What is the main focus of Reference Data Management?

What is the main focus of Reference Data Management?

• A. Controlling data volume
• B. Control over defined domain values and their definitions
• C. Data archival processes
• D. Data analytics optimization

Answer: (B)

The main focus of Reference Data Management is indeed centered on controlling defined domain values and their definitions. Reference data refers to the set of permissible values for a given data element, which helps maintain consistency across different databases and applications. This involves managing the codes, categories, and classifications that are used to ensure that data is standardized and can be universally understood and used within an organization. By controlling these domain values and their definitions, organizations can improve data interoperability, reduce data redundancy, and ensure that data is used accurately across various processes. This management practice is crucial for data quality and governance, as it helps to prevent discrepancies that might arise from using different interpretations of the same terms or values. The other options, while they touch on important aspects of data management, do not capture the specific aim of Reference Data Management as effectively. For instance, controlling data volume is more associated with data storage and management practices rather than defining standardized value sets. Data archival processes deal with the preservation of data for long-term storage and retrieval, while data analytics optimization focuses on improving the performance and outcome of analytical processes. None of these directly address the importance of managing and standardizing domain values, which is the essence of Reference Data Management.

The main focus of Reference Data Management is indeed centered on controlling defined domain values and their definitions. Reference data refers to the set of permissible values for a given data element, which helps maintain consistency across different databases and applications. This involves managing the codes, categories, and classifications that are used to ensure that data is standardized and can be universally understood and used within an organization.

By controlling these domain values and their definitions, organizations can improve data interoperability, reduce data redundancy, and ensure that data is used accurately across various processes. This management practice is crucial for data quality and governance, as it helps to prevent discrepancies that might arise from using different interpretations of the same terms or values.

The other options, while they touch on important aspects of data management, do not capture the specific aim of Reference Data Management as effectively. For instance, controlling data volume is more associated with data storage and management practices rather than defining standardized value sets. Data archival processes deal with the preservation of data for long-term storage and retrieval, while data analytics optimization focuses on improving the performance and outcome of analytical processes. None of these directly address the importance of managing and standardizing domain values, which is the essence of Reference Data Management.