Hello there!
For context, we have a service department that sends work orders between each other, until now we have measured their volume based on who opens and who closes the work order, but we realized that sometimes, work orders are sent between departments and that we want to measure as "processed by".
We have a raw data table that gives us the following data:
In below example, with our past count only group A & B recieved stats for it.
We want to count to enable group C to get 1 processed for this record.
Thankful for any information that assists.
Bonus question: Can anyone point me to resources that can help in creating a timeline for a case, for below example that would be:
A->C->B with timestamps and duration on each group.
Unique ID | Open Group | Closed Group | Resolved Group | Current Assignment | History Assignment | History Previous Assignment | History Post Assignment | History Start Time | History End Time |
884934 | A | B | B | B | A | C | 13 dec 2016 13:49:59 | 13 dec 2016 14:09:09 | |
884934 | A | B | B | B | A | C | B | 13 dec 2016 15:57:44 | 13 dec 2016 15:58:18 |
884934 | A | B | B | B | A | B | B | 14 dec 2016 09:02:44 | 14 dec 2016 09:03:12 |
Hi @CauseAndEffect ,
If you still have the issue, then this might be a direction for a solution.
I loosely translated your data model to the following:
A TicketStats table was created using:
TicketStats = filter(
distinct(
Union(
SUMMARIZE ( 'Tickets', Tickets[ID], Tickets[Open] ) ,
SUMMARIZE ( 'Tickets', Tickets[ID], Tickets[Step2] ) ,
SUMMARIZE ( 'Tickets', Tickets[ID], Tickets[Step3] ) ,
SUMMARIZE ( 'Tickets', Tickets[ID], Tickets[Closed] )
)
),
not ( [Open] ="")
)
The column name is [Open], because that is the column used in the first summarize.
Hope this helps,
Jan
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