Data Explorer: Fields in the Requisition Opportunities Dataset

Data fields are used to compose your dashboard charts in Visual Insights. The Data Explorer gives you the ability to pull fields from a dataset into a chart that you can then filter and/or pivot to visualize the insights you wish to see. This article lists all data fields available in the Requisitions opportunities data set.

Please see the attached Excel file for details on the differences between data field types and groups. Given the large number of available data fields, we recommend using a keyword search in this file (Mac: ⌘ + F  || Windows: Ctrl + F ) to locate fields in the data fields table.

For instructions on how you can use the data fields in the 'Requisition opportunities' data set to build custom charts, refer to our help article on how to use the Data Explorer to build custom charts.

Understanding Data Fields

Data fields are organized by type and group in the picker. There are also functional considerations to consider when including specific data fields in a custom chart. This section provides an overview of data field types, groups, and functional considerations.

Types of Data Fields

There are three data field types you can select from when building a custom chart:

• Dimensions: Fields for non-aggregate data. Dimensions consist of a single data point.
• Measures: Fields for aggregating data, such as counts, sums, and averages.
• Filter-only fields: Nearly all dimensions can also be used as filters. However, some fields can only be used as filters. These fields typically contain logic for advanced features.

Data Field Groups

In the 'Requisition opportunities' data set, data fields are organized into the following groups:

• Requisitions
• Requisition financials
• Requisition fields (1:n)
• Requisition fields are chosen by name
• Requisition fields pivot
• Postings
• Posting opportunities
• Posting opportunities conversion (1:n)
• Posting opportunities pipeline history (1:n)
• Dates and durations
• Period-over-period analysis
• Dynamic date by interval
• Formatted dates
• General

Functional Considerations

Some data fields exhibit unique behavior when added to a custom chart. Look for the following signifiers when adding data fields to understand how they will display in your chart:

• Fan outs
  If a data field has '(1:n)' appended to the end of its name, it means that field may result in a fan out when the report is run. A 'fan out' occurs when multiple results are returned for a single field. For example, if a  'Requisition fields (1:n)' data field is added to a chart visualized as a table, that field may yield multiple rows for a single opportunity (one for each stage it reached in the pipeline). To prevent the fanning out of multiple fields in a chart, try using 'Summary list' fields (see below).
• Summary lists
  If a data field ends with '(list)', the field returns multiple results when the report runs, and those results are contained in a single cell as a comma-separated list. For example, if an 'Opportunity sources (list)' data field is added to a chart visualized as a table, opportunities with multiple sources will have them listed in a single cell, with each source separated by a comma.
• Hyperlinks
  If a data field has '(hyperlink)' appended to the end of its name, it means that the results in this field will be clickable. Clicking the result will bring you to the corresponding item in your Lever environment. For example, if a 'Requisition ID (hyperlink)' field is added to a chart, clicking a posting title in the resulting chart will open that posting in Lever (in a new browser tab). User access permissions will be enforced when an item is clicked (i.e., if a user clicks the hyperlink for an item they do not have access to, Lever will prevent them from seeing it).