To unpivot monthly columns, keep the identifying fields and move each month heading into a new name column beside its value. Dataset Unpivot Tool reshapes a wide table into a long table, which can make repeated measurements easier to group or check.
Identify what stays attached to each value
Suppose an illustrative table has region, jan and feb columns, with EU/10/20 and US/30/empty as its two rows. Region is the identifier. Jan and feb are the value columns. Name the new fields month and amount.
With empty-value removal on, the long result contains EU/jan/10, EU/feb/20 and US/jan/30. The empty US February cell produces no row. With removal off, retain that empty observation deliberately instead of assuming it represents zero.
Choose the identifier and value columns separately. A column cannot belong to both groups. The new name and value headings must differ from one another and from the retained identifier headings.
Check row expansion before transforming a large table
Two source rows with two selected value columns can create four result rows before empty values are dropped. A table with many month columns can therefore grow substantially even when the original row count looks small.
Estimate the expansion by multiplying the source rows by the selected value columns, then consider the empty-value policy. The tool has a separate result-row and cell cap, so narrowing the selected columns may be necessary for a very wide source.
Verify one source record across all its output rows. Its identifier should stay unchanged, each selected heading should appear in the name field and each value should come from the corresponding source cell.
Preserve the distinction between blank and zero
An explicit zero is a value and should remain attached to its month. A blank may mean missing, not collected or inapplicable. Choose the drop-empty option according to that meaning and record the choice when sharing the transformed table.
Data Quality Rule Checker can check required identifiers or expected value patterns in the reshaped table. It checks the rules you enter rather than deciding what a blank means in your business process.
If you later summarize the long table with Pivot Table Builder, use the same identifiers and month field, and consider whether aggregation could merge duplicate observations. A reshape is not a guarantee that pivoting back will reproduce every source detail.
Inspect the full download, especially the final record. Keep the source copy and the selected column plan so the transformation remains reversible in your workflow even when omitted blanks cannot be inferred from the long table alone.