Downtime and Availability report using different issue types

Hi all. I need a little help if possible please.

I’m trying to build a report which shows the following:

  • Minutes of planned downtime - this is captured in issue type: Incident
  • Minutes of unplanned downtime - this is captured in issue type: Sub-Task
  • Uptime % - this is captured in issue type: Sub-Task

I’ve got the measures set up so the values are populated in a table in EazyBI, but because I am getting the information from different issue types, the table also contains information that I don’t want to appear in the chart. I hope that makes sense. I’ve included 3 images below to hopefully make this a little clearer.

  • The first is of the chart in PPT to show something similar to what I am trying to achieve.
  • The second is the table of data that contains the information I need, plus the info I don’t need
  • The third is of the combi bar and line chart that the table above generates. I have circled the three elements I want to remove from the chart

I suppose put simply, I want the combi bar and line chart, but so that it only shows:

  • Inc-Impact Downtime in Minutes Unplanned Downtime
  • Downtime in Minutes Planned Downtime
  • Inc-Impact Dynamic Uptime % Unplanned Downtime


Thanks in advance!

Hi @dwightman ,

From the screenshots, it seems you can define calculated measures, narrowing the scope of the specific measure. That will require removing the dimension returning Unplanned/Planned downtime from the report columns. For example, the calculated measure for “Inc-Impact Downtime in Minutes Unplanned Downtime” could look similar to the one below:

([Measures].[Inc-impact Downtime in Minutes],
[DIMENSION_NAME].[Unplanned Downtime])

Replace DIMENSION_NAME with the name of the dimension in columns. The formula in the calculated measure forms a tuple. With this example, you should be able to define the two other calculations. See more details about tuples and calculated measures on the eazyBI documentation page.

Roberts //

Thank you so much @roberts.cacus!