In data management, what is normalization?

Normalization is indeed the process of organizing data to minimize redundancy. This technique is crucial in database design, as it helps ensure that the data is stored efficiently and consistently, reducing the likelihood of anomalies during data operations such as insertions, deletions, and updates. By dividing a larger database into smaller, more manageable pieces and defining relationships between those pieces, normalization helps to ensure that each piece of information is stored only once, which not only conserves storage space but also contributes to data integrity.

In more practical terms, normalization often involves creating tables for different entities and linking them through foreign keys. This structure allows for more robust and efficient data management since there are clear definitions of how data elements relate to one another, thus making it easier to maintain and query the data without unnecessary duplication.

The other options refer to processes that either do not address the issue of redundancy or pertain to entirely different aspects of data management. For example, optimizing data retrieval speed is related to indexing and query performance rather than the organization of data. Duplicating data is contrary to the goal of normalization, which explicitly seeks to eliminate such repetition. Additionally, converting data formats is about changing the way data is represented, which does not inherently involve organizing or structuring it for minimal redundancy