Request a demo
Fill out the form and
we’ll be in contact shortly
So many estate decisions come down to the same question in the end: can you trust the data?
A board asks if a building can be closed. A CFO wants to know why their portfolio costs what it does. A sustainability lead is asked to prove a reduction, not estimate one. In all these conversations, the numbers only count if nobody in the room can question them.
JLL's latest Global Occupancy Planning Benchmark Report suggests most organisations still can't say that with confidence. And it's quietly undermining everything else they're trying to do.
The most telling finding in this year's report isn't a utilisation figure. It's a priority. Improving space data accuracy is now the number two objective in corporate real estate globally. Ahead of cost reduction. Ahead of hybrid programmes. Behind only portfolio optimisation itself.
If we think about what that actually means, the people running the world's biggest portfolios, 84 organisations covering 716 million square feet, have concluded that before they can optimise anything, they need to be able to believe their own numbers.
Only 7% of organisations rate their data capability as excellent. Nearly half sit at average or below. One in ten don't audit their space data at all. At the same time, 90% now say they use utilisation data in planning decisions, up from 70% a year ago.
Put those two findings together.
Nine in ten organisations are making decisions with data that fewer than one in ten would call excellent.
Part of the problem is what most organisations are actually measuring. Badge swipe data is still the dominant method, used by 92% of organisations, and I understand why. It's cheap, it's already there, and it produces a number.
But a badge swipe tells you one thing: a person walked through a door at a point in time. It doesn't tell you where they went. It doesn't tell you which desk they used, whether the eight-person meeting room held two people all afternoon, or whether the whole floor emptied out at 14:00.
JLL makes the same point in the report: building-level badge data can tell you how many people came in. It can't tell you which seats they used or which spaces were never touched.
That matters because almost every decision worth making happens below building level. You don't close a badge reader. You close a floor. You consolidate a team, repurpose a room, change a desk ratio. If your data stops at the front door, so do your decisions.
Here's the bit I think gets missed. High-level snapshot data doesn't just limit what you can see. It limits what you can do. Every significant estates decision has opposition: the department that doesn't want to move, the leader defending their floor, the assumption that 'we're full on Tuesdays.' The moment someone in the room can say 'that's just badge counts, it doesn't show what's really happening,' the decision stalls and everyone defaults to the status quo.
Estates don't change because a dashboard exists. They change when the evidence can't be argued with.
Data that can be challenged doesn't drive change. It gives people a reason to put the decision off. That's the real cost of the snapshot. Not the inaccuracy itself, but all the decisions that never get made because of it.
AUDE published a framework last year that puts proper language around this. It describes six levels of digitisation split into two very different states. Levels one to three are the static digital estate: data gets captured, put on dashboards and analysed.
Levels four to six are the smart digital estate: data flows in real time, systems respond automatically and eventually run themselves. It was written for universities, but the thinking applies to any estate.
The word 'static' provides a clear definition. A static estate isn't an estate without data. It might be drowning in it. An estates leader at a large UK university put it to me simply:
“We're data rich, but insight poor”.
A static estate is where all of the data describes the past. Last quarter's utilisation report. Last month's badge counts. The survey from the spring. People make decisions retrospectively, and by the time the analysis lands, the building has already moved on. That's most estates today. Retrospective inputs can only ever produce retrospective management.
A smart estate is a different thing altogether. Data is collected and analysed as things happen, so the estate can respond in real-time. Adjusting systems, releasing space, flagging problems before they become costs rather than reporting on them afterwards. AUDE calls the move from level three to level four a critical juncture, and it's right. It's the point where the estate stops being described by data and starts being run by it.
And this is exactly where accuracy stops being nice to have and becomes the entry requirement. In a static estate, an error in the data is annoying. A person will probably spot it, question it and find a way to work around it. In a smart estate, there's no person sitting between the data and the action. A wrong signal doesn't produce a wrong report. It produces a wrong decision, executed automatically, over and over.
Retrospective data can carry a margin of error because a human is in the loop. Real-time operation can't, because the data is the loop.
If you want an estate that acts on its own information, that information has to be right, at the level where the action happens, essentially all the time. You can't build a smart estate on data you'd have to double-check.
Getting to a smart estate probably means you're going to have to think about integration of systems. Occupancy feeding building management systems through open standards like BACnet and MQTT. Space data linking to booking systems, workforce planning, energy platforms and, increasingly, AI planning tools. The smart building is really just systems talking to each other, and every one of those conversations is only as honest as the data being passed along.
Integration doesn't average out bad data. It multiplies it. Connect an inaccurate occupancy feed to your HVAC and you don't get a smart building; you get a building that confidently heats empty floors. Feed stale space records into a predictive model and, as JLL puts it, the model will forecast the past, not the future.
The report backs this up in its AI findings. Only 8% of organisations have got beyond pilots in occupancy planning, and when asked what's holding them back, 34% say poor data quality outright. It's not just cost and infrastructure. They're admitting the data itself isn't ready. The 73% with data governance programmes have a head start. The 27% without are building on sand. And this isn't just a property problem. Gartner has estimated that poor data quality costs the average organisation $12.9 million a year, and that 59% of organisations don't measure their data quality at all. Sound familiar? One in ten estates teams aren't auditing their space data either. The pattern is the same right across the business. The cost just hides in different budgets.
That's the real meaning of the static-to-smart journey. Every layer you add, whether it be dashboards, analytics, integrations, automation or even AI, amplifies whatever sits underneath it.
Accurate data compounds value at every level. Inaccurate data compounds error, faster and more expensively than you ever could manually. The organisations that get to smart first won't be the ones that bought the most technology. They'll be the ones whose foundations could take the weight.
As we know, the estate is usually the second biggest cost after people. JLL puts global utilisation at 56% against a 74% target. That gap is floors being heated, cleaned, leased and maintained for people who aren't there. You can't recover that cost with confidence unless you can prove, at space level, exactly where the waste is.
These are the decisions careers and capital get staked on. Consolidations, disposals, lease exits, refurbishments. Multi-million, mostly irreversible commitments. The cost of measuring properly is a rounding error against the cost of getting one of those wrong, or the cost of not being able to make it at all.
On top of that, the regulatory direction only goes one way. Sustainability reporting increasingly wants evidenced reduction, not estimates. An assumed saving is worth nothing in an audit. And there's a harder problem coming behind it. Most organisations have already taken the easy wins on Scope 1 and 2: greener tariffs, LED replacements, plant upgrades. Those levers are close to being exhausted, and a greening grid will only carry Scope 2 numbers so far. What's left is demand. The gas and electricity a building burns is driven by how it's actually used, and you can't cut energy for occupancy you can't see. The research backs this up. Studies across commercial buildings put the savings from factoring real occupancy into building operation at 10-40%, and around half of a typical commercial building's energy is used outside working hours. Heating, cooling and lighting space nobody is in.
Everyone is heading to the same point: the remaining reductions will have to come from fine-tuning buildings around real usage, hour by hour and space by space. That's an accuracy problem before it's an energy problem.
And the point the whole industry has now agreed on: accurate data is the ticket to everything that comes next.
A static estate can limp along on approximations because people catch the errors. A smart one can't.
The organisations investing in data quality today aren't just improving their reporting. They're buying the only route from retrospective management to real-time intelligence.
One more finding from the report worth mentioning. Change management programmes have dropped to 31% of organisations even as attendance mandates rise, and JLL warns that structure without support produces compliance, not engagement. I'd say the same about data. A number without trust produces reports, not decisions. Accuracy is what turns information into authority.
Anything less isn't occupancy intelligence. It's an expensive way of describing the past.
