“Can you clean up this spreadsheet?” is one of those requests that sounds small and turns into a weekend. After enough of them, I built a repeatable process — and it runs on free tools.
First, define “clean”
Clean for what? A file ready for analysis is different from one ready for import into a CRM. Before touching anything, I ask what the output needs to do. Half of data work is agreeing on the definition of done.
Convert into a format you can reason about
Data arrives in every shape: JSON exports, nested fields, inconsistent CSVs. A free JSON to CSV converter flattens structured data into rows I can actually inspect and edit. Doing this in the browser means no uploading someone’s customer list to a service.
Standardize before you spot-check
- Dates → one format, always.
- Names → consistent capitalization, trimmed whitespace.
- Categories → controlled vocabulary, no “USA / U.S. / United States.”
- Empty cells → decide a convention and apply it everywhere.
Then verify, in both directions
Spot-check random rows against the source, and reconcile totals. If a column should sum to a known number, make it sum. This is the step clients remember when they audit your work.
Summarize what you changed
I hand back a short changelog, not just the file. A free summary generator helps turn my rough notes into a clean handover doc. Clients trust work they can understand.
The honest truth about data work
The tools are easy; the judgment is hard. Free converters handle the format mechanics. Deciding what “correct” means — and proving it — is the actual service. That’s why it’s still worth paying a human for.
The converter and summary tool are both free and browser-based, part of the free AI tools set.
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