Cleaning your own file for your own analysis is a low stakes activity. Cleaning a file other people will build on is a different job, and these are the practices that make the difference.
1. Never edit the original
Work on a copy and keep the source file exactly as it arrived, including its filename and date. It is the only reference point you have when a result is questioned, and it costs nothing to keep.
2. Agree what a duplicate is before you remove any
Is one person at two companies one record or two? Is a franchise location a separate account? Two people will answer differently, and the answer changes the row count that everyone downstream builds on. Write the rule down in one sentence before the first merge.
3. Profile before you fix
Look at row counts, blank counts and distinct values per column first. Ten minutes of profiling tells you which columns are actually broken and stops you spending an afternoon on a column nobody uses.
4. Normalize before you match
Trimming and case folding will collapse a large share of duplicates on their own, and they make everything the matcher does afterwards more accurate. The full sequence is in data cleaning techniques.
5. Keep an audit log, and keep it with the file
Every change, the original value, the new value, the rule and who approved it. Store it beside the cleaned file rather than inside a tool, because the question always arrives after the tool session has closed.
This is the practice most teams skip and the one that pays off hardest, usually about three weeks later in a meeting about why a number moved.
6. Fix it upstream when you can
If the same problem shows up in every export, the export is the problem. A form without validation, an import run twice, a field people fill in freehand. Cleaning the same defect every month is a maintenance contract you never signed.
7. Clean on a schedule, not in a panic
A file cleaned before every send stays small enough to review by eye. A file cleaned once a year, the week before a migration, is a project. Regular passes also mean the rules stay familiar, so the review is fast.
If you want the review step built in rather than bolted on, that is what this data cleaning tool is: every fix is a line you approve, and the audit log comes out as its own file. Teams evaluating options can start with data quality tools.