What is Data Normalization?
Companies and any other big organizations, like goverments, often collect data form multiple sources in different formats and structures, leading to inconsistencies and redundancies. This is where data normalization comes into play.
Data normalization is the process of cleaning up and structuring collected information to make it more clear and machine-readable. The main goal is to organize data in a standardized format reducing duplicates and dependency within stored information and making it easier to interpret and use.
Normalized vs. Denormalized Data
Normalized data structures are favored for transactional systems that require strict data integrity. They follow specific rules, such as normalization forms, and save information into multiple related tables. Relationships between these tables are established through keys, such as primary and foreign keys (usually unique identificators). In contrast, denormalized data structures are often preferred for analytical systems that prioritize query speed and simplicity. Denormalized databases combine and merge information from multiple tables into a single structure, optimizing query performance and simplifying data retrieval.
1. How to Normalize Data?
The basic steps to normalize data effectively are:
1. Identify the Entities
Begin identifying the main entities or objects that need to be stored in the database. For example, in an e-commerce system, entities may include customers, products, orders, and suppliers.
2. Define Attributes
Determine the attributes or properties of each entity. For example, a customer entity may have attributes such as customer ID, name, address, and contact details.
3. Normalize Tables
Break down the data into separate tables, ensuring each table represents a single entity or concept. Set the primary key for each table, which uniquely corresponds to each record.
4. Establish Relationships
Define relationships between the tables using primary and foreign keys. For example, a customer ID in the orders table can be a foreign key referencing the customer table’s primary key.
5. Refine Normalization Levels
Ensure the normalized tables adhere to the desired normalization levels (1NF, 2NF, 3NF). Review the tables for any potential anomalies or violations of normalization principles and make necessary adjustments.
2. Types of Data Normalization
The top five data normalization forms are:
First Normal Form (1NF)
The first normal form (1NF) focuses on eliminating duplicate data and organizing it into separate tables with a unique identifier or primary key. It ensures that each column in a table contains only atomic values and that there are no repeating groups or arrays of values.
Second Normal Form (2NF)
The second normal form (2NF) builds upon 1NF addressing the issue of partial dependencies. It ensures that all non-key attributes in a table depend on the entire key, eliminating dependencies on only a part of the primary key.
Third Normal Form (3NF)
The third normal form (3NF) extends the normalization process by eliminating transitive dependencies. It ensures that non-key attributes depend only on the primary key and do not have indirect dependencies on other non-key attributes. This form helps minimize data anomalies.
Boyce-Codd Normal Form (BCNF)
The Boyce-Codd normal form (BCNF) is a stricter form of normalization that addresses all possible dependencies within a table. It eliminates any non-trivial functional dependencies on candidate keys by decomposing the table into smaller tables. BCNF ensures that each attribute in a table is functionally dependent on the entire primary key.
Fourth and Fifth Normal Forms (4NF and 5NF)
The fourth and fifth normal forms (4NF and 5NF) are advanced normalization forms that deal with multivalued dependencies and join dependencies. These forms are less commonly used compared to the previous three since they address specific situations where the data has intricate relationships.
3. Data Normalization Examples
To illustrate the process of data normalization we will progressively normalize the data using the normalization forms discussed earlier.
Example 1: Denormalized Data
In a denormalized example, the asset name, category, and tags are stored in a single table without proper separation of data elements.
Asset Table:
| Asset ID | Asset Name | Category | Tag |
| 1 | Laptop Lenovo | electronics | Laptop, Lenovo |
| 2 | Practity project | education | Practity, projects |
| 3 | office chair | furniture | office, chair |
Example 2: First Normal Form (1NF)
By separating the data into multiple tables, we achieve the first normal form, ensuring that each column contains only atomic values and there are no repeated groups.
Asset Table:
| Asset ID | Asset Name |
| 1 | Laptop Lenovo |
| 2 | Practity project |
| 3 | office chair |
Category Table:
| Asset ID | Category |
| 1 | electronics |
| 2 | education |
| 3 | furniture |
Tags Table:
| Asset ID | Tag |
| 1 | Laptop |
| 1 | Lenovo |
| 2 | Practity |
| 2 | projects |
| 3 | office |
| 3 | chair |
Example 3: Second Normal Form (2NF)
To achieve the second normal form, we separate the categories into different tables and create a relationship between the Asset and Category tables using the AssetCategory junction table.
Asset Table:
| Asset ID | Asset Name |
| 1 | Laptop Lenovo |
| 2 | Practity project |
| 3 | office chair |
Category Table:
| Category | Category_id |
| electronics | A1 |
| education | A2 |
| furniture | A3 |
AssetCategory Table:
| Asset id | Category_id |
| 1 | A1 |
| 2 | A2 |
| 3 | A3 |
Tags Table:
| Asset ID | Tag |
| 1 | Laptop, Lenovo |
| 1 | Lenovo |
| 2 | Practity |
| 2 | projects |
| 3 | office |
| 3 | chair |
Example 4: Third Normal Form (3NF)
To achieve the third normal form, we separate the tags into a separate table and create a relationship between the Asset and Tag tables using the AssetTag junction table.
Tag Table
| Tag ID | Tag |
| 1 | Laptop |
| 2 | Lenovo |
| 3 | Practity |
| 4 | projects |
| 5 | office |
| 6 | chair |
TagAsset ID
| Tag ID | Asset ID |
| 1 | 1 |
| 2 | 1 |
| 3 | 2 |
| 4 | 2 |
| 5 | 3 |
| 6 | 3 |
4. Benefits of Data Normalization
Easier Sorting and Handling of Data
Normalized data is easy to handle, facilitating the work of users, data professionals and engineers. It allows for efficient sorting, filtering, and data manipulation, making daily tasks simpler and more efficient.
Normalized data makes searching for specific terms or entities easier with shorter SQL queries. It strengthens connections between related data elements, enabling improved information retrieval and analysis. For example, with a reduced number of columns, users can view more records on a single page, enhancing visualization and facilitating data exploration.
Optimized Storage Space
As data volume continues to grow exponentially, data normalization significantly contributes to storage space optimization and save costs associated with house keeping.
Seamless Integration with Data Analysis Tools
A normalized database can be smoothly connected to data processing and analysis tools. These tools rely on accurate and standardized data to generate insights and produce correct outputs. Without data normalization, these solutions may not have accurate information to work with, leading to incorrect analysis and decision-making.
Better Quality Outputs
Clean and standardized data produces better results. Normalized data enhances the quality of outputs generated from data analysis and reporting.
5. Best Practices for Data Normalization
Analyze the Data
Understand the data model, its structure, relationships, and dependencies. This analysis helps identify the entities, attributes, and their relationships, guiding the normalization process.
Apply Normalization Forms Incrementally
It is recommended to apply the normalization forms incrementally, starting with the first normal form (1NF) and progressing to higher forms. This gradual approach allows for a systematic and manageable normalization process.
Establish Proper Relationships
Define relationships between tables using primary and foreign keys to ensure data integrity and maintain referential integrity. Properly defining relationships helps avoid data anomalies and inconsistencies.
Ensure Atomicity
Each attribute in a table should represent an atomic value. Avoid storing multiple values within a single attribute, as it violates the principles of normalization. Decompose the data into separate attributes to achieve atomicity.
Consider Performance and Scalability
While normalization improves data integrity, it can impact performance and scalability. Strike a balance between normalization and the specific requirements of your system. Denormalization techniques, such as adding calculated fields or using caching strategies, may be necessary in certain cases to enhance performance.
Document the Normalization Process
Maintain documentation of the normalization process, including the decisions made, entity-relationship diagrams, and table structures. Documentation serves as a reference for future development, maintenance, and collaboration among team members.
Validate and Verify the Normalized Data
After normalization, validate and verify the data to ensure its accuracy and consistency. Perform tests and checks to confirm that the normalized data meets the desired objectives and resolves any previous data anomalies.
Regularly Review and Update the Data Model
Data requirements may evolve over time, and new data elements may emerge. Regularly review and update the data model to accommodate changes and ensure the continued effectiveness of the normalized data.
Choose Appropriate Tools and Technologies
Select tools and technologies that support data normalization features, such as database management systems or data integration platforms. Utilize software that offers functionalities specifically designed for data normalization, simplifying the process and reducing manual efforts.
6. Key Takeaways
Data normalization is a crucial process in organizing and structuring data. It simplifies data management processes, improves search and query efficiency, and enables better decision-making. By applying normalization rules and forms, businesses can achieve a standardized data format, optimize storage space, and ensure accurate analysis and reporting.
In conclusion, data normalization is a powerful tool for businesses to streamline their data management processes, improve data quality, and make informed decisions. By embracing data normalization, organizations can unlock the full potential of their data and gain a competitive edge in today’s data-driven landscape.
Remember, data normalization is not a one-time task but an ongoing process that requires continuous monitoring and adjustment.
