The user who posted this knows exactly what they need, clean, paired data that reveals mismatches, but they're stuck in the gap between a messy CSV and a usable table. That frustration is familiar to anyone who has stared at comma-separated cells and wished the software would just *understand* the relationship between the items. The good news is that this isn't a problem with Excel. It's a problem with how we've been taught to think about spreadsheets.
The core issue here is structural. Data like "Apples, Apples, Bananas" in one column and "A123, A123, B456" in another isn't really two columns, it's two lists pretending to be cells. The user's instinct to reach for Power Query is correct, but the barrier isn't technical. It's the assumption that transforming data should require that level of effort. When a tool forces you to split columns, then re-pair them manually, it's not a skill gap, it's a design gap. The spreadsheet should already recognize that the nth item in Fruits corresponds to the nth item in Codes, and it should offer to unstack them without a multi-step ritual.
What this user is actually asking for is a system that treats data as connected, not isolated. They want to verify that Apples always maps to A123, and they want to surface the exceptions without building a custom pipeline. That's not a niche request. It's the daily reality for anyone managing inventory, product catalogs, or survey responses. The fact that you have to ask "how do I align these columns" is a sign that the tool is making you do the thinking it should be doing for you.
We believe the future of spreadsheets isn't about more features, it's about fewer steps. AI-native tools can already parse this kind of comma-separated mess, infer the pairing, and present the clean result as a starting point, not a destination. The user's goal, identifying mismatched codes, should be a one-click insight, not a Power Query tutorial. Until then, the workaround exists, but the real solution is a tool that sees the structure hiding in the chaos.