Data cleaning isn't glamorous, but it is where real analysis begins. When a beginner like UndercoverRadish dives into a mixed-format date column and emerges with a working Power Query process, that's not just a small win, it's the foundation of trustworthy analytics. Too often we see people stop letting data problems masquerade as pivot table problems, only to discover the real issue was hidden in inconsistent formatting. Radish's question about whether their solution actually handled `yyyy/mm/dd` correctly is exactly the kind of scrutiny that separates a one-off fix from a repeatable system.
The approach here is smart, even if the confidence in it is shaky. Power Query's `try...otherwise` pattern is a pragmatic way to handle ambiguous dates: try the British interpretation first, fall back to the American one. That works for most cases, but Radish is right to wonder about the `yyyy/mm/dd` entries. That format is unambiguous to a machine, year first, then month, then day, so Power Query likely parsed it correctly regardless of locale. The real test comes with entries like `08-mar-26`. That's not covered by the current logic, and Radish knows it. Adding a third `try` for `"en-GB"` with a format mask, or using `Date.From([Appointment_Date], "dd-MMM-yy")`, would close that gap. The scale question, 600 records versus 5,000, is less about the cleaning logic and more about validation. At 600, you can spot-check every ambiguous row. At 5,000, you need a repeatable audit step, like flagging rows where the day value exceeds 12, because those can only be day-first. That is the kind of defensive thinking that turns a good process into a great one.
What makes Radish's story resonate is the honesty about not fully understanding why it worked. That is not a weakness; it is the beginning of mastery. The same curiosity that drove them to ask ChatGPT for a dummy dataset is what will push them to test every edge case. We have seen this pattern before in workflows like automating individual timesheet PDFs for 60+ employees from one template, where the real breakthrough isn't the automation itself but the realization that a single, well-structured process can scale across dozens of variations. Radish is on that same path. The immediate next step is to add a validation column that flags any date where the parsed month and day could be swapped, then manually inspect those. Over time, that manual check becomes a rule, and the rule becomes a reusable function.
The concrete takeaway: your cleaning process is only as good as your ability to explain it to someone else. If you cannot describe why each step exists and what edge cases it handles, you have not finished the job. Radish's process works for most of their data, but the ambiguous `08-mar-26` entry is a reminder that "works for most" is not the same as "works reliably." The next iteration should include explicit handling for abbreviated month names and a way to log which locale was chosen for each row. That transparency is what turns a one-time cleanup into a repeatable, trusted pipeline.