Podcast: Turning data into everyday decisions with Workplace Intelligence

Charlotte
So we’ve all been there. A stuffy meeting room, a heating schedule that doesn’t match how anyone actually uses the building, a booking system that says one thing while the floor clearly tells you another. Most people just live with it. But here with me today is Scott Murphy of Conscia UK, who has spent his career helping organisations use technology to improve people’s lives.

And that is just as relevant here today, from the campus that stopped guessing which lecture halls needed heating to the hospital that could finally see air quality in real time. So today we’re talking through various moments where the numbers maybe have surprised people in the room, certainly surprised me while I was doing the research. So welcome back to In the Thick of It. So welcome, Scott. Nice to see you again.

Scott Murphy
Nice to see you, Charlotte.

Charlotte
So should we jump straight into the numbers? Because these numbers are quite fascinating, aren’t they? And yeah. And and surprising because I think as a person who uses office buildings on a regular basis, you kind of know some of this from an anecdotal point of view.

But to see it so proven in data is fascinating.

And and there was one research that we found, wasn’t there, that we talked about where there was a four week study of an organisation that had around 1,400 desks.

When you looked at the data holistically across the building, the claim rate looked quite comfortable at 67, 71%.

So you could realistically look at that and go, okay. Well, that’s two thirds utilisation. There’s room to grow there with capacity.

But actually, when they broke that data down into more granular ways by floor, by department, it actually revealed quite severe pockets of overcrowding next to completely empty places which might have had a 150 unused desks in there, which actually reflected work levels of around $2,000,000 around various different decision scenarios.

So I think that’s quite a textbook version, isn’t it, of that overcrowding issue that can hide under data that isn’t quite granular enough?

Scott Murphy
Yeah. A 100%. And so we battled with the same thing when we were thinking about how do we solve this problem that is in the industry today about getting the data you need to be able to make these decisions. And, you know, you mentioned it in the intro, but my background has been in technology and trying to use technology to help people achieve outcomes. And there’s been lots of different applications for that over the years.

And we’ve been working in this workplace arena for probably two to three years now, I would say. And when we first came to market, our focus was based on our roots. It was the network. The network is the foundation for all knowledge we used to talk about.

We talked about how it was so important to get the infrastructure in place first, and then layer that up with things like access points and switches to give you the information you need. And there is a lot of merit in that. And there are still some people that take that same approach and there’s huge value in that. But what we found actually was that for some people that was a blocker because you just can’t make that big an investment at a frequent level.

And for other people that just wasn’t granular enough. So typical networking data is fairly high level. You see what’s happening at a floor. You see what’s happening at an office level.

And if you’ve invested a lot of money, you may see that in particular pockets, but rarely.

So what we needed was a solution that actually gave you the detail underneath.

And getting that next layer detail is crucially important. Because like you say, the trends at a high level can paint one picture, but the story underneath is anything but as simple.

Charlotte
Well, and also people when you talk about workplaces in this regard, I mean, typically it’s called smart workplaces, but it’s about workplace efficiency and experience, however you coin it.

People tend to automatically think about an office building

Scott Murphy
They do.

Charlotte
Which can be more homogeneous than other buildings, such as the ones I know you’re used to working with organisations in health care, in education, higher education. And that can be very different at a granular level, can’t it?

Scott Murphy
Oh, it can. Yeah, hugely different. You think about a hospital, for example, a single floor could have multiple different rooms. Some of those rooms could be surgery rooms.

Some of them could be daybeds. Some of them could be X-ray rooms. How do you get a picture of what is happening and what your utilisation actually is? Like you say in that example before, if I’ve got 70% utilisation across the board, perhaps that’s good.

But maybe my X-ray rooms are at a 100% capacity. Maybe I’ve got people waiting for beds and I just can’t surface them. Getting to that level of detail is where you truly understand what is happening in your environment and more importantly, what you need to do about it.

Charlotte
Yeah. Because we were talking before, weren’t we, about context?

Scott Murphy
Yes.

Charlotte
And that’s the that’s the layer that often gets missed with these types of when you’re only focused on the technology and not the context or the people. Yeah. You can miss those, like you say, those scenarios, which look good at the top level, but are presenting challenges day to day for real people. Yeah. Especially in a hospital.

Scott Murphy
Absolutely. Yeah. And what we might find is that when a particular type of space becomes a 100% utilised, then people start bleeding over into other spaces, which then skews the data for that space. So again, a space looks like it’s more utilised than it actually is.

And that’s because people are adapting. They’re making do with what they’ve got. And we see this all the time. If if something isn’t giving us what we need, then we adapt and we use something else to fill that need.

So if I need a meeting room for just two people, but none are available, I’ll book a meeting room for six because it’s free.

And we see that sort of behaviour all the time, but we’d never truly know that if we didn’t have the data to support it. Yeah. So, data is critically important in all of these decisions, making sure we get the granular so we can fully understand.

Charlotte
Because it’s not just about overcrowding and utilisation, is it? Air quality is also really And, you know, particularly in hospitals, but everywhere, because I know Harvard University did a study where they showed that a mid level CO2 range cognitive function was reduced by 15%. But at a higher range, it was reduced by more than 50%, which is quite a jump. Yeah. And if you think about that, it’s bad enough in an office environment. If you think about a surgeon Yeah. That’s a little worrying.

Scott Murphy
It’s very worrying.

Charlotte
Yeah. So it’s there’s been lots of initiatives, haven’t there, about how you can use this to improve this data to improve air quality?

Scott Murphy
Yeah, absolutely.

And the great thing about today’s world is that there are loads of places that we can get that data. So I mentioned before, when we first came to market, we had a network led approach with good reason, because the network is very important as a foundation for a lot of things. But that didn’t necessarily get people the data they wanted, and for some it restricted them because they thought that was the investment that needed to happen first.

Charlotte
Right.

Scott Murphy
Of course, today we’ve got sensors for everything. You can really easily get a temperature sensor, stick it with a bit of sticky tape on the wall, and connect that to the Internet to give you the information you need. Like, getting that data is really easy now.

But sometimes that simplicity creates another problem, which is we get these sensors all over the place, and we almost have the opposite problem, which is now too much data. Too much data in too many places. And how do we actually understand that? Is it enough to know that the temperature is a certain level? Or actually, do I want to know how that’s impacted by the people that are in the room? Is that temperature rising when the windows are open or when they’re closed? Because that’s a big difference.

And so the important thing for us when we’re trying to solve all of this problem is, yes, let’s get the data and let’s get the data in whatever way we can because there are multiple ways to get data. But crucially, once we’ve got that data, let’s make sure we understand it. And the only way you understand it really is to get all of that data in one place so that you can see it, and then contextualise it, play that data against each other so you can see how all of the things interact, and then present it in a way that IT wants to see it, facilities want to see it, your end users want to see it. Bringing all of that together in one place is really important.

Charlotte
Yeah. It’s true, isn’t it? Because and it reminds me of that Queen Elizabeth Hospital in Birmingham. They did a real time test on air quality within their hospital.

And and one of the things they did, which I thought was quite interesting, is they analysed the traffic air quality outside of Because the to your point, if you’ve got a window open and you’re getting readings inside the room, that again could give you a false reading if you’ve missed the context that the window is open. Yeah. So those kind of examples are interesting, aren’t they?

Scott Murphy
Yeah. It’s it’s really interesting. And I’ve been trying to work out at home when I’ve got my fan in my office. What is the best way to use that fan?

Should I have windows open? Should I not? Haven’t quite worked it out yet. But again, what would be really useful for me to know is what is the temperature of that room when I’ve taken into account all the variables?

Yeah. Do I need to use the fan as much as I do? I’ve got it on 09:00 till 05:00 at the moment every single day. Yeah.

But maybe I don’t need to. Maybe there’s other ways, better ways to manage it. So yeah, understanding context is really important.

And of course, comes from data, but it’s not only data that adds into that context. There’s a lot of other things you need to know.

Charlotte
Well, it’s like we were talking about on a previous episode. Though, for the fan issue, would recommend ceiling fans.

Scott Murphy
Yes.

Charlotte
I have those in my house. They were there when I bought it. And they’re great. They’re so much better than those standalone fans.

Anyway, I digress. Helpful tip for doing a heat wave. But it’s like we’ve talked about before, isn’t it? That that layering of you’ve got to have the technology, you’ve got to know the context, but you’ve also got to know the people.

And people are a nightmare. They all make decisions on the hoof and they’ll behave completely unpredictably.

Scott Murphy
They will. Yeah.

So it’s

Charlotte
looking at it over time is really important, isn’t it?

Scott Murphy
It is really important. Yeah. You’ve got to understand not just the data, but how people are using things. I liken it to my home scenario.

I invested in a smart thermometer so that I could control my heating when out and about. And the benefit of that is you know how often your heating is on and I thought, great. I can control how much we’re using this and start getting insight. But of course, I share the house with my wife and my kids.

And as much as I might want to control it, I’m not in control of how much it’s used. Yeah. And when I’m out and about and away from the house, I don’t know the context. I see it and think, oh, why is the heating gone on again?

Yeah. But I don’t know the situation that’s going on. So Yeah. The the data gets you so far.

Understanding of the context and how people are using it is something else.

Charlotte
It’s something else. Definitely.

And and that kind of leads on to the other point that we’ve talked about before, which is energy usage. And and this is obviously where a lot of this started, wasn’t it? You know, a lot of this data and analysis and this move into workspace efficiency was led initially by energy efficiency and cost reductions, obviously.

So UCL did a did a study around mapping occupancy data against seat locations in order to try and make energy usage more efficient. And it’s quite surprising what you can achieve, isn’t it? Even now when a lot of the basics have been done.

Scott Murphy
Oh, yeah. You can achieve a huge amount just by having the right insight, knowing when to turn things off like lighting, knowing when to turn a monitor off on your desk, knowing when to adjust the heating in a meeting room. All of these little things in isolation are not very much, but when you add them up across a massive building, across multiple buildings, they make a huge difference.

And energy monitoring is really important in this environment because, see, in order to achieve a lot of this stuff, you need to invest money. And when you invest money in something, you need to be confident that the amount of money you invest is gonna produce a return that’s greater than the investment that you’ve made. And of course, we can talk about intangible benefits quite a lot. We can talk about the efficiency that comes from having a meeting room when I need one and the ability to meet with clients and make really important decisions.

And a lot of those stuff are quite soft benefits. And we can put numbers on those and we can analyse them and we can justify them. But when you see hard facts, when you see the amount you were spending versus the amount you are spending Just it changes everything. It makes that investment so much easier, makes you so much more confident in the decisions that you’re making, and it frees up money to spend in other areas.

And we see this all the time. Something like smart workspaces is not just an IT conversation. It’s also a facilities conversation, a HR conversation, a finance conversation.

And although the investment may be driven from one of those individual departments, eventually over time, the benefit spreads across everything. If we start capturing data based on occupancy and how people are using the space, that may ultimately fund a deeper investment into Wi-Fi infrastructure so that we can get more accurate data and maybe start tracking assets as well as people, Start actually tagging stuff as it goes around an environment. Yeah. And this is where we get the greater investment into the organisation as a whole.

And the benefit is just tangible across loads of different areas.

Charlotte
Yeah. The asset one is quite interesting, isn’t it? Because you can you can do that with hospital beds, for example, like the gurneys.

Scott Murphy
Yeah.

You

Charlotte
know, that will sometimes go missing and you could have extra people in beds if you finally knew where they were.

Scott Murphy
Yeah. Exactly. And it’s important for cleaning as well. So it’s a use case that a lot of time people overlook, but we’ve got a lot of clients that work in like life science and research, and they’ve got some really expensive machinery that is locked down and only used by certain people, and it needs proper cleaning at the end of the day.

Yeah. When you’re trying to organise a cleaning schedule, when you haven’t got the data, you say, well, come in at the end of every day or come in at the weekend because we’re always busy during the day. But actually, maybe people are finishing up at 02:00. Maybe it’s never used on a Wednesday.

Maybe we don’t need to clean it every day. Actually, can we get data versus, you know, how people are using it to then make those decisions? Yeah. Becomes it again.

Another really useful benefit.

Charlotte
Yeah, definitely. And I and I and going back to the energy usage one, what I find quite fascinating is you don’t need to have massive incremental changes with energy like just, you know, changing a temperature by one degree, you know, because if you think about big distribution centres, for example, for logistics companies, you know, you’ve got you’ve got to have the doors open. Yes. But in the middle of winter, you need to heat the space if there are people working in the space. Yeah.

But I remember looking at stories where, you know, people had done this sort of optimisation that we’re talking about and understanding when people were in certain sections of the warehouse, for example, the heating would follow them around.

Yeah. It’s it’s but the savings were significant.

Scott Murphy
Yeah. Yeah. You don’t need a lot, do you? Mean, you can you make a few adjustments in enough spaces and the effects are cumulative and yeah, they really do make a big difference.

Charlotte
They do. And I think to your point that you made a few minutes ago, I think when doing all of this and looking at getting the right data, but also looking at the data in the right way, as you said. So overlaying that context, overlaying the human needs as well as the commercial needs.

Then using that data to make decisions then leads to better outcomes, which could be people experiences, but it could also be financial. And that’s great for the CFO, isn’t it? Because the CFO would probably previously hear slightly competing objectives, more anecdotal type stories.

Scott Murphy
Yeah.

Charlotte
Big promises from vendors. Yeah. So how so them navigating through that. And I think I love this phrase, a precisely quantified current state cost is a phrase a CFO would love.

Scott Murphy
Yes.

Charlotte
And if you can show how you move the needle from that to actually, this is what we can realistically save you. I mean, that’s a huge outcome.

Scott Murphy
Oh, yes. It’s a massive outcome. And an outcome like that requires intention.

And this is something that we’re really conscious of in Conscia is that, of course, when we talk about smart environments, we’re talking about data because data is driving everything. But data in and of itself isn’t really valuable.

What’s valuable is understanding that data so that you can drive a particular outcome and a particular decision. So when we think about this, you know, our offer from Conscia is largely based around the things most people talk about. We’ve mentioned them in this podcast, data around occupancy, data around air quality, data around energy. But what we do with our clients is we then map that to the so what conversation.

You want to look at occupancy, why is that? Are you trying to reduce real estate? Are you trying to improve collaboration between certain teams? Are you trying to minimise overcrowding?

You know, in the COVID area, as we call it, feels like ages ago, doesn’t it?

Charlotte
It does. I know. It was six years ago.

Scott Murphy
It’s

Charlotte
Yeah. That surprises me when I say it

Scott Murphy
out loud.

I know. I try not to say it out loud. But, you know, the amount of people that can be in a room in a confined space. Yeah. You know, there are reports that we can generate on that to see if people are safe. If somebody comes down with an illness, can we track who they’ve interacted with and what spaces they’ve shared with people? Yeah.

You know, understanding why you’ve got the data is what’s ultimately going to drive that outcome. And it’s the same for air quality and energy. We’re not just capturing the data for the sake of it so that it paints a pretty picture. But what is it we actually want to do with that data? How are we going to manage that data to affect something, to achieve something? So that when we’re sitting in front of our leadership team and we’re making decisions about a downsize or an investment, that there is physical, tangible data that backs it up and a trail that you can evidence that shows why we’re collecting that data and what outcome it’s producing.

Charlotte
So do you find Yeah, because I think that’s all really important because and I guess my question is, what do you find drives a lot of these projects? Like, what kickstarts an organisation into looking at this stuff? Like, thinking about those examples, like Queen Elizabeth Hospital being concerned about air quality.

Does it does it come from a particular department? Does it as you were saying, they need to know why they want it in the first place. So are there sort of your top three areas that drive it?

Scott Murphy
There are. And I think a part of this creates us a different problem that I think is worth discussing as well. But I think if you’re in a facility style environment, you are driven by space utilisation and optimisation, making sure we’ve got enough space for the people that need to use it. If I’m in a hospital environment, I’m concerned with how quickly my patients can be seen, how quickly can they get to an expert, how quickly can I get them a bed?

And in education, I’m thinking about how can my spaces facilitate the way that my students are engaging? How do they find the people that they need to and work with the people they need to?

And all of these things drive a certain behaviour. So it always starts with some level of problem. I think where the disconnect comes, we discussed this in the other episode.

It’s actually really easy to solve those problems because anyone can go out and they can buy a sensor and they can get exactly the information they want to see. If if I really wanted to, even though I’m not in any way sanctioned to do this in my office, if I wanted to know how a particular area was used, I could buy a sensor and just plug it in and hope no one notices. And I could get the data I need to get. So it’s super easy to get the data.

I think where the challenge comes in though, is when you scale that, when you start to add sensors to multiple rooms, when you start to factor in all of the individual environments that have to get data, all the multiple different sources that you can get that data from, then the challenge becomes much bigger. It’s not have I got the data, it’s how do I see that data? How do I use that data? How does it drive a decision?

So that’s the area we’re most focused on, I guess.

Charlotte
And not doing that in a vacuum because, you know, you’re one person in one office with one sensor. You would solve the problem from your perspective quite naturally Yeah. But without understanding why it might be like that in the first place Yeah. How that then has a trickle effect, you know, in my background originally was in supply chain and it was always a mantra, don’t fix that problem over here without looking 40 steps down the line because that could create a huge problem over there with the best of intentions.

Scott Murphy
It can. And we had a fascinating example. Just recently, we went to see a client and we’re approaching the meeting room that we were going to be sat in, and we looked outside and there were there were four different sensors that had been put on the wall. And we said, so what are all those sensors getting?

And the answer was, well, I don’t know. That one belongs to finance. That one belongs to IT. That one belongs to someone else.

Unless you’re joining this stuff up, it’s impossible to get a true picture. Yeah. And this is what we’re really trying to drive ultimately is a lot of customers have already invested in this stuff because it’s so easy to do. Right?

Yeah. And it’s interesting and it’s exciting.

And in fact, research shows us that I think up to 75% of people in public sector and I think commercial as well have invested in at least one sensor, but only two thirds of those people actually feel ready to deploy it and get use out of it. Yeah. So, you know, we need to understand that people have invested in this already. They’re already collecting data. So the problem is not how to get the data. The problem is how do I see that, interpret it, see everything as a whole and ultimately drive the decisions that I wanna make.

Charlotte
Yeah. And that’s I think you raise a good point where, you know, people get quite excited about it, I think. But often there can be quite significant savings from the very unglamorous things, can’t They’re they’re not the so shiny bits. But once you like you say, you got four sensors in a meeting room, what are they telling you? It could be something really basic, but it could be something really insightful once you join the dots on those plus the other ones.

Scott Murphy
Yeah, absolutely. And we talked about it quite a few times, haven’t we? But the context is really important is, you know, energy consumption might look higher on a Tuesday than it did on a Monday. But that might not be a bad thing.

That might be a thing that is a natural occurrence of the fact there’s more people in the office, more people using the technology that we’ve given them. So it could be a good thing. It’s only a problem if that energy is rising without that broader context that’s happening. So, yeah, having independent sensors gathering independent data and giving you independent stats is not necessarily helpful in and of itself.

It’s only when you piece it together to understand the full piece.

Charlotte
Yeah. And I guess what that gives eventually is a sense of predictability because we’ve talked a lot about how unpredictable people are. Yes. But they often have predictable reactions to certain situations that repeat. Yes. So if the same scenarios happen over time, if there’s a heat wave, if there’s a big sporting event going on or whatever something that or something that would impact occupancy, for example, or energy consumption.

So I guess after they’ve been on this journey for a little while, they start to get a sense of patterns and predictability, which must help.

Scott Murphy
Massively. Yeah. And so we’re big believers in automation and we do a lot of automation, but we always make sure our starting point is understanding. Understand before you automate. Yeah.

And we talked about it a little bit, but, you know, the main offer that we bring as Conscia is a centralised platform that gets all the data in one place. And once you’ve got all that data in one place, you do start to understand. You start to understand what your trends are, where your occupancy is, how your air quality is performing, what your CO2 levels are like.

You can start to understand all of that. And then eventually, you realise is from that understanding, you often make the same decisions. You often make the decisions about when to release a meeting room or when to release a desk or when you want to alter your temperature and turn it up, turn it down, what have you. And initially, you make all those decisions manually.

And then eventually, realise I make the same ones over and over again, we can probably automate this. Yeah. And that’s where the integration into things like building management systems become really important. Again, not trying to create a silo where you’ve got the data but can’t do anything with it, but actually take that data and use it to tell something else what to do.

Yeah. And that’s where that automation piece comes in, integrating with BMSs, controlling temperature, controlling lighting, all based on data and understanding.

Charlotte
And that’s a really good point as well because it’s, you know, you and I have both worked in technology for a long time. And I think I you can automate too early.

Scott Murphy
You can.

Charlotte
And and then that again causes all sorts of problems down the line. But, yeah, automating where you see repeatability is so crucial, isn’t it?

Scott Murphy
Yeah. We we had an excellent example at home recently.

So I’ve got two young kids and in the last year or so, we’ve we finally got babysitters in so that we can go out and enjoy our life

Charlotte
That’s a magical moment, isn’t it?

Scott Murphy
It’s magical. But we’ve also got smart lighting at home. We’ve got Hue. Yeah. And we had this issue where I often went up to bed before my wife and she was left downstairs.

And whenever she came up to bed, she never turned the light off. So we decided we would automate it. When it got to 11:00 at night We would turn off all the lights in the house because We would be in bed.

And then when we started getting babysitters in, we forgot to change that rule.

So we had babysitters in, it would get to 11:00 and it’d be sat in complete darkness. All the lights have gone out. Wondering what had gone on.

So, you can automate too soon. Yeah. Yeah. You have to understand everything.

Charlotte
Yeah. Understand all the criteria, all the context, as we’ve said all the Yeah. Way No. It’s interesting. And I and I think, you know, there’s when you’re do you find that when you talk to people in finance? Because, you know, we’ve talked a lot about facilities and HR and IT, which are obviously right on the front line.

But do you find that finance who can be sometimes in a bit of an ivory tower, do they understand or how what’s their viewpoint on it? Well, I’m not gonna say do they understand because they’re very intelligent people. But what’s their viewpoint on it typically?

Scott Murphy
I think, you know, every finance director differs, but I think, you know, most of them understand numbers and talk in numbers. So if we can demonstrate the amount of money that we are predicting to spend in order to generate the data And then the amount of money we think we’re going to save by having that data, then that tends to be a conversation that they like. Yeah.

And sometimes that data can be tangible, like physical numbers. Other times it can be a bit softer in terms of productivity. But most of the time is physical numbers that make the difference. And that was ultimately what got our finance director interested. So we’ve developed all of this proposition. We’ve started using it internally. And the first question that our financial adapter asked is, what’s it going to save me?

Charlotte
Yeah. Yeah.

Scott Murphy
You know, and some of the times that saving is in the immediate, it is we’re in this existing space.

And it’s going to save us x amount per year by understanding. Other times, it’s more long term. So, know, we, for example, we’ve got three offices at the moment. We’ve got one in Oxford, one in Portsmouth and one just outside of Swindon.

And we’re trying to think at the moment, what office space do we need? Do we need three? Or do we just actually need one? Because they’re all quite close together.

Yeah. And if we do just have one, how many desks do we need? How many meeting rooms do we need? What sort of communal space do we need?

We could make all of those decisions blind and we could hope.

But then that’s quite difficult to really work out whether that decision was right or not.

Charlotte
Well, it can be an expensive thing Yeah.

Scott Murphy
Yeah.

Or we could use data and we could show how people are using meeting rooms, show how often people are coming into the office, what sort of trends there are, and then that can inform what sort of space we need.

So then the financial savings come in a slightly different form. They come in a reduced spend in something in the future. So lots of different ways to measure it, but generally it’s numbers driven.

Charlotte
Yeah. Yeah.

Well, this is something we could talk about for quite some time, I think.

Partly because I think we both find it quite interesting. But and thank you for your conversation, for your time.

But now we need to put you in the hot seat. Again. So, yes. Again. So you should be a little bit more comfortable with it. It’s not your first time anymore.

So just some quick fire questions. Again, one or two sentences just to answer them quickly.

So first question. If you could only get a customer to track one thing that would move the needle quickly for them, what’s the one you’d pick?

Scott Murphy
I think I would choose air quality. Because air quality is something that is really easy once you understand to adjust. And as we spoke about small adjustments can make quite a big impact.

Charlotte
Thank you. Excellent. Second question.

What’s what do you typically see as the fastest win with an organisation, something they could realistically do this quarter, not in the long term?

Scott Murphy
I would say understanding occupancy. Occupancy is probably the easiest thing to track. There’s a lot of technology out there that can track it. And sometimes people already have the technology that’s tracking it for them, and they just haven’t fully realised it.

It also immediately changes behaviour.

Charlotte
Great. Thank you. I love I do love this section because it does force you, doesn’t it? To to concentrate.

Scott Murphy
I can see your eyes looking at me.

Charlotte
Yeah. Know. What’s he gonna say?

Third third and final question. When you think about the people who actually work in these buildings and not the budget holders, what difference have you seen it make to people day by day?

Scott Murphy
I think it makes a huge difference in lots of different ways, but I’d say the main one is just being equipped to make the decisions about when you go into the office and how you spend your time when you’re there.

Lots of people work from home now. And the office is a great space to be if you can fully utilise it for your benefit. So if I understand what the office can provide me, and more importantly, it can provide what I need, then I will more likely go in.

Charlotte
Excellent. Which is a big objective for a lot of people. Excellent. Thank you again, Scott. Pleasure. Great conversation.