When creating queries, I've noticed that there are multiple different metrics about tickets that are solved. What is the difference between Solved tickets and Tickets solved?
The Solved tickets metric can be found in the Tickets dataset. This metric tells you the number of tickets that are currently in a solved status in your account.
For example, by using the Solved tickets metric with the Assignee name attribute, you can see a snapshot of the number of tickets that are currently in a Solved status organized by what agent those tickets are currently assigned to. But, as soon as one of those tickets switches back to Pending or Open it will no longer show in the report.
The Tickets solved metric can be found both in the Tickets dataset and the Ticket updates dataset. This metric tells you the number of tickets that changed from a different status to a Solved status or a Closed status at any point. This metric excludes tickets moving from a Solved status to a Closed status so as not to double count tickets.
In the Ticket updates dataset, the data this metric shows is dependent on how you use it. For example, you can use it along with the Ticket group attribute to see how many tickets were moved to a Solved or Closed status by each of your agent groups regardless of what status those tickets are in now.
In the Tickets dataset, the metrics each specify the timeframe themselves. For example, Tickets solved - last 7 days. To see a list of the tickets solved metrics in the Tickets dataset, visit this anchor link and scroll up a bit: Ticket dataset.
A note on time
Generally, when using a time axis, like ticket solved date, you can use the Tickets dataset with the solved tickets metric and you will see the tickets that were solved on the given day. So, there is no need to switch over to the Ticket Updates dataset to try and get day-by-day information on what tickets were moved to a solved status.
If you want to see a snapshot of how many tickets were in a solved status on any given day, use the Backlog dataset to do that. Learn more about that unique dataset here: Analyzing your ticket backlog history with Explore.