 Hey, welcome everyone to CUBE Conversations. This is John Furrier, Founder of SiliconANGLE. We are here live in Palo Alto. We're here with Tyler Bell, Vice President of Prog with Factual. For CUBE Conversations around big data and business, Tyler, welcome to the CUBE Conversations. Thank you, John. So big data, obviously, we've been covering for a long time, and data's the hottest thing right now. People say data's the next oil, data lakes, data oceans, as I say, but data's actually central to the value proposition of this next generation of social, mobile, cloud, and certainly cloud has propelled data as a first-class citizen in the developer framework and to business models. So I want to just get your take on what you think about the big data space in terms of, for the folks out there who are looking at using data to recast their business models, look at application development, and figuring out how to use data to enable their business for this next generation. Yes. Yeah, one reason you see and hear of all these metaphors around data is because people are trying to grab various sort of ideas in their head and better understand how to articulate this new value, which is something we haven't really seen before. And really what it is, is that you see the separation of data from the program, and really that's where things come into its own. It's really only relatively recently that the idea of data as an asset has come to fruition. And with that mental separation, we now have the tools that just work with the data independent of the business process. And so as data has grown in quantity, yes. But I think it's also the mental shift, is that people, executives, technicians are understanding that data as an asset means that I can build a motor, I can broaden my wall against the competition, and actually do things in the sector that haven't been seen before. I'd like to get your take on how data has changed, how the mindset around data has changed in terms of how people acquire data, what to acquire, what to look for, databases and a lot of those technologies and around how the tooling's changed into this new generation. But first, tell the folks out there about Factual. What are you guys doing, what's your business, what's your business model and what is the vision of Factual? Yeah, well Factual's a data platform obviously, that's why I'm here. We focus specifically on location data. So, and that can take many different forms. And I think each form is particularly important. The first one is location data in the terms of commercial places. So all the bricks and mortar stores around the globe. So our foundational product layer is about 70 million places in 50 countries. And we use that, that's valuable because it's of course valuable in any kind of mapping or social discovery app or check-ins, any kind of review app, requires that seed data around which they can wrap their own content, much like a pearl is formed around a small grain. And then of course on top of that, we have the idea of working with location data. So where users go over time, where people return to, what's important to them, what can we learn about people based upon and behavioral patterns based upon where they go. And so that's taking say your location data from your app, overlaying on top of our places with some machine learning and then we return information about your user base. And that new enriched data can be used by you to personalize how your app behaves and also make sure that you're producing more relevant content. I had a chance to speak with Gil, your CEO and he talked about, you guys have been around for multiple years doing a lot of hardcore development and knowing the company, you guys are a lot of data geeks and you guys are, you guys are digging into the data. Who are some of your customers today using Factual and why are they using Factual? Well, we're very keen to announce two new partners or I guess you could say refreshed partners that are coming to Factual to take a bigger bite out of our data. It used to be that when you launched, say a mapping product or a local product or any kind of application, you start out in one country and you learn about that then you grow out country by country. Increasingly organizations are saying, look, when I launched this mobile app, I want to go global from ground one. I want to know the entire geographic landscape in front of me. So companies come to Factual for our global places data, the international data, as well as the suite of tools we have around it. And the two that we're announcing now are Bing and Yelp are coming on board and they're taking a big bite out of our international data which is lovely to see, they're good partners. So you guys essentially acquire data from the internet public data and you also have source data. Is that just like locations, their addresses? What are some of the data types and sources that you guys acquire? So this specifically relates to our places data. So that's that foundational product that I spoke of earlier. And Factual excels in areas, not where there's too little data but actually there's far too much. And really that's the internet pretty much, the entire to the internet. So far too much data, it's heterogeneous, it's mixed up, it's erroneous, fragmentary and it's dated as well. This stuff moves very, very quickly. In fact, we just released a blog post talking about our tens of millions of updates and deletions and refreshes and new places that we've added just in the last quarter. So everything's moving and what Factual wants to do and what we've done is build a crucible. And basically you take all this poor data, this bad data, you take some very high quality trusted data that we get from partners, hotel chains will give us data, partners who work with hotel chains and restaurant chains give us data. We put it in this big melting pot. We ignite the switch and all the impurities are burnt off and we're left with this cool glowing nugget of pure canonical data. You know, I love open source. Open source has been a great boom for us in the industry you're seeing that reflected now with cloud and other things, especially in software side. So data in a way has to be open. So the one observation we always hear is I have some data but if I just can put it into a pool if you will and make it better can I get other data? So everyone has data, right? That's clear. But then folks wanna know what to do with the data. So like how to make sense of the data. So can you just explain that concept of, I call it silo data, what are you gonna call it? You know, if I'm a hotel chain, I got all kinds of customer data, I got product information about my hotel, but I'm just a hotel, I'm not a restaurant. So this is the challenge, right? Is that internet companies have faced how do you one make all that data work together? Yes, yeah. And there's a lot of like, there's a lot of new ones. Also a lot of like hard stuff under the covers like cleaning the data, explain that whole concept out, this notion of openness and having data sets from different sources come together. Yeah, so with that one question you've hit upon about five vital aspects of data. So I'll break them up and address them as I can. The first with the idea of openness, we sort of the tech sector or individuals don't have a very good mental model of how to share data yet. That's something that doesn't exist because the general thought process, the belief system, from your company's lawyers, town two, the executives and product managers, is that data is a zero. And cultural privacy. Yeah, yeah, well, I mean, so privacy, let's fork that off. That's hugely critical and I don't wanna just touch upon it. But so here we will deal with just data about businesses, about places. So that's not terribly private, sort of where this office is located. You want people to know where this office is located. Retail establishments want to make sure their data is clean and well-broadcasted across the internet because it's good for them and it's good for the consumer. Everybody wins. But there's no model for non-zero sum data. So if I have data about places, I will very often be reluctant to exchange it or share it out unless there's a clear value that's articulated. And so people still think of data as a zero sum game whereas if I give you data, then I've somehow lost value. When in truth, factual's view is that data is more powerful and in most cases, not all, more powerful when it's shared. So if you think of the, there's think of, I guess the two examples I use for open data which are most valuable. One is technological and the other's natural. The technological one is GPS. So with GPS, that's the most successful open data play ever because by having those birds in the sky, we've now opened up the world's geographic data and all sorts of businesses flourish on top of that open data set. The other one of course is on the natural side is a seed, right? So if I have a seed and I have an apple orchard, apples create seeds or I can cut a bit off and splice it and give it to you. You can grow your own orchard. Now we're both better off because we both have orchards, we have cross-pollination. It's not that I'm chopping down my tree and then giving you an apple. And so I use those metaphors because they sort of articulate opposite extremes of where we are now which is fundamentally people are thinking, well, I'm told with the big data movement that all data has value. I don't know what that value is yet and so I'm gonna just lock it down. And factual's proposition is that when you share data it's more powerful. So for example, I have a business about a place. I give it to you. Let's say I give you one million bits of data. Records about places. You correct bits on every one. If you share those edits, corrections back with us, we both share ownership of that data. So you've helped us, we've helped you and we shake hands and walk away happy. So in any ecosystem you mentioned this is an enablement model because that sends you what you're saying is this is an enablement opportunity for folks non-zero some game as you mentioned. This is a phases of innovation. There's build, grow, value or monetize. Build, grow, monetize. Well kind of in that build mode now people are looking at holistically at data. Can you share your thoughts on where people are at right now based on your experience with the company talking to big platforms that have data and as people who don't have the big platforms there might be a small business sharing their address kind of like a yellow pages model on the web that want to take advantage of what the big guys have in platforms. So how do they build and how do they grow using data? Yeah you know I think there was a with sort of the advent of big data and let's say sort of three years ago was just a finger in the air sort of chalk mark about what I'm talking. The whole idea was that there are now new tools that I can use to better understand my content. How do I get those tools in? How do I find the expertise to work with the tools? And I think those kinds of questions on the operational side have now become a little bit more refined. So I may not need that. I can now use a service. Microsoft is pushing the ML as a service now of course. So it's no longer necessary of a question of I have to have all these assets. It's become much more subtle. And that subtlety is really reflected in well I may not need to work with big data myself but I can identify someone who can process it for me and make it usable. So that's something factual it does certainly. Or I can hire a service that allows me sort of on tap computational resources that I require. And so what you're seeing is a maturing of the industry where it's no longer a question of sort of a panic knee jerk. I need to have all this. And it's much more of understanding but what are the data assets? What are the raw material? What are the computational resources? And then the head count that I need to deliver. But in the overall spectrum of things it's sort of we're still in January on the calendar year of big data. So you think it's clearly build mode right now. People in build out mode not so much growth focus. So maybe some of the big boys maybe but. Yeah you know it's certainly not growth phase because that implies that you've actually sort of locked it in and you know exactly what you're doing. I think it's still so early that we're still learning the potential. Whenever just like whenever any new technology is brought on board the first question people ask is can it do what I can do already? And then once that you can tick that box people begin to say oh look at all these new ways that I can do things. We're actually now have the technical vocabulary to expand my product in ways that we're just not possible before. And so I think we're right on that cusp where people are ticking the box and saying okay what can I do now that's new. Tyler you're a vice president of product team and knowing how product teams work you have to balance kind of two roles. You gotta look inward to the company to know the assets and the mojo that you have from a technology and staff standpoint and then out to the customer facing market on the go-to-market side and balancing that roadmap what to deliver they basically call the MVP minimum viable product. So gotta ask you how do you balance the roadmap from what you can deliver today and what the market's ready for and specifically you know getting in there and getting your success beach head if you will for factual. And then what do you sequence downstream. So some people call it headroom or whatnot. So you know you guys have a unique very valuable product. What is your focus there? I mean how do you look at that and what are you doing specifically today in market. Yeah you know I'm glad you asked me this question that sounds so trite but I'm glad you asked because it's one of the most difficult things that we work with because what we found is we're not a company that starts out with the marketing message and then sort of looks down. We start out with the data and we say look we have all these fantastic resources. We have an expertise in location and data computation and there are some very clear pain points in market needs. So we come up from the data side rather than down with a message. And I think that's very powerful because our data is the best that's out there certainly. So the product is sound. But what's more important is that or I guess what's most revealing is that the difficulty that factual has is taking all these data wonks as you noted taking our products and basically understanding that they need to be reinterpreted for people in entirely different sectors. So how do we take this valuable packet of data about a place or about a person or someone's movements over time and how do we articulate that value not as raw JSON but actually as a marketing message to say these are the wonderful use cases you can do. So I mean I guess as an exemplar it was only until probably about a year, a year and a half ago that we had any kind of maps on our site. So we deal with location data but it's all articulated as just chunks of code basically. And now we're beginning to say look this really requires and we can do some really cool visualizations with the material that we have. So to your question the big balance that I have there is working with my team on the outbound side to ensure that we can articulate a coherent message but also always articulate the strength of the data. We never want to compromise what we have. We wanna say look we have confidence metrics and percentile metrics that we give you when you consume our data rather than what other folks say that which is yes this person's a golfer no this person's a golfer. We can say yeah we're pretty sure this person's a golfer and we're being open and transparent with you or at least as far as the data allows us to be. It's always a challenge. The beauty of data is in the eye of the beholder and it's really challenging for a company of your excellence to kind of have that approach because you can have make data for every diverse use case on the planet and be intensely in markets. So the broader market opportunity really is the scale point for you as a company. When do you see that straight and narrow on the broader market opportunity? So that's the next question I wanna ask you to kind of end the segment is if you look at the data strategy holistically and say okay we see that data will be a big market and we know that that's growing. We see evidence of that. But now we have a consumerization trend that's really driving the data equation and it's not just about the product. Product places and places and whatnot. It's about the audience themselves and the consumers who actually want value. And so now you have a consumer angle and you have some people who have been consumer facing. I wanna know all about the consumer, psychographic, demographic, all kinds of data there. I have all this data on the products that are out there. How do you integrate those two concepts? Yeah. You've touched upon a very important point and I'd break that up into two answers. The first is by starting with the observation is that Twitter's really the first mechanism where the consumer's been allowed to talk back to the product or talk back to the brand. And that's just a sea change of how consumers engage with products and brands and places, stores, et cetera. And I think that it's because we no longer have a simple one-way communication, it's become two-way, it's become a conversation. What you're seeing is twofold. One is on the technical side, data is being used to better understand the consumers through the products that they engage with in the same way that we want to better understand the places by the people that engage with them. And so you're seeing now the raw materials for a very virtuous circle that's moving us away from this shotgun approach where it's sort of one-to-many messaging and that's what advertising is, performs very poorly in your face, rarely relevant to the marketing message which is much more one-to-one. We're actually engaging with each other now. And brands and advertisers are learning about that, but we now have the data that begins, allows us to enable that. And you know, obviously data is, we are data geeks and I'm a data geek and I self-confess, but essentially what you're referring to is these new protocols are being developed. I mean essentially conversation is a handshake. If I have a conversation with you and you don't respond, you're not handshaking with me, you're not connecting, that's measurable, that's unique. Data opportunity. So I got to ask you about the personalization and the targeting and you're seeing a lot of folks go for the ad serving market as ad tech is the first kind of obvious low-hanging fruit, but what's beyond ad tech in terms of data? When you talk about the consumers connecting with brands, when you have real time, and which essentially location data is about real times, where people do, where they are, how does that change the game in terms of the data opportunity for you guys and for your customers? Locations has two sides to it. One's the real-time thing, where's the user now and that's a hugely strong signal, but there's also the contextual side, where have they been, where do they go to over time, because that's a great signal about what's important to this individual. To my earlier point about people picking up a new technology and saying can I do with it what I already do, they're now picking up information about the user and saying can I monetize that and so can I take this new data that factual provides and put this into an extant advertising model and I see a bump and that's a good thing, but where the future really is is the idea of personalization and so the reason that your phone behaves much like mine and others in this room is that the phone, the device, the applications know so little about who you are as an individual and there's all kinds of reasons for that, the siloed nature of mobile apps, the whole idea of Google and Apple and they're not just about introducing the consumer almost in their entirety, so there's all sorts of reason why the device knows so little about who I am as a person and because it doesn't have the data about me, it can't anticipate my needs, it can't articulate and surface things to me that are interesting and I think most importantly it can't filter all the crap that happens around us on a day to day level and really surface only the things that are interesting and so what we're seeing now, I think and what I'm keen to ensure that factual plays a part thereof is that data about individuals must be handled in a hugely private and anonymous way ideally but nonetheless contain information that allows applications and devices to better engage with us as consumers and anticipate our needs and that's really the future. So my final question is kind of just to sum up the segment about factual and the data opportunity is if someone comes to you and they're a total stranger or a friend who's not in the business and says, now tell me about this experience, all this data's out there about me and my phone where I go, real time, I'm in the crowd, I'm on Twitter, I'm on Facebook, I'm on my databases, I'm at work, this consumerization trend that's blending business in IT and the consumer apps, what is the most important thing that you would tell that person about what's really happening here? What is the value message that you would share with that person? Yeah, I would say that we are now in the middle of a massive shift where consumers actually begin to understand and control their own data. Now, that may be directly as a so-called personal data locker, there's various reasons I don't think it's necessarily gonna come to fruition in that form but also indirectly in that I can understand which brands to engage with and what kind of data I can share off my device about me. And the reason that that's so critical is that consumers are increasingly understanding that the data they contain and release about themselves has tangible value. You can go out and you can buy a database that has just a phone identifier and then gender and that sells for a big chunk of money and the data's absolutely rubbish but it gives you an indication that in aggregate information it gives you first a clear message that developers just don't know who the user is. They don't even know their gender and you can't ask because people are like, why are you asking me? So what you're seeing now is an artifact of that sort of users understanding the data that they emit and control and its value is that product managers are changing their mindset and they're saying, look, if we want to know a user's gender or if we want to know a user's location, we have to give them something that actually merits our maintenance of that valuable data point. And so for example, if you're a song app and you basically release music to your consumers either on demand or by broadcast, what you can do is you can say, well, I want to get location in because it helps me really understand who that consumer is but I'll tell the consumer, look, you don't have to do this, it's totally under your control but if you do, because we know all the cool bands that you like and your preferences, we'll tell you when they're in town and we'll give you special deals. So it's a question of incentives but I think more importantly, it's gonna be a question of new experiences. What data, when I release data as a consumer, what unparalleled novel experiences can I have that make my life easier? So there has to be a transactional exchange of some sort. So for the opportunity on the other side of the coin for the business is what then? To exchange value in sight? Indeed, yes. So the business, the publisher, produces a better experience, it's much more engaging and I think that value right there is absolutely fantastic. Like if I have an app which is much more anticipatory if even the most basic one of routing, for example, if this app can save me 10 minutes on my way into work then that's a very good value that's exchanged but that's a pretty facile example. We're at Tyler Bell, Vice President prior to the factual making tools and automation and technology, making data make sense. Thanks for joining us here on the CUBE Conversations. Thanks for watching.