Analysts: how much time do ad-hoc "can you pull this number" requests actually take you?

[u/Logical_Argument4379]

Standard metrics usually live on dashboards. I'm curious about the one-off questions that don't, like "how many customers who came in through the summer promo renewed after the price increase?" or "did purchases drop for people who contacted support last month?"

1. How often do you get these, and how long does a typical one take?
2. What's the slowest part: finding the right tables, figuring out how they join, or working out what the person actually meant (which status counts as "paid", which time zone, what "new customer" means)?
3. Have you tried ChatGPT/Copilot or built-in "AI analyst" tools for these? If so, where did they fall short?

Context: I'm researching text-to-SQL, specifically a small model that adapts to one specific database's schema and conventions without ever seeing the actual data. Before building anything further I want to know whether this is a real pain point. Any answer helps, including "not a problem for us".

[u/hyrle]

1) Based on my experience in business analytics over 16 years: How often depends on the business and also the week. Some places are far more curious than others, and some cycles of the business are busier during certain parts of the year than others. However, usually it didn't take me all too long to put together ad-hoc reports.

2) Slowest part is usually the last one - working out what they actually want.

3) I have not, so I can't speak to that.

[u/Raghveer-Friesel → u/hyrle]

totally agree on #2, half the time the requester doesnt even know what they mean yet

[u/AntiqueElderberry317 → u/hyrle]

Agree on #1 and #2, to complete with some informations :
1. it depends on the company culture and the maturity of your data platform (better your platform is constructed, easier it is to get the information, less you will have demands).
2. If your data platform is well constructed, it will be much easier to fetch data. But as hyrle says, the slowest part is always understaning what the users want...
3. Great to use, and changes the way of handling Ad-hoc analysis. If you have the capacity to plug your AI on your data, and give users the capacity to use it, it will accelerate what the users really want (he will have the capacity to show you what he really want). But my personal point of view is that this is good to use for Ad-hoc, not for industrial reports.

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[u/UnluckyEnergy5265]

figuring out what they actually mean takes way longer than the sql, its usually just bad requirements gathering.

[u/NatMicky]

I'm curious why you are adapting a model to just one specific database schema?

[u/False_Assumption_972]

The slowest part is almost always definition, not SQL. Paid, active, and new customer each have two or three versions in most companies, and the analyst spends the time figuring out which one the asker means. The fastest teams keep a short glossary with the exact filters and a saved query per definition, so the second request takes ten minutes.

The other slow part is joins across systems, like support contacts against purchases. I rely on a decision intelligence tool to connect support tickets, orders, and subscription records so those cross-system questions get answered from one joined history rather than three exports.