{{Infobox
bodystyle = width:20em; |
title = Hide Columns |
titlestyle = |
headerstyle = |
labelstyle = width:33% |
datastyle = |
image = 200px |
imagestyle = |
header1 = |
label1 = Type |
data1 = CXO-Cockpit Tips and Tricks |
header2 = |
label2 = Audience |
data2 = CXO-Cockpit Captain |
header3 = |
label3 = Difficulty |
data3 = Medium |
label4 = Applies to |
data4 = Multi-Column Template,Columns List |
In some cases it is useful to have columns hidden based on the Point of View selection.
For example, if your company does not want to compare to the forecast until the month of April has reached (typically because the information in the forecast is not relevant until that month), we recommend to apply custom calculations to hide these columns.
In this example, we would use the MEA (measure) dimension to create a custom calculation to apply the logic for hiding the column with
WHEN
PER.PER.CurrentMember.Name = 'Jan' or
PER.PER.CurrentMember.Name = 'Feb' or
PER.PER.CurrentMember.Name = 'Mar'
THEN
NULL
ELSE
MEA.MEA.None]
END
If you apply this measure to the columns of the forecast, these columns will automatically hide when the point of view is set to Jan, Feb or Mar (be aware that dependent on your configuration of your Hyperion system, you will have to use 'Jan' or 'January' etc)
In order to complete this functionality, you have to put the suppression setting of these columns to 'SuppressNodataandZeros'.
It is important to know that all variances referring to this column, also disappear, because the VAR function result for variances referring to this NULL column also returns NULL. Please be aware that you also have to apply 'SuppressNodataandZeros' to the Var columns.
This tip is also applicable for other common situations, such as hiding forecast versions that are irrelevant for that specific month. If you include all forecast versions in the columns and apply different measures to these columns so that only one of the forecast versions shows up for any given month, you end up with a column that only shows the comparison to the relevant forecast version.