The era of big data has made data collection, storage, and analysis a top priority for businesses of all sizes, especially large-scale enterprises. Companies are developing and using databases to manage all the information effectively, thus searching for best practices to handle data, one of which is database normalization.
In this article, we’ll explain what data normalization is and how it works, discuss why it’s so important, and share some tips on how you can benefit from data normalization by employing this practice in your business.
Understanding data normalization
Simply put, data normalization is the development of clean and easy-to-use data, which can improve overall data management. During this process, data is reorganized within a database in a way that users can properly employ that database for further queries and analysis.
Data normalization focuses on the two key goals: getting rid of duplicates within a dataset and logically grouping data together. To eliminate the copies, one must go through the whole dataset and erase redundant information. 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. You might want to have related values hand in hand to analyze them together, and this is what you get after normalizing your data. Dependent data would appear in a close range within the dataset.
How does data normalization work?
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). Each of them defines how to put entity types into a series to grow the level of data normalization.
First normal form (1NF)
1NF is a fundamental part of data normalization, which guarantees no recurring entries in a group. To qualify as 1NF, 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 data applies a whole set of 1NF requirements, you must ensure that information has one primary key by placing all data subsets in multiple rows to separate tables. Finally, 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 in the primary 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.
When should you use data normalization?
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 duplicates and require ‘cleaning’ for further processing and analysis.
Benefits of normalized data
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.
First, data normalization reduces the size of a database. The enormous quantity of memory 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.
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.
Advantages data normalization offers 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 are no redundancies or mistakes, the data will be considerably cleaner, and you won’t have to play around with it as you change information.
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 data normalization makes the process smoother.
Along with the above advantages, data normalization may bring significant benefits to specific people. 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 modeling for the data or are responsible for dataset maintenance – data scientists and business analysts stand to benefit significantly from the data normalization process.
Risks of avoiding data normalization
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.