◆ Microsoft Power BI

Convert data types in Power BI

This is real work, not a feature someone invented — it comes from real job ads and real questions people asked. Below are four ready AI prompts: get it done, make it easy for the next person to say yes to, work out the right move when you are stuck, and stop it coming back.

4prompts

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskThe 'Order ID' column in the sales dataset is currently showing up as text, but I need to sort…+
The 'Order ID' column in the sales dataset is currently showing up as text, but I need to sort and filter it numerically for the inventory report. Convert the 'Order ID' column to a whole number data type, ensuring no data loss.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
BImprove — make it easier to acceptBefore I hand this sales dataset to the operations team, make sure the 'Order ID' column is in…+
Before I hand this sales dataset to the operations team, make sure the 'Order ID' column is in a format that allows for proper numerical sorting and filtering. Review the column's current data type and convert it if necessary, flagging any potential issues with the conversion.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentI tried to convert the 'Revenue' column to a decimal number, but now I'm seeing errors and…+
I tried converting the 'Revenue' column to a decimal, and now I have errors.
I tried to convert the 'Revenue' column to a decimal number, but now I'm seeing errors and blank values, and I can't figure out why. The finance team needs these numbers accurate for month-end. What just happened, and what's the best way to fix this conversion without losing the critical revenue figures?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI consistently hit data type conversion errors when pulling in new data sources, which always…+
I frequently run into data type conversion errors when integrating new data sources.
I consistently hit data type conversion errors when pulling in new data sources, which always delays my reporting for the marketing team. I end up spending hours cleaning up individual columns. What habit should I change in my data ingestion process to proactively identify and handle these type mismatches more efficiently?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

honest answers, no sign-up

Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Microsoft Power BI's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.