Forum Discussion
2 date fields in different table
having a calendar table makes your life much easier (because you get to use the time intelligence functions)
you can do those calculations without it but you'll have to write a bit more
look here at this link provided by Matt
http://www.powerpivotpro.com/2015/02/create-a-custom-calendar-in-power-query/
Your graph is exactly what I need. Here is what I have:
Sorry for the picture, for that metrics I am not using any measures, hope that screenshot is better
If that can help!
Well, first thing I would try would be to relate your Date from Forecasting to your Date in YTD. Then, I would see if you can drop down your Date column in your Axis and see if you can choose something like "Day". Is the format of your date something like:
1/30/2016
?
My X-axis is set to "Continuous" and "Linear" if that makes a difference.
Maybe one of those is a cumulative/running total? Why is the difference so much?
Try creating Runing Totals for each like so
Forecast RT = CALCULATE ( SUM(Table[Forecast], FILTER(ALL(Table), Table[Date] <=MaX(Table[Date]) ) )
Actual RT = CALCULATE ( SUM(Table[Actual], FILTER(ALL(Table), Table[Date]<=MAX(Table[Date]) ) )
I'll try that but to answer your question, depending the date I select, one will show and dupicate the total.
So if I select the date under forecast, I will have the forcast per day with no probelem and the impression served will be the total of all impressions, if I select the date under Impression served, I will have the impressions per day with no prob and the forecast will be the total forecasted impressions :(
I'm pretty sure that if you create a table of just dates and relate both Impressions and Forecast to it like in my model, you will get the correct lines.
Here is what is going on, it just dawned on me. Because there is no relationship between Forecast and Impressions, it is doing exactly what you said, it is simply summing one or the other (all of them) for every date because there is no context filter for date essentially. So the aggregation gets everything. That is why one is so much higher than the other and why it is the same number for all dates.