Mastering the Art of Coordination in Commercial Real Estate Data and Teams

Introduction to Coordination in CRE

Drew: Welcome back to the Peak Property Performance Podcast. You got me, Drew Hall. And Bill's here with us. Hey, Bill.

Bill: Hello, everyone. Welcome back. Happy to have you. We've talked about three of the Cs so far, clarify, connect, and collect. And so the next one in the lineup is coordinate. So let's spend today's session talking about what that means. What does it mean to coordinate? What are we coordinating?

Drew: Right. And where, you know, again, commercial real estate, it's an old industry. So what are we coordinating? We're talking specifically about coordinating teams, about coordinating systems, about coordinating data.

Bill: Exactly. Yeah. And then there's that data again, we keep coming back to data, data, data, because that's where the power lies, those systems that have been in the physical facilities for so long, getting at the data that those hopefully are generating, or at least finding out whether they can generate the data like we talked about in previous episodes.

Understanding Data Types and Privacy Concerns

Drew: So one of the things that we talk about in Peak Property Performance in the book is kind of a baseline introduction to the concept of different data types, you know, like whether there's just a single data source or blended data sources, like bringing multiple data sources together from multiple systems. And that's where the power really, really comes in, in this coordination effort.

Bill: Yeah, I had a call today with a property in Southern Georgia, and they, you know, it's existing property. So he's talking about, we don't need the data, our systems just work. And then I just started asking questions. Well, what if you could, and what if you had this data from one system and this data from another, and you were able to look between it to see what correlations? And he said, well, give me some examples. So we started giving some examples from other clients. And then I think he had an aha moment, like, okay, that would be valuable.

Drew: But he made the jump towards user data. You'll hear us say that a thousand times on this podcast. When we talk about data, we're talking about systems data owned by the property. We do not touch user data.

Bill: That's right. Yes. You'll probably get tired of me saying it, but we are very particular about privacy. I don't want to be the one that gets used and I don't ever want to use somebody's data. Systems data is different, owned by the property.

Drew: So blended data would be taking one, two or more disparate data sources and aggregating them into a data warehouse, data lake, it could be a big spreadsheet. I mean, it doesn't have to be big and fancy data, but blending them gives you the ability to start looking for correlations, whether or not you lay AI on it or we do for you, it's up to you.

Bill: But we're just talking about, remember the first three chapters, connect, collect the after clarify, of course, but now we're going to get into coordinate. What do you do with it? Now that you have it. So you're starting to get data show up.

Drew: Yeah, definitely. Where's it coming from? So that's why Drew, I liked the way you started out with understand data types.

Bill: Yeah. And I want to make one quick point on the whole user data thing, because it is, it is funny. I mean, I often don't remember or I don't go to that first, the whole user data, because like you said, we don't want it. We don't want it. I mean, there's, there's certain pieces of data that are, that are required in terms of, you know, onboarding onto the, onto the platform, et cetera, et cetera. But yeah, beyond the ability to troubleshoot, if there's an issue and things like that, or to, to authorize access to specific things, like we just don't want it. We do. That's, that's not. We certainly don't want to know what somebody is doing on any system that is up to their, to their company. That's fine.

Drew: Yeah. This specific question is, are you tracking what they do where they're buying dinner? It's like, Oh no, no, no, no, no, no. We're not geofencing where we're not capturing where they're shopping so that you can sell them ads. This, that is not what we're talking about.

Bill: Exactly. Yeah. That's what you're looking for. Thank you. But that's not us.

Drew: Right. Exactly. We're talking about your data, Mr. Prospect, your data, your buildings. Like it was cottage style, multiple buildings, each one of them had systems. There was multiple networks. It wasn't that old of a property, but it was quite convoluted because there was really no digital strategy when it was built.

Bill: Yeah. Well, and you know, it ties right into that conversation you were talking about having had even just today, you know, selecting your initial plays, like you just so happened to on the fly in the conversation you were in today, come up with a scenario that you thought would be intriguing based on that particular conversation.

Blended Data and the 55 Plays Strategy

Drew: I don't know that we've shouted out to the 55 yet, right? The 55 plays as we call them, but it's really like different ways that you can bring together different data sources so that you can blend them and do something with them, that whole actionable intelligence thing.

Bill: So that's what we say here. And in the book for coordinators, we talk about selecting just like three, you know, consider all the possibilities. And we provide that list of 55 for consideration. It's a huge boost to get you started, but don't, don't get overwhelmed. Just pick three. And as we say, consider the impact, consider the achievability and consider the learning opportunities based on those three.

Drew: So lots of options, but don't be overwhelmed. I encourage people to pick ones they know they can finish. Don't pick the ones that are going to have the biggest impact. Some of those are going to be the longest. Every once in a while you'll find a low hanging fruit that is, quote unquote, easy or quick. Usually they're not easy. They do take detailed work.

Bill: But pick one you know you can finish because the last thing your team wants to see is something that never finishes or even worse, a failure. Costs money and time and they saw nothing of it. So pick ones, you know, you can accomplish and, and build from there. Like say, look, we did this that took us this long and you know, now we're going to do this or what do you think we should do?

Drew: So 55 plays are different data types, single data source, right? Going back to the 55. If you had these two or these three, imagine if you had those data sources and you put them together, what you could get. If you had this and this or this, this and this, you could get this. It's just 55 ideas spread out over the six digital infrastructure domains to give ideas. It's by far not all you can do and by far not what you have to do. It's meant to be idea generating, but they're all things that have and are done.

Project Planning and Scalability in Data Management

Bill: Yeah. It just creates that structure so that you, so that you have a path forward, you know, it goes along with the project plans that you'll inevitably need to create. That's what we talk about is creating project plans. So you can like measure as you go and you, you, you bear in mind, you're not just your, your tactical goals here, but also your strategic goals as well.

Drew: Well, the project plan is going to help you and your team, right? We've seen all kinds of approaches, going to help you figure out whether or not you're doing this in-house, you're going to hire it out. You're going to blend it. You're going to bring somebody in to teach you how to do it and then leave, or like they're going to come in and teach you how to do it and stay. Like there's, there's, think about how you would do anything, not necessarily new, but you have the choice to build or buy or hire a consultant to teach you implement.

Bill: So, you know, build, buy, might maybe even put acquire on there. If your company is acquisitive, go find a solution that works and then implement it. I don't know what time constraints are driving you. If time is no issue, maybe you want to do it in-house. If you just want to learn how to do it fastest, maybe you want to hire a consulting firm or a partner to run part of it. And you have skillsets in-house. We see that hybrid approach works great, especially in the first couple of years. And then you start to find a groove and you, you can pour resources in it as the financial results from this project come in.

Drew: Yeah. Yeah. Well, and one of the things you mentioned right there was constraints. So we talk about making sure that what you're doing here as you go, making sure that it's scalable. So it's, it's important to bear in mind as you, as you, you know, flesh out, like, what are my, what are my three, what are my three plays? As you start to kind of unravel that to make sure that you have not just the path forward, but you have the capacity, the ability to scale as you bring in, you know, maybe you're, you find a little bit of data trickling from one particular system, but you discover that there's, there's the ability to get a lot more data from it. So just making sure that you're able to, to get that, to get those data sources in full and nothing's like spilling off the edge, you know? So it's kind of a capacity, scalability considerations.

Integration vs. Coordination of Data Systems

Bill: Well, I want to throw in one more idea, and that is the idea of integration. I mean, I've had very large firms say to us, you know, optic wise, our systems are all integrated. We already have our data. And I asked very specifically, well, where's your data? Is it in your data repository or is it just being shared by your vendors? Which is great being shared by your vendors. If one of them needs data from another system and it helps their system be more efficient, that's wonderful. But where's the data and can you use it for other things? Can you use it to triangulate or four or five, six, you know, degrees of separation and then run your analysis across it? Whether or not that includes machine learning is a moot point, but integration doesn't solve the problem.

Drew: So by coordination, we're talking about the data it's in your repository. It doesn't necessarily have to be in your building or in your, or in your direct control, but yours, like, can you run queries on your own data? Most of the time, integrated systems don't let you do that. They can, you just have to ask. You have to connect, collect, and then coordinate, right? Of course you have to clarify it first, but you have to connect to those systems so you can collect the data. So in your coordination efforts, talk about, okay, these systems are, they truly are integrated. How can I use that data to help a whole nother set of systems that these can't see?

Bill: Yeah. And I think too, oftentimes like you can drive right at the heart of it with just a relatively simple question. You know, a question like, what's the air conditioner running in this building in that conference room on the fourth floor yesterday at 2:45 PM? Yes or no. And it may be, it may be relatively easy to answer that question, maybe, but then let's pretend that you can answer that question. The natural followup question is why?

Drew: Why was it running yesterday? And that answer may be very simple, because the thermostat was set to this, but the temperature as measured by the sensor that's limited to whatever was that. So therefore, boom, the compressor kicks on and we run the air conditioner. That's a pretty conventional old school reality, right? But when we talk about blended sources, there could be lots of different reasons why the air conditioner would kick on or wouldn't kick on, because there's a larger scope, a larger decision. There's a larger pool of decision making, larger pool of data to consider whether or not, like as you instruct the system what to do, it's way more flexible than just temperature is not matching what temperature needs to be, you know, like a traditional thermostat decision.

Drew: So it's all about, I find that there are some questions and it depends on the site. There's some questions that allow you to identify those gaps really quickly. And you can sense, I mean, it gives you an idea of the power as well. If you can answer a question like that more completely, like, oh yeah, the AC did kick on. I wonder if that's what I want. I had something come up yesterday, different data set, similar circumstance. Client said, we sub meter all of our apartments, you know, some units, sometimes facility, multifamily doesn't because they have to pay one more bill. But in this case, it was paid for by apartment. And then I said, well, where's the data? And they say, it's just a billing system. It passes through and we integrate it with our lease management system and it shows up on the bill.

Leveraging Data for Operational Efficiency

Drew: I said, but if you had the data from those meters, couldn't you tell if maybe there was a leak in an apartment? Maybe, you know, something's running all night, even if it's a slow leak. Like if you had the data in real time, wouldn't that turn into a leak detection system of itself? Not nearly as sensitive as a true leak detection system because it was an older property, you know, and they were trying to deal with insurance rate increases. And I said, if you have that, couldn't you put alarms on it and show that to your underwriter? And they just had an aha moment. And then it dawned on them, okay, it is my data, but I don't have it. It's sitting in my sub meter vendors database. So now they're going to go get it and use it and set alarms when things start peaking.

Drew: If something is running between 12 and five consistently, like people are going to take showers between 12 and five, but if it's using this maybe, so they're starting to set up limits, within a system they didn't have to pay for. They're just building it because they have the data. Absolutely. Yes. And they have some very smart people on their team that are doing it. They didn't have to do it. It was just a question I asked, like, what could you do if you had the data? All of a sudden, they're not going to go or worry about that. They're going to use it offensively to bring down their insurance increases or reduce the kick in the butt from the insurance increases. And there is a win.

Drew: Consider infrastructure needs is along that line. You don't have to go buy a system necessarily. If you have the data, you can, especially in existing properties that don't have big CapEx or capital improvement projects, budgets, I should say. You can do so much with the data with a creative team. Coordinating is also coordinating your team, not just the data, but telling them what the goals are, what the plays are, encouraging them to have input, encouraging them to take a play and run with it. Like, okay, what would you like to do next? And they've always had something they wanted to try. Give them the right to try it and let them fail. I mean, tell them it's okay to fail. Either way, you're going to know more when it's done than you do now. And they learn something.

Drew: That's not really a failure, but say you set the goal to reduce water usage by 1% and it doesn't. All of a sudden, now you're monitoring leaks because maybe you can't get people to use less water, but I can prevent the next leak from flooding three floors down. It's a very long example. Sorry about that.

Bill: No, no, no. It's funny. I was thinking like at worst, you're tripping up the trail. You're still headed to your destination. But they were doing it with a system they already had. They just weren't pulling the data out and using the data. So there was, other than the manpower, there was no extra cost in it.

Drew: Yeah. That's beautiful. Yeah. That's a great example. So we love it when we find stuff like that. So- Oh, yeah. Yeah. Stay focused and keep the strategy in mind, but include the team. You'll hear us talk again about, I think it's chapter six, where we talk about the sixth C, which we'll get to the champion, but that is including the team and looking at the portfolio or the property as a whole, rather than diving down deep. This, we're talking about getting into coordinating, which means diving down deep in one or more systems or one or more databases. Hopefully it's just one data lake by the time you're done.

Conclusion and Future Topics in CRE Coordination

Bill: That's great. I mean, I think that's a pretty good general coverage of that fourth C, coordinate. I agree. So I'm going to wrap it right there and maybe tease the next one when we come back next time, we'll talk about that fifth C, which is control. But in the meantime, thanks for joining us and yeah, have a great day, everyone. See you on the next episode. Don't forget to like and subscribe. We appreciate all that. If you have any ideas, send us a note, you know how to reach us. Thank you.

Drew: Thanks guys.