Half of audit work is cleaning data nobody cleaned

Someone asked what Alteryx is actually useful for in audit. One reply cut through everything:

Cleaning garbage data is about 50% of our job, so that itself is super useful.

That’s not specific to Alteryx. It’s not specific to audit.

Every data pipeline I’ve worked on — invoice processing, bank reconciliation, reporting automation — has a cleaning step that takes longer than anyone planned for. Extra spaces in vendor names. Date formats that change depending on who exported the file. Amounts in one column for some rows, split into debit and credit for others. A reference field that’s consistent 90% of the time and creative the other 10%.

The tools that help most are the ones that make data cleaning repeatable. Power Query for Excel-based work. Python for more complex transformations. Alteryx when the data is large and the logic is complicated enough to warrant a visual pipeline.

The thing audit teams use these for — joining a population to the trial balance to test completeness, filtering journal entries for high-risk fraud patterns, pulling a fixed asset rollforward from a subledger — none of that is magic. It’s structured data manipulation applied to questions auditors already know how to ask.

The barrier was never the question. It was the cleaning step before you could ask it.

That step is automatable. And when it is, the actual work — the judgment, the interpretation, the professional conclusions — gets more of the time it deserves.

The data is almost always messier than expected.

Build the cleaning layer first.