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Transform data 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
See the prompt↓All Microsoft Power BI tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskTake the raw sales data from the Q3 CRM export, clean up the 'Customer Name' field to remove…+
Take the raw sales data from the Q3 CRM export, clean up the 'Customer Name' field to remove leading/trailing spaces and special characters, and then format the 'Revenue' column as currency. Load this into the 'Sales Performance' report data model.
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 present this Q3 sales data to the executive team, make sure the 'Revenue' and 'Profit…+
Before I present this Q3 sales data to the executive team, make sure the 'Revenue' and 'Profit Margin' columns are easily digestible. Highlight any outliers in sales performance by region, and flag any data quality issues that might make someone question the numbers.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentThe finance director is asking why the Q3 sales report shows different numbers than their…+
The finance director just asked why the Q3 sales report numbers don't match their spreadsheet.
The finance director is asking why the Q3 sales report shows different numbers than their spreadsheet, and I can't immediately see a discrepancy in our source data. I'm afraid this will undermine confidence in our reporting. What's the most likely reason for this mismatch between the report and their manual calculation, and what's the quickest way to diagnose it without causing more alarm?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI'm constantly losing hours every week manually cleaning up messy data from different source…+
I keep spending too much time manually cleaning data before it even gets into a report.
I'm constantly losing hours every week manually cleaning up messy data from different source systems before I can even start building reports. This eats into my analysis time and delays insights. What habit should I change to prevent this recurring data prep bottleneck and ensure cleaner data from the start?
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.