Working with datasets

Creating, cloning and refreshing a dataset, and the reason to leave the standard datasets alone entirely.

Creating, cloning and refreshing a dataset, and the reason to leave the standard datasets alone entirely.

Work with them

1. Open the datasets in Explore.
2. Clone one to make your own version.
3. Add calculated attributes or metrics to the clone.
4. Build reports from it.

Clone rather than edit

The standard datasets are shared by every report using them, including the standard dashboards. Adding to one affects all of them, and removing something breaks reports you did not know existed.

Why you would clone at all

To add calculations your organisation needs everywhere: a grouping of ticket types into categories you use, a flag for your own definition of an escalation.

Putting those on the dataset means every report can use them, instead of the same formula being retyped in twelve places.

Keep the number small

One custom dataset per real need. Five near-identical clones produce reports that disagree, and nobody can say which is authoritative.

Name it so people can tell

Include what makes it different from the standard one. "Tickets with product grouping" is useful; "Tickets copy 2" guarantees the wrong one gets used.

Document the calculations

A calculated field is a decision about how your business defines something. Write down what it means and why, because in a year the formula will be readable and the reasoning will not.

Refreshing

Datasets update on the refresh cycle. A newly added calculation applies from the next refresh, so a blank column immediately after saving is usually just timing.

See also

• Creating a standard calculated attribute
• What a dataset is and why picking the right one matters
• How often Explore refreshes your data