Here’s a revised version of your article with improved grammar while maintaining the original structure: Learn about database normalization, a crucial procedure in data collection that reduces redundancy and boosts integrity. Discover how to efficiently collapse tables to maximize your database work.Within the field of information technology, effective and structured data collection via various automated systems, such as CRM, is one of the key components in enhancing efficient business operations.
A system that can automatically process this data is not affected by inconsistent or erroneous data, nor by data redundancy.Effective data collection processes require sound data management procedures. One of the techniques used to achieve this goal is database normalization. Normalization of data serves the purpose of producing non-redundant and well-structured data that can be easily examined, analyzed, and modified in order to provide data that is accurate and consistent.
Most of us have probably encountered this concept, but we don’t fully understand what normalization is and how important it is in data handling. This article will discuss database normalization in detail, including its concepts, goals, and benefits that result from its application.
Definition of Database Normalization
Database normalization is the process of organizing data in a database to reduce redundancy and enhance data integrity. The processes involved in database normalization include cleaning or arranging data to create well-structured information in accordance with established standards. In the normalization process, tables in the database are arranged so that each table contains only information directly related to a specific entity.Normalization is carried out by breaking down large tables into smaller tables, thereby reducing data duplication and facilitating maintenance. This process also aims to ensure that the data stored in these tables can be easily accessed, modified, and updated without causing inconsistencies.The purposes of database normalization include:
- Eliminating Data Redundancy
- Reducing Data Complexity
- Simplifying the data analysis and organization process
Stages of Database Normalization
In a database, there are several levels of normalization stages. The stages in data normalization generally consist of 1NF to 5NF.
1NF
First Normal Form (1NF): A table is said to be in the first normal form or 1NF if it meets the following criteria:
- It does not have multi-value attributes (there are no repeating groups of data).
- Each cell contains only one unique single value.
At this level, tables containing repeating elements are broken down into smaller tables.
| NIM | Name | Address | Postal Code | Course Content | SKS | Value |
| 1234 | elin | Ciganitri | 40240 | User Experience | 4 | 85 |
| 1235 | budi | Buah Batu | 40257 | Accounting | 4 | 86 |
| 1236 | bagus | Buah Batu | 40257 | Database | 2 | 82 |
| 1237 | ara | Buah Batu | 40257 | Database | 2 | 81 |
| 1238 | cimut | Buah Batu | 40257 | Programmer | 3 | 80 |
| 1239 | citra | Cimahi | 40260 | Programmer | 3 | 80 |
2NF
A table is in the second normal form if it meets the requirements of 1NF and all non-key attributes depend on the primary key. At this level, all attributes that are not directly related to the primary key are moved to another table. Here are some requirements that must be met for the 2NF stage in database normalization:
- All attributes must depend on the primary key.
- If partial dependency is found, then the attribute must be separated into another table and should be supported by a foreign key.
Eliminate partial dependence in the following way:
- Separate the student ID (NIM) into a new table, the Student Table, which contains information on Name, Faculty, and Study Program.
- Separate the course codes into a new table, the Course Table, which contains information about the Courses and Credits.
- The primary key is the Student ID (NIM) and the Course Code.
- The remaining attributes and the primary key become a new table, the Score Table.
Student Table
| NIM | Name | Address | Postal Code |
| 1234 | elin | Ciganitri | 40240 |
| 1235 | budi | Buah Batu | 40257 |
| 1236 | bagus | Buah Batu | 40257 |
| 1237 | ara | Buah batu | 40257 |
| 1238 | cimut | Buah batu | 40257 |
| 1239 | citra | Cimahi | 40260 |
Course Table
| Course Code | Course | SKS |
| M001 | Usee Experience | 4 |
| M002 | Accounting | 4 |
| M003 | Database | 2 |
| M004 | Programmer | 3 |
Value Table
| NIM | Course Code | Value |
| 1234 | M001 | 85 |
| 1235 | M002 | 86 |
| 1236 | M003 | 82 |
| 1237 | M003 | 81 |
| 1238 | M004 | 80 |
| 1239 | M004 | 80 |
3NF
A table is in the third normal form when all non-key attributes that depend on other non-key attributes are moved to another table, ensuring that all non-key attributes depend directly on the primary key. Here are some requirements that must be met for the 3NF stage in database normalization:
- There is no transitive dependency (i.e., non-key attributes depend on other key attributes).
- Eliminate the following transitive dependency:
- Separate the study programs and faculties into a new table, the Study Program Table, which contains information about the Study Program and Faculty. In this case, the non-key attribute is the Study Program, which will form the new table and become the Primary Key.
Student Table
| NIM | Name | |
| 1234 | elin | |
| 1235 | budi | |
| 1236 | bagus | |
| 1237 | ara | |
| 1238 | cimut | |
| 1239 | citra |
Postal Code Table
| Postal Code | Address |
| 40240 | Ciganitri |
| 40257 | Buah Batu |
| 40260 | Cimahi |
Tabel Mata Kuliah
| NIM | Course | SKS |
| M001 | Usee Experience | 4 |
| M002 | Accounting | 4 |
| M003 | Database | 2 |
| M004 | Programmer | 3 |
Tabel Nilai
| NIM | Course Code | Value |
| 1234 | M001 | 85 |
| 1235 | M002 | 86 |
| 1236 | M003 | 82 |
| 1237 | M003 | 81 |
| 1238 | M004 | 80 |
| 1239 | M004 | 80 |
BCNF
Boyce-Codd Normal Form (BCNF): The Boyce-Codd Normal Form is an enhancement of 3NF where a table must meet 3NF and every determinant is a super key. (superkey).
4NF
Fourth Normal Form (4NF): A table is in the fourth normal form if it meets BCNF and has no multi-valued dependencies. At this level, tables with multi-valued dependencies are broken down into smaller tables to eliminate anomalies.
5NF
Fifth Normal Form (5NF): The fifth normal form relates to join dependencies. A table is in the fifth normal form if it meets 4NF and all existing join dependencies have been resolved.
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The Purpose of Database Normalization
There are several main objectives of database normalization that make it an important part of good database design:
Reducing Data Redundancy
One of the main goals of normalization is to reduce data duplication. By breaking down large tables into smaller tables, we can minimize unnecessary data repetition. This helps save storage space and improves data management efficiency.
Improving Data Integrity
Normalization helps maintain the consistency and accuracy of data. By organizing data into structured tables, the risk of data anomalies, such as unsynchronized or conflicting data, can be minimized.
Facilitating Data Maintenance
A normalized database is easier to maintain because the data is organized neatly and structured. When there are changes or updates to the data, corrections can be made more quickly and accurately without affecting the entire database.
Improving Query Performance
With efficiently organized data, the performance of queries or data requests can be improved. Normalization allows databases to retrieve and process data more quickly, especially when dealing with large amounts of data.
Testing Data Normality and Its Relation to Database Normalization
One term that is often associated with database normalization is the data normality test. The data normality test is a statistical procedure used to determine whether the distribution of data in a dataset follows a normal distribution. Although these terms are similar, the data normality test is different from database normalization. Normality tests are more commonly used in statistical analysis; this type of analysis can be assisted by statistical tools such as SPSS.
Meanwhile, database normalization focuses on organizing the data structure within database tables.Nevertheless, these two concepts are interconnected in data management. In some cases, well-normalized data in a database can facilitate the process of normality testing, as well-structured data is easier to analyze.
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Benefits of Database Normalization
The application of database normalization in data management provides a number of significant benefits. Some of the main benefits of normalization are:
Space-Saving Storage
By reducing data redundancy, normalization helps save storage space. Unnecessary duplicated data will be eliminated, making the overall size of the database smaller.
Easier Data Management
A normalized database is easier to manage because the data is organized neatly and structured. Maintenance, updates, and deletions can be carried out more efficiently and with a lower risk of data inconsistency.
Improving the Speed and Efficiency of Queries
A normalized database allows queries or data requests to be executed more quickly and efficiently. This is especially important when dealing with large amounts of data, where query performance can affect data access speed.
Preventing Data Anomalies
Normalization helps prevent the occurrence of data anomalies, such as inconsistent or contradictory data. By organizing data into structured tables, the risk of data inconsistency can be reduced.
Facilitating Database Structure Changes
When there are changes in the database structure, such as the addition of new attributes or changes in relationships between tables, a normalized database is easier to adjust without disrupting the entire system.
Challenges in Database Normalization Implementation
Although database normalization has many benefits, there are also several challenges that need to be addressed in its implementation. One of the main challenges is the increased complexity that comes with higher levels of normalization. The higher the level of normalization, the more tables need to be managed, which can complicate the maintenance and management of the database.In addition, in some cases, overly strict normalization can lead to a decrease in query performance. This happens because data spread across multiple tables requires more complex joins or table merging to obtain the necessary information. Therefore, a balance between normalization and denormalization is necessary to ensure that database performance remains optimal.
Conclusion
Database normalization is one of the most important techniques in designing efficient and structured databases. By reducing data redundancy, enhancing integrity, and facilitating maintenance, normalization plays a significant role in ensuring that the data stored in the database can be managed effectively. Although implementing normalization can be complex and presents its own challenges, the benefits gained from its application far outweigh these difficulties, especially in long-term data management.Through normalization, databases can be designed better, making stored data more organized, easily accessible, and reliable. Thus, normalization becomes one of the crucial steps in supporting effective data management in various modern information systems.
Reference
Kidd, C. (2024, April 18). Data & database normalization explained: How to normalize data. Splunk. https://splunk.com/en_us/blog/learn/data-normalization.html
Gustiana, I. (n.d.). Normalisasi data [PowerPoint slides]. UNIKOM.
Penulis : Meilina Eka A
