Back to blog
Data normalization is the development of clean and easy-to-use data. During this process, data is reorganized within a database in a way that users can properly employ that normalized database for further queries and analysis.
In this article, we’ll answer the “What is data normalization and how it works?” question in more depth. We’ll then discuss why it’s so important, and share some tips on how you can benefit from employing data normalization in your business.
So, what is data normalization? It’s a process of eliminating data duplication, ensuring logical data storage, and maintaining data integrity.
To exlude the copies, you must go through the whole dataset and erase data redundancy. If not removed, such data might spoil the analysis later on, as these values are not precisely what you need.
Grouping information is another essential step in “cleaning” the data. To ensure logical data storage, you might want to have related values hand in hand analyze them together, and this is what you get after normalizing your data. Dependent data would appear in a close range within the dataset.
When looking into data normalization, you might come across the “data denormalization” term. While normalization is all about data integrity and eliminating redundancy, denormalization is the opposite method – it turns the normalized data into redundant, information-filled schema; it’s needed because an overly normalized structure results in query processor overhead. Because of the very essence of this method, it might be difficult to maintain data integrity during denormalization.
Now that we have a rough idea of what data normalization is, it’s time to dig deeper into how it works in practice. Even though the process might differ a bit depending on the type of database and collected information itself, some key steps are often involved.
As mentioned, data normalization starts from removing duplicates. Then, it continues with solving all the issues in case any conflicting data appears before moving forward. Third, formatting follows up, turning the data into easy-to-process information. Eventually, data gains a way more organized structure after it’s consolidated.
Digging into the specifics, there are three primary forms of data normalization, namely first, second, and third normal form (NF). Let’s dive into each one a bit deeper.
First normal form (1NF)
The first normal form (or 1NF in short) is a fundamental part of data normalization, which guarantees no recurring entries in a group. To qualify as the first normal form, each cell must contain a single value, and each record must be unique.
Second normal form (2NF)
2NF is the second step of eliminating data redundancy. After a whole set of 1NF requirements is applied, you must ensure that information has one primary key by placing all data subsets in multiple rows to separate tables. Then, you would be able to create relationships through new foreign key labels.
Third normal form (3NF)
When all 2NF requirements are applied, data can appear in the 3NF rule. Following that, data in a table must depend on a primary key. You should move all data affected by a change of the key to a new table.
The given guidelines will become more apparent as you better understand the normalization forms, and dividing your data into tables and levels will turn out to be straightforward. These tables will thereby make it simple for anybody in an organization to collect data and guarantee that it’s accurate and not duplicated.
Mainly, data normalization should be part of each data management-related process. A database needs to get rid of possible errors to serve its function well in further data processing and analysis.
Besides, data normalization aids in formatting gathered data. Without being able to view and study collected data, a company risks having the majority of information unused, taking up space and providing little value to the business. Failing to make the most of data may be a huge setback if you consider how much money companies are willing to spend for data gathering and database architecture.
One example of data that requires significant data normalization efforts is web scraped data. Even though web scraping is an essential component of market research, brand protection, ad verification, and many other use cases, collected data won’t be of great help until it’s put into a clear and structured way. Immediately after you get the scraped content, it might contain duplicate information and require ‘cleaning’ for further processing and analysis.
Making data analysis easier is the key reason to normalize data. However, there are many other motives to employ this procedure in a business, all of which are highly advantageous.
1. Reduced database size
First, data normalization reduces the size of a database. The enormous quantity of memory and disk space required to store and analyze a large dataset is often a significant concern. While technological advancements have increased the capacity and efficiency of storage options, we now find ourselves in a situation where gigabytes, terabytes, and larger storage alternatives are no longer sufficient. As a result, reducing disk space is a crucial issue, and data normalization may offer great help to that.
2. Improved performance
What’s more, taking up less disk space improves performance. You can perform data analysis more efficiently when a dataset isn’t clogged with useless information. If you’re having trouble with your data analytics, normalization can undoubtedly come in handy for your database.
3. Easier to alter and update
Advantages of data normalization extend even beyond disk space and its impact. You’ll also find it simpler to alter and update data in your database if you follow this approach. Because there’s no data redundancy, it’ll be considerably cleaner, and you won’t have to play around with it as you change information.
4. Makes multiple-source data tracking easier
Many companies look at their databases’ data to see how they may improve themselves. This may be a challenging process, especially if the information they have comes from various sources. Let’s say a business has a query on sales numbers related to customer engagement on social media. It can be challenging to examine the data with so many different sources, yet if you standardize data, the process will be so much smoother.
5. Convenient for individual users
Along with the above advantages, data normalization may bring significant benefits to specific individuals. If you’re involved in data gathering, management, and organization, you’ll want to make the most out of your data. The same boat carries those who do statistical data modeling or are responsible for dataset maintenance – data scientists and business analysts stand to benefit significantly from the data normalization process.
6. Faster to answer questions
Once data normalization becomes a bit more manageable, you can start organizing data without further modifying it. Instead of trying to translate unstructured data that wasn’t properly stored, different teams within the company can save time and solve various questions more efficiently.
7. Improved segmentation
Finally, data normalization vastly helps with lead segmentation and group analysis. You can quickly split groups into multiple categories based on different criteria, like industry types or titles. For example, if you want to filter out high-priority leads, data normalization allows you to pick specific valuables that define a high-priority lead – this way, you won’t be spending time on less important tasks.
Data normalization is significant when several teams are utilizing the same data source or interacting through data. The more data sources and the more participants are in this process, the higher the risk of non-normalized data, which can result in specific values being lost.
Another scenario where you might experience significant losses is having cluttered data. Yet, without data normalization, you wouldn’t even be able to calculate what these losses are. It would gradually become one of the primary causes of data unusability. Sidelong, the proportion of wasted data at your company represents the loss incurred due to failing to normalize data.
Data normalization aids businesses in getting the most out of collected data by optimizing dataset infrastructure, improving disk space and performance, and making it easier for employees to handle the information they work on. This significantly enhances further data processing and analysis, which are essential components in business operations.
Having in mind the importance of data and the resources companies put into accessing such data, employing it in the right way is a must for a business to achieve the maximum of what data can offer and, of course, to avoid significant losses.
About the author
Former Content Manager
Monika Maslauskaite is a former Content Manager at Oxylabs. A combination of tech-world and content creation is the thing she is super passionate about in her professional path. While free of work, you’ll find her watching mystery, psychological (basically, all kinds of mind-blowing) movies, dancing, or just making up choreographies in her head.
All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.
Get the latest news from data gathering world
Forget about complex web scraping processes
Choose Oxylabs' advanced web intelligence collection solutions to gather real-time public data hassle-free.
Scale up your business with Oxylabs®
GET IN TOUCH
Certified data centers and upstream providers
Connect with us
Advanced proxy solutions
oxylabs.io© 2023 All Rights Reserved