>> Announcer: Live from Las Vegas, it's theCUBE, covering IBM Think 2018. Brought to you by IBM. >> Welcome back to theCUBE. We are live at the inaugural IBM Think 2018 event. I'm Lisa Martin with Dave Vellante. And our first guest, on day one of our coverage, is Jerry Thompson, the Chief Revenue Officer of Identity Guard. Hey Jerry, welcome to theCUBE. >> Thank you, well, it's a pleasure to be here. >> So tell us about Identity Guard. What are you guys, what do you do and how are you working with IBM? >> Yeah, Identity Guard is a, is a subsidiary of Intersections. We are a publicly-traded company and we're only in the identity and privacy space. So we, today, protect about 1.4 million people's identities. They, it's a subscription-based service. And two and a half years ago, we made the decision to, to basically invent identity 2.0 and the only way to do that was to use artificial intelligence technology, so we went to Watson to do that. >> This is a giant leap that you mentioned. >> Huge. >> So let's kind of, maybe, break that down a little bit and really talk about what you're doing here that was really transformative. >> Yeah, so, identity protection companies today only look at structured data. And, basically, we look at structured data and we look at it in arrears, so we can't do anything proactive or preventive. We knew if we used Watson in an AI technology, we could monitor unstructured data, which is probably 90% of all the data out there about any of us. And in order, in doing so, we could do preventive and predictive analysis of your personal information, privacy and your identity. So there was a quantum leap to go from just reacting to actually proactively protecting people's identity and privacy. >> So could you take us through, sort of, the journey that you went on to go from, sort of, where you were to where you are now and where you're headed? >> Yeah so, I mean, it starts like every other company with Watson. We took the tour of the Watson building. Went upstairs to the glass conference rooms and in that conference room, waiting for us, was the CIO of Watson. >> Dave: When was this? >> Two and a half years ago. >> Okay. >> And we explained the problem we were trying to solve. And from that day forward, IBM has been an amazing partner for us, amazing partner. So we did all of the things. We went through a Scrum, we wrote some product code, we did, you know, proof of concept, and when we were convinced that we could actually reinvent this industry, we went all-in. >> Keep going. >> And that was two and a half years ago. >> So, so, so a lot of people would say "Okay, Watson's a heavy lift, "you got to have a lot of services." It sounds like you did but the outcome is really what you're driving toward. So what was the outcome you were looking for and what'd you have to do to get there? >> Yeah so, I mean, at the highest level, we wanted to protect not only your financial and credit data, but all of the data that's out there about you and your partner, spouse, wife and kids. And in order to do that you need a processing engine that actually is intelligent. So that was the journey in Watson. We have found it to be not a big, heavy lift. We had the right kind of data scientists and we knew the problems we were trying to solve. Not in the abstract, in the particular. We defined the stories and the categories that we wanted to play in. We defined the product as we wanted to launch it. We knew it was going to be a one to two year run because you have to invent it, create it, then you have to play with it, right? You have to run it through the machine, so, >> Iterate. >> Right, and iterate. So, in order to do that, we knew the timeframe so we were never frustrated. And, along that journey, we came up with other things that we thought would be amazing to include in the service so, like cyberbullying technology, geolocation technology. All kinds of other things where only Watson would help us do that. >> And, and the data scientists were on your team >> Our team, yeah. or IBM brought those to the table? Okay, so you >> Yeah, no, IBM always let us reference their, but we have a handful in Virginia and some more in California in our development center. >> So you're one of the lucky ones who had a team, a bench, of data scientists >> Yes. >> at your disposal to go, is that right? >> Yeah, I wouldn't say a deep bench, but we've added to it over time, as you, as you get into the way you want to solve this problem. >> And, and how, specifically, are you using Watson? Can you give us, add some color on the APIs that you're using >> Sure. >> and how you're applying them? >> So we use natural-language processing because we pour amazing amount of data through the Watson funnel. Social media data, geolocation, Alchemy News. And we need the natural-language to actually jump and, and search for key words and key intimates. We use emotion analysis API, sentiment analysis API for context. So we're reading social media posts, your kids' posts. Your kid might say "Boy, I killed it "on the soccer field today." That's not a threat, right, that's just a statement. You have to add context to the statement. In order to do that, we use emotion and sentiment APIs. We use visual image recognition for inappropriate things that might be coming through. We use Alchemy News, which I believe is Discovery today. We're in the process, with the help of IBM, to create a library, a language, around emojis. Some emojis can be very threatening in the way they're used and the context they're used. You have to be able to read it, intelligently read it, and then put it in context to the string of texts or Instagram posts or whatever, that are going back and forth. So we, we've really taken this holistic view of what Watson can do, help us do for unstructured data and, in that process, it made our ability to monitor structured data better. We learned a lot. So we actually got benefits on both sides of our business. >> So you talked about this quantum leap that, that you made to identity 2.0. Also, what you're doing, in your space is quite pioneering in that, you're >> Yes. >> the only, first and only company, in the space that's using AI. Cyberbullying is such a hot, very challenging topic and, and sadly one that's very much needed in terms of identity. >> Right. >> But why do you think it is that, that Identity Guard is, is so pioneering in this space? >> Yeah, you know, we've always been, we, first of all, Identity Guard invented the identity business 23 years ago. We're the first ones to ever do it, first ones to do credit scores, reports. So we've always innovated in this space. The, the challenge for us as a public company, our biggest competitor is the credit bureaus, right? And the credit bureaus are low-cost providers and, and, candidly, I think they stamp out innovation in our field because they just want it to be about credit data. They don't want it to be about other things. So it was time for somebody to take this leap to predictive and preventive technologies, not just reactive. The rear view mirror can tell you a lot but it can't help you protect today, and that's what we've been doing in our space. >> Well the dossier from a credit bureau is so limited. >> Right. >> It doesn't provide context. You know, your score goes up or down for weird reasons. 'Cause people are doing credit pulls or whatever it is. You don't really have a context of what's going on there. So, so my question to you, Jerry, is where do you see innovation going in this space? Obviously data is involved and the credit bureaus have data but where is innovation going to come from in the next five to 10 years? >> Yeah, you know, I think it's the, we're going to figure out how to harvest data that's out there and then score that data so that we can help you and your family stay safe. Nobody today wants to have no internet, right? The internet's opened up an amazing amount of capability for people. But, but you have to have a way to play in it without it being too dangerous. And I believe we can use Watson. That's our, it's been our theory from day one. We can use Watson to level the playing field, right? Not, not really get an advantage, but to level the playing field, especially for families where not everybody is aware of all of the malfeasance that's out there on the internet, right? >> Right. >> People are always looking to harvest our data and to use it in a malicious way. Especially kids and minors, right? They're at risk for cyber, you know, predation and stalking and cyberbullying and, and parents today know it's a big issue. >> Okay, go ahead please Lisa. >> I was just going to say, in terms of expectations, you're saying it's to level the playing field with the cyber criminals, the stalkers, in the next, you know, can we look at timeframe? Think that you'll get ahead of that to start actually preventing some of this cyberbullying going on? >> You know, I, that's a good question. I will tell you right now, our ambition is to level the playing field. It's tilted this way today. I think what will happen is technology's like geolocation. It seems, first of all geolocation is not really relevant without Watson Discovery, right? You need all of this massive data going on in the locations that you're relevant in to help us protect you. But I believe, based on the early science that we're doing with IBM, that we can actually help a kid, somebody's stalking them from, you know, four states away but it says it's the little boy across town, we can actually stop things like that happening using the processing and the algorithms that we're doing using Watson. So there are, there are relevant areas that I think we can have a massive impact on the privacy and the protection of people and their families. >> I want to come back to innovation, so data is clearly a key component of that. You're extending the data model into unstructured data. I'm hearing that, correct? >> Yes. >> Also, AI, machine intelligence is another part of that. What about scale? Scale and network effects >> Yeah. >> and that sort of component of innovation. >> That had to be >> Does that come from cloud, is that where it's coming from? >> That had to be part of this. So we, along with all of our competitors in the existing 1.0 business, we use a hard-coded platform. >> Right. >> Right, I mean, if you want to change something, you have to get out a sledgehammer and a chisel and it takes a year. We built Watson using AWS, so we've used all the best tools, the fastest tools. We've run scale testing, you know, and, and the beautiful thing about our business, we're a digital business, right, so our factory's open 24 hours a day, 365 days a year. Our shopping carts never close. You can always, you know, subscribe to the Identity Guard With Watson service. So we needed the cloud to give us the scale. We also needed the platform to be able to plug in and unplug the APIs. Some partners may not want social media monitoring. Some partners may not want this, so we didn't have to hard-code our product. We actually built three services and we can unplug any of the services. >> So, when you say you're a digital business, it strikes me that your data model is not in a bunch of silos. >> Correct. >> You've got a data model that's accessible, maybe through sets of APIs, et cetera, that your human experts can go attack. >> Correct. >> Is that a fair assertion? >> Yeah, that's fair. One other thing about Watson. We were going to use Watson from day one, I was convinced. And I was the one that took the company on this journey. But the other thing I like about Watson is that you don't, Watson doesn't keep the data, right? We talked to the other big players in this field and one of their mandates is, they always keep the data. All of it. And, and Watson shreds the data and we don't keep all the data. So think of all the social media and other data that flows through this funnel. People out there want to keep it so then they can reverse profile consumers or cohorts or, Watson shreds the data. You're not in the, you're not in the spoofing or spying business, nor are we. So that was also a really important consideration. >> Yeah, I said that at the top, that you're, you're going to hear this from Ginni tomorrow. I can almost guarantee ya, she's going to say that we're not in the business of trying to re-mine your data and re-target. >> Right. >> But, so that was, I was going to ask you why Watson. That was one reason. What about the quality of the, of the machine intelligence? >> Yeah. >> You hear a lot, you know, you hang around Silicon Valley, "Oh yeah, Watson." How does it compare, in your view? >> Yeah. >> You're a practitioner who's, you know, you're familiar with all this. >> So they have more refined, first of all, more APIs, right? More, some of them not relevant to us, the medical ones, which are amazing and fascinating, >> Yeah, but, yeah. >> but they had more structured APIs and a better road map on where they were going. And what we found from day one is that, if we defined something, they would say "We'll jump in and help", right? It's really important when you're the first one, you know, the tip of the spear, you don't know, you don't know what you don't know. And we found from day one, the IBM team has treated us like we're General Electric, right? Or General Motors, right? We're just, you know, a couple of hundred million dollar company trying to make a big difference in a important space. And they have treated us like a Fortune 100 company from day one and really appreciate it. >> So as >> And their science is so good. >> Sorry there, as the CRO, going from identity 1.0 to 2.0, this journey that you're on. You mentioned competition. How many, talk to us about the actual financial impact to the company that you can say that you've been able to achieve on this journey to identity 2.0. Presumably, leaving some of your competition back in the 1.0 land. >> Yeah, yeah, actually, our competition will be behind us for at least a couple years 'cause it takes a couple years. You know, you don't do this quickly. So we are out, we launched, we launched Watson in December. We actually launched, we distribute our product through partners, most of it, 90%. 10%, people come to our site and sign up online but we launched 21 partners in January, 11 in February, 13 in March we'll launch. So by the end of the year, we predict we'll have about 200 Watson partners distributing our product, which would give us a huge head start and advantage over anybody else. Once you see what we're doing and you see what else, the 1.0 version, it's almost impossible to pick 1.0. It's impossible, right? So our job is to get more, create more awareness in the distribution channels so that people are, are understand that Watson is out there and available. >> And, and this is a subscription service, I think you said, upfront? >> Yeah. >> And you've got different tiers, etc? >> Yes, yes. >> And you guys have a couple of, of sessions >> that you're participating in at the event? >> We do. >> Yeah, I know that we're on tomorrow afternoon and I believe Wednesday morning. >> Great. >> So, yeah. >> Well Jerry, thanks so much for stopping by theCUBE >> You're welcome. >> and sharing what you guys at Identity Guard are doing with data, >> Thank you. >> I mean, it's fascinating. >> Appreciate you talking to us. >> Dave: Thanks for coming on. >> Yeah, thanks, pleasure. >> And we want to thank you for watching theCUBE. I'm Lisa Martin with Dave Vellante again. This is day one of theCUBE's three days of coverage at the inaugural IBM Think 2018. Stick around, we'll be right back with our next guest after a short break. (bright music)