I just completed a Financial Performance Dashboard for AfriPay Solutions Ltd under the mentorship of Mubar Dauda.
This was not just a Power BI project. It was a real fintech business problem: turning fragmented financial data into decision-ready insights.
๐ฃ๐ฎ๐ด๐ฒ ๐ญ ๐ผ๐ณ ๐๐ต๐ฒ ๐๐๐ต๐น๐ฒ๐๐ฒ๐ซ ๐๐ฎ๐ฟ๐ฒ ๐ฑ๐ฎ๐๐ต๐ฏ๐ผ๐ฎ๐ฟ๐ฑ ๐ถ๐ ๐๐ฎ๐ธ๐ถ๐ป๐ด ๐๐ต๐ฎ๐ฝ๐ฒ.
3,323 injuries.
โฌ6.33M in expenses.
52.4 average recovery days.
But the numbers arenโt the point.
This is what happens when a cohort moves past
โhow do I build this?โ
and starts thinking
โwhat should this do?โ
โ KPI cards built with HTML visuals
โ Blink notifications that flag critical values
โ Cards that flip to reveal deeper context
This isnโt a report.
It responds.
It guides.
It makes decisions easier.
That shift didnโt happen by accident.
It came from weeks of:
โ Wireframing before building
โ Getting the data model right
โ Writing DAX with intent
Now itโs showing up in the output.
Page 2 is coming.
More refinement on Page 1.
๐ง๐ต๐ฒ ๐ณ๐๐น๐น ๐ฟ๐ฒ๐๐ฒ๐ฎ๐น ๐ถ๐ ๐ฐ๐ผ๐บ๐ถ๐ป๐ด ๐๐ผ๐ผ๐ป.
#Datafam
๐ ๐ผ๐๐ ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐ฐ๐ผ๐๐ฟ๐๐ฒ๐ ๐๐๐ผ๐ฝ ๐ฎ๐ ๐๐๐ซ.
Mine doesnโt.
Weโre wrapping up the Power BI series at Data with Danny
and before closing out, I introduced my students to two things most instructors arenโt teaching yet.
๐๐ถ๐ฟ๐๐ ๐๐น๐ฎ๐๐ฑ๐ฒโ๐ ๐ ๐๐ฃ ๐๐ฒ๐ฟ๐๐ฒ๐ฟ, connected directly to Power BI.
What that unlocked:
โ Model structure set up automatically
โ Measure folders organised without manual work
โ 35+ DAX measures written and optimised
In under 5 minutes.
Something that would normally take hours.
๐ฆ๐ฒ๐ฐ๐ผ๐ป๐ฑ ๐๐ต๐ฒ ๐๐ง๐ ๐ ๐ฉ๐ถ๐ฒ๐๐ฒ๐ฟ ๐๐ถ๐๐๐ฎ๐น.
Most people donโt know Power BI can render HTML.
Once you know this, your KPI cards, injury summaries, and dynamic text
stop looking like dashboards
and start looking like products.
But before we touched any of it, I told my students this:
AI will never replace someone who understands the fundamentals.
It will replace someone who doesnโt
and thinks AI is a shortcut around learning them.
We spent weeks building the foundation first:
โ Data model
โ Relationships
โ DAX logic
Because if you donโt understand what a good measure looks like,
you wonโt know when AI writes a bad one.
๐๐ ๐ถ๐ป ๐๐ต๐ฒ ๐บ๐ถ๐ฑ๐ฑ๐น๐ฒ.
๐ก๐ผ๐ ๐ฎ๐ ๐๐ต๐ฒ ๐ฏ๐ฒ๐ด๐ถ๐ป๐ป๐ถ๐ป๐ด.
Thatโs the difference between working smart
and getting lucky.
#Datafam
๐ ๐ ๐๐๐๐ฑ๐ฒ๐ป๐๐ ๐ฑ๐ถ๐ฑ๐ปโ๐ ๐ธ๐ป๐ผ๐ ๐ฑ๐ฎ๐๐ฎ ๐ฎ๐ป๐ฎ๐น๐๐๐๐ ๐๐๐ฒ ๐๐ถ๐ด๐บ๐ฎ.
Honestly, that reaction told me everything.
Today we covered wireframing in Power BI.
The moment I showed them how a wireframe catches gaps before a single DAX measure is written โ
the questions got intense.
One student asked:
โSo wireframing can actually prevent build problems?โ
Yes.
Thatโs exactly the point.
Most people jump straight into Power BI and start building.
Then halfway through they realise:
โ The layout doesnโt tell a story
โ A measure they need doesnโt exist yet
โ The stakeholder wanted something completely different
Wireframing fixes all of that before it becomes expensive.
Discovery โ Wireframe โ Data Model โ Build โ Ship
Thatโs the real Power BI development lifecycle.
Not โopen Power BI and start dragging visuals.โ
๐ง๐ต๐ถ๐ ๐ถ๐ ๐๐ต๐ ๐ ๐๐ฒ๐ฎ๐ฐ๐ต ๐๐ต๐ฒ ๐ฝ๐ฟ๐ผ๐ฐ๐ฒ๐๐, ๐ป๐ผ๐ ๐ท๐๐๐ ๐๐ต๐ฒ ๐๐ผ๐ผ๐น.
#Datafam