>> Announcer: From Seattle, Washington, it's theCUBE! Covering AWS IMAGINE Nonprofit. Brought to you by Amazon Web Services. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in the waterfront in Seattle, Washington, it's absolutely gorgeous here the last couple of days. We're here for the AWS IMAGINE Nonprofit event. We were here a couple weeks ago for the education event, now they have a whole separate track for nonprofits, and what's really cool about nonprofits is these people, these companies are attacking very, very big, ugly problems. It's not advertising, it's not click here and get something, these are big things, and one of the biggest issues is human trafficking. You probably hear a lot about it, it's way bigger than I ever thought it was, and we're really excited to have an expert in the field that, again, is using the power of AWS technology as well as their organization to help fight this cause. And we're excited to have Brad Myles, he is the CEO of Polaris and just coming off a keynote, we're hearing all about your keynote. So Brad, first off, welcome. >> Yeah, well thank you, thank you for having me. >> Absolutely, so Polaris, give us a little bit about kind of what's the mission for people that aren't familiar with the company. >> Yeah, so Polaris, we are a nonprofit that works full-time on this issue. We both combat the issue and try to get to long-term solutions, and respond to the issue and restore freedom to survivors by operating the National Human Trafficking Hotline for the United States, so, it's part kind of big data and long-term solutions, and it's part responding to day-to-day cases that break across the country every day. >> Right, in preparing for this interview and spending some time on the site there was just some amazing things that just jump right off the page. 24.9 million people are involved in this. Is that just domestically here in the States, or is that globally? >> That's a global number. So when you're thinking about human trafficking, think about three buckets. The first bucket is any child, 17 or younger, being exploited in the commercial sex trade. The second bucket is any adult, 18 or over, who's in the sex trade by force, fraud, or coercion. And the third bucket is anyone forced to work in some sort of other labor or service industry by force, fraud, or coercion. So you've got the child sex trafficking bucket, you've got the adult sex trafficking bucket, and then you've got all the labor trafficking bucket, right? You add up those three buckets globally, that's the number that the International Labour Organization came out and said 25 million around the world are those three buckets in a given year. >> Right, and I think again, going through the website, some of the just crazy discoveries, it's the child sex trafficking you can kind of understand that that's part of the problem, the adult sex trafficking. But you had like 25 different human trafficking business models, I forget the term that was used, for a whole host of things well beyond just the sex trade. It's a very big and unfortunately mature industry. >> Totally, yeah, so we, so the first thing that we do that we're kind of known for is operating the National Human Trafficking Hotline. The National Human Trafficking Hotline leads to having a giant data set on trafficking, it's 50,000 cases of trafficking that we've worked on. So then we analyzed that data set and came to the breakthrough conclusion that there are these 25 major forms, and almost any single call that we get in to the National Hotline is going to be one of those 25 types. And once you know that then the problem doesn't seem so overwhelming, it's not, you know, thousands of different types, it's these 25 things, so, it's 18 labor trafficking types and seven sex trafficking types. And it enables a little bit more granular analysis than just saying sex trafficking or labor trafficking which is kind of too broad and general. Let's get really specific about it, we're talking about these late night janitors, or we're talking about these people in agriculture, or we're talking about these women in illicit massage businesses. It enables the conversation to get more focused. >> Right, it's so interesting right, that's such a big piece of the big data trend that we see all over the place, right? It used to be, you know, you had old data, a sample of old data that you took an aggregate of and worked off the averages. And now, because of big data, and the other tools that we have today, now actually you can work on individual cases. So as you look at it from a kind of a big data point of view, what are some of the things that you're able to do? And that lead directly to, everyone's talking about the presentation that you just got off of, in terms of training people to look for specific behaviors that fit the patterns, so you can start to break some of these cases. >> Exactly, so, I think that the human trafficking field risks being too generic. So if you're just saying to the populace, "Look for trafficking, look for someone who's scared." People are like, that's not enough, that's too vague, it's kind of slipping through my fingers. But if you say, "In this particular type of trafficking, "with traveling magazine sales crews, "if someone comes to your door "trying to sell you a magazine with these specific signs." So now instead of talking about general red flag indicators across all 25 types, we're coming up with red flag indicators for each of the 25 types. So instead of speaking in aggregate we're getting really specific, it's almost like specific gene therapy. And the data analysis on our data set is enabling that to happen, which makes the trafficking field smarter, we could get smarter about where victims are recruited from, we could get smarter about intervention points, and we could get smarter about where survivors might have a moment to kind of get help and get out. >> Right, so I got to dig into the magazine salesperson, 'cause I think we've all had the kid-- >> Brad: Have you had a kid come to you yet? >> Absolutely, and you know, you think first they're hustlin' but their papers are kind of torn up, and they've got their little certificate, certification. How does that business model work? >> Yeah, so that's one of the 25 types, they're called mag crews. There was a New York Times article written by a journalist named Ian Urbina who really studied this and it came out a number of years ago. Then they made a movie about it called "American Honey," if you watch with a number of stars. But essentially this is a very long-standing business model, it goes back 30 or 40 years of like the door-to-door salesperson, and like trying to win sympathy from people going to door-to-door sales. And then these kind of predatory groups decided to prey on disaffected U.S. citizen youth that are kind of bored, or are kind of working a low-wage job. And so they go up to these kids and they say, "Tired of working at the Waffle House? "Well why don't you join our crew and travel the country, "and party every night, and you'll be outdoors every day, "and it's coed, you get to hang out with girls, "you get to hang out with guys, "we'll drink every night and all you have to do "is sell magazines during the day." And it's kind of this alluring pitch, and then the crews turn violent, and there's sometimes quotas on the crew, there's sometimes coercion on the crew. We get a lot of calls from kids who are abandoned by the crew. Where the crew says, "If you act up "or if you don't adhere to our rules, "we'll just drive away and leave you in this city." >> Wherever. >> Is the crews are very mobile they have this whole language, they call it kind of jumping territory. So they'll drive from like Kansas City to a nearby state, and we'll get this call from this kid, they're like, "I'm totally homeless, my crew just left me behind "because I kind of didn't obey one of the rules." So a lot of people, when they think of human trafficking they're not thinking of like U.S. citizen kids knocking on your door. And we're not saying that every single magazine crew is human trafficking, but we are saying that if there's force, and coercion, and fraud, and lies, and people feel like they can't leave, and people feel like they're being coerced to work, this is actually a form of human trafficking of U.S. citizen youth which is not very well-known but we hear about it on the Hotline quite a lot. >> Right, so then I wonder if you could tell us more about the Delta story 'cause most of the people that are going to be watching this interview weren't here today to hear your keynote. So I wonder if you can explain kind of that whole process where you identified a specific situation, you train people that are in a position to make a difference and in fact they're making a big difference. >> Yeah. So the first big report that we released based on the Hotline data was the 25 types, right? We decided to do a followup to that called Intersections, where we reached out to survivors of trafficking and we said, "Can you tell us about "the legitimate businesses that your trafficker used "while you were being trafficked?" And all these survivors were like, "Yeah, sure, "we'll tell you about social media, "we'll tell you about transportation, "we'll tell you about banks, "we'll tell you about hotels." And so we then identified these six major industries that traffickers use that are using legitimate companies, like rental car companies, and airlines, and ridesharing companies. So then we reached out to a number of those corporate partners and said, "You don't want this stuff on your services, right?" And Delta really just jumped at this, they were just like, "We take this incredibly seriously. "We want our whole workforce trained. "We don't want any trafficker to feel like "they can kind of get away with it on our flights. "We want to be a leader in transportation." And then they began taking all these steps. Their CEO, Ed Bastian, took it very seriously. They launched a whole corporate-wide taskforce across departments, they hosted listening sessions with survivor leaders so survivors could coach them, and then they started launching this whole strategy around training their flight attendants, and then training their whole workforce, and then supporting the National Human Trafficking Hotline, they made some monetary donations to Polaris. We get situations on the Hotline where someone is in a dangerous situation and needs to be flown across the country, like an escape flight almost, and Delta donated SkyMiles for us to give to survivors who are trying to flee a situation, who needs a flight. They can go to an airport and get on a flight for free that will fly them across the country. So it's almost like a modern day Underground Railroad, kind of flying people on planes. >> Jeff: Right, right. >> So they've just been an amazing partner, and they even then took the bold step of saying, "Well let's air a PSA on our flights "so the customer base can see this." So when you're on a Delta flight you'll see this PSA about human trafficking. And it just kept going and going and going. So it's now been about a five-year partnership and lots of great work together. >> And catching bad guys. >> Yeah, I mean, their publicity of the National Human Trafficking Hotline has led to a major increase in calls. Airport signage, more employees looking for it, and I actually do believe that the notion of flying, if you're going to be a trafficker, flying on a Delta flight is now a much more harrowing experience because everyone's kind of trained, and eyes and ears are looking. So you're going to pivot towards another airline that hasn't done that training yet, which now speaks to the need that once one member of an industry steps up, all different members of the industry need to follow suit. So we're encouraging a lot of the other airlines to do similar training and we're seeing some others do that, which is great. >> Yeah, and how much of it was from the CEO, or did he kind of come on after the fact, or was there kind of a champion catalyst that was pushing this through the organization, or is that often the case, or what do you find in terms of adoption of a company to help you on your mission? >> That's a great question. I mean, the bigger picture here is trafficking is a $150 billion industry, right? A group of small nonprofits and cops are not going to solve it on their own. We need the big businesses to enter the fight, because the big businesses have the resources, they have the brand, they have the customer base, they have the scale to make it a fair fight, right? So in the past few years we're seeing big businesses really enter the fight against trafficking, whether or not that's big data companies like AWS, whether or not that's social media companies, Facebook, whether or not that's hotel companies, like Wyndham and Marriott, airlines like Delta. And that's great because now the big hitters are joining the trafficking fight, and it happens in different ways, sometimes it's CEO-led, I think in the case of Delta, Ed Bastian really does take this issue very seriously, he was hosting events on this at his home, he's hosted roundtables of other CEOs in the Atlanta area like UPS, and Chick-fil-A, and Home Depot, and Coca-Cola, all those Atlanta-based CEOs know each other well, he'll host roundtables about that, and I think it was kind of CEO-led. But in other corporations it's one die hard champion who might be like a mid-level employee, or a director, who just says, "We really got to do this," and then they drive more CEO attention. So we've seen it happen both ways, whether or not it's top-down, or kind of middle-driven-up. But the big picture is if we could get some of the biggest corporations in the world to take this issue seriously, to ask questions about who they contract with, to ask questions about what's in their supply chain, to educate their workforce, to talk about this in front of their millions of customers, it just puts the fight against trafficking on steroids than a group of nonprofits would be able to do alone. So I think we're in a whole different realm of the fight now that business is at the table. >> And is that pretty much your strategy in terms of where you get the leverage, do you think? Is to execute via a lot of these well-resourced companies that are at this intersection point, I think that's a really interesting way to address the problem. >> Yeah, well, it's back to the 25 types, right? So the strategies depend on type. Like, I don't think big businesses being at the table are necessarily going to solve magazine sales crews, right? They're not necessarily going to solve begging on the street. But they can solve late night janitors that sometimes are trafficked, where lots of big companies are contracting with late night janitorial crews, and they come at 2:00 a.m., and they buff the floors, and they kind of change out the trash, and no one's there in the office building to see those workers, right? And so asking different questions of who you procure contracts with, to say, "Hey, before we contract with you guys, "we're going to need to ask you a couple questions "about where these workers got here, "and what these workers thought they were coming to do, "and we need to ID these workers." The person holding the purse strings, who's buying that contract, has the power to demand the conditions of that contract. Especially in agriculture and large retail buyers. So I think that big corporations, it's definitely part of the strategy for certain types, it's not going to solve other types of trafficking. But let's say banks and financial institutions, if they start asking different questions of who's banking with them, just like they've done with terrorism financing they could wipe out trafficking financing, could actually play a gigantic role in changing the course of how that type of trafficking exists. >> So we could talk all day, I'm sure, but we don't have time, but I'm just curious, what should people do, A, if they just see something suspicious, you know, reach out to one of these kids selling magazines, or begging on the street, or looking suspicious at an airport, so, A, that's the question. And then two, if people want to get involved more generically, whether in their company, or personally, how do they get involved? >> Yeah, so there are thousands of nonprofit groups across the country, Polaris is in touch with 3,000 of them. We're one of thousands. I would say find an organization in your area that you care about and volunteer, get involved, donate, figure out what they need. Our website is polarisproject.org, we have a national Referral Directory of organizations across the country, and so that's one way. The other way is the National Human Trafficking Hotline, the number, 1-888-373-7888. The Hotline depends on either survivors calling in directly as a lifeline, or community members calling in who saw something suspicious. So we get lots of calls from people who were getting their nails done, and the woman was crying and talking about how she's not being paid, or people who are out to eat as a family and they see something in the restaurant, or people who are traveling and they see something that doesn't make, kind of, quite sense in a hotel or an airport. So we need an army of eyes and ears calling tips into the National Human Trafficking Hotline and identifying these cases, and we need survivors to know the number themselves too so that they can call in on their own behalf. We need to respond to the problem in the short-term, help get these people connected to help, and then we need to do the long-term solutions which involves data, and business, and changing business practice, and all of that. But I do think that if people want to kind of educate themselves, polarisproject.org, there are some kind of meta-organizations, there's a group called Freedom United that's kind of starting a grassroots movement against trafficking, freedomunited.org. So lots of great organizations to look into, and this is a bipartisan issue, this is an issue that most people care about, it's one of the top headlines in the newspapers every day these days. And it's something that I think people in this country naturally care about because it references kind of the history of chattel slavery, and some of those forms of slavery that morphed but never really went away, and we're still fighting that same fight today. >> In terms of, you know, we're here at AWS IMAGINE, and they're obviously putting a lot of resources behind this, Teresa Carlson and the team. How are you using them, have you always been on AWS? Has that platform enabled you to accomplish your mission better? >> Yeah, oh for sure, I mean, Polaris crunches over 60 terabytes of data per day, of just like the computing that we're doing, right? >> Jeff: And what types of data are you crunching? >> It's the data associated with Hotline calls, we collect up to 150 variables on each Hotline call. The Hotline calls come in, we have this data set of 50,000 cases of trafficking with very sensitive data, and the protections of that data, the cybersecurity associated with that data, the storage of that data. So since 2017, Polaris has been in existence since 2002, so we're in our 17th year now, but starting three years ago in 2017 we started really partnering with AWS, where we're migrating more of our data onto AWS, building some AI tools with AWS to help us process Hotline calls more efficiently. And then talking about potentially moving our, all of our data storage onto AWS so that we don't have our own server racks in our office, we still need to go through a number of steps to get there. But having AWS at the table, and then talking about the Impact Computing team and this, like, real big data crunching of like millions of trafficking cases globally, we haven't even started talking about that yet but I think that's like a next stage. So for now, it's getting our data stronger, more secure, building some of those AI bots to help us with our work, and then potentially considering us moving completely serverless, and all of those things are conversations we're having with AWS, and thrilled that AWS is making this an issue to the point that it was prioritized and featured at this conference, which was a big deal, to get in front of the whole audience and do a keynote, and we're very, very grateful for that. >> And you mentioned there's so many organizations involved, are you guys doing data aggregation, data consolidation, sharing, I mean there must be with so many organizations, that adds a lot of complexity, and a lot of data silos, to steal classic kind of IT terms. Are you working towards some kind of unification around that, or how does that look in the future? >> We would love to get to the point where different organizations are sharing their data set. We'd love to get to the point where different organizations are using, like, a shared case management tool, and collecting the same data so it's apples to apples. There are different organizations, like, Thorn is doing some amazing big data-- >> Jeff: Right, we've had Thorn on a couple of times. >> How do we merge Polaris's data set with Thorn's data set? We're not doing that yet, right? I think we're only doing baby steps. But I think the AWS platform could enable potentially a merger of Thorn's data with Polaris's data in some sort of data lake, right? So that's a great idea, we would love to get to that. I think the field isn't there yet. The field has kind of been, like, tech-starved for a number of years, but in the past five years has made a lot of progress. The field is mostly kind of small shelters and groups responding to survivors, and so this notion of like infusing the trafficking field with data is somewhat of a new concept, but it's enabling us to think much bigger about what's possible. >> Well Brad, again, we could go on all day, you know, really thankful for what you're doing for a whole lot of people that we don't see, or maybe we see and we're not noticing, so thank you for that, and uh. >> Absolutely. >> Look forward to catching up when you move the ball a little bit further down the field. >> Yeah, thank you for having me on. It's a pleasure to be here. >> All right, my pleasure. He's Brad, I'm Jeff, you're watching theCUBE. We're at AWS IMAGINE Nonprofits, thanks for watching, we'll see you next time. (futuristic music)

>> 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)