Data hygiene is important in budgeting because accurate data ensures valid budgets. Which two practices help maintain data quality?

Data hygiene is important in budgeting because accurate data ensures valid budgets. Which two practices help maintain data quality?

• A. Enforcing consistent definitions across datasets
• B. Regular data cleansing
• C. Archiving old data periodically
• D. Regular data cleansing and enforcing consistent definitions

Answer: (D)

In budgeting, data quality hinges on cleaning data and making sure everyone uses the same definitions across datasets. Regular data cleansing tackles errors, duplicates, and inconsistencies in the data itself—cleaning up miskeyed figures, resolving duplicates, and addressing gaps or outliers so the numbers you rely on are trustworthy. Enforcing consistent definitions across datasets ensures that terms, categories, units of measure, time periods, and currencies mean the same thing everywhere data comes from. Without this alignment, similar numbers can be interpreted differently, causing misclassification and unreliable budgets. Together, these practices address both the accuracy of the data and its semantic consistency, which is essential for valid budgeting. Archiving old data helps with storage and performance but doesn’t directly improve the current data’s accuracy or the uniformity of definitions, so it doesn’t enhance data quality in the budgeting process by itself.

In budgeting, data quality hinges on cleaning data and making sure everyone uses the same definitions across datasets. Regular data cleansing tackles errors, duplicates, and inconsistencies in the data itself—cleaning up miskeyed figures, resolving duplicates, and addressing gaps or outliers so the numbers you rely on are trustworthy. Enforcing consistent definitions across datasets ensures that terms, categories, units of measure, time periods, and currencies mean the same thing everywhere data comes from. Without this alignment, similar numbers can be interpreted differently, causing misclassification and unreliable budgets.

Together, these practices address both the accuracy of the data and its semantic consistency, which is essential for valid budgeting. Archiving old data helps with storage and performance but doesn’t directly improve the current data’s accuracy or the uniformity of definitions, so it doesn’t enhance data quality in the budgeting process by itself.