ChatGPT for Excel and data analysis: where it stops

ChatGPT is a genuinely excellent analyst — for one file, for one person, right now. That's not the job most teams actually have.

By the Quiriz Team · Published August 17, 2026 · Competitor features verified August 2026

Upload a spreadsheet to ChatGPT, ask a question, and it will write and run real code against your data. For a one-off question about a file on your desktop, it's hard to beat, and we're not going to pretend otherwise.

The job most teams have looks different: eleven exports from five systems, a report that has to come out the same way every month, and six colleagues who need answers without becoming spreadsheet people. Here's the honest comparison.

The short version

	Quiriz	ChatGPT
Datasets you can keep	100+	20–40 files per project, 10 per upload
Answers in Excel / Sheets cells	`=QUIRIZ.ASK()` returns the value	Sidebar works on the open workbook
Keeping data current	Drive, OneDrive, email, Excel, Sheets, Slack	Manual re-upload — project files don't sync
Same number every cycle	Governed metrics compiled to SQL	Re-derives from scratch each run
Ask from Slack	Answers from your datasets	Searches your Slack messages, not your data
Cost for 12 occasional askers	Included — no per-asker seat	~$25/seat/month, each
Who sees which data	Company, business unit, team, project	No dataset-level access control
One-off analysis of a file you have open	Good	Excellent — ChatGPT wins this
Statistics, forecasting, ML	Not supported	Excellent — ChatGPT wins this

1. The file ceiling

5files per project — Free

25files per project — Plus

40files per project — Pro, Business, Enterprise

10files per upload, maximum

Check this one first, because it's arithmetic rather than opinion: how many exports does your monthly reporting pack touch? If it's more than a few dozen, ChatGPT Projects can't hold them — on any tier.

Quiriz stores 100+ datasets and picks the right ones from your question, so nobody has to remember which file holds what. The answer names the datasets it used.

Quiriz relationship view showing four HubSpot datasets connected by detected join keys: sales pipeline with 180 rows, call logs with 583 rows, CRM contacts with 180 rows and CRM revenue and lifecycle with 90 rows, joined on hubspot_owner_id and hs_analytics_source — Quiriz detects how your datasets relate and joins across them — so a question can span sources without you assembling anything first.

2. Answers in your spreadsheet, from data that isn't open

ChatGPT for Excel and Google Sheets shipped in May 2026 and it's a good product. Note the constraint though: it reads the workbook you have open. It's an assistant for the file in front of you, not a window onto the other eleven.

Excel with the Quiriz add-in open. The formula bar contains =QUIRIZ.ASK asking for a probability weighted sales forecast and quota for H2 2026 by month, and the cells below show the resulting month, forecast and quota table. The sidebar offers pushing the active sheet to Quiriz or importing a file. — `=QUIRIZ.ASK()` returns the number in the cell — computed against datasets you loaded weeks ago, in files nobody has open. It refreshes like any other function.

The test that separates them: open a blank sheet and ask about last quarter's revenue.

3. Keeping the data current

ChatGPT Projects hold files persistently — that part works. The problem is month two. Project files are static: you upload once and update manually when they change. Projects don't sync with the source document. The Google Drive connector is Workspace-only and, by OpenAI's own account, does little with Sheets and Excel files — the two formats that matter here. And since August 2026, individually authorised sync connections are gone; only administrator-managed sync remains, which turns a user task into an IT ticket.

So somebody re-uploads. Every cycle. And every stale answer traces back to whether they remembered.

Quiriz: point at a folder and bulk upload, connect Drive or OneDrive for automatic updates, email a file in, or push from Excel, Sheets or Slack. Reports that depend on a dataset re-run themselves when it refreshes.

4. The same number every time — with no setup project

Ask ChatGPT the same question about the same data twice, a month apart, and you can get two different numbers. That isn't a bug and better prompting doesn't fix it — it's what happens when a probabilistic model writes the query fresh on every run.

Two things drift independently: how the question becomes a query, and which rows it actually sees — past the context budget, retrieval pulls fragments, and a total computed over a fragment is wrong without telling you.

You can fix both in ChatGPT — by building a data stack. Point it at a governed semantic layer through a connector and it returns trusted numbers. That needs a warehouse, a modelling project, an engineer, and the plumbing between them.

Quiriz ships that layer in the box. Define what "revenue" means once, in a builder with no SQL. From then on the definition compiles to a query and runs against every row — same answer on Tuesday as last quarter, whoever asks, from whichever surface.

Quiriz answering in a Slack thread: asked for total Shopify sales in 2025, it replies with $349,874.55 net and lists product sales after discounts, refunds deducted, that shipping fees and sales tax are excluded, and that paid and refunded orders dated in 2025 are included — A governed answer states its own definition — after discounts, refunds deducted, shipping and tax excluded. The caveats arrive with the number instead of living in someone's head.

5. Ask in Slack — and colleagues don't need a seat

ChatGPT has a Slack app, and it's worth knowing exactly what it does, because the names are confusingly similar: it searches your Slack messages, threads and files to ground answers in conversation history. It answers questions about what your team said. It doesn't answer questions about your revenue data.

Quiriz's Slack bot answers from your datasets, in the channel where the question was asked — and the people asking don't need seats. With ChatGPT, the twelfth colleague who asks one question a month is another ~$25/month subscription. You pay for your data, not your people.

6. Speed — and what's actually being compared

We asked both the same question, on the same data, in August 2026: "Show net sales by month in 2025."

<10sQuiriz

42sChatGPT

ChatGPT analyses by loading rows — into a context window, or into a Python sandbox where it writes and runs code. Quiriz compiles the question to SQL and runs it against the database. No file to parse, no dataframe to load, no sandbox to start.

But the timing is the smaller point. The two answers aren't the same artifact. One arrives with a definition behind it and per-step rows you can download and reconcile; the other arrives as a table. Both are fast. Only one is checkable.

Where ChatGPT genuinely wins

For one file and one question, dragging it into a chat beats setting anything up. For statistics, forecasting, regression or machine learning, ChatGPT writes and runs real Python — Quiriz answers business questions over your data, it doesn't fit models. For open-ended exploration where you don't yet know the shape of the question, it's the better tool. And if you're one person and nobody else needs the answers, most of this page doesn't apply to you.

You probably shouldn't choose. Use both.

The split is clean, and it follows one rule: a language model shouldn't be the thing that computes the number.

Quiriz — the number

Ingest messy exports from many systems
Join across datasets
Governed metric definitions
The same figure every cycle, for everyone
Per-step rows for audit

ChatGPT — everything after it

Narrative and recommendation
Decks, memos, client emails
Charts, dashboards, presentation
Forecasting, statistics, modelling, code
Exploring what the number means

In practice: ask Quiriz the governed question, download the per-step rows, and hand that CSV to ChatGPT for the chart, the deck or the write-up — it's now working from a defined, auditable number instead of guessing at your raw file. Or drive Quiriz from an agent directly: we expose a CLI and MCP surface, so a tool-using assistant can query the governed layer instead of re-deriving an answer from files it half-read.

And close the loop. When ChatGPT enriches or reshapes that data, push it back into Quiriz. It becomes a governed dataset again — scoped by team and project, refreshable, and askable from Excel, Sheets and Slack by people who were never in your chat. Otherwise good analysis stays trapped as one person's conversation: no lineage, no sharing, gone next month.

What each is for: ChatGPT is a brilliant analyst who starts fresh every morning with no memory of what your team agreed "revenue" means. Quiriz is the institutional memory that hands it the right number to work from.

Also compare: Claude for Excel · Microsoft 365 Copilot · AI for Google Sheets · AI in Excel: the full guide

Try it on your own data. No signup — upload a messy export and ask it something.

Try with your data → Excel add-in Sheets add-on Slack demo

ChatGPT features and pricing verified August 2026. OpenAI ships quickly — if something here is out of date, tell us and we'll correct it.

Related: AI Pricing Comparison 2026 — verified per-seat prices for Copilot, ChatGPT, Claude and Gemini and what a 10-person team really pays. AI Hallucination Statistics 2026 — why the same model scores 0.7% or 94% depending on the test.