This Reddit user has put their finger on a problem that should be infuriating, not routine. When a single column contains numbers, text, currency symbols, and stray notes, the spreadsheet has already failed you. The software is asking you to become its janitor before you can be its analyst, and that is a broken promise.
What this user describes is the silent tax of working with data. You export a file, it looks clean to the eye, but the moment you ask it to do real work, sort, filter, calculate, the whole thing collapses. The column is not uniform. It is a mess dressed up in rows and cells. And the tool you are using, rather than helping you fix it, demands that you diagnose and repair every inconsistency yourself. That is not a feature of a mature tool. That is a legacy limitation that we have all accepted for far too long.
The practical cost here is not just the time spent cleaning data manually. It is the friction that stops people from doing the analysis they actually wanted to do. You see a problem, you open a file, you try to run a simple calculation, and you hit a wall. You then spend fifteen minutes converting text to numbers, stripping symbols, and deleting notes. By the time the column is clean, you have forgotten what question you were trying to answer. The tool has shifted your focus from insight to maintenance.
This is where smarter AI tools change the equation. The goal should not be to make manual cleaning faster. The goal is to make it unnecessary. A column of mixed types is not a puzzle for the user to solve. It is a signal that the tool should recognize and act on. Normalization, converting everything in a column to a consistent, usable format, should happen automatically, in the background, before you ever click sort. The user should not have to know how it works. They should just see a column that works.
The practical point is this: do not accept a tool that treats dirty data as your problem. If you find yourself manually stripping dollar signs or reformatting dates every time you open a file, you are not doing data work. You are doing data repair. The technology exists to handle this automatically, and it should be the baseline, not the premium feature. Demand a tool that meets your data where it is, not one that expects you to meet it in a perfectly clean state.