Understanding datasets

Explore Professional or Enterprise









Your Zendesk data is split into different datasets. Each dataset contains metrics and
attributes that you can use to create 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.
This article contains the following topics:

Understanding the available default datasets
Understanding dataset structure

Related articles:

Working with datasets
Setting dataset permissions



Understanding the available default datasets

The table below describes the datasets that are available for each product.


Tip: Need help finding the right metrics and
attributes to measure success and make smart business decisions? If so, check
out Top 18 customer service metrics to
measure.




Table 1. Explore default datasets







Zendesk product area
Dataset name
What it contains




Support
Tickets
Information about ticket details, like ticket ID and
assignee. Does not include ticket update events. Full list of metrics and
attributes



Updates history
Information about updates made to tickets during their
lifetime. Full list of metrics and
attributes



Backlog history
Information about your unsolved tickets at the end of a given
date. Full list of metrics and
attributes



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.
Full list of metrics and
attributes



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.
Full list of metrics and
attributes



Knowledge
Knowledge Capture
Information to help you understand the efficiency of
selecting articles to deflect support tickets. Full list of metrics and
attributes



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.
Full list of metrics and
attributes



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. Full list of metrics and
attributes



Search
Information about the searches that users performed and the
terms they searched for in your knowledge base. Full list of metrics and
attributes



Community
Information about the activity in your community forums,
including the number of posts and comments, upvotes and
downvotes, community members, and more. Full list of metrics and
attributes



User session
Information about user session activity from the help center
to assess how effectively visitors are locating the answers they
seek. Full list of metrics and
attributes



Page efficiency
Information about help center page analytics to pinpoint
self-service content that effectively provides answers, as well
as content that may need enhancement. Full list of metrics and
attributes



Quick answers
Information to help you understand how your customers
interact with your help center, including what they’re searching
for and how successfully they find answers. Full list of metrics and
attributes



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. Full list of metrics and
attributes



Engagement
Information about your customer engagement using
Chat. Full list of metrics and
attributes



Chat Concurrency
Information about your agents' handling of concurrent chat
engagements. Full list of metrics and
attributes



Voice
Calls
Information about your call center and agent
activity. Full list of metrics and
attributes



Answer Bot
Article Recommendations
Information about the performance of help center articles
automatically recommended to customers. Full list of metrics and
attributes



Flow Builder
Information about bot performance across Zendesk
channels. Full list of metrics and
attributes



Omnichannel
Agent state
Information about how groups and agents spend their time
across channels. Full list of metrics and
attributes



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



Custom queues
Information about queue volume and efficiency over time. For
example, the maximum, minimum, and average of queue wait
time. Full list of metrics and
attributes



Engagement
Information about agent contributions in any given ticket.
Includes metrics like engagement duration and assignment to
first reply time. 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. Full list of metrics and
attributes



Intelligent triage
Metrics and attributes that relate to tickets enriched with
intent, language, and sentiment.Full list of metrics and
attributes



Copilot suggestions
Information about how agents engage with Zendesk AI
copilot-powered suggestions, including similar tickets, merging
suggestions, suggested macros, suggested replies, and quick
answers. Full list of metrics and
attributes



Copilot auto assist
Information about how agents are using auto assist and
checking procedure adoption, ticket volume, number of executed
actions, and acceptance rate. 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.