What is Data Normalization?
January 18, 2021
Analysts and investors rely on data for everyday decision-making processes. Data can elevate investment decisions, improve AI-based recruitment, and even streamline business operations. However, well-structured data is difficult to come by, as there are many barriers when it comes to obtaining standardized and easy-to-read datasets. Luckily, data normalization offers a solution to this problem.
What is data normalization?
Database normalization is the process of structuring a database according to what’s called normal forms, with the final product being a relational database, free from data redundancy. More specifically, normalization involves organizing data based on assigned attributes as a part of a larger data model. The main objective of database normalization is to eliminate redundancy, minimize data modification errors, and simplify the query process.
Ultimately, normalization goes beyond simply standardizing data, and can even improve workflow, increase security, and lessen costs. This article will unpack the significance of database normalization, its basic structure, as well as the advantages of normalization. Let’s first take a look at why normalization is important and who uses it.
The main objective of database normalization is to eliminate redundancy, minimize data modification errors, and simplify the query process.
Why is normalization important?
Data normalization is an essential process for professionals that deal with large amounts of data. For example, crucial business practices such as lead generation, AI and ML automation, and data-driven investing all rely on large sums of data and relational database records. If the database is not organized and normalized, something as small as one deletion in a data cell can set off a sequence of errors for other cells throughout the database. Essentially, in the same way as data quality accounts for the accuracy of the information, data normalization accounts for the organization of said information.
Who uses normalization?
While database normalization may seem conflated with computer jargon, you’d be surprised how many professionals utilize the normalization process. Essentially, all software-as-a-service (SaaS) users can benefit from database normalization. This includes people that regularly parse, read, and write data, such as, data analysts, investors, and sales and marketing experts.
Implementing normalization throughout your databases, regardless of your business type (B2B, B2C, or an agency), will most likely see improvements in workflow optimization, file size, and even cost. But what exactly is normalization?
The data normalization process
Normalization organizes columns (attributes) and tables (relations) of a database according to a set of normal form rules. These normal forms are what guide the normalization process, and can be viewed as a sort of check and balance system that maintains the integrity of dependencies between the attributes and relations. The normalization process aims to ensure, through a set of rules (normal forms), that if any data is updated, inserted, or deleted, the integrity of the database stays intact.
So what exactly are normal forms?
Normal forms were first introduced in the 70s by Edgar F. Codd, as a part of a larger organizational model for the standardization of relational database structures. As previously mentioned, normal forms, at their core, reduce data redundancy and aim to create a database free from insertion, update, and deletion anomalies. Normal forms do this by singling out anomalies that undermine the dependencies between attributes and relations and editing them to fit a standardized format that satisfies sequential normal forms.
After years of advancement and refinement, data normalization has six normal forms, known as 6NF; however, most databases are considered normalized after the third stage of normalization, known as 3NF. Going further, we will focus on normal forms 1NF through 3NF, as they are the primary stages of normalization. It’s also important to note that normalization is a cumulative process. For instance, in order to move onto the second normal form (2NF), the first normal form (1NF) must be satisfied. With that said, let’s get started with normal forms.
First normal form (1NF)
The first normal form is the foundation of the rest of the normalization process. It is referred to as the primary key and involves minimizing attributes and relations, columns and tables respectively. To do this, one must first start by removing any duplicate data throughout the database. Removing duplicate data and satisfying the 1NF includes:
- There is a primary key - no duplicate n values within a list or sequence.
- No repeating groups.
- Atomic columns - cells have a single value and each record is unique.
Second normal form (2NF)
Once 1NF is satisfied, one can move onto 2NF. The second normal form requires that subgroups of data that exist in multiple rows of tables are removed and represented in a new table with connections made between them. Essentially, all subsets of data that can exist in multiple rows should be put into separate tables. Once this is done relationships between the new tables (the subgroups of data that were rearranged) and new key labels can be created.
- 1NF is satisfied.
- Removes partial dependencies - relations (tables) with a primary key containing two or more attributes are relocated to a new table with new key labels created that correspond to a primary key.
Third normal form (3NF)
Following the logic of 2NF, the third normal form also requires that 1NF and 2NF are satisfied. 3NF states that no non-primary key attribute (column) is transitively dependent on the primary key. Therefore if the primary key is substituted, inserted, or deleted then all the data (that is transitively dependent upon that primary key), must be put into a new table.
- 1NF and 2NF are satisfied.
- There is no transitive dependency for non-primary attributes.
4NF and beyond
While normalizing your database in accordance with 4NF, 5NF, and 6NF, is recommended, most relational databases do not require more than 3NF to be satisfied to be considered normalized. The benefits of data normalization beyond 3NF don’t always cause significant errors when there are updates, deletions, or insertions of data. However, if your company utilizes complex datasets that get changed frequently, it is recommended that you also satisfy the remaining normal forms.
5 advantages of data normalization
Let's take a look at some of the advantages of data normalization.
- Improved overall database organization
After normalization, your database will be structured and arranged in a way that is logical for all departments company-wide. With increased organization, duplication and location errors will be minimized and outdated versions of data can be more easily updated.
- Data consistency
Consistent data is crucial for all teams within a business to stay on the same page. Data normalization will ensure consistency across development, research, and sales teams. Consistent data will also improve workflow between departments and align their information sets.
- Reduces redundancy
Redundancy is a commonly overlooked data storage issue. Reducing redundancy will ultimately help reduce file size and therefore speed up analysis and data processing time.
- Cost reduction
Cost reduction due to normalization involves a culmination of the previously mentioned benefits. For instance, if file size is reduced, data storage and processors won’t need to be as large. Additionally, increased workflow due to consistency and organization will ensure that all employees are able to access the database information as quickly as possible, saving time for other necessary tasks.
- Increased security
Because normalization requires that data is more accurately located and uniformly organized, security is significantly increased.
In all, data normalization is an essential part of business for all those dealing with large datasets. Not only is it important to obtain quality data, but it is also important to maintain it through normalization. Analysts, recruiters, and investors alike will benefit from data normalization.
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