Maison Lumière · Competency campaign analytics Sample

A Self-Service campaign: your administrator configured the frameworks and the invitation list, EVA ran the assessment conversation with each person, and this page is computed from what came back. Every figure responds to the filters above.

Each row shows the distribution across the four levels and, on the right, the EVA Competency Level: the mean of the level values over answered responses, on the scale from 1 to 4. The distribution is the primary reading; the mean is a summary.

The same competencies read across four cuts of the population. Competencies are the rows because their names are long; the cut is in the columns. Cells are the EVA Competency Level, coloured against the campaign mean for that competency, so a red cell is below this organisation's own norm rather than below an external standard. Groups smaller than five people are hidden.

When someone reports Advanced or Mastery, EVA asks for one worked example and scores the written answer against the CAR rubric: Context, Actions, Results. The score describes how well the claim is evidenced in that answer. It does not re-rate the person and it is not a performance measure.

Pick any two people from the list. The chart overlays their reported levels across the selected framework; the table underneath ranks where they differ most. Use it for a development conversation, a pairing decision or a succession discussion, not as a ranking.

Six questions EVA asks at the end of the same conversation. They cost the person a minute and they tell you three things a competency framework cannot: who would move, who would recommend you, and who is already working with AI. All of it is stated intention on one day, not a commitment, and none of it belongs in a selection decision.