Which of the following is an example of data enhancement?

Which of the following is an example of data enhancement?

• A. Implementing referential integrity checks
• B. Adding auditing and reference vocabularies to a data set
• C. Correcting coding inaccuracies
• D. Identifying stale business rules

Answer: (B)

Data enhancement refers to the processes involved in improving the quality and context of data by augmenting it with additional information, which ultimately increases its usability and value for analysis and decision-making. The choice that represents data enhancement is the addition of auditing and reference vocabularies to a data set. This act expands the dataset by integrating structured vocabularies and standards that can provide context, enhance metadata, and enable better data interpretation and analysis. It enriches the existing data, making it more informative and operationally relevant for users. In contrast, implementing referential integrity checks focuses on ensuring that relationships between data entities in a database adhere to certain rules. While this is crucial for maintaining data quality, it does not directly enhance the data itself. Correcting coding inaccuracies addresses flaws or errors in the data but does not involve adding any new elements to enrich it. Identifying stale business rules involves recognizing outdated processes or rules that might affect data usage, but it does not involve enhancing the dataset with additional value or context.

Data enhancement refers to the processes involved in improving the quality and context of data by augmenting it with additional information, which ultimately increases its usability and value for analysis and decision-making.

The choice that represents data enhancement is the addition of auditing and reference vocabularies to a data set. This act expands the dataset by integrating structured vocabularies and standards that can provide context, enhance metadata, and enable better data interpretation and analysis. It enriches the existing data, making it more informative and operationally relevant for users.

In contrast, implementing referential integrity checks focuses on ensuring that relationships between data entities in a database adhere to certain rules. While this is crucial for maintaining data quality, it does not directly enhance the data itself. Correcting coding inaccuracies addresses flaws or errors in the data but does not involve adding any new elements to enrich it. Identifying stale business rules involves recognizing outdated processes or rules that might affect data usage, but it does not involve enhancing the dataset with additional value or context.