Filter on any of the responses that do not say “January” and update them to “January”. This is

because filtering and updating are data cleansing techniques that can be used to ensure data

consistency, which means that the data is uniform and follows a standard format. By filtering on any

of the responses that do not say “January” and updating them to “January”, the analyst can make

sure that all the responses for the month of January are written in the same way. The other steps

arenot appropriate for ensuring data consistency. Here is why:

Deleting any of the responses that do not have “January” written out would result in data loss, which

means that some information would be missing from the data set. This could affect the accuracy and

reliability of the analysis.

Replacing any of the responses that have “01” would not solve the problem of data inconsistency,

because there would still be two different ways of writing the month of January: “Jan” and “January”.

This could cause confusion and errors in the analysis.

Sorting any of the responses that say “Jan” and updating them to “01” would also not solve the

problem of data inconsistency, because there would still be two different ways of writing the month

of January: “01” and “January”. This could also cause confusion and errors in the analysis.