Your Zendesk data is split into different datasets. Each dataset contains metrics and attributes that you can use to build queries and generate reports. You must select a specific dataset before you can query your data with Zendesk Explore.
For help choosing and managing datasets, see Working with datasets.
Use this article to help choose the right dataset for your reports and to learn more advanced information about how datasets store your business information. This article contains the following topics:
Choosing the right dataset
Datasets are available for the following products. Choose a product to read more about each dataset and discover the metrics and attributes you can use with them:
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Support datasets
- Tickets: Key ticket metrics like number of tickets, ticket activity, and ticket type.
- Ticket updates: Information about updates made to tickets during their lifetime.
- Backlog history: Information about your unsolved tickets at the end of a given date.
- SLAs: Information about your Service Level Agreement (SLA) performance.
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Talk datasets:
- Calls: Information about your call center and agent activity.
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Chat datasets:
- Chat engagement: Information about your customer engagement using Chat.
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Guide datasets
- Answer Bot: Information about the effectiveness of your Answer Bot use.
- Knowledge Capture: Information to help you understand the efficiency of selecting articles to deflect support tickets.
- Team Publishing: (Guide Enterprise only) Information to help you understand your team activity in Guide including when articles are created, published, edited and more.
Understanding dataset structure
Explore datasets contain all of the available information for your product, or product features. In order to query your data efficiently and avoid duplicate or inconsistent data, Explore groups your data into multiple data tables. You can think of a data table as a kind of "box" in which your data is stored. Each data table is not isolated, they are joined to one another by connection points special attributes that act as unique identifiers for each row of data in the table.
In the example diagram below, ticket data is stored in the Tickets data table and user data is stored in a separate Users data table. These data tables are joined in the datasets using connection points special attributes.
For example, Ticket ID is the connection point for the Ticket data table but Requester ID is the connection point for the Users table.
When a user runs a query, Explore determines which tables contain the required metrics and attributes and if the tables need to be joined. If the required metrics and attributes are located in one table then no connections (or joins) are made. An example of this is a query that counts ticket IDs by status.
However, if the required metrics and attributes are in multiple data tables then the tables will be joined. An example of this is a query that counts ticket updates by assignee name. In this case, the Ticket updates, Tickets, and Users tables are joined to generate the result.
Explore data tables are connected using the LEFT JOIN method. This means that when the tables are joined, the query returns all rows from the table on the left, even if there are no matches from the table on the right. In the example above, a count of ticket ids by assignee name will return all tickets with or without an assignee.
In some cases, it is technically not possible to store data in multiple data tables due to the high volume of the data or high speed of the query execution required. An example of this is the Backlog dataset. This uses only one table for storing data.
6 Comments
I've created a query for Call Wait Time. The default brackets are:
0-5 Sec, 5-15 Sec, 15-30 Sec, 30-60 Sec, 60-300 Sec, >300 Sec, and "Not Recorded". What does the "Not Recorded" bracket mean?
Is the image in the example wrong? Ticket ID is the connection point for the Ticket data table, but a Chat associated with Ticket ID 1203 is shown to be linked to a Ticket with Ticket ID 1202.
Hi Dennis, you were indeed correct and this graphic has now been corrected. Thank you very much for pointing this out!
On Wednesday, April 8, from 8:00am-10:00pm CDT, we'll have a special guest team of experts on hand to answer your questions about Explore: Reporting & Insights Migration in your Zendesk Explore instance for an AMA-style conversation!
Just click the "new post" button and write up your question. Be sure to be as detailed as possible. The best questions include:
Questions posted to this topic in advance of the start time will be answered first thing on the 8th, so if you've got something you want our experts to look at, post it today!
We track how many support articles are written and edited each month. Currently, I'm able to pull that data out of Insights. Is there any discussions being had about that data moving to Explore?
Another item we track, is how many new customers a month. One way to track that is to see how many new 'organizations' are added each month. I can't pull that data out of Explore either.
I would like to have both of these items on a dashboard for my team to review.
Thank you!
Hi Krista Shaver thanks for the questions.
We are looking to add article data to Explore before the end of this year. Keep an eye out on our announcements section for more details.
Your feedback on organizations is great. Would you please consider adding that to our Explore Feedback section where members of the product team will review it and folk can also vote on it - https://support.zendesk.com/hc/en-us/community/topics/360001200913
Thanks!
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