Time and Activity: Distribution and consistency
Objective. Read a spread of attendance or utilisation across people or days and tell the difference between the level (how high) and the consistency (how even) - two different findings.
This screen answers 'is the team even or lumpy, and who sits at the edges?' It shows the distribution of a measure across people or days rather than a single average, so outliers and spread are visible.
See it in the demo
What this screen answers
A Sales or CSM person opens this when an average alone is hiding the story. It answers 'behind the team number, how spread out are people (or days), and who are the outliers?' A single mean can be identical for a steady team and a volatile one, so this view exists to show the shape, not just the midpoint - the people or days at the low and high edges are where a manager usually needs to look.
The task
Scope to one team and period, choose the measure, and read the shape before any single figure: where does the bulk sit, how wide is the spread, and are there clear outliers at either end. Then separate two questions on purpose: what is the level (is the middle high or low) and what is the consistency (is the spread tight or wide). Note outliers for Investigate rather than concluding anything about them here.
How to read it
The headline misread is confusing consistency with level. A tight distribution means people are alike, not that they are good - a team can be very consistent at a low level, or inconsistent around a high average. 'Consistent' and 'high' are independent findings and must be stated separately. Also beware small groups: with few people or days, one outlier can dominate the shape and the spread is unstable. And the level still carries the usual denominator caveat - utilisation spread is over available hours, so coverage gaps can stretch the distribution artificially.
Honest limits
A distribution shows variation, not its cause and not whether it is a problem. Some spread is legitimate (different roles, tenure, shift patterns, client mix), so a wide shape is not automatically bad. It also does not show output - evenly utilised people can deliver unevenly. Treat outliers as candidates to investigate with context, not as rankings, and confirm legitimate sources of spread with the customer.
Interpretation check
Consistency is not level. A tight spread only means people are alike, not that they are performing well, and a high average can hide wide variation. State 'how high' and 'how even' as two separate findings.
Practical task
In the demo, pick a team and write two separate one-line findings: one on the level (where the middle sits) and one on the consistency (how wide the spread is), plus the one outlier you would send to Investigate.
Self-check
Team A and Team B have the same average utilisation. What can this screen still reveal?
Different shapes. One may be tight around the mean, the other spread with outliers at both ends. The averages match but the management story is different.
A manager says 'the team is very consistent, so we are in good shape.' What do you correct?
Consistency is independent of level. A team can be consistently low. Confirm the level separately before calling it good.
A distribution looks extreme for a 4-person team. What caution applies?
Small samples are unstable - one outlier dominates the shape. Do not over-read spread on tiny groups.
Why is a wide spread not automatically a problem?
Role, tenure, shift and client-mix differences create legitimate variation. Confirm the sources of spread with the customer before treating it as an issue.
Verified vs demo. Screen is live in the demo at /#/distribution; exact controls and figures are SME-validation-pending.