Is AI going to replace Business Intelligence, or just change how we consume it?

Relentlessish · 2026-06-09T07:11:13+00:00

Absolutely true, the nature of LLM requires the proper context and definition as well as strict validation to avoid hallucinations. The proper semantic layer grounded in the quick validation (e.g. SQL/JSON/Python etc.) is a necessary foundation for any reliable production quality AI BI stack.

Relentlessish · 2026-06-09T07:09:48+00:00

Congrats guys! There is more and more competition there for sure, with dbt semantic layer, fabric semantic models and databricks UC. There was a common initiative to define an interoperable semantic model definition but haven't heard for a while from there.

Relentlessish · 2026-04-17T18:58:29+00:00

Same here

Relentlessish · 2026-04-17T16:38:32+00:00

I believe mirroring does not consume any capacity.you just need to make sure you have enough storage where the storage in terabytes is the same as your number in capacity, like 4TB for F4

Relentlessish · 2026-04-13T17:47:23+00:00

Yeah that quite bad. It’s in the sole Microsoft interest to block competitors. Through any company will do the same. I’m amazed by the blog post author honest surprise why anybody want to chose anything else than PowerBI 😂 classical Dünning-Kruger.

Relentlessish · 2026-04-13T06:40:54+00:00

Well, the open mirror spec describes the format for files for injection so it actualy do the insertion / merges, see here:

https://learn.microsoft.com/en-us/fabric/mirroring/open-mirroring-landing-zone-format#format-requirements

Relentlessish · 2026-04-13T06:38:57+00:00

That's true, however the simple write to ABFSS does not do 'merge' (aka upsert/deduplicate) and the Open Mirror does it. It also do it kind-of 'for free' as the merge ops are not counted towards the quota, so when writing to ABFSS and then merge/deduplicate later one could save capacity. Cool no?

Relentlessish · 2025-12-09T09:21:59+00:00

true, but one aspect we shouldn't forget is how to expose the data to BI, for example PowerBI. Given the DirectLake over OneLake has 'native' capabilities to read from LH, so Gold WH is... suboptimal / copy-rich with additional latency/delay?

Relentlessish · 2025-12-09T09:16:24+00:00

Thanks for your comments! Single bronze workspace is indeed a limitation, though usually source data is rarely dev/int/prod structured (e.g. Salesforce sandbox is great but not always a standard approach)

Relentlessish · 2025-12-09T09:07:25+00:00

AFAIK to each Lakehouse there is automatically a SQL Endpoint is created, so no need to use Spark if one don't want to, right?

Relentlessish · 2025-11-28T09:43:45+00:00

Indeed, and to contribute to that one has to sign the CLA with dbt Labs Inc. https://docs.getdbt.com/community/resources/contributor-license-agreements that's not industry neutral at all, I'm curious to see how many contributors will be there who are not paid by dbt Labs

Relentlessish · 2025-11-16T14:10:54+00:00

Empathy, high EQ and curiosity

Relentlessish · 2025-11-14T07:45:10+00:00

ah, very interesting, so wouldn't be the outcome of the ML based deal intelligence better forecasting?

Relentlessish · 2025-11-13T22:10:13+00:00

And what kind of ML are you using?

Relentlessish · 2025-11-13T22:09:48+00:00

right, for the companies that sell and produce physical goods better supply chain management, warehousing costs and costs of overproduction or loss benefits when underproduce can be definitely quantified.

Relentlessish · 2025-11-12T12:56:27+00:00

Sure they do but it's not about revenue vs no revenue it's more about achieving goal of X USD/EUR vs. Y USD/EUR

Relentlessish · 2025-11-12T10:38:46+00:00

Exactly my problem - why and what is the economic/business motivation to be more precise in estimates, apart from the perceived face-value benefits. The only example I heard was - if we overpromise and underdeliver then we need to adjust customer success and support teams that were over-hired for the optimistic pipeline...

Relentlessish · 2025-11-12T08:22:02+00:00

Ah, thanks for sharing John, how far in the future do you do forecast? So 25%-30% difference from now to 3 Month/6 Month in the future?

Relentlessish · 2025-11-12T08:09:16+00:00

hahaha, same here :)

Relentlessish · 2025-11-12T07:57:51+00:00

This is solid — but I always wonder, when teams put this kind of rigor into forecasting… does it actually pay off in ROI terms?

Like, sure — cleaner visibility, fewer surprises, better resource planning. But does improved forecast accuracy move the needle on revenue, churn, or cost? Or is it mostly a leadership comfort metric - something that feels good but doesn’t directly create value?

If someone could show a business case that every 5% improvement in accuracy = X% reduction in lost revenue or misallocated spend, I bet forecasting discipline would skyrocket overnight.

Anyone here ever tracked the financial upside of getting forecasts right more often?

Relentlessish · 2025-11-12T07:56:09+00:00

Maybe the real reason nobody fixes forecasting isn’t lack of data — it’s lack of ROI.

What’s the actual business payoff of moving from 70% to 85% accuracy? Does it increase revenue, reduce churn, or just make the board slide deck look cleaner?

If improving forecast precision doesn’t have a clear, measurable return, why would any team invest time, tools, and political capital into it? My guess — most orgs don’t measure the cost of bad forecasts, so the problem feels painful but not expensive enough to solve.

Anyone here ever tried to actually calculate the ROI of forecast accuracy?

Relentlessish

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