I'm currently working on reviewing data from our past year. Simple start is comparing our volume to the previous year. The problem I'm running into is the difference in number presented between the Tickets dataset and the Ticket Updates dataset.
Over in the Tickets dataset, I create a simple query with the following:
Columns: Ticket created - Month
Rows: Ticket created - Year (filtered for only 2019 and 2020)
Over in the Ticket updates dataset, I create another simple query with the following:
Metrics: COUNT(Tickets created)
Columns: Update - Month
Rows: Update - Year
The first thing that popped out was the fact that the Ticket Updates dataset query showed a 3x difference in tickets created for a particular month than the Tickets dataset. Other months also show smaller differences as well (typically less than 10 tickets).
(Note: We moved to Zendesk in the middle of 2019 which may account for the insane difference in the Ticket Updates dataset.)
To troubleshoot, I added the metric COUNT(Tickets solved) to the Ticket Updates dataset query. Sure enough, that brought the inaccurate number more inline with the Tickets dataset count. (There are still minor differences in other counts between the two datasets too.)
But now if you compare the Solved number presented by the Ticket Updates dataset, they don't match (sometimes by nearly 40x) the numbers presented in the Tickets dataset with the following query:
Metrics: COUNT(Solved tickets)
Columns: Ticket solved - Month
Rows: Ticket solved - Year (filtered for only 2019 and 2020)
The reason I've used the Ticket Updates dataset in the past is because I want to show both Created and Solved in charts. But seeing the difference in numbers has me concern as to which is giving the correct numbers I should be looking at, especially once I start adding in other criteria to compare counts.
Could someone help explain why there is a (sometimes jarring) difference in numbers and how I should be comparing tickets created and solved?