What Is Data Normalization, and Why Is It Important?

Introduction 

Data is the foundation of the modern world. It’s a resource that provides more insight into how our businesses are performing. The importance of data depends on what kind of business you have and what you want to do with it. If you run an e-commerce store, then data will be critical for understanding your customers. If you run a service-based business, data will help you understand how your employees perform in their roles. 

Data is growing at a phenomenal rate, with more than 2.5 quintillion bytes created every day. This data can be used for everything from improving the customer experience to discovering new opportunities for your business. 

Clean data is the basic need for accurate insights and decision-making. If your data isn’t clean, you risk making decisions based on faulty information. You might think that your company is growing when it’s actually shrinking, or vice versa. You might think that a new product is performing well when actually it’s selling poorly. 

When you can trust the information you’re working with, you can make better-informed decisions, which will help your business grow. 

What is Data Normalization? 

What do you mean by normalization of data? Data normalization is the process of organizing and transforming data to improve its structural integrity, accuracy, and consistency. Data normalization is also an important part of database design. 

Data normalization is adopted because it helps to ensure that data will be consistent. This is important because if the data is inconsistent, it will be more difficult to derive useful insights from it. 

Data normalization also helps to ensure that data is reliable and accurate. By ensuring that the data is consistent and reliable so that we can have greater confidence in our analyses. 

Data Normalization: How Does it Work? 

Data in a database is organized by normalizing it. In order to accomplish this, it is necessary to create tables and link the tables together in accordance with data protection principles and to increase the adaptability of the database by eliminating duplication and inconsistent reliance on tables. 

As a result of redundant data being stored on disks, disk space is wasted and maintenance issues arise. Whenever it is necessary to modify data that is already present in multiple locations, the update needs to be done in the same manner everywhere where the data exists. A customer’s address can be easily changed in the Customers table rather than any other table in the database if the information is kept solely in the Customers table and not in any other tables. 

It would be perfectly acceptable for a user to search through the Customer database to find a specific customer’s address. However, it would not be appropriate for the employee who makes the phone call on behalf of that customer to do the same. Due to the fact that the wage of an employee is associated with or dependent upon the employee, it must be transferred to the Employees table. Due to inconsistent dependencies, it may become difficult for you to access certain data because the path you would follow to find them may be incomplete or damaged, making them difficult to access. 

What Is the Need for Data Normalization? 

Now, let’s understand why data normalization is important. To understand what data normalization is we need first to know that are numerous crucial business activities that rely on large amounts of data and relational database records, such as lead generation, artificial intelligence (AI), machine learning (ML), and data-driven investing, for instance.  

Something as small as deleting one data cell within a database can set off a chain of events that result in a series of errors occurring throughout the entire database if the database is not organized and normalized. Like data quality, data normalization refers to the organization of information, while data quality refers to its accuracy. 

The need for data normalization is to avoid redundancy and inconsistency in a database. When data is not normalized, it can lead to a lot of complications in the future. The database will become large, complex, and not suitable for any type of analysis. Data normalization helps in designing a database effectively so that it can be used easily. 

Advantages Of Data Normalization 

The advantages of data normalization include the following: 

• Ensures reduced redundancy: Reduced redundancy is a big benefit of normalization. Data redundancy means storing the same data in multiple places, which can be inefficient and costly. Normalizing your data ensures that it’s all stored in one place so that you don’t have to worry about lost or corrupted information. 
• Eases data analysis: Normalization makes it easier to analyze your data because it’s all stored in one place and organized in a logical, standardized way. This means you can use your database management system (DBMS) to run reports on it or perform queries that would otherwise be impossible if the data were not normalized. 
• Easy maintenance: Normalized data is easier to maintain because it only has one version of each piece of information rather than multiple versions. This means there’s less chance of human error and inconsistency in your database. 
• Cost reduction: Normalized data helps you reduce costs in several ways. First, it reduces the amount of storage space required for your database because there is one version of each piece of information. Second, it makes it easier to reuse and share data with other applications because all the data is standardized and organized in a logical way. 

• Easy to access: A normalized database is much easier to access than a denormalized one. Because all the data is stored in one location, it’s easy to find and retrieve specific information. It’s also easier to combine data from multiple sources into a single report or analysis. 
• Reduces errors: A normalized database is much less likely to contain errors than a denormalized one. Because all the data is stored in one place and organized in a logical way, there are fewer chances for mistakes during data entry or retrieval. 
• Increased security: A normalized database is more secure than a denormalized one. Because all the data is stored in one location and organized in a logical way, it’s much easier to protect against unauthorized access. It’s also harder for someone with malicious intent to change or delete information because they would have to go through multiple steps instead of simply looking up what they want directly from someone else’s computer. 
• Data Consistency: Normalization also ensures consistency. Data is consistent when it’s the same in all related tables and columns, which helps you avoid errors in your data. Normalization helps keep your data consistent and reliable so that you can make better business decisions with confidence. 

As a whole, data normalization plays an essential role in business for those who have to deal with large datasets as a part of their daily operations. Aside from obtaining high-quality data, it is also very important to maintain it through normalization in order to ensure that it remains accurate. As a result of data normalization, it will benefit both analysts and recruiters as well as investors. 

Disadvantages Of Data Normalization 

Data normalization is a process that can make data more consistent, accurate, and complete. While this can be an advantage for many businesses, it does have its disadvantages. 

One of the most significant disadvantages of data normalization is that it takes time to normalize data. Data must be analyzed and standardized before it can be stored in a normalized database. This process can take days or even weeks, depending on how much data needs to be processed. 

Data Normalization requires additional disk space to store normalized databases because they are larger than their non-normalized counterparts. This means that more hardware is required for storing and accessing normalized databases than non-normalized ones, which increases costs for both the business owner and customers. 

Data normalization may not be appropriate for some types of data due to its distribution across multiple tables (e.g., customer name, address). Normalization would require creating one table per field instead of one table per entity (e.g., customer). 

In order to normalize data, you need to understand the relationships between the different sets of data and how they relate. For example, if you have a table of customers and their orders, you need to know how many orders each customer has placed and how many items are in each order. 

Data normalization can also create problems if you want to use the data in ways other than what it was originally intended for. For example, if your goal is to create an average order size for each customer, then this would not be possible if you normalized the data because you would no longer have access to individual customers’ orders or order sizes. 

Conclusion 

Organizations continue to use data at an unprecedented scale, which makes data normalization a top priority. After gaining a basic understanding of how data normalization works, it is time to take a deeper dive into how it works and how it affects your business. Hence, it is recommended that you pursue UNext’s  Integrated Program In Business Analytics in collaboration with IIM Indore.