Use card date filter values as variables in Beastmode calulations




We've been struggling with YoY comparison since we signed with Domo about two years ago. We quickly been able to do what we wanted with Beastmode calculations. We built some awesome charts like the one shown below where we compare the current and previous year, the budget for current year and the growth all on the same chart.


The limitation of using beastmode calculations is this limits the card to show only 2 years of data and makes the date filter useless, while for analysis purpose, the business absolutely needs to be able to play with the date filters to see Y-1 vs Y-2, Y-2 vs Y-3 etc. Right now, to fulfill this important need, we would have to duplicate the cards and obviously, create a complete new set of cards and beastmode calculations for each of previous years.


With the deployment of the new YoY function, we tought maybe we could have been able to replace beastmode calculations and play with date filters on top of the card, but unfortunately, we can't because the function is limited to one dimension only making this kind of graph impossible to build.


YTD Sales Growth vs Plan - DCI (USD).png

So the idea I watnt to suggest here to fix this is to be able to use the date filter values into Beastmode calculations as variable/function. I think this should not be so difficult to implement for Domo's developpers and would fix a bunch of issues like that.



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  • This idea would be very usefull. In fact, not only a date filtered get value fonction should be accessible in beast mode but all filtered value (page filter, card filter or Quick filter).



  • btm



    Thank you for submitting this @ManseauN. I am assigning to our product manager @ckwright to review and comment.

  • Hi All,

    We also have often found ourselves in the situation were we could have taken major advantage by the possibility to pass a value selected in the filter to a beast mode.

    So for us it would be a great improvement.

    Thanks for submitting the idea!


  • Hi, 

    Are there news of this request? 
    We are really struggling without this feature!

    Hoping for a "yes, it's been implemented!" ?



  • I wish we could change the subject of this idea.  I partially want to create a new one.  The idea of being able to leverage a value from a filter in a Beast mode is huge!  Example.


    When we process returns we track whether the reason for the return originates form the customer or from us.  We would like to see a chart that shows total returns as a percentage of sales, then be able to click a filter to alter the graph to then show only the returns where we were the root cause (reason for the return).  as of today if we were to filter, we would end up with returns being 100% of sales as we would then only have the records where there was a return of that type.  All others would be omitted. 


    So instead being able to dynamically calculate the numerator based on the presence of a filtered value would be AMAZING!   That is just one idea but I could see so many different places where this would be beneficial. 

  • jaeW_at_Onyx
    jaeW_at_Onyx Budapest / Portland, OR 🔴

    just change how you structure your data.

    On your Sales data add an 'isReturn' binary (true /false) and ReturnReason.

    Jae Wilson
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  • Agreed.  @jaeW_at_Onyx I can think of a number of different ways that I could structure the data in order to achieve the same goal.  However all alternatives that I can think of have drawbacks/limitations.


    currently we can see the sale of an item and if it was returned in the same record.  I think what you are suggesting is to split the return and the sale into 2 different records.  IF we were to do that we would lose the visibility, in the same record, to whether or not the item was returned.  Could still get there but would be more work.


    I could also summarize the data and just have 3 different views, but then we loose the granularity of the data and we have yet another dataset.  I try to not have dataset proliferation as it becomes very difficult to remember what every dataset is used for.  we tend to try to make our data sets as versatile as possible and only create new ones when absolutely necessary.   


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