The analyst who posted this knows exactly what they need, and they're right to feel stuck. The problem isn't their skill, it's that the tools they're using were never designed for the data volume their job now demands. They're doing manual stitching across chunks of a 3-million-cell export limit, feeding old-school stakeholders who won't touch a dashboard, and trying to build a "set it and forget it" system that Excel was never meant to be. Our take is simple: stop forcing Excel to be something it isn't, and start treating your data pipeline like a modern analytics stack, even if your output is still a spreadsheet.
The core tension here is familiar to anyone who works in CPG or retail analytics. You need the flexibility of Excel for your buyers, but the data has outgrown the tool. Power Query is the right answer for the stitching, it can ingest those chunked exports, merge them, and refresh automatically when you drop new files into a folder. But Power Pivot alone won't solve the scale problem if you're still hitting cell limits on the output side. The real unlock is building a lightweight data model in something like Power BI or a cloud database, then using Cube formulas or connected PivotTables to serve your stakeholders the Excel files they want, without ever opening a flat file that breaks. That's the "set it and forget it" they're after: a system where the heavy lifting happens upstream, and the spreadsheet is just the viewport.
The AI piece is worth pausing on. The user mentions feeding final PivotTables into an LLM to write recaps, and that's a smarter starting point than most people assume. Clean, structured data in a PivotTable is already a great input for an LLM, it has clear headers, aggregations, and context. The mistake would be trying to skip the data model step entirely and relying on ChatGPT to write Python scripts that patch over broken workflows. That approach works for one-off tasks, but it doesn't solve the repeatable, refreshable pipeline that this analyst needs. Focus first on getting the data model right, then layer in the LLM for the narrative layer, not the other way around.
For anyone reading this who feels like their skills froze in 2020, the path forward is narrower than it seems. You don't need to learn Python or become a data engineer. You need one good course on Power Query and data modeling, a willingness to let go of VBA, and a clear boundary between your data pipeline and your presentation layer. The analyst who posted this is already halfway there, they know what they want, they know what's broken, and they're asking the right questions. The next step is to stop treating Excel as the engine and start treating it as the dashboard. That shift is what separates the VLOOKUP era from the data model era, and it's within reach.