I agree that the term "self-service analytics" (especially the 'analytics' part) and "insights" just passes the wrong image of the real need of business users out there. It mixes 'strategic insights' with 'operational needs'. And I think self-service needs to be about operationalizing data. Sales managers are not necessarily looking to 'analyze' data or 'get an insight'. They need answers from data to manage their team. They need to track well-defined KPIs. See how their salespeople are doing and be able to have a productive meeting to tell them what they are neglecting. Customer success people need to "pull some data real quick" on the usage of the product by a certain client before a meeting.
These things happen all the time. And yet most companies out there think that the solution is to just build a bunch of dashboards, foreseeing what everyone will ask in the future. And then nobody checks the dashboards. Or finds the right one. And then they have a team of SQL translators pulling data for ad-hoc questions. That's silly IMO.
I'm obviously biased as a founder of a self-service analytics company based on AI (https://www.veezoo.com). But this is just my 2 cents on a topic I really care about.
Also related: https://news.ycombinator.com/item?id=36049449
Kinda crazy how they didn't check again the responses of GPT in the video before marketing it. Reminded me of the Bing and the Bard demo mistakes earlier this year.
People usually perceive a trade-off between precision and ease of use. I think that, if done naively, natural language interfaces will indeed lead to imprecise and inconsistent answers. But it doesn't have to be like this.
From my experience building Veezoo[1], natural language interfaces for data can be very powerful for business users as long as it explains back understandably what the query does, allows to modify them by clicking and it relies on a set of vetted dimensions/measures, e.g. in a semantic layer. Also, UX solutions[2] like Autocompletion (for discovery and disambiguation) are way more important for these more specific use cases of natural language interfaces than for an open-domain chatbot like ChatGPT. This usually helps a lot with the issues you've mentioned.
There are many questions that are hard to write a query for but are simple to formulate in a relatively precise way (or allow the user to correct it by clicking). This is especially useful for the average business user that doesnt know SQL or struggles with "precise" tools for data analytics.
If you like the idea of asking questions from your data in plain English, there are also other alternatives to Power BI, like Tableau's Ask Data, ThoughtSpot and Veezoo (free for 5 users). Here is a series of comparison videos to show their strengths: https://veezoo.com/compare-solutions/
Oh, ThoughtSpot. I spent a couple of months testing that out. At the time, it was hard to deploy amd didn't "scale down". That is, the smallest size we could deploy was something like 750 Gigs.
Also, the UI looked half baked, but I'm sure they have fixed that by now
These things happen all the time. And yet most companies out there think that the solution is to just build a bunch of dashboards, foreseeing what everyone will ask in the future. And then nobody checks the dashboards. Or finds the right one. And then they have a team of SQL translators pulling data for ad-hoc questions. That's silly IMO.
I'm obviously biased as a founder of a self-service analytics company based on AI (https://www.veezoo.com). But this is just my 2 cents on a topic I really care about.