Integrity and D. Consistency. This is because integrity and consistency are two of the best reasons to
validate data for quality control purposes, which means to check and ensure that the data is
accurate, complete, reliable, and usable for the intended analysis or purpose. By validating data for
integrity and consistency, the analyst can prevent or correct any errors or issues in the data that
could affect the validity or reliability of the analysis or the results. Here is what integrity and
consistency mean in terms of data quality:
Integrity refers to the completeness and validity of the data, which means that the data has no
missing, incomplete, or invalid values that could compromise its meaning or usefulness. For
example, validating data for integrity could involve checking for null values, outliers, or incorrect data
types in the data set.
Consistency refers to the uniformity and standardization of the data, which means that the data
follows a common format, structure, or rule across different sources or systems. For example,
validating data for consistency could involve checking for spelling, punctuation, or capitalization
errors in the data set.
The other reasons are not the best reasons to validate data for quality control purposes. Here is why:
Retention refers to the storage and preservation of the data, which means that the data is kept and
maintained in a secure and accessible way for future use or reference. Retention does not need to be
validated for quality control purposes, because it does not affect the accuracy or reliability of the
data itself.
Transmission refers to the transfer and exchange of the data, which means that the data is moved or
shared between different sources or systems in a fast and efficient way. Transmission does not need
to be validated for quality control purposes, because it does not affect the completeness or validity
of the data itself.
Encryption refers to the protection and security of the data, which means that the data is encoded or
scrambled in a way that prevents unauthorized access or use. Encryption does not need to be
validated for quality control purposes, because it does not affect the uniformity or standardization of
the data itself.
Deletion refers to the removal and disposal of the data, which means that the data is erased or
destroyed in a way that prevents recovery or retrieval. Deletion does not need to be validated for
quality control purposes, because it does not affect the meaning or usefulness of the data itself.
