Splitting a text field can populate several neighboring columns. Preserve the original and clear a deliberate output area before applying the operation to a working dataset.
Identify the actual separator
Inspect several records, including ones with missing fields. A comma, tab, pipe, or multicharacter token can separate fields, but punctuation inside a name or address may be part of the field itself.
For a simple delimiter-separated column, use Data > Split text to columns and choose the separator. Automatic detection is convenient, but check its result rather than assuming it inferred your schema correctly.
Use a formula when retaining the source
A helper formula such as =SPLIT(A2,"|",FALSE,FALSE) separates pipe-delimited text while retaining empty fields. The final FALSE matters when consecutive delimiters represent a missing field.
For multicharacter delimiters, the split_by_each argument controls whether each delimiter character is treated separately. Test that setting explicitly. SPLIT is not a complete parser for every quoted CSV structure, so use a suitable file import for complex CSV data.
Reconcile the resulting columns
Compare the number and meaning of fields in each sample. Check consecutive separators, leading or trailing separators, and values containing punctuation. Ensure no neighboring formulas or records were overwritten.
Keep raw text until the split has been verified and column types are set appropriately. A postal code may need text storage while an amount needs numeric interpretation. Do not treat a successful visual split as proof that dates, identifiers, and decimal values retained their original meaning. Define how malformed records will be reviewed before repeating the cleanup on a larger import.
References
Reviewed against the linked official product guidance on November 3, 2026. Features, limits, screen labels, and access can vary by account, platform, region, and organization policy.