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You don’t need to be a scientist to unlock the insights in your data. In fact, DataCamp’s Maggie Remynse and CBRE’s HoChun Ho say the secret to using data is hard-earned industry experience.
You Showed Me: Putting data to work in CRE while keeping it protected
January 31, 2023 35 Minute Listen
You Showed Me: Putting data to work in CRE while keeping it protected
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Spencer Levy
If I were to lead with the fact that this show was devoted to talking about data, I could almost see some of your eyes glazing over. But this topic is more than a discussion of numbers. It is about the future of business, about state of the art decision making and, for those of you in our core audience, about the nuances and challenges of working with data in commercial real estate. So you do the math. And by the way – spoiler alert – it's also a show about pickleball.
Maggie Remynse
A lot of times, you know, we hear data and it's kind of this scary monster in the corner. And so we think that it's something that's kind of abstract and hard to really get a grasp on. But my job is really to help you understand that even if you are not a data scientist, you can still find value in data and provide value to your organization by understanding data.
Spencer Levy
That's Maggie Remynse, Vice President of Curriculum for DataCamp, an EdTech company devoted to sharing information on data and how to use data, from the very basics to more advanced education on data collection, interpretation and more. She's also one of the best pickleball players in the country.
HoChun Ho
I think data is a fun topic, and commercial real estate data is extremely fun. And I think we have a completely different set of challenges than other industries.
Spencer Levy
And that's HoChun Ho, CBRE’s Head of Enterprise Data Governance. With three decades of experience, he's an expert on collecting, safeguarding and all forms of best practices in this evolving, increasingly vital field. On this episode, data and data literacy – and a dash of pickleball for good measure. I'm Spencer Levy and that's right now on The Weekly Take.
Spencer Levy
Welcome to The Weekly Take and this week we are going to be talking about data literacy, starting with Maggie Remynse. Maggie, thank you for joining the show.
Maggie Remynse
Thank you for having me, Spencer.
Spencer Levy
And then we're joined by our own HoChun Ho. HoChun, thanks for joining.
Hochun Ho
Thank you for having me here.
Spencer Levy
So let's begin with you, Maggie. Data is a topic that we hear a lot about, but I would say it's still a little bit of a mystery to many of our guests, including many times me. So, Maggie, why don't you tell our guests who you are and what you do?
Maggie Remynse
Sure. Yeah. As you said, I am the VP of curriculum at DataCamp. DataCamp is an EdTech company. So what we do is we educate the masses on data all the way from being a beginner, a complete beginner, just trying to figure out what data really means, up to you know, I'm trying to become an advanced machine learning data scientist and everything in between. So I've spent the last several years in this space and also working in banking and in FinTechs, really trying to educate the masses on data.
Spencer Levy
We seem to hear a lot that data is the new oil. That it is the most valuable single thing out there because of what it can do today, but more importantly, perhaps for its potential to make great changes in the future, both for its predictive capabilities before we even get into AI. Maggie, what do you think?
Maggie Remynse
I completely agree with that statement, Spencer. Data is the new oil, but just like oil, you have to know how to work with it for you to find the actual value. If you just were to dig in your backyard and find oil, you would probably be like, okay, now what? And that's the same thing that a lot of people run into with data. I might have a lot of data, but I have to actually know how to process it. And you said predictive, and that's a great word to really think of, is that we have to use it to predict. We also have to be there and educated enough to be able to interpret whatever those predictions end up being for us to really find the value from data.
Spencer Levy
And sometimes the value is surprising. Sometimes we don't even know what the data might do for us.
Maggie Remynse
100%. I mean, a lot of times if you go into looking at data or working with data with this idea of already what the outcome is going to be, you're going to miss a bigger picture. So going in with an open mind and being able to explore it holistically and not necessarily having that predetermined outcome. And also relying on those industry experts. Relying on your data analyst or data scientist and your people within your organization who have a lot to say about data and know the ins and outs of the data they're working with those are the ones that can help provide that value.
Spencer Levy
HoChun to you, data is the new oil. What's your point of view?
HoChun Ho
Oh, I love it. Every once in a while we're going to come up with a new analogy so I have heard that data has to be the new asset. And just like computers and tables and chairs and whiteboard and the new oil, I would love to develop more on the oil analogy. The first person that found oil in his backyard or her backyard probably did not think that oil can be used that way. So in a way, I like the analogy for that. My responsibility of data governance is really creating the refinery of oil and exploring new possibilities using data for a company. So from that standpoint, I love the analogy.
Spencer Levy
I think that people are afraid of data more than they love data. Most people – not you two, you two are experts in the field, this is what you do, but I think people that are outside of the data arena – they ask the question, what's in it for me? How people should use data to close sales, to give clients more information. So HoChun what do you say to those people?
HoChun Ho
I think data is a fun topic, and commercial real estate data is extremely fun. And I think we have a completely different set of challenges than other industries.
Maggie Remynse
Obviously, data is complex, but it can also add a lot of value to a lot of different users. And it's really about if you learn how to actually utilize the data that's in front of you, the data that's given to you. You call me a data expert, which I appreciate, but I would hardly say I'm a data expert myself. I've just learned how to use data in the way that is necessary for the role that I'm in. And that's the same thing that can be said for anybody in commercial real estate. So if I'm a real estate broker, I need to learn how to communicate effectively with my clients, utilizing the data that I have to be able to do that. I can utilize the data insights that are at hand or figure out better ways to underwrite my deals quicken to be able to really drive an insight from what I want. You know, if I'm an analyst internally I need to understand like what are the data fields what are those data elements that are really valuable to the company and that I can utilize easily and quickly to generate better insights. So it's really about that subject matter expertise of understanding the role you're in and really where data can add value because it's not going to be the end all, be all, no matter where you are. But data shouldn't be an inhibitor for the role that you're in. I have a friend that's in sales, not real estate sales, but similar in sales. And he started to utilize data to share with his team. He's a manager of, you know, how many prospecting calls they are doing as a team each week. How many in-person meetings they were having each week and sort of showing the trends of how quickly those led to deals. And his team was very surprised that this data could then predict closing of deals because it wasn't necessarily thought of in that way. It was just, oh, I need to hit this metric, this number, this arbitrary number that the company is throwing at me to hit instead of like these are the types of behaviors that we're trying to incentivize because we know that they're going to lead to success.
Spencer Levy
Maggie, you've spent a career in data as have you HoChun. Maggie, how did you get into it and why?
Maggie Remynse
For me, getting into data was kind of an accident. I was initially in the sciences, so I studied both biology and chemistry. And as an undergrad student and worked in literal pharmaceutical formulations where I was making drugs, like the drugs, like the COVID vaccines that you see or, you know, the over-the-counter Tylenol. And my biggest passion in working with drugs was actually looking at all the quality metrics of like making sure I had the highest purity and really making sure that the data, when I would do testing on things, turned out to be proper. And so that really kind of inspired me to get into data. But even at that point, I had no idea that it was data. So getting an MBA, I learned a lot more about data and ended up working at a bank and realized that you can make a career really being in the data space and helping people to make sure their data is properly cleaned and had the highest levels of quality. And that's really what inspired me to get into it. Then working with executives in banking saw the opportunity and the need for not only good data quality, but also in educating people on how to use data properly. A lot of times, you know, we hear data and it's kind of the scary monster in the corner and so we think that it's something that's kind of abstract and hard to really get a grasp on. But my job is really to help you understand that even if you are not a data scientist, you can still find value in data and provide value to your organization by understanding data.
Spencer Levy
So let's go to a real world example, if we can. I'm going to turn to both you, Maggie and HoChun. Give me one example of where you say, wow, this is an example I use of how data made a difference to this company. Maggie, do you have any examples like that?
Maggie Remynse
I mean, we can think about Amazon. I'm sure everybody here has an Amazon Prime account, I've had my Prime account for eight years, nine years or something like that now. And every time I go into Prime it recommends me something. I may not necessarily buy it, but a lot of times those things are accurate in what I'm looking for. Or if I go in and look for a certain product, it says, Oh, this product is normally bought with this one and we would recommend this one. And those are things that don't necessarily have immense value to my everyday life, but they do make things a lot easier. Same thing can be said when you go into your Netflix or any streaming service account now you have. Most likely it's telling you like, oh, this movie or this show you will probably like based on the other ones that you've watched historically. They're able to actually tell me like, hey, we know you well enough to tell you that you're probably going to like that. And it definitely makes things a lot easier. We don't necessarily need to think all the time, which maybe is not the best idea, but in these everyday moments where you're just trying to decompress, it can add value and take away some kind of stress to making a decision.
Spencer Levy
Well, I think I'm the bane of the existence of Spotify and Pandora because I have on my playlist, in addition to Neil Diamond and Tom Jones, I have Black Sabbath and Led Zeppelin, so they just can't figure me out. Maybe I can't figure out myself, but that's a pretty wide palette of tastes. But I'm with you completely on that example. HoChun any examples that you could point to of how a company changed because of its use of its algorithm, its interpretation of data.
HoChun Ho
In my career, I've worked for insurance companies several times. And when I look at commercial real estate right now, when we say we are using advanced analytics or machine learning, trying to drive some value, some people don't feel comfortable with that. Some people question whether I should trust an algorithm as opposed to our brokers or our experience. I think it's important that we leverage our wisdom and leverage our experience. But if you look at insurance companies before they even issue a policy for you, they kind of know the risks they're getting into. They know how to do underwriting. And there is a very complex process in the background doing underwriting and actuarial services to figure out the risks. Capital markets, companies don't do the same thing. So in a way, we are really playing catching up in our industry to apply those very proven technology that you and I have been using and feeling comfortable with and we're doing business with these industries for ever. Therefore, I think when you look at the very established practices of using data, I think there is a lot of opportunities and a lot of experiences we can learn.
Spencer Levy
I remember there was an expression ‘garbage in, garbage out’, the good stuff doesn't go into the model, good stuff is not coming out. But what it speaks to is there are challenges of not only the input of the data, but collecting it at all. HoChun, can you speak to some of the challenges of just collecting the data?
HoChun Ho
The data gathering is probably the single biggest challenge in the commercial real estate business, especially to our brokers. Because they really need to have access to complete, accurate, timely data for real estate opportunities, the availabilities. We spend a lot of money getting that information to them so they can do their job. And the data we're collecting is not easy data. Space can be repackaged. So a building, a floor, a suite, a campus, all of these are in our remit to support our business peers. And the data collection to deal with data with a fluid structure is really, really challenging. And then we have a number of different perspectives in the business services we provide. We do business for occupiers, we do business with investors and landlords and all kinds of real estate stakeholders and they all have their own needs the way they look at space. In our business, the data challenge is not about volume, it's about variety. It's about the timely access to the data that's hard to get.
Maggie Remynse
Just like any industry CRE is just like the rest, in the sense that you have to know the industry to really be able to find value with the data in hand. A lot of times when you're able to find value, especially in a business like CRE, it's about the external data sources coupled with those internal data sources that are really going to make a huge difference in the long run. HoChun’s areas is data governance, you have to have the right data within your organization and the clean data that you want to use. But you also really need to understand the market and having a little bit of extra knowledge on your competitors or the locations of your buildings or the areas that you're trying to look into can really go a long way. So utilizing external data sources like land registers or immediate press releases or company filings and disclosures, public records, all those different things together with your own internal data that you are amassing. And coupled with the actual subject matter knowledge that brokers have, you can really add a lot of value in any industry, and especially the commercial real estate. Because I do think that this is an area that it's very untapped with data at this point. And so there's a lot of opportunity for somebody going back to the oil analogy, hitting the oil mine and taking it all the way.
HoChun Ho
And if we look at other industries, how they benefited from making the best use of data. I always say that banking knows how to sell you the eighth credit card when on average every household already has seven. And telecom companies would, you know, many years ago would tell you that deposit $0.25 to extend another 30 seconds off your call. They know how to do that in real time. In that kind of a quick speed of value. We are sitting on really a goldmine and we are not really using the value of it yet. And that is why it's so exciting to come to the real real estate industry. But it's not to say that we don't have challenges managing space data iss really, really hard.
Spencer Levy
One of the things that has always troubled me is that when people put data of any type into a system, they would expect it to be clean, to be consistent. And then the problem is very often they will reach conclusions that I don't always agree with. And because they will sometimes not jive with real, real world experience. And at that point, I guess you got to go back to scratch that well, why doesn't this match with real world experience? And maybe there was something wrong, not necessarily with the information that's put in there. It might be perfect, but the interpretation of it. Maggie, what's your point of view?
Maggie Remynse
That's very true. Data is only as good as the people who are actually working with it and interpreting those results. A lot of times assumptions can be made because I don't necessarily have the background that somebody who's an expert in the commercial real estate business might have. And so if I were working with somebody who actually knows the business much better, they might be able to help interpret those results or make assumptions in the data that I wouldn't have actually drawn, that can help make different decisions long term. And so data is not supposed to be a roadmap to the gold mine. It's supposed to be an addition that will help you get there maybe quicker or find an insight or something that you're not necessarily thinking about. It's really supposed to help broaden your thought process rather than become the only thought.
Spencer Levy
Well, I like the way you framed it, is that this is, quote, not supposed to be the ticket to the gold mine. But I would suggest to you that many people do put it that way. People have high, high, high expectations for data. Now, let me be direct here. I'm in the commercial real estate business. I want the best data I could possibly get. But the problem is that the business of commercial real estate, we're dealing with assets and decisions that are not widgets. Each asset, even one that is right across the street that is identical construction with the same tenants is a different building. And its different decisions need to be made. So, HoChun, in a world where you are tasked with data governance, how do you deal in the commercial real estate context with the fact that we're more different than others.
HoChun Ho
I often tell people that I had a lot of years working in data and I've been in commercial real estate for about nine. And I was humbled in the first three months. Honestly, this is hard. And you're absolutely right, especially at the data we're dealing with, the two buildings on the same block may have completely different values to the tenants or to the landlords. So how do we actually provide the information needed by the business to Maggie's point, for the business to come up with the interpretation? And that's my job. Now, I don't pretend that I can tell the business in data governance that we know the differences, we know how to use the data. However, it's more or less like preparing water for or oil for people or for the business to consume. I want them to be able to read the label, to know the processes that we went through to purify the water. I want them to know the expiration date. I want them to know the ingredients. And I want them to know everything they can possibly know about the source and who took care of the data. Therefore, they come up with the best judgment and they come up with the interpretation. So there are a lot of things we do to really help the business, to interpret the data, and that's to the challenge. I still think it's hard even after we do all of this. That's why it's fun to be in commercial real estate.
Maggie Remynse
We shouldn't be allowing a machine necessarily to make all these decisions. Now, maybe at some point in time we'll have enough data that we will quantify as enough data to really help us make certain types of decisions. But exactly, data is really supposed to be that starting point and is supposed to help get us going. And maybe do some of the things that are boring and mundane and monotonous, like, you know, looking up all of the information in the area. Maybe you can easily find all this information through data and look at all the comps and all that stuff that maybe would have been a manual process before, but now we can utilize data to do that in a quicker way. And again, like banking, you look at the banking industry in general, there's data they have for like my grandparents have accounts from when they first got married 50 some years ago. Very few industries have that kind of information that they can really go back in time and look at. That's also why we see some of the inconsistencies maybe are like we're not able to make as clear of decisions. And so I do think long term, we'll be able to get to a better point where maybe we have enough data or the right data, however we want to quantify that to help us make better decisions. But at the end of the day, you still need somebody there to kind of say yes or no and to really interpret it and to see if it makes sense. Because the machine doesn't know if it makes sense they're just going to tell you that, oh, this is like what's most plausible.
Spencer Levy
HoChun there's another concept out there that we would like to discuss that's called data literacy. What does that mean?
HoChun Ho
So if you go online to look for a definition on the Internet, you're going to find a lot of different definitions. I actually have mine. I actually believe that data literacy in the corporation is the skills and knowledge to read and interpret the company's data, business data to form an opinion. Now, there is some loaded information here. It's the skill, it's the knowledge, it's the ability to read, but also interpret the data and form an opinion. You notice. I'm not saying that from a decision to form an opinion. And I believe that if you have the skills and knowledge and the ability to interpret, then you are a data literate person.
Maggie Remynse
I would agree with that. I think the only thing I would add is that there is a different level of data literacy for pretty much all users and all people, and that's expected. We wouldn't expect everybody to have the same. So being able to read, work with, analyze and communicate with data means slightly different things to different user groups.
Spencer Levy
Got it. So residential real estate has got a lot of very good websites that I use myself, including Zillow. I don't see commercial real estate getting there any time soon because if I want to rent 10,000 feet in Los Angeles, Los Angeles is 75 different cities stuck together called Los Angeles. And so I don't even know where I'd begin on that type of search. You can do hotel decisions online. Certainly you can do airline decisions online, and you're getting close to residential decisions as well. I don't know if we'll ever get there in commercial. What do you think, HoChun?
HoChun Ho
Well, first of all, I think commercial real estate is a big aggregate term that represents a lot of different services. I certainly agree with you, Spencer. In certain areas that we may see slower adoption of using data to win. But if you look at are there other areas, just because we are not the pioneer, we can be the best follower because we have all the exemples coming from other industries. There's a lot of things we can learn from other industries. And the other thing is that just like the automotive industry, cars are talking back. Our buildings are talking back, too. So it really depends on how we leverage the information to create opportunity for certain types of business cases or use cases. Certainly in equipment management, for example, in that area all the equipment now can talk back to us. We have a lot of opportunities to do a better job of maintenance. So there are similar areas that I believe that we are going to see some leapfrog. Another advantage is that in our industry there is a concept of operational intelligence. So we use drones, we use robots, all these new ways of getting things done generates the kind of data other industries don't see. So I think we have a great opportunity to leapfrog just because we're not the pioneer.
Spencer Levy
Well, I'm glad you use the term leapfrog because I'm doing a presentation this afternoon and my theme this Spring is video games, and Frogger is one of the games that’s featured in my presentation, jumping from one to the next. But what you suggested is a term that I use all the time HoChun, which is called the quote-unquote “second-mover advantage”. Basically looking for analogous industries and say, okay, residential real estate figured it out, maybe logistics figured it out with Wal-Mart and Home Depot and other large big box stores, and commercial real estate maybe we haven't figured it out yet, but we'll get closer. What do you think?
HoChun Ho
If you look at all the EVs and Tesla and other companies and how the cars are using data to produce new products? One has to believe that there are ways to do that since the buildings are really talking to us equipment or talking to us.
Spencer Levy
But there are limitations, right. And I think that the question is how far are you prepared to go? So it really comes down to sometimes the cost benefits of the end result. In the meantime, we can still do the analysis. What do you think, Maggie?
Maggie Remynse
You really hit exactly what I was going to say, is the cost benefit of actually building an app or pulling all those types of data together. I mean, you mentioned the private housing sector, I would say that there's a huge demand for housing, private housing in a sense that maybe there's not the same demand in all levels of CRE. You can go in and you have like the corner store at your, you know, bodega basically and that's a much smaller building than the 10,000 square foot unit that you're looking for in Los Angeles right now. And those are quite different needs. And so aggregating all that data together, it could be challenging for any individual to do that or any companies do that. But also you're meeting different market demands there. You're talking about small mom-and-pop type of private industries or private like little shops versus massive corporations that might be renting big businesses or buildings all on itself and everything in between, which is quite different user groups that you're really targeting there. And the demand and the competition is quite different within that. I know I worked at a bank previously and they had a lot of their lock and key under a lot of their commercial real estate holdings, and I don't know if they would have wanted to relinquish some of the data that might be necessary even to go into building like a publicly available tool in a sense. Now, if you're talking about those individuals, like if I were to want to rent a small building or an office space, that's a much different level and I might not be as concerned about the data privacy or, you know, relinquishing some of that information. And I might be willing to pay a little bit more because I don't have any knowledge of it. Now, people like you, Spencer, who have so much knowledge and this is your career, you might be like, I don't know if I really want to have all this publicly, this information publicly available as competition gets much larger and much different when you're talking at scale.
Spencer Levy
So let's turn now to the G in ESG. We talk a lot about the E and the S – environmental and social. But HoChun, the G in governance, ESG, that's kind of what you do with respect to data., data governance. Are they the same governance or is it unrelated?
HoChun Ho
They're not the same, but they're definitely related. And data governance has been a business practice for many years, and ESG, certainly the practices and desire have been there for a while, but the term is a relatively new term. They're related in the sense that data governance and governance in ESG both focus on protection. But in addition to data protection for data governance, we are also focusing on data enablement. So if you look at some of our typical data governance deliverables – of documenting the data in the dictionary, in a data catalog, in business glossaries, or monitoring data quality and remediated data defects – these are not exactly in the sense of protecting the data, but also getting into business enablement and providing better results with better data and kind of territory. So they are very much related, but not exactly the same.
Spencer Levy
Got it. Got it. So now, Maggie, we're going to get to the most important part of today's show. I would have been remiss if I didn't mention to our listeners that Maggie, in addition to her data prowess, is a world class pickleball player, which is now the hottest sport. So first of all, tell us about that, Maggie. How did you get into pickleball and tell us about what you do there?
Maggie Remynse
Yeah, sure. Pickleball is the best. If you haven't played, definitely go out. If you haven't even heard of it, then I don't know where you've been living at this point. But yeah, pickleball. I started playing pickleball back in 2017. I played college tennis and have been playing tennis for the majority of my life for longer than I can remember. And as a person who played a high level sport growing up and through college, getting out of college and leaving the tennis world was a challenge for me to like, put away my competitive spirit. I always tell my team we're competing whether they know it or not. I'm competing. You know, we're having a walking race and we're probably competing, whether you thought it or not. And so I was always looking for some way to kind of get my competitive edge again. And my sister actually introduced me to pickleball. At first it was just something fun to do, but quickly with it literally the first week I played, I was asked to play a tournament and the rest is history, really. I've been competing at the professional level since 2018 at the pro level. Have got up to number seven in the world, currently ranked just inside the top 20 in the world. And I compete in pickleball regularly. It's definitely a great thing to get to do. I'm lucky to still be able to do it and work full time. I will say my sister and I are the only two players in the pro space, male or female, that work full time outside of playing pro. So we were definitely lucky and just happy that I can still compete with these youngsters now.
Spencer Levy
Good for you. Now is there, like, a professional pickleball association?
Maggie Remynse
Yeah. So there's a couple of different areas where you can play pickleball or through pro. There's the PPA, the Pro Pickleball Association, the APP, the Association of Professional Pickleballers and Major League Pickleball, MLP. So a lot of people have probably started to hear of the Major League Pickleball because those are the likes of LeBron James and Kevin Durant and, you know, other big names and Mark Cuban. So like lots of big names have bought teams and Major League Pickleball. So you can be drafted to a Major League Pickleball team. Last year, I played on Major League Pickleball and my team got second and we finished third in the second one.
Spencer Levy
So if LeBron and Kevin Durant were on the other side of the net, you are taking them down?
Maggie Remynse
Absolutely. I mean, you know, I'm not going to beat them in basketball, but I'm sure we're going to beat them in pickleball.
Spencer Levy [00:30:54] Got to ask, Maggie, can data make you a better pickleball player?
Maggie Remynse
Data can absolutely make you a better pickleball player. By knowing exactly where your strengths are or where your weaknesses are or where the trends are within a match, it can help you decide how to practice, and it can help you decide how to really compete and what shots to hit at different times throughout a match. Right now, a lot of what you do is just kind of based on general knowledge or that gut instinct of, Oh, I think this has worked in the past. But being able to rely on data will change the way people are really thinking. And it’s done this in a lot of other sports at this point in time. You look at so many other sports and the way that data has been incorporated, it truly changes the way that you're able to practice. I know as a kid when I was in tennis, I would really utilize the tools around me and I would count how many balls I was hitting in when I would see that on different devices and I could actually really proactively work on different shots based on the data telling me that maybe one shot was weaker or better than another. And that hasn't really came into pickleball yet, but it's something that's truly an opportunity in that if somebody is able to easily capture this kind of data, especially as a pro, I would totally be interested in it. And I know a lot of amateurs would be as well, because they all want to figure out how to improve their game. So in the short answer, yes, everybody can utilize data to improve their standing and make them a better athlete in general, but it's just really like, how accessible is this for those people or for anybody in a sense, and how can we really incorporate it in the long term?
Spencer Levy
Let's turn to some final thoughts about the data and the world where we're going to and visions for the future of data, of A.I., of machine learning. Maggie five years from now, what's your vision?
Maggie Remynse
I do see, like, things like ChatGPT taking up more space in different people's lives, whether it's our personal lives or their work lives. Chat GPT is an AI driven tool that basically acts like Google. Chat GPT is something that will do similar, however also add a lot more color and context. You can get complex answers and anybody can utilize the results from it to really enhance and augment your own decision making. But it shouldn't be utilized instead of that. We should still really formulate our own opinions and have educated assumptions and ideas behind it. And so I would hope that going forward people would be able to use those things responsibly, but also not fully rely on it to be their everyday life.
Spencer Levy
HoChun what’s your perspective? Five years from now where are we going to be with data governance and the like?
HoChun Ho
I think we're looking at a lot of automation and we're looking at a lot of opportunities, but nothing can be automated unless you can manually go through that once in your head, right? So the human interpretation, the human involvement is still important. I'm really hopeful that one day all commercial real estate participants, stakeholders will be operating on a level of data advantage that's available to all of us. And I'm certainly hopeful that as long as we play within the do's and don'ts of compliance protection, some of the guardrails in five years, in ten years, we're going to get there with data.
Spencer Levy
And so on behalf of The Weekly Take, I want to thank first Maggie Remynse, the Vice President of curriculum at DataCamp for joining us on today's show. Thank you.
Maggie Remynse
Thank you, Spencer. Thanks for having me.
Spencer Levy
I then want to thank our friend and colleague, HoChun Ho the Head of Enterprise Data Governance at CBRE, also for showing us the way in the data world. Thank you, HoChun
HoChun Ho
Thank you, spencer.
Spencer Levy
For more on the topic of the show, check out our Web site at CBRE.com/TheWeeklyTake. But if you did enjoy the pickleball portion of our program, we've got a treat for you next week, a show that's all about sports, competition and business featuring athletes in the working world who take their weekend warrior status to the next level. From an open-water swimmer who recently crossed the English Channel to a world-class triathlete and indeed, more from Maggie Remynse on the power of pickleball, and more. So come back to hear from our winning team next week, but not before you share this program with your own team. And also subscribe, rate and review us wherever you listen. Thanks for joining us. I'm Spencer Levy. Be smart. Be safe. Be well.
Spencer Levy
If I were to lead with the fact that this show was devoted to talking about data, I could almost see some of your eyes glazing over. But this topic is more than a discussion of numbers. It is about the future of business, about state of the art decision making and, for those of you in our core audience, about the nuances and challenges of working with data in commercial real estate. So you do the math. And by the way – spoiler alert – it's also a show about pickleball.
Maggie Remynse
A lot of times, you know, we hear data and it's kind of this scary monster in the corner. And so we think that it's something that's kind of abstract and hard to really get a grasp on. But my job is really to help you understand that even if you are not a data scientist, you can still find value in data and provide value to your organization by understanding data.
Spencer Levy
That's Maggie Remynse, Vice President of Curriculum for DataCamp, an EdTech company devoted to sharing information on data and how to use data, from the very basics to more advanced education on data collection, interpretation and more. She's also one of the best pickleball players in the country.
HoChun Ho
I think data is a fun topic, and commercial real estate data is extremely fun. And I think we have a completely different set of challenges than other industries.
Spencer Levy
And that's HoChun Ho, CBRE’s Head of Enterprise Data Governance. With three decades of experience, he's an expert on collecting, safeguarding and all forms of best practices in this evolving, increasingly vital field. On this episode, data and data literacy – and a dash of pickleball for good measure. I'm Spencer Levy and that's right now on The Weekly Take.
Spencer Levy
Welcome to The Weekly Take and this week we are going to be talking about data literacy, starting with Maggie Remynse. Maggie, thank you for joining the show.
Maggie Remynse
Thank you for having me, Spencer.
Spencer Levy
And then we're joined by our own HoChun Ho. HoChun, thanks for joining.
Hochun Ho
Thank you for having me here.
Spencer Levy
So let's begin with you, Maggie. Data is a topic that we hear a lot about, but I would say it's still a little bit of a mystery to many of our guests, including many times me. So, Maggie, why don't you tell our guests who you are and what you do?
Maggie Remynse
Sure. Yeah. As you said, I am the VP of curriculum at DataCamp. DataCamp is an EdTech company. So what we do is we educate the masses on data all the way from being a beginner, a complete beginner, just trying to figure out what data really means, up to you know, I'm trying to become an advanced machine learning data scientist and everything in between. So I've spent the last several years in this space and also working in banking and in FinTechs, really trying to educate the masses on data.
Spencer Levy
We seem to hear a lot that data is the new oil. That it is the most valuable single thing out there because of what it can do today, but more importantly, perhaps for its potential to make great changes in the future, both for its predictive capabilities before we even get into AI. Maggie, what do you think?
Maggie Remynse
I completely agree with that statement, Spencer. Data is the new oil, but just like oil, you have to know how to work with it for you to find the actual value. If you just were to dig in your backyard and find oil, you would probably be like, okay, now what? And that's the same thing that a lot of people run into with data. I might have a lot of data, but I have to actually know how to process it. And you said predictive, and that's a great word to really think of, is that we have to use it to predict. We also have to be there and educated enough to be able to interpret whatever those predictions end up being for us to really find the value from data.
Spencer Levy
And sometimes the value is surprising. Sometimes we don't even know what the data might do for us.
Maggie Remynse
100%. I mean, a lot of times if you go into looking at data or working with data with this idea of already what the outcome is going to be, you're going to miss a bigger picture. So going in with an open mind and being able to explore it holistically and not necessarily having that predetermined outcome. And also relying on those industry experts. Relying on your data analyst or data scientist and your people within your organization who have a lot to say about data and know the ins and outs of the data they're working with those are the ones that can help provide that value.
Spencer Levy
HoChun to you, data is the new oil. What's your point of view?
HoChun Ho
Oh, I love it. Every once in a while we're going to come up with a new analogy so I have heard that data has to be the new asset. And just like computers and tables and chairs and whiteboard and the new oil, I would love to develop more on the oil analogy. The first person that found oil in his backyard or her backyard probably did not think that oil can be used that way. So in a way, I like the analogy for that. My responsibility of data governance is really creating the refinery of oil and exploring new possibilities using data for a company. So from that standpoint, I love the analogy.
Spencer Levy
I think that people are afraid of data more than they love data. Most people – not you two, you two are experts in the field, this is what you do, but I think people that are outside of the data arena – they ask the question, what's in it for me? How people should use data to close sales, to give clients more information. So HoChun what do you say to those people?
HoChun Ho
I think data is a fun topic, and commercial real estate data is extremely fun. And I think we have a completely different set of challenges than other industries.
Maggie Remynse
Obviously, data is complex, but it can also add a lot of value to a lot of different users. And it's really about if you learn how to actually utilize the data that's in front of you, the data that's given to you. You call me a data expert, which I appreciate, but I would hardly say I'm a data expert myself. I've just learned how to use data in the way that is necessary for the role that I'm in. And that's the same thing that can be said for anybody in commercial real estate. So if I'm a real estate broker, I need to learn how to communicate effectively with my clients, utilizing the data that I have to be able to do that. I can utilize the data insights that are at hand or figure out better ways to underwrite my deals quicken to be able to really drive an insight from what I want. You know, if I'm an analyst internally I need to understand like what are the data fields what are those data elements that are really valuable to the company and that I can utilize easily and quickly to generate better insights. So it's really about that subject matter expertise of understanding the role you're in and really where data can add value because it's not going to be the end all, be all, no matter where you are. But data shouldn't be an inhibitor for the role that you're in. I have a friend that's in sales, not real estate sales, but similar in sales. And he started to utilize data to share with his team. He's a manager of, you know, how many prospecting calls they are doing as a team each week. How many in-person meetings they were having each week and sort of showing the trends of how quickly those led to deals. And his team was very surprised that this data could then predict closing of deals because it wasn't necessarily thought of in that way. It was just, oh, I need to hit this metric, this number, this arbitrary number that the company is throwing at me to hit instead of like these are the types of behaviors that we're trying to incentivize because we know that they're going to lead to success.
Spencer Levy
Maggie, you've spent a career in data as have you HoChun. Maggie, how did you get into it and why?
Maggie Remynse
For me, getting into data was kind of an accident. I was initially in the sciences, so I studied both biology and chemistry. And as an undergrad student and worked in literal pharmaceutical formulations where I was making drugs, like the drugs, like the COVID vaccines that you see or, you know, the over-the-counter Tylenol. And my biggest passion in working with drugs was actually looking at all the quality metrics of like making sure I had the highest purity and really making sure that the data, when I would do testing on things, turned out to be proper. And so that really kind of inspired me to get into data. But even at that point, I had no idea that it was data. So getting an MBA, I learned a lot more about data and ended up working at a bank and realized that you can make a career really being in the data space and helping people to make sure their data is properly cleaned and had the highest levels of quality. And that's really what inspired me to get into it. Then working with executives in banking saw the opportunity and the need for not only good data quality, but also in educating people on how to use data properly. A lot of times, you know, we hear data and it's kind of the scary monster in the corner and so we think that it's something that's kind of abstract and hard to really get a grasp on. But my job is really to help you understand that even if you are not a data scientist, you can still find value in data and provide value to your organization by understanding data.
Spencer Levy
So let's go to a real world example, if we can. I'm going to turn to both you, Maggie and HoChun. Give me one example of where you say, wow, this is an example I use of how data made a difference to this company. Maggie, do you have any examples like that?
Maggie Remynse
I mean, we can think about Amazon. I'm sure everybody here has an Amazon Prime account, I've had my Prime account for eight years, nine years or something like that now. And every time I go into Prime it recommends me something. I may not necessarily buy it, but a lot of times those things are accurate in what I'm looking for. Or if I go in and look for a certain product, it says, Oh, this product is normally bought with this one and we would recommend this one. And those are things that don't necessarily have immense value to my everyday life, but they do make things a lot easier. Same thing can be said when you go into your Netflix or any streaming service account now you have. Most likely it's telling you like, oh, this movie or this show you will probably like based on the other ones that you've watched historically. They're able to actually tell me like, hey, we know you well enough to tell you that you're probably going to like that. And it definitely makes things a lot easier. We don't necessarily need to think all the time, which maybe is not the best idea, but in these everyday moments where you're just trying to decompress, it can add value and take away some kind of stress to making a decision.
Spencer Levy
Well, I think I'm the bane of the existence of Spotify and Pandora because I have on my playlist, in addition to Neil Diamond and Tom Jones, I have Black Sabbath and Led Zeppelin, so they just can't figure me out. Maybe I can't figure out myself, but that's a pretty wide palette of tastes. But I'm with you completely on that example. HoChun any examples that you could point to of how a company changed because of its use of its algorithm, its interpretation of data.
HoChun Ho
In my career, I've worked for insurance companies several times. And when I look at commercial real estate right now, when we say we are using advanced analytics or machine learning, trying to drive some value, some people don't feel comfortable with that. Some people question whether I should trust an algorithm as opposed to our brokers or our experience. I think it's important that we leverage our wisdom and leverage our experience. But if you look at insurance companies before they even issue a policy for you, they kind of know the risks they're getting into. They know how to do underwriting. And there is a very complex process in the background doing underwriting and actuarial services to figure out the risks. Capital markets, companies don't do the same thing. So in a way, we are really playing catching up in our industry to apply those very proven technology that you and I have been using and feeling comfortable with and we're doing business with these industries for ever. Therefore, I think when you look at the very established practices of using data, I think there is a lot of opportunities and a lot of experiences we can learn.
Spencer Levy
I remember there was an expression ‘garbage in, garbage out’, the good stuff doesn't go into the model, good stuff is not coming out. But what it speaks to is there are challenges of not only the input of the data, but collecting it at all. HoChun, can you speak to some of the challenges of just collecting the data?
HoChun Ho
The data gathering is probably the single biggest challenge in the commercial real estate business, especially to our brokers. Because they really need to have access to complete, accurate, timely data for real estate opportunities, the availabilities. We spend a lot of money getting that information to them so they can do their job. And the data we're collecting is not easy data. Space can be repackaged. So a building, a floor, a suite, a campus, all of these are in our remit to support our business peers. And the data collection to deal with data with a fluid structure is really, really challenging. And then we have a number of different perspectives in the business services we provide. We do business for occupiers, we do business with investors and landlords and all kinds of real estate stakeholders and they all have their own needs the way they look at space. In our business, the data challenge is not about volume, it's about variety. It's about the timely access to the data that's hard to get.
Maggie Remynse
Just like any industry CRE is just like the rest, in the sense that you have to know the industry to really be able to find value with the data in hand. A lot of times when you're able to find value, especially in a business like CRE, it's about the external data sources coupled with those internal data sources that are really going to make a huge difference in the long run. HoChun’s areas is data governance, you have to have the right data within your organization and the clean data that you want to use. But you also really need to understand the market and having a little bit of extra knowledge on your competitors or the locations of your buildings or the areas that you're trying to look into can really go a long way. So utilizing external data sources like land registers or immediate press releases or company filings and disclosures, public records, all those different things together with your own internal data that you are amassing. And coupled with the actual subject matter knowledge that brokers have, you can really add a lot of value in any industry, and especially the commercial real estate. Because I do think that this is an area that it's very untapped with data at this point. And so there's a lot of opportunity for somebody going back to the oil analogy, hitting the oil mine and taking it all the way.
HoChun Ho
And if we look at other industries, how they benefited from making the best use of data. I always say that banking knows how to sell you the eighth credit card when on average every household already has seven. And telecom companies would, you know, many years ago would tell you that deposit $0.25 to extend another 30 seconds off your call. They know how to do that in real time. In that kind of a quick speed of value. We are sitting on really a goldmine and we are not really using the value of it yet. And that is why it's so exciting to come to the real real estate industry. But it's not to say that we don't have challenges managing space data iss really, really hard.
Spencer Levy
One of the things that has always troubled me is that when people put data of any type into a system, they would expect it to be clean, to be consistent. And then the problem is very often they will reach conclusions that I don't always agree with. And because they will sometimes not jive with real, real world experience. And at that point, I guess you got to go back to scratch that well, why doesn't this match with real world experience? And maybe there was something wrong, not necessarily with the information that's put in there. It might be perfect, but the interpretation of it. Maggie, what's your point of view?
Maggie Remynse
That's very true. Data is only as good as the people who are actually working with it and interpreting those results. A lot of times assumptions can be made because I don't necessarily have the background that somebody who's an expert in the commercial real estate business might have. And so if I were working with somebody who actually knows the business much better, they might be able to help interpret those results or make assumptions in the data that I wouldn't have actually drawn, that can help make different decisions long term. And so data is not supposed to be a roadmap to the gold mine. It's supposed to be an addition that will help you get there maybe quicker or find an insight or something that you're not necessarily thinking about. It's really supposed to help broaden your thought process rather than become the only thought.
Spencer Levy
Well, I like the way you framed it, is that this is, quote, not supposed to be the ticket to the gold mine. But I would suggest to you that many people do put it that way. People have high, high, high expectations for data. Now, let me be direct here. I'm in the commercial real estate business. I want the best data I could possibly get. But the problem is that the business of commercial real estate, we're dealing with assets and decisions that are not widgets. Each asset, even one that is right across the street that is identical construction with the same tenants is a different building. And its different decisions need to be made. So, HoChun, in a world where you are tasked with data governance, how do you deal in the commercial real estate context with the fact that we're more different than others.
HoChun Ho
I often tell people that I had a lot of years working in data and I've been in commercial real estate for about nine. And I was humbled in the first three months. Honestly, this is hard. And you're absolutely right, especially at the data we're dealing with, the two buildings on the same block may have completely different values to the tenants or to the landlords. So how do we actually provide the information needed by the business to Maggie's point, for the business to come up with the interpretation? And that's my job. Now, I don't pretend that I can tell the business in data governance that we know the differences, we know how to use the data. However, it's more or less like preparing water for or oil for people or for the business to consume. I want them to be able to read the label, to know the processes that we went through to purify the water. I want them to know the expiration date. I want them to know the ingredients. And I want them to know everything they can possibly know about the source and who took care of the data. Therefore, they come up with the best judgment and they come up with the interpretation. So there are a lot of things we do to really help the business, to interpret the data, and that's to the challenge. I still think it's hard even after we do all of this. That's why it's fun to be in commercial real estate.
Maggie Remynse
We shouldn't be allowing a machine necessarily to make all these decisions. Now, maybe at some point in time we'll have enough data that we will quantify as enough data to really help us make certain types of decisions. But exactly, data is really supposed to be that starting point and is supposed to help get us going. And maybe do some of the things that are boring and mundane and monotonous, like, you know, looking up all of the information in the area. Maybe you can easily find all this information through data and look at all the comps and all that stuff that maybe would have been a manual process before, but now we can utilize data to do that in a quicker way. And again, like banking, you look at the banking industry in general, there's data they have for like my grandparents have accounts from when they first got married 50 some years ago. Very few industries have that kind of information that they can really go back in time and look at. That's also why we see some of the inconsistencies maybe are like we're not able to make as clear of decisions. And so I do think long term, we'll be able to get to a better point where maybe we have enough data or the right data, however we want to quantify that to help us make better decisions. But at the end of the day, you still need somebody there to kind of say yes or no and to really interpret it and to see if it makes sense. Because the machine doesn't know if it makes sense they're just going to tell you that, oh, this is like what's most plausible.
Spencer Levy
HoChun there's another concept out there that we would like to discuss that's called data literacy. What does that mean?
HoChun Ho
So if you go online to look for a definition on the Internet, you're going to find a lot of different definitions. I actually have mine. I actually believe that data literacy in the corporation is the skills and knowledge to read and interpret the company's data, business data to form an opinion. Now, there is some loaded information here. It's the skill, it's the knowledge, it's the ability to read, but also interpret the data and form an opinion. You notice. I'm not saying that from a decision to form an opinion. And I believe that if you have the skills and knowledge and the ability to interpret, then you are a data literate person.
Maggie Remynse
I would agree with that. I think the only thing I would add is that there is a different level of data literacy for pretty much all users and all people, and that's expected. We wouldn't expect everybody to have the same. So being able to read, work with, analyze and communicate with data means slightly different things to different user groups.
Spencer Levy
Got it. So residential real estate has got a lot of very good websites that I use myself, including Zillow. I don't see commercial real estate getting there any time soon because if I want to rent 10,000 feet in Los Angeles, Los Angeles is 75 different cities stuck together called Los Angeles. And so I don't even know where I'd begin on that type of search. You can do hotel decisions online. Certainly you can do airline decisions online, and you're getting close to residential decisions as well. I don't know if we'll ever get there in commercial. What do you think, HoChun?
HoChun Ho
Well, first of all, I think commercial real estate is a big aggregate term that represents a lot of different services. I certainly agree with you, Spencer. In certain areas that we may see slower adoption of using data to win. But if you look at are there other areas, just because we are not the pioneer, we can be the best follower because we have all the exemples coming from other industries. There's a lot of things we can learn from other industries. And the other thing is that just like the automotive industry, cars are talking back. Our buildings are talking back, too. So it really depends on how we leverage the information to create opportunity for certain types of business cases or use cases. Certainly in equipment management, for example, in that area all the equipment now can talk back to us. We have a lot of opportunities to do a better job of maintenance. So there are similar areas that I believe that we are going to see some leapfrog. Another advantage is that in our industry there is a concept of operational intelligence. So we use drones, we use robots, all these new ways of getting things done generates the kind of data other industries don't see. So I think we have a great opportunity to leapfrog just because we're not the pioneer.
Spencer Levy
Well, I'm glad you use the term leapfrog because I'm doing a presentation this afternoon and my theme this Spring is video games, and Frogger is one of the games that’s featured in my presentation, jumping from one to the next. But what you suggested is a term that I use all the time HoChun, which is called the quote-unquote “second-mover advantage”. Basically looking for analogous industries and say, okay, residential real estate figured it out, maybe logistics figured it out with Wal-Mart and Home Depot and other large big box stores, and commercial real estate maybe we haven't figured it out yet, but we'll get closer. What do you think?
HoChun Ho
If you look at all the EVs and Tesla and other companies and how the cars are using data to produce new products? One has to believe that there are ways to do that since the buildings are really talking to us equipment or talking to us.
Spencer Levy
But there are limitations, right. And I think that the question is how far are you prepared to go? So it really comes down to sometimes the cost benefits of the end result. In the meantime, we can still do the analysis. What do you think, Maggie?
Maggie Remynse
You really hit exactly what I was going to say, is the cost benefit of actually building an app or pulling all those types of data together. I mean, you mentioned the private housing sector, I would say that there's a huge demand for housing, private housing in a sense that maybe there's not the same demand in all levels of CRE. You can go in and you have like the corner store at your, you know, bodega basically and that's a much smaller building than the 10,000 square foot unit that you're looking for in Los Angeles right now. And those are quite different needs. And so aggregating all that data together, it could be challenging for any individual to do that or any companies do that. But also you're meeting different market demands there. You're talking about small mom-and-pop type of private industries or private like little shops versus massive corporations that might be renting big businesses or buildings all on itself and everything in between, which is quite different user groups that you're really targeting there. And the demand and the competition is quite different within that. I know I worked at a bank previously and they had a lot of their lock and key under a lot of their commercial real estate holdings, and I don't know if they would have wanted to relinquish some of the data that might be necessary even to go into building like a publicly available tool in a sense. Now, if you're talking about those individuals, like if I were to want to rent a small building or an office space, that's a much different level and I might not be as concerned about the data privacy or, you know, relinquishing some of that information. And I might be willing to pay a little bit more because I don't have any knowledge of it. Now, people like you, Spencer, who have so much knowledge and this is your career, you might be like, I don't know if I really want to have all this publicly, this information publicly available as competition gets much larger and much different when you're talking at scale.
Spencer Levy
So let's turn now to the G in ESG. We talk a lot about the E and the S – environmental and social. But HoChun, the G in governance, ESG, that's kind of what you do with respect to data., data governance. Are they the same governance or is it unrelated?
HoChun Ho
They're not the same, but they're definitely related. And data governance has been a business practice for many years, and ESG, certainly the practices and desire have been there for a while, but the term is a relatively new term. They're related in the sense that data governance and governance in ESG both focus on protection. But in addition to data protection for data governance, we are also focusing on data enablement. So if you look at some of our typical data governance deliverables – of documenting the data in the dictionary, in a data catalog, in business glossaries, or monitoring data quality and remediated data defects – these are not exactly in the sense of protecting the data, but also getting into business enablement and providing better results with better data and kind of territory. So they are very much related, but not exactly the same.
Spencer Levy
Got it. Got it. So now, Maggie, we're going to get to the most important part of today's show. I would have been remiss if I didn't mention to our listeners that Maggie, in addition to her data prowess, is a world class pickleball player, which is now the hottest sport. So first of all, tell us about that, Maggie. How did you get into pickleball and tell us about what you do there?
Maggie Remynse
Yeah, sure. Pickleball is the best. If you haven't played, definitely go out. If you haven't even heard of it, then I don't know where you've been living at this point. But yeah, pickleball. I started playing pickleball back in 2017. I played college tennis and have been playing tennis for the majority of my life for longer than I can remember. And as a person who played a high level sport growing up and through college, getting out of college and leaving the tennis world was a challenge for me to like, put away my competitive spirit. I always tell my team we're competing whether they know it or not. I'm competing. You know, we're having a walking race and we're probably competing, whether you thought it or not. And so I was always looking for some way to kind of get my competitive edge again. And my sister actually introduced me to pickleball. At first it was just something fun to do, but quickly with it literally the first week I played, I was asked to play a tournament and the rest is history, really. I've been competing at the professional level since 2018 at the pro level. Have got up to number seven in the world, currently ranked just inside the top 20 in the world. And I compete in pickleball regularly. It's definitely a great thing to get to do. I'm lucky to still be able to do it and work full time. I will say my sister and I are the only two players in the pro space, male or female, that work full time outside of playing pro. So we were definitely lucky and just happy that I can still compete with these youngsters now.
Spencer Levy
Good for you. Now is there, like, a professional pickleball association?
Maggie Remynse
Yeah. So there's a couple of different areas where you can play pickleball or through pro. There's the PPA, the Pro Pickleball Association, the APP, the Association of Professional Pickleballers and Major League Pickleball, MLP. So a lot of people have probably started to hear of the Major League Pickleball because those are the likes of LeBron James and Kevin Durant and, you know, other big names and Mark Cuban. So like lots of big names have bought teams and Major League Pickleball. So you can be drafted to a Major League Pickleball team. Last year, I played on Major League Pickleball and my team got second and we finished third in the second one.
Spencer Levy
So if LeBron and Kevin Durant were on the other side of the net, you are taking them down?
Maggie Remynse
Absolutely. I mean, you know, I'm not going to beat them in basketball, but I'm sure we're going to beat them in pickleball.
Spencer Levy [00:30:54] Got to ask, Maggie, can data make you a better pickleball player?
Maggie Remynse
Data can absolutely make you a better pickleball player. By knowing exactly where your strengths are or where your weaknesses are or where the trends are within a match, it can help you decide how to practice, and it can help you decide how to really compete and what shots to hit at different times throughout a match. Right now, a lot of what you do is just kind of based on general knowledge or that gut instinct of, Oh, I think this has worked in the past. But being able to rely on data will change the way people are really thinking. And it’s done this in a lot of other sports at this point in time. You look at so many other sports and the way that data has been incorporated, it truly changes the way that you're able to practice. I know as a kid when I was in tennis, I would really utilize the tools around me and I would count how many balls I was hitting in when I would see that on different devices and I could actually really proactively work on different shots based on the data telling me that maybe one shot was weaker or better than another. And that hasn't really came into pickleball yet, but it's something that's truly an opportunity in that if somebody is able to easily capture this kind of data, especially as a pro, I would totally be interested in it. And I know a lot of amateurs would be as well, because they all want to figure out how to improve their game. So in the short answer, yes, everybody can utilize data to improve their standing and make them a better athlete in general, but it's just really like, how accessible is this for those people or for anybody in a sense, and how can we really incorporate it in the long term?
Spencer Levy
Let's turn to some final thoughts about the data and the world where we're going to and visions for the future of data, of A.I., of machine learning. Maggie five years from now, what's your vision?
Maggie Remynse
I do see, like, things like ChatGPT taking up more space in different people's lives, whether it's our personal lives or their work lives. Chat GPT is an AI driven tool that basically acts like Google. Chat GPT is something that will do similar, however also add a lot more color and context. You can get complex answers and anybody can utilize the results from it to really enhance and augment your own decision making. But it shouldn't be utilized instead of that. We should still really formulate our own opinions and have educated assumptions and ideas behind it. And so I would hope that going forward people would be able to use those things responsibly, but also not fully rely on it to be their everyday life.
Spencer Levy
HoChun what’s your perspective? Five years from now where are we going to be with data governance and the like?
HoChun Ho
I think we're looking at a lot of automation and we're looking at a lot of opportunities, but nothing can be automated unless you can manually go through that once in your head, right? So the human interpretation, the human involvement is still important. I'm really hopeful that one day all commercial real estate participants, stakeholders will be operating on a level of data advantage that's available to all of us. And I'm certainly hopeful that as long as we play within the do's and don'ts of compliance protection, some of the guardrails in five years, in ten years, we're going to get there with data.
Spencer Levy
And so on behalf of The Weekly Take, I want to thank first Maggie Remynse, the Vice President of curriculum at DataCamp for joining us on today's show. Thank you.
Maggie Remynse
Thank you, Spencer. Thanks for having me.
Spencer Levy
I then want to thank our friend and colleague, HoChun Ho the Head of Enterprise Data Governance at CBRE, also for showing us the way in the data world. Thank you, HoChun
HoChun Ho
Thank you, spencer.
Spencer Levy
For more on the topic of the show, check out our Web site at CBRE.com/TheWeeklyTake. But if you did enjoy the pickleball portion of our program, we've got a treat for you next week, a show that's all about sports, competition and business featuring athletes in the working world who take their weekend warrior status to the next level. From an open-water swimmer who recently crossed the English Channel to a world-class triathlete and indeed, more from Maggie Remynse on the power of pickleball, and more. So come back to hear from our winning team next week, but not before you share this program with your own team. And also subscribe, rate and review us wherever you listen. Thanks for joining us. I'm Spencer Levy. Be smart. Be safe. Be well.
Guests
Maggie Remynse
Vice President of Curriculum, DataCamp
Maggie oversees the planning and development of content (courses, projects, and practice pools) at DataCamp, an online learning platform that focuses on strengthening the data skills of its students. Classes cover wide-ranging topics such as data science, machine learning, Power BI, Tableau, and data literacy. Additionally, Maggie is one of the top professional pickleball players in the world.
HoChun Ho
VP, Digital & Technology
HoChun oversees Data Governance at CBRE. The scope includes global data ownership and stewardship, master data governance, data quality, reference & meta-data management and overall data roadmap.
Host
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Spencer Levy
Global Client Strategist & Senior Economic Advisor, CBRE
Spencer Levy is Global Client Strategist and Senior Economic Advisor for CBRE, the largest commercial real estate services firm in the world. In this role, he focuses on client engagement and public-facing activities, including thought leadership work performed in conjunction with CBRE Research. He also serves as Co-Chair of the Real Estate Roundtable’s Research Committee.