What is the importance of data normalization in data analytics?

Data normalization is a crucial process in data analytics for several reasons, as it helps improve the quality, consistency, and efficiency of data analysis. The importance of data normalization includes:

1. Eliminating Redundancy

• Prevents data duplication: Normalization organizes data into tables and relationships, reducing the risk of storing the same data in multiple places.
• Saves storage space: By removing redundant data, normalization ensures that datasets remain compact, saving storage space and reducing maintenance complexity.

2. Enhancing Data Integrity

• Ensures data accuracy: Normalization enforces rules (such as referential integrity) that ensure data consistency, avoiding issues like conflicting or outdated information.
• Prevents anomalies: It reduces the risk of insertion, update, and deletion anomalies, which can lead to incomplete or erroneous data.

3. Improving Query Performance

• Efficient querying: Normalized data is structured in a way that makes querying more efficient, especially in relational databases. Smaller, more organized tables allow for quicker lookups and data retrieval.
• Faster analytics: Normalized data reduces the computational overhead during complex analytics processes, as less redundant data needs to be processed.

4. Facilitating Data Relationships

• Creates logical data structure: Data normalization breaks data into logical groups and defines relationships between them, which simplifies analysis and enables clearer insights.
• Improves scalability: When datasets grow, normalized structures make it easier to scale, as it simplifies table extensions and modifications without affecting the overall system.

5. Data Consistency Across Systems

• Supports integration: Normalized data is easier to integrate with other systems or databases. This is especially important when working with distributed databases or when merging data from multiple sources.
• Avoids data conflicts: By ensuring that the same data is stored only once, normalization minimizes discrepancies when data is modified, ensuring consistent values across systems.