Why Our Approach to Hotel Data Earned a Patent and Prepares Hotels for AI - Clark Brayton, Joseph McGroarty & Pritesh Patel, Actabl

Joseph: My name is Joe McGroarty. I am leading the data and analytics team at Actabl. I am the one that is lucky enough to have a team of people to help us bring all this data together to show the hotel industry what we can do with it.

Clark: I am Clark Brayton. I'm the senior data analyst for Actabl. I have the privilege of also working with multiple teams and team members, making hotel life easier by using our products.

Pritesh: I'm Pritesh Patel, director of product, leading over integrations, and I've had the pleasure in the last decade to work closely with hotel technology partners bringing in data from disparate systems into a normalized layer that we're gonna be talking about today.

Josiah: It's good to see you all. Thank you for making time to chat. We recently announced something I was really excited to share with the world, a data patent that is providing our customers with a lot of capabilities. I feel like for as long as I've worked in hospitality, data and the use of it, and how it's processed and managed, it feels like it's always been a challenge across so many different contexts.

So I'm excited to get into this. Joe, I wanna go to you first. I wanna hear from your perspective, what was the challenge from your perspective that we were facing, you found our customers facing as it relates to data?

Joseph: Well, there's a lot of it. I think that is one of the biggest challenges. There's a lot of it, and it's very disparate. Hotel companies will do a lot of the labeling and tagging a different way. So to be able to bring all that data in is one real challenge. We're talking about terabytes and so much data, at some point it's hard to fathom.

But the fact that it's different everywhere, to be able to bring all that data into one place and be able to normalize it, and to be able to use it has been such a big benefit of what's going on with this patent.

Josiah: Awesome. Pritesh, I wanna go over to you. You're kind of overseeing a bunch of stuff here, including integrations, which I imagine brings this all to life. What were you seeing on the data side?

Pritesh: Yeah, absolutely. Just going back to the last 10 years of journey since I originally joined Actabl. One of the main things that we helped bring together was just the amount of systems that are at a hotel. A typical hotel has about 10 to 15 systems, and that scales from limited all the way up to luxury and convention hotels, and resorts can go 40 plus systems. So as an owner and operator, you're multiplying whatever number of hotels you have, let's just say at least 10 at a minimum, but maybe close to 50, 100 hotels.

You're multiplying that by the number of systems. Now you're talking a real problem. Every system talks differently. Every system has a different way of communicating with other systems. There's just a lot of data, and it's all there. But just 10, 15 years ago, everything was just done manually via Excel. Everything was rolled up via macros, just a normal person using their knowledge and bringing all the data together. So that's been the biggest problem, and that's what we seem to run into with almost any operator and owner. How can we bring...

We have a ton of data. We want to do something with it. We need to find what that action is. However, we don't know what to action on without having the proper insights. And insights is backed by data. If you don't have a way to normalize that data, you don't have a way to look at everything at the same common language.

And that seems to be the biggest problem, at least from my conversations with many, many hotel operators, all the way from above property all the way on property. This is the biggest challenge, and that's what we've solved with this patent.

Josiah: Amazing. I wanna come back to this idea of normalization in a moment, but Clark, I wanna go over to you. It's been awesome to work with you on some of the recent reports that we've published on hoteldata.com. Everyone loves seeing those reports. There's so much that you and our team do behind the scenes to make those happen.

You've been working in and around hotel data for a while, and I'm curious from your perspective in the hotel industry, what are some of the challenges with data that you've observed?

Clark: I would definitely piggyback on the statements both Joe and Pritesh have made so far. It comes down to data, data volume, and to paraphrase, data, data everywhere, but what the heck do I do with it? It was very hard to be actionable. It's the value of time. It was time spent creating the roll-up reports that would then be emailed or faxed or way back when, sometimes actually snail mailed to the corporate office.

So there was that time gap. And to paraphrase another old adage, time equals money. It's that opportunity cost is the opportunity lost in the case of time. And that was the hardest part because you spent a lot of time looking backwards. Does Joe have this report ready? Does Pritesh have his ready? As the GM or the corporate operator, I was always waiting on something.

With the advent of normalization, it's available at a click of a mouse, and so your efficiency just goes through the roof. And there are additional benefits. You're not using your printers as much, your toner, your paper, all those other consumables. It just makes everything go faster. And in business, the battle goes to the swift.

Josiah: I want to pivot a little bit and talk about what the patent that we recently announced allows us to do, what allows our customers to do. I'll include a link in the show notes where they can read that whole thing. We went into a fair amount of detail, but I wanna pick up on the thread we were talking about a moment ago around normalization.

So maybe for somebody who's just not super close to hotel tech or hotel data, what is this normalization process? What does it do and how does it work? What are the mechanics behind it? What does it allow us to do?

Clark: It's the efficiency. Each hotel has a PMS system to use an internal hospitality acronym, which think of a brand. You go to the front desk, you check in at a Hilton or a Marriott, et cetera, and they have a proprietary system that contains your guest information, and you go through the check-in process and eventually the check-out process.

All that information is needed to operate a hotel effectively on a daily, monthly basis. Once you have all that information, how do you put it together at scale, as we've been talking about?

That's where the normalization comes in because each brand is proprietary. Not every report, which is to Pritesh's prior statement, someone would have to do this manually via Excel. We do that automatically. We take all those disparate sources and normalize them, AKA translate them to the management company's master account system. So it's immediate visibility as soon as we receive the data.

Josiah: Pritesh, I would love to hear your take on this. Clark is a named inventor on this patent. You are as well, Pritesh. Tell us a little bit more about this. How does this work?

Pritesh: Absolutely. Just to double-click into what Clark mentioned. We talked about how there's just a ton of data. It lives everywhere. I talked about a typical hotel ecosystem in terms of technology and software. It can scale anywhere from fifteen to forty plus hotel systems.

The thing is, there are different vendors, there are different partners, and the way they structure their data, everything is just different. None of it speaks the same language. Accounting system, reservation system, revenue management systems, OTAs, so many systems, all producing data in different formats, different naming conventions, and often meaning different things, or different meanings for the same thing.

So if you wanted something basic like, "Hey, what is my revenue?" Well, that's not just one number. That's a stitch of multiple data points from different systems. Reconciling all of that, figuring out what needs to roll up to that definition of revenue, is very...

That's the problem that we've solved for. What we are doing in our technology here at Actabl is bringing that data into a normalized layer. What does that mean? We're using a tag-based approach. This is a little bit different from the chart of accounts or the USALI format. That is a rigid format in the accounting standards. But when it comes to data, there's that standard data, structured data, and then there's the unstructured data. And then there's also the data that is tied around KPIs, like your occupancy and all that stuff.

So we're bringing all of that data into a normalized tag-based tagging. Those tags are what drives our data across all of our customers. As we're bringing in data from different hotels, we're normalizing them to those tags, and those tags are what we're using to speak the same language in the system. And that's the value that Actabl provides, and that is what hoteliers are looking for when they come to us with problems.

And that's the solution we provide. Bringing them that all-in-one reporting. We're brand agnostic. We bring in independent hotels, all the major brands, all that data comes in. And now as an owner and operator, when you're looking at, or when you're asking the question, what is my revenue? You're looking at a P&L, you're looking at your various reporting, all of that language kind of syncs up to that same language.

So what we've done is essentially solve for that inefficiency of bringing all that data together. And then also we're saving time. Clark mentioned time equals money. So all that time that's spent wrangling data, you're just figuring out what needs to be reported rather than figuring out what you need to fix to make the guest experience better, or what you need to add to your hotels to make the guest experience better.

Josiah: I love it. Just to briefly respond to that, Pritesh, I think what you and Clark mentioned is exciting for me because I've worked with a lot of different hotel companies, and it feels like this has just been an ongoing challenge because of all the items that you've outlined, Pritesh.

And you kind of think about, for the leaders that are listening to us, these are very important decisions with a lot of money on the line. It's really important to get this stuff right. And so this isn't just about creating a pretty dashboard. These are people making hugely significant decisions based on what they're seeing here.

And so it's critical that the data can be relied on to make those decisions. But Joe, I'd like to go to you to close us out and talk a little bit about what are some of the outcomes of this? What does this allow our customers to do?

Joseph: Yeah, it's a great question. What we've been talking about here a lot is getting this data together and how difficult that challenge was for hotels. Talking about large amounts of data, putting it into Excel. I think Excel is around 1.4 million rows, and we all know it doesn't act very well past a few hundred thousand.

Actabl is bringing in and moving tens of millions of rows of data every day. And because we're able to bring all this in, and because it's normalized with this patent and the way that we do this with ProfitSword data, we're able to do a lot of different things.

We already mentioned hoteldata.com. One of the main drivers behind that is all of this data that lives inside of Actabl's data lakehouse that we're then able to hand over to Clark and the experts, to analyze it, work with the rest of the team to then publish some really interesting artifacts out there for hoteliers to read and even make decisions from.

It also gives us the ability to do some benchmarking as well. Because we have all this data, we can understand what segments are being more successful. How is the revenue doing? We're able to then evaluate that against different segments within the hotel ecosystem.

Josiah: I wanna get your take on what this allows in a more AI-enabled world. At recent conferences and conversations, I hear so many people saying, "Hey, we're kind of running our data through XYZ process." And sometimes it's interesting. We're gonna talk more in this series that we're doing each week around things to think about. I feel like in all these conversations I'm having, it all comes back to the inputs. That's where you gotta start because otherwise...

AI can create these interesting charts or insights, but it's all based on how reliable your data is. So I wonder if you could speak more in this sort of AI-enabled world, why this is so important. I think you touched on this a little bit, but why this matters so much now more than ever.

Joseph: Yeah, you can't just throw a bunch of random data into an AI and ask questions and expect a good answer. You can get an answer, and people might even make decisions based on those answers. But if you don't have the data normalized, if it's not sitting in one place that you can ask questions of, then you're gonna get a lot of hallucinations inside of the AI answers.

So one of the things that we think about so much here at Actabl is context. Context matters so much with AI, and being able to have this data normalized gives us a focus point to be able to put a lot of context on top of it. What does revenue mean? What other types of drivers are there for profitability? Those types of things we can start defining better because we're working with one tag instead of working with a hundred tags from a hundred different sources. It makes it a lot easier to be able to ask these questions.

Something that we've done is we're battle tested. We're hardened. We've been doing this for a couple years. We've been building up this data lakehouse that has this really great data in there. And because we've gone through it, we've learned our lessons, and we're in a really good place. It takes a lot of work. That's just something that I tell anybody. It's not easy. It takes work. It takes time. We could actually use these lessons to go in and then help some of our customers too.

Josiah: Before we go, I was thinking of maybe doing a speed round, because I like to leave people with, "Hey, what's something I could take into my next meetings or ask my team about or ask my technology partners about?"

Clark: It's all about time. From a technology perspective, how much time does your IT group spend monitoring these systems that we can normalize automatically for you? How much time does your accounting team spend gathering reports, putting them together? We do this automatically. It goes on and on and on.

If you're a hotel operator, I need to find out how much I'm spending on labor now so I don't overspend tomorrow, as an example. Everything in hotels is time-sensitive. It's one of those few things that literally expires on a daily basis. If I don't sell that room today, I can't sell it for yesterday tomorrow.

Same thing for all your seats in your restaurant. I can't turn back time. With our system, you don't need to. You can be more proactive with it.

Josiah: That's great advice. Pritesh, anything come to mind for you?

Pritesh: A couple of ways. So the first one is just kind of readiness. One of the questions that comes to mind is, how many systems are running at the property today? And do they actually talk to each other? Are you collecting that data anywhere to do anything meaningful with?

And then really just looking at AI readiness. Are you ready for the next frontier of hospitality? What I mean by that is, is your hotel structured in a successful way in terms of your systems and your tech stack that help you unlock the next frontier, which is powering hospitality through AI?

And then really the third one, which is related to a solution like Actabl. Based on what you know today about your business, your hotels, your guests, how confident are you making any staffing decisions, pricing decisions, based on the data that you have with you today?

Josiah: Love it. Joe, anything to add?

Joseph: For sure. Yeah. Where does your data live? If it's all over the place, it's not gonna work. Who are the people to help you get that data ready? What are the roles that you need? It can't just be... it needs to be somebody specialized in it. How do you clean that data? How do you standardize that data? How do you normalize that data to be able to use it? What data warehouse vendor do you use? There's so many questions out there. It's really hard to get the data to a place, large data, disparate data, to a place that's really usable. And if you aren't familiar with it, it becomes very difficult to even know where to get started.

Josiah: Amazing. Amazing questions. A lot of things to think about. I'll include a link in the show notes as well, where we can continue the conversation. Encourage everybody who's listening to reach out to us. We'd love to talk with you about this. Joe, Clark, and Pritesh, thank you so much for running through this with me today.

Joseph: Thank you.

Clark: Thank you.

Pritesh: Thank you so much. It was a pleasure.