In this episode, Francis Adrian Viernes, CFA, MSF, CCREP, PMDSA, Chief Data Scientist and Head of Data Analytics at Megaworld Corporation, discusses how they are making waves with its innovative use of artificial intelligence (AI) to create safe and secure townships. Francis delves into Megaworld's plans for expanding AI applications beyond accident detection, such as weather forecasting, crowd monitoring, people counting, and air quality monitoring. He also shares his perspective on how Megaworld's digital transformation initiatives will shape the future of urban living in the Philippines.
Megaworld Corporation is a subsidiary of Alliance Global Group, Inc., one of the largest conglomerates in the Philippines. Megaworld is listed on the Philippine Stock Exchange, with a market capitalisation of USD 1.06B (₱59.56B).
In this episode, Francis Adrian Viernes, CFA, MSF, CCREP, PMDSA, Chief Data Scientist and Head of Data Analytics at Megaworld Corporation, discusses how they are making waves with its innovative use of artificial intelligence (AI) to create safe and secure townships. Francis delves into Megaworld's plans for expanding AI applications beyond accident detection, such as weather forecasting, crowd monitoring, people counting, and air quality monitoring. He also shares his perspective on how Megaworld's digital transformation initiatives will shape the future of urban living in the Philippines.
Megaworld Corporation is a subsidiary of Alliance Global Group, Inc., one of the largest conglomerates in the Philippines. Megaworld is listed on the Philippine Stock Exchange, with a market capitalisation of USD 1.06B (₱59.56B).
The AIBP ASEAN b2b growth podcast is a series of fireside chats with business leaders in Southeast Asia focused on growth in the region. topics discussed include business strategy, sales and marketing, enterprise technology and innovation.
Vanessa - AIBP:Hello, and welcome to the ASEAN B2B growth podcast, where we sit down with individuals responsible for driving growth within their organizations here in Southeast Asia. My name is Vanessa, and I'll be your host for today. We have a very special guest joining us, Mr. Francis Adrian Viernes, Chief Data Scientist, Head of Data Analytics / Data Science at Megaworld Corporation. Megaworld, founded in 1989, is a real estate company listed on the Philippine Stock Exchange, boasting a market cap exceeding US $1.1 billion. Without further ado, may I invite Mr. Francis to give us a brief introduction of yourself, give us an idea of your background and perhaps share some personal hobbies.
Francis Adrian Viernes, Megaworld:Okay, so thank you for that, Vanessa. And it's actually very nice to be invited in the podcast and meet you again. So well, as you know, in my capacity as the chief data scientist and the head of data analytics, we create here data science models, but not just the fancy data science models, there are a lot of ways to approach data. Working with a big company like Megaworld Corporation, and by extension, the Alliance Global Group, which is a very large conglomerate in the Philippines, it's actually very interesting. One thing that I found is the larger the company is, sometimes simple data science models or even simple analytics, sometimes does wonders for driving growth. And that's because you have the power of big data to support you. Now, let me discuss a bit why that data that I am handling is a bit more interesting. So AGI, as a company and its a conglomerate. We have many subsidiaries that are dealing with different operations, but here the core is all about real estate. And maybe if I would like to summarize it for you, the best word might not actually be real estate, maybe its lifestyle. AGI, Alliance Global Incorporation in the Philippines is all about lifestyle. Of course the leader in there is Megaworld Corporation, there being townships, where each of our townships have a very different personality. So if you go here, and I would like to invite you or anyone here listening this podcast, you go to the Venice Grand Canal in McKinley Hill, it will give you a glimpse of like, Italy. If you go to our Eastwood city, it's like our unique Hollywood, where you see the local stores having their names, imprinted over the sidewalks with stars, very similar to Hollywood. And we have as well, other townships that are being plan, say for example, like the Newport kind of look like Macau. So if you've been there, you've seen really the advocacy of our chairman, where for our chairman, there are some Filipinos who might not be able to go regularly or visit some of the places and maybe Megaworld as a company and lifestyle brand, would want who will bring that to the Philippines. It's really nation building. Other brands are of course McDonald's, as you know, very powerful in terms of merchandise. We also have to make our lifestyle more in hotels, and on top of the Megaworld hotels. So you also have a different good sibling company. Under the AGI conglomerate used to travelers, holder of a lot of the five star hotels here - Mariott, Sheraton, Hilton. And recently with hotel Cora. And we also have Emperador, the world's largest and best selling brandy, right. And we also have startup builders at giant and bigger which compete to grab and actually many more than import. So a lot of those companies are under the AGI and of course if you, look at that different data. How they talk together is a little bit interesting. So this is why I think this position has a very huge potential.
Vanessa - AIBP:But can you share a little bit more with us about how you're actually driving growth and innovation for Megaworld with regards to the current role that you're sitting on as the chief data scientist and head of data analytics?
Francis Adrian Viernes, Megaworld:Okay, so when I think any data scientist has that opportunity to actually provide the growth that the the company needs just by analyzing, for example, what strategies in the past have worked for them, which brought in more sales and which one haven't been as successful. So really, at its simplicity, or at score, this is how we as data scientists drive value. And, and you know what, for a big company like Megaworld Corporation, and for, hopefully a lot of conglomerates here in SEM region, we have the benefit of having a huge data set that you can analyze, and you can further Submit. The benefit of that interesting way for someone for Megaworld who by the way, is celebrating its 35th year by this year, is that you'd be able to see what we call in data science, the domain shift. But the more well, maybe the simpler term is like the generational shift in terms of behavior and how you would not want to point your company to adapt to the trends. Now, the more flashy ones, which may not be as applicable to other datasets with other conglomerates and other companies as we do invent things for people science models, or ai, ai products that say, for example, the accident detection, which happily won the award from AIBP, and also we're developing ourselves some type of geospatial project as well is we want to develop products that will will of course and enhance the safety of our labors. Because the product is a bit different. When they say that the products are different. Not a lot of suppliers will rush to produce or to mass produce the AI projects that we need. Let's say for example, unlike you know, for example, we have McDonald's, right? Hunger is a basic is a basic or for food is a basic need that McDonald's can mass produce, which is for example, burger fries, and everybody needs that. So there is some pointing to scaling or producing a lot. But for some reason, this product is the township solution, integrated development. And let me explain what that is. Integrated Development is like creation of a mini city, where components are present. That's why it's called integrated, integrated, meaning there's hotel, there's residential, there's malls, and they all harmoniously work together. And because of that, that kind of brought up sometimes have less suppliers. So for example, compared to the bigger or the more, the like developer, which could lose a lot of abilities, you may have aI products that serve you on building the habit, but because your product is integrated, the harmonious integration of all these components, that product may not necessarily be available, and we sometimes want to develop that. So that we can enhance the safety but we will also be moving forward with the other objectives, which is of course to increase operational efficiency and of course, maybe in the future increase the profits.
Vanessa - AIBP:Then, earlier, you spoke about the accident detection software that won the AIBP asean enterprise innovation awards for the Philippines last year. You know, it represents cutting edge computer vision model trend using AI mainly to identify and analyze vehicular collisions on the road. Perhaps you can share with us what inspired the creation of this software. You know, how do you see Megaworld utilizing data science and AI to address the unique challenges and opportunities faced within these townships in the Philippines?
Francis Adrian Viernes, Megaworld:Oh, well the opportunity or the idea of developing accidents that that kids overcame from analyzing that there are not major accidents, minor accidents and bicycle accidents is in the culture. And you know, this is what separates us from other developers because you are an integrated development when degraded developer integrated developments have functionality of a mini city. And a mini city has what access its own fire, fire crops, policemen for orders. So what happened is that sometimes with the during the pandemic, a lot of people have started the spikes. The new word, creating your cities, for example, in a lot of our cities here in a Sea region would probably be like that you were you thinking of the huge the huge amount of bikes, right? You maybe are just so to the bike lanes, they're kind of Nason or recent for a lot of their cities, right. So that being the case, there is new there are new developments are here in yourself sitting either high amount amount of accidents, also, particularly maybe because that a lot of people started to also just learn. So maybe that's also one of the reasons I'm just thinking right now. But so as an integrated developer whose job is to keep everyone safe inside your own project, we needed to find a faster way to respond. Because sometimes some of those who might actually fall, let's say, for example, maybe an old person, you might want to respond, respond faster, because increase the chance of making that injury less severe, with faster response. And as you know, with accidents, sometimes the difference that like difference between life and death can just mean a couple of seconds, right? If often heard this in a lot of your movies, for example, or being advertised, or anecdotes where if only the ambulance have been here a few seconds before we would have administered emergency emergency response and he would have lived or she would have lived, right something like that. So that being the case, this inspired us to actually create one. Why create because we have search for suppliers. And no suppliers are able to provide this because again, the need or the demand is very low. The demand is very low because the creator of integrated townships or developments are actually with you. So because there's no supply for that, why would I create a Demand just sue me for the kind of product? There's a new one in the Philippines, right? So that that dilemma allowed us to, hey, maybe we can invent it? Right? There's, there's a lot of technologies gotten a little bit better. And we have very talented, even young professionals right now who are more about the work, implement implementing their, their ideas. And maybe if we need for all of this, we'll find that solution here.
Vanessa - AIBP:Can you share some specific insights or like metrics from the deployment of this particular software in southwood cities that led to policy improvements, safer roads, you know, what, what was some of the results that were achieved in the initial pilot?
Francis Adrian Viernes, Megaworld:Okay, so for the accident detection software, it's able to detect when two objects are about to collide, it uses or it predicts the trajectory. For example, one object is going 90 degree and one object is going 45 degree using the speed as well, okay, these two will collide. More often than not, we have a lot of false positives, because of course, if you driven in an emerging economy, you know that they're very expert in avoiding those collisions. So it would look like in the camera, the bee would collide, but they would actually stop. But sometimes if it does happen, then we are notified by an alarm. So now, in the southwood city, there were around 400 incidents of that we were developing here that it's all minor. It's all just a bicycle accident. So nothing really classified as a major traffic accident. Right? So using this, we were able to pinpoint or locate on that CCTV, and we were able to see what spot it actually, that accident actually occurs. Of course, we coupled that with the vanilla dashboard, where the locations are happening. So we have a heat map. That means the case, we were able to now review the scheme there and devise a new traffic flow, eliminating almost 90% of the accident, if I'm not mistaken around 90% So the accidents are so much new after that, because, of course it's not Just the detection at the end of the day, accident detection is just accident detection just effects. It's about our response. But it wouldn't have happened. If the detection were able to report and notify what time and what location is happening. And it gets for a lot of our going back and circling back to your first question, that is where data scientist actually does driving, or data scientists do drive the growth or produce values, it's really where you can if you gather the data, and you can pinpoint the pain point, actually, or that which you want to resolve, it's easier to tap. Because without data, what a company would be doing is to blindly lie in the thick of, or maybe use intuition, which, by the way, may always make may may actually be correct. So some of our experienced leaders have intuitions that are so powerful that they are more powerful than the data and that that happens. But what sometimes, because of the wide variety, maybe, for example, for Megaworld already have a lot of subsidiaries, maybe it will be very helpful to really just employ an app fast, because it's no secret that a larger company asked me find it more difficult to respond faster, because of the size of the data scientists you can pinpoint. The data or the insights produced by for example, for the accident detection, makes it a little bit faster to develop that.
Vanessa - AIBP:Understand, you know, in such instances, when you are rolling out certain projects, certain initiatives that are driven from the data science team, how do you usually get the buy in from the management team? Is it something that you would have to present a proof of concept convinced them? Then in such instances, how would the company then decide, you know, what kind of resources are they going to prioritize and educate, for example, in this instance, the accident detection software?
Francis Adrian Viernes, Megaworld:Okay. Well, there are many ways to actually get your proposal across, but one that I found very, quite very far for is actually to present what you've said, the proof of concept or a prototype. I guess it's that not that uncommon. You watch series like Shark banks, they kind of need to see what they're investing into. And that's just being a good investor or a good management, for example. So I often come to them and show a proof of concept. And then of course, now with the, with the resource, a resource, it's actually now you, for for investors, if they liked the prototype, okay, how much do you need, of course, we might have some some benchmark. And of course, doing some studies are comparing with how much it will cost by outside. Or maybe, for example, for the accident, how much it would cost to have to insure all those who had accidents, maybe that would be a basis of costs, right. But it's really just one one way, because for example, for a project, like accident detection, where it's really about safety, sometimes just using the word safety, should be enough for for the management. Yes, we want safety, even though it's expensive, of course, that's too expensive, we're gonna have to have it down. But the considerations are a little bit different. Say, for example, we are going to propose a data science project or a proposal where an existing competitor or maybe a commercialized product exists, that may be the main consideration would really just be the total costs, because it already exists. Maybe also not the total costs, I think, there are a lot of possibilities, but I'm just throwing it over here. So that many people can plan accordingly. matches the total cost will be the quality of response. And so so this is something that not a lot of people are going to be talking about, but when you when you have this data products, it's very dependent on the data in the room. So if the data is loaded upon is actually quite different from yours, then it's possible that the accuracy is so much less, for example, a lot of maybe the providers that are coming from America have used data that are coming from America, and therefore when you use it in deployed here in Asia, where Demographics The landscape are different, it might not be as accurate. So let me share with you something, for example, On the age and gender detection, you also develop something like that, oh, by the way, that can be a prototype for you, you ever visit here, but having to develop that, and we all know that facial structure of the West, the Caucasians are a bit different. And therefore, when we try the models that have been created using those, then the predictions for us happily would be that we're too young, like I'm 16 years old, because probably, if we compare our facial structure with theirs, it's really a bit more, but we can look younger, I think that it's a bit too based on the model. So that being the case, is a consideration of course this commercializing quality as well. And sometimes even the privacy concerns that's also not being talked about. So this is why in the next FAQ for this one, let me drag this to the company here is point to creating your own data science lab, it doesn't need to be large. But creating your own data science lab that create data products for you. Makes a lot of sense, because of the last, sometimes the quality of data and the number of privacy concerns, because you don't want your data because Okay, so for number one for the quality concern, how do we resolve the quality concern, then you have to give data to the supplier so that they will retrain the model based on your data, if you're not comfortable with that, but you know, they can resell that because once the model has learned, see here, we're going very technical right now, but a lot of our intellectual property or data privacy concerns, right, it's all about protecting the data, not the product but the data produced. So for example, if a machine learning model has been trained on that data, that model is just as valuable. But that's not what is being protected for data privacy, it's just the sensitive data. Now, because you have a model that has predictive predictive on this sensitive data, if another supplier uses that with the other company, they can say, but I didn't use, I didn't say I didn't sell your data, right, it's just a model that I learned how to create right now. So that being the case, if you don't want that, then the data science lab, maybe just two to three or four persons wouldn't be able to do something about that. Just for example, just to give you an example, for the CCTV, the placement of your CCTV so it's, it's depends on how you would put it right. But now, that being the case, that also affects the quality of accident detection, how you're the angle of your CCTV training, all of that are a little bit important. So that, so So those are the strong considerations on whether to create or to buy costs definitely is, is is a consideration, especially for companies that have that require proof of concept. But you know what, it's not that difficult right now to propose, because of the massive investments being showered upon that. So if you can do it a little bit lower than that, your job with analytics is easier.
Vanessa - AIBP:Thank you very much for sharing, Francis. You know, the accident detection software is just one of the many technological innovations developed by Megaworld. You know, you mentioned earlier about the TAT Lab as well. Can you share a little bit more about that with our audience how, you know, it supports the development deployment of machine learning models and AIs for you know, like you mentioned also, township safety and security.
Francis Adrian Viernes, Megaworld:Okay, so right now, our next project is actually the creation of rooms where that will it's a natural extension of CCTV, so you haven't yet CCTVs CCTVs are having a static right we don't move and because of that, you're managing a city even though that city has a lot of CCTVs the average coverage is around 50% Meaning there's 50% of the Dodgers surveyed is that that's why when I was watching all this all this series for example, and I'm a very big fan of by the way I fail to discuss my personal hobby and it's actually true crime to crime and watching all this years eat as he asean series actually, and with for true crime. Even this mystery novels, for example, a lot of the murder sometimes outside happens on dark alley. Is example or darkest, but the in between of the streets, for example, because all of those aren't corporate bases, the rooms when they roam around the township immediately complements or enhances your coverage area, to the areas that the CCTV are not really capturing. And that by itself is one way for us to further disease the agenda. Right? So we're developing it with smart capabilities. And hopefully it will be able to count to monitor. And hopefully, we don't expect, we don't want another pandemic clapping. But if a pandemic does happen, you can also monitor two objects being close to one another for social distancing all of that we can deploy once we have this moving, CCTV, moving CCTV, aka grow.
Vanessa - AIBP:Understand, understand, and you know, in addition, again, to the accident detection software, and you know, I remember in recent conversations, you were talking about utilizing similar models for weather forecasting. You know, you spoke earlier about crop monitoring people counting, and also in some instances, air quality monitoring. Can you share some about your future plans to develop such initiatives?
Francis Adrian Viernes, Megaworld:Yeah, so for weather monitoring, we actually do have now, and that's a service we give our your papers. So by the way, is one thing you need to know about that for weather and air quality. They're a little bit localized. So like, I've always been saying this to just learn this. So for example, when you get the forecast for at Metro Manila, you get a forecast for a particular place and say you're going out of the country, go to Singapore, you go to Malaysia. Yeah, this is actually through I think, when you invited us to the Malaysian AIBP conference, we were there, asking the driver about the weather forecast. And so if you go to Google and search it, it will give you a general forecast, meaning the forecast or most of the location in Malaysia, for example, Kuala Lumpur. But what would happen is that sometimes, as you can often often see, probably is that there are times that one plays is actually encountering the rain, you walk a few steps, or a few meters far away from that place, it's not raining. That's because weather is a bit localized. So in order to we're utilizing this actually, it's a it's a supplier provider, because they already they already are able to provide this a much cheaper cost us this is a image of the cloud to predict if that image is actually right now, very interesting, when I'm talking to you, I'm looking at not in there's a portion here, a little bit darker, like scary, dark, but looking at a few more buildings here to newer, so that is he uses those image to predict the probability of rain. And with that your forecast for weather for that particular location is a little bit more accurate. Right. Now, why is this important? This is important because sometimes those weather disruption, disruption of business operations, we have a lot of videos where they have multiple offices. So advance are knowing in advance what would be happening to your localized place, would therefore make you plan in advance whether you're going to divert your operations to your other satellite offices. And in therefore we would experience a greater, more. What more convenient was business at your business will be new in it is better. For air quality, same goes. Because for example, we want it we're all about safety. Now. We're all about health. If for example, in the future, this is a plan that we're doing right now, we haven't implemented but we have plans to do it as hard a component for the health module of the township. If, for example, the health quality severely drops at a particular place, maybe what we can do is to stop incoming vehicles as a priority is the township. So stop incoming vehicles, lose it off, and maybe, maybe say that it's for for air quality purpose, right. So all of that are in the pipeline in we're very excited about it.
Vanessa - AIBP:And in the broader context of smart city development, how do you see Megaworld digital transformation initiatives shaping the future of townships, urban living in the Philippines?
Francis Adrian Viernes, Megaworld:I see Megaworld as kind of the leader, we can up just in the Philippines, but maybe in the whole of the ASEAN region. And here's why. You're the concept why I really like the laboratory here in the data science. terminology. Yep. In science in a lot of governments have gotten this, a lot of government have implemented what they call special economic zones. So the similar to that concept, for example, if you can learn what works in the small scale, then you can try to emulate the successes on a much bigger scale townships are smaller scales. Right. So that being the case, it has the potential, if you can have an experiment and what makes a small city or township it works, whether we harmonize those results and actually be scaled up. Because if we start with a larger city, the process of MLP is high, it's going to take too long, because you don't know where to start, the mistakes will be more expensive, because it's a larger scale, you're gonna spend a lot of money, but by doing that to a smaller city first, so we have cities here that are serving around less than 20 hectares, for example, maybe that can be a good sample for that, and the successes that you can do there, you can engage with the loops. And whatever successes we have there, you can also carry out what works, you can leave the leave out. So that by itself makes us potentially one of the largest web visitors in this space.
Vanessa - AIBP:Understand. So to conclude start small and then scale big once you get it right. You know, what's the next big project you have in the works? You know, we've heard a lot about generative AI, chat GPT... As a data scientist yourself, this must be an area that you're looking at as well.
Francis Adrian Viernes, Megaworld:Of course, of course, in in what I'm doing right now is actually trying to consolidate all of this one and see how we can leverage a smaller scale of the generative AI product that for example, for our executives, and you want to ask, we'll give you the answer. Right. But it's a it's it's something that we are also having in the pipeline, maybe once I'm done with it before that meeting, whatever, as an entry into our the next round of AIBP already.
Vanessa - AIBP:Yeah, definitely looking forward to more projects, more initiatives that you are leading for Megaworld data site, what are your views of future business growth for your industry for Megaworld? And outside the industry? What other areas within ASEAN that you see most growth over the next three to five years?
Francis Adrian Viernes, Megaworld:Okay, well, I'm definitely gonna be a bit more advice. But also, there will be more of a little bit of back and up with more checking results. I think for Megaworld, really, we're reaping the benefits of data science approach, I do think that we're going to have the growth is going to be exciting if you don't see that a lot with companies that are already a massive scale, but Megaworld is, but but with a newer, innovative approach to decision making, like for example, right now, how we are integrating our data sources, I think that the growth prospects are going to be nice. It's actually going to be true for most companies. Remember, we're all coming from from pandemic. So if that's our base case, everything is going to be going to be increasing. And that's good for everyone because everyone benefits from it. But in terms of the countries that are more excited for this year, Asean, I'm gonna have to also say the Philippines. So there are a lot of forecasts in, you just looked at the forecasts that are being made by the World Bank or the International Monetary Fund, for example, the IMF, ADB. And I think even there's another but not sure if that's useful, but there's another popular will survey survey group that we set. The growth forecast for developers are a little bit higher than the asean six and I think And well, maybe when you think about it, it's also because you've also lagged for quite some time. And it's not unfeasible to think that way. Right. So, for example, because all of your other neighboring countries are much into already considered developing faster. So us developing a little bit faster this year in the coming years are not surprising. But more than that, just to share with you some of the newer stuff we've been tracking, because we also do that for Megaworld, we also look at the development, the transportation systems have a lot of the transportation projects here are quite massive. you an example from my last reading, may or may not we may not be the final count, but 51 projects are scheduled to even subway. So I know for a fact for Malaysia, they have already already developed once and for persons reports specially Singapore for very developed public transport. But it's just interesting to see that those plants are being highlighted right now. Do you think that's the first that you need to well pay attention to in order for the company that you
Vanessa - AIBP:Understand. Thank you very much for, for sharing, Francis, and thank you very much for joining us on the ASEAN b2b growth podcast. Philippines is definitely one of the markets that we are all very excited about. You've got a good demographics and a good GDP growth. So for sure, we will be keeping an eye out for Megaworld, what you guys are working on as far as the other enterprises in the Philippines. Thank you again for joining us.
Francis Adrian Viernes, Megaworld:Thank you Vanessa and see you soon.
Vanessa - AIBP:Thank you, Francis, see you.
Voice Over:We hope you've enjoyed the episode. For more information about business growth in the ASEAN region, please visit our website www dot IoT business hyphen platform.com