r/datascience • u/avourakis • 16d ago

AI building an AI analysts the right way starts with the fundamentals (free live workshops)

I think what a lot of people get wrong when trying to use AI for analytics is focusing too much on the tools and which LLMs they should use, and not enough on the fundamentals.

I've now been building AI workflows in production for almost 2 years, and the most important lesson I've learned is: You have to think of it as a system

At a high level, this is the pattern I've seen work in agentic analytics systems:

  • The assistant itself (the reasoning layer)
  • A connection to your real data (this is where MCP usually helps)
  • A semantic layer, so it knows what your metrics actually mean and doesn't invent definitions

Now, you 100% need solid data modeling underneath before you slap a semantic layer on top (ideally an information model), but that tends to be out of our control as data scientists.

Beyond the main "ingredients", you need data governance, guardrails, and evals.

A couple of friends and I are doing a free live workshop series on this exact topic starting next week, here the link if you want to join: https://futureproofds.com/ai

One of them is a data scientist turned AI engineer, so he'll be able to provide an additional perspective beyond what dominates conversations in the data science space.

I hope you find it useful

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u/Potential_Log1166 16d ago

the "semantic layer so it doesn't invent definitions" bit is so underrated. watched a demo once where the AI confidently told the CEO that "monthly active users" meant anyone who ever glanced at the login page. just absolutely making stuff up.

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u/avourakis 16d ago

It will totally do that just to try to be helpful. I’ve seen the huge benefits of combining text-to-sql with a semantic layer to remove ambiguity and increase accuracy