I'm obtaining wrong results from a DAX formula and I can't understand why.
In my database I have articles that are composed by multiple tools, which are produced from blank tools. One blank can be used to produce multiple tools. I need to calculate blank sales by 3 time periods: last 6, last 12 and last 24 months.
This is my Power BI model:
The time period table I used for the time period slicer and the measure look like this :
To obtain Blank's sales volumes, I created 3 measures:
When I use the last formula, which I thought would have returned the right amount of Blank sold by article by time period, I obtain strange results.
When I select "last 24 months" time period, everything looks fine:
When I select "Last 12 months", the total is fine, but the total by article is wrong:
Finally, if I select "Last 6 months" time period, all the results are totally wrong:
The curious fact is that I checked the result by executing a sql query on the database, and the DAX formula returns the right result (so 1466 for the selected time period), but only when used in a card, without filtering it by Article number.
I have no other filters that affect the visuals.
Could you help me understand why I'm not obtaining the right result, or suggest a better way to reach the desired results?
I'm guessing (at least part of) the problem is that you are backing up from different end dates because LASTDATE(Sales[DocumentDate]) can return different values for different ArticleNo.
I'm not sure what value you actually want for that date, possibly LASTDATE('Dates Table'[Date]), but I'm pretty sure you want it consistent across different ArticleNo.
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I've been Googling around this problem for hours and haven't found a solution that suits my needs.
I have a large data set with agent activities and the total time in seconds each activity lasts. I'm pulling this together in a matrix, to display agent names on the left and the start date of each week across the top like so:
This is working as intended (I've used a measure to convert the seconds into hours) but I need the average of the displayed weeks as another column, or to replace the Total column.
I've tried solutions involving DAX measures but none are applicable, likely because I'm using a custom column (WeekStart) to roll up my numbers into weeks. Adding more complexity is I have 2 filters on the matrix; one to exclude any weeks older that 5 weeks in the past and another to exclude any future weeks.
In Excel I'd just add another column next to the table, averaging the 5 cells to the left of it. I could add it to the data table with a SUMIFS checking the Activity date is within the week range and dividing the result by 5. I can't do either of these in PowerBI and I'm new to the software so I'm at a loss as to how to do this.
I'm trying to figure out the difference between the in-application modifier as_rate() and the rollup function per_second().
I want a table with two columns: the left column shows the total number of events submitted to a Distribution (in query-speak: count:METRIC{*} by {tag}), and the right column shows the average rate of events per second. The table visualization applies a sum rollup on left column, and an average rollup on the right column, so that the left column should equal the right column multiplied by the total number of seconds in the selected time period.
From reading the docs I expected either of these queries to work for the right column:
count:DISTRIBUTION_METRIC{*} by {tag}.as_rate()
per_second(count:DISTRIBUTION_METRIC{*} by {tag})
But, it turns out that these two queries are not the same. as_rate() is the only one that finds the expected average rate where left = right * num_seconds. In fact, the per_second() rollup does this extra weird thing where metrics with lower total events have higher average rates.
Is someone able to clarify why these two functions are not synonymous and what per_second() does differently?
I was creating some analysis on revenue for past years. One thing I noticed is measures of revenue for each month of a year are same for every year's corresponding months. That is revenue for April 2015 is same as revenue for April 2016.
I did some searching to solve this problem. I found that our measure column 'Revenue' is aggreagted based on time dimension as 'Last(sum(revenue))'. So actual revenue values of April 2019 is considered by OBIEE as last and copied to other year's April month revenue.
I can understand that keyword 'last' may be the reason of this, but shouldn't year, quarter, month columns choose exactly those numbers that corresponds to that date? Can someone explain how this works and suggest solutions, please?
Very simply put: The "LAST" is the reason. It doesn't "copy" the value though. It aggregates the values to the last existing value along the dimensional hierarchy specified.
The question is: What SHOULD that Saldo show? What is the real business rule?
Also lastly: Using technical column names and ALL UPPER CASE COLUMN NAMES in the BMM layer shouldn't be done. The names should be user-focused, readabla and pretty. Otherwise everybody has to go and change it 50 times over and over in the front-end.
It's been a year since I posted this question,but a fix for this incorrect representation of data was added today. In the previous version of rpd, we used another alternative solution to this by creating two measure columns of saldo ( saldo_year and saldo_month) and setting level for them at year and level respectively and using them both in an analysis. This was a temporary solution until we did the second version of our rpd since we realized that structure of the old one wasn't completely correct and it was easier and less time consuming to make it from ground and create a new one than to fix the old one.
So as #Chris mentioned, it was all about correct time dimension and hierarchies. We thought we created it with all requirements met, but recently we got the same problem in our analyses. Then we figured out that we didn't set id columns as primary key in month and quarter logical levels. After we got the data we want. If anybody faces this kind of problem, then the first thing to check in rpd is how the time dimension and hierarchy is defined, how logical levels and primary keys and chronological keys are set in hierarchy.
I have a sheet where I record my working hours (this is more for me to remind me to stop working than anything else). For every day, I have three possible shifts - early, normal & late, and I have a formula which will sum up any times put into these columns and give me the daily total hours.
To summarise the duration of time spent working in a day, I use the following formula: =(C41-B41)+(E41-D41)+12+(G41-F41) which is:
early end time minus early start time
normal end time minus normal start time PLUS 12 hours
late end time minus late start time
Which gives me output like this:
What I cannot seem to achieve is, the ability to sum the daily totals into something which shows me the total hours worked over 1-week. If I attempt to sum the daily totals together for the example image shown, I get some wild figure such as 1487:25:00 when formatting as 'Duration' or 23:25:00 when formatted as 'Time'!
All my cells where I record the hours worked are formatted as 'Time'
When using arithmetic operations on date values in Google Sheets, it's important to remember that the internal representation of a date is numeric, and understood as the number of days since January 1, 1970.
What follows from that, is that if you want to add 12 hours to a time duration, you should not write "+12" because that will in fact add 12 days. Instead add "+12/24". In other words, try the following formula instead of the one you are using now:
=(C41-B41)+(E41-D41)+(12/24+G41-F41)
I'm using Cognos Report Studio. I'm pretty new to the software. But anyway, I've created a query that is meant to count the number of days between two dates. There are multiple records and I need the average of all the days. I'm able to do all of this. But my result is 6.57254211... I want this number to be rounded. But I can't seem to figure out how to do this. I achieved the average by applying an aggregate function. I though the round would be applied the same way. But there is no Round in the rollup aggregate function option. I also tried to use _round() in my data item code, but that returned an error. Plus, I'm pretty sure that just rounds each individual number I get, not the average of all of them. Anyone know how to do this?
I was able to round my average by creating a query calculation and doing _round([Weekdays]) for the code. [Weekdays] being the average I need rounded.