It's Thursday afternoon. Your Head of Facilities has a report due to leadership by Friday. She's pulled data from the occupancy sensor platform, the desk booking system, the BMS, and two separate spreadsheets maintained by different teams. The numbers don't quite agree. The space names don't match up. One system says the third floor is running at 60% occupancy. Another suggests it's barely used.
She's spent the better part of two days trying to reconcile the data, and she still doesn't trust it. By the time the report lands on the leadership team's desks, it's already out of date, and everyone in the room knows the numbers could be challenged.
This isn't an unusual week. For a lot of workplace and facilities teams, this is every week.
There's a tempting assumption buried in how many organisations think about this problem: that if they just had more data, or better data, things would be clearer. It's the wrong framing.
"The challenge today isn't collecting more data," says Nathan Colle, Head of Professional Services & Operations of Metrikus. "Most organisations already have plenty of it. The difficult part is making sense of that data altogether."
In a typical mid-market enterprise, building data sits across sensors, booking platforms, access control systems, energy management tools, FM ticketing software, and standalone spreadsheets, often managed by entirely different teams with different priorities. Each of those systems describes the building in its own language. Space names don't always align. Occupancy is measured differently from one platform to the next. Even straightforward questions like which floors were busy last Tuesday can become surprisingly difficult to answer consistently.
The problem is compounded by organisational structure. The team managing the BMS has different priorities from the team managing desk booking. The facilities team running the ticketing system isn't necessarily talking to the people pulling occupancy reports. Nobody owns the joined-up picture, because joining that picture isn't anyone's job. It falls to whoever needs to answer a question, usually at short notice, and usually with a deadline attached.
The result is that the data exists, but it can't really be used. Not quickly, not confidently, not without someone spending significant time pulling it all together first.
When organisations think about the cost of fragmented building data, they tend to think in terms of software licences and integration projects. But the real cost is harder to quantify and therefore easier to ignore.
"The hidden cost that you never really see," says Colle, "is probably time, trust, and wasted effort."
Time is the most visible of the three. Workplace teams spend days preparing reports that should take hours. Facilities managers end up pulling together information manually that, in a better-connected system, would already be available in a single view.
Industry research consistently suggests that data professionals spend upwards of 80% of their time finding, preparing, and validating data, leaving less than 20% for actual analysis. For workplace teams without a dedicated data resource, that imbalance is likely even more pronounced.
Trust is the cost that compounds. When different systems produce slightly different answers to the same question, confidence in the data erodes. And once the data is distrusted, it stops being used. Leadership waits longer for information. Decisions get delayed. Teams default to anecdote and gut feel, not because they want to, but because it feels safer than presenting numbers that might be challenged.
"People will start to lose confidence in the data itself," says Colle, "because different systems are telling a slightly different story."
Wasted effort is the least visible cost of all. It's the energy spent validating rather than acting. The hours given to reconciling reports that should already be aligned. The collective resource absorbed by a process that produces uncertainty rather than clarity.
The operational consequences tend to reveal themselves in a specific way. A workplace team knows something isn't right: employees are complaining that the building feels overcrowded, that meeting rooms are impossible to find on certain days. But the data says utilisation is fine. There's a gap between what the numbers show and what the building actually feels like.
That gap exists because the data is being read in isolation. One system shows aggregate occupancy. Another shows room bookings. Neither is joined to environmental data, or to access control, or to the FM ticketing system that's logging complaints. Nobody has a complete picture. And because nobody has a complete picture, the response is almost always reactive.
"Teams end up reacting to the complaints and the issues after they happen," says Colle, "rather than spotting any early patterns and being able to proactively resolve any issues that might occur before they become a problem."
This is where the hidden cost becomes a real operational one. Fixed cleaning schedules run regardless of where people actually are. Heating and cooling systems operate on assumptions rather than live data. A floor that's consistently over capacity on certain days doesn't get flagged until someone complains. And even then, the team has to go back to multiple systems to understand what actually happened and when. The building isn't being managed on evidence. It's being managed on lag.
The organisations that are starting to move past this aren't necessarily spending more on data collection. They're changing how they connect what they already have.
The shift is from a model where building data lives in separate systems and gets pulled together periodically, usually by hand and usually for a specific report, to one where that data is continuously connected and available in a form that operational teams can actually use. When operational teams and leadership are looking at the same picture, built from the same underlying data, the nature of the conversations they can have changes.
When that connection happens, things change quickly. The conversation internally shifts from "what do the numbers say?" to "what's actually happening, and what should we do about it?" Decisions that were previously made on perception or anecdote start being made on a clear, shared view of how the building is operating.
The data problem most organisations have isn't a shortage. It's a usability gap, and increasingly, the most effective workplace teams are closing it.
If you're thinking about how to get more from your existing building data, we're always happy to talk through what that looks like in practice. Explore how Metrikus connects building intelligence.