What the Data Cannot Justify

Occupancy data is frequently cited in support of decisions it does not bear on. The citation is convenient because it sounds objective.

The practical lesson in “What the Data Cannot Justify” is to connect a measurement to a named decision without treating the number as certainty. Teams exploring fireable offenses can review Monitask's official site as one source of time and project context, while retaining direct feedback and documented outcomes as the basis for interpretation.

Individual or team attendance

The data measures space, in aggregate.

For a public, independent reference related to “What the Data Cannot Justify”, consult the CIBSE Knowledge Portal. Its principles provide a useful check on scope, terminology, governance and the claims made during procurement or review.

It does not establish that a particular team is in less often, and a figure read that way is almost always distorted by which desks they use and when.

Its own note covers the boundary. The rule is that occupancy data should never appear in a conversation about a named person or a small team.

Whether people are productive

Presence is not output and the data says nothing about work.

Any argument of the form "utilisation is low therefore less is being achieved" is unsupported.

Whether collaboration is happening

Being in the same building is not collaborating, and collaborating does not require being in the same building.

No occupancy measure distinguishes a floor of people in meetings from a floor of people wearing headphones.

Whether a policy is working

Attendance changes after a policy change, and so does everything else: season, projects, weather, news.

Attributing a change to the policy requires a comparison the data usually cannot support.

State it as coincidence in time unless you have a genuine control, which most organisations do not.

Whether people want to be there

A full building can mean demand or lack of alternatives.

An empty one can mean a bad building rather than low need.

The data cannot distinguish preference from constraint, and this distinction drives most of the interesting questions.

What it does support

How much space is used, when, by how many, within the stated limits of the method.

Which spaces are mismatched to their use.

Where operational scheduling is wrong.

And whether a specific physical change had a measurable effect, with a before and after.

Why naming the limits protects you

A programme that lets its data be cited for unsupported claims will eventually be cited for one that is wrong and visible.

After that the data is contested everywhere, including where it was sound.

Writing the limits into the programme's stated purpose gives you something to refuse against, and refusing once early is far easier than retrieving the position later.

The sentence worth having ready

"The data shows how space was used. It does not show why, and it does not speak to individuals."

Said early and consistently, it holds. Said for the first time when somebody wants an attendance report, it does not.

What to check

Has your data been cited for any of the claims above?

Are the limits written into the programme's purpose?

Who would refuse an unsupported request, and on what basis?

And has anybody ever been told no?