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"We started working on a smart building at the Museum of London Docklands because we wanted to get our hands dirty on a smart building. We really got as much into the market as we possibly could and learned as much as we could. All of those learnings transferred directly into the new project at Smithfield.”
—Steve Watson
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Episode 164 is a conversation with Jason Pohl from Buildings IOT from and Steve Watson from The Museum of London.
Episode 164 features Jason Pohl from Buildings IOT and Steve Watson from The Museum of London and is our 10th episode in the Case Study series looking at real-life, large-scale deployments of smart building technologies. These are not marketing fluff stories, these are lessons from leaders that others can put into use in their smart buildings programs. This conversation explores The Museum of London’s new museum that will be a world leading smart museum using the latest technology and data science to minimize energy use. Enjoy!
You can find Steve on LinkedIn.
Introduction (1:48)
Intro to Steve (2:25)
The vendor team (5:39)
Introduction to Jason (6:55)
The data problem (9:30)
Day to day operation (11:45)
Technology stack (13:11)
Vendor options (15:52)
Steps in the process (17:49)
FDD results (25:30)
Lessons learned (32:30)
Challenges (37:01)
Advice for others (42:46)
Music credits: There Is A Reality by Common Tiger—licensed under an Music Vine Limited Pro Standard License ID: S577122-16073.
Note: transcript was created using an imperfect machine learning tool and lightly edited by a human (so you can get the gist). Please forgive errors!
Steve Watson: [00:00:00] We started working on the smart building at the Museum of London Docklands because we wanted to get our hands dirty on a smart building and this was the perfect place where we can experiment and then transfer everything that we learned from this process into our new museum which is currently in construction.
Um, so we, we put in the infrastructure and Uh, we engaged the facility management team, and we started to discuss benefits, um, with senior management, and we shared the data with, um, some research organisations, universities, and we really got as much into the market as we possibly could. learn as much as we could.
And then all of those learnings, um, from, you know, the, this, the work that we did at the Museum of London Docklands transferred directly into the new project at Smithfield.[00:01:00]
James Dice: Hey friends, if you like the Nexus podcast, the best way to continue the learning is to join our community. There are three ways to do that. First, you can join the Nexus Pro Membership. It's our global community of smart building professionals. We have monthly events, paywall deep dive content, and a private chat room, and it's just 35 a month.
Second, you can upgrade from the Pro Membership to our courses offering. It's headlined by our flagship course, the Smart Building Strategist. And we're building a catalog of courses taught by world leading experts on each topic under the smart buildings umbrella. Third and finally, our marketplace is how we connect leading vendors with buyers looking for their solutions.
The links are below in the show notes. And now let's go on to the podcast.
Welcome to the Nexus podcast. This is the latest episode in our series, diving into case studies of real life Large scale deployments of smart building technologies. And I emphasize real life because we're not here to create a fluff story. We're here to [00:02:00] share real lessons from leaders so that others can put this to use in their smart buildings programs.
Today, our story is coming out of the Museum of London, which currently has two locations, one of which is in the process of moving sites and with plans to reopen in stages starting in 2026. The new museum will be a world leading smart museum, using the latest technology and data science to minimize energy use and the day to day operations.
The voice you heard at the beginning was Steve Watson, Technical Building Lead at the Museum of London. Steve, can you introduce yourself and tell us about your background, please?
Steve Watson: Hi, James. Um, thanks for inviting me on. Um, so I'm originally from Melbourne. I've been in the UK for the last 15 years, um, and, uh, my career has spent, has been spent, uh, working on either cultural institutions, um, or in corporate businesses, and I tend to switch back and forth.
Um, but I've, I've been lucky enough to work [00:03:00] on some amazing buildings, um, the Royal Academy of Arts in Piccadilly, um, and then in at the National Arts Centre and also the Performing Arts Centre in Melbourne. I started my career as an electrician, so I've worked on site, um, then went into project work, um, I've worked as both a client and a, um, contractor, also in facilities management, um, and again worked on both sides of the fence, so facilities management.
And that's, you know, over 30 years of experience in buildings. So, um, I find myself, um, in the smart buildings world now, um, because I see that as the solution to a lot of problems and a lot of things that people have to overcome, um, when having to deliver or meet the expectations of building owners and building occupiers.
James Dice: Totally. That perspective from both sides of the fence is super important in a role like yours. Um, It's interesting how [00:04:00] you've, uh, you know, worked in these career, these, um, cultural institutions throughout your career. I'm wondering what excites you about these types of buildings. Well,
Steve Watson: um, you
know, like
they're quite often the, um, some of the, um, highlight buildings in a city, you know, or the, you know, you get to work with, um, some really beautiful buildings and you get to mix with some really interesting creative people.
The Royal Academy of Arts, for example, has most of the heavyweight architects in the UK as members. Um, and you get to rub shoulders with some pretty talented people within the buildings environment. I think, um, you know, like the preservation of old buildings, but with new technology. It's something I've always loved, you know, I've always liked the idea of old cars with new technology in them as well.
And, um, same with buildings. And I think that's, you know, key to the preservation and, um, the sustainability of the older stock of buildings [00:05:00] going forwards.
James Dice: Awesome. That's super cool. So what we're going to do is we're going to do a little, um, project overview case study, if you will, around One building that you guys are making smart, like you said, sort of exploring.
Um, and then we're going to like use that to talk about how it's influencing the decisions on, on the new building. Um, so for the project that you've already done, can you talk about, um, who your vendor team is, how many buildings are we talking about? What's how big is the building and when did this start and what have been your sort of like high level results on this?
Just kind of give us a quick overview of the project.
Steve Watson: Yeah, sure. So, the Museum of London Docklands is, um, 129, 000 square feet. Um, it's open to the public, museum, and, um, I started working on that, uh, probably 45 years ago was the initial discussions, um, with a company called OneSite [00:06:00] Solutions. Uh, they're a master systems integrator and a BMS technology vendor in London, and then through them started working with, uh, buildings IRT.
And, you know, together we've worked through the challenges of, of understanding the BMS data or, you know, like I suppose we've addressed a lot of the challenges which a lot of existing buildings face now. And that is the data problem and, um, how to get over that too, you know, to make. The value of data available.
James Dice: Cool. Yeah, and I just realized for my next London trip, I'm going to have to come, come over. I like to come over to London to watch soccer or football for you guys, but, uh, I'm going to have to add the museum to my next, my next trip. Absolutely. Let me know. So, we also have Jason Pohl here, who's Customer Success Manager at Buildings IoT.
Welcome, Jason. Can you introduce yourself?
Jason Pohl: Yeah, thanks, James. So I'm with [00:07:00] Buildings IoT, and fundamentally, my job is just to help the customers get the value and utilization that they expect out of the software that we deploy with them. Right. So I might reach back into implementation or project work to help speed things along.
But the core focus in my day to day work with folks like Steve is Really just supporting them in the kind of human change management, I like to call it, where you have some new tools, some new data available to you, and you're going through the transformation process of integrating those into your facility operations there.
So I've spent a couple years now doing this type of work, but also I have some IT background and spent a little bit of time in the military as well. So I think. Just working in different environments and corporate real estate or with vendors and [00:08:00] understanding how people work really has been instrumental in being able to be effective in supporting folks like Steve when they're going through.
A project like we had been engaged with on them at the Documents Museum there.
James Dice: Yeah, totally. And, and just to be clear and clarify this in my mind, you guys are a US based software company. You partner with Master Systems Integrators in the UK on projects like these to actually be your sort of boots on the ground support.
Is that right?
Jason Pohl: Yeah, exactly. We do have a couple of folks in the UK recently opened an office in Leeds. Yeah. Yeah. Yeah. But yeah, fundamentally we're having our US based team do some of the onboarding work, but we've got tremendous partners like OneSite Solutions that are doing that groundwork for us.
Makes sense.
James Dice: All right, let's tell the story of this, this program in a little more detail here. So Steve, can you talk about why you started down this path and sort of what your goals were? Sure. And I'll caveat this with, um, we, we [00:09:00] did, uh, sort of, um, You know, overview article on our website about this project.
And, um, Natalie, who wrote it from the Billings IoT side, she said, Steve loves data. Um, and I thought that was a funny, funny thing to say about someone that's, you know, sort of running, running a facility. Can you talk about like what you, what she meant by that? Why you love data and, and sort of, you mentioned a couple minutes ago, the data problem.
So can you talk about like why you started down this path and like sort of what the data problem is?
Steve Watson: So, um, the data problem has always been there, and that is that over time, um, there's rarely enough governance in a building to maintain good quality data, you know, of building data. And so it degrades over time.
But I'm, you know, like, I'm very focused on the changing relationship that we have with data now. And the way we use our mobile phones to, um, count our steps or to suggest a travel [00:10:00] route or even to record historical data like taking photos. Um, that's changed in the last 10 to 15 years, um, with smartphones.
So the same thing I think is happening in buildings now. Um, where we, we know that there's a lot of data generated by the engineering systems in the building. Um, and we're hungry for that data. So, Steve Loves Data means I want that data and I want it to be in a, in a format that, that I can do things with.
And it's not just me, you know, like I can sense the excitement, um, amongst computer science students who, um, do not have domain knowledge, but Who can quickly understand, um, the basics of how building works and want to work with the data. And, and they feed back to me the issues with the data as well, you know, um, initially, what does this abbreviation mean?
You know, what's the, what's the naming structure? [00:11:00] Is there a naming structure? Um, and so, you know, I think those are the data problems that we can solve and, and um, that we're working on.
James Dice: Yeah. It seems like sometimes so much of it is just getting it in a format that, Computer science folks or people that are going to do stuff with it, the format that they expect to see, right?
And, and so much of OT data is not in any format that, uh, someone can do something with and that they expect to see. So when I think of a museum, you know, the projects I've done in museums in the past, energy audits and retrofits and those types of things, um, I think about the constant temperature and humidity requirements.
Can you talk about sort of the, the more. technical aspects of the day to day operation of the facility?
Steve Watson: Yes, so there's a couple of situations. One is where you've got your own collection and you need to keep that in a stable environment. Um, there is normally a conservation team in a museum and a collections [00:12:00] team and an exhibition team and all of them are focused on keeping a stable environment, which means a steady relative humidity.
And a steady temperature. Um, over recent years those conditions have relaxed a little bit. Um, which is great, which means that they're not, it's not as um, energy intensive. Um, but you know, like, quite often um, there's, there's a lot of art stored in old salt mines, um, where the conditions are absolutely perfect.
Um, and, um, Air conditioning is, in a museum and gallery, is one of the biggest expenses, not just on the energy it consumes, but on the maintenance and the replacement costs of that equipment. The importance of air conditioning is, um, is tied to the preservation of the objects, and we preserve the objects because they, we use them to tell stories, and that's what we do in the museum.
We tell stories. In our case it's about London, and it's for Londoners and visitors. Um, [00:13:00] that's, you know, the, that's the purpose of, of maintaining the close control conditions.
James Dice: Awesome. So let's talk about the technology piece. So when you guys think about your technology stack now versus what it was like before, can you talk about what, what that was before and like, what were the problems with that from, you know, we talked about the data piece, but, um, what beyond data, what were the problems with it?
Jason Pohl: Yeah. So, uh, I know Steve has at the museum, the existing BMS and. There was another long term data store where a lot of the energy meter data was stored. And then, of course, you'll have your lighting and the sensor data as well. So, I think one of the things that was interesting that we were able to help solve for him is just bringing those all under one API and one front end.
And making it a little easier, proving that on the long term, preserving the data [00:14:00] quality, you know, can be done in a kind of more regimented way where it's not one place for your energy data, one place for your BMS data, but Place where those two things are married together and you're training it all and it's going to be available for, as Steve mentioned, some research students, right?
Or to pull from if he has some consultants. We've added some consultants that he has working together with him into the platform so that they can go in and easily look at the data themselves there, right? So previously, maybe the where that information is at is not easily accessible, right? So just One of the standard benefits of SaaS and a cloud platform is that he wants to add a couple users to look at the various things that we have connected.
That's a straightforward process and it will all be there, you know, with the relationships as well. So that's the main thing that comes to mind with kind of where he was [00:15:00] at. Uh, where we've got to today.
James Dice: Yeah. And if I, if I can just summarize what both of you have kind of said, all of these different systems were not connected in any way.
So if you had data in one and data in the other, there's no way to sort of bring that together, no easy way to bring that together. So you mentioned metering, you mentioned HVAC, you mentioned, um, lighting control. Um, and just all the different ways in which that data can exist in all those different systems, you're bringing it into one place, modeling the data, and then allowing it to be used through, you know, various different ways.
So Steve, you've been around the industry for, you said, 30 years. Um, you've been on the vendor side as well. You were probably exposed to a lot of. different ways to solve this problem, right? The, the, there's a lot of vendors out there. Can you talk about what options you were considering and sort of why you made this selection you did in terms of working with Billings IoT and sort of choosing the way they do things from a, from a software perspective?
Steve Watson: You know, like the, the two things that I [00:16:00] find, um, really, really important is the relationship with the company, um, and in this case with Jason, and to be able to work with someone. And the other thing that I find really important is the interface. Um, so this is not even getting into the back end or the technical side of things.
Those two, um, the interface is super important first. Because, um, if, if it doesn't, if, if it's not likeable, then you've already got a problem, you know. And I think, I think as, um, consumers of data now, we're becoming more and more picky actually. You know, we want our, I'd love my relationship to building data to be same as my relationship to, say, retail data and apps, you know, that, that I get.
I don't, I don't think we're quite there yet, um, but, um, looking at the interface that Buildings IoT provided and the ease of which I was able to pull data together, like disparate [00:17:00] data together, um, was super important. And then it's, it's all about the relationship and, you know, like I said earlier, you know, if, if, if we're going to create something, we need to get onto a level, um, where we understand each other and we are creative.
You know, we, we, we, we can see. A vision of the future and we work together to build that. I think that's really, really important. Absolutely.
James Dice: No, that's great. And so as you guys think about the project and the journey you're on, can you talk about the main phases of this deployment? Um, so what we've heard so far, right.
Is that there's, um, an existing building you're upgrading digitally. There's a new building coming online. Can you talk about the ways in which you've sort of gone through this process from deciding to move forward to, you know, where you're at today?
Steve Watson: First thing was, um, you know, I realized that our BMS data in the Museum of London Docklands, um, wasn't up to scratch and wasn't really, [00:18:00] um, usable.
So at the same time we were working with Arup, um, who are the engineering design consultants on the major project, and, um, we developed a naming convention, um, we used their naming convention and, you know, modified it a little bit. We also linked it to the BDNS, um, and, um, and we came up with a naming convention that we would use both the prototype and, um, the new building.
So that synergy set us off down a data renaming route, um, and then, um, that was an important part. And then, um, the next phase was to work with OneSite Solutions. So installing the infrastructure. So we had to get controllers, so Trend controllers had to get, um, or Honeywell controllers. They're, they still had, were working on their own land, some of them, so a lot of that got moved over to IP.[00:19:00]
Um, we installed an instance of Niagara and I think it was around about that point that we started working with OneSite Solutions. Um, and they, uh, prepared the data, I think initially to go to an MQTT broker. And, um, I'm not sure if you're Jason, if you're getting the data natively ahead of Niagara or whether you get it via the broker.
Jason Pohl: Yeah, absolutely. So we actually are grabbing it straight out of Niagara in this instance. But also we do have something that is. Um, we've been working on is we can configure a program and get it out that way as well. But in this particular case, right, we have, uh, where Steve just described the, you know, incredibly important prerequisite work that happens before a solution like ours can be set up.
Once you have a decent amount of good quality data, right, we can just come in and do that [00:20:00] basic driver integration or, uh, as you know, uh, More future looking way. Use the MQTT framework there and bring it out into our cloud so that we can just have a very light touch in the BMS. Like that's a hugely important theme for most of our customers that come to us is.
They're not always owning or supporting the BMS, right? So we just want to very simply get the data into our cloud so that we can then do the normalization and integration and apply all the features that are going to show up on the, on the front end there. So. I think with the folks at one side, they did really a lot of the hard work, the light work of preparing the building for us to come in and do the, the integration and the normalization aspects of it.
And once we got into that in earnest, that bi directional communication with them, where [00:21:00] it's not a traditional kind of vendor customer relationship, but a true partnership in the sense that there was some stuff there that was existing that needed a little bit of tweaking. They helped us get that solved.
So. When we go turn on the fault detection diagnostics, we can have faith that they're, they're valid, right? On a good, clean data there.
James Dice: Awesome. Oh, the thing, the themes I'm hearing really align with what we try to educate buyers and the market around is, is what you're talking about is like getting your, your different layers of the stack.
solidified before you start to add on software. So what we've talked about is the device layer, all of your different systems. We've talked about getting the network layer, right, Steve, you talked about getting them onto IP, Jason and Steve, you talked about the data layer and getting all the data normalized and available in the cloud.
Um, This, this sort of pattern you guys are talking about is a very standard one, and one that I feel like keeps coming up in every smart building story, right, is, is we have to get those three layers of the infrastructure right [00:22:00] before we start to talk about what can we do with the data and any sort of software application that comes next.
That being said, I'd love to talk about the software piece. Next. So one of the things that you guys have done here is you, you know, Jason, you've talked about FTD a few times you're doing FTD and we'll talk about that in a second, but it sounds like there's also, um, kind of like a visualization layer and, and you're, you're, you're allowing the users and operators and Steve to sort of explore the data on 3D floor plans of the museum.
Can you talk about that piece?
Jason Pohl: Yeah, absolutely. So we really deliver on point and to. Distinct manners. One is you just come in, you get in some analytics, you want the data layer from our API, and the other is what Steve has, where we're going to deliver that, but also provide some more traditional BMS functions of it's a floor plan.
And then we integrate the, the relationships that we [00:23:00] model into that, right? So he goes to the floor plan, says the room number, he clicks on that area of the museum and it says, it's this piece of equipment and it's serving these rooms. And then he can click on a relationships tab. It says, Hey, this piece of equipment is getting water from the condenser water plant.
And. Maybe the air handler is serving some terminal units, right? So, the mirroring of those two things definitely is, you know, tremendously valuable when you are going through this transformation process that Steve is. So, you might have folks looking at it that are used to a BMS, right? So, not just providing some traditional table views, right?
But having that with your analytics analysis. And the spatial modeling overlaying on a floor plan really eases the transition of someone who might not be used to looking at a piece of software that's providing you very specific guidance on, on the data that we're [00:24:00] integrated in. So it's, it's exciting because that's not always.
A scenario that is possible given, you know, everybody has a snowflake paradigm, right? It's good when there's someone like Steve that's able to, um, procure a solution that can provide that functionality to ease that transition. And Steve, you got any other color to add to that?
Steve Watson: Yeah, I just think, um, you know, that when we're talking about the engineers who have to run the building, they need the information.
as easily as possible. And they need to, they need to trust the information. They need to have the information in a way that they can interpret and make decisions on. And I think that's what we're trying to do. You know, we're trying to get accurate information or, um, accurate analysis to the engineers in the best way possible, um, for them to act on.
James Dice: Love that. Okay, let's talk about FDD next. So, um, When I [00:25:00] think about what you guys just shared, it's like taking the software layer of your BMS and making it a little bit super powered by the data model and, um, the ability to have these relationships built in and those types of things. FDD really takes it up to the next level of something that, um, Typically, the BMS is not going to provide the engineers and operators running the building probably aren't used to that level of analysis of the data.
So can you guys talk about like what FDD means and sort of what some of the results have been there?
Jason Pohl: Absolutely. So I'll tee you up, Steve, for this one. So for us, when the majority of our customers, of course, including Steve, are coming and saying we want analytics running on top of it, And one of the things that is a huge part of just the normal customer success workflow is helping folks looking at that for the first time and identifying what their immediate next steps [00:26:00] are when they log in and they see, Hey, my, I have a fan status command mismatch, or there's some, there's some pumps running on the plant that aren't supposed to be, or even something as simple as, Hey, the value on your sensor that's battery powered has been stuck for a number of hours.
Like, you know, take a peek at it. Maybe you need a new battery, right? And. Going from a, this is an alarm on the BMS, you have to react immediately to, you have this information that's based off of the historical data, and you can organize your workflow a little bit around that. And that's, you know, a, the beginning part of this overall journey that, that Steve's on, right?
So, this is the existing building, and he's understanding how he's going to be able to take this, This transformation and apply it to the new construction, right? Cause it's not just technology. Like I love talking about how analytics work and the power they can provide, [00:27:00] but ultimately the software is as good as the people using it.
And it's really a joy in my day to day work to see folks understand how they can change their workload, make it a little easier and just. Reduce that, that learning curve that any new tool will incur when you are just changing how things have, have always been done. So we're very much in the beginning of that journey, and it's good.
And one of the things that Steve and his team have done very well is understand that you want to get started on that, As, as early as possible. We really can't understate the value of just getting started, you know, your, uh, 500, 000 or however many square feet your, your new museum is going to be when it opens.
That's not when you want to be touching new technology, uh, for the first time, right? We're not a couple reps under your belt there. So, Steve, what else might I have missed in that kind of general recap that you want to add some additional color to?
Steve Watson: [00:28:00] Um, not much, I think that was pretty thorough, but one thing I would say is that I think with um, a traditional BMS, I don't think fault detection and diagnosis was ever done very well.
You know, uh, certainly you got a lot of alarms, um, but you didn't, you didn't get, um, a sentence with it or you didn't get a suggestion of what might be wrong, um, and that's, that's next level. Um, the other thing which is really important for us is that, um, identifying a fault, It doesn't fix the fault necessarily and, um, and working with Jason and a company called FlowPath, um, who are also based in the States, um, they, they were using their, um, CMMS system.
Um, they've created an API so that, uh, the insights, The analysis that gets performed in on point is transformed into work orders in the work order system. And of course at the start of when we connect we get this big [00:29:00] backlog of issues that we work through. But as we clear that we're starting to see real insights turn into real work orders and someone complete that work order and closing the loop.
And that's, that's super exciting.
James Dice: Yeah, super exciting. You guys are, you're preempting the questions I was going to have. So, so can you guys talk about, um, was the CMMS or, um, I forget what you guys call it in the UK, CAFM, CAFM. Was that in place already or was FlowPath a new vendor as well? And so you guys had to figure out how do we implement this new software and integrate it with defaults?
Can you talk about that sort of transition?
Steve Watson: FlowPath was actually recommended through discussions with Jason, and I don't think you're too far away from each other geographically. And, um, and then I got in touch with those guys and we talked through their product and how they work and, um, and it's a really good interface.
Really, really [00:30:00] great. Um, another great company, another company that focus on, um, relationship management, um, which is super important. And they feel like they're at the beginning of a great journey. Um, and that's the kind of people we want to work with. Um, that's the non technical side.
Jason Pohl: Yeah. And I mean, just full disclosure, we hadn't worked with Lopat before, right?
Steve came to me and he said, Hey, I want to have an integration. Do you have any suggestions? I said, Hey, well, we have this one integration that's built. There's this other company that I, like you said, geographically nearby in Atlanta. I've talked with them. They seem like they align with how we do business and what you're trying to achieve.
Just have a chat with them. See if it's useful, right? And once he had that initial chat, I just, again, talked with the technical folks at FlowPath and set them up with an API account, and this is the most exciting technical bit for me is, they just built the integration right into our API. [00:31:00] We didn't have to build.
Anything for them, right? And the way that it was structured, it was a pretty straightforward process for them. And, you know, gives Steve the same experience that he was describing with, with Allpoint. It's a very modern front end and it's able to work through the steps that he needs. I think you talked about like being able to assign to different contractors, right?
Or even just kind of creating a simple URL that someone can go to and see what they need. The work order is, so that was not something that we planned, right? We, we had our scope for. The, uh, the analytics and having some BMS and some visualization platform. And then after we got through that, you know, Steve said, Hey, what about, uh, integration into a work order system?
And I think it's one of the kind of key values we can provide. Just how we deliver the product is we don't have to build everything for our customers, right? We're going to make your data available and you want to have another vendor come in and [00:32:00] do an integration for something. By all means, have a, have a chat with them and we can set them up and they can be on their way.
James Dice: Absolutely. So those are the sort of big things that you guys are doing with the data at the, what we call the application layer. So Steve, can you talk about the, um, lessons learned here? And what I mean by that is what, what have you, what has happened on this project from a results perspective that you will then apply to the new building that's coming online in a couple of years?
Steve Watson: You know, the main, um, thing that I, I want to. Um, carry forward into the new building is that we need to work with the right companies and At the same time we need to own our data and be market aware I'm expecting big things from The, um, Smart Buildings, uh, sector over the next five years and, um, you know, we'll be involved in everything that happens and we want to know about that.
The way we're [00:33:00] working through the technical submissions, uh, process on the new project It means that the, we're looking at the data that the technology, like a phone call unit, for example, the data that is going to be exposed. And we're doing that much earlier in the project than you would normally do that.
Um, and we're looking at, uh, you know, what data is going to get exposed. Is it going to the BMS or is it an IP device? If it's an IP device, all of that as a way forward in the project. Um, so we're kind of getting in the weeds earlier in the project and that's, that's really good. Thank you. That's super good.
We're getting into it, you know, before, um, the trade contractor arrives on site with equipment that does or doesn't do things that we're not aware of.
Jason Pohl: Yeah, I think I'll just echo that this is something that we've seen across other customers as well, right? Especially when we are, you know, engaging on some MSI work stateside where it is a new construction, [00:34:00] right?
And there's a consulting process of, hey, here's the overall timeline on this new build. And here's what our outcomes are, right? Thinking about, hey, they've got DIP 25 and I know exactly what they're looking for. Tell me what I need to be aware of so that I don't get to a point where a systems integrator is coming in and putting a device online and we identify, oh, that network is not connected to the network that it needs to get other data from, right?
So, If you are, as the example Steve provided, like the, the fan coil unit, right? We know what we expect to see from, uh, an FCU and it's pretty well known. I think at this point we have this ontology alignment project. It's open. You know, we can go and see how we would tag that piece of equipment and what the data is available.
And just being able to point to that for folks. In a construction process, say, [00:35:00] hey, uh, yeah, we know it's probably too early to maybe define the exact point list, but you can point to this and say, this is what we're generally expecting. And we're going to run analytics on it. And that can not always happen before submittals, especially when you're having things come from the design engineer.
And all the information isn't known, but they can choose the right equipment so that you're not having as many revisions through that process or having a gap. Right. It's definitely something that we always love to see where you have a new construction project, or even just a substantial retrofit, and they're asking these questions early enough in the process so that they're not engaging a vendor to do analytics at a point where We come in, take a peek under the hood and say, well, there's not a lot of great analytics that we can do because the data's not there.
So [00:36:00] it's a, you don't know what you don't know scenario, right. And going through the process at the Docklands museum. Seeing what data he has on those existing pieces of equipment, I think is what Steve is describing and informing, Hey, this is what we're expecting on these new equipment as well.
James Dice: Yeah, I can't, I can't even summarize how many projects I've worked on where you get to the point where it's too late.
And you realize you don't have the data that you expected. What, let's talk about, um, we, we said at the outset of this that we, we would share sort of some lessons learned and challenges with implementing this approach. I feel like you guys have said the word journey several times. Um, talked about this as a learning experience so that you can apply it in the future.
So let's not act like this has been easy, right? So let's, let's, let's share some sort of challenges. Um, Yeah. So let's maybe go through one, two, three challenges so that people can get a sense of the sort of obstacles that you had to overcome and whatever advice you'd have for other people as [00:37:00] they sort of encounter those.
Jason Pohl: For sure. All right, Steve, I'm going to tee you up on the first one here. So we definitely touched on the data quality aspect a lot. I think what's not always said is you may have really competent, qualified people, you're engaged in scope to deliver this project, but they're going to come in and something else has already been done, right?
So thinking about, uh, pieces of equipment in the BMS. There were controller upgrades, right? And there's some assumptions made through that process to get that data into the new Niagara system. Right. And it's not always. Anyone's fault that some data is mapped wrong that is currently engaged on the project, right?
So we had to do some fixes out there and it wasn't anything that we did wrong or anything that one side did wrong or Steve provided bad documentation. It was no fault of anyone that was engaged in the project, [00:38:00] right? Having the right folks to hark back to that point Steve was making earlier involved means that we could actually.
Go through the work to solve it, right? And go in and remap some stuff in the DMS where that's not always possible. And it's always going to be a little messy when you're connecting into systems that are existing, especially if they're existing and they were upgraded to a new IP controller, but maybe during that process it didn't include a full field verification of everything that you're seeing on those controllers there.
So, you know, Steve, what else can you add on that aspect for me?
Steve Watson: Well, I think the point is about getting started, isn't it? And getting to those, um, issues as quickly as possible and working through them. Yeah, I mean, you have to, you have to get started and not expect things to be easy or, um, or perfect. Um, but then, um, working through it with the understanding that, um, Every [00:39:00] hurdle is an opportunity to get closer to what we want.
All right. What other challenges did you, did you experience? The relationships that form during the process of, of building the smart infrastructure and, and working with, um, vendors is super important. Um, and you know, I think that we're still working together because it's been a success. So, you know, I think, I think that's something that you, you know, we're constantly evaluating and, and it's, um, making the right decisions there, um, are very, very important.
Jason Pohl: So, I'm hearing you correctly, Steve. Just finding the right people is the challenge, right? Because it's not necessarily before you even get to selecting the technology, right? It's like, the people, and you know, we just happen to be the folks in this scenario that can support the work that you need to do to get to the [00:40:00] cool guy, shiny widget stuff at the end of the road there.
Um, and I think, um, That's something that we've seen in this smart building, and it's especially the contracting space for a long time, right? It's like relationships still drive a lot of business, right? And on the technical side, for me, drives the success of a project where you are connecting to things that you didn't install, right?
And you're, you're modeling data that you didn't initially integrate from the field. And that, that truly collaborative process, uh, is not always for the taints of heart, right? When you get your hands on some data that is needing a couple extra hours of, of cleansing, uh, you really got to be invested in the customer's outcome to do that, right?
Because, uh, some people can raise the change order flag and say, Ah, you know, this wasn't in scope, right? But I think we really had folks, um, that rolled up their sleeves and, and just helped us get to the eventual [00:41:00] outcome of it. Uh, having some good data there that we can do something with.
James Dice: Makes a ton of sense.
Steve, can you talk a little bit about the, sort of the, the financial side of this? The, the, the business case for investing in, um, another layer of software, or in this case data layer and application layer. So, you know, typically if you think about the museum's budget before you had a BMS budget and lighting control budget, et cetera, and now you're adding on these different software layers, how do you think about paying for it?
And was that a challenge in sort of making that business case? Um, yeah,
Steve Watson: I mean, it's always a challenge and you always need to be able to articulate it. Um, I was actually surprised that it's not as expensive as you might think. Um, to set up the BMS data and the meter data on our infrastructure to buy an instance of Niagara.
Um, to pay for the time for people to work with the data. Um, it's not a very [00:42:00] expensive exercise at all, you know, and, um, and the value that we get out of it, um, we, we, we can, we can save ourselves the money by, through preventative maintenance, um, and through the insights, you know, like, and it's, I know it's difficult for, um, for you to put that business case down on paper.
But, you know, anyone who's been in the industry long enough. Knows the feeling of, of, the, you know, the, the dread of a big piece of equipment going down. Um, that you really know you, you perhaps could have known or you could have done something to do, um, beforehand.
James Dice: No, I mean, I mean, just understanding how you as the, you know, buyer and decision maker think about this is, is awesome.
So let's think about others that are like you out there, Steve, that are listening to this. If you had to give them a roadmap or a playbook, what would the steps be that you would tell them to go through to sort of copy this approach?
Steve Watson: I think I'd advise them to build a prototype. [00:43:00] Start small, um, you know, get an instance of Niagara, um, they have to engage with, um, the, um, with the smart building community and they really need to engage with data science.
I guess I started listening to, um, Um, Data Skeptic in 2018 or something like that, and your episodes as well, James, um, around 2020 or something like that. And I always said to people, you need to be listening to one podcast a week at least, you know, and a book, a book a month. From a client's perspective, they really need to get educated, um, so they can have decent conversations.
And I don't think, um, the technical side of it, although it can be laborious, I don't think it's overly, um, difficult. There's plenty of skilled people around who can do with it, as long as, um, you work together and, um, you're able to have a meeting of minds, I guess, over, um, what the vision is and where you want to get to.
James Dice: So if [00:44:00] I'm hearing you correctly, the answer to the playbook is to, to engage with the Nexus podcast and the Nexus community. . Uh, that's a selfish interpretation of what you just said, but, um, yeah, I got, you heard,
Jason Pohl: I heard the same thing, James. Yeah.
James Dice: Jason, what do you, what would you say in terms of like, talking to other buyers out there about following this path?
Jason Pohl: I think I can't understate the willingness that Steve has to. Just get started and be curious about the process, right? Many times when you are thinking about doing something for the first time, just the thought of getting started is, is overwhelming, right? And, but once you get started, it's actually almost always not as bad as you imagined.
And for the Museum of London team, there's, what I've picked up through this process is, The organization is, you know, mature in a way that Steve's empowered to make these decisions to say, Hey, let's actually try this. [00:45:00] Don't know exactly how it's going to end up, but we know it's going to be worth it.
We're going to learn a lot on the way. And the first step is not to always go out and find the software, but to have the conversations with your stakeholders internally, to get a little buy in to say, Hey, I want to try something new. I think I have a general idea on how it's going to work, but I really won't know until we start, and that is easier said than done, right?
For folks that have gone through it. And, you know, I've gone through different portions of my career where somebody reached out and said, this is what you should be doing, you know, and they didn't have to help me. And just building a little bit of a network and not being afraid to reach out and say, Hey, can you help me do this?
I think people that have gone through a transformation, they're always eager to help the next person. If they have experienced the pain, they don't want the next guy to have to learn the same lessons. Right. So really, you know, find a community. I can [00:46:00] help you through that process and, you know, we've like the building intelligence group.
They have a lot of chapters everywhere, right? There's one here in Atlanta. And we do that where it's just we want to get together once a month or something. Talk about what's working, what's not working. And that FaceTime with other folks that are going through the same thing can really give you a light bulb moment of, okay, I know what I need to do differently to really get to a new outcome there.
James Dice: Yeah, I'm hearing a couple of things from you guys. One is, um, we like to say the role of the smart building champion. So Steve, having that, that role that's internal to the organization, willing to sort of shepherd or, um, you know, whatever metaphor you want to use, you Bring forward the, the smart buildings program within the organization.
Um, that's how we teach it in our course. We kind of think of our course as like creating smart building champions. Um, but there's some, some free content out there for anyone that's looking to find, you know, figure out what that [00:47:00] role looks like and we'll link to those in the show notes. The other thing I'm hearing from you guys is the, the value of finding software IOT that are, that are not just software providers, right?
It sounds like the partnership. Yep. And the ability to sort of, uh, sort of walk you through that as an external voice as well. Um, anything I'm missing you guys that we haven't covered that you think would be good for the audience to, to hear before we, before we take off?
Steve Watson: Uh, the only thing I would, I would suggest is that, um, there's, um, a really, um, large community of researchers working in this field as well, um, in the universities.
And there's some great stuff coming. There's some really good stuff coming using large language models and, um, And using natural language interfaces into building data. Um, and I can't wait for it.
Jason Pohl: Yeah, I think we've hit on everything that I really just have learned and wanted to share through this whole process and the only thing that I can think to add on top of it really is just that, you know, as you go [00:48:00] through a process and you're trying to identify what it is that you need to do to get started is just start with the outcomes.
Right, like what is it that you want to achieve and Don't go too wide, right? Choose one or two things you're really trying to achieve and then go through a process find partners and products that can help you achieve that and then build upon it once you get that first outcome out of the way there. It can be easy to say I want FTD and automated control and I want an API that has all my data available, right?
And those are all cool things that I love doing, but If you just choose one, it's a lot easier to get started.
James Dice: That's, yeah, that's great advice. And that's one of the things we're trying to do with this case study series is really show what, what else is everyone out there, out there doing, and then what are the, the obstacles to get in there to allow others to sort of visualize themselves going through the same process.
So thank you both for coming on the show and sharing this journey with us. And maybe you'll come back after the [00:49:00] new building is built and we can talk about what it was like on the construction process.
Rosy Khalife: Okay, friends, thank you for listening to this episode. As we continue to grow our global community of changemakers, we need your help. For the next couple of months, we're challenging our listeners to share a link to their favorite Nexus episode on LinkedIn with a short post about why you listen. It would really, really help us out.
Make sure to tag us in the post so we can see it. Have a good one.
"We started working on a smart building at the Museum of London Docklands because we wanted to get our hands dirty on a smart building. We really got as much into the market as we possibly could and learned as much as we could. All of those learnings transferred directly into the new project at Smithfield.”
—Steve Watson
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Episode 164 is a conversation with Jason Pohl from Buildings IOT from and Steve Watson from The Museum of London.
Episode 164 features Jason Pohl from Buildings IOT and Steve Watson from The Museum of London and is our 10th episode in the Case Study series looking at real-life, large-scale deployments of smart building technologies. These are not marketing fluff stories, these are lessons from leaders that others can put into use in their smart buildings programs. This conversation explores The Museum of London’s new museum that will be a world leading smart museum using the latest technology and data science to minimize energy use. Enjoy!
You can find Steve on LinkedIn.
Introduction (1:48)
Intro to Steve (2:25)
The vendor team (5:39)
Introduction to Jason (6:55)
The data problem (9:30)
Day to day operation (11:45)
Technology stack (13:11)
Vendor options (15:52)
Steps in the process (17:49)
FDD results (25:30)
Lessons learned (32:30)
Challenges (37:01)
Advice for others (42:46)
Music credits: There Is A Reality by Common Tiger—licensed under an Music Vine Limited Pro Standard License ID: S577122-16073.
Note: transcript was created using an imperfect machine learning tool and lightly edited by a human (so you can get the gist). Please forgive errors!
Steve Watson: [00:00:00] We started working on the smart building at the Museum of London Docklands because we wanted to get our hands dirty on a smart building and this was the perfect place where we can experiment and then transfer everything that we learned from this process into our new museum which is currently in construction.
Um, so we, we put in the infrastructure and Uh, we engaged the facility management team, and we started to discuss benefits, um, with senior management, and we shared the data with, um, some research organisations, universities, and we really got as much into the market as we possibly could. learn as much as we could.
And then all of those learnings, um, from, you know, the, this, the work that we did at the Museum of London Docklands transferred directly into the new project at Smithfield.[00:01:00]
James Dice: Hey friends, if you like the Nexus podcast, the best way to continue the learning is to join our community. There are three ways to do that. First, you can join the Nexus Pro Membership. It's our global community of smart building professionals. We have monthly events, paywall deep dive content, and a private chat room, and it's just 35 a month.
Second, you can upgrade from the Pro Membership to our courses offering. It's headlined by our flagship course, the Smart Building Strategist. And we're building a catalog of courses taught by world leading experts on each topic under the smart buildings umbrella. Third and finally, our marketplace is how we connect leading vendors with buyers looking for their solutions.
The links are below in the show notes. And now let's go on to the podcast.
Welcome to the Nexus podcast. This is the latest episode in our series, diving into case studies of real life Large scale deployments of smart building technologies. And I emphasize real life because we're not here to create a fluff story. We're here to [00:02:00] share real lessons from leaders so that others can put this to use in their smart buildings programs.
Today, our story is coming out of the Museum of London, which currently has two locations, one of which is in the process of moving sites and with plans to reopen in stages starting in 2026. The new museum will be a world leading smart museum, using the latest technology and data science to minimize energy use and the day to day operations.
The voice you heard at the beginning was Steve Watson, Technical Building Lead at the Museum of London. Steve, can you introduce yourself and tell us about your background, please?
Steve Watson: Hi, James. Um, thanks for inviting me on. Um, so I'm originally from Melbourne. I've been in the UK for the last 15 years, um, and, uh, my career has spent, has been spent, uh, working on either cultural institutions, um, or in corporate businesses, and I tend to switch back and forth.
Um, but I've, I've been lucky enough to work [00:03:00] on some amazing buildings, um, the Royal Academy of Arts in Piccadilly, um, and then in at the National Arts Centre and also the Performing Arts Centre in Melbourne. I started my career as an electrician, so I've worked on site, um, then went into project work, um, I've worked as both a client and a, um, contractor, also in facilities management, um, and again worked on both sides of the fence, so facilities management.
And that's, you know, over 30 years of experience in buildings. So, um, I find myself, um, in the smart buildings world now, um, because I see that as the solution to a lot of problems and a lot of things that people have to overcome, um, when having to deliver or meet the expectations of building owners and building occupiers.
James Dice: Totally. That perspective from both sides of the fence is super important in a role like yours. Um, It's interesting how [00:04:00] you've, uh, you know, worked in these career, these, um, cultural institutions throughout your career. I'm wondering what excites you about these types of buildings. Well,
Steve Watson: um, you
know, like
they're quite often the, um, some of the, um, highlight buildings in a city, you know, or the, you know, you get to work with, um, some really beautiful buildings and you get to mix with some really interesting creative people.
The Royal Academy of Arts, for example, has most of the heavyweight architects in the UK as members. Um, and you get to rub shoulders with some pretty talented people within the buildings environment. I think, um, you know, like the preservation of old buildings, but with new technology. It's something I've always loved, you know, I've always liked the idea of old cars with new technology in them as well.
And, um, same with buildings. And I think that's, you know, key to the preservation and, um, the sustainability of the older stock of buildings [00:05:00] going forwards.
James Dice: Awesome. That's super cool. So what we're going to do is we're going to do a little, um, project overview case study, if you will, around One building that you guys are making smart, like you said, sort of exploring.
Um, and then we're going to like use that to talk about how it's influencing the decisions on, on the new building. Um, so for the project that you've already done, can you talk about, um, who your vendor team is, how many buildings are we talking about? What's how big is the building and when did this start and what have been your sort of like high level results on this?
Just kind of give us a quick overview of the project.
Steve Watson: Yeah, sure. So, the Museum of London Docklands is, um, 129, 000 square feet. Um, it's open to the public, museum, and, um, I started working on that, uh, probably 45 years ago was the initial discussions, um, with a company called OneSite [00:06:00] Solutions. Uh, they're a master systems integrator and a BMS technology vendor in London, and then through them started working with, uh, buildings IRT.
And, you know, together we've worked through the challenges of, of understanding the BMS data or, you know, like I suppose we've addressed a lot of the challenges which a lot of existing buildings face now. And that is the data problem and, um, how to get over that too, you know, to make. The value of data available.
James Dice: Cool. Yeah, and I just realized for my next London trip, I'm going to have to come, come over. I like to come over to London to watch soccer or football for you guys, but, uh, I'm going to have to add the museum to my next, my next trip. Absolutely. Let me know. So, we also have Jason Pohl here, who's Customer Success Manager at Buildings IoT.
Welcome, Jason. Can you introduce yourself?
Jason Pohl: Yeah, thanks, James. So I'm with [00:07:00] Buildings IoT, and fundamentally, my job is just to help the customers get the value and utilization that they expect out of the software that we deploy with them. Right. So I might reach back into implementation or project work to help speed things along.
But the core focus in my day to day work with folks like Steve is Really just supporting them in the kind of human change management, I like to call it, where you have some new tools, some new data available to you, and you're going through the transformation process of integrating those into your facility operations there.
So I've spent a couple years now doing this type of work, but also I have some IT background and spent a little bit of time in the military as well. So I think. Just working in different environments and corporate real estate or with vendors and [00:08:00] understanding how people work really has been instrumental in being able to be effective in supporting folks like Steve when they're going through.
A project like we had been engaged with on them at the Documents Museum there.
James Dice: Yeah, totally. And, and just to be clear and clarify this in my mind, you guys are a US based software company. You partner with Master Systems Integrators in the UK on projects like these to actually be your sort of boots on the ground support.
Is that right?
Jason Pohl: Yeah, exactly. We do have a couple of folks in the UK recently opened an office in Leeds. Yeah. Yeah. Yeah. But yeah, fundamentally we're having our US based team do some of the onboarding work, but we've got tremendous partners like OneSite Solutions that are doing that groundwork for us.
Makes sense.
James Dice: All right, let's tell the story of this, this program in a little more detail here. So Steve, can you talk about why you started down this path and sort of what your goals were? Sure. And I'll caveat this with, um, we, we [00:09:00] did, uh, sort of, um, You know, overview article on our website about this project.
And, um, Natalie, who wrote it from the Billings IoT side, she said, Steve loves data. Um, and I thought that was a funny, funny thing to say about someone that's, you know, sort of running, running a facility. Can you talk about like what you, what she meant by that? Why you love data and, and sort of, you mentioned a couple minutes ago, the data problem.
So can you talk about like why you started down this path and like sort of what the data problem is?
Steve Watson: So, um, the data problem has always been there, and that is that over time, um, there's rarely enough governance in a building to maintain good quality data, you know, of building data. And so it degrades over time.
But I'm, you know, like, I'm very focused on the changing relationship that we have with data now. And the way we use our mobile phones to, um, count our steps or to suggest a travel [00:10:00] route or even to record historical data like taking photos. Um, that's changed in the last 10 to 15 years, um, with smartphones.
So the same thing I think is happening in buildings now. Um, where we, we know that there's a lot of data generated by the engineering systems in the building. Um, and we're hungry for that data. So, Steve Loves Data means I want that data and I want it to be in a, in a format that, that I can do things with.
And it's not just me, you know, like I can sense the excitement, um, amongst computer science students who, um, do not have domain knowledge, but Who can quickly understand, um, the basics of how building works and want to work with the data. And, and they feed back to me the issues with the data as well, you know, um, initially, what does this abbreviation mean?
You know, what's the, what's the naming structure? [00:11:00] Is there a naming structure? Um, and so, you know, I think those are the data problems that we can solve and, and um, that we're working on.
James Dice: Yeah. It seems like sometimes so much of it is just getting it in a format that, Computer science folks or people that are going to do stuff with it, the format that they expect to see, right?
And, and so much of OT data is not in any format that, uh, someone can do something with and that they expect to see. So when I think of a museum, you know, the projects I've done in museums in the past, energy audits and retrofits and those types of things, um, I think about the constant temperature and humidity requirements.
Can you talk about sort of the, the more. technical aspects of the day to day operation of the facility?
Steve Watson: Yes, so there's a couple of situations. One is where you've got your own collection and you need to keep that in a stable environment. Um, there is normally a conservation team in a museum and a collections [00:12:00] team and an exhibition team and all of them are focused on keeping a stable environment, which means a steady relative humidity.
And a steady temperature. Um, over recent years those conditions have relaxed a little bit. Um, which is great, which means that they're not, it's not as um, energy intensive. Um, but you know, like, quite often um, there's, there's a lot of art stored in old salt mines, um, where the conditions are absolutely perfect.
Um, and, um, Air conditioning is, in a museum and gallery, is one of the biggest expenses, not just on the energy it consumes, but on the maintenance and the replacement costs of that equipment. The importance of air conditioning is, um, is tied to the preservation of the objects, and we preserve the objects because they, we use them to tell stories, and that's what we do in the museum.
We tell stories. In our case it's about London, and it's for Londoners and visitors. Um, [00:13:00] that's, you know, the, that's the purpose of, of maintaining the close control conditions.
James Dice: Awesome. So let's talk about the technology piece. So when you guys think about your technology stack now versus what it was like before, can you talk about what, what that was before and like, what were the problems with that from, you know, we talked about the data piece, but, um, what beyond data, what were the problems with it?
Jason Pohl: Yeah. So, uh, I know Steve has at the museum, the existing BMS and. There was another long term data store where a lot of the energy meter data was stored. And then, of course, you'll have your lighting and the sensor data as well. So, I think one of the things that was interesting that we were able to help solve for him is just bringing those all under one API and one front end.
And making it a little easier, proving that on the long term, preserving the data [00:14:00] quality, you know, can be done in a kind of more regimented way where it's not one place for your energy data, one place for your BMS data, but Place where those two things are married together and you're training it all and it's going to be available for, as Steve mentioned, some research students, right?
Or to pull from if he has some consultants. We've added some consultants that he has working together with him into the platform so that they can go in and easily look at the data themselves there, right? So previously, maybe the where that information is at is not easily accessible, right? So just One of the standard benefits of SaaS and a cloud platform is that he wants to add a couple users to look at the various things that we have connected.
That's a straightforward process and it will all be there, you know, with the relationships as well. So that's the main thing that comes to mind with kind of where he was [00:15:00] at. Uh, where we've got to today.
James Dice: Yeah. And if I, if I can just summarize what both of you have kind of said, all of these different systems were not connected in any way.
So if you had data in one and data in the other, there's no way to sort of bring that together, no easy way to bring that together. So you mentioned metering, you mentioned HVAC, you mentioned, um, lighting control. Um, and just all the different ways in which that data can exist in all those different systems, you're bringing it into one place, modeling the data, and then allowing it to be used through, you know, various different ways.
So Steve, you've been around the industry for, you said, 30 years. Um, you've been on the vendor side as well. You were probably exposed to a lot of. different ways to solve this problem, right? The, the, there's a lot of vendors out there. Can you talk about what options you were considering and sort of why you made this selection you did in terms of working with Billings IoT and sort of choosing the way they do things from a, from a software perspective?
Steve Watson: You know, like the, the two things that I [00:16:00] find, um, really, really important is the relationship with the company, um, and in this case with Jason, and to be able to work with someone. And the other thing that I find really important is the interface. Um, so this is not even getting into the back end or the technical side of things.
Those two, um, the interface is super important first. Because, um, if, if it doesn't, if, if it's not likeable, then you've already got a problem, you know. And I think, I think as, um, consumers of data now, we're becoming more and more picky actually. You know, we want our, I'd love my relationship to building data to be same as my relationship to, say, retail data and apps, you know, that, that I get.
I don't, I don't think we're quite there yet, um, but, um, looking at the interface that Buildings IoT provided and the ease of which I was able to pull data together, like disparate [00:17:00] data together, um, was super important. And then it's, it's all about the relationship and, you know, like I said earlier, you know, if, if, if we're going to create something, we need to get onto a level, um, where we understand each other and we are creative.
You know, we, we, we, we can see. A vision of the future and we work together to build that. I think that's really, really important. Absolutely.
James Dice: No, that's great. And so as you guys think about the project and the journey you're on, can you talk about the main phases of this deployment? Um, so what we've heard so far, right.
Is that there's, um, an existing building you're upgrading digitally. There's a new building coming online. Can you talk about the ways in which you've sort of gone through this process from deciding to move forward to, you know, where you're at today?
Steve Watson: First thing was, um, you know, I realized that our BMS data in the Museum of London Docklands, um, wasn't up to scratch and wasn't really, [00:18:00] um, usable.
So at the same time we were working with Arup, um, who are the engineering design consultants on the major project, and, um, we developed a naming convention, um, we used their naming convention and, you know, modified it a little bit. We also linked it to the BDNS, um, and, um, and we came up with a naming convention that we would use both the prototype and, um, the new building.
So that synergy set us off down a data renaming route, um, and then, um, that was an important part. And then, um, the next phase was to work with OneSite Solutions. So installing the infrastructure. So we had to get controllers, so Trend controllers had to get, um, or Honeywell controllers. They're, they still had, were working on their own land, some of them, so a lot of that got moved over to IP.[00:19:00]
Um, we installed an instance of Niagara and I think it was around about that point that we started working with OneSite Solutions. Um, and they, uh, prepared the data, I think initially to go to an MQTT broker. And, um, I'm not sure if you're Jason, if you're getting the data natively ahead of Niagara or whether you get it via the broker.
Jason Pohl: Yeah, absolutely. So we actually are grabbing it straight out of Niagara in this instance. But also we do have something that is. Um, we've been working on is we can configure a program and get it out that way as well. But in this particular case, right, we have, uh, where Steve just described the, you know, incredibly important prerequisite work that happens before a solution like ours can be set up.
Once you have a decent amount of good quality data, right, we can just come in and do that [00:20:00] basic driver integration or, uh, as you know, uh, More future looking way. Use the MQTT framework there and bring it out into our cloud so that we can just have a very light touch in the BMS. Like that's a hugely important theme for most of our customers that come to us is.
They're not always owning or supporting the BMS, right? So we just want to very simply get the data into our cloud so that we can then do the normalization and integration and apply all the features that are going to show up on the, on the front end there. So. I think with the folks at one side, they did really a lot of the hard work, the light work of preparing the building for us to come in and do the, the integration and the normalization aspects of it.
And once we got into that in earnest, that bi directional communication with them, where [00:21:00] it's not a traditional kind of vendor customer relationship, but a true partnership in the sense that there was some stuff there that was existing that needed a little bit of tweaking. They helped us get that solved.
So. When we go turn on the fault detection diagnostics, we can have faith that they're, they're valid, right? On a good, clean data there.
James Dice: Awesome. Oh, the thing, the themes I'm hearing really align with what we try to educate buyers and the market around is, is what you're talking about is like getting your, your different layers of the stack.
solidified before you start to add on software. So what we've talked about is the device layer, all of your different systems. We've talked about getting the network layer, right, Steve, you talked about getting them onto IP, Jason and Steve, you talked about the data layer and getting all the data normalized and available in the cloud.
Um, This, this sort of pattern you guys are talking about is a very standard one, and one that I feel like keeps coming up in every smart building story, right, is, is we have to get those three layers of the infrastructure right [00:22:00] before we start to talk about what can we do with the data and any sort of software application that comes next.
That being said, I'd love to talk about the software piece. Next. So one of the things that you guys have done here is you, you know, Jason, you've talked about FTD a few times you're doing FTD and we'll talk about that in a second, but it sounds like there's also, um, kind of like a visualization layer and, and you're, you're, you're allowing the users and operators and Steve to sort of explore the data on 3D floor plans of the museum.
Can you talk about that piece?
Jason Pohl: Yeah, absolutely. So we really deliver on point and to. Distinct manners. One is you just come in, you get in some analytics, you want the data layer from our API, and the other is what Steve has, where we're going to deliver that, but also provide some more traditional BMS functions of it's a floor plan.
And then we integrate the, the relationships that we [00:23:00] model into that, right? So he goes to the floor plan, says the room number, he clicks on that area of the museum and it says, it's this piece of equipment and it's serving these rooms. And then he can click on a relationships tab. It says, Hey, this piece of equipment is getting water from the condenser water plant.
And. Maybe the air handler is serving some terminal units, right? So, the mirroring of those two things definitely is, you know, tremendously valuable when you are going through this transformation process that Steve is. So, you might have folks looking at it that are used to a BMS, right? So, not just providing some traditional table views, right?
But having that with your analytics analysis. And the spatial modeling overlaying on a floor plan really eases the transition of someone who might not be used to looking at a piece of software that's providing you very specific guidance on, on the data that we're [00:24:00] integrated in. So it's, it's exciting because that's not always.
A scenario that is possible given, you know, everybody has a snowflake paradigm, right? It's good when there's someone like Steve that's able to, um, procure a solution that can provide that functionality to ease that transition. And Steve, you got any other color to add to that?
Steve Watson: Yeah, I just think, um, you know, that when we're talking about the engineers who have to run the building, they need the information.
as easily as possible. And they need to, they need to trust the information. They need to have the information in a way that they can interpret and make decisions on. And I think that's what we're trying to do. You know, we're trying to get accurate information or, um, accurate analysis to the engineers in the best way possible, um, for them to act on.
James Dice: Love that. Okay, let's talk about FDD next. So, um, When I [00:25:00] think about what you guys just shared, it's like taking the software layer of your BMS and making it a little bit super powered by the data model and, um, the ability to have these relationships built in and those types of things. FDD really takes it up to the next level of something that, um, Typically, the BMS is not going to provide the engineers and operators running the building probably aren't used to that level of analysis of the data.
So can you guys talk about like what FDD means and sort of what some of the results have been there?
Jason Pohl: Absolutely. So I'll tee you up, Steve, for this one. So for us, when the majority of our customers, of course, including Steve, are coming and saying we want analytics running on top of it, And one of the things that is a huge part of just the normal customer success workflow is helping folks looking at that for the first time and identifying what their immediate next steps [00:26:00] are when they log in and they see, Hey, my, I have a fan status command mismatch, or there's some, there's some pumps running on the plant that aren't supposed to be, or even something as simple as, Hey, the value on your sensor that's battery powered has been stuck for a number of hours.
Like, you know, take a peek at it. Maybe you need a new battery, right? And. Going from a, this is an alarm on the BMS, you have to react immediately to, you have this information that's based off of the historical data, and you can organize your workflow a little bit around that. And that's, you know, a, the beginning part of this overall journey that, that Steve's on, right?
So, this is the existing building, and he's understanding how he's going to be able to take this, This transformation and apply it to the new construction, right? Cause it's not just technology. Like I love talking about how analytics work and the power they can provide, [00:27:00] but ultimately the software is as good as the people using it.
And it's really a joy in my day to day work to see folks understand how they can change their workload, make it a little easier and just. Reduce that, that learning curve that any new tool will incur when you are just changing how things have, have always been done. So we're very much in the beginning of that journey, and it's good.
And one of the things that Steve and his team have done very well is understand that you want to get started on that, As, as early as possible. We really can't understate the value of just getting started, you know, your, uh, 500, 000 or however many square feet your, your new museum is going to be when it opens.
That's not when you want to be touching new technology, uh, for the first time, right? We're not a couple reps under your belt there. So, Steve, what else might I have missed in that kind of general recap that you want to add some additional color to?
Steve Watson: [00:28:00] Um, not much, I think that was pretty thorough, but one thing I would say is that I think with um, a traditional BMS, I don't think fault detection and diagnosis was ever done very well.
You know, uh, certainly you got a lot of alarms, um, but you didn't, you didn't get, um, a sentence with it or you didn't get a suggestion of what might be wrong, um, and that's, that's next level. Um, the other thing which is really important for us is that, um, identifying a fault, It doesn't fix the fault necessarily and, um, and working with Jason and a company called FlowPath, um, who are also based in the States, um, they, they were using their, um, CMMS system.
Um, they've created an API so that, uh, the insights, The analysis that gets performed in on point is transformed into work orders in the work order system. And of course at the start of when we connect we get this big [00:29:00] backlog of issues that we work through. But as we clear that we're starting to see real insights turn into real work orders and someone complete that work order and closing the loop.
And that's, that's super exciting.
James Dice: Yeah, super exciting. You guys are, you're preempting the questions I was going to have. So, so can you guys talk about, um, was the CMMS or, um, I forget what you guys call it in the UK, CAFM, CAFM. Was that in place already or was FlowPath a new vendor as well? And so you guys had to figure out how do we implement this new software and integrate it with defaults?
Can you talk about that sort of transition?
Steve Watson: FlowPath was actually recommended through discussions with Jason, and I don't think you're too far away from each other geographically. And, um, and then I got in touch with those guys and we talked through their product and how they work and, um, and it's a really good interface.
Really, really [00:30:00] great. Um, another great company, another company that focus on, um, relationship management, um, which is super important. And they feel like they're at the beginning of a great journey. Um, and that's the kind of people we want to work with. Um, that's the non technical side.
Jason Pohl: Yeah. And I mean, just full disclosure, we hadn't worked with Lopat before, right?
Steve came to me and he said, Hey, I want to have an integration. Do you have any suggestions? I said, Hey, well, we have this one integration that's built. There's this other company that I, like you said, geographically nearby in Atlanta. I've talked with them. They seem like they align with how we do business and what you're trying to achieve.
Just have a chat with them. See if it's useful, right? And once he had that initial chat, I just, again, talked with the technical folks at FlowPath and set them up with an API account, and this is the most exciting technical bit for me is, they just built the integration right into our API. [00:31:00] We didn't have to build.
Anything for them, right? And the way that it was structured, it was a pretty straightforward process for them. And, you know, gives Steve the same experience that he was describing with, with Allpoint. It's a very modern front end and it's able to work through the steps that he needs. I think you talked about like being able to assign to different contractors, right?
Or even just kind of creating a simple URL that someone can go to and see what they need. The work order is, so that was not something that we planned, right? We, we had our scope for. The, uh, the analytics and having some BMS and some visualization platform. And then after we got through that, you know, Steve said, Hey, what about, uh, integration into a work order system?
And I think it's one of the kind of key values we can provide. Just how we deliver the product is we don't have to build everything for our customers, right? We're going to make your data available and you want to have another vendor come in and [00:32:00] do an integration for something. By all means, have a, have a chat with them and we can set them up and they can be on their way.
James Dice: Absolutely. So those are the sort of big things that you guys are doing with the data at the, what we call the application layer. So Steve, can you talk about the, um, lessons learned here? And what I mean by that is what, what have you, what has happened on this project from a results perspective that you will then apply to the new building that's coming online in a couple of years?
Steve Watson: You know, the main, um, thing that I, I want to. Um, carry forward into the new building is that we need to work with the right companies and At the same time we need to own our data and be market aware I'm expecting big things from The, um, Smart Buildings, uh, sector over the next five years and, um, you know, we'll be involved in everything that happens and we want to know about that.
The way we're [00:33:00] working through the technical submissions, uh, process on the new project It means that the, we're looking at the data that the technology, like a phone call unit, for example, the data that is going to be exposed. And we're doing that much earlier in the project than you would normally do that.
Um, and we're looking at, uh, you know, what data is going to get exposed. Is it going to the BMS or is it an IP device? If it's an IP device, all of that as a way forward in the project. Um, so we're kind of getting in the weeds earlier in the project and that's, that's really good. Thank you. That's super good.
We're getting into it, you know, before, um, the trade contractor arrives on site with equipment that does or doesn't do things that we're not aware of.
Jason Pohl: Yeah, I think I'll just echo that this is something that we've seen across other customers as well, right? Especially when we are, you know, engaging on some MSI work stateside where it is a new construction, [00:34:00] right?
And there's a consulting process of, hey, here's the overall timeline on this new build. And here's what our outcomes are, right? Thinking about, hey, they've got DIP 25 and I know exactly what they're looking for. Tell me what I need to be aware of so that I don't get to a point where a systems integrator is coming in and putting a device online and we identify, oh, that network is not connected to the network that it needs to get other data from, right?
So, If you are, as the example Steve provided, like the, the fan coil unit, right? We know what we expect to see from, uh, an FCU and it's pretty well known. I think at this point we have this ontology alignment project. It's open. You know, we can go and see how we would tag that piece of equipment and what the data is available.
And just being able to point to that for folks. In a construction process, say, [00:35:00] hey, uh, yeah, we know it's probably too early to maybe define the exact point list, but you can point to this and say, this is what we're generally expecting. And we're going to run analytics on it. And that can not always happen before submittals, especially when you're having things come from the design engineer.
And all the information isn't known, but they can choose the right equipment so that you're not having as many revisions through that process or having a gap. Right. It's definitely something that we always love to see where you have a new construction project, or even just a substantial retrofit, and they're asking these questions early enough in the process so that they're not engaging a vendor to do analytics at a point where We come in, take a peek under the hood and say, well, there's not a lot of great analytics that we can do because the data's not there.
So [00:36:00] it's a, you don't know what you don't know scenario, right. And going through the process at the Docklands museum. Seeing what data he has on those existing pieces of equipment, I think is what Steve is describing and informing, Hey, this is what we're expecting on these new equipment as well.
James Dice: Yeah, I can't, I can't even summarize how many projects I've worked on where you get to the point where it's too late.
And you realize you don't have the data that you expected. What, let's talk about, um, we, we said at the outset of this that we, we would share sort of some lessons learned and challenges with implementing this approach. I feel like you guys have said the word journey several times. Um, talked about this as a learning experience so that you can apply it in the future.
So let's not act like this has been easy, right? So let's, let's, let's share some sort of challenges. Um, Yeah. So let's maybe go through one, two, three challenges so that people can get a sense of the sort of obstacles that you had to overcome and whatever advice you'd have for other people as [00:37:00] they sort of encounter those.
Jason Pohl: For sure. All right, Steve, I'm going to tee you up on the first one here. So we definitely touched on the data quality aspect a lot. I think what's not always said is you may have really competent, qualified people, you're engaged in scope to deliver this project, but they're going to come in and something else has already been done, right?
So thinking about, uh, pieces of equipment in the BMS. There were controller upgrades, right? And there's some assumptions made through that process to get that data into the new Niagara system. Right. And it's not always. Anyone's fault that some data is mapped wrong that is currently engaged on the project, right?
So we had to do some fixes out there and it wasn't anything that we did wrong or anything that one side did wrong or Steve provided bad documentation. It was no fault of anyone that was engaged in the project, [00:38:00] right? Having the right folks to hark back to that point Steve was making earlier involved means that we could actually.
Go through the work to solve it, right? And go in and remap some stuff in the DMS where that's not always possible. And it's always going to be a little messy when you're connecting into systems that are existing, especially if they're existing and they were upgraded to a new IP controller, but maybe during that process it didn't include a full field verification of everything that you're seeing on those controllers there.
So, you know, Steve, what else can you add on that aspect for me?
Steve Watson: Well, I think the point is about getting started, isn't it? And getting to those, um, issues as quickly as possible and working through them. Yeah, I mean, you have to, you have to get started and not expect things to be easy or, um, or perfect. Um, but then, um, working through it with the understanding that, um, Every [00:39:00] hurdle is an opportunity to get closer to what we want.
All right. What other challenges did you, did you experience? The relationships that form during the process of, of building the smart infrastructure and, and working with, um, vendors is super important. Um, and you know, I think that we're still working together because it's been a success. So, you know, I think, I think that's something that you, you know, we're constantly evaluating and, and it's, um, making the right decisions there, um, are very, very important.
Jason Pohl: So, I'm hearing you correctly, Steve. Just finding the right people is the challenge, right? Because it's not necessarily before you even get to selecting the technology, right? It's like, the people, and you know, we just happen to be the folks in this scenario that can support the work that you need to do to get to the [00:40:00] cool guy, shiny widget stuff at the end of the road there.
Um, and I think, um, That's something that we've seen in this smart building, and it's especially the contracting space for a long time, right? It's like relationships still drive a lot of business, right? And on the technical side, for me, drives the success of a project where you are connecting to things that you didn't install, right?
And you're, you're modeling data that you didn't initially integrate from the field. And that, that truly collaborative process, uh, is not always for the taints of heart, right? When you get your hands on some data that is needing a couple extra hours of, of cleansing, uh, you really got to be invested in the customer's outcome to do that, right?
Because, uh, some people can raise the change order flag and say, Ah, you know, this wasn't in scope, right? But I think we really had folks, um, that rolled up their sleeves and, and just helped us get to the eventual [00:41:00] outcome of it. Uh, having some good data there that we can do something with.
James Dice: Makes a ton of sense.
Steve, can you talk a little bit about the, sort of the, the financial side of this? The, the, the business case for investing in, um, another layer of software, or in this case data layer and application layer. So, you know, typically if you think about the museum's budget before you had a BMS budget and lighting control budget, et cetera, and now you're adding on these different software layers, how do you think about paying for it?
And was that a challenge in sort of making that business case? Um, yeah,
Steve Watson: I mean, it's always a challenge and you always need to be able to articulate it. Um, I was actually surprised that it's not as expensive as you might think. Um, to set up the BMS data and the meter data on our infrastructure to buy an instance of Niagara.
Um, to pay for the time for people to work with the data. Um, it's not a very [00:42:00] expensive exercise at all, you know, and, um, and the value that we get out of it, um, we, we, we can, we can save ourselves the money by, through preventative maintenance, um, and through the insights, you know, like, and it's, I know it's difficult for, um, for you to put that business case down on paper.
But, you know, anyone who's been in the industry long enough. Knows the feeling of, of, the, you know, the, the dread of a big piece of equipment going down. Um, that you really know you, you perhaps could have known or you could have done something to do, um, beforehand.
James Dice: No, I mean, I mean, just understanding how you as the, you know, buyer and decision maker think about this is, is awesome.
So let's think about others that are like you out there, Steve, that are listening to this. If you had to give them a roadmap or a playbook, what would the steps be that you would tell them to go through to sort of copy this approach?
Steve Watson: I think I'd advise them to build a prototype. [00:43:00] Start small, um, you know, get an instance of Niagara, um, they have to engage with, um, the, um, with the smart building community and they really need to engage with data science.
I guess I started listening to, um, Um, Data Skeptic in 2018 or something like that, and your episodes as well, James, um, around 2020 or something like that. And I always said to people, you need to be listening to one podcast a week at least, you know, and a book, a book a month. From a client's perspective, they really need to get educated, um, so they can have decent conversations.
And I don't think, um, the technical side of it, although it can be laborious, I don't think it's overly, um, difficult. There's plenty of skilled people around who can do with it, as long as, um, you work together and, um, you're able to have a meeting of minds, I guess, over, um, what the vision is and where you want to get to.
James Dice: So if [00:44:00] I'm hearing you correctly, the answer to the playbook is to, to engage with the Nexus podcast and the Nexus community. . Uh, that's a selfish interpretation of what you just said, but, um, yeah, I got, you heard,
Jason Pohl: I heard the same thing, James. Yeah.
James Dice: Jason, what do you, what would you say in terms of like, talking to other buyers out there about following this path?
Jason Pohl: I think I can't understate the willingness that Steve has to. Just get started and be curious about the process, right? Many times when you are thinking about doing something for the first time, just the thought of getting started is, is overwhelming, right? And, but once you get started, it's actually almost always not as bad as you imagined.
And for the Museum of London team, there's, what I've picked up through this process is, The organization is, you know, mature in a way that Steve's empowered to make these decisions to say, Hey, let's actually try this. [00:45:00] Don't know exactly how it's going to end up, but we know it's going to be worth it.
We're going to learn a lot on the way. And the first step is not to always go out and find the software, but to have the conversations with your stakeholders internally, to get a little buy in to say, Hey, I want to try something new. I think I have a general idea on how it's going to work, but I really won't know until we start, and that is easier said than done, right?
For folks that have gone through it. And, you know, I've gone through different portions of my career where somebody reached out and said, this is what you should be doing, you know, and they didn't have to help me. And just building a little bit of a network and not being afraid to reach out and say, Hey, can you help me do this?
I think people that have gone through a transformation, they're always eager to help the next person. If they have experienced the pain, they don't want the next guy to have to learn the same lessons. Right. So really, you know, find a community. I can [00:46:00] help you through that process and, you know, we've like the building intelligence group.
They have a lot of chapters everywhere, right? There's one here in Atlanta. And we do that where it's just we want to get together once a month or something. Talk about what's working, what's not working. And that FaceTime with other folks that are going through the same thing can really give you a light bulb moment of, okay, I know what I need to do differently to really get to a new outcome there.
James Dice: Yeah, I'm hearing a couple of things from you guys. One is, um, we like to say the role of the smart building champion. So Steve, having that, that role that's internal to the organization, willing to sort of shepherd or, um, you know, whatever metaphor you want to use, you Bring forward the, the smart buildings program within the organization.
Um, that's how we teach it in our course. We kind of think of our course as like creating smart building champions. Um, but there's some, some free content out there for anyone that's looking to find, you know, figure out what that [00:47:00] role looks like and we'll link to those in the show notes. The other thing I'm hearing from you guys is the, the value of finding software IOT that are, that are not just software providers, right?
It sounds like the partnership. Yep. And the ability to sort of, uh, sort of walk you through that as an external voice as well. Um, anything I'm missing you guys that we haven't covered that you think would be good for the audience to, to hear before we, before we take off?
Steve Watson: Uh, the only thing I would, I would suggest is that, um, there's, um, a really, um, large community of researchers working in this field as well, um, in the universities.
And there's some great stuff coming. There's some really good stuff coming using large language models and, um, And using natural language interfaces into building data. Um, and I can't wait for it.
Jason Pohl: Yeah, I think we've hit on everything that I really just have learned and wanted to share through this whole process and the only thing that I can think to add on top of it really is just that, you know, as you go [00:48:00] through a process and you're trying to identify what it is that you need to do to get started is just start with the outcomes.
Right, like what is it that you want to achieve and Don't go too wide, right? Choose one or two things you're really trying to achieve and then go through a process find partners and products that can help you achieve that and then build upon it once you get that first outcome out of the way there. It can be easy to say I want FTD and automated control and I want an API that has all my data available, right?
And those are all cool things that I love doing, but If you just choose one, it's a lot easier to get started.
James Dice: That's, yeah, that's great advice. And that's one of the things we're trying to do with this case study series is really show what, what else is everyone out there, out there doing, and then what are the, the obstacles to get in there to allow others to sort of visualize themselves going through the same process.
So thank you both for coming on the show and sharing this journey with us. And maybe you'll come back after the [00:49:00] new building is built and we can talk about what it was like on the construction process.
Rosy Khalife: Okay, friends, thank you for listening to this episode. As we continue to grow our global community of changemakers, we need your help. For the next couple of months, we're challenging our listeners to share a link to their favorite Nexus episode on LinkedIn with a short post about why you listen. It would really, really help us out.
Make sure to tag us in the post so we can see it. Have a good one.
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