What is Data Normalization? | 4 Key Types, Concepts, Benefits & Limitations

It isn’t a secret that the world has now entered the Big Data age. Almost every organization, especially huge corporations, collects, saves, and analyses data in order to expand. Data management is commonplace in most daily corporate processes, including instruments like Databases, CRM platforms, and Automation Systems.

If you’ve worked in a company for any length of time, you’ve most likely come across the term Data Normalization. Data Normalization is a best practice for processing and utilizing stored data, and it’s a procedure that can aid a company’s overall success.

Here’s all you need to know about Data Normalization, as well as some key pointers to keep in mind before you start the process.

What is Data Normalization?

The production of clean data is generally referred to as Data Normalization. However, when you dig a little deeper, the meaning or goal of Data Normalization is twofold:

• Data Normalization is the process of organizing data such that it seems consistent across all records and fields.
• It improves the cohesion of entry types, resulting in better data cleansing, lead creation, and segmentation.

Simply said, this procedure entails removing Unstructured Material as well as Redundancy (duplicates) to ensure logical data storage. When Data Normalization is done correctly, standardized data entry is the result. This technique, for example, applies to the recording of URLs, contact names, street locations, phone numbers, and even codes. These standardized data fields can thus be quickly grouped and read.

Why do you Need Data Normalization?

As data becomes more useful to all types of businesses, the manner it is arranged in mass amounts becomes increasingly important. It is clear that when Data Normalization is done effectively, 

• it results in a better overall business function, 
• from assuring email delivery to preventing misdials and 
• improving group analysis without the fear of duplicates. 

Consider what would happen if you left your data in disarray and missed out on significant growth opportunities as a result of a website that wouldn’t load or notes that didn’t reach a vice president. None of this screams progress or success. One of the most critical things you can do for your company right now is to choose to standardize data.

Who Would Require Normalized Data?

• Every company that wants to succeed and develop must execute Data Normalization on a regular basis. It’s one of the most critical things you can do to eliminate inaccuracies that make Data Analysis complex and time-consuming. 
• When updating, adding, or removing system information, such errors are common. An organization will be left with a well-functioning system that is full of useful, beneficial data once data input error is eliminated.
• An organization can make the most of its data and invest in data collection at a higher, more efficient level with Normalization. Examining data to improve a company’s operations becomes less difficult, especially when cross-examining. 
• Data Normalization becomes an invaluable technique for individuals who routinely integrate and query data from Software-as-a-Service(SaaS) applications, as well as those who gather data from a range of sources such as Social Media, Internet Sites, and more.

How Does Data Normalization Take Place?

Now is the time to point out that your Data Normalization will differ based on the type of data you have. Data Normalization is the process of adopting a consistent format for all data throughout an organization.

• Miss EMILY will be written in Ms. Emily
• 8023097864 will be written 802-309-7864
• 24 canillas RD will be written 24 Canillas Road
• GoogleBiz will be written Google Biz, Inc.
• VP marketing will be written Vice President of Marketing

Experts agree that there are five main guidelines or “Normal Forms” for accomplishing Data Normalization, in addition to fundamental formatting. Each rule focuses on categorizing entity types into a number of groups based on their complexity. Although the principles for Normalization are considered to be guidelines, there are times when deviations from the form are required. When it comes to variations, it’s crucial to think about the implications and oddities.

For the sake of simplicity, the first and three most prevalent variants are described at a high level.

Types of Normalization Forms

Let’s understand the different Normal Forms with the following example:

First Normal Form (1NF)

The simplest kind of Data Normalization is 1NF, which assures that no two entries in a group are identical. The following rules must be followed for a table to be in the first normal form:

• Each cell should only have one value.
• Each record should be distinct from the others.

The above table in 1NF:

Second Normal Form (2NF)

All subsets of data that can be placed in multiple rows are placed in distinct tables in a 2NF table. The following rules must be followed for a table to be in the second normal form:

• It should be in the 1NF.
• Any subset of Candidate keys should not be functionally dependent on the Primary key.

It’s obvious that you won’t be able to move further with creating a simple database in 2NF unless you partition the table above.

Third Normal Form (3NF)

The following rules must be followed for a table to be in the 3NF:

• It should be in the 2NF.
• It should not have any functional dependencies that are transitive.

A Transitive Functional dependency occurs when a change in one column (that isn’t a primary key) affects all of the other columns.

Here’s an example of 3NF in a SQL database:

The table is in 3NF because there are no transitive functional relationships.

Boyce Codd Normalization: The Special 4th Normal Form

Codd and Boyce Normal Form, often known as 3.5NF, is a higher variant of 3NF. A BCNF is a 3NF table that doesn’t have any candidate keys that overlap. To be included in BCNF, a table must meet the following criteria:

• It should be in the 3NF.
• X should be a super key for each functional dependency (X → Y).

BCNF Example: 

The original database before Normalization:

• Since teacher is not a candidate key, Teacher-> subject violates BCNF.
• Divide R into R1(X, Y) and R2(R-Y) if X->Y violates BCNF.
• As a result, R is separated into two relations: R1(Teacher, subject) and R2(Teacher, subject) (student, Teacher).

All of the irregularities that existed in R have now been eliminated in the aforementioned two relations.

What are SQL Keys?

In SQL, a KEY is a value that is used to uniquely identify records in a table. An SQL KEY is a single column or a group of fields that are used to identify rows or tuples in a table. SQL Key helps establish a relationship between many tables in a database by identifying duplicate information. Non-key columns are columns in a table that aren’t utilized to uniquely identify a record.

Primary Key

A Primary key is a single column value that is used to uniquely identify a database entry.

It has the following characteristics:

• It is not possible for a Primary Key to be NULL.
• A primary key value has to be one-of-a-kind.
• The primary key values should only be altered on rare occasions.
• When a new record is inserted, the primary key must be provided a value.

Secondary Key

Candidate keys that aren’t the Primary key are referred to as Alternate Keys. For a table, there can only be one primary key. As a result, all other Candidate Keys are referred to as Alternate or Secondary Keys. They can also be used to uniquely identify tuples in a table. They are not used as the Primary Key as Database Administrator decided to use a different key as the primary key.

Foreign Key

The main key of another table is referenced by a Foreign key. It aids in the connection of your Tables.

• The name of a foreign key can differ from the name of its primary key.
• They do not have to be unique, unlike the primary key, to ensure that rows in one table correspond to rows in another. Most of the time, they aren’t.
• Primary keys cannot be null, but foreign keys can.

What are the Advantages of Data Normalization?

Normalizing a database has numerous advantages. The following are some of the most significant advantages:

• Normalization can be used to resolve Database Redundancy or Data Duplication.
• By applying normalization, you may reduce the number of Null Values.
• As a result of less Data Redundancy, the database becomes more compact.
• Reduce or eliminate Data Modification issues.
• It makes the query easier to understand.
• The database structure is now more clear and more understandable.
• It is possible to increase the database without compromising existing data.
• Since the table is tiny and more rows can be accommodated on the data page, finding, sorting, and indexing can be faster.

What are the Limitations of Data Normalization?

• As information is dispersed over more tables, the necessity to join tables grows, making the task more time-consuming. In addition, the database becomes more exciting to recognize.
• Tables will include codes rather than true information since rehashed data will be stored as lines of codes rather than genuine data. As a result, there is always a requirement to visit the query table.
• The information model is built for applications, not for impromptu questioning, hence the data model turns out to be extremely difficult to ask against. (An impromptu question is one that cannot be answered before the question is asked.) It is made up of a SQL that is built up over time and is usually done by work area cordial question devices.) As a result, displaying the knowledge base without knowing what the client wants is problematic.
• The exhibition becomes increasingly slow as the typical structure type advances.
• To carry out the standardization cycle effectively, accurate information on the many conventional structures is required. Unscrupulous use can result in an abysmal plan full of substantial anomalies and data irregularities.

Conclusion

As organizations expand their businesses, managing large volumes of data becomes crucial for achieving the desired efficiency. Data Normalization powers stakeholders and management to handle their data in the best possible way. In case you want to export data from a source of your choice into your desired Database/destination then Hevo Data is the right choice for you! 

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations, with a few clicks. Hevo Data with its strong integration with 100+ sources (including 40+ free sources) allows you to not only export data from your desired data sources & load it to the destination of your choice, but also transform & enrich your data to make it analysis-ready so that you can focus on your key business needs and perform insightful analysis.

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