Clean blank cells in your data for a smarter pivot table.

This user's macro is a perfect illustration of the gap between what spreadsheets *can* do and what they *should* do. The problem isn't the data, it's the tool. Five thousand rows, ten minutes of waiting, and a script that hunts for blank cells one by one? That's not a workflow. It's a workaround.

The core issue here is structural. The original data has too much information crammed into column A, with award numbers, grant codes, and object codes all stacked in a single column. To make a pivot table work, the user needs those values separated into their own columns. So they wrote a macro to shift cells, create blanks, and then copy the value from the cell above into each empty space. It's clever. It's also exhausting. The macro works, but it treats the symptom, blank cells, rather than the root cause: a data layout that fights the user at every step.

What this user really needs is a tool that understands data hierarchy from the start. An AI-native spreadsheet doesn't require you to build scripts around its quirks. It can recognize that "AWD-154" is a parent category, that "GR234" and "Obj-123" are nested under it, and that the blank cells below each award label are not errors, they're implicit repetitions. Instead of forcing the user to write a loop that copies and pastes for ten minutes, the tool should simply fill those blanks intelligently, in seconds. The pivot table would then see a clean, labeled table without the user ever having to touch a macro.

Our take is straightforward: if your spreadsheet requires a custom script just to prepare data for a pivot table, the spreadsheet is failing you. The user here is doing the right thing, they are automating a tedious process. But they deserve a system that makes that process unnecessary. Stop writing macros to fix broken layouts. Start using a tool that builds the right structure for you. The data is already telling you what it needs. Listen to it, and let the software do the rest.