>> From Cambridge Massachusetts, it's The Cube covering MIT Chief Data Officer, and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. (electronic music) >> Welcome back to MIT in Cambridge, Massachusets everybody. You're watching The Cube, the leader in live tech coverage. My name is Dave Vellante I'm here with Paul Gillin. Day two of the of the MIT Chief Data Officer Information Quality Conference. One of the things we like to do, at these shows, we love to profile Boston area start-ups that are focused on data, and in particular we love to focus on start-ups that are founded by women. Julie Johnson is here, She's the Co-founder and CEO of Armored Things. Julie, great to see you again. Thanks for coming on. >> Great to see you. >> So why did you start Armored Things? >> You know, Armored Things was created around a mission to keep people safe. Early in the time where were looking at starting this company, incidents like Las Vegas happened, Parkland happened, and we realized that the world of security and operations was really stuck in the past right? It's a manual solutions generally driven by a human instinct, anecdotal evidence, and tools like Walkie-Talkies and video cameras. We knew there had to be a better way right? In the world of Data that we live in today, I would ask if either of you got in your car this morning without turning on Google Maps to see where you were going, and the best route with traffic. We want to help universities, ball parks, corporate campuses do that for people. How do we keep our people safe? By understanding how they live. >> Yeah, and stay away from Lambert Street in Cambridge by the way. >> (laughing) >> Okay so, you know in people, when they think about security they think about cyber, they think about virtual security, et cetera et cetera, but there's also the physical security aspect. Can you talk about the balance of those two? >> Yeah, and I think both are very important. We actually tend to mimic some of the revolutions that have happened on the cyber security side over the last 10 years with what we're trying to do in the world of physical security. So, folks watching this who are familiar with cyber security might understand concepts like anomaly detection, SIEM and SOAR for orchestrated response. We very much believe that similar concepts can be applied to the physical world, but the unique thing about the physical world, is that it has defined boundaries, right? People behave in accordance with their environment. So, how do we take the lessons learned in cyber security over 10 to 15 years, and apply them to that physical world? I also believe that physical and cyber security are converging. So, are there things that we know in the physical world because of how we approach the problem? That can be a leading indicator of a threat in either the physical world or the digital world. What many people don't understand is that for some of these cyber security hacks, the first weak link is physical access to your network, to your data, to your systems. How do we actually help you get an eye on that, so you already have some context when you notice it in the digital realm. >> So, go back to the two examples you sited earlier, the two shooting examples. Could those have been prevented or mitigated in some way using the type of technology you're building? >> Yeah, I hate to say that you could ever prevent an incident like that. Everyone wants us to do better. Our goal is to get a better sense predicatively of the leading indicators that tell you you have a problem. So, because we're fundamentally looking at patterns of people and flow, I want to know when a normal random environment starts to disperse in a certain way, or if I have a bottle neck in my environment. Because if then I have that type of incident occur, I already know where my hotspots are, where my pockets of risk are. So, I can address it that much more efficiently from a response perspective. >> So if people are moving quickly away from a venue, it might be and indication that there's something wrong- >> It could be, Yeah. That demands attention. >> Yeah, when you go to a baseball game, or when you go to work I would imagine that you generally have a certain pattern of behavior. People know conceptually what those patterns are. But, we're the first effort to bring them data to prove what those patterns are so that they can actually use that data to consistently re-examine their operations, re-examine their security from a staffing perspective, from a management perspective, to make sure that they're using all the data that's at their disposal. >> Seems like there would be many other applications beyond security of this type of analysis. Are you committed to the security space, or do you have broader ambitions? >> Are we committed to the security space is a hundred percent. I would say the number one reason why people join our team, and the number one reason why people call us to be customers is for security. There's a better way to do things. We fundamentally believe that every ball park, every university, every corporate campus, needs a better way. I think what we've seen though is exactly what you're saying. As we built our software, for security in these venues, and started with an understanding of people and flow, there's a lot that falls out of that right? How do I open gates that are more effective based on patterns of entry and exit. How do I make sure that my staffing's appropriate for the number of people I have in my environment. There's lots of other contextual information that can ultimately drive a bottom line or top line revenue. So, you take a pro sports venue for example. If we know that on a 10 degree colder day people tend to eagres more early in the game, how do we adjust our food and beverage strategy to save money on hourly workers, so that we're not over staffing in a period of time that doesn't need those resources. >> She's talking about the physical and the logical security worlds coming together, and security of course has always been about data, but 10 years ago it was staring at logs increasing the machines are helping us do that, and software is helping us do that. So can you add some color to at least the trends in the market generally, and then maybe specifically what you're doing bringing machine intelligence to the data to make us more secure. >> Sure, and I hate to break it to you, but logs are still a pretty big part of what people are watching on a daily basis, as are video cameras. We've seen a lot of great technology evolve in the video management system realm. Very advanced technology great at object recognition and detecting certain behaviors with a video only solution, right? How do we help pinpoint certain behaviors on a specific frame or specific camera. The only problem with that is, if you have people watching those cameras, you're still relying on humans in the loop to catch a malicious behavior, to respond in the event that they're notified about something unusual. That still becomes a manual process. What we do, is we use data to watch not only cameras, but we are watching your cameras, your Wi-Fi, access control. Contextual data from public transit, or weather. How do we get this greater understanding of your environment that helps us watch everything so that we can surface the things that you want the humans in the loop to pay attention to, right? So, we're not trying to remove the human, we're trying to help them focus their time and make decisions that are backed by data in the most efficient way possible. >> How about the concerns about The Surveillance Society? In some countries, it's just taken for granted now that you're on camera all the time. In the US that's a little bit more controversial. Is what your doing, do you have to be sensitive to that in designing the tools you're building? >> Yeah, and I think to Dave's question, there are solutions like facial recognition which are very much working on identifying the individual. We have a philosophy as a company, that security doesn't necessarily start with the individual, it starts with the aggregate. How do we understand at an aggregate macro level, the patterns in an environment. Which means I don't have to identify Paul, or I don't have to identify Dave. I want to look for what's usual and unusual, and use that as the basis of my response. There's certain instances where you want to know who people are. Do I want to know who my security personnel are so I can dispatch them more efficiently? Absolutely. Let's opt those people in and allow them to share the information they need to share to be better resources for our environment. But, that's the exception not the norm. If we make the norm privacy first, I think we'll be really successful in this emerging GDPR data centric world. >> But I could see somebody down the road saying hey can you help us find this bad guy? And my kids at camp this week, This is his 7th year of camp, and this year was the first year my wife, she was able to sign up for a facial recognition thing. So, we used to have to scroll through hundreds and hundreds of pictures to see oh, there he is! And so Deb signs up for this thing, and then it pings you when your son has a picture taken. >> Yeah. And I was like, That's awesome. Oh. (laughing) >> That's great until you think about it. >> But there aren't really any clear privacy laws today. And so you guys are saying, look it, we're looking at the big picture. >> That's right. >> But that day is coming isn't it? >> There's certain environments that care more than others. If you think about universities, which is where we first started building our technology, they cared greatly about the privacy of their students. Health care is a great example. We want to make sure that we're protecting peoples personal data at a different level. Not only because that's the right thing to do, but also from a regulatory perspective. So, how do we give them the same security without compromising the privacy. >> Talk about Bottom line. You mentioned to us earlier that you just signed a contract with a sports franchise, you're actually going to help them, help save them money by deploying their resources more efficiently. How does your technology help the bottom line? >> Sure, you're average sporting venue, is getting great information at the point a ticket is scanned or a ticket is purchased, they have very little visibility beyond that into the customer journey during an event at their venue. So, if you think about again, patterns of people and flow from a security perspective, at our core we're helping them staff the right gates, or figure out where people need to be based on hot spots in their environment. But, what that also results in is an ability to drive other operational benefits. Do we have a zone that's very low utilization that we could use as maybe even a benefit to our avid fans. Send them to that area, get traffic in that area, and now give them a better concession experience because of it, right? Where they're going to end up spending more money because they're not waiting in line in the different zone. So, how do we give them a dashboard in real time, but also alerts or reports that they can use on an ongoing basis to change their decision making going forward. >> So, give us the company overview. Where are you guys at with funding, head count, all that good stuff. >> So, we raised a seed round with some great Boston and Silicon Valley investors a year ago. So, that was Glasswing is a Boston AI focused fund, has been a great partner for us, and Inovia which is Canada's largest VC fund recently opened a Silicon Valley office. We just started raising a series A about a week ago. I'm excited to say those conversation have been going really well so far. We have some potential strategic partners who we're excited about who know data better then anyone else that we think would help us accelerate our business. We also have a few folks who are very familiar with the large venue space. You know, the distributed campuses, the sporting and entertainment venues. So, we're out looking for the right partner to lead our series A round, and take our business to the next level, but where we are today with five really great branded customers, I think we'll have 20 by the end of next year, and we won't stop fighting 'till we're at every ball park, every football stadium, every convention center, school. >> The big question, at some point will you be able to eliminate security lines? (laughing) >> I don't think that's my core mission. (laughing) But, optimistically I'd love to help you. Right, I think there's some very talented people working on that challenge, so I'll defer that one to them. >> And rough head count today? >> We have 23 people. >> You're 23 people so- >> Yeah, I headquartered in Boston Post Office Square. >> Awesome, great location. So, and you say you've got five customers, so you're generating revenue? >> Yes >> Okay, good. Well, thank you for coming in The Cube >> Yeah, thank you. >> And best of luck with the series A- >> I appreciate it and going forward >> Yeah, great. >> All right, and thank you for watching. Paul Gillin and I will be back right after this short break. This is The Cube from MIT Chief Data Officer Information Quality Conference in Cambridge. We'll be right back. (electronic music)