What Is Data Normalisation? (Definition, Types and Benefits)

By Indeed Editorial Team

Published 16 October 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Normalising data is a crucial process for companies that want to maintain their operations and expand their business. With normalisation, a business may maximise its data and invest in data collection at a higher, more efficient level. If you're a database specialist or want to start a career working with data, understanding normalisation may help you enhance the integrity of a company's database. In this article, we define data normalisation, outline the types of normalisation, discuss its benefits and highlight the anomalies associated with non-normalised data.

What is data normalisation?

Data normalisation is the process of organising data into similar entries so that analysts and other interested parties can more easily interpret and categorise information. Normalisation involves structuring data according to specified properties. The primary goals of normalising data are to remove duplicate data, reduce data modification errors and simplify the querying process.

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5 types of data normalisation

Data professionals can use established normalisation approaches to increase data accuracy. There're five normal form levels, starting from the first normal form to the fifth normal form. Each level has its own set of methods for normalising data, which lowers the possibility of overlooking erroneous data. Here's more information on the five types:

  • First normal form (1NF): This level of normalisation eliminates duplicate data and allows for the creation of distinct tables for groupings of relevant data. This is the most basic process of normalising data.

  • Second normal form (2NF): This normalisation level eliminates subgroups of data that appear in several rows of a table and replaces them with new tables that have links to one another.

  • Third normal form (3NF): This level eliminates columns unrelated to the data's primary key or first column. There has to be continuity between the first column and the rest of the data.

  • Fourth normal form (4NF): This level removes any dependencies between multiple values so no other data is affected if one value changes. This may help to avoid creating duplicate entries from numerous account variables.

  • Fifth normal form (5NF): This form contains no joint dependency and divides into sub-relations. It also meets all restrictions discussed in forms one through four.

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Normalisation phases

The following are the normalisation phases that might help you normalise data:

  • Phase one: This phase makes a table for each value or finds duplicated fields in a database and moves them to a new table. Consider adding a key to duplicated fields if you decide to transfer them to another table.

  • Phase two: This phase creates links between values in multiple tables. For example, you may associate account names with phone numbers to guarantee that each account name has the correct contact information.

  • Phase three: This phase adds links between the primary key and non-key columns. This might assist you in connecting the data so that the information remains consistent and relevant.

Why is normalisation important?

Normalising data is necessary for people who often work with significant volumes of data. Lead generation, artificial intelligence (AI), machine learning (ML) automation and data-driven investment are just a few key business processes that depend on massive amounts of data and relational database records. A single deletion in a data cell might trigger a chain of errors in other cells across an unorganised and un-normalised table or database. Normalising the data can reduce this risk.

Benefits of normalising data

Here are the benefits of normalising data:

Removes redundant data and abnormalities

When you use normalisation techniques on a database, you can rapidly find and fix duplicate data so that it doesn't affect the integrity of the database. Normalisation groups together redundant data, making it easier to identify. Normalisation techniques can also aid in detecting anomalies by allowing them to stay at the bottom or top of your database.

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Removes unnecessary data connections

Normalisation procedures aid in the removal of data connections that are unrelated to the primary data that the company uses. These approaches may separate data into independent tables that no longer have links to one another. In a database, for example, you may enter customer account names and revenue data together. The normalisation may separate account names from income data, allowing you to study each individually if values change.

Prevents data deletion

Using well-known normalisation procedures can help you avoid losing data related to the primary key data. Normalisation establishes links between the primary data and data directly affecting the primary data. These links can enable the deletion of unnecessary data while leaving critical information intact.

Optimises data storage space

The normalisation may aid in the removal of redundant data from databases. Normalisation procedures, for example, may decrease duplicate values in a database. Removing significant amounts of duplicate data may free up storage capacity for future data.

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Enhances data access and interpretation

Normalising datasets may make it easier for people to read and utilise a database. This is beneficial if many of the users are from outside your department. The normalisation may help new users understand how to use and analyse the database, giving you more time to evaluate and update data.

Creates a logical data map

Taking an unorganised database and converting it into a more organised and user-friendly spreadsheet may help make a database usable for departments or people that utilise the data. It may also assist you in swiftly organising and making available information. Normalising data may help guarantee that you don't have duplicate or obsolete data.

Enhances data consistency

Normalising data may help increase the accuracy of the information in a database. It may delete duplicate data sets across departments and prevent obsolete datasets from overwriting valid data. The normalisation may also enhance the database's data structure by removing incorrect linkages between tables.

Establishes data connections

Normalising data can show departments how data from separate tables relate to one another. It may also aid in identifying data links and identifying data discrepancies or information inaccessibility. It's also a crucial component for swiftly building data management software systems.

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Saves both time and money

Once you have normalised data, you may discover that it can save the organisation time and money. Departments can get information more quickly because the data is more logically accessible. Because you've previously validated the correctness of current data, normalisation may lessen the time it takes to add new data information.

Data anomalies

When attempting to load an integrated conceptual model into a database management system without normalisation, several issues, or anomalies, can arise. These issues stem from relationships that emerge directly from user views. Here are the three categories of anomalies with examples:

Update anomaly

An update occurs because of updated information. Here's an example:

A company has 10 columns in a table, two of which are employee name and employee address. If one of the employees moves, it may be necessary to update the table. If the table isn't normalised, one employee may have numerous entries, and you may miss one while editing them.

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Insertion anomaly

An insertion anomaly occurs when there is information that still needs to be inserted. Here's an example:

A school database has a table with four columns. These columns are student ID, student name, student address and student grades. While it's possible to fill up the first three qualities upon a student's enrolment, the fourth attribute may have a NULL value until the student has earned at least one grade.

Deletion anomaly

This anomaly implies missing table data. Here's an example:

A school database has columns for student ID, student name, course and address. If a student drops out, the school can remove the record in the database that corresponds to them. This can also erase the information on the course, even though the school determines the course and not the individual student.

Frequently asked questions about normalising data

Below are answers to frequently asked questions about normalisation:

Which companies normalise data?

Any company that utilises big data to monitor consumer information and evaluates trends can normalise data to have clean databases. Most companies gather specialised data sets to help identify various details on their clients' purchases, storing essential consumer information and organising other critical data categories. Businesses that hold customer data may utilise normalisation to simplify the analysis and help reduce inaccuracies in their digital records.

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What effect does normalisation have on marketing?

Marketing depends on various data sources to create campaigns and identify target audiences. The generation of data for market segmentation becomes possible by normalising data, which enables marketing experts to conduct in-depth analyses of consumers' characteristics to better determine how to communicate with them. Normalisation can also prepare data for email marketing campaigns.

What are keys in normalisation?

There are three keys in normalisation which help you establish relationships between different columns and tables in a data set. These are the primary key, the foreign key and the unique key. The primary key identifies each entry in a table, preventing null or missing entries, while the foreign key is for columns dependent on other tables' primary key values. The unique key identifies each row as distinct and allows for null values.

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