 Give us the brief summary, the brief history of Enigma and explain what we mean when we talk about real-world data, which is what we're talking about today. So we founded Enigma in 2012 really on the premise that we failed to connect the dots during the financial crisis. You saw people giving out trillions of dollars of loans without understanding the fundamental variables that actually made for a risky investment or not. Home ownership and unemployment were skyrocketing very much so in the same fashion and people were just looking at the default rates of the last six months of these home ownership loans. And what we understood was that there was a lot of buzz about data, but there was a big difference in between data that was generated on the Internet through user activity. Take the Facebooks, the Googles of the world. Most of what you hear about when you hear about AI and big data, which is definitely already an antiquated word, really stems from that kind of activity and that kind of application. The reality though is that there's this whole world of real-world data that we've decided to collect and merge into one unified knowledge graph at Enigma, things like cargo container shipments, corporate registration records, loans, unemployment, government contracts, the footprint of retail activity in a given location, and the real-world turns out to be much hairier and complex to model. One way you've described in the past is bits versus atom, and especially to conference like this, a lot of people are focused on the former, maybe not the latter. Yeah, I have this notion and this way of describing it. When you think about data, you think about instrumentation. How is the data collected? We like to say that there's a world of bits, so to speak, log data, click data, all kinds of data that's collected really through activity by a machine, whereas this world of data that's instrumented in atoms, things that are observed, things that are reported, things that are measured, and the world of atoms is much more chaotic. It is not a one-to-one comparison. Every different government agency has a different way of classifying things. Every different city has a different way of doing a health inspection or something along those lines, and it's all about reconciling that, really resolving, moving away from data to entities. And so tell us a bit about Enigma Public, that you hoover up all this data from public databases, things like what's on container ships and restaurant health records and all these kinds of things, and then you put it all online for free, which is odd. Yeah, so I'll give you a little bit of our basis. The foundation of our company is built on public data. We feel that open data, data that comes from governments that's generated by reporting activities and accountability, that becomes a substrate of a certain kind of truth that society needs to transact. We need to know who owns this apartment so that no one can fraudulently sell an apartment. And we use that data to build essentially a corpus of people, companies and places, but we're a very civically-minded company. We believe that our mission is to basically improve the world around us, and one of the things that we do from a transparency perspective is take all of our raw data as much as we've collected in the public, and just put it back out there. Anyone can go to public.enigma.com and browse over 100,000 data sets. Journalists have used it to break stories, researchers use it, consultants use it. It's just our way of connecting back with the community and also understanding and sensing what data is used for things. We get a lot of ideation from the community in that respect. Do you have any favorites that you've added to the database recently or anything we should look at? There's some really wild stuff in there. Oh, for sure. I mean, we've been working on a project recently with a couple organizations to basically stand-up tools for fighting human trafficking. We work with a lot of banks, big customers, folks like American Express, folks like BlackRock, BB&T. We work a lot in the financial services industry. One of the things that we do is help them fight financial crime, and a very specific topic of that which is near and dear to us is sex trafficking, and it is quite difficult for banks to fight people using their services, basically traffic people. You develop these patterns or what they call them, basically topologies, queries that you can use, taking client and bank transaction data, merging it with external data. Just the other day, we got a list of every single transportation hub in the United States. Where are the big bus switching terminals? Where are the big trucking stops? Where are the big bus switching for weird transactions at those places? Someone, a nail salon suddenly charges $100,000 near one of those places, the type of flag that you can use, and I've been knee-deep in a couple of those datasets these days. And then more broadly, the business of the company, you've been through a few rounds of funding now. Correct. You've been through a few rounds of funding for a few hundred people. When you do mix public and private data like that, what do your clients come to you for? Can you explain a bit? You work a lot with financial services at Ride Pharmaceuticals, that kind of thing. How does that public data help them make better decisions with all of the enormous databases that they already produce privately about their users? Absolutely. Listen, at the end of the day, all of these companies want to provide better client experiences. The biggest problem for a lot of these companies is risk management, understanding who they're doing business with, understanding whether an adverse event, a pill that was taken and someone had a headache or vomited, like why and where that is happening, if there's a pattern or trend. And basically what we do is we give them context to their internal data. Someone applies for a credit card. It's really hard to give credit to small and medium businesses. Very difficult. In fact, we've raised $100 million and we're rejected for a credit card by many banks just because they don't have the fingerprint that very, very large companies have. I know famously that a couple billion dollar startups like Spotify were rejected for credit cards because they didn't have a real phone number. They had a VoIP number. So we're transitioning from old ways of screening people to new ways and what we do is we take a lot of this public data, external data, data that we gather from other partners as well and provide basically the fingerprint of a company, person or place that can be used to make decisions and that gets injected into their workflow and their decision making processes be it credit card underwriting or pharmaceutical safety triage. So there's a lot of startups here and just a lot of bigger tech companies too. Do you think that, I can imagine your answer because of the company you work at, but do you think that they are overlooking real world data, public data, if you run a social network or something that exists mainly in that world of bits, what could they learn from this data about bus terminals or container ships, that kind of stuff? I mean, absolutely. There are so many canonical examples of why this data has just tremendously strong signal. There's famous thing, Google Flu Trends. I don't know if you remember this, where Google basically was collecting billions of searches across its platform for I'm sick, I'm cold, I'm sneezing, I'm coughing a lot, trying to predict the flu. So it turns out that they stopped this program after a couple of years. It was a failure, they missed the mark by certain magnitude in predicting flu trends and flu outbreaks, whereas the Center for Disease Control has a more old school method of hospitals reporting into this, much more manual, right? But the signal was much stronger. And the signal was stronger because at the end of the day there's a real world supply chain of the data versus this kind of large-scale approximation. And when you're serving an ad on the internet, you can kind of know who the person is. But if you're trying to find someone who's smuggling weapons, you better be sure that you match their name against your client database. So there's just a granularity to real world data and a signal that's much harder to work with, but we've seen to be potentially a much stronger signal. Yeah. You and your co-founder of the company met while you were philosophy students. Is that right? Let's get philosophical here because there's a lot of the chat we've had at Slush this year around data. You know, we had, like, Commissioner Vestiger yesterday talk about trust and how maybe big platforms are abusing the data they have and public trust, that kind of stuff. And also, there was a talk yesterday, there was a Google exec who was talking about Google+, and he sort of hinted that they had dodged a bullet by it not becoming more popular, which meant that they had just avoided all of the morass that, like, you know, Facebook and some other companies are in now. We had talked before about the ethical supply chain of data. You've seen the insight of lots of public and private databases. Can you just explain to me what you mean by the ethical supply chain of data and what works, what doesn't, what are the common pitfalls you see of these, of collecting information about people, essentially, right? Yeah, absolutely. I mean, to get philosophical for a second, the whole point is it's not an epistemological problem, i.e., it's not what your data is about, but it's about where it's collected, how it's collected, how it's used. I mean, listen, today we, you know, there's a data set that we provide to all fire departments in the United States, pro bono, to help them find slum landlords and inspect for fire, fire detectors and apartments, and that data set could be easily used by an insurance company to profile minorities in neighborhoods that are, you know, a more disenfranchised insurance loans. That's called redlining. It's like an actual term in the industry. So you need to understand, you know, not only what your data is going to be used for, but how is it collected? Was this data collected by, you know, hacking a, you know, database of private information, or was this data collected without the user's knowledge, or was this data collected even badly from a quality perspective? You have to think about, from an ethics perspective, it's really not just what the data says, or what the attributes are at face value. It's the whole intention behind it that matters. And I think, you know, people are starting to wake up to this much as they have in other industries. Yeah. And, you know, speaking of waking up to that, you know, we're in the land of GDPR here and fairly strict regulations about the use and abuse of personal data, you know, a big criticism that a lot of tech companies have is that things like GDPR, those kind of opt-in regulations can be kind of a hurdle to collecting data and stopping innovation, all these kinds of things. I mean, how would you, your company lives on data? That's what its main asset is. I mean, where do you come down on that debate? Well, listen, so it's just an interesting question for us. I mean, frankly, you know, we deal in a lot of data that by law has been made public for the purposes of increasing trust in the system. So we deal in a category of data that's certainly, to begin with, has a strong ethical basis and rationale. But we mix it with a lot of other external data. We mix it with internal private data. So we're very conscious about GDPR. I'd say the point that I think people are missing, and this is traditional of folks who have not gone deeply into compliance is that it's not just about the burden of complying to this regulation. I think consumer sentiment has fundamentally changed. It is not just about regulation coming in and protecting us for these edge cases. I think people are really frustrated by how large monopolistic technology companies have abused their understanding of people's behavior. And that has changed. And GDPR is a regulation. But more importantly, consumer sentiment will be the driving force here. And without the users, these companies don't exist. They don't actually provide a value or a service or a good. And take an example like the car industry where 20 years ago, Ralph Nader in the United States was made famous for this, lobbied for car safety and seat belt regulation, and that was a big thing. Today, I think every car advertisement is like, we rank number one. They literally show you the car crashing into a wall in a test dummy. That is the ad for cars. Or much like the food industry today, regulation on food organic has become the highest way to sell a product with much higher margin. I think we're going to see the same thing in data and privacy. I think our society needs it. I think the internet is a wonderfully connected place. But we've already seen the abuses to it in the US, vis-a-vis the democratic system, literally being undermined by abuses of the ways in which we interact on the internet. I'll put it that way. This is not the usual way, especially at conferences like this that we talk about tech innovation. But are there things around data governance, around practices, around data that private companies can learn from the public sphere? You go in and you pull this data about that comes mainly from governments. And like you said, their practices are pretty strict. And I mean, what can the private sector learn from the public sector and that? Well, so I think, first of all, I think they both have a lot to learn from each other, for sure. But if the private sector can learn anything, is that at the end of the day, you're delivering services to people. And these people are your constituents. You may call them users. You may attach growth rates to them. But they will overthrow you. And there's a certain kind of at the end of the day honesty and principled way about thinking about citizens that the government does that have rights that are kind of inalienable. I think a lot of those principles are things from a moral perspective that private companies should adhere to, not just because it's the kind of right thing to do, but because it's good business. I mean, take a look at the brands that are being hit by a lot of this stuff on the internet. Like the Facebooks of the world and the Googles of the world. It was only 10 years ago when we were talking about don't do evil. And today we're talking about spying on people all over the place. This stuff moves fast. Consumer sentiment moves extremely fast. And there's no way to stop that other than doing the right thing. I suppose another thing that Enigma especially is around, I guess, transparency, you could call it, broadly speaking, you put a lot of your data online, freely available APIs, all this kind of stuff. Most companies for which data is core guard it pretty jealously, both for regulatory and for competitive reasons. I mean, are people a bit too anxious about it? Should they be more open with it? Would that solve some of the problems about trust? Yeah, there's definitely a lot of talk about open banking. So for instance, I bank with a variety of banks in the United States, some of them make their APIs available to companies, some don't. And there's been companies who've had massive success working in this ecosystem, like a company called Played in the United States. The notion is, at the end of the day, the ecosystem needs to thrive on more standards, more protocols, more connectedness, less silos. Just take a look at the history of the internet. We started with Wald Gardens. We started with AOL and Prodigy. And we've actually opened up to the browser. This will happen for real world data too. This will happen for company data too. And it will fundamentally require a negotiation of the data rights with the user for this to happen, right? I think companies are afraid that their data is core. It's not a really strong idea, because at the end of the day, you can always open up just enough to let other people interact. The analysis is what's core. For us, our IP is not just the data that we have, but the insight that we produce from it. That's really what we kind of keep to ourselves as much as possible. It's the learnings, it's the understanding. The infrastructure is getting commoditized. Just look at what AWS is doing. Data will get commoditized. Just look at what everyone else is doing. Will remain is always knowledge, right? Knowledge is the most valuable thing we believe in our industry. And that's definitely the trend that we're betting on. And I think whether people wake up to that or not is there's plenty of value to be made along the way. Right, speaking of value, so how would you describe the business model of Enigma then? We got a bit philosophical there. Talking about trust, transparency, public data and all this kind of stuff. So to get down to kind of brass tags, like how do you make money from this? So we have a very large corpus of data that we give out a lot of the raw materials for free. The refined goods, which we merge into a knowledge graph is what we sell to folks, gets sold on an API basis. And most of our kind of best use cases, we go even beyond, right? We're not just a content provider. We're actually providing the data linking technology, the ability to match our external data to theirs, other external data to theirs, and provide an insight and make a decision. So in a lot of verticals, be it KYC or sanctions compliance or anti-money laundering, underwriting, some real world marketing use cases, we're actually making a decision for the client using their data, analyzing that, and that's a service on top. So it's data and decision support workflows. Right, I see. Are there any other sectors that you have your eye on? What's the most exciting thing that might be untapped for a company like yours? We're starting to get into these heavier industry sectors, like CPG, logistics, energy. My background is in alternative energy. I think for us, one of the more exciting things is expanding in the US. That's definitely where we are at the moment. But getting into some markets that are perhaps not attended to as strongly as they are, places like Africa, very interesting to enigma. We have a growing number of conversations and partnerships brewing there around things like getting access to credit. There's just a huge problem of folks who are not banked in Africa. I mean, people literally go out of their way to manage the cash, the little cash that they have, so providing fundamental core identity solutions to folks. That's something that we're very, very interested in. We always like to go where folks tend not to immediately. As part of what we do, we're a little enigmatic about things, no pun intended. But I think you'll see a lot of stuff like that coming from us in the future. And so to wrap up, in the little time that we have left, if there was one thing that the audience should take away when they go back and look at the data that they're collecting and how they're collecting it and what they're doing with it and all that kind of stuff, you work with huge amounts of data, which we've been talking about. There's a lot of startups here that are maybe just starting to do that and maybe not thinking through the implications of that. What should they take away from this? I'd say that the biggest thing is, I'll give you a two-fold answer. One is, I think the most interesting use cases are offline. They are not about getting an ad or getting someone to convert on something online. Like that is how we're gonna change the world, right? Not, and I use this sentence very sparingly outside of the trips that I make to San Francisco to meet investors, right? No, but I'm very concerned about these things. So think about whether what you're doing has an impact in the real world. And the second is this notion of consumer sentiment around privacy. It is not about slowing down innovation because suddenly now we have to click on an extra button to get to where we need to get to... Good technology makes those experiences disappear. So the technology we'll get there for us to comply to privacy in a very automated way. We do a lot of that work with some partners. But think about the consumer sentiment that's coming. Think about the fact that people no longer trust you and maybe even need you, right? To manage their social experiences online. Yeah, it's a bracing point to end on. Thank you very much for sharing that and thanks so much for joining us. Thank you. All right. That was fun. That was good.