What does data duplication signify in a data management context?

What does data duplication signify in a data management context?

• A. The presence of multiple copies of the same data
• B. The absence of necessary attributes in data records
• C. The failure to comply with legal data requirements
• D. The inconsistencies in data processing workflows

Answer: (A)

Data duplication in a data management context refers to the presence of multiple copies of the same data. This situation occurs when the same piece of information exists in more than one location within a database or across different systems. Such duplication can lead to several challenges, including increased storage costs, potential confusion for users, and discrepancies in reporting if different copies of the same data are updated independently. The presence of multiple copies can hinder data integrity and processing efficiency since managing and updating several copies becomes cumbersome. Additionally, it can complicate data governance and compliance efforts, as organizations strive to maintain a single version of the truth. Recognizing and addressing data duplication is vital for ensuring the quality and reliability of data management practices. Other options presented do not accurately capture the essence of data duplication. Having missing attributes deals more with data completeness, compliance issues relate to regulatory adherence, and inconsistencies in workflows pertain to operational challenges rather than duplication of data itself.

Data duplication in a data management context refers to the presence of multiple copies of the same data. This situation occurs when the same piece of information exists in more than one location within a database or across different systems. Such duplication can lead to several challenges, including increased storage costs, potential confusion for users, and discrepancies in reporting if different copies of the same data are updated independently.

The presence of multiple copies can hinder data integrity and processing efficiency since managing and updating several copies becomes cumbersome. Additionally, it can complicate data governance and compliance efforts, as organizations strive to maintain a single version of the truth. Recognizing and addressing data duplication is vital for ensuring the quality and reliability of data management practices.

Other options presented do not accurately capture the essence of data duplication. Having missing attributes deals more with data completeness, compliance issues relate to regulatory adherence, and inconsistencies in workflows pertain to operational challenges rather than duplication of data itself.