 Hello everyone, I'm John Furrier with SiliconANGLE. This is CUBE Conversations here in Palo Alto, and I'm here with Tyler Bell, Vice President of Product at Factual. Tyler, welcome to the CUBE Conversation. Thank you, John. About big data. So, you know, we had a segment earlier about Factual and your business. Fascinating and intrigued by it. I think a lot of folks will soon understand the impact of what you guys have done. Certainly unique from a business perspective and also from a data perspective, looking at the data holistically, looking at these new cutting edge trends. I think the next wave of innovation is upon us, and you guys are going to be a big player in that. So, I want to talk about this wave two of innovation. You know, in the social world, cloud mobile and social, something we've been covering on SiliconANGLE since 2010, since we found it, it's creating massive innovation. Look at what Amazon has done, certainly on the startup side, with the cloud, with DevOps. You're seeing mobile ops really exploding to a level that everyone kind of expected, didn't expect, but it's totally relevant. But now social media has kind of been combined with what I call big data. I mean, now, didn't know you talked to it's cloud mobile, big data, cloud mobile, social, but you know, social media is about data. It's about connected people. It's about people communicating directly with products and brands. So I want to ask you about this wave of innovation, wave one and wave two. And one of the things that we're looking at is that we think that we're in this wave two transition, where I think you guys are very relevant. So I want to ask you about the waves of innovation with this new generation of users, new generation of applications, new generation of infrastructure. How do you see this wave two coming? I mean, wave one is early adopters, the tooling's embryonic, it's growing. And so what's your take on this market in terms of transition wave one to wave two? Yeah, I think a lot of it is what's happening on the business side is that we're moving the camera lens and by moving the camera lens we get a whole new perspective on the kind of opportunities as well as the data that is being produced. So if you're the only guy with a telephone, the user experience isn't very good. If there's two of you, it's good for both of you and there's not much signal. When you have millions of people, I learn a tiny bit about you from one conversation you have, a text, a chat, a photograph you take for example, by itself that's not very interesting. But when you begin to look at broad trends across billions of inputs, then you actually begin to extract signal from the noise and that's really where that camera lens is moving to. It allows us to look upon data as an asset and we are now creating so much data, just how we move, how we engage with each other, how we engage with businesses, the shows that we watch. We have a small intricate sensor platform in our pocket that just picks up everything. All of that data can be collected and aggregated to not so much just to learn about you and that's important. We can talk about that another time. But more importantly I think as separate data points to understand the collective and that collective can be something simple like weather or it can be something more complex about how people move through an arena over time. And David Lothan and I talk about this on theCUBE going back into our fifth season. So we've done a lot of the storage, cloud, big data events and now continuing to do more events, talk to the thought leaders. And it's interesting, it's hard to kind of put my finger on the lens and the prism that people look at. If you're a storage guy selling storage, you want the storage opportunities massive with data. You're an application developer, you say, okay, I can use data in my app and if you're a business, you now can look at data differently because now it's available. So the question I have for you, what's driving innovation in this next wave of, is it all the above? Is it the infrastructure, the business drivers? What are the key elements of this next wave? Because there are people building out new things with data. It could be new software, it could be Hadoop, as it could be applications. It could just be a business model. So what's your take on that, on those segments? It's the, I think the first, well, we already have the, we now have the infrastructure in place, right? So the storage is there and the asynchronous processing is there. So that's a hugely critical component, but it's a means rather than an ends. Obviously it's an end for the vendors, but it's the means to new and exciting businesses and where the innovation is happening. And really what that entails, I think what's driving it is this holistic understanding of how you are engaging with your consumers. So take, for example, search. I mean, it's something that we've all touched upon. It's now something that Google dominates, but when you form a search query, you will add a string and that search query is metadata around. It's basically who you are and where you are. And then that search query is analyzed for entity recognition. And so you get results back and then Google learns a lot about you and the process and that doesn't necessarily help them as an individual, but looking across the broadest possible array of those signals they're getting in, plus what they get off the phone and everything, they now have this panopticon of who you are as a consumer. And they build pretty tight walls around that. And I think the interesting things that are happening now is basically like any population under duress, whenever there's pressure, either if you look at sort of biology, if there's pressure on the food chain or for mates, interesting things begin to happen. And this is what's happening in the competition now is that app developers and publishers are keen to use these new data assets to think around Google, to think around their ownership of the consumer. And they're saying, how can I form a chain between my product and my consumer? And how can I enrich that engagement by itself without having to bring in a large scale monolith? And Factual's trying to help them. We're trying to help them and say, look, we can help you enrich your data to better articulate the requirements of your user and anticipate those requirements as well. Yeah, I mean, certainly the trends and the buzz around this next wave and the wave of innovations key, you're hearing people say mobile first, cloud first, data first, what we say. Certainly the market opportunity has massively grown. So it's a massive growth opportunity. The question is, where is the underserved market right now in terms of are people just not having the right tooling? Is it the platform that the folks that are moving in building out, whether it's infrastructure, containers or software, or just having a boatload of data themselves, they have to deal with it. So I guess I want to get your perspective on that from Factual's standpoint, you guys offer a variety of solutions, but what's going on in the customer's mind? Are they, what do they need the aspirin? Do they need more picks and shovels? I mean, what metaphor? Where would you, how would you describe that? Where are we in the state of the evolution there? Yeah, well, I mean, certainly Factual positions itself as the pick and shovel vendor. I guess the term that I prefer to use is force multiplier. So everybody has data assets about either sort of, you know, about the songs that they have a database of songs and their signatures, or they have a database about their consumers and the books that you read. And everyone is looking to see how can we better leverage that data resource. And some of it is going to be through machine learning and preference models about, so I can better surface sort of shopping what you might like in advance. This is what your friends like, type engagements. We've all seen that now. But the problem there is that it's isolated. So if you think about store brands or indoor, you know, iBeacons or indoor beaconing positioning, brands tend to think about the consumer only when they are in their store. And so when they leave their store, they kind of fall off the radar. So increasingly, whether it's a store and a brand or whether it's what kind of music this user likes, we have these silos that have developed as an artifact of the mobile process, as an artifact of how we think about data as a zero-sum asset. And the interesting things are happening now is when we can better understand the value of exchanging data across these silos. So number one is protecting user privacy and protecting user expectations. But the data doesn't have to be about a user. So if I can, you know, this is one reason and probably the critical reason I'd articulate that Factual is working in location now because location acts as a foreign key in understanding anything. And with this foreign key, you can hook in all sorts of different assets, including music and book preferences and similar. And so the innovations and the opportunities are happening now when data is understood as an asset that can be leveraged better when it's combined with another data set. Here, the founder of Factual is Gil who applied semantics which went on and sold to Google. But I want to focus on the word semantic. Obviously data is very semantic driven. The semantic has obviously been around for a while that concept, but we are truly living in that age now where the semantic internet is existing in multiple dimensions, not just web. You got mobile, you got user, you got new interaction data you mentioned briefly around connected consumers. Where's all this going and what do customers of yours need to understand to take advantage of that opportunity? Because as new things come out, it's always hard at first. And then innovation makes things simpler, reduces steps to do things. What are those things going on in your customer environment right now where they can look at that and say, hey, okay, I agree, it's a big opportunity with data. I know there's all this semantic stuff going on out there. What do I do? Where do I start? And how do I get it to be easier to get to the value piece? Yeah, yeah. So, I mean the idea of semantics and sort of the trend towards semantic search, you see this on tech headlines but actually from a consumer angle, like don't talk to me about semantics. Like I did this for years in graduate school, don't talk to me about semantics. Because it's- It's all semantic. Yeah, because it's very, it's basically sort of esoteric and encapsulated. But the idea of meaning and understanding is absolutely critical. So the way that I tend to articulate that shift, which I think is very important, but the semantic web sort of, there's a lot of hand wavy is probably a disparaging term around it. But you know, a lot of these things, I spent, I was involved- It's a guiding principle at this point. Yeah, in a way. It's a very good, it's a great North Star, right, for the way things that have gone. And I would articulate that it's less about building ontologies and much more about use case driven understanding of things. And so what I mean by that is how do we move away from the world where it's all keyword driven? I remember when I showed up at Yahoo in 2006, 2007, and I was blown away that all these things were coming across Yahoo's pipes and nothing was being disambiguated. So that's the semantic term for saying, all right, I've got something that says Apple. I'm gonna talk about the fruit of the computer company. And I joined Yahoo because their places team did exactly that. No, I found Springfield in the name, using all the logic and algorithms, which Springfield is actually most likely the one being referred to here. And that really is the future of search. It's the future of consumer engagement because what you're doing is taking an uninformed string and this come through voice. It can come through text, search. It could be a chunk of text in a news article. And you are annotating that. You are disambiguating the entities and you're saying that this article talks about these places and these people over time. There's been technologies now that can do that, but basically outside of the annotation, not much else happens. It may be used in your search index. That's reasoning, basically. You're basically developing some algorithmic reasoning. Yeah, well, I mean, you have to, I mean, what we used to do on the geoside is we said, okay, this person has talked about Rome. Now we have a probability matrix that says that it's very likely that Rome and Italy that we all know and love, unless you're in Rome, Georgia. And then the closer you are to Rome, Georgia, the more likely you are to be speaking about that specific entity. So what we're talking about now is the idea of intent. And so whenever you have a stream of communication, whether it's from a consumer to a platform or between two consumers, the better you can understand the entities that are involved in the intent, the better you can anticipate the consumer's next needs and the better you can enrich the results that are returned from that engagement. So you guys have a lot of customers that are early adopted that deal with a lot of data. So now we're moving into the mainstream of big data. You're talking about disambiguation. We're talking about reasoning. This is complex science, right? So for us geeks, we'd love to talk about that, but let's get back to reality for a second. For the folks out there who go to work, try to do a job, go home, kick the dog and play with their family and all that stuff. So okay, what's the bottom line for them? It's hard to do what things are going on in your mind in this wave two of social connected data or data in general, what are the things that are gonna be abstracted in a way that are complex? How does someone get data? How do they engage with data, use data to get to some value in a very simple way? What are the key things that you see that are key to do items that will help get this underserved market, using data, playing with data, working with data, developing with data? Really making that data citizen a key part of the development and in products. Yeah, I think if we do our job correctly, then the citizen isn't thinking about data, right? I mean, it's always a very esoteric word in the first instance and people are hearing it more now, but unless you were sort of involved in computation or on a big numbers heavy project, 10 years ago data was not a particular vocabulary term we used outside of the next generation, Star Trek. So what you're seeing now, I think, and the general trend if we do our job correctly is that the consumer isn't going to be thinking about data but they will understand that there's a value exchanged and so coming back to the idea of how consumers engage with any kind of business, it can be bricks and mortar, it can be a brand online, it can be a God forbid, a cable company. If they can understand and articulate how the value about how they engage with this or other applications, if they can exchange that value in return for novel and interesting experiences, then I think we will have been doing our data right. And I think we're moving the pendulum away from, if you give me data, I give you a coupon, right? That's too facile an exchange, but if you say give me data or allow me to work with this other application that you engage with and we'll do this behind the scenes and therefore your calendar is synced with X. So even any kind of cross calendar syncing is a very sort of primitive but a very handy way to demonstrate that just allowing data to operate on the platform side and talk to each other makes life easier. And fundamentally it's gonna be about new experiences, it's gonna be about extracting, for the consumer extracting value where it wasn't before, but fundamentally I think so much of it will be about convenience. How can I look at my phone less? How can I learn more about what's important to me with less effort or distraction? You mentioned in a previous segment, my note here says from our last segment, you said data is more powerful when it's shared and this is coming back to openness and you mentioned satellites are up there and now that satellite data is free and open and people playing with that and recently the missing airlines and the Malaysia Airlines, people were using the open data to co-find things so you see that element of the crowd. So I gotta ask you about the crowd sourcing trend. You're seeing certainly social media drive that where people are sharing more and sharing data in itself, not just sharing updates, which essentially is data if you will. But okay, data is more powerful if you share it. What things are going on around the data market that you guys are involved in that enables more of the crowd sourcing. And what do you think of the crowd sourcing trend? Is the crowd a factor? Cause now you have elections being shaped or you saw some tweets earlier in the New York Times folks talking just this morning about the Turkey situation also we saw what happened in Ukraine and we saw what happened in Egypt. So data, free data creates new opportunities. So what's your take on that crowd sourcing trend? Early stages, developing, what's needed? Well, I think we're now out of the point where people have stopped laughing at crowd sourcing attempts and now they're quite frightened by them actually. Frightened by it, what I mean by that is the incumbents are frightened by it. The example I tend to use most is open street map. So this was started by a fellow named Steve Coast just a handful of years ago who was frustrated with dealing with very expensive proprietary map data. And so he got a bunch of friends together in the UK and they went around with GPS units and bicycles on the Isle of Wight in the UK and they mapped that out in a couple of days. And this caught on and more and more people started doing this. And what you have now is a global map that has higher resolution and detail in many parts of the world than their commercial equivalents. And it has I think over sort of one million one million plus contributors. And so everybody laughed at open street map and said don't be ridiculous. You know the money we spend in the technology behind this but Steve and early OSM enthusiasts were pressing enough to know that this has become so much easier to actually input data into a shared resource. And then we can all use that shared resource. And you see companies are moving away now from the proprietary map vendors because this open street map resource has become increasingly better. And it's not just a question of the absolute quality like how does it compare now against the commercial competition but actually the improvement rates. So you know very often commercial guys go up like that they've got fixed budgets but if people are enthusiastic the quality will just absolutely skyrocket. Yeah and the openness thing is really key and I want to ask that you quickly move to the trend or where the crowd is growing and that is obviously on the public certainly Twitter has hashtags on TV and you're seeing you know just a few years ago and wave one a few social networks Facebook and LinkedIn and Twitter and now Google plus is out there. So you're seeing a proliferation of social networks as a social network for everything. So the challenge is the data is residing in there and there's a post on the verge this week by a writer who's just out of college saying all my friends now aren't the friends I had in college and they have bloated social graph and all this content and news feed is irrelevant and seeing stuff that I don't want to see. So do people prune their social graph and it brings up the question of the data. LinkedIn doesn't actually want to share the data very often. Twitter has data. These are silos. So if openness is power how do these guys get the data out there? That's a challenge. It's an innovation dilemma. What's your take on that? It's gonna be user driven or consumer driven. All these companies their strength is in owning that graph. Facebook sort of opened it up and then locked it down. Twitter did the same thing. LinkedIn never really opened it. So all these folks want to control that as an asset but fundamentally the value is gonna be when that asset is better exploited and by exploited I mean leveraged to its full potential for consumer benefit and usually that's through sharing. I don't think it's going to be the kind of sharing where sort of you and I exchange data but rather- It's aggregate sharing. Yeah it's gonna be much more sophisticated and basically data can be shared between silos when there's a clearly articulated benefit to the user because users will espouse that and they will support it and generally if these product managers are true to there if they generally want to create compelling consumer experiences they will identify opportunities where data can be shared. So it's a capitalistic model, right? It's like opportunities. So if Yahoo wants to move the ball down the field they could be more open and get more of that data and maybe get some of those Facebook users or other users holding back the data but you guys have success with companies who have opened up their data. So what examples can you highlight that would be kind of telegraphing the trajectory of how these siloed data providers need to open up or things that they might want to do? Well we're actually in the stage now where in terms, let's look at two things. One is place data which is names, address, phone numbers about businesses and companies are comfortable sharing that data with factual because we will either give them data back or we will clean it and normalize it. It's not personal data. They might have one million records if they share with us then we give them access to 25 million. So there's a value that we can articulate and then our partners can engage with us and that's great. You know, and for brands we say look give us your data, we will clean it, normalize it, distribute it to all of our fantastic partners. So that's a very lucid narrative and we're engaging in that successfully. Personal data about individuals is never going to be, I don't think it's ever, we're ever going to see an environment where we can pull all kinds of data about individuals and sell it on. That's what various folks within the ad tech stack used to do is they used to pull in data from different streams and audiences and then sell these audiences on. And a lot of folks still do this, right? That's the big thing. But especially because factual deals with location which is a very intimate, very private signal we don't want to interdict that. We don't want to get between you as a publisher and then your users. So what we say is look, even though factual is engaged in this open data platform for places, when we work with your location data about your consumers, we, if you give us the data, we'll first ask that you hash the user ID because we don't want to know who your users are. Give us the data, we process it, then we give it back enriched and we don't give it to anyone else. What we do do is we can improve our models and our understanding of human behaviors. But we never take your data and cross-sell it or cross-reference it because the legal teams would allow it, the product teams would allow it and the consumer teams would allow it. And I don't see those expectations changing hugely. I think we're still going to be walking that careful line whereby we can create fairly intimate profiles about an individual that are trusted and retained within the original resource that you as a consumer shared your location with. You've been in the industry for a while and have an advanced degree in all kinds of sciences. What is the DNA of Factual for the folks out there watching? I mean, I'm sure you guys are onboarding a lot more people now. You have a lot of funding, certainly, Andreessen Horwitz Index Ventures, right, whether tier one VCs are invested. What's your culture over there? I think we look for the unusual. We don't take people who espouse a party line or they recite what they read on TechCrunch. We're looking for outliers. We're looking for individuals who are so intelligent or so different, they may not come across well in the first interview. We hope to look around that. I'm an archeologist by training. I don't think any kind of tech company would... Digging for data brings on a whole new meaning. They like that, don't they? And it's heavy on the CS. It's heavy on data science. But our outbound folks on the BD side, everybody's data savvy. But everybody, you can't paint them with any kind of brush that suggests they're like everybody else. And so that's, as a colleague, as a member of the factual team, I think that's what I respect most is that everybody is sort of, we're not part of a default template, but also there's just a huge element of trust there. I have to trust the engineering team. I have to trust me and we all rely very, very heavily on our teammates. So there's a data IQ minimum, if you will, kind of an algorithm, necessarily. So it's a data first kind of algorithm you guys look at. Absolutely important, yes. Like if you don't, if you're not willing to come to grips with the underlying just tenor, that's the, that is the music that's played at factual is the music of data. And you've got to tune your ear in. And we won't accept people that say, well, look, this is a widget. I'm just going to shift it or I'm going to process it. And I don't really care about the fundamentals. My last question as we end this segment is explain to the folks out there what's going on in the industry. As you mentioned, some headlines that are being written in the press and then articles written that, you know, the press needs to up their game in terms of getting more savvy on some of these emerging tech trends that are game changing for businesses and certainly building profitable companies. Where are we in the big data business to use that term loosely about, you know, big data wash, but there's a big marker. How do you explain to folks what's going on in the big data market with the vendors? Some people sell software, some people sell infrastructure and people sell data cleaning solutions. What's that, what's the industry landscape look like? How do you shape that out? Well, we're certainly in a backlash, right? And so all the hype of, all the hype of big data has not necessarily come to fruition. Hype by definition rarely does. And so you're seeing a backlash and where people are saying, look, well, this use case that you cited previously at Google Flu Trends is the use case most commonly pointed to. What do you promise previously was either misleading or hasn't come to fruition? And that's understandable, but it's not correct. The biggest advances of the big data side of things is that it's affecting your everyday products and engagements, it's understanding how risk is calculated, it's understanding how to make sure that your Uber is in the right place at the right time. Data is driving these experiences and by itself it's transparent. Data doesn't get in the way, it actually facilitates these experiences. And so certainly where we are, if we're again, if we're doing our job correctly is that data is an unseen unheard force in the background that's provided me with new and more valuable consumer experiences. What's the transformation of the data business in your mind? If you look at folks out there and I want you to share it from a perspective of audiences that are watching, the CIO, the service provider, the telco, the app developer, what is the big aha around the big data? What is transforming, and what advice would you give that person? What advice would you give the person out there on? I'm into the data, I see the future, what do I do? What's your advice? Yeah, a lot of different advice those different organizations, certainly. You know, on the, I guess perhaps as an example, I can highlight usually how brands like a big box store would usually deal with geographic information. So the physical locations of their stores. And it used to be the ownership of sort of the planning department or the business intelligent department. And it's usually a fellow with a geographic information system on a computer in the basement. And that geographic data has slowly moved out of the basement and from the bearded fellow into the marketing department. Because the data is slowly, it's being recognized that yes, although we may, although our competitors may use this to plan where their next store is, the benefits of opening the data up outside and combining it with others means that if we expose and normalize data set of our store locations, we get better likes on Facebook, we get better tweets about it. All the content becomes normalized and structured. And that means we can better understand our business. And so there's always going to be that trade-off. And you know, this is what I'd say is that the idea of take a look at your data assets and leverage them to the best of your opportunity. Very often it's not going to be, it's not going to be all win. There will be some disadvantage or at least a perceived disadvantage, but the opportunities that sharing data creates are so much more significant. We're here with Tyler Bell with the product team at Factual. We're here at SiliconANGLE's office in Palo Alto for CUBE Conversations. Go to siliconangle.tv for more coverage and more CUBE Conversations. Thanks for watching.