Which method is considered essential for achieving data quality in database management?

Data normalization is a fundamental method in achieving data quality in database management. This process involves organizing the data in a way that reduces redundancy and dependency by dividing the data into related tables, ensuring that each piece of information is stored only once.

By following normalization principles, such as ensuring that each table has a primary key and that relationships between tables are well-defined through foreign keys, data integrity is enhanced. This structured approach prevents issues such as data anomalies, inconsistencies, and corruption, all of which can significantly impact the reliability of the database.

The other methods mentioned, such as data duplication, could lead to inconsistencies and increase the risk of errors since it involves storing the same data multiple times. Data isolation generally refers to the separation of different datasets which can hinder the integration necessary for comprehensive analysis. Data fragmentation, while it may improve performance in certain contexts, does not inherently contribute to data quality and can complicate data recovery and consistency.

Overall, data normalization is vital for maintaining a high standard of data quality, which is crucial for effective database management and ensuring accurate data retrieval and analysis.