Dataview Types

The type of Dataviews that you use should be based on the type of information that is most relevant for the work you do.

For example:

Employee Dataviews can be useful for front-line managers who want to view data that is summarized by employee.

Business Structure Dataviews can be useful for higher level managers who want a broad view of the organization that allows them to drill down to the root cause of performance and other issues.

Business Structure Dataviews are usually configured to show information at a specific level in the business structure. Metrics that appear in these Dataviews usually contain more detailed information from a lower level of a business structure. For example, if the structure is:

  • Region
  • District
  • Store
  • Department
  • Job
  • Employee

and the Dataview shows data at the Store Level, then you should have the ability to drill down to see more detailed information at the next consecutively lower level in the business structure of the Store you have selected (for example, the list of departments in that Store).

Note: You can only view the levels in the Business Structure for which you have access.

You can personalize each type of Dataview in a similar way to focus on relevant statistics in the data However the business structure dataview allows you to drill down to more granular levels in your organization, for example, by region, store, and department all the way down to the employee level.

Business Structure Time Series Dataviews (Analytics only)

Business Structure Time Series Dataviews show organization-related Analytics data over time. Metrics and KPIs from the Analytics component can be summarized by calendar day, week, month or quarter to allow users to identify trends in key areas like overtime and absenteeism.

Employee Time Series Dataviews (Analytics only)

Employee Time Series Dataviews show employee-related Analytics data over time. Metrics and KPIs from the Analytics component can be summarized by calendar day, week, month or quarter to allow users to identify trends in key areas like overtime and absenteeism.