Your Zendesk data is split into different datasets. Each dataset contains metrics and attributes that you can use to create Explore reports. You must select a specific dataset before you can create a report.
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.
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Understanding the available default datasets
The table below describes the datasets that are available for each product.
| Zendesk product | Dataset name | What it contains |
|---|---|---|
| Support | Tickets | Information about ticket details, like ticket ID and assignee. Does not include ticket update events. |
| Updates history | 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. Available only if you have tickets with SLA policies applied. See Defining and using SLA policies. |
|
| Group SLAs | Information about your group service level agreement (SLA)
performance. Available only if you have tickets with SLA policies applied. See Defining group SLA policies for internal teams. |
|
| Guide | Knowledge Capture | Information to help you understand the efficiency of selecting articles to deflect support tickets. |
| Team Publishing | Information to help you understand your team activity in
Guide, including when articles are created, published, edited
and more. Available only on Enterprise plans. |
|
| Knowledge Base | Information to help you understand how often your help center articles are being viewed, which articles are being voted up or down, and more. | |
| Search | Information about the searches that users performed and the terms they searched for in your knowledge base. | |
| Community | Information about the activity in your community forums, including the number of posts and comments, upvotes and downvotes, community members, and more. | |
| Messaging and live chat | Messaging tickets | Information about all messaging channels, including web, mobile, and social messaging channels. Includes number of tickets, resolution times, satisfaction, and more. |
| Engagement | Information about your customer engagement using Chat. | |
| Chat Concurrency | Information about your agents' handling of concurrent chat engagements. | |
| Talk | Calls | Information about your call center and agent activity. |
| Answer Bot | Article Recommendations | Information about the performance of help center articles automatically recommended to customers. |
| Flow Builder | Information about bot performance across Zendesk channels. | |
| Omnichannel | Agent state | Information about how groups and agents spend their time across channels. |
| Agent state daily |
Information about how groups and agents spend their time across channels, aggregated daily. Full list of metrics and attributes |
|
| Agent productivity |
Information about work items offered and assigned to agents and how agents used their capacity. Full list of metrics and attributes |
|
| AI | Generative AI agent tools | Information about agents’ usage of the following generative AI features: summarize, expand, and make more friendly and make more formal. |
| Intelligent triage | Contains metrics and attributes that relate to tickets enriched with intent, language, and sentiment.Full list of metrics and attributes |
Understanding dataset structure
Explore datasets contain all of the available information for your product. 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; instead, they're 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 report, Explore determines which tables contain the required metrics and attributes and whether 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 report 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 report 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 report 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's technically not possible to store data in multiple data tables due to the high volume of the data or high speed of the report execution required. An example of this is the Backlog dataset. This uses only one table for storing data.