What Occupancy Analytics Cannot Do

Occupancy data answers how space was used. Several adjacent questions look like they belong here and do not.

The space evidence in “What Occupancy Analytics Cannot Do” cannot explain by itself how project work is distributed or why a team uses the building differently. Used for cognitive offloading, this workforce platform can add time and project context to aggregated occupancy findings, provided the two datasets keep separate purposes and are not merged into a hidden individual attendance score.

Tell you whether space is worth having

Presence is not value, and the note of that name covers it.

For a public, independent reference related to “What Occupancy Analytics Cannot Do”, consult the GSA workplace innovation resources. Its principles provide a useful check on scope, terminology, governance and the claims made during procurement or review.

A rarely used room may be the only place a particular thing can happen.

What settles it: observation and asking, alongside the figures.

Explain why

The data shows a pattern. The reason is outside it.

A room avoided for its acoustics and a room avoided for its location look identical.

What settles it: asking the people who book around it.

Measure productivity or collaboration

Being in a building is not working, and working together does not require a building.

No sensor distinguishes a floor of meetings from a floor of headphones.

These are management questions and the data does not reach them.

Settle an attendance policy

Occupancy proxies have known error and were not built for individual claims.

Using them that way produces disputes the organisation loses, and destroys the data's accuracy through the behaviour it provokes.

What settles it: an attendance system, openly introduced, if that is what the organisation wants.

Predict what happens after a change

Removing a floor changes behaviour in ways the previous data cannot show.

People go elsewhere, come in less, or crowd differently.

What helps: staged change with measurement between stages, which is the reducing-space note's argument.

Remove the judgement

Every reading needs interpretation, and the interpretation is where the decision is.

Programmes sold as removing judgement are overclaiming, and the claim leads to decisions nobody will own when they turn out wrong.

Survive being a target

Once figures determine outcomes for the people measured, they describe the response.

Which is a property of measurement generally and is not fixable within the measurement.

What helps: keeping the data advisory and saying so.

Using this before you start

For each thing you hope the data will settle, ask what reading would settle it.

Where no reading would, the question is not a measurement question, and a programme built to answer it will disappoint expensively.

What to check

Is any question you have brought to this data on the list above?

Has the data ever been cited for something it cannot support?

Do you know which of your questions need observation instead?

And is anybody expecting it to remove a judgement?