Best ChatGPT apps for data analysis and spreadsheets
If you want ChatGPT to actually work with your data instead of guessing at it, the apps worth installing are the ones that connect to a real source: Coupler.io to pull numbers in from a hundred places, Formula Genius when the job is a stubborn Excel or Sheets formula, ThoughtSpot Spotter to ask your warehouse a question in plain English, and Hex when you need a real SQL-and-Python notebook. Each one runs inside the conversation, so the data comes to the chat rather than you copy-pasting rows into a prompt. Below is the set I'd reach for, grouped by the stage of the analysis you're on.
I curate a directory of apps that run inside ChatGPT, so I watch which jobs keep coming up. "Data analysis" isn't one tool — it's a chain: get the data in one place, shape it, ask it questions, run something heavier when a plain question isn't enough, and read what your users are doing. The apps below map onto that chain.
Get your data into one place
Analysis stalls before it starts when the numbers live in six different tools. These two are the plumbing.
Coupler.io connects business data from 100+ sources — Shopify, ad platforms, CRMs, analytics — into dashboards and warehouses. If your monthly report means exporting four CSVs and stitching them by hand, this is the app that ends that ritual, and it's the one closest to a spreadsheet workflow since it can land the data where you already work.
CData Connect AI is a managed connection layer for 350+ data sources — databases, SaaS APIs, and warehouses — through a single endpoint, without writing connection code. I'd use it when the data I need is behind a database or an API that ChatGPT can't reach on its own.
Work in a spreadsheet
Most people's "data analysis" is still a spreadsheet, and the wall is usually a formula that won't cooperate.
Formula Genius generates formulas and queries for Excel, Google Sheets, SQL, and regex. You describe what you want in words — "sum column D only where the date is last month" — and it writes the formula. It's the app I'd keep open on any day that involves a spreadsheet and a formula I can half-remember but not quite write.
Ask your data in plain English
Once the data is in a warehouse, the point of a ChatGPT app is to skip the SQL and just ask.
ThoughtSpot Spotter bills itself as your AI data analyst — natural-language business-intelligence queries against enterprise data. You ask "which regions grew fastest last quarter" and it answers from the governed data, not from a hallucination.
Omni Analytics answers questions across your BI tools and warehouses and, importantly, links back to the source. When a number is going into a decision, the source link is the difference between trusting it and re-checking it by hand.
Cube sits as a semantic layer over your warehouse, so questions get answered against consistent, defined metrics. If your team has ever argued about two different values for "active users," this is the layer that settles which definition wins before the question is even asked.
Run a real notebook
Sometimes a plain question isn't enough and you need to actually transform the data. These bring the notebook into the chat.
Hex runs SQL and Python notebooks with collaborative data exploration. It's the one I'd pick when the analysis needs a join, a cleanup step, and a chart — the things a single question can't do — but I still want to drive it from the conversation.
Deepnote runs agent-driven data workflows and notebooks. The angle here is letting an agent do the repetitive notebook steps for you, which suits a workflow you run the same way every week.
MotherDuck runs serverless DuckDB queries for fast analytical workloads. When the dataset is large enough that a spreadsheet chokes but you don't want to stand up a whole warehouse, this is the fast middle path.
Read product and user behavior
If your data is really "what are people doing in my product," the analysis lives in a product-analytics tool, and querying it from chat beats clicking through dashboards.
Amplitude searches and analyzes product analytics — user behavior, funnels, and retention. I'd use it to ask where users drop out of a signup flow without building the funnel chart by hand first.
PostHog queries product usage and funnel metrics for your app. It covers similar ground to Amplitude, so pick the one your product already sends events to rather than adding a second.
Turn documents into structured data
Talonic extracts structured data from documents and lets you ask questions about them. This is the bridge for when your "data" is trapped in PDFs or scanned reports — it pulls it into a structured shape you can actually analyze instead of read.
How to choose
Don't install all twelve. Work backward from where your data already sits: if it's in spreadsheets, start with Formula Genius; if it's scattered across tools, start with a connector like Coupler.io or CData Connect AI; if it's already in a warehouse, an ask-in-plain-English app like ThoughtSpot Spotter or Omni Analytics gets you furthest fastest; and if the analysis is genuinely heavy, a notebook like Hex is worth the extra setup. The whole point is to keep the data and the question in the same window.
For the full list as it grows, see the Analytics and Data category in the directory.
*Data current as of 2026-07-17. These are apps that run inside ChatGPT, selected from my directory's Analytics and Data category; I include tools that do real analysis work and describe each from its own stated function, not from marketing claims.*
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