Normalizing data: The what, why and how
Types of data normalization
To improve data accuracy, data analysts can use well-established normalization techniques on their databases. These normalization techniques are typically divided into levels one through four, but they can reach level eight if analysts require more thorough techniques. It is less likely that inaccurate data will be overlooked because each level has its own distinct procedures for normalizing data.
Here are the different types of data normalization methods:
What is data normalization?
Data normalization is the process of standardizing the information in an unstructured database. This can aid in lowering data duplication and enhancing data integrity in general. Organizations might have different criteria for normalizing data and information. A business might standardize data fields to include company names, account names, minimal address details, or phone numbers, for instance. By doing this, the business may find it simpler to read and analyze the data in the database.
10 reasons why it’s important to normalize data
Here are 10 reasons to normalize your data:
1. Corrects duplicate data and anomalies
When you use normalization techniques on your database, you can quickly spot and remove duplicate data without affecting the rest of the database. By combining duplicate data, data normalization facilitates identification. Because normalization methods allow anomalies to stay at the top or bottom of your database, they can also aid in the identification of anomalies.
2. Removes unwanted data connections
Data normalization techniques aid in the removal of data connections that are unrelated to the primary data used by your organization. These techniques enable the division of data into various tables that are no longer related to one another. For instance, you might enter customer account names and sales data together in a database. Account names and revenue data can be separated using data normalization, allowing you to analyze each piece of information separately in the event that values change.
3. Prevents data deletion
You can avoid deleting data related to the primary key data by using well-known data normalization techniques. To achieve this, data normalization creates connections between the primary data and the secondary data that have a direct impact on the primary data. These connections can highlight unnecessary data without affecting important information.
4. Optimizes data storage space
Data normalization can help remove unnecessary data from databases. For instance, normalization techniques could help eliminate duplicate values from a database. You can free up more storage for future data by deleting large gigabytes of redundant data.
5. Adds new data
Prior to adding new data, normalizing your database can help the database sort and condense the new data. This can speed up the process of adding information to your database by ensuring that it only retains pertinent data. This can also enhance the business’s capacity to assess information from updated databases and use that information to inform decisions.
6. Improves access and interpretation of data
Normalizing datasets can facilitate user interpretation and facilitate effective database use. If many users are from outside your department, this could be helpful. In order to give you more time to review and add data, data normalization can make it simpler to instruct new users on how to use and analyze the database.
7. Creates a logical map of data
Making a spreadsheet more organized and user-friendly from an unstructured database can help make a database useful for departments or people using the information. Additionally, it can facilitate information organization and accessibility. Data normalization can also ensure that you don’t have duplicate data or out-of-date data information.
8. Increases data consistency
The reliability of the data in a database can be increased by normalizing the data. It can eliminate redundant data sets from various departments and stop stale datasets from replacing accurate information. Your database’s data hierarchy can be improved by data normalization because you can get rid of erroneous links between tables.
9. Creates data connections
Data normalization has the benefit of demonstrating to departments the relationships between data from various tables. Additionally, it can aid in the identification of data connections, data inconsistencies, and information accessibility issues. It’s also a crucial element for quickly implementing data management software systems.
10. Saves time and money
Once your data has been normalized, the business may find that it can save time and money. Because the data is probably more logically accessible, departments might be able to retrieve information more quickly. Because you’ve already ensured the accuracy of the existing data, data normalization can also speed up the process of adding new data.
Phases of data normalization
You can choose the best methods to use within your organization by being aware of the various types of data normalization. The three normalization phases are listed below to assist you in normalizing your data:
Tips to consider before normalizing data
There are a few things you may want to take into account before implementing data normalization processes in your databases, such as:
Data normalization examples
Here are some instances of normalizing data in a database:
Numerous formats of phone numbers are present in Johns database, including “5553217894” and “(555) 3217894.” John uses hyphens to format all phone numbers in order to ensure consistency, for example, “555-321-7894 Because John divides the collection of numbers into three distinct sections, the company’s departments can dial clients’ numbers correctly.
Duplicate information prevention
In Maria’s database, client names for open accounts are listed using middle name abbreviations, such as “John A Wright. To make this information more clear, Maria decides to capitalize every client name in her database, such as “John Andrew Wright” By formatting name information in this way, Maria’s database will be kept duplicate-free and information retrieval will be more accurate.
Please be aware that Indeed is not connected to any of the organizations mentioned in this article.
What is data normalization and why normalization is so important?
In order to reduce data redundancy and boost data integrity, a relational database is structured according to a number of so-called normal forms in a process known as database normalization. In plainer language, normalization ensures that all of your data appears and reads uniformly across all records.
Why is it necessary to normalize data before analyzing?
Due to the possibility that different variables in your data set have varying units of measurement For instance, one could be the cost, another the production in numbers, another the percentage interest in the purchase, etc. To obtain a reasonable correlation analysis among all such variables, data must be normalized.