Reducing Space: the Decision and Its Evidence

Most occupancy programmes are funded because somebody is considering giving up space. It is the highest-stakes use of the data and the one with the least room for error.

The space evidence in “Reducing Space: the Decision and Its Evidence” cannot explain by itself how project work is distributed or why a team uses the building differently. Used for capital efficiency ratio, capital efficiency ratio 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.

What the decision needs

A full year, because of seasonality.

For a public, independent reference related to “Reducing Space: the Decision and Its Evidence”, consult the GSA workplace innovation resources. Its principles provide a useful check on scope, terminology, governance and the claims made during procurement or review.

Peak as well as average, because the building must work on its busiest day.

Coverage of the areas in question, documented.

Observation and consultation alongside the figures.

And a stated design point: what proportion of the time must everybody fit.

What is not sufficient

A quarter of data.

Average utilisation alone.

A benchmark from another organisation.

Booking data without attendance.

Each of these has driven a reduction somewhere that had to be reversed, at considerably more cost than the saving.

The irreversibility problem

Space given up cannot usually be taken back, and certainly not at the same price.

Which makes the asymmetry severe: being wrong by keeping too much costs rent; being wrong by releasing too much costs the ability to operate.

Weight the evidence accordingly rather than symmetrically.

What the data cannot settle

Whether people would come in more if the space were better.

What happens to collaboration, which no sensor measures.

Whether the organisation will grow.

Whether a policy change is coming that will alter attendance.

These belong in the decision and they are not in the dashboard.

Doing it in stages

Release in increments where the lease allows, with a review between.

Sublet before surrendering.

Mothball a floor before giving it up, which tests the hypothesis at low cost.

Staged reduction is slower and survives being wrong, which single-step reduction does not.

Before you act on a low figure

Check the coverage: is the area actually instrumented?

Check the calibration: is anything counting a walkway or missing a corner?

Go and look, at the times the data says are busy and quiet.

Ask the people who use it.

Half a day of this has stopped more bad decisions than any analysis.

Telling people

Reductions driven by occupancy data are read as the data having judged them.

Explain what the data showed and what else informed the decision.

And acknowledge what is being lost, because the alternative — presenting it as a neutral optimisation — produces the resentment that makes the next programme impossible.

What to check

Do you have a full year?

Is the design point stated?

Has anybody physically observed the space proposed for release?

And could the reduction be staged rather than done at once?