Great post Christian. It seems like for some orgs (like my own), as soon as there's a comfort level in being able to use the prompt-friendly tech on our own data we could see a massive jump for our internal BI/analytics departments. I think it's one part not wanting to bulk up staffing on the roles can do it with more complex tech and a second part not feeling comfortable just dropping years of business data into models where we have no idea who's using them and for what.
Thanks Mike. I agree that there's a major comfort-level component to BI/analytics adoption. The obstacle to adoption that I see for BI use-cases is that it's so much harder for the average user to validate whether the results the machine spits out are correct. It's *relatively* easy for me to decide whether a qualitative response from GPT makes sense. But because of the idiosyncracies of companies' internal data, unless I'm restricting what the machine is analyzing to a specific table my data team has cleansed and fool-proofed for me, I have no way of knowing whether the arithmetic was performed on the right fields, nor do I have any prior knowledge of how to filter the results (in effect the WHERE clauses in a SQL query). I do think it's solvable, but I haven't seen a solution for this yet. I'd love to be proven wrong on this if you've seen any solutions that get at this problem!
Great post Christian. It seems like for some orgs (like my own), as soon as there's a comfort level in being able to use the prompt-friendly tech on our own data we could see a massive jump for our internal BI/analytics departments. I think it's one part not wanting to bulk up staffing on the roles can do it with more complex tech and a second part not feeling comfortable just dropping years of business data into models where we have no idea who's using them and for what.
Thanks Mike. I agree that there's a major comfort-level component to BI/analytics adoption. The obstacle to adoption that I see for BI use-cases is that it's so much harder for the average user to validate whether the results the machine spits out are correct. It's *relatively* easy for me to decide whether a qualitative response from GPT makes sense. But because of the idiosyncracies of companies' internal data, unless I'm restricting what the machine is analyzing to a specific table my data team has cleansed and fool-proofed for me, I have no way of knowing whether the arithmetic was performed on the right fields, nor do I have any prior knowledge of how to filter the results (in effect the WHERE clauses in a SQL query). I do think it's solvable, but I haven't seen a solution for this yet. I'd love to be proven wrong on this if you've seen any solutions that get at this problem!