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Why Is Database Normalization so Important?


The famous author Marie Kondo has popularized the idea: that getting organized is the biggest favor you can include in your daily routine. Organizing your company or catalog information is even more important than being able to find that pair of socks in your drawer every morning.

But are there any special rules for achieving this?

No one doubts the purpose of a database: it’s a system for organizing your product and company information. Database normalization is a technique that’s recommended as a follow-up tool to help refine your organizing, although not all types of companies will find it compulsory or even useful.

Let’s see if that’s what your company might need.

→ Related content: How to create and optimize an online store database

What Is Database Normalization?

Following the creation of a product database, normalization is the next key step, as this process removes any type of error, anomaly or redundancy that might exist in the design of your tables and in the links  between different sources of information.

Why do we normalize a database? Reasons include, to avoid data being replicated in various tables at the same time or unrelated product data being gathered together in the same table.

In addition, this technique makes it possible to make your databases more logical and natural, reducing their size and simplifying the structure to make product data easier to locate, contrast and retrieve.

However, it’s commonly heard among programmers that normalizing data is not a priority task. Is that a complaint or a reality?

Certainly, it does require a significant investment of time and effort. Then there’s its main disadvantage: that normalized databases do slow down processes considerably.

That may be true, but it’s worthwhile undertaking a process of database normalization in order to avoid other problems that may arise later on. The tasks you avoid doing now will have to be taken care of at some point in the future. Also, it’s better to do them while control of your product databases haven’t yet reached an unsustainable level.

→ Also check out: The best Master Data managing software

Objectives of Database Normalization

It may seem confusing as to what normalization is really for; this is a summary of the objectives of database normalization:

  • To correct duplicate data and database anomalies.
  • To avoid creating and updating any unwanted data connections and dependencies.
  • To prevent unwanted deletions of data.
  • To optimize storage space.
  • To reduce the delay and complexity of checking databases when new types of data need to be introduced.
  • To facilitate the access and interpretation of data to users and applications that make use of the databases.

Types of Database Normalization

Databases can be classified by their level of normalization, from level 1 to level 5 (sometimes up to 8 different types are mentioned).

This means that level 1 (First Normal Form or 1NF) is the simplest and most basic way of normalizing data, up to 5NF, the most sophisticated. The latter is rarely used and it is more common to see the first three types.

  • 1NF: Removes duplicates and creates separate tables for groups of related data.
  • 2NF: Removes subgroups of data present in multiple rows of a table and creates new tables, with connections between them.
  • 3NF: Deletes columns that do not depend on the main key value.
  • 4NF: Deletes all dependencies between multiple values.

The requirements for meeting each level of normalization expand to include more complex techniques, although this doesn’t mean they’re then improved for all companies.

Knowing the type and the specific organizational needs of your business is key to choosing the right database normalization process (or even the best combination of rules from different types of normalization).

You might also try: Everything you need to know about data lifecycle management

Database Normalization Phases

Now, how do we tackle the actual process of database normalization?

To make it easy, we recommend you follow the phases of database normalization starting with level 1.

This is a very general summary of the process, the details of which you should check with the database designers:

  • Phase 1: Create different tables for each value, or locate duplicated fields in a table, place them in different tables, and link a key to each.
  • Phase 2: Create connections between values in different tables. For example, between a table of Colors and a table of Sizes of a clothing product.
  • Phase 3: Add connections between the main key columns and the non-key columns.

Read more: Do you need a distributed database? Find out here!

Advantages of Database Normalization

A Logical Map

The storage and mapping of data is more logically arranged and therefore its usefulness for any department using the tables is doubled. Normalizing product data means that the information is always organized and stored in its proper place, without duplicates or outdated versions.

Data Consistency

The reliability of the data is increased for all those involved in accessing the databases, and there will be greater consistency in the information stored. It avoids the following errors: outdated versions being saved, duplicated data being found in different sections of your company, and different types of links between product data being encountered without a clear hierarchy.

Sound Data Connections

The main advantage of normalizing data, apart from clearing out redundancies, is the design of a complete data system that will show how data from different tables relates one to another. It will facilitate the recognition of data connections as well as correcting any inaccessibility or inconsistency of information within the product database.

Related content: What is a SKU? Discover the cornerstone of your product database

Connection to Other Systems

A database normalization process is essential for enabling the implementation of any data management software system, such as a product information management (PIM) tool. With good basic organization, installing this system is quicker and easier, and it can easily be linked to the company’s data sources without delays or the need to correct synchronization problems.

Increased Security

Security is increased, since normalization ensures data can be more accurately located.

Cost Savings

You’ll find it much easier to maintain the databases you already have and to make new additions. It’s also faster to connect data sources to any internal or external system, as no revisions will be necessary to ensure that the data sent is correct.

Keep reading: The best Master Data Management systems currently available

Conclusion: Should you normalize your databases?

It depends on each case, but as a general rule the most sensible advice is “if you can, go for it”.

Among the reasons to decide whether or not to normalize data, you can check:

  • Whether you use NoSQL databases.
  • Whether you have the right resources (since it’s a demanding process that will need full time dedication for a while).
  • If you work with very complex designs which don’t fit well with a standard normalization process.
  • Whether you’re just creating a trial database, that won’t therefore need a perfect formalization from the outset.

Want to know the best way to ensure control of your databases that’s 100% accurate and secure? Centralize and synchronize your data in one place with a PIM: try it for free for 30 days and make it easy and convenient for your team to work with information whenever they need.

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