Navigating Workplace Analytics for Hybrid Work : Ep 10

About the Episode

In the era of hybrid work models, understanding workplace analytics has become paramount. Join us as we delve into the significance of data sources, why they’re crucial now more than ever, and how they’re reshaping the way we utilize facilities.

Gone are the days of disparate systems and fragmented data sources. Today, we witness a shift towards centralized platforms that offer comprehensive insights into facility management and space performance. With an abundance of data at our fingertips, the challenge lies in effectively analyzing it to drive informed decisions.

From utilization data to energy consumption patterns, the complexity and variability of workplace metrics have skyrocketed. However, advancements in technology have enabled us to process vast amounts of data efficiently, leveraging cloud-based solutions for enhanced accessibility and scalability.

Join us to learn about how workplace analytics is changing modern work environments and the power it has to transform them. The key is to ask: Do we have the right information to make good choices? How can we optimize space planning and measure effectiveness across our real estate portfolio?

By harnessing workplace analytics, organizations can unlock a plethora of benefits, from optimizing energy usage and maintenance to reducing total operating costs. Moreover, predictive analytics allows us to anticipate future trends and adapt proactively, while performance scoring provides tangible metrics for evaluating space efficiency.  From predictive insights to actionable strategies, we uncover the key to unlocking the full potential of your workplace.

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Episode Transcription

Jennifer Heath 0:15

Hello, everybody, welcome to another episode of the Hybrid Hangout podcast with Brian Haines and myself, Jennifer Heath. We are always excited to get together and talk about the hot topic of the day. We appreciate you all listening and supporting us. We always have lots of great ideas for things that we want to talk about. There’s so many interesting topics right now. And today, we are going to dive in on the topic of workplace analytics. It’s something we talk about all the time. And I think we’re almost at a point where we’ve talked about it so much that we’re sort of too high up in the clouds almost when we talk about it. So today, we really want to get in to some of the nuts and bolts, talk about different data sources, how things have changed over the years. And really just take a look at it in terms of why it is such an important topic. So Brian, any thoughts from you before we get started? I’ve got a few good questions for you. But I’ll give you a minute to say hello.


Brian Haines 1:13

Hey, thanks, Jen, always look forward to these discussions. Analytics is something that is a super hot topic right now. It’s one of our fastest growing segments, we see such an massive interest in it. But it’s really gotten a lot more powerful, and also a lot more complex than what we may have seen in the past. So really looking forward to having an opportunity to talk about it today. And why it’s such a powerful tool to help our clients really move their real estate portfolios forward in a really, highly performant way, in a sustainable way. So it’s pretty exciting.


Jennifer Heath 1:52

Awesome, so let’s just start there. Let’s talk about why it is such a hot topic. What is it about coming out of the pandemic and the challenges that facilities or real estate teams are facing today? Why is analytics such an important part of the solution?


Brian Haines 2:06

Yeah, a couple of things pop into my mind. First of all, I think we may have mentioned this before in a previous podcast, but the way that people are using facilities is really, really different than it used to be. It’s kind of pretty chaotic in a lot of ways. It used to be, you know, everyone came to the office, they said at their desk, they said at the same desk every single day, they sat there nine to five, they got to the end of the day, Monday through Friday, they went home. Now it’s really, really quite different. Even in organizations that have not gone completely hybrid. It’s still pretty complex when you look at the actual utilization data, and we like to sample a lot of it. When we look at it, because we like to understand global trends as a company, that’s just something we’re fascinated by. It’s highly variant. And it’s actually difficult to calculate unless you’ve got a real analytics platform that’s being fed by a lot of different data sources. And I gotta tell you, it can’t be just one. It has to be a lot, you really want to truly understand the accuracy of your real estate portfolio.


Jennifer Heath 3:17

So talk a little bit about how data analytics have changed in the last 20 years. The first thing that comes to mind, for me, is disparate systems, that we have been able to make some progress towards having data more centralized, more aggregated, what are some of the changes that you’ve seen?


Brian Haines 3:36

Yeah, so when I started my career early on, Jen, this was a longtime, early 90s, working in space and occupancy planning, I was building databases around, you know, allowing us to be able to track space, you know, millions and millions of square feet of space at a university. And the analysis of that was primarily done by one person with a spreadsheet. That’s a big difference, right? So it was like one person a spreadsheet, as you can imagine the amount of data in that spreadsheet was pretty limited, I think. And they were essentially trying to perform analysis, by doing things such as manually writing formulas and calculating fields and looking for inconsistencies in the data. It was really, you know, at least back then was really highly specialized, the kinds of people that did this type of work. And they weren’t really, you know, they weren’t spitting it out real time. They were sort of doing an analysis and then you would get a report. That report, you know, maybe once a quarter it may be once a year but something that someone worked on a tremendous amount primarily in spreadsheets with limited amount of data. Now, you flash forward to now, we have massive computing power, we got massive potential access to data inputs. And I think the difference is, you know, like I was saying that person way back then was really looking at a pretty discrete set of data. Now we do what we like to consider multi data point analysis, which I know I mentioned before, and that is taking data inputs from all aspects of operating your facilities and real estate portfolio. And using those to build the answers that you’re looking for, or the insights.


Jennifer Heath 5:38

Yeah, and I think another part of it, that’s interesting, and I’ve talked about this in in terms of other aspects, and the transition to hybrid work, is the simple transition to the cloud of so many systems, it makes all that data more accessible. You’re not having to pull it out of one system, do some sort of an FTP file transfer, or whatever it might have been, a spreadsheet upload, trying to bring all these data sources together. Today, we’re in a much better position to access and use API’s and bring data in from different areas, so that you do have that centralized capability. Let’s talk a little bit about different data sources. Because we look at a lot of different types of data at FM:Systems. Again, we talk a lot about multi data point analysis and the fact that you do need those different perspectives and viewpoints. And what utilization is, what performance is, how you measure that. What do you think, are the key data sources for a facilities manager or real estate professional? And what do you think are going to be the trending ones over the next few years?


Brian Haines 6:46

Yeah, the key data sources to me it always comes down to one thing, do you have the right data to answer the questions that you’re trying to ask or, you know, sort of tease out inconsistencies in the way you’re operating your facilities, looking for opportunities to optimize what you’re doing. If I you know, going back turning the wayback clock, again, not back to the early 90s, but this, this one is only back to about 2015. The first time we released utilization capabilities, was really looking at, you know, a single sensor type. And that was a work point center, you know, that would go under people’s desks, and we were using things like passive infrared to detect whether or not someone was there. And then we were getting, you know, pretty good result, but they were incomplete. It was really just telling you, you know, what the utilization of an of a desk was. And for the most part, when you’ve got assigned seating, the utilization of a desk is, is almost pretty close to being, you know, what you expect, because someone’s expected to sit somewhere, the utilization problem was not nearly as complex. Well, when it started to get more complex, we started having people interact with facilities in different ways coming in at different times. And we started to add data sources. Back at the beginning of the pandemic, we saw sort of, you know, back around 2020, we saw the sort of advent of a number of different sensor types that went up in the ceiling, over doors and things like that. It started to give us a more complete picture. Right, so now we’ve got multiple data sources coming in, we’ve got work point data, we’ve got area data, we’ve got entry access point data. And we started build, sort of really a much more robust model, if you will, with multi data points, really starting to look at utilization. Then we started to layer on even more data, and that data included things like, you know, how many people are coming in and out of doors? What’s the data coming from our booking system? Like, are people booking space? And are they booking it and not using it? Are they not booking it when they should be booking it and using it and really looking at a lot of different factors, such as data, we started the layer and even more like badging data like people badging in and out, data coming from a lighting systems, data coming from Wi Fi. And as you can see, the multi data point analysis, just around the topic of utilization, started to get really deep, really intense, and really broad. And let me tell you, humans are not good at that. They’re just, they’re just not I mean, I know a lot of brilliant people in the world. But they can’t in real time, do trillions of calculations on data coming in from all over the place. That’s where analytics really shines and it gets even better. And I’ll wait for your additional questions because it gets broader. Because I think we solved the utilization problem. I think, you know, FM:Systems has got a great solution. There’s other products in the market that have a solution for being able to do this. I think we’re really getting an understanding of how people are using buildings, but then it’s on to the next. It’s on to the next big challenge, which we’ll talk about in a few minutes.


Jennifer Heath 10:09

Yeah. So one comment I will make there, one of my favorite, like cross analysis points is the bookings reservations- the bookings data versus the utilization data. To be able to see what the intention is, versus what the actual behavior was, what actually happened. I think that is such a telling story in so many different areas. So that to me, that’s one of my first great examples about why multi data point analysis matters. Because if you’re only looking at your bookings data, you’re seeing what they intended to do. It’s only when you bring in that next data source, that you understand what actually happened, and you can start to connect some of those dots. Why did they book the space, if they didn’t want to use it? There’s a myriad reasons why that might be. But if you don’t even know that that’s a disconnect that’s happening, you can’t start to address some of those other challenges. So I think that’s such a good example of why bringing multiple data sources together is important. Let’s talk about that from the standpoint of facility management and facility performance. Because while utilization does still impact that, there are a whole list of other things that have to be considered if you’re thinking of it in terms of operations.


Brian Haines 11:25

Sure, so when you ask yourself, alright, now that we’ve solved this sort of utilization thing, we know when people come in, we know they go, we know who they spend time, we know when they’re booking, and they’re not showing up. We know all of those things. We can calculate those things and then use analytics to really allow us to provide insights. But the next question is the so what question, right? Like, what does that really allow us to do? Well, one of the first things that started to allow us to do is, frankly, better space planning. As people started to evolve their real estate portfolios, and started refocusing space and started looking at things like the support of better collaboration spaces, and less individual workstations and things like that. Utilization data was and is now allowing us to make much better decisions, it’s helping us to have the right amount of real estate that provides the right amount of functionality to the people being able to use the space. So that’s kind of a big, so what, but there’s also a follow on, so what, and that is you can actually take that utilization data, and you can use it to measure sort of the effectiveness of pretty much all other real estate functions inside of either an individual building on a floor or even across an entire portfolio. And that’s where we really start to see, you know, let’s say cloud based, data warehouse based, analytic solutions with data coming in from a lot of different systems, a lot of different sensors, looking at different functional areas of real estate, is really allowing us to be able to do stuff that frankly, we couldn’t have dreamed about even just a half decade ago, it’s really powerful.


Jennifer Heath 13:15

So I’m really interested in talking about when you look at improving and optimizing facility performance, the first thing that comes to mind, for me is very much about energy consumption, and energy distribution, but also the maintenance of all those different machines and elements throughout the building. What different data sources can people harness to start to get more predictive and prescriptive in how they’re operating the buildings themselves?


Brian Haines 13:47

Yeah, you’re kind of looking at the mythical total operating cost calculation that people have been trying to do for years by, you know, gathering together things like energy usage, maintenance costs, and things like that and build it into kind of a total cost of ownership. Once again, I think due to the limits of humans, we can’t be in all places at all times. And we’re not on all the time, we have to rest, we go home. Those buildings continue to operate. And, you know, once humans are not involved, what is that building, really providing as a function? I think that’s a big question that we all need to ask ourselves. Because if we’re only using a building, or facility, or even a portfolio campus, maybe 8-10 hours, 12 hours a day, what are we doing with the rest of that time? Things are running, lights are on, equipment’s running. And we all know this, if you’ve got a low mileage car, for instance, an analogy, it’s probably going to last longer. The maintenance costs are going to be a lot less than a vehicle that you’re putting tons and tons of miles on right. Unless you’re that outlier that loves to have a car with a half million miles on it, but for the most part, things break down more quickly, the more heavily they’re used. So we’re looking at things like, you know, what is the appropriate amount of let’s say, operational bandwidth do we provide to people who are using a building? And does it really matter when no one’s there? Should we turn it off? Should we reduce energy usage? Should we reduce equipment cycle time like things going on and off all the time if nobody’s there. I really think it’s an opportunity. It’s really complex. It’s not easy, right? And it involves things in a lot of organizations such as it’s not always that clear, right? It’s not nine to five anymore. It’s a lot different than that. Could be shiftwork, it could be people coming in at night to do things. But can we actually use analytics and intent data. And intent data is when people say they’re going to be there, or you know, let’s say analysis over time, says that Brian comes in every Wednesday, then next Wednesday, he’s probably going to be here, right? That’s measuring intent, can we take that data and apply it to the total cost of operation of a facility? Right? I think, for far too long, we’ve looked at the total cost of operation over like a 24 hour period or a 365 day period, instead of looking at it applied to when the building’s actually being used. And the rest is, frankly, just waste. Its weight, it’s assets sitting there that no one’s using that we’re paying for, we’re running energy to, we’re using up all kinds of utilities, we’re providing security, all kinds of things. And no one’s there. Frankly, it’s a giant waste.


Jennifer Heath 16:36

Totally agree. So you touched on one of the other topics that I really want to touch on, which is the whole concept of predictive analytics. Predicting that you always come in every other Wednesday. So you’re probably going to be coming in on this Wednesday. Talk a little bit about where you see that going over the next few years. What are some real world examples for facility managers, you know, people who are workplace experience directors? How can they start leaning into some of that predictive capability to improve what they’re doing?


Brian Haines 17:15

Yeah, so first of all, one thing that analytics can do is not only tell you what’s happening, but if you take that data, and you look at trends, you can extend those trends out into the future, and have a high predictability around things continuing to repeat based upon large datasets, impact, that’s simple, right? We can extend that out, we could use things like benchmarks and benchmarks could be looking at benchmark geographically, it could be looking at me comparing myself against other organizations that do what I do, maybe I’m in health care, maybe I’m in higher ed. But also benchmarks around costs, like looking at things like what’s the total cost of operating a facility in New York, or Chicago versus L.A. versus Des Moines. And we can start taking that data and we can extend it out really into the future, right? So we can predict costs in the future, based upon what we’re doing now, on these large datasets, we can also bend to those things. So which I think is really the opportunity. Because if we see, at some point in the future, maybe it’s 18-24 months into the future, we’re projecting that timeline out, where something’s going to go wrong. Like let’s say, this facility is going to dip below a certain utilization rate, or it’s going to go above a certain utilization rate. It may be signaling to me bigger things, like, maybe I don’t need this building, or maybe I need more of this building, right? Like those kinds of things. It also allows us to be able to do things like predict, like capital planning, being able to do things such as, listen, if I, if I have a heating ventilation and air conditioning unit on top of this building, and I’m using it heavily and it’s supposed to last five years. And I’m looking at utilization and the utilization is way beyond what it should be. Well, that piece of equipment may end of life before I’m planning and so I can start looking at that. The other thing is, is that, you know, through smart building technology, building control systems, and the equipment now is actually reporting itself to us. And if you’re listening, and that listening is primarily through technology to reg devices, and being able to take that data and feed it into a cloud system. You know, a lot of those building systems are telling you how healthy they are or how unhealthy they are. It’s not like going in for a yearly checkup. Essentially, they’re telling you on a daily basis how well they’re doing. So we can start looking at that data and start to look for those building systems to start to get sick, if you will. All right. So things are starting to fail, a pump is starting to fail. And if I’m in downtown Phoenix, Arizona, and it’s July, and my HVAC system is going to fail based upon predictive analysis and I know my utilization is going to be really high in the summertime, I may have a big problem, like nobody wants to work in a building that’s 120 130 degrees, it’s a really bad situation. That could be an unexpected capital expense, that I could get ahead on, right, I could plan or maybe do some maintenance, maybe get that equipment really operating better extend its useful life, sort of give it a health check. And I’m using all of that massive amount of data to help me make those better decisions. So extending the life of the building, making better decisions around the building, extending the life of the equipment. All of that is enabled by what we’ve been talking about.


Jennifer Heath 20:45

At one point that you mentioned that, I think it’s an interesting element of this, as well is being able to compare different aspects of your portfolio for the purpose of either shrinking or expanding. So there’s a lot of research right now that, you know, whatever percentage of people are planning to shrink their real estate over the next five years, the percentages that are planning to increase it, whatever it is, they need to have some kind of objective data that they’re basing that decision on. And one thing we do in FMS:Insights, our analytics platform that I think is so interesting, and compelling, is our performance scoring. Where you’re able to compare different factors across different facilities, and look at things like the maintenance tickets versus the operating costs, look at the utilization versus the operating cost. And you can pick and choose which one of those aspects is most important to you, based on what’s going to be most important to your workforce and your portfolio as a whole. And I think that’s a really powerful idea that you’re not always just comparing apples to apples, sometimes you have compare the apples to all the other fruit types in the car, you have to be able to compare all these different things. And think about it in terms of what brings the most value to your organization. Is it about expanding real estate and having different kinds of space? Is it about reducing costs and allowing more flexibility, every organization is going to have a different value driver in that, and being able to pull those different levers and look at that analysis from those different perspectives is so powerful.


Brian Haines 22:24

Yeah, and Jen, I think every organization probably has different values that they want to apply to all of those functions, right. So if I’m looking at total costs, and I’ve got, you know, some real estate with a lot of employees in a kind of a low cost area of the country where real estate and lease costs, and even construction costs are pretty inexpensive as compared to maybe having a marquee building in a really expensive market. You know, your values about the way you measure those two pieces of real estate is going to be different, like one might be like absolutely where, you know, you’ve got visitors, big clients coming in, you want to take them to the New York City office, right, you might not want to take them to the campus out in the middle of nowhere. And so there’s a value weighing, also, we weigh things such as different functional aspects of what needs to be accomplished in those facilities. So if I’ve got a healthcare organization, for instance, and I’ve got two buildings, one of them is an office building, where we’ve got a lot of people doing things like billing and insurance tracking, and the other one is a hospital, I’m going to apply different values to that. So in the hospital, there’s exam room privacy, there’s operating room privacy, there’s this, way that we look at space, which is different than the way we look at office space. And if you can imagine our clients, they don’t have one building, they’ve got sometimes 10s, 100s, 1000s of buildings, all performing all kinds of different functions in support of the overall mission of that specific organization. And so the way they measure how analytics affects the way they think about this building could be not just different amongst different clients, but even drastically different amongst an individual organization, which is kind of fascinating, right? You’re taking a massive amount of data, and you’re trying to make really insightful judgments about what you’re going to do. You really have to take all of those things into effect. A nd that’s really complicated even more and you know, I don’t keep I don’t mean to keep dinging on human beings. We’re just not good at it unless you’re maybe a PhD from an economics somewhere. I don’t know a lot of facility teams that have PhDs in economics looking at their data, right? They need something that’s going to be clear and concise. The days of that, like dedicated analysts sitting there with the Excel spreadsheet are gone. We need to be able to provide this really complex information with the value judgments baked in. It’s a really consumable way for someone looking at it. So they’re not making that judgment call, right? It’s built in.


Jennifer Heath 25:14

So another data source that we have not touched on so far, is the concept of environmental monitoring. That there are all these different environmental factors that you can bring in. Why is that one so important? There’s an obvious one related to indoor air quality that we want to make sure we have high quality of air, in our facilities, it impacts productivity, it impacts health. But environmental factors, in general, can inform a lot of other decisions, talk a little bit about how we use that in terms of analytics.


Brian Haines 25:47

Yeah, so I love the way you gave the sort of the multiple points of analysis for analytics, because people often think of indoor air quality as just, you know, how it relates to the air we breathe, and, you know, providing that information to the occupants of the facility so they can see you know, the wellness score of the facility and things like that all incredibly important, it also gives us the opportunity to be able to make, you know, real time or in the future operational changes to a building, you know, increasing airflow, being able to, you know, do things such as- flashback couple of years, reduce the transmission of communicable diseases- because we’re, you know, having better airflow, better filtration, all of that coming in. So that’s part of the indoor air quality picture as well. The next one, which you didn’t mention is, you know, I look at sustainability in a couple of different ways. Sustainability is the right thing to do, so that we could continue to have a healthy, whole planet in support of, you know, a global view of really providing the best environment to everyone who’s on the planet. That’s a really, really strong component of sustainability. The flip side of it is energy usage and carbon footprint, right. So there’s kind of two sides to that coin. One is, you know, in support of sustainability, healthier environment. The other one is, if you’re using less energy, creating less carbon, having a smaller carbon footprint, your costs are gonna go down. I mean, that’s kind of a no brainer, right? Like you get them both, like you get a healthier environment. And guess what, everybody knows this, healthier environment, guess what goes up- productivity. Productivity goes up when you’re in a healthy environment, and costs go down. So productivity up so people are doing more, they’re getting a better result from the investment that you’re making and the people who work for your organization. On the flip side of that, w e can also operate our building more while providing a better environment like it makes such sense. It’s kind of crazy not to do that. Right. So anyway, I think all of those factors in sustainability. And then the last component of that is, when you have the people factored in terms of utilization, that makes it a little bit more complicated, right? So if I have the most sustainable building in the world, and no one’s using it, is it really sustainable, right? Like the carbon footprint that went into building it and putting it there and putting all the manufacturing and all the systems and stuff. So I can say, oh, my gosh, this is the most sustainable building in the world. But no one’s in here. Is it providing value, versus a building that was built that way, and is a delight, and everyone wants to go to right? So there’s another component as well.


Jennifer Heath 28:33

Yeah, and that’s another great example of why multi data point analysis is important. If you’re just looking at one set of metrics, and you’re hitting goals over here, you might give yourself an A plus score, but then when you compare it to some other key metrics, to your point, if utilization is low, are you really getting an A plus in sustainability? So just continuing to add that context, add those different perspectives, we’re going to get smarter and smarter about how we’re delivering our workplaces and how we’re operating our facilities. All right, last question. For you, Brian. We’ve been on this topic a while. And I want to know, if you are someone who was just getting started, if you’re in a very manual situation today, you still got data in spreadsheets, what’s the best first step? If you can go with a single data source to start? Where would you start?


Brian Haines 29:26

I always go back to the same thing. And that is, first of all, you’ve got to figure out what you’re trying to solve. Right? So what you’re trying to solve will drive the data that you need to help make that decision. So figuring out what your goals are, what your objectives are, you know, is it as a culture, is it increasing indoor air quality, is it maximizing utilization? What is it? Is it all of those things? And then looking at ‘Do we have the data to be able to do that?’ The second thing is data quality of what you do have and whether or not it’s accurate enough or reliable enough for you to be able to produce analytic results that you feel confident in. So if I’m a young person, or I’m just entering the profession, and I’ve got to report like, if I go to my manager, do I feel confident that what I’m placing in front of them is accurate? That’s another really important component. So figuring out what you’re trying to answer, making sure that you have the data in support of that. And then data quality, I think those things all go hand in hand to that answer that question.


Jennifer Heath 30:28

Perfect. All right, that brings me to the end of my questions. This has been a great discussion today. Did you have any closing thoughts?


Brian Haines 30:38

No, you know me, I love this discussion. I think the closing comment is that, you know, the more this data gets complex and bigger, we do multi data point analysis, the bigger the need is to get help when it comes to analyzing what you’re looking at. We’re working on a lot of new capabilities around AI and machine learning that allows us to be able to provide what we call narratives, in other words we’re looking at those analytics views and instead of having that person from the 1990s who’s in that spreadsheet, you know, basically it’s telling you what the answer is or what the insights is in plain human text. In a way that I could read it. So, it’s like partnering with the data if you will, in a way that it’s almost talking to you. So those narratives are really important. And also the ability to be able to ask questions the way a human would ask and get answers back that makes sense. I think that those things right now are being applied to all of this multi data point analysis and information systems in ways that are going to allow us to answer really big questions. And I’m excited about that.


Jennifer Heath 31:50

It is exciting too because it makes it so much more accessible. You don’t have to be a data analyst to be able to come in and get value out of some of these different data sources and systems. You can rely on AI and machine learning and different tools to make that data and those insights accessible to the person who needs to know in order to make the right set of decisions. All right Brian, appreciate your time today, always a good discussion. And we will look forward to seeing everyone on the next Hybrid Hangout.

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