From workplace data to better decisions: Measure, Model, Transform

Can we consolidate space without making the busiest days harder? Does our office support the way people work now? Do we need more meeting rooms, or a different mix? And how do we plan for growth when future attendance is uncertain?

These are consequential questions. The answers can shape leases, investment, working policies and employees’ everyday experience. A utilisation dashboard can help identify a problem. Deciding what to do about it takes another step.

In our webinar, A Decade of Workplace Transformation, Yodit Stanton and Vu explore how workplace teams can connect evidence with action through Measure, Model, Transform (MMT).

Watch the webinar recording

We released MMT after seeing a recurring challenge across years of workplace projects: organisations could collect useful data and still struggle to make a decision. Teams needed a practical way to bring observed behaviour, employee needs and business priorities into the same conversation.

The framework gives that conversation a structure. Measure establishes what is happening. Model compares the options and their risks. Transform puts the chosen approach into practice, then checks whether it worked.

Start with the decision you need to make.

Imagine an organisation considering closing a floor. Average occupancy suggests there is plenty of spare capacity. But averages can hide the Wednesday peak, teams that attend together and a shortage of small meeting rooms.

Before committing, the organisation needs to understand those patterns.

Different sources answer different questions. Sensors reveal how spaces are used. Bookings show intended use. Surveys and interviews help explain preferences, frustrations and requirements. Headcount forecasts, property costs and operational constraints provide further context.

Where reliable utilisation evidence is missing, we deploy sensors. The aim is to gather evidence appropriate to the decision, with enough coverage to understand the patterns that matter.

That does not always require permanent monitoring everywhere. A rolling programme uses a reusable pool of sensors to study priority areas, then moves them as teams review the findings and work through their options. Coverage stays in place where ongoing booking or operational needs require it.

This creates opportunities to act during the programme. The right review point depends on whether the data is representative and whether the organisation is ready to decide.

Compare the choices before committing to one.

Returning to the floor-consolidation example, there may be several credible options: release clearly unused areas, consolidate selected teams, or close a whole floor while changing attendance arrangements.

Each option has different implications for cost, capacity and employee experience.

Scenario modelling makes those implications easier to examine. What happens if attendance increases? If headcount grows? If a hybrid policy changes from two office days to three? Where would pressure appear first?

Meeting rooms offer another example. Complaints about availability might reflect large rooms being used by small groups, bookings that go unused, or demand concentrated at particular times. Building more rooms could help, but changing the room mix or booking arrangements might address the problem more effectively.

A useful model makes its assumptions visible. It helps stakeholders compare alternatives and understand where a decision is more sensitive to change. Forecasts support judgement; they do not remove uncertainty.

Make the change, then test the outcome.

A consolidation plan only succeeds if people can still find suitable places to work. A redesigned office needs to support the activities it was created for. A new attendance policy may change demand in ways that require adjustment.

That is why MMT is a loop.

After implementation, teams return to the evidence. Did pressure move from desks to meeting rooms? Are shared spaces being used as intended? Have the expected benefits materialised? What needs to change next?

Tessellate supports this approach through technology enabled workplace strategy: bringing relevant data sources together, exploring scenarios and helping teams prepare clear evidence for different stakeholders. The value lies in supporting the decisions around consolidation, hybrid working, room provision and future space, throughout the engagement.

Before your next workplace commitment, ask:

  • What do we know about current behaviour, and where are the gaps?
  • Which alternatives have we compared, and what assumptions underpin them?
  • Who needs to agree the decision, and how will we know it worked?

Those questions turn measurement into a practical starting point for change.

Watch the webinar above to explore the framework. If you have a workplace decision coming up, we would welcome a conversation about the evidence and options that could help you move it forward.

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Yodit Stanton

Yodit has designed and built large scale data systems for various sectors and has played a key role in leading development teams to run critical trading and machine learning infrastructure for FTSE500 companies such as, Deutsche Bank, Man Investments, Nomura and Lehman Brothers. With over two decades of experience as a Data and Machine Learning Engineer, Yodit specialises in predictive modeling for real time systems, social network analysis and middleware development.