The best AI tool for exploratory analysis
for data analysts
We tested the best AI tools for exploratory analysis for data analysts in 2026. Here's what won, and what the runners-up are good for.
Julius AI
After testing against real data analysts workflows in Q1 2026, Julius AI is the clear winner for exploratory analysis. It excels where other tools fall short: conversational data analysis. The gap between Julius AI and the runners-up is meaningful in day-to-day use.
What separates Julius AI from the competition is how it handles the edge cases that come up in real data analysts work, not just the showcase demos. For data analysts specifically, that distinction matters more than raw benchmark scores.
What it gets right
- Natural-language analysis with visible code
- Runs real Python, not just descriptions
- Charts and explains results automatically
Where it falls short
- Large datasets hit upload/processing limits
- Generated code still needs verification
- Not built for production pipelines
The runners-up
ChatGPT (Advanced Data Analysis)
ChatGPT runs Python on your data to answer exploratory questions with charts and explanations, much like Julius. It is the convenient choice if you already pay for ChatGPT and want quick answers from a spreadsheet. Julius is more purpose-built for repeated data work, but for ad-hoc exploration ChatGPT is right there.
ThoughtSpot
ThoughtSpot’s natural-language search (Sage) lets non-analysts explore large, governed datasets by typing questions, returning instant visualizations. It scales to far bigger data and more users than a chat-on-a-CSV tool. Best for organizations that want self-serve exploration across the whole company rather than individual file analysis.
Hex
Hex combines notebooks, SQL, Python, and AI assistance with strong collaboration and warehouse integration, aimed at analytics teams rather than solo non-coders. It is the power option when exploration involves real queries and reproducible analysis shared across a team. Pricier, but justified for teams of several analysts.
Common questions about AI for exploratory analysis
Do I need to know Python to use Julius?
No, you ask in plain English. But the visible code means coders can verify and learn, getting the best of both.
How big a dataset can it handle?
Browser-based tools have upload and processing limits; very large datasets belong in a warehouse with SQL or a notebook tool like Hex.
Should I trust its charts and stats?
Verify them, check the code and sanity-check aggregations and methods. It's a fast draft, not an unaudited source of truth.
Julius or Hex?
Julius for fast solo exploration; Hex for collaborative, reproducible, governed team analysis.
Not a data analyst?
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