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Shachar Meir

Why Self-service Analytics Keeps Failing (AI doesn't fix it)

Published 2 months ago • 1 min read

Hi Reader,

AI makes self-service analytics look inevitable.

The pitch sounds something like this:

→ Plug an AI agent into a clean semantic layer
→ Add good metadata and context
→ Let stakeholders ask questions in plain English
→ Done

Everyone gets answers instantly.
No dependency on data teams.

Sounds great.

But here’s the problem:

Self-service analytics has been “almost working” for over 20 years.​
And success rates are still… disappointing.

Why?

Because it quietly relies on a set of assumptions:

  1. Stakeholders know what they actually want
  2. Stakeholders can express it clearly in words
  3. Words always mean the same thing to everyone
  4. Stakeholders understand the data well enough and want to be self-served
  5. Stakeholders even need that level of flexibility in how they consume data

If you’ve worked in data, you know how shaky these assumptions are.

AI makes the interface better.
It doesn’t fix the underlying problem.

I’m not saying it won’t work at all.

But expectations need to be… realistic.

In this mentoring conversation with Omar Brid, we break this down properly:

→ where self-service actually works
→ where it fails (and why)
→ what data teams should do instead

Watch it here 👇

Why Self-Service Analytics Keeps Failing Why Self-Service Analytics Keeps Failing [30:19]
Why self-service keeps failing, and how to make it work?

Have a great weekend!

Regards,
Shachar

P.S. I've recently launched my website – if you’re serious about growing beyond execution work and into real impact, it’s worth a look.

♻️ Please share these links with 2 colleagues or friends to help them grow:

Shachar Meir

Data Advisor

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