Data Explorer: Fields in the Opportunities Dataset

Data fields are used to compose your dashboard charts in Visual Insights. The Data Explorer enables you to extract fields from a dataset into a chart, which you can then filter and/or pivot to visualize the insights you wish to see. This article lists all the data fields available in the Opportunities dataset.

Please see the attached Excel file to learn about the differences between the types and groups of data fields. 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 using the data fields in the 'Opportunities' data set to build custom charts, refer to our help article on how to use the Data Explorer to create custom charts.

Understanding data fields

Data fields are organized by type and group in the picker. There are also functional considerations to keep in mind 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
  Dimensions are fields for non-aggregate data. Dimensions consist of a single data point.
• Measures
  Measures are fields for aggregate 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 have logic related to advanced features.

Data field groups

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

• Requisition financials
• Common attributes
• Conditions
• Dates and durations
• People
• Other attributes
• Dynamic date formats
• Static date formats
• Filters
• Ids
• UTC stat date formats
• Offer fields by name

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, suppose a  'Pipeline history (1:n)' data field is added to a chart visualized as a table. In that case, 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 has '(list)' appended to the end of its name, it means that if the field returns multiple results when the report is run, those results will be 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 be 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 'Posting title (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).

Data fields in the Opportunities data set