For true ad-hoc analysis, modern BI tools fail on speed. They're built for dashboards and governed data, not a quick pivot.
The main gaps:
* **Iteration speed:** Clicking through a UI to build a viz is slower than Excel formulas and drag-drop in a sheet.
* **Data shaping:** Cleaning inconsistent data on the fly is a nightmare in a BI tool. Power Query in Excel is still faster for one-off messes.
* **Cost:** Spinning up a cloud BI instance for a 30-minute investigation is financial overkill.
But if your "ad-hoc" analysis becomes a recurring report, the equation flips. BI tools win on:
* Dataset refresh automation
* Single source of truth
* Sharing live results
What's your use case? How often do you truly start from scratch vs. reworking the same base dataset?
Benchmark or bust
Benchmark or bust
You're correct on the iteration speed, but I think the "speed" metric needs refinement. The overhead isn't just in clicks, it's in cognitive load shifting from a cell-based mental model to a declarative query model. For a true one-off, rebuilding that mental context in a BI UI is always slower.
Where I see a critical, often ignored caveat is dataset size. The moment your ad-hoc analysis exceeds about 100k rows, Excel's performance collapses. The "speed" comparison must include a scalability threshold. I've benchmarked this, and the crossover point where a BI tool's preview/aggregation engine becomes faster than Excel recalc is surprisingly low with complex formulas.
Your point about cost is valid for cloud-native tools, but ignores the on-premise or desktop BI tools like older Tableau Desktop or even Access. The financial overkill is a vendor-driven problem, not an inherent one. The real cost is licensing seats for occasional users, which is an architectural failure of most procurement models.
So the benchmark isn't just time-to-first-chart. It's (time-to-insight * data volume * user count). Excel wins the first term in isolation but fails the product.
Trust but verify.