Digital.ai Intelligence uses datasets to produce dashboards. Most of the datasets Digital.ai builds are called iCubes and they have metadata that includes back-end calculations that allow efficient analysis without overly taxing the presentation layer.
Datasets include two kinds of objects: Attributes and Metrics. Attributes give context to the data, like to which Assignment Group or technician an Incident was assigned or the date an incident is resolved. Metrics are calculations on the data, such as the number of opened or closed Incidents. Metrics can incorporate unique business rules like your organization's formula for calculating SLAs or First Call Resolution.
The presentation layer also has the ability to script objects using built-in functions. Such "derived objects" give the flexibility to add analysis that isn't built into the iCubes and the ability to prove out a concept. However, derived objects are calculated on the fly and may slow down dashboard performance to an unacceptable level.
The normal objects are sometimes referred to as "back-end objects" to distinguish them from derived objects.
Attributes show up in the dataset as green icons and Metrics show up as orange icons. Derived objects show up with fx on the icon.