This user's predicament is exactly where traditional spreadsheets break, and it's time to name that limitation plainly. Excel is a fine tool for a thousand rows and a dozen columns, but when you face 14,035 rows and 466 columns, you are no longer working with a spreadsheet, you are wrestling with a database that refuses to admit what it is. The request for a "hierarchical grouping" that preserves every column while answering questions about order counts, product mixes, and product age is not a feature request. It is a signal that the tool itself has become the bottleneck.
The practical problem here is structural. Excel organizes data in flat grids, and grouping 466 columns without losing information requires either collapsing data into summaries (which discards detail) or expanding into unwieldy nested layouts (which breaks the grid model). Power Query and PivotTables are workarounds, but they force users to reshape their data into a format the tool prefers, not the format their questions demand. The user already hit this wall: they tried converting to a table and found it "can't quickly convert this much data." That is not a user error. That is the tool telling you it was never built for this scale or this kind of relational thinking.
What this situation reveals is a gap between the questions people need to ask and the answers their software can provide. The user wants to know what percentage of address locations have a certain product mix, or how many locations have multiple products and what those products are. These are not exotic queries. They are basic relational questions that a properly designed data system should answer in seconds. Yet the user is stuck searching forums for a "good tip" on a 14,000-row sheet, because Excel treats every row as an independent island rather than as part of a hierarchy of orders, addresses, and products.
The solution is not a better Excel trick. The solution is a tool that understands hierarchy natively, one that lets you group by order number, then by address, without dropping a single column or forcing you to flatten your data into a table first. An AI-native spreadsheet that interprets your intent, preserves your structure, and lets you ask questions in plain language would turn this user's frustration into a workflow. Instead of hunting for the right menu option, they would type "show me all order numbers with more than one address" and get the answer immediately. That is not a distant future. It is the minimum viable expectation for anyone managing data at this scale. The user's problem is not their data. It is the tool they are using to understand it.