 4247567652 will be written as 424-756-7652
• 24 Larrys AVE, NY will be written as 24 Larrys Avenue, New York

Furthermore, normalizing data en masse involves building tables and linking them together, following practices to protect and reduce anomalies. Various data normalization techniques and practices exist, requiring careful consideration and implementation.

What Is An Example Of Data Normalization? 

Here are a few instances:

What Are The Principles Of Data Normalization?

Data normalization principles ensure efficient database organization, free from redundancy and inconsistencies. There are three common normal forms, each with its own rules. These principles guide database organization and maintain data consistency.

1. First Normal Form (1NF):

– Atomic data, also known as one value per table cell, is required.

– Each row must be distinct and have a primary key to identify it.

2. Second Normal Form (2NF):

– All non-key attributes must be dependent on the primary key in order to function.

– No partial dependencies, which necessitates that each non-key attribute rely entirely on the primary key rather than just a portion of it.

3. Third Normal Form (3NF):

– Every non-key attribute needs to function independently of the others.

– No transitive dependencies, which means that non-key characteristics cannot rely on other non-key characteristics.

Boyce-Codd Normal Form (BCNF) and higher levels of normalization are used to minimize data redundancy, ensure consistency, and improve database performance and efficiency. 

Additionally, BCNF restricts determinants to being candidate keys, allowing them to serve as primary keys independently. These principles help create a more organized, efficient, and reliable database structure.

Why Is Data Normalization Important?

#1. It reduces duplicate data

Normalizing data reduces duplicates, prevents issues like misrouted leads and misaligned teams, and is crucial for a quality data management workflow.

#2. Improve marketing segmentation.

Marketing teams will benefit from data normalization by being able to segment leads more precisely, especially using job titles, which can vary significantly between businesses and industries. To help segment and prioritize outreach, you can apply common tags or labels across a lengthy list of these values.

#3. It enhances performance and metrics.

Poorly maintained databases can hinder performance analysis. Therefore, standardizing data formatting makes it easier to analyze, such as when calculating the number of contacts with a job title like “director” in a campaign. 

What Is Machine Learning Data Normalization?

Normalization is a technique used in data preparation for machine learning to change numeric columns’ values to a common scale without distorting differences. It is not required for every dataset but is necessary when features have different ranges. 

Although there are so many feature normalization techniques in Machine Learning, only a few of them are most frequently used. These are as follows:

• Min-Max Scaling: 

The Min-Max scaling technique shifts and rescales attribute values in a dataset, resulting in a range of 0-1, enhancing its accuracy.

• Standardization scaling:

Standardization scaling, also known as Z-score normalization, centers values around the mean with a unit standard deviation. Additionally, it involves removing attributes and dividing the resultant distribution by the standard deviation.

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References:

BMC

Pipeline

Medium