Gil Elbaz of Factual

Welcome to the Puck, Venture Capital and Beyond, a show that explores the evolving landscape of the venture capital world. We'll have candid conversations with today's VCs and entrepreneurs who are shaping those changes. I'm Jim Beer, managing partner of Bier and Troff and president of CMG Advisors. This podcast brings change makers to the table to discover the inner workings behind decision making strategies and ultimately how they got to where they are today. I think the holy grail is to have a single view of your customer and to serve them as best as you can based on who they are online and who they are offline and build their trust that you can do so in a way that protects their privacy, but delivers the most wonder to them.

Today, Gil Alvez joins us to talk about his fascinating journey from founding of applied semantics to the development of the Google footprint in Santa Monica, and eventually to the creation of Factual, an information sharing startup focused on solving the big data issues of our times. I'd love to start in the beginning to kind of like how we got to where we are today with Factual because I'm sure there's as you went down the road, there's reasons the twist went the way they went. So do you wanna give us a little background in terms of I don't even know where you're from, by the way. Well, I I grew up in Cincinnati and San Antonio. So what brought you to California originally?

From a very early age, I was just absolutely in love with math and actually in love with data. I remember I grew up in Cincinnati and San Antonio. One of the really interesting moments in my life was 1981. Actually, 1980. We were one of the first communities to get cable TV.

On cable TV, you get all the regular channels, some bonus channels that we we fell in love with, like HBO. Sure. And my two favorite channels, the weather channel and the financial news channel. In both of those, you had a real time feed of data constantly streamed to you. In one case, the weather.

In one case, ticker symbols. I started charting these things. It was the only real time access I had. You know, I liked looking at sports scores in the newspaper, but you had to wait twenty four hours for that with cable TV numbers coming in at you. And I started just writing them down as fast as I could.

You know, tracking data without a computer is not easy. No. That's true. When I finally got a computer, it became the perfect tool to track all my data. So I had this weird I don't know if it was a quirk or addiction, but I just I love the fact that there was this information, and I I felt like there was something to it that it was important.

I think it took a while before I understood how fundamental it is to business and innovation. That came later. But it all started with this love of information and much easier to access than having to wait for my parents to drive me to the library or the bookstore. Got it. And then from there and that love, what prompted you to come to Southern California?

I got into Caltech. Okay. One of the best technical schools. So when I got in, I felt like, okay. I I have to go.

I got in. All of a sudden, I find myself in Los Angeles. I haven't spent any time here before that. Very happy that Caltech brought me to California because I certainly feel like I was as an entrepreneur as a kid. And and today I I felt like I was destined to be here.

Definitely the right place for me. I feel very much like I was born to be an entrepreneur in California. Well, what's fascinating for me is I'm a Southern Californian and when you look at venture capital and the Internet, there's so much talk about up north. And when you look at Southern California and the trajectory, it's really exciting. I mean, you building out Google's office in Santa Monica, what you've done to actually help put Southern California on the map is extremely exciting to me and the other people I've interviewed in the sense that we have always been the kind of stepchild, so to speak, relative up north.

And I was here twenty years ago when people were talking about convergence, you know. And entertainment was gonna but it's really happening now. I mean, there is really a technology hub in Southern California that you helped really spawn and put on the map. So in terms of kind of where you started in Southern California, do you wanna tell us a little bit about that? Sure.

Southern California, LA was a very, very different place in the late nineties when I got back here. I I did have a stint of six or seven years in Silicon Valley after college. That was a tremendous experience and an opportunity to work at a number of fast growing companies and learn. I decided to come back to Southern California where I had some roots and a place that I loved, so I came back to start the company. As I said, it was a very different place.

I didn't know a single angel investor. There were only just a a handful of venture capitalists, and many of them weren't right here in Los Angeles. For example, there was Enterprise Partners. We had to go way down to, to Oceanside, closer to San Diego. There just wasn't a lot of activity.

So that represents a whole set of challenges that entrepreneurs today don't have to face. There are a lot of other challenges that they do have to face. An incredible amount of competition. So many people trying to enter this fun roller coaster of a game. When I got started, it was very much a project of deep passion and love of data and information.

So, yes, we were starting a company, but it was really born from this is something the world needs. The Internet was fairly new phenomenon, and we saw that it was increasingly disorganized. And so you had search engines that were trying to clean this up. You had Yahoo and its directory approach that was trying to clean up this crazy mess of information. And I just thought that was the most important project that I could work on, and we built a new technology to try to do this but better.

Effectively, we were competing with Google. I didn't realize it at the time, but that was, of course, quite an ambition. So can you, for those people listening that are not technologists, help explain kind of what that initial creation was and how it initially came to you and then how you brought it down so that we now it's going on in the web every day, right? And we know it works but how do you actually explain it in a way for those people like, again, myself and others that are not technologists so that we can understand how you got from the idea to actually making it a reality. Yeah.

And we're talking now way back to Oingo applied semantic space. Absolutely. Yeah. Again, the the broad landscape was that there was too much information. People couldn't find what they were looking for.

There was disorganization. And it's not just about search. It's really about disorganization of society. If people don't know about each other and don't know that there are related concurrent projects going on, how are we gonna have an efficient economy or society? That's really what the web brings about is the potential for just incredible collaboration among billions of people to solve grand challenges in the world and to do so in a more efficient way.

But it does all come down to whether the information technology works or doesn't work. In particular, what we saw was that computers had problems multiple meanings. Dolphins could refer to a football team or it could refer to the sea mammal. That was one of the fundamental issues that we saw was that computers use too linear of an an approach to processing text and we're doing exact matching. And as we got deeper and deeper into this, we realized that we could build a technology that read an entire page of text and understood it at a deeper level, understood the key themes that were there and key topics, words that weren't even on the page.

And this is still a big problem that software engineers grapple with, the fact that too many technologies are based on exact keyword matching. And sometimes what you need to find is the meaning behind the words. Interesting. But doesn't that also tie into advertising at some level? In other words, I get what you're saying in terms of it reads a page and it some level?

In other words, I get what you're saying in terms of it reads a page and it makes sense of it. That makes sense. But then, how did that become monetized and actually lead to some of the creative things that are going on in the advertising world today? We started out focusing believing that we could revolutionize search, and we had a good start. We had a search engine.

We were getting traffic. At some point, we were starting to lose the battle with some of the other high growth search engines, and it turned out that this exact same technology is incredibly valuable for advertising, a specific kind of advertising, and that is contextual advertising. That's where you want a display ad, back in those days, it was all about display ads, that matched the mindset of the consumer that was reading this content. And at the time, advertisers didn't realize they want it. In fact, we were told that's not how advertisers buy.

They buy on demographics. I want to target young active males. Instead, we were saying we could target based on the fact that you're reading something about travel, related to travel. The problem with keyword matching is an article about travel may never use the word travel. You have to infer that somehow based on a computational process.

We call it natural language understanding that reads several paragraphs, processes it, and then decides to what degree is this about travel, then it can be the perfect place for American Airlines to place an ad. In fact, we saw the metrics. You were going to get much higher click through, much higher engagement. Ultimately, that meant great monetization opportunity for them and for us. Interesting.

Okay. That makes sense. I wanna spend a little time on this for a second. One of the things about factual as I understand it is that you've created an opportunity where anyone can get this access. That it's not just information owned by the Googles or the Facebooks of the world.

In a sense, it's making it available to people and there's a huge benefit for all the customers, obviously, the people that use it making this available. When you look at what's going on in the world today where you've got a company like Amazon who's going out and building these mega warehouses and essentially is able to ship you a product within twenty four hours and they've got all the information in the world on customers because they're gathering it. And then you've got the Walmart's and the Costco's and the Staples and the Toys R Us and they're all out there trying to compete against that. Is there anything about what Factual does that can give retailers in this new economy an opportunity to do creative things that are not being done that give them a chance to go up against the big boys? Yeah.

Yeah. That's very much the story. So Pliesimandex ended up being part of Google through an acquisition. There I was at Google already with this deep world view that data drives the world. But then at Google, I saw it at a scale that you couldn't possibly imagine.

This is a company that knows how to collect data, has the trust of well over a billion consumers, is incredibly adept at taking this data and turning it back into value that they give back to the consumer. This is a company that it didn't feel like, to me, can be stopped. And this is back in 2003 when I got there. Even I couldn't imagine that they would move into industry after industry that seemed very far removed at the time from search. They're becoming the future of television, you know, along with Netflix and a couple others.

It looks like they're gonna be the future of transportation along with Uber and Tesla and a few others. It's all back to the data moat that they have. I think that's the single most important moat. After a few years at Google, I left realizing that the single thing that will keep the rest of the Fortune 500 challenged is this Google data moat. I would say it's also a Facebook and Amazon data moat.

Those companies look like they are becoming the triopoly of consumer data. I left Google in 2007 to create a data company that would help enable every other company to be as innovative as Google. You need the data to do it. I think there's every opportunity for every other company to hire data scientists and software engineers. There's a lot of creativity out there.

Sometimes they just they don't have access to the same data and that that was really the opportunity, again, to be neutral, meaning we're not competing against our customers. We are willing to provide this core data to the banking industry, to marketers, to the rest of the app developers that are not these big tech behemoths. And now I can see how that levels the playing field. Again, one of the ironies or challenges is in this world of everybody knows who Google is, everybody knows who Amazon is, everybody knows who Facebook is and presumably everybody's gonna know who factual is and is getting there in the same way. But if I'm somebody out there competing and I've got all this data and I've got these products, I still have to drive people to my site.

And that's one of the disadvantages. I think when you look at the number of products that Amazon has versus Walmart or if you look at the percentage of retail sales on the internet that Amazon has versus all the others, it's staggering. Just like I'm sure you saw with search. Right? That the amount of search going through Google is staggering.

Are you seeing companies taking advantage of your data and doing really interesting things? And in terms of where the world is going, where do you see innovation and use of your data? Again, for boring people like me, I'm driving around and I'm, you know, looking for my new Starbucks or I'm looking for this. And obviously, I never even thought. Like, I just assumed that data was just there.

Like, oh, you're on this app, you're on this app. And to stop and realize, oh, actually, somebody had to put that data together and make it available to somebody just is a whole new way of looking at the world. But when people come to you and they're licensing your data or getting access to it, how are you seeing it being creatively applied in this world right now? So I've said that Amazon, for example, has this tremendous moat of data, but it's not the end of the story. As powerful as they are, they still represent a small single digit fraction of the country's total GDP or total amount of retail expenditure.

Still a tremendous amount of what we spend is offline walking into stores, and most of those stores aren't Amazon. A few are. Right. We're seeing that in our local mall here. Right.

They're great. But there's still every opportunity for every other retailer to start applying smart data practices and to invest in data so that they can match Amazon's capability to be intelligent, predictive, and contextual when dealing with people that are walking into the store. It really just takes focus and investment to do a lot of these things that are now possible. One of those things you can do is develop this deep one to one relationship with your customer, build their trust so that they share data with you. Starbucks, for example, has a great app that I use all the time.

And the more I use it, the more they know about me. The more that they know about me multiplied by the data scientists that they have that can add value to it. They can offer me opportunities and coupons just at the right time and keep me as a loyal customer. That's an example of the kind of thing that a leading forward thinking retailer can do. There's a tremendous amount of insights that one can pull if one studies all of the traffic data.

So what are the new trends? When somebody goes to the movie theater today, where are they coming from? Are they walking? Are they driving? After the movie's done, do they go and get dinner?

Is that a thing, or are people going home earlier? And if you slice this by different, let's say, age groups and demographic groups, how are trends changing? If you're not on top of this kind of data, you're just gonna be much less strategic than well, certainly less than Amazon. So using that as an example, if I go to an AMC or a landmark movie, for instance, and they're tracking that data. Is that factual tracking that data for them or are they tracking that data?

How does that actually in the real world, how does that work? There are many apps that are able to convince users that they should opt in and share location with these apps because they get a tremendous exchange of value. So these apps take Uber, for example, extremely popular app. It helps people. They need to know where you are in order to serve you.

They can leverage this data not just to serve you immediately, but also to build up more and more learnings around things that they need to do to be strategic to provide a solid service. For example, they have to predict I just went to the Hollywood Bowl a couple weeks ago. They have to be able to predict that after concert, there's gonna be a huge demand Right. Of people. And the only way to do that well is to leverage big data, data from a tremendous number of people over time that can teach a big computer system how to predict the future.

So using that as an example, which is a great one because some of us have gone to the Hollywood Bowl and had a nightmare experience with the $80 surge in price because to your point, there aren't enough cars and it's all being figured out. Does Factual get involved in that? I mean, using it as an example, like, do you work with a company a company like Uber? And, again, if this is proprietary, we don't have to use specific examples. But is that helping people figure out where the world is going in that one?

Is that something you actually work with the Ubers and the Lyfts of the world to help them do? Generally, yes. Location data and location intelligence is what Factual does. There's several layers there. The foundational layer is understanding at every point on the globe what's there.

It seems basic, but it's actually a very hard problem to have mapped out by business or named point of interest close to every square inch of the globe. So we've done this in 52 countries anyway, where we have a 130,000,000 businesses and points of interest, where we understand not just the names and name variance of these places, address, the geo coordinates, but also deep deeper understanding of the nature of these businesses, categorization into a taxonomy so that you can look up all hardware stores, all four star hotels, all sushi restaurants, whatever the category is. Is. And then on and on and on, we have a ton of, factual attributes that we build. And to do this and keep the data very fresh, knowing that something like a quarter of businesses churn, go out of business each year and new businesses pop up, that in itself is a tremendously difficult project and we've had to apply a lot of AI and machine learning and automation to do this at scale, to do it relatively cheaply, relatively few people compared to the companies that came before us that were working on these kinds of problems.

Yeah. That's right. A piece of it. A big piece of it. I mean, I can't even imagine how you would keep it all current.

But, again, if you feel comfortable talking about it, it makes sense. Using the Hollywood Bowl as an example because it's a very tangible thing. There's a Hollywood Bowl. There's thousands of people going to the Bowl. There's Lyft and Uber dropping people off and the concert's a couple hours long and then people are gonna need to get picked up.

Who's using factual in that context to help solve that problem? Well, we can count many of these big tech companies like Uber, Snap, Apple, Facebook, and others as customers that rely on on our location data to build better location based apps. We've been working on this problem for we founded the company ten years ago and have been working on location data for nine years. It's a deep amount of focus, and it's taken a while to get to the point where we are today as the trusted leader in location data. A lot of that is technology.

A lot of that is the sweat around serving customers and listening to customers and iterating again and again and just building a better and better product and service. Interesting. But using the example you use with the Hollywood Bowl of Uber needs to know how to map out how many cars to have at the Hollywood Bowl, in that scenario, it's not relevant how many Starbucks there are, but it is relevant how many people they dropped off at the Hollywood Bowl and how long the concert is. Right. So do you actually have that information in terms of how there's a concert tonight at the bowl that gets out at this time and is that the kind of information that would be helpful to Uber in that example?

You just use that as an We have technologies that assist with turning the raw data that comes from the device. So Uber's app, to use that example, can access the GPS location. It's a latitude longitude. So these two long decibel numbers that represent a precise location. What we found generally in the industry is that that data is underutilized.

The power of location is underutilized. There's more that you can do with it if you leverage a set of predictive and analytical technologies on top of that raw data. So we don't collect that raw data. That's between an app and its user. But once all these numbers are collected, turning it into an understanding we call this observation graph technology, understanding what are the patterns of movement.

Again, are people walking? Are they walking a long way to get home? Do they need to drive? Are they coming from a long way away? Are they taking public transportation?

So understanding the overall patterns of movement is one of the key use cases for our set of technologies. So following along these lines of patterns of movement, there's also the question of how are companies gonna compete with Amazon. I know retailers, for instance, are focusing on experiential shopping. How, for instance, is the information that you have, the factual information that you put together, how might that be helpful to retailers? Data is certainly a very key ingredient to power predictive, relevant, delightful consumer experiences.

The full innovation around experiential, it requires much more than data. It also requires a whole creativity and capability around understanding how to connect emotionally with your customers. And that's by the way, that's something that I don't think a single large tech company can do across every imaginable persona, potential group of people to sell a product to. So that really leaves the opportunity for a whole range of other companies that are experts at building certain product categories and connecting with their consumers in new ways. I think they need to be more adept at leveraging all sorts of data and location data being a key part of that.

So we are starting to work with many of these innovative companies to help their app do a better job of bridging the divide between the offline experience and online. I think that's really the holy grail is to have a single view of your customer and to serve them as best as you can based on who they are online and who they are offline and build their trust that you can do so in a way that protects their privacy, but delivers the most wonder to them. So that's interesting. So there's been a lot of buzz around artificial intelligence, especially with respect to the growing use of bots in customer service and virtual voice assistance. Is there any part of what Factual is doing that relates to this area?

Well, so first of all, voice enabled search is certainly the future. People love it. Frankly, it doesn't even work that well yet. It's getting there. But even with what it is, people love it.

To be able to talk to a machine naturally, to ask a question, to do so without having to pull out a phone and put your password in and change your context. You know, we're in the kitchen saying to Alexa, set a timer or play this song or make a phone call. You know, Google and Apple also have popular devices as well that do some of the same things. One of the interesting things about voice enabled searches, it's not like web search where you can offer 10 choices. With web, you know, here's the 10 blue links.

One of them should suit your fancy. With voice, it has to get it right the first time. It's gonna be really annoying if it says here's 10 things that might be right. So these companies are putting a huge amount of investment into further improving the search algorithms. That's where it comes to AI.

You need to leverage the most sophisticated AI and a tremendous amount of data in order to perfectly respond to the request. From our advantage point at Factual, we're seeing more and more demand for data so that answers to questions can be as on point as possible. Right. Exactly. And as much as there's been a lot of discussion about AI, there is also the related issue of an evolving labor market.

Is there any way that you see the job market being affected by big data? Being connected from wherever you are and whatever your situation is, I think is gonna be hugely positively impacted by this new data economy. For example, while there are people in cities that aren't close to the jobs, we're talking about maybe elderly populations that don't want a full time job or can't get to the job. How can you contribute from a computer anywhere? How can you contribute to the global economy?

We're seeing, I would say, factual is a customer of such capability. We work with a company called TaskUs that helps us find people to do micro jobs. It's a little bit a little bit of an analog to an Amazon Mechanical Turk, which pairs up a need for a micro project with somebody who can provide that help. You know, who can help us translate this one sentence? There's another one called Fiverr, which is really fun.

It's a index of services. Somebody has a unique capability. It could be translation. It could be a thousand other things. A voice.

I paid somebody on 5 or $5 in order to say something as part of a joke in a celebrity voice. There's so many skills that people have and they can contribute, but the fundamental gap is just matching the the supply of that talent to the need. And the Internet now turning into a index database of world talent, I think, is taking us closer to this holy grail where anybody can contribute. So use an example. Do you actually have people that work remotely that do specific projects for you?

Yeah. For us, they'll help verify certain pieces of information where we want somebody to do research and make sure as part of a test process that a certain business really is there. They'll do internet research for us on an individual business level. Wow. That's fascinating.

Shifting gears, I know that you were involved in the LAVC scene. Do you wanna tell us a little about ten one ten? Of course. Ten one ten is an early stage venture fund. I founded it, launched it along with, close friend and partner, David Waxman.

He does a lot of the heavy lifting these days. I have much of my hands full with, running factual, but I also contribute in numerous ways and work closely with David on ten one ten. We also have a terrific associate, Austin, and we have a great set of advisors and people involved and venture partner, my brother being one of them, Eitan, is very involved in adding a ton of value. We've put ourselves out there as the techie folks, deep in tech. We're operators between David and I.

We started five companies. If you include my brother, we've started eight. We really can commiserate with the entrepreneurs that we work closely with. We know what they're going through. We can help in numerous ways, both decent knowledge base experience and and network over the years.

So, I see some of the work that David does to help entrepreneurs get from seed stage to ready for series a. That just amazes me in terms of the value that he can add by helping to build out a team, provide great guidance. He's extremely hands on. So really excited about paying it forward, paying it back, I should say, to the LA region, which we're very focused on. We're not exclusively focused on it from an investment thesis, but we're very focused on, and there's just so much opportunity here.

LA is going to create many of the big winners in the next decade. It also happens to be a terrific financial opportunity to invest in LA. You just said something that's fascinating, which is that LA is gonna create these companies. And I can see your excitement. What do you see changing in Southern California in terms of that next generation that wasn't, for instance, here in 1998?

One of the things that's sort of a self fulfilling prophecy, there's this confidence and this activity that's kept people here over last five or seven years. If you go back to 1998, the percentage of engineering grads that fled for Silicon Valley was very high. I've heard of numbers as high as 70%. It's a very different story over the last five to ten years where a lot of our top talent, and there's no shortage of talent here. We have many of the best engineering schools.

Now they're staying. Five or ten years later, they are experienced individuals that have had these tremendous growth experiences, whether it's at Tinder or Snap or these other companies that have added a tremendous amount of value and innovation. Now they're becoming next time spawned entrepreneurs and doing their own thing and bringing that experience and newfound confidence into the region. We're also seeing that investors believe that there's no reason why the next massive winner can't be here. There's no reason why it's destined to be in Silicon Valley.

So you just see a lot more money pouring. Probably an example is a bird that just raised $400,000,000 even with a very small team and revenue base, huge potential of course. You weren't seeing those kinds of bets in the region ten or fifteen years ago. One of the things I'm waiting for is for that billion dollar fund in LA, that consistent place where people will write the larger check so that you don't have the winner here, but then that winner has to get funded from outside of our ecosystem. Do you think that's gonna change in the next few years?

It's in the process of changing. So we're seeing great funds with very talented teams that are raising increasingly larger funds, certainly upfront and Greycroft and Pritzker bringing a lot of money into the environment. Bee Capital, we're seeing more and more. Still not enough capital to fund, so there still is plenty of going up north to hit Sandhill Road. I did that myself both at Applied Semantics and also at Factual.

At Factual, we were raising our series a in 2010. So even in those eight years, the landscape has changed dramatically. I think there's gonna be more funds and more partners placed here, but that's gone slower than many of us have anticipated. Part of the reason is because it's so easy to fly up north. There's a feeling that it's a workable solution.

Right. But I'm surprised. I would think that more institutional money would wanna back VC in LA. To your point, I think it will happen and it's Yeah. You're seeing the funds get larger and larger.

So eventually, we'll have them. I'm ready. I've been waiting for this for a while and I remember when, this was years ago, when Green Dot did get its funding and they were talking about, oh, well, the board was saying, oh, you need to go talk to Sequoia, you need to go talk to Ben. You know, it's like, wait a second. What about in our back yard?

Let's fund these companies down here. In addition to doing all the wonderful things you're doing with Factual, you also seem to have the time to be trying to make the world a better place in the non profit arena as well. Do you wanna tell us a little bit about the organizations you've started or you're on the board of that you're excited about? One example is the Common Crawl Foundation. It's a nonprofit that I started around the same time as Factual about ten years ago.

My feeling was that access to data is a critical element if you're going to understand the world and then therefore innovate and build new services. Common crawl sought to build and offer for free the single largest corpus of human information, which as far as I know, still believe is the web itself. What I hadn't seen anybody do is crawl the web and make it available in bulk at scale. Anybody can try to crawl the web, but there's a lot of blockers. So what we've done at Common Crawl is we've assembled a body of knowledge, a body of data about counting in the tens of billions of web pages.

We have a relationship with Amazon where they give us a huge amount of storage space for free as part of their Amazon public datasets program. And then any researcher in the world that wants to understand the human condition, wants to understand language or research trends in society, they can download or process this body of knowledge. It's right there for them, and they don't have to try a page at a time to try to pull it down. We see an amazing number of academic papers where they no longer have to go join Google and do research at Google because Google has this data. Now they can do it while in school.

They can do it from the privacy of their own garage, working as an independent researcher. They can access Amazon's huge compute infrastructure and try to understand things about the world. If somebody's trying to do research and they want access to it, do they have to apply to your program to be able to get access to it? How is it accessed if somebody's interested in doing a research project? There's no application and there's very little in the way of any gating.

They don't have to register. You need an account on Amazon. You have to agree to our terms of service that says that you aren't going to distribute this information in a way that violates copyright law. That makes sense. But we make it very, very easy for an academic or a startup person or a researcher to access it very quickly.

There's technical documentation at commoncrawl.org that explains and provides tutorials. So we spend a lot of time talking about data and the history and so forth. I know there's some fear out there with people worrying about things and they've talked about Facebook and stuff. But how do you see data being used in a good way to make the world a better place? There's so many ways that I see in the future that there will be companies that aggregate a whole network of remote sensor data in order to provide solutions to the grand challenges of our time.

The biggest network of sensors right now on our mobile devices that can capture location and movement and other issues. One idea that comes to mind is Kel Tec created a technology where it analyzes literally the vibrations through the accelerometer on the phone in order to detect earthquakes and to do so with a much greater accuracy and much lower cost because it doesn't require any new hardware. Any type of opportunities to have a deeper understanding of crises as they're happening and predictive abilities to figure out how to respond to these, that's gonna add a lot of value to society. The question is, are we as consumers willing to trust companies to aggregate our data for the common good? And I think one of the things that's inspiring about your story is that you've really gone out there and said that there are these big companies that have data that are using it in very proprietary ways and in kind of big brother ways for their own benefit.

But that this is data that can be democratized and to be used for creative things. And what's fascinating to me is that, yes, you have for profit businesses but you're also doing these non profit businesses and trying to facilitate our creativity so that we don't have to be afraid of data. We can have trusted people that are using that data to make us feel safer and to solve these great challenges of today. I see it very much as a double bottom line opportunity. There is a lot of demand for data and data science capabilities.

So there are companies that are willing to pay. But at the same time, we're seeing left and right and we're investing more in opportunities where people can take aggregated data and predictive models and get ahead of problems before they really plague us. There's examples in understanding epidemics before they spread in the health care space. There's examples of understanding crises as they're unfolding figuring out how first responders can help. We're just seeing so much innovation with turning data into action.

On the next episode of the Puck, join us for an in-depth conversation with Iba Ho, cofounder and general partner of FICA Ventures. Eva shares not only her experience and the path that drove her towards FICA, but also her views on some of the most salient social challenges facing LA today. This is one you won't wanna miss.