I am facing this below issue.
I need to calculate Budget variance % which is ((Actual Cost -Budget)/Budget)*100. So for that we need to sum the actual cost for a particular month say, January,2021 and have to subtract budget for that particular month(Jan) only, for each costcenter , and we have got only single budget for every month for each cost center whereas we have actual cost for each day in a month ,for each cost center ,which will be summed up. Actual cost and budget are coming from 2 diff tables say A & B respectively. Table A is having columns named Cost center,Date,ActualCost,OrderID. Table B consists of columns named, Cost center,Date,Budget. Date column is having diff set of dates in two diff tables A & B. So, when I am trying to do the required calculation, its throwing error . Can anyone please help me with the steps to follow to calculate this Budget variance?
Related
I've sifted through threads and videos trying examples, but I still seem to be stuck. Please help? DAX newbie here.
I have a data set with yearly sales with granularity by geography, device type, and another column with ~6 different technical specifications. I would like to display a dynamic waterfall chart that shows yearly changes in sales based on selected slicers/filters in the workbook.
1) Resolve in one step
=CALCULATE(SUM(Data[$]))-CALCULATE(SUM(Data[$]),PARALLELPERIOD(Data[Date],-1,YEAR))
2) Create an intermediate value for last year sales and subtract from this year's sales
LY Sales = CALCULATE(SUM(Data[$]),DATEADD(Data[Date],-1,YEAR))
VARIANCE = [$] - [LY Sales]
I would like to create a calculated column or measure that shows the time elapsed in any given month. Preferably as a percentage.
I need to be able to compare productivity (rolling total) over a month's period between different months.So creating a percentage of time passed in a month would put every month on a level playing field.
Is there a way to do this?
Or is there a better way to compare productivity between 2 months on a rolling basis?
EDIT
I am graphing sales on a cumulative basis. Here is a picture of my graph to demonstrate.[][
Ideally I would like to be able to graph another person's sales on the same graph for a different month to compare.
The problem is each month is different and I don't think power bi allows much customization of the axes.
So I figured a potential solution would be to convert months to percentages of time passed, create two separate graphs and place them on top of each other to show the comparison of sales.
Using percentages doesn't sound right here: one person's "productivity" in February will appear lower than another person's productivity in March just because February has 3 less days.
Just use [Date].[Day].
To answer the original question (even though it shouldn't be used for this), month progress percentage calculated column:
MonthProgress% =
var DaysinMonth = DAY(
IF(
MONTH(MyTable[date]) = 12,
DATE(YEAR(MyTable[date]) + 1,1,1),
DATE(YEAR(MyTable[date]), MONTH(MyTable[date]) + 1, 1)
) - 1
)
return MyTable[date].[Day]/DaysinMonth*100
Also check DAX functions PARALLELPERIOD and DATEADD.
This is the solution I settled on.
I customized the ranges for the x and y axes so they match.
For the y-axis, I simply put the range from 0 to 50+ our highest month.
For the x-axis, I created a column with the DAY function so I got a number assigned to each day of the month which allowed me to manually set the chart range from 0 to 31. I have asked another question on how to only get workdays.
I have a table in Power BI, where I have two columns like Date and Daily Targets. Daily Targets are always same on the same date so I need a measure to only SUM 1 value for 1 date instead of calculating every row because these two columns contains duplicate values. Please see at attached screenshot for the data table.
As you look into my data, there are two distinct dates and all I need is when I add this Daily Target Column in any visualization, instead of adding 11653+11653+11653 for 3rd Jan, it should only Sum 11653 for 3rd Jan. Please help me with it, I will be very grateful to you.
To get a measure that takes the maximum value of the Daily Target by date, you can do something like this:
Daily Target = SUMX(GROUPBY(Table1, Table1[Date], "Max Daily Target", MAXX(CURRENTGROUP(), [DailyTarget])), [Max Daily Target])
Assuming your table is called Table1
The inner GROUP BY says to identify the highest daily target for each date. This assumes any given date will only have a single daily target (you could equally pick the MIN or AVG as they should all result in the same number). Note, if you have a single date with 2 different daily targets, this formula will fall down because it will only pick the biggest.
The outer SUMX sums each day's biggest daily target. This is important if you are aggregating by month or year. At the end of January, you want to have up to 31 daily targets added together.
Note: In general, I would roll up the daily target by day before loading the data into Power BI. It's not fully clear from your screenshot why you have records at a lower granularity, so I can't explain how I'd do it in your particular case. However, this post by DAXPatterns.com does go into how to handle "sales vs. budget", which may be relevant to you: http://www.daxpatterns.com/handling-different-granularities/
I am working on a report that has data by month. I have created a measure that will calculate a cost per unit which divides the sum of dollars by the sum of production volume for the selected month(s):
Wtd Avg = SUM('GLData - Excel'[Amount])/SUM('GLData - Excel'[Production])
This works well and gives me the weighted average that I need per report category regardless of if I have one or multiple months selected. This actual and budget data is displayed below:
If you take time to total the actual costs you get $3.180. Where I am running into trouble is a measure to sum up to that total for a visual (This visual does not total sadly). Basically I need to sum the aggregated values that we see above. If I use the Wtd Avg measure I get the average for the total data set, or .53. I have attempted another measure, but am not coming up with the correct answer:
Total Per Unit Cost = sumX('GLData - Excel','GLData - Excel'[Wtd Avg])/DISTINCTCOUNT('GLData - Excel'[Date])
We see here I return $3.186. It is close, but it is not aggregating the right way to get exactly the $3.180:
My Total Per Unit Cost formula is off. Really I am simply interested in a measure to sum the post aggregated Wtd Avg measure we see in the first graph and total to $3.180 in this example.
Here is my data table:
As you probably know already, this is happening because measures are dynamic - if you are not grouping by a dimension, they will compute based on the overall table. What you want to do is to force a grouping on your categories, and then compute the sum of the measure for each category.
There are 2 ways to do this. One way is to create a new table in Power BI (Modeling tab -> New Table), and then use a SUMMARIZE() calculation similar to this one to define that table:
SUMMARIZE('GLData - Excel',[Category],[Month],[Actual/Budget],"Wtd Avg",[Wtd Avg])
Unfortunately I do not know your exact column names, so you will need to adjust this calculation to your context. Once your new table is created, you can use the values from that table to create your aggregate visual - in order to get the slicers to work, you may need to join this new table to your original table through the "Manage Relationships" option.
The second way to do this is via the same calculation, but without having to create a new table. This may be less of a hassle. Create a measure like this:
SUMX(SUMMARIZE('GLData - Excel',[Category],[Month],[Actual/Budget],"Wtd Avg",[Wtd Avg]),[Wtd Avg])
If this does not solve your issue, go ahead and show me a screenshot of your table and I may be able to help further.
I am pretty new to MDX but I know what I want accomplish but its proving very hard. Basically, I have a dataset where each row is a sale for a customer. I also have postcode data and the UK population at each ward.
The total population in each ward is then divided by the count of the wardcode within the data set - e.g. ward A had a population of 1,000. I have ten customers who live in ward A and so the population value is therefore 1,000/10.
So as long as there are no other dimensions selected, only the region hierarchy, I can then drill up and down and the population penetration as count of customers / calculated population value is correct. However, as soon as I introduce more dimension the total population will not sum to its true value.
So I therefore need to do the calculation above within the cube and I am trying to find the MDX function(s) to do this.
Esentially something like -
step 1) sum the number of ward codes (the lowest level of the Geographic hierarchy) and group this by the distinct ward code, eg wardcodeA = 5, wardcodeB=10 etc.
Step 2) Then take the population in each ward (which could be stored as the total at ward level and taking the average) and then divide this by the result of the previous step
step 3) sum the results from each ward at the currently select Geographical level
The fact other dimensions are changing the value of customers / population means that something in your modeling is wrong.
You should have a fact table (can be a view/concept) like this :
REGION_ID, CUSTOMER_COUNT, POPULATION_COUNT
Once you got this create a fact table and a specific measure for counting customers and population with a single dimension linked. This is the main point, do not link your measures with dimension that are not needed.