241–250 of 311 posts
This sounds great... until that 5(or whatever) % error margin kicks in, a bad result is given, a decision on that data is made, and the company loses $$$$$, just to save a few 100k here or there.
While this is very cool, SQL was designed to be used by business people. We need to go back to that model, where we train the business people who need these analytics how to use SQL to uncover the result. That along with a rigorous policy for including the queries that produced the result so the query logic can be checked would go a long way to actually taking advantage of the data we're collecting as businesses.
I have been spectacularly unable to get any business people to learn SQL. I even find that very few will bother with reporting tools (eg Tableau) for simple self-service. Instead, the expectation is that there will be a programmer/DBA/report writer position dedicated to producing analytics "on-demand."
I imagine SQL would be harder and more annoying for biz folks. A programmer might be right "expert" to get the SQL working (Usually the programmer itself is a generalist when trying to write SQL for analytics).
A text-to-SQL tool will help create the queries quickly; it is much easier for an engineer to verify the generated queries and the results of those queries.
Earlier quoted context omitted.
I've done too little SQL to be traumatized by it, but I experienced it plenty of times doing more complex data processing on the backend. I would e.g. spend a week on first discussing a solution, then researching both theoretical background and relevant libraries, and finally writing code to do the math and render the answers (probability distributions). I eyeball them, they seem to all match expectations, so I decla…
The traumatising experience of SQL is that the scenario you describe being scale-invariant. The smallest question and the largest question both have the potential to be completely wrong for any inane reason. Often turns out that metrics the company has been using for months are totally bogus too in my experience, which is not a fun conversation to have. So the life of an SQL analyst in their early days (it gets bette…
Anyone who's been asked more than a couple of times for data that requires a non-trivial bit of ad-hoc SQL will know the sinking "oh shit" feeling that comes when you subsequently realise you borked the query logic in some subtle way and have accordingly emailed out a completely bogus answer/report. From the article it doesn't seem that GPT is significantly better or worse than a human in this regard, although an exp…
You can ask an analyst to double check their work, and a good one will find any such mistakes. If you ask GPT-3 to correct an error it always confidently tells you you're wrong and it made no mistake (in my experience). It's funny but I imagine more annoying when it's eventually integrated into a paid service people rely on.
Anyone who's been asked more than a couple of times for data that requires a non-trivial bit of ad-hoc SQL will know the sinking "oh shit" feeling that comes when you subsequently realise you borked the query logic in some subtle way and have accordingly emailed out a completely bogus answer/report. From the article it doesn't seem that GPT is significantly better or worse than a human in this regard, although an exp…
I've done too little SQL to be traumatized by it, but I experienced it plenty of times doing more complex data processing on the backend. I would e.g. spend a week on first discussing a solution, then researching both theoretical background and relevant libraries, and finally writing code to do the math and render the answers (probability distributions). I eyeball them, they seem to all match expectations, so I decla…
If you really want to play for keeps then on Monday you can explain that you had a fantastic idea over the weekend for how to improve the results and make them more accurate.
Earlier quoted context omitted.
As if the analyst doesn't get the results wrong! For 1/50 of the price, maybe a few more errors are acceptable, even.
Which errors are you okay with?
Earlier quoted context omitted.
As if the analyst doesn't get the results wrong! For 1/50 of the price, maybe a few more errors are acceptable, even.
Which errors are you okay with?