[zurfer]

that is correct. GPT-4 is good on well-modelled data out of the box, but struggles with a messy and incomplete data model.

Documenting data definitely helps to close that gap.

However the last part you describe is nothing new (BI teams taking credit, and pushing on problems to data engineers). In fact there is a chance that tools like vanna.ai or getdot.ai bring engineers closer to business folks. So more honest conversations, more impact, more budget.

Disclaimer: I am a co-founder at getdot.ai :)

[lmeyerov]

Agreed, maybe I wasn't clear enough. I don't view it as BI team vs platform team vs whoever. Maybe a decrease in the need for PhD AI consultants for small projects, or to wait for some privileged IT team for basic tasks, so they can focus on bigger things.

Instead of Herculean data infra projects, this is a good time for figuring out new policy abstractions, and finding more productive divisions of labor between different days stakeholders and systems. Machine-friendly abstractions and structure are tools for predictable collaboration and automation. More doing, less waiting.

More practically, an increasing part of the Louie.ai stack is helping get the time-consuming quality, guardrails, security, etc parts under easier control of small teams building things. As-is, it takes a lot to give a great experience.

[sagaro]

There used to be a company/product called Business Objects aka BO (SAP bought them), which had folks meticulously map every relationship. When done correctly, it was pretty good. You could just drag drop and get answers immediately.

So yes, I can understand if there is incentive for the startups to invest in Data Engineers to make well maintained data models.

But I do think, the most important value here is not the chatgpt interface, it is getting DEs to maintain the data model in a company where product/biz is moving fast and breaking things. If that is done, then existing tools (Power BI for instance has "ask in natural language" feature) will be able to get the job done.

The google moment, the other person talks about in another comment, is where google or 1998 didn't require a webpage owner to do anything. They didn't need him/her to make something in a different format. Use specific tags. Use some tags around key words etc. It was just "you do what you do, and magically we will crawl and make sense of it".

Here unfortunately that is not the case. Say in a ecom business which always delivers in 2 days for free, a new product is launched (same day delivery for $5 dollars), the sales table is going to get two extra columns "is_same_day_delivery_flag" and "same_day_delivery_fee". The revenue definition will change to include this shipping charges. A new filter will be there, if someone wants to see the opt in rate for how many are going for same day delivery or how fast it is growing. Current table probably has revenue. But now revenue = revenue + same_day_delivery_fee and someone needs to make the BO connection to this. And after launch, you notice you don't have enough capacity to do same day shipping, so sometimes you just have to return the fee and send it as normal delivery. Here the is_same_day_delivery_flag is true, but the same_day_delivery_fee is 0. And so on and on...

Getting DE to keep everything up to date in a wiki is tough, let alone a BO type solution. But I do hope getdot.ai etc. someone incentivizes them to change this way of doing things.