Normalization removes redundancies and inconsistencies in data. For example, in a school, it would be logical to look at a student table to update the addresses, but it would be redundant and unproductive to also list all students' classes in that same table; those should be listed in a separate class table. This would then allow teachers and administrators to easily view and analyse student-related data.
This is essentially the power of data normalization, which means making it easy to consume and analyse data by ensuring that all the data entering your system is “clean,” in the sense that it’s not redundant or unstructured. While that’s easier said than done in the era of data sprawl, there are things you can do to help ensure data normalization.
Let’s take a look at what data normalization is and why it’s important, data normalization forms, data normalization benefits, and data normalization challenges.
What Is Data Normalization?
Data normalization, as implied above, is simply the act of “normalizing” your data by making sure it’s not redundant or unstructured, both of which make it much harder to manage and use because it’s harder to organize. The normalization process involves dividing large tables into smaller, related tables to reduce data duplication.
Data normalization is a key part of data integrity and data hygiene. When it comes to data hygiene, think of normalization as the act of brushing your teeth every morning and every night to avoid having to see the dentist. The cleaner your teeth, the less money you spend on taking care of them and the more efficient you can be with the various resources you use to clean your teeth, like toothpaste, floss, and water.
Common Normalization Forms
Data normalization forms are essentially rules categorized into tiers. Data must meet the first tier’s rules before you can apply the second tier’s rules, and the second tier’s rules before you apply the third tier’s rules, and so on.
There are several normalization forms, each with specific requirements:
First Normal Form (1NF)
The most basic form of data normalization, 1NF ensures there are no repeating entries in a group. This means every cell only has a single value and every record is unique.
Second Normal Form (2NF)
2NF builds on the rules of 1NF, requiring all entries to adhere to all the 1NF requirements and also have one primary key applied. The primary key rule requires a separate table to be created for every data subset. The data in each table can then be linked with foreign key labels.
Third Normal Form (3NF)
3NF mandates all entries to adhere to all the 2NF requirements and also requires that if any changes are made to the primary key, all impacted data must be put into a new table.
Boyce-Codd Normal Form (3.5NF)
The Boyce-Codd Normal Form, also known as the BCNF or 3.5NF, is a 3NF table that doesn’t have overlapping candidate keys. It ensures that every determinant is a candidate key and eliminates redundancies related to functional dependencies.
Fourth Normal Form (4NF)
4NF requires BCNF adherence and addresses multi-valued dependencies, ensuring that data is not duplicated across multiple rows.
Fifth Normal Form (5NF)
5NF deals with cases where multiple overlapping relationships exist and ensures that overlapping relationships are represented in separate tables.
Benefits of Data Normalization
Data normalization offers numerous benefits, including:
Data Storage Optimisation
One of the primary benefits of data normalization is that it frees up storage space—meaning it allows you to refine your data analysis to a specific data set. Less space means quicker data retrieval. Your database has less ground for your data management systems to cover, making it much easier for them to find information. This also means you have more room to store other data that you want to save for later.
As an example, consider a typical retail customer experience scenario: Someone’s at a store about to buy a sweater and you want to give this customer a highly personalized offer on a pair of pants to go with the sweater. If your data management system has to sift through a junk-littered data warehouse to find the one good usable offer based on this customer’s preferences, by the time it actually presents that offer, it’s likely going to be too late and the customer has left the store.
Easier Maintenance
Data normalization also allows for easier maintenance and updates. Changes are typically localized to specific tables, reducing the risk of errors and making it easier to maintain and update the database schema.
Flexibility
Normalized databases are also more flexible and adaptable to changes in business requirements. Adding new data or modifying existing structures can be done with less impact on the overall database design.
Consistency
With normalization, data is stored in a consistent manner across tables, reducing the likelihood of conflicting information. This consistency enhances the overall quality of the data.
Data Normalization Challenges
While normalization provides many benefits, there are some potential trade-offs, including:
Complexity
As the level of normalization increases, the database schema becomes more complex. Joining normalized tables in queries can be more intricate, potentially leading to increased query complexity and execution time.
Performance of Read-intensive Operations
In scenarios where read-intensive operations are prevalent, normalized databases might experience performance challenges. Joining multiple tables to retrieve data can be resource-intensive, especially in large databases.
Training and Education
Data normalization can be quite complex, which means you’ll need to have that knowledge somewhere on your team or in your company. If you don’t already have it, you’ll need to find a way to get it, which could mean spending money on a third-party resource.
Over-normalization Risk
Over-normalization, or applying normalization beyond what is necessary, can lead to unnecessary complexity and performance issues. It's important to strike a balance and normalize the data to an appropriate degree based on the specific requirements of the application.
Update Anomalies
While normalization helps eliminate certain types of anomalies, it may introduce update anomalies. For example, updating a piece of information in one table may require updates in multiple places, which can increase the likelihood of errors and inconsistencies.
Increased Storage Requirements
In some cases, normalization can lead to increased storage requirements. This is because normalized tables may require additional storage for primary and foreign key relationships, and the benefits of space optimisation may not be fully realized in certain situations.
Impact on Write Performance
While read operations may be impacted by the complexity of joins, write operations, such as inserts and updates, can also be affected. Normalized tables may require multiple operations to maintain referential integrity, potentially impacting write performance.
Query Optimisation Challenges
Optimizing queries on highly normalized databases may require careful consideration and tuning. Database administrators may need to create appropriate indexes and analyse query execution plans to ensure optimal performance.
Denormalization
An alternative to data normalization is data denormalization. Where data normalization involves dividing larger tables into smaller tables, denormalization merges data into a single table that can be queried faster.
Data normalization is generally better in scenarios with a lot of costly table joins, while denormalization is more useful when there are many costly join queries. Normalization provides better data integrity and optimised disk space and is generally better for data consistency and faster data insertion and deletion. Denormalization is usually a better choice when you need faster search and want to optimise read performance. Denormalization can also lower the overhead costs created by over-normalized data or complicated table joins.
It’s important to strike the right balance between normalization and denormalization. The level of normalization should be tailored to the specific requirements of the application and the expected patterns of data retrieval and modification. It’s very common to see business applications in 3NF as that strikes a balance in performance for reads, writes, and indexes of relational databases. In many cases, more is not better. The additional normalization can actually slow down a system by causing excessive reads and writes, creating lags and excessive locking when doing joins on tables.
Conclusion
Data normalization improves data integrity, searchability, and analysis by applying a specific set of rules around how data is entered into databases. The common data forms, which are the rules used to dictate how data is entered, are 1NF, 2NF, 3NF, BCNF, 4NF, and 5NF.
While data normalization offers clear benefits such as flexibility, storage optimisation, and easier maintenance, it also has potential drawbacks, like complexity and decreased write performance.
That said, data normalization is a key part of maintaining a well-structured and efficient database and is an important data-cleaning step in any data pipeline, adding efficiencies and reducing costs.
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