All You Need to Know

Over the years, the need for and reliance on data on decision-making has topped the chart.

The constant involvement in investment decisions, improvement of AI-based recruitment, and streamlining of business operations is a testament to the value of data; a deep understanding of big data can be used to drive growth.

Whether large or small, enterprises collect and manage data using automation systems, CRM platforms, or databases. However, data in its many forms and entry models gets redundant and inconsistent, posing a challenge to realizing optimal datasets.

Despite such challenges, companies are interested in leveraging valuable insights from ever-growing information piles. Data normalization provides a solution to this problem. This stack overflow thread on data normalization shows that normalization has existed since the 1970s.

In this piece, we’ll spotlight data normalization, its importance, the different normal forms, and denormalization. We’ll also provide a step-by-step guide for normalizing tables and some real-world use cases. Let’s delve right in.

What is Data Normalization, and Why is it Important?

Data normalization entails organizing a database to provide a coherent model for data entry based on normal forms and ending up with a relational database as the final product. To normalize a data set, you need to eliminate redundancy in data and, as a result, save on disk space. The whole process aims to standardize data and reduce modification errors, allowing it to be easily queried and examined.

Consider processes that work with extensive data. For instance, lead generation, data-driven investments, artificial intelligence (AI), and machine learning (ML) automation. Without organization, events like the deletion of data cells offset errors. Additionally, the quality of your data determines its accuracy. This, sequentially, calls for using a set of practices to protect the data, reduce anomalies, and unlock multiple benefits.

Data normalization improves the overall architecture of your database, arranging it into consistent logical structures. Consistent data in your enterprises keeps everyone on the same page, spanning stability in research, development, and sales teams, enhancing the overall workflow. Besides reducing disk space, normalizing data speeds up processing time, analysis, and data integration.

Regarding costs, a reduction cascades from other benefits. For example, reducing file size drives to using less storage and smaller processors. With an enhanced workflow, seamless accessibility to the database saves time, improving a company’s overall productivity.

Moreover, normalizing data improves security, successively from uniform organization. This has led to developers adopting data normalization in object-oriented programming to optimize their projects, elevating their flexibility and expandability.

Unraveling the Data Normalization Process

Equipped with a solid background on data normalization and how it can help your organization, the next step is to learn how the process works. Depending on your specific type of data, normalization may look different.

A great approach starts with pointing out the need for normalization. It could be communication issues, unclear reports, poor data representation, etc. Precise needs are a pivot to the next step, choosing the right tools.

Since the tech landscape is ever-evolving, the market is flooded with multiple IT asset management software tools for all business sizes. The best solution will have normalization features. In fact, some tools like InvGate Insight will do all the normalization as per your IT inventory.

Although such tools are helpful, you’ll need to understand the underlying logic normalization; we’re covering them in the next section. The rules defined here guide how you establish relationships between tables.

cryptoshitcompra.com/wp-content/uploads/2023/12/All-You-Need-to-Know.png", "imageCurrentSrc": "", "targetWidth": "975", "targetHeight": "548", "scaleAttr": "", "dialogLabel": "Enlarged image" } } }» data-wp-interactive=»» class=»wp-block-image size-full wp-lightbox-container»>

Next, you need to examine the relations between your tables and determine attributes, dependencies, and primary keys. This, in turn, reveals the anomalies you need to address. You can now apply normalization rules matching your specific needs for the dataset. Simply put, you’re splitting tables and creating relations across them using keys to ensure each piece of info gets stored in one place.

Lastly, validate the information for precision, integrity, and consistency. If potential errors arise due to the normalization process or anomalies, you may need to adjust… Consider documenting the normalized data structure for future updates and seamless maintenance. For the documentation, include the schema, table relationships, primary and foreign key constraints, and dependencies.

Also read: How to Create Foreign Key Constraints in SQL

What are the Different Normal Forms?

Data normalization is built on a set of rules called normal forms. The rules are characterized by tiers, with each rule building on its predecessor – you can only apply a second tier if the first is met, a third one if the second one is met, and so on. Six normal forms are available, but a database is considered normalized upon reaching the third stage. Let’s dive into each.

First Normal form (1NF)

As the most basic normalization technique and the foundation, this step eliminates redundant entries in a group – each record must be unique. This means having a primary key – no n values within a list, no repeating groups, and atomic columns where each cell has a single and distinct record that cannot be further divided. For example, you can have records with a column titled name, address, gender, and a purchase.

Second Normal Form (2NF)

Having satisfied the 1NF rules, you can now proceed to 2NF. The goal is to remove repetitive entries by ensuring that subgroups of data in multiple rows of tables are extracted and presented in a new table with connections spanning across them. For clarity, all subsets of your data existing in multiple rows are put into separate tables.

Next, you can create relations between the new tables and label keys. This implies removing partial dependencies where relational tables having primary keys with two or more attributes are mapped to a new table with key labels corresponding to the primary key. The linking can be done using foreign key constraints.

To build up on the above example, the purchase records, say cookies (and their types), are placed into another table, with a corresponding foreign key for each person’s name.

Third Normal Form (3NF)

To perform 3NF, the second normal form needs to be met, which, in turn, requires that the first normal form is ratified. The rule here is that 3NF data models should only depend on the primary key – no transitive functional dependencies. If you modify the primary key (by deleting, inserting, or substituting it), all data, depending on the primary key, is channeled to a new table.

Picture a record with names, addresses, and gender. If you change a person’s name, their gender may be modified. To fix this, the 3NF gender gets a foreign key and a new table to store the data. We’re taking on an example to showcase this; read on. By now, your data is normalized. But let’s review the higher levels.

Fourth Normal Form (4NF)

Higher variants of 3NF – Boyce-Codd Normal Form (BCNF) – credited to Raymond F. Boyce and Edgar F. Codd, build on the Boyce-Codd framework, which addresses multivalued dependencies. And now that the data is normalized, they aren’t commonly used. However, companies working with complex data sets that frequently change should consider satisfying the remaining norms.

As expected, 4NF can only be realized if 3NF has been met. Any non-trivial dependencies are eliminated, excluding tiers for a candidate key. A BCNF table follows that for every functional dependency (X -> Y). An example would be a table with `projects`, `employees`, and their skills. By applying 4NF, you can split into two tables, illustrating this in the step-by-step guide.

Before advancing to 5NF, the essential tuple normal form (ETNF) is an intermediary. It is used with constraints from join and functional dependencies. Here’s the proposal by ResearchGate to prevent redundant tuples in relational databases if you’d like to explore the details.

Fifth and Sixth Normal Form (5NF and 6NF)

Alternatively referred to as project-join normal form (PJ/NF), this step eliminates cyclic dependencies in tables and attributes. It targets cases where a combination of attributes is used as keys in a table. A table falls into this category if it complies with 4NF and cannot be further split into smaller tables without using data. The aim is to pinpoint and seclude multiple semantic relations.

And before you get to 6NF, the highest level, we have domain key normal form (DK/NF). A database cannot have constraints past the key and domain options. In this case, all constraint relations are a logical sequence of defined keys and domains. With 6NF, a database must meet 5NF rules and does not support non-trivial join dependencies.

What is Denormalization?

Denormalization involves optimizing the database by adding redundant data to one or more tables. The process doesn’t imply ‘reverse normalization’, but it’s a technique employed after normalization.

You’re introducing pre-computed redundancy through an approach that solves issues upcoming from normalized data. This approach involves splitting tables, adding derived and redundant columns, and mirroring databases.

By tuning the performance of databases, denormalization supports time-critical operations. As an outcome, you can retrieve data faster based on fewer joins. Additionally, you simplify queries (and have fewer bugs to fix) because there are fewer tables to work with.

On the downside, you introduce a few tradeoffs; data redundancy means more storage, inconsistency, updates, and inserts come with expenses, and it relatively increases code complexity.

How to Normalize Tables: A Step-by-step Guide

Let’s give an example to go through all nominal forms. We’ll begin with a standard table illustrating the progression from 1NF to 6NF. Consider a table with information about a library’s books, authors, and genres.

The table is not in 1NF as there are multivalued attributes (Authors and Genres), and rows aren’t unique.

First Normal Form

To achieve 1NF, split multivalued attributes into separate rows. This makes all cells have unique values; there are no repeating groups.

Second Normal Form

With 1NF satisfied, proceed to 2NF. Remove partial dependencies by creating separate tables for related info. We decompose the table into Books, Authors, and Genres tables, linking them through foreign keys.

Authors:

Genres:

Books:

BookAuthors

BooksGenres: