While the structured self-assessment are designed to help ensure accurate data capture, there may be cases where self-assessed profiles may require a level of validation to improve data accuracy. Where it may not be practical or cost effective to have everyone's profile validated, these analytics can help pinpoint those profiles that have an anomaly which may require investigation.
Step-by-step guide
In the Admin Console, select the Analytics
Under the Analytics select the Out-Liers Page:
Use the Export functionality to export the lists of people from the appropriate panels
| Info |
|---|
The Out-Liers are based on a the analysis of everyone's Self-Assessed profile using a set of algorithms that look for anomalies that indicate a persons profile may require to be at least reviewed. e.g. Excessively high skills counts may indicate exaggeration or there may be a high variance between their Generic Levels Of Responsibility (LOR) and the skills that have been chosen. |
Related articles
| Filter by label (Content by label) |
|---|
| showLabels | false |
|---|
| max | 5 |
|---|
| spaces | com.atlassian.confluence.content.render.xhtml.model.resource.identifiers.SpaceResourceIdentifier@4b921ae |
|---|
| showSpace | false |
|---|
| sort | modified |
|---|
| typeshowSpace | pagefalse |
|---|
| reverse | true | labels |
|---|
validation Analytics Out-Liers| type | page | | cql | label in ( "validation" , "out-liers" , "analytics" ) and type = "page" and space = "STXKB" |
|---|
| labels | validation Analytics Out-Liers |
|---|
|