Join us at FabCon Atlanta from March 16 - 20, 2026, for the ultimate Fabric, Power BI, AI and SQL community-led event. Save $200 with code FABCOMM.
Register now!Learn from the best! Meet the four finalists headed to the FINALS of the Power BI Dataviz World Championships! Register now
I am trying to proivide a day over day, week over week and month over month comparision (percentage) for future commission earned by travel agents. Our fiscal year is from October to September if that matters. I already have my date table.
I am working with a Direct Query connection.
Day over day is tricky because we do not have transactions every day
Week over week is also tricky because I am unsure of how to group the weeks and then do the calculation.
Month over month I would also need the calculation.
This is an excel mockup of how the data looks in SQL for simplicity.
This is my current matrix visual in PowerBi
Rows = Transaction Date
Columns = Departure Date
Values = Future Commission Amounts
Solved! Go to Solution.
Hi @Anonymous ,
Has your problem been solved by @amitchandak 's suggestion?
If you need further help, please feel free to let me know.
Best Regards,
Community Support Team _ kalyj
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi @Anonymous ,
Has your problem been solved by @amitchandak 's suggestion?
If you need further help, please feel free to let me know.
Best Regards,
Community Support Team _ kalyj
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
@Anonymous , create a date table either in power bi or in the database, for a week you can create rank on YYYYWW . A direct query can also support the date table created in power bi.
WOW
new columns in date table, if in db, create as per DB code
Week Start date = 'Date'[Date]+-1*WEEKDAY('Date'[Date],2)+1
Week End date = 'Date'[Date]+ 7-1*WEEKDAY('Date'[Date],2)
Week Rank = RANKX(all('Date'),'Date'[Week Start date],,ASC,Dense)
OR
Week Rank = RANKX(all('Date'),'Date'[Year Week],,ASC,Dense) //YYYYWW format
measures
This Week = CALCULATE(sum('Table'[Qty]), FILTER(ALL('Date'),'Date'[Week Rank]=max('Date'[Week Rank])))
Last Week = CALCULATE(sum('Table'[Qty]), FILTER(ALL('Date'),'Date'[Week Rank]=max('Date'[Week Rank])-1))
DOD
This Day = CALCULATE(sum('Table'[Qty]), FILTER(ALL('Date'),'Date'[Date]=max('Date'[Date])))
Last Day = CALCULATE(sum('Table'[Qty]), FILTER(ALL('Date'),'Date'[Date]=max('Date'[Date])-1))
Last Day = CALCULATE(sum('Table'[Qty]), previousday('Date'[Date]))
YOY
FY Start in OCT
YTD Sales = CALCULATE(SUM(Sales[Sales Amount]),DATESYTD('Date'[Date],"9/30"))
Last YTD Sales = CALCULATE(SUM(Sales[Sales Amount]),DATESYTD(dateadd('Date'[Date],-1,Year),"9/30"))
Power BI — Year on Year with or Without Time Intelligence
https://medium.com/@amitchandak.1978/power-bi-ytd-questions-time-intelligence-1-5-e3174b39f38a
https://www.youtube.com/watch?v=km41KfM_0uA
Power BI — Qtr on Qtr with or Without Time Intelligence
https://medium.com/@amitchandak.1978/power-bi-qtd-questions-time-intelligence-2-5-d842063da839
https://www.youtube.com/watch?v=8-TlVx7P0A0
Power BI — Month on Month with or Without Time Intelligence
https://medium.com/@amitchandak.1978/power-bi-mtd-questions-time-intelligence-3-5-64b0b4a4090e
https://www.youtube.com/watch?v=6LUBbvcxtKA
Power BI — Week on Week and WTD
https://medium.com/@amitchandak.1978/power-bi-wtd-questions-time-intelligence-4-5-98c30fab69d3
https://community.powerbi.com/t5/Community-Blog/Week-Is-Not-So-Weak-WTD-Last-WTD-and-This-Week-vs-Last-Week/ba-p/1051123
https://www.youtube.com/watch?v=pnAesWxYgJ8
Day Intelligence - Last day, last non continous day
https://medium.com/@amitchandak.1978/power-bi-day-intelligence-questions-time-intelligence-5-5-5c3243d1f9
Share feedback directly with Fabric product managers, participate in targeted research studies and influence the Fabric roadmap.
Check out the February 2026 Power BI update to learn about new features.
| User | Count |
|---|---|
| 53 | |
| 47 | |
| 32 | |
| 16 | |
| 15 |
| User | Count |
|---|---|
| 86 | |
| 71 | |
| 38 | |
| 28 | |
| 25 |