How to remove duplicate offers.

The question seems simple on the surface: remove duplicate rows from a spreadsheet. But the rules this user lays out reveal something far more interesting. They are not just cleaning data. They are navigating a logic problem that depends on context, on which dealership a customer visited, on whether the same vehicle appears across multiple locations. It is the kind of task that exposes the gap between what traditional spreadsheets were designed to handle and what people actually need to do with them every day. A post on SharePoint data collection that has frustrated countless teams sits right next to this one, and both point to the same underlying tension: our tools were not built for the messy, rule-driven reality of modern data work. Read the full story in Job has me doing a needlessly complicated task.

The user asks whether a macro is the only solution. It is a reasonable question, and the honest answer is that it should not have to be. When the logic requires conditional handling — duplicate removal within a single dealership versus across multiple dealerships — you are asking the spreadsheet to understand intent, not just values. Traditional formula-based approaches struggle here because they treat every row as equal. A macro can do it, but it turns a data-cleaning task into a development project. That is a cost most people do not realize they are paying until they are already deep in VBA. What is worth exploring is whether the right tool can handle this kind of conditional logic natively, without forcing you to script your way out of a problem that should feel intuitive. When your workflow depends on layered rules like these, the tool you choose should meet you at the complexity rather than pushing you past it.

There is also something worth naming about the data itself. These are extracted rows from a website, which means someone, somewhere, designed a system that outputs this structure without considering downstream usability. The user is not generating this data for fun. They are trying to make sense of it so they can act on it. That downstream clarity is where real productivity lives, not in the extraction step. Whether the source system can be adjusted or the output needs to be wrangled after the fact, the goal remains the same: turn raw rows into something you can trust and use. That is a problem worth solving well, because it repeats every time the extraction runs.

The deeper question lurking here is whether we still accept that data cleaning is a manual chore. Someone in another thread is wrestling with a similarly tangled SharePoint setup, and another is navigating the evolving rules around AI agent access. These stories share a common thread: the tools we rely on are straining under the logic we ask of them. As data volumes grow and the rules governing that data get more specific, the ability to handle conditional deduplication, multi-source reconciliation, and context-aware filtering becomes less of a nice-to-have and more of a baseline expectation. The question worth watching is not whether a macro can solve this. It is whether we are willing to explore tools that solve it the way we actually think about it — with clarity, without ceremony, and without turning every cleanup task into a development sprint.