How do you support data quality and governance with data normalization?

Data Normalization is the technique of dividing the data into multiple data to reduce redundancy and achieve data integrity. You need to have a data modeling skills and understanding of data domain.

Data normalization is one of the way to make data read easy and more structured. It helps you organize data entries to ensure they appear similar across all fields and records, making information easier to find, group and analyze.

Data normalization consists of modeling data in a way to reduce the abnormality. For this purpose you may need to do so many insert, update, and delete manually, to shape your data in an integrated form. One of my main issues in this type of effort is making a decision for a really messy column. For example, a column that consist of a hundred unique data but with a little compromise you can summarize this column to ten unique value which will give you a better big picture when you are looking at your data.

Data normalization plays a vital role in supporting data quality and governance by structuring and standardizing data consistently Here's how: Consistency: Data normalization reduces redundancy, ensuring uniform and consistent data values Accuracy: Eliminating duplicates and inconsistencies improves data accuracy and reliability Efficiency: Normalized data is easier to update and maintain, reducing the risk of errors Data Integrity: It enforces referential integrity, preventing data anomalies and maintaining data quality Compliance: Normalization aligns data with regulatory and governance standards, ensuring compliance with data policies and regulations Data normalization is a fundamental technique that enhances data quality and governance.

Data Normalization is a process of modeling data from a denormalized one-big table to multiple tables and linked via primary keys and foreign keys to establish reference between tables.

This topic quite challenging as platforms like GCP are prepared to enable queries on larger tables without normalization. However, normalized data allows you to enable data integrity (enforce relationship between data assets), data consistency (data is what it is supposed to be throughout your data ecosystem) and easier maintenance processes (changes are centralized). This is also one of the reasons why Reference Data Management is quite a big deal in larger organizations taking the leap into Data Driven objectives, you will need to enable a solid reference data strategy to support your normalized data.

Data normalization is crucial because it ensures data accuracy, reduces redundancy, saves storage space, and improves query performance. It maintains data integrity, simplifies maintenance and application design, enhances data security, and supports scalability. Essentially, normalization makes data organized, efficient, and reliable, facilitating better decision-making and analysis.

A major benefit of normalized data is flexibility. In the rapidly changing world of data tools and business requirements, normalization can make it easier to adapt to change. When data is organized in a structured and normalized way, it's typically more flexible and adaptable to evolving needs.

Data normalization is crucial for supporting data quality and governance by: - Ensuring consistent, accurate data without redundancies or inconsistencies. - Minimizing errors, providing reliable and trustworthy information. - Establishing structured rules that align with data standards and governance practices. - Preventing anomalies such as data redundancy and update issues, ensuring data integrity. - Enabling informed decision-making by offering reliable, consistent data. - Organizing data logically for efficient storage, retrieval, and analysis. - Serving as the foundation for maintaining high-quality data essential for effective governance practices.

To implement data normalization, first, identify the database tables and their relationships. Then, apply normalization rules (such as 1NF, 2NF, 3NF) to eliminate redundancies and inconsistencies. Break down large tables into smaller ones and establish clear relationships using primary and foreign keys. Ensure each piece of data is stored in only one place to prevent redundancy. Regularly review and adjust the database schema as the data evolves, maintaining the normalized structure. Utilize database management tools that often have built-in features for normalization. Testing the database structure with real data and refining the design based on performance and efficiency is also crucial.

In a usual setup, source systems would have multiple tables as sources. However, it would not necessarily follow the structure of how you would want to group your data - OLTP vs OLAP. That's when one would consider building a denormalized table, before applying data normalization.

My first step would be analyzing the data set, use cases, and the future state or scalability. There are normalization Trade-offs and it's not always the best choice in every situation. Make sure it is worth the increased complexity in queries or potential performance issues, and when denormalization might be a more suitable approach. You will see this more often the closer the data sits to Reporting and Analytics. In these environments, data is structured to simplify complex analytical queries and aggregations, which is more efficient for reporting purposes.

To implement data normalization for data quality and governance: - Identify distinct data entities in the database and their relationships. - Apply normalization forms to organize data, reducing redundancy and dependency. - Break down data into smaller, related tables to prevent duplication. - Maintain consistency by adhering to normalization rules, preventing anomalies. - Validate and optimize the normalized structure for storage and query efficiency. - Align normalization practices with governance policies and standards. - Document the normalized structure and provide user training for better understanding and uniform data handling.

From a technical perspective, one of the most significant benefits of data normalization is the reduction of redundancy and the elimination of the risks of inconsistencies. Normalization promotes efficient data retrieval and query performance by breaking down large tables into smaller ones. This reduces the amount of data that needs to be processed during queries, resulting in faster response times. It also facilitates indexing and improves the overall database structure, optimizing query execution. On the flip side, data normalization can present challenges. The process often requires a deeper understanding of the data and complex schema design.

Data normalization ensures accurate and reliable data by eliminating redundancies and inconsistencies. It reduces storage space, enhances query performance, and simplifies data maintenance. Normalized data supports efficient application design, improves data security, and facilitates scalability, providing a solid foundation for reliable decision-making and analysis.

Benefit of normalization is lessening data redundancy. That results to saving storage. However, with the rise of virtually unlimited storage space, some organizations decide to not apply data normalization. Most users with basic knowledge of SQL would prefer to build a short select statement rather than joining tables without considering the impact to performance.

Benefits of Data Normalization: Data normalization maintains consistency by eliminating redundancy, ensuring accuracy and reliability. It enhances data quality, reducing errors and inconsistencies. By optimizing storage, it minimizes redundant data, saving space. Moreover, normalization enhances query performance, enabling faster data retrieval and analysis. Challenges of Data Normalization: Normalization may result in intricate database structures, potentially impacting query speed. It might introduce complexities during updates, leading to anomalies. Users might find it challenging to comprehend complex data relationships, requiring additional effort for understanding and utilizing the data effectively.

Balancing data normalization and denormalization involves a careful evaluation of specific business requirements and query patterns. Normalize data to ensure accuracy, consistency, and efficiency in most operations. However, for frequently executed complex queries, consider denormalization.

Assess Needs: Understand system requirements for data access, modification, and performance Start with Normalization: Ensure data consistency, minimize redundancy, and maintain integrity Monitor Performance: Identify where normalized structures impact query efficiency Strategic Denormalization: Apply denormalization selectively to critical areas affecting performance Implement Caching: Use caching mechanisms to improve query speed, reducing denormalization needs Regular Evaluation: Continuously assess system performance and user requirements to adjust normalization or denormalization levels Hybrid Approaches: Consider hybrid models combining both strategies for optimized data management without compromising integrity or performance

In general, it's advisable to normalize your base data layer, such as a Data Warehouse, as extensively as performance considerations permit. This approach can entail creating a semantic layer for analytics and reporting. By doing so, you lay the groundwork for a flexible infrastructure that can readily adapt to future requirements. Updating the semantic layer is typically a more efficient process than making substantial changes at the warehouse level, which could have cascading effects on every upstream application. Regardless of your decision, comprehensive documentation is a crucial element to ensure clarity and maintain the integrity of your data environment.