"Data is the new oil, but also the new pollution." – data scientist Doc Ligot

Magatang ARO podmates, si how we severino mo li? With another accomplished and interesting Filipino, Dominic Ligot, who is also called Doc, but without the medical degree. He is a data scientist and technologist who has been thinking about how data can improve society but also harm the world. He'll explain why. Magatang ARO sa Yodok. Ay, magatang ARO sila hard. Thanks for having me. You're welcome, Doc. And thank you for making the time to be with us today. But first, Doc, I need to make a disclosure for our listeners. You and I are both on the board of editors of the Philippine Center for Investigative Journalism. We are colleagues there. However, you're not a journalist. You're our data and tech guy on the board. Because people might wonder, what's a tech guy doing on the board of a journalism organization? We've always known that there's really no conflict. But just for the benefit of listeners, what should journalists be thinking about in this age of big data? And before you answer that, what is big data? I mean, that's kind of a buzzword now. A big data. You yourself have been using that. Any big data and why is it useful for journalists? And from there, we can talk about how is it useful for the non-journalists in the world? Okay. Let's talk about that. Well, first, please. Of course, there has been this recent debate then. I don't know if we'll call it a debate. The same thing about what is a journalist nowadays? Of course, there's the traditional, I would say, those who got educated in journalism, those who work in news, public affairs. But because it's nowadays, there's also a term that's emerging. We can talk about later called data journalism, which is you get data. You write stories about it. And I think I'm overlapped with what I do in my profession. I would classify myself more as a data analyst. And there's a lot of analysis happening in journalism. So, okay. So, we'll go back to our original question. The thing we don't see in data is a bit of information, whether it's numbers, facts, or data. But, again, big data, probably the term big data, it's been around for a while, maybe at least 10, maybe 15 years. The traditional, I would say, appreciation. The thing about it is IT. It's data that they call it the three V's, volume, velocity, and variety. For example, volume, volume is the easiest to appreciate. The fact that we had floppy disks, and the data was measured in kilobytes. And then later we had hard disks, and the data is measured in mega bytes, nowadays, terabytes. And then, when the internet came about, the data just became much, much bigger, gigabytes. And terabytes, petabytes, that's what they call it. And it's really just the volume aspect of it is, it's just inconvenient to move around. It's not that everything could probably fit in a floppy disk. Nowadays, we don't even use floppy disks. And the average size of, let's say, a typical MS Word document or an Excel sheet. Actually, it's bigger than the average size of a hard disk back in the day. I think the one that was hard disk was 20 megabytes. And I was just working on an Excel sheet that was 100 megabytes in size. So, not that much of a normal storage media. So, volume is one. Variety is another. That's the case when you're using data. It's very straightforward. Spreadsheets may be mostly war facts and figures that you can fit in a page. When it starts to become big data, it means mixing data of all sorts, everything from numbers to pure text, data from social media, payments. So, the context for Variety is companies now struggle with different types of data. Unlike that, if it was just numbers, it's on a spreadsheet. And then finally, velocity. I think this is the most compelling part. If you think about the typical company, they do financial reports every month. Maybe they do sales reports every week. So, very batch oriented, the product of the data. Maybe you do an annual report once a year. But because everything in our lives now are connected online, actually data is real time. You're generating data as you speak. It's being recorded on digital media. As I speak, data points are getting generated. And then the moment you share it on social media, more data points are getting generated. So, the characteristics of big data. The size of it, the variety of it, and how it creates updated. And the total amount is overwhelming. So, the point that you see everything they do is data component. Well, of course, it's not enough to just have data. You also need to generate insight from the data. And more importantly, you have to generate action from the insight from that data. So, you need those three things for data to be useful. And so, companies struggle with this all the time. It seems to be quite simple, but it's deceptively simple. What did you mean by that? How can companies today use data? And what are they missing out on? So, let's start with the fascination or obsession of data now. It's directly a result of technology, how it works. It's data that you can see. But it was just in the form of books or printed media. So, because technology at that time only allowed you to store data that way. So, when you do things, data is an afterthought. It's a way of recording what you did. It's the past. And then as slowly technology started to be part of whatever you do. For example, when we graduated from yellow pads to laptops. Now, data is part of the process of doing whatever business you have. So, data is now an enabler rather than just the output. And that changes a lot of things. So, maybe the stage one is data is just the outcome or a recording of the outcome. Now, data is part of the process of generating an outcome. And then I think the next stage which we're seeing now is data actually is the business or data is the product. So, we're going to hit on our 10 companies like media companies, for example, content companies, social media companies. You're missing out on the essence of what you're doing. It's actually data. And to the point that we're coming to a, I think we're now transitioning to an age. When because of data, you don't really need humans anymore. For example, on the newsfeed of Facebook. It's not the same thing that you do. It's an automated process. That's an algorithm. That's another word we can discuss. Because it used the data already that existed, let's say in Facebook, to create a pattern. The pattern is now being used to automate things. So, that's a lot of change. So, when I say company struggle with this, it's not that it's the most part of organizations, the way we're organized. The roles in a company that haven't changed much. Maybe probably over the last century, you still have presidents, vice presidents, and whatever people doing the same thing. I see in an era where data was just an afterthought. Leon, what happens when data is the one making decisions? Or algorithms are the one making decisions? How does that change? How companies operate? Mianjonayan, matagal na process. But those companies, let's say the newer ones, for example, start-ups, who were digital from the get-go, data driven from the get-go. Nafikita, natin, how they are slowly replacing traditional businesses. Another case in point is retail, Amazon. That selling stuff on the internet was a joke, right? And Amazon struggled historically with their profitability. But as more and more people started using the internet as a medium, or not just communication, but also for commerce, it makes less sense not to open a brick and mortar shop. Or at least you should definitely have an online presence. You should have a physical, a tout dito store. And I think the most extreme version was, no look up pandemic. So the need for some strange reason, aka a virus, dig up what you need to buy. So now, your traditional view, if I'm a retailer, I'm a restaurant, I'm a fast food, now you would never have dreamt going online. Now you have to do it. It just changes everything the dynamics of it. Even the media industry, the journalism industry is also affected by that. Well, of course, online shopping is many people's experience with big data. Because all of these online retailers, even the search engines, they're crunching all of the numbers related to your previous choices, your history on the internet. And recommending all kinds of things for you to buy. If you bought a particular book, it's going to recommend you, and the next time you go to Amazon, similar books that you might want to buy. And it's pretty effective, no? And ganun din ang newsfeed natin. Ganun din ang when we go to a news website. When we read a particular article, let's go on to recommend for us to read, you know, related stories or previous stories about the same topic, et cetera, no? So that's a common enough experience for many of us. But is there unexplored data territory for companies? Some real world examples that you've been thinking about, that this industry should be mining this kind of data set to improve its performance or to improve its service to the public. I think, Shemere, Mahabang, and there's a lot you can think about. But let's bring it back to some of the basic things. And the other thing that's going on is, you have a product, you have a service, there's a consumer, you try to sell to that consumer. So the marketing function, let's start with the marketing function. Before big data became a thing, how did people sell stuff? If it's not face to face, if it's not word of mouth, you relied on advertising. So I think that's one area where big data has changed the game because an abang mechanism of advertising back in the day, you have a lot of, let's say, media channels, radio TV, billboard. And the people who manage these channels need to justify kind of the, there's a term called reach in some marketing. They say, "Helen bang nano o no od ng, let's say, a show, like Robin Chan, or something like that." And that becomes a value you can attach to, let's say, a time slot in traditional broadcast. So the value of that time slot now becomes relative to the value of your product. If I want to sell diapers, just for example. And I know that those who are probably mothers, just hypothetically, then I would like to advertise there. And then the marketing research industry supports that because they're still doing service, they're still doing ratings. Now, what happened to the social media? It's the same principle you're still doing targeting. But then you can go right to, for example, if, for example, a company like Facebook, knows exactly who the mothers are because of their Facebook posts or because of the pictures they post. Then that becomes a data point that they can offer to advertisers. Now, okay, you don't even need to advertise on TV anymore. We can give you the news feed of mothers and it's going to cost you this much. And in many other applications of data, it's the same thing. You didn't really change the process, but you, because you have so many data points, the same information, you can break it down to a very, very minute targeting opportunity. What we've been talking about so far, Doc, is big data in the service of selling and advertising, to turn profit. But you've also been thinking about using data to save lives. Data, big data in the service of the public interest, of public health. You are one of those who developed this very innovative tool to predict the HAT spots. This is in early 2020 when we were on the cost of the COVID pandemic. The pandemic, the pandemic, the pandemic, the pandemic, the pandemic. But you're already thinking about epidemics. What particular to the Philippines and many other tropical countries. You want global recognition for this Dengue HAT spot prediction tool. Tell us about this in a layman's language. I know that certain public health risks and dangers have been predicted through Google searches, right? When people are searching, let's say, cures for high fever or treatments for the flu, etc. It seems like they predict through Google searches, if you're looking for a lot of things, especially in particular places. But you went beyond that, went beyond simple Google search analytics. Yeah, so let's roll the clock back to 2019. The project was called Aedes. It was named after the Mosquito that spreads Dengue, the Aedes Egyptian Mosquito. But it's also an acronym for Advanced Early Detection and Exploration Service. I'm sure the word Dengue was in the middle of it at some point, but because that's where it started. Okay, so it wasn't just the name of Mosquito. It was actually an acronym. Yeah, well, we were trying to be creative with recall. Okay. Yeah, may recall a shop. Project Aedes we call it. And it was a rumor. This was circa September, October of 2019. And then this NASA hackathon happens every year, by the way. And it was our first time to to join it. There was a Filipino team that already had won it a year ago, 2018. They were featured also in the media. So the idea of winning a NASA hackathon was so far fetched. I mean, think about it. No, it's a global hackathon. Anyway, so the inspiration was we, so I was running a consultancy and Tamakau, right about that time. We were already thinking about pivoting into social good because we we've been doing a lot of commercial work at that time. And so surely there's a way we can use all this data skills and technology to benefit society, apart from selling more diapers and beer kind of kind of work. So we saw the hackathon as an opportunity to get started. So try and get our hackathon to see what it's all about. The premise of the hackathon was simple. At least you could select challenges because they one of the challenges which we found compelling was use satellite data and other data sets to help with sustainable development goals in the UN SDGs. And they listed a few. One was health. And at that time, since we're not COVID, the epidemic was dangga. I don't know if people remember we were at a five year high in 2019. The highest dangga ever recorded in the past five years. So this is why don't we try solving this dangga problem. And when we sat down and tried to look at the literature, tabaka, Google searches one technology that has been used in influenza. So we thought, "Basidengga, what the name?" And then there was other technologies like looking at weather patterns because mosquitoes breed in damp moisture, places with moisture. So baka may affect, umulan lang baka maraming the mocha after. And then the last part which we felt was the most compelling is if you can detect where the moist, the kind of the moist, or stagnant water is from space, then that's the place where you treat the, I'm not the mosquitoes. Now you can tell health officials and LGUs, "Now you go behind this church, there's probably a pool of water there." So for the concept, and the good news was a lot of this had already been done in some paper in the past we met with a number of researchers found their work. But no one ever thought of putting it together in one solution. So we did that in the hackathon and we won the local leg, then later won the global leg. And this is where it gets interesting. So now we have a tool that based on Google and rainfall can tell you whether you'll have X number of cases, based on historical patterns. And then the same tool also tell you and by the way the mosquitoes are probably in mother Ignatia, coroner, P-mog, or something like that. That's a perfect tool. You know when and you know where. But then two things happened. One, the COVID, so the interest in Denge and incidentally the cases of Denge dropped in 2020 to be replaced by COVID cases. And when we met government, health organizations, because they were too distracted by COVID, the interest in our tool dropped dramatically. So we kind of felt like COVID stole our thunder there. But we continued working on the tool and it was sometime late 2020 when the current little interest this time from there's this organization called Geo Group on Earth Observations. It's actually NASA and the space agencies, including ours, CPSA partnering with the UN and who uses satellite data for sustainable development. We'll be back soon. So you know when we raised our hands, this is our project, what do you think? We actually got awarded again a second time along with other institutions that were doing the same thing. There was actually another Denge project in Vietnam. And shortly after that UNICEF came knocking and they said Denge is one of many mosquito barn diseases brought by EDG. So if you have that solution, maybe we can bring it to other countries who have similar problems. So that's actually where it is now. Now we're being helped by UNICEF make it a global tool. And since that time, it's become a little public good. Major delayed reaction, but I think the solution is still ongoing. Your data was mostly Philippine data, and if so, has your tool been used to address the Denge problem in the Philippines? Any real world applications are part of this innovation? On the national front, it didn't push through yet because COVID is all our thunder. But now we're in discussions with several LGUs to pilot it. And then at the same time, Because we used a lot of technology that we developed for COVID response. Of course, you can't detect COVID from space, but at least the way the data was being processed, calculating epidemic metrics. We were one of the first COVID traffic that came out globally. We had this tool called the Corona traffic. We were collaborating with people in Malaysia. It was actually recognized by WHO. I don't know if you remember that time, roughly around April, May, June of 2020. We went from no one was tracking COVID to suddenly everyone was tracking COVID, so around that time. Then we also worked with UNDP in 2021 to use data analytics for under the man tracking vaccines and hesitancy to vaccines. That got used by DOH to craft communications to increase vaccine acceptance. I think that was largely successful. So, the lesson is, if you're dealing with data, if you're not married to one concept, you can instantly bring it to another area of application. COVID was certainly a big deal in the past two years. When you were interviewing back in 2020 about this award-winning project ADS, the Deng hotspot prediction tool, one possible application of the concept is to weed out fake news during infotomics. As we know, later in that year, in the fall, we had falsehood about this information about the pandemic. There were people who were denying that there was a pandemic. That COVID was real. And then, of course, there was a lot of this information later on about cures and vaccines. And there was also fake cures and all kinds of stuff coming out to confuse the public. And it might have even caused lives. So, to apply by on, to apply by the technology and helping weed out falsehood in our information ecosystem. And then, how could you use this platform, the tool to weed out not just to predict where Deng is going to happen, but to identify and remove from the ecosystem of falsehoods? There is a term that's under the mainstream called infotomic. It also started sometime in 2020 at this mainstream where, there's also this deluge of information. Some of it is true, some of it is not true. And so, in a way, the original project kind of branched out in several directions. So, that direction of mine got the attention of Facebook. So, we were working with Facebook on developing tools that can measure the virality of an infotemic. And there's this, you want to test, treat and vaccinate the people who are pandemic. So, going on this infotomic, you want to know who's causing the fake news, do some contact tracing, measure the reproductive number. So, we were able to do that. And then, find out whether we can inoculate a population or vaccinate them. So, when I mentioned that vaccine piece that we did with DOH, that was our way of helping, I guess, the acceptance of vaccines. We identified the reasons why people were hesitant, we identified the reasons why they actually wanted to get vaccinated, divided the entire Philippine population into segments. And then, as in any end of the mantra, we had to measure early adopters. So, having identified who the early adopters were was useful, because they don't have to waste resources on people who want to get vaccinated. And you go for the ones who are skeptical. Now, here's an interesting piece. This is communication theory, there's this concept called a diffusion of innovation. But early adopter, there's no problem. But before you get to early majority, which is kind of mass acceptance, you will be able to see from the data from social media, which we were tracking. There were concerns that the early adopters didn't care about. Like, for example, one of the big causes skepticism was side effects. So, you have a skeptic of vaccine side effects. The vaccines can actually kill you. Another cause of concern was they didn't believe COVID was actually true. It's a conspiracy. So, these are the concerns that are now more prevalent in the majority. You're trying to win them over. And then one key roadblock was also trust in the government. So, I don't trust the government. Therefore, I don't trust the vaccines. The government is pushing. Probably corruption. So, having identified these parang triggers, the same way, you would identify parang markers in a virus. You would see a text from the text analysis of the posts in social media. Then helping the OHCraft specific narratives to counter them was important. Number one, convince yourself that COVID is really, really true. No, it's not inventor. But if you stop there, you're just fear mongering. It's a parang says, "I don't know if I'm going to be able to do it." So, you have to convince people that the vaccines actually work. So, if I actually have examples of this, you will notice the communication of DOH generally changed. Between May 2021 and later. Up until that point, a lot of communication and DOH is very factual, very scientific. Of course, there are doctors. So, you see a lot of info about efficacy, you know, how do you get your jargon or papers. Well, that doesn't move behavior. People zone out about that. The early adopters love it. Some of them are very highly educated to begin with. But the majority is not as good as the other ones. So, the communication changed from very fact-based to human-based. You show people getting back to work because they were vaccinated. You see senior citizens happily getting job. And then most importantly, calling out the fake news when you see it. There's another research that proved that. You can inoculate people against fake news, by showing them examples of other fake news. And then they will be able to see it. Now they will challenge any form of alarm messaging or politically motivated messaging. But that was the challenge at that time. So, in a way, we didn't intend for the DANGA project to end up there. But because the technology we developed allowed us to get data, the big data, we talked about the big data. We saw that big data is really just a reflection of what people are doing on the ground. So, I think that's the biggest takeaway. That's what people do at home or at work, their sentiments. Because they leave a data trail online. And if you can mind that data trail, you have a lot of opportunities to intervene. Hopefully for good. Doc, how about digital transformation? How do you define that? Before you answer, let's pause for a break. Is it a buzzword? No, you're digital transformation. What do you understand about that? Well, digital transformation is another one of those terms that came right around the same time as big data and artificial intelligence and. I think these are all part and parcel of this trend, which I love to talk about, called the fourth industrial revolution. And again, without getting into too many terminologies, the big change causes a fourth industry. So, there have been three industrial revolution already. I think the first one was team, the second one was electricity, the third was information technology. Up until the third, our relationship, we have a relationship in machines. It's been very one way. You tell a machine to do something and it should do it or it mail functions and you have to fix it. However, in the fourth industrial revolution, dialing as a data, machines can now function independent of humans. You can program them, give them patterns, leading up to the Hollywood definition where you have robots working on their own. So, digital transformation in a way describes that phenomena, but for companies where ask, up until the third industrial revolution, very one way to get into the company, you have managers creating plans and people following the plan. And then data is just a recording of what happened, whether you succeeded or not. Now, data takes center stage. If data is not the product you're selling, it's actually used in decision making. So, a lot of the trend in digital transformation is figuring out what technology you need to process data within your company. Same applies to government. Why are we still, for example, in the recent, this recent event with the SWD, why are in its 2020 and we're asking people to line up for financial aid when we can certainly deliver that through digital means like mobile apps and GKAS, even indigents have mobile phones. And paper, why do we insist on doing everything on paper? on paper. I'm looking for a palette. and it's very inefficient. It's prone to error or fraud, but if you digitize that, then you reduce all of that errors, reduce, reduce that to fraud. And maybe the most blatantly obvious example is elections. Once upon a time, we had to line up and fill up a paper, well, we still fill up paper, but the counting is no longer done manually. You can automatically count it. So it pervades everything. That's what digital transformation is all about. You're moving from an analog kind of existence to not just digital, but now data driven, data that you're going to be able to take. Yeah, but at the same time, you know, you're going to get a lot of energy. It's a lot of skepticism about automated elections, because so much of the process is not visible, not to the naked eye. And unlike, you know, there are people who are nostalgic about the blackboard method of counting votes, you know, you can see the number of candidates who are voting, and then you can see the number of candidates who are standing at the count. So it's like for some people, for a lot of people, there was a much more transparent way of electing our leaders. But I guess someone like you would say, well, that's why you have cybersecurity. That's why you've got, oh, and there's a digital way, also of making sure cheating doesn't happen, or manipulation doesn't happen. Yeah, well, my reaction to that, and I'm definitely one of the people who would want to have more transparency to the point of opening source code, the Mangagayana. If we can learn from what happened in the previous industrial revolution, for example, when the industrial revolution, this is kind of a factoid. I love talking about that. I love talking about the word sabotage. The root word is sabotage, the shoe, because in the first industrial revolution, when manual work, craftsmans work started becoming mass production because of machines. That means what's the job of these are manual laborers. And there were riots all over the world that these civilized world at that time, where workers were attacking machines. And the shoes were used to kind of, there's another expression called throw a monkey wrench into a machine. So, good on. So the term sabotage, you want to sabotage the machine by throwing your sabotage into it. The same thing happened again when we transitioned from horses to cars in the late 1800s, before the Spanish flu, there was this big health emergency called the Manure Crisis. The problem was, everyone used horses. And they were having a horse craft, all over the place. To the point that it was suffocating major cities like London and New York, then the first time the fossilizer was in Australia. So, I don't have to use Manure as much. And they wouldn't solve it. Waste experts, urban planning, horse experts. What are you going to do about the horse and the Manure produces? The solution was to remove the horse from the equation and make the carriage run itself. So, a lot of horse experts, can you imagine at that time? We're jobless. And didn't matter how good you were with horses, you were useless in an automobile. And that's what we saw here with this Fort Industrial Revolution. There's this, they call it a digital divide. If you're too used to the prior paradigm, you're suddenly pushed into a new paradigm. Suddenly, election results are done in 15 minutes. We were just talking about the things that we call the NAMFrell counts. People are shocked. Is it really that fast? Must be cheating. And so, what I'm trying to say is it's a characteristic of these jumps, not in technology. There will be people who will fall through the cracks get left behind. It can be very jarring. And somewhere in the middle, the transition between second and third, the man, when people were used to print media and then the Oso in radio and then the Oso in TV. Back in the day, you guys would know this better. The TV wasn't supposed to be, it wasn't held as in high esteem as news on a broadsheet, and then later, you find out, TV is faster. The broadsheet will take 24 hours to print something. TV, you know, a good, right there and then breaking news. And then now we're transitioning to social media, very, very on the on the on the minute you know, if flow information. There was this information back then, but it was kind of harder to do because you had a lot of structures in place. People who were in news desks, in broadsheets, in TV, the head fact checkers, and the broadcast was limited to people with franchise. Hi on the internet. Gives everyone the ability to broadcast. So, but not everyone does fact checking. So, that's kind of the latter point I want to make, whenever we have these APWALS technology industrial revolution, there are some of them, but people have to also aggressively proactively love before controls and change quickly. Now, I'm worried personally, I was saying called, everyone loves saying data is the new oil. I also say it's the new pollution because it's also being used to attack society to destroy traditional structures. And I'm worried that the system will be able to do so. But later, if it becomes automated, it's going to be very hard to fight automated fate news. That's why we've been, I've been reading the alarm bell on the fix for a long time now. So far, so good. Depending on what you're doing, in a mainstream way, to spread this information that can easily be done. And there could be other ways, to use technology to spread falsehoods. We just have to be number one very vigilant and be aggressive about it when we see it. And one problem actually in detecting a lot of this information out there is the tech platforms, the tech technology companies have been quite slow. In developing ways of searching in foreign languages. So I'm more interested in Filipino, because a lot of Filipino spreading falsehoods are used for vernacular. And these tech companies, and then you insert weird characters or just like, you know, the fake news is very strong. I know that if you're looking for a lot of falsehoods. But anyway, I wanted to ask you, just before we end this topic on digital transformation, because the speed of the transition has been quite blinding. And then you can see it's a pandemic. We don't have time to do actual physical interactions and shopping. For example, you know, also the G-Cash online shopping. And even, I mean, whenever I write very recently, I had medical exam, I had medical tests. And then the results came in all on my phone. What are the current industries that are ripe for digital transformation? So that they can be used for more useful, more profitable, or viable as industries? Yeah, well, if I generalize, usually the industries that have high retail touch. So you notice, again, past moving consumer goods, banking, telecommunications. No one has to tell again. Retail to a certain extent. And then for the second tier, you can see hospitality hotels, transportation, like booking online tickets, planning your trip, trip advisor, Agoda. So I'm not really are the ones with kind of more of a B2B or less retail touch, industrial companies, for example, manufacturing logistics. Although they were already data driven to begin with, you're kind of like old school data driven, like third industrial revolution data driven, only because it worked already for them. You have efficiencies already, even without adding big data and artificial intelligence. So I'm a reagent. So there's like two fronts, at least two fronts that I see. On the high touch retail side. Now I think the middle, the middle ground is like media. That's a content. There'll be more and more, I would say, personalization of kind of the experience. For example, the shift from going to a mall to relying on online delivery. That has become highly personalized. So there's no one, one size fits all experience anymore. Like if you went to a mall, you know, it's about the storefront. Everyone sees the same storefront. Now I go to Lazada or Shoppy or order online. It's that order is for me and me alone. And I get to judge that experience personally. So it's the same trend happening on various fronts, Fintech, even Telco. On the more industrial side, I think it's going to be more about a battle for productivity. Like how fast can you produce your product? How efficient? And more importantly, how do you keep the value of your, in a way of your supply chain? Especially now that costs are on the way up. It's a natural outcome of many major crises, inflation. So cost becomes a premium, no? Related to marginalization. And the biggest cost in any company is usually people. Now, I think that's why a lot of, I would say advocates, activists were lobbying for data ethics are watching the jobs front. Because he had an easiest cost to reduce. Now, again, I'm going to buy a machine, buy by 100 workers, instantly cut payroll and call centers in under example. Let's have some shot bots instantly cut a thousand heads. And you might lose a little bit of the quality of the call, but the business survives now. Well, you actually have that, yeah, you have that conversation and journalism also, right? I said, there's there's artificial intelligence that can produce stories reports based on sports scores. So you don't need that game. And then you're more or just you're results, right? And add a few adjectives here and there. And the same thing with reporting on the stock market. But that they're used to be full time journalists just covering the stock market and reporting on ups and downs. So I mean, your conversation in journalism about that is, well, that means that we more of us will have to shift to more explanatory or analytical journalism and let the machines do the grant work of reporting sports scores or the ups and downs of the stock market. As an outsider looking in, although more and more I'm getting involved in the journalist conversations, remember when when when a company like Raptor came out, it was in a weight-trail blazing because they had no franchise. They didn't need any broadcast capability, it's purely online. And in fact, that's been used against them in some for a lot of the new snow, but they are a news organization. They're organized similarly. But they've utilized the digital format fully. Now, my question with that would be, how come we haven't seen more organizations like that? I mean, a lot of the like Jamie has a digital arm, EBS also had one. But it's still the same players. But one thing that has happened in Quenya, the banking, is you have a lot of fintech companies supplanting banks, PayPal, paymaya. To the point that the banks are forced to either come up with their own or merge with these players. So in a way, that's my fearless forecast. We need more digital news outfits, maybe some of them very niche. Because they have the cost barrier to entries very low. And you don't have the franchise question. Makakat talunan nalang seguri is on the expertise. Because do you still hire expert journalists or people trained? I would say there's probably value to that too. You don't want to lose that. Hence this blogger versus journalists debate. But also changing the entire experience of getting facts and news. And then a tie or both in PCIJ, I think that format could get a renaissance. Because you short form-- I mean, that's the trend. But when people get interested in a topic, they have nowhere else to go. I mean, we keep pedaling. Now you have to go to a place to read in depth, exposies and investigations. So I feel that there's an opportunity for that format to come back in a good way. Well, doc, no. I mean, before we leave that topic, because you mentioned their opportunities, their technical opportunities now for rappler like online organizations. But we can't overlook the political barriers and risks. Namah, we know the experience of rappler. They've taken advantage of the rise of social media. And but social media has also been the source of a lot of their problems, right? A lot of this information about them has been spread through social media. And journalists in general have been demonized. So maybe that could explain why people might have the skills and the opportunity. Right. Yeah. Maybe they'd rather go into something, or maybe they'd rather wait out this era. Unfortunately, it's lasting a long time. But one industry that I wanted to mention to you is agriculture. Because you mentioned telcoms and retail and a lot of the early adopters. But I've heard conversations that they have transformed their agriculture. So that farmers grow and market the right crops and produce at the right time. And they're not duplicating so that it affects prices. Because the problem of insan is a lot of farmers are producing the same goods or the same crops at the same time. And therefore they're not earning as much. You're destroying the market for themselves. Yeah. It's opposed to finding out who's growing what, when, and seeing the opportunity there. And then it seems like a cool thing. And a lot of farmers now have cell phones. So they have access to technology. So why can't they receive that kind of information based on people like you crunching big data sets to help farmers? Yeah. It's good you mentioned that. That's actually another thread from the Dengue work that's Anupayinit's infancy. Because we have been to be working in food security. I guess the commercial aspect of that would be the agriculture and the food industries. Ignoring what I would call structural problems, which I can elaborate on later. I think no one is stopping anyone from getting into AgriTech in a big way. The immediate anulang would be getting over the initial skepticism by farmers themselves. Because he didn't even see that exposed to digital technology as much in general. And they've known one form of doing business forever and improving to them that it can work. And agricultural cycle can take months from planting to growing and then harvesting and then repeating the cycle. So you need to partner with maybe established farms to prove it. We have actually started doing some of that as early as 2020 while the pandemic was raging. Now the bigger problems also remain. The fact that farmers, small farmers rarely have the scale to maximize technology. So there has to be a mechanism to give them technology and at a very good cost. Because if you're traditional IT approach, you can tell that they won't invest. So it's good that's where partnering with maybe some NGOs to be the catalyst to adopt that technology can work. Or maybe government like the OST has an agricultural army and pick card. They do a lot of research in that place. And then also the, I guess the whole food, I mean, we're suffering shortages in some commodities. It's not just because of current events. The fact that we are inefficient, we have the structure and potential monopolies and cartels messing up the situation. That's all a data problem. So if anything, it should probably start with better data gathering and open data about where agriculture should be. And just like one area that's still pretty democratic is maps. No one can stop you from taking a satellite photo of the entire country. And then using that to map areas which are either high yield, low yield, prone to disasters and floods and not. And then offering that maybe as a service to farmers. It's in its infancy. But everywhere else in the world, the world is the most important thing. Because the labor component of a traditional farm. So anyway, you can improve the productivity of a farm. As tremendous impact terms of food production or country. Ironically, the countries that are smaller than us are more efficient in producing their whatever little agricultural land can produce. We have a lot of unutilized inefficient farm land. It's just waiting to get unlocked. So yeah, that's definitely an area of interest personally for me. Yeah. And we have less land than a lot of countries. And the little land that we have for farming is not being efficiently used. So Sinosawemo, better data analytics and better data science has applied to agriculture can improve all of that, especially productivity of our farms. In question, I'm going back to food security. Like this has always been a nagging thought. I'm still getting to the bottom of why some provinces are net positive and some are net negative. It can happen. You know, minimum equal production. Like that's our net positive net negative. When the things of food production production. So they have to bring in food or basic commodities from outside the province. So again, the data will show it better. But some of our imports could have been self-sustaining if our regions talk to each other better. Things like that. Plus the fact that our capital. So there is an inherent cost to transport goods all over the place. These are all data problems that could be, you know, could be tackle using big data. And then I remember somebody pointed out in a Twitter space, I was in a Twitter space that how can we do imports during the time when we also have large harvests. So on the air, you really penalize your farmers because they're now competing with the cost of the imported goods. Who didn't that be scheduled more efficiently again I'm not allowed to. a green person, not yet at least. But it certainly sounds like a problem data could solve. We're going to see the seasonality of the harvest and see whether we could plan the imports better. So there's a lot of untapped potential. And looking at both the good and the bad side of these things. Good points. Well, this has all been fascinating. We want to thank you, Doc, for sharing your thoughts and your time. And but more importantly, for using big data for public health and for the public good. Mabuhayka Dominic Ligot. Marami Marami Salamat, then. And yeah, hope to talk to you again. [MUSIC PLAYING] This episode was produced by the team of you, Mariyanga, and Chansel Bador, and edited by JR MacToto. With the wonderful people of JMA News and Public Affairs digital, don't forget to like and subscribe. Till the next part, Mabuhayka Yoat, Ingat Lagi. [MUSIC PLAYING]