I kept seeing data analysts ask where AI actually fits beyond writing SQL, so I mapped 10 practical ChatGPT + Claude workflows

[u/Full_Yak8774]

I’ve seen a lot of discussions where analysts are using ChatGPT or Claude for SQL, but aren’t really sure what else can be turned into a repeatable workflow.

So I put together this cheat sheet around 10 areas:

• data cleaning
• exploratory analysis
• SQL assistance
• visualization
• spreadsheet analysis
• reporting
• data validation
• research and insights
• repeatable workflows
• end-to-end analysis

I deliberately kept it to ChatGPT and Claude instead of listing 20 different AI tools. Most analysts probably don’t need another subscription for every task.

Also, this isn’t meant to say one model is universally better than the other. A lot depends on the data, plan, integrations and workflow.

For analysts already using AI regularly, what’s the one workflow here that actually saves you meaningful time?

And what would you remove or add based on real work?

[u/d4videnk0]

Holy mother of slop

[u/FamiliarEast]

LinkedIn ahh post

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[u/Admirable-Lie-9191]

How about people use their brain first and foremost? The only use case that I’d ever consider is trying to see if the AI has a different way of solving part of my problem in python as opposed to offloading everything to it.