When the Data Contradicts What People Say
Staff say the building is full and the sensors say it is half empty. Both are usually right, and the resolution is informative.
Reading · Analysis
This conflict arrives in every programme. Treating it as people being wrong is the most common and least productive response.
The boundary discussed in “When the Data Contradicts What People Say” also matters when digital work systems are introduced alongside workplace analytics. A team evaluating explore the platform in the context of self report bias should state the purpose, use only necessary settings and explain clearly what managers can review before any data is collected.
The usual shape
Occupancy reports 45%. Everybody says they cannot find a desk, a room is never free, the building is packed.
For a public, independent reference related to “When the Data Contradicts What People Say”, consult the CIBSE Knowledge Portal. Its principles provide a useful check on scope, terminology, governance and the claims made during procurement or review.
The instinct is to show them the data.
That ends the conversation and loses the information in the complaint.
Why both can be true
Averages hide peaks. 45% average with 90% on Tuesday afternoons is a building that feels full when people are in it.
Distribution. Half the desks used means nothing if the used half is the half people want and the rest is by the lifts.
Findability. Space that exists but cannot be located or booked is functionally absent.
Timing. Everybody wants a room at ten and at two; the rest of the day it is empty.
And coverage gaps, which make busy areas invisible.
Treating the complaint as data
A reported shortage is a measurement of experience, which is the thing you are ultimately trying to manage.
The useful question is not "are they right" but "what are they describing".
Ask: when, where, trying to do what. Those three answers usually locate the problem in the data within an hour.
What it usually turns out to be
A peak-hour problem rather than a capacity problem.
A specific area or a specific room type.
A booking system that makes space hard to find.
Or a type of space that does not exist: somewhere to take a call, somewhere quiet.
Each has a cheap answer and none is "build more".
When the data is wrong
It happens: a sensor counting a walkway, a gap over the busy corner, a timeout mis-set.
The complaint is frequently the first symptom, and dismissing it means the error persists.
Check the data before trusting it over the room.
The credibility cost
A programme that uses its numbers to tell people their experience is mistaken loses the building.
After that, every figure is contested and the programme stops being able to inform anything.
Which is a practical reason, beyond the obvious one, to take the complaint seriously.
How to report the resolution
"You said the building is full. The figures show Tuesday afternoons at 90% and Fridays at 20%, and the shortage is in small rooms. Here is what we are changing."
That reply uses both sources and is believed.
What to check
When staff last contradicted your data, what happened?
Did anybody check the sensors before dismissing it?
Do your reports show the peak as well as the average?
And is there a route for people to report a shortage that reaches whoever reads the data?