Structure of Zendesk Explore Datasets
How are Zendesk Explore datasets structured?
Zendesk Explore datasets are structured using multiple data tables connected by unique identifiers. This structure helps in efficient data querying and avoids duplication.
Data tables are joined using connection points, like Ticket ID for tickets and Requester ID for users. This allows for complex reports that pull data from multiple tables, ensuring comprehensive insights.
More related questions
What are Zendesk Explore datasets?
Zendesk Explore datasets are collections of metrics and attributes used to create reports. Each dataset is specific to a product and must be selected before report creation. These datasets help you analyze various aspects of your business, such as…
What default datasets are available in Zendesk Explore?
Zendesk Explore offers several default datasets, each tailored to specific products like Support, Guide, and Talk. These datasets include metrics and attributes relevant to their respective areas. For instance, the Support dataset includes ticket…
Can I use custom fields in Zendesk Explore datasets?
Using custom fields in Zendesk Explore datasets can be challenging, as they may not be directly available. If you're trying to use custom fields in a specific dataset, like the Sales database, you might need to explore alternative methods or…
Why do solved ticket metrics differ between datasets?
Solved ticket metrics may differ between the Tickets and Ticket Updates datasets due to the way data is recorded and updated. The Tickets dataset might show a different value than the Ticket Updates dataset because they track different aspects of…
How do data tables connect in Zendesk Explore?
In Zendesk Explore, data tables connect using the LEFT JOIN method, which ensures all rows from the left table are included, even if there are no matches in the right table. This connection method allows for comprehensive reporting by joining…
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