>> Live, from Barcelona Spain, it's theCUBE, covering Cisco Live! Europe. Brought to you by Cisco and its ecosystem partners. >> Welcome back to theCUBE, here at Cisco Live! 2019 in Barcelona, Spain, I'm Stu Miniman, happy to welcome to the program a first-time guest, but someone I've known for many years, Jason Edelman, who is the founder of Network to Code. Jason, great to see you, and thanks for joining us. >> Thank you for having me, Stu. >> Alright, Jason, let's first, for our audiences, this is your first time on the program, give us a little bit about your background, and what led to you being the founder of Network to Code. >> Right, so my background is that of a traditional network engineer. I've spent 10+ years managing networks, deploying networks, and really, acting in a pre-sales capacity, supporting Cisco infrastructure. And it was probably around 2012 or 13, working for a large Cisco VAR, that we had access to something called Cisco onePK, and we kind of dove into that as the first SDK to control network devices. We have today iPhone SDKs, SDKs for Android, to program for phone apps, this was one of the first SDKs to program against a router and a switch. And that, for me, was just eye-opening, this is kind of back in 2013 or so, to see what could be done to write code in Python, Seer, Java, against network devices. Now, when this was going on, I didn't know how to code, so I kind of used that as the entrance to ramp up, but that was, for me, the pivot point. And then, the same six-week period, I had a demo of Puppet and Ansible automated networking devices, and so that was the pivot point where it was like, wow, realizing I've spent a career architecture and designing networks, and realizing there's a challenge in operating networks day to day. >> Yeah, Jason, dial back. You've some Cisco certifications in your background? >> Sure, yes, CCIE, yeah. >> Yeah, so I think back, when this all, OpenFlow, and before we even called it Software-Defined Networking, you were blogging about this type of stuff. But, as you said, you weren't a coder. It wasn't your background, you were a network guy, and I think the Network to Code, a lot of the things we've been looking at, career-wise, it's like, does everyone need to become coders? How will the tools mature? Give us a little bit about that journey, as how you got into coding and let's go from there. >> Yeah, it was interesting. In 2010, I started blogging OpenFlow-related, I thought it was going to change the world, saw what NICRO was doing at the time, and then Big Switch at the time, and I just speculated and blogged and really just envisioned this world where networks were different in some capacity. And it took a couple years to really shed light on management and operations of networking, and I made some career shifts. And I remember going back to onePK, at the time, my manager then, who is now our CEO at Network to Code, he actually asked, well, why don't you do it? And it was just like, me? Me, automate our program? What do you mean? And so it was kind of like a moment for me to kind of reflect on what I can do. Now, I will say I don't believe every network engineer should know how to code. That was my on-ramp because of partnership with Cisco at the time, and learning onePK and programming languages, but that was for me, I guess, what I needed as that kick in the butt to say, you know what? I am going to do this. I do believe in the shift that's going to happen in the next couple years, and that was where I kind of just jumped in feet first, and now we are where we are. >> Yeah, Jason, some great points there. I know for myself, I look at, Cisco's gone through so much change. A year ago, up on stage, Cisco's talking about their future is as a software company. You might not even think of us as networking first, you will talk to us about software first. So that initial shift that you saw back in 2010, it's happening. It's a different form than we might have thought originally, and it's not necessarily a product, but we're going through that shift. And I like what you said about how not everybody needs to code, but it's this change in paradigms and what we need to do are different. You've got some connections, we're here in the DevNet Zone. I saw, at the US show in Orlando last year, Network to Code had a small booth, there were a whole bunch of startups in that space. Tell us how you got involved into DevNet, really since the earliest days. >> Yes, since the early days, it was really pre-DevNet. So the emergence of DevNet, I've seen it grow into, the last couple years, Cisco Live! And for us, given what we do at Network to Code, as a network-automation-focused company, we see DevNet in use by our clients, by DevNet solutions and products, things like, mentioned yesterday on a panel, but DevNet has always-on sandboxes, too. One of the biggest barriers we've seen with our clients is getting access to the right lab gear on getting started to automate. So DevNet has these sandboxes always on to hit Nexus API or Catalyst API, right? Things like that. And there's really a very good, structured learning path to get started through DevNet, which usually, where we intersect in our client engagement, so it's kind of like post-DevNet, you're kind of really showing what's possible, and then we'll kind of get in and craft some solutions for our clients. >> Yeah, take us inside some of your clients, if you can. Are most of them hitting the API instead of the COI now when they're engaging? >> Yeah, it's actually a good question. Not usually talked about, but the reality is, APIs are still very new. And so we actively test a lot of the newer APIs from Cisco, as an example. IOS XE has some of the best APIs that exist around RESTCONF, NETCONF, modeled from the same YANG models, and great APIs. But the truth is that a lot of our clients, large enterprises that've been around for 20+ years, the install base is still largely not API-enabled. So a lot of the automation that we do is definitely SSH-based. And when you look at what's possible with platforms, if it is something like a custom in Python, or even an ANSEL off the shelf, a lot of the integrations are hidden from the user, so as long as we're able to accomplish the goal, it's the most important thing right now. And our clients' leaderships sometimes care, and it's true, right? You want the outcome. And initially, it's okay if we're not using the API, but once we do flip that switch, it does provide a bit more structure and safety for automating. But the install base is so large right now that, to automate, you have to use SSH, and we don't believe in waiting 'til every device is API-enabled because it'll just take a while to turn that base. >> Alright, Jason, a major focus of the conference this year has been around multi-cloud. How's that impacting your business and your customers? >> So, it's in our path as a company. Right now, there's a lot of focus around multi-cloud and data center, and the truth is, we're doing a lot of automation in the Campus networking space. Right, automating networks to get deployed in wiring closets and firewalls and load balancers and things like that. So from our standpoint, as we start planning with our clients, we see the services that we offer really port over to multi-cloud and making sure that with whatever automation is being deployed today, regardless of toolset, and look at a tool chain to deploy, if it's a CI/CD Pipeline for networking, be able to do that if you're managing a network in the Campus, a data center network, or multi-cloud network, to make sure we have a uniform-looking field to operations, and doing that. >> Alright, so Jason, you're not only founder of your company, you're also an author. Maybe tell us about the, I believe it's an update, or is it a new book, that recently got out. >> Yes, I'm a co-author of a book with Matt Oswalt and Scott Lowe, and it's an O'Reilly book that was published last year. And look, I'm a believer in education, and to really make a change and change an industry, we have to educate, and I think the book, the goal was to play a small part in really bringing concepts to light. As a network engineer by trade, there's fundamental concepts that network engineers should be aware of, and it could be basics and a lot of these, it could be Python or Jinja templating in YAML and Git and Linux, for that matter. It's just kind of providing that baseline of skills as an entrance into automation. And once you have the baseline, it kind of really uncovers what's possible. So writing the book was great. Great opportunity, and thank you to Matt and Scott for getting involved there. It really took a lot of the work effort and collaborated with them on it. >> Want to get your perception on the show, also. Education, always a key feature of what happens at the show. Not far from us is the Cisco bookshop. I see people getting a lot of the big Cisco books, but I think ten years ago, it was like, everybody, get my CCIE, all my different certifications updated, here. Here in the DevNet Zone, a lot of people, they're building stuff, they're building new pieces, they're playing in the labs, and they're doing some of these environments. What's your experience here at the show? Anything in particular that catches your eye? >> So, I do believe in education. I think to do anything well, you have to be educated on it. And I've read Cisco Press books over the years, probably a dozen of them, for the CCIE and beyond. I think when we look at what's in DevNet, when we look at what's in the bookstore, people have to immerse themselves into the technology, and reading books, like the learning labs that are here in the DevNet Zone, the design sessions that are right behind us. Just amazing for me to have seen the DevNet Zone grow to be what it is today. And really the goal of educating the market of what's possible. See, even from the start, Network to Code, we started as doing a lot of training, because you really can't change the methodology of network operations without being aware of what's possible, and it really does kind of come back to training. Whatever it is, on-demand, streaming, instructor-led, reading a book. Just glad to see this happen here, and a lot more to do around the industry, in the space around community involvement and development, but training, a huge part of it. >> Alright, Jason, want to give you the final word, love the story of network engineer gone entrepreneurial, out of your comfort zone, coding, helping to build a business. So tell us what you see, going forward. >> So, we've grown quite a bit in the past couple years. Right now, we're over 20 engineers strong, and starting from essentially just one a couple years ago, was a huge transformation, and seeing this happen. I believe in bringing on A-players to help make that happen. I think for us as a business, we're continuing to grow and accelerating what we do in this network automation space, but I just think, one thought to throw out there is, oftentimes we talk about lower-level tools, Python, Git, YAML, a lot of new acronyms and buzzwords for network engineers, but also, the flip side is true, too. As our client base evolves, and a lot of them are in the Fortune 100, so large clients, looking at consumption models of technology's super-important, meaning is there ITSM tools deployed today, like a ServiceNow, or Webex teams, or Slack for chat integration. To really think through early on how the internal customers of automation will consume automation, 'cause it really does us no good, Cisco, vendors, or clients no good, if we deploy a great network automation platform, and no one uses it, because it doesn't fit the culture of the brand of the organization. So it's just, as we continue to grow, that's really what's top of mind for us right now. >> Alright, well Jason, congratulations on everything that you've done so far, wish you the best of luck going forward, and thank you so much, of course, for watching. We'll have more coverage, three day, wall-to-wall, here at Cisco Live! 2019 in Barcelona. I'm Stu Miniman, and thanks for watching theCUBE. (electronic music)

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hi everyone. Welcome back to the cubes. Virtual coverage of AWS reinvent 2020 virtual. We are the cube virtual I'm John ferry, your host with my coach, Dave Alante for keynote analysis from Swami's machine learning, all things, data huge. Instead of announcements, the first ever machine learning keynote at a re-invent Dave. Great to see you. Thanks Johnny. And from Boston, I'm here in Palo Alto. We're doing the cube remote cube virtual. Great to see you. >>Yeah, good to be here, John, as always. Wall-to-wall love it. So, so, John, um, how about I give you my, my key highlights from the, uh, from the keynote today, I had, I had four kind of curated takeaways. So the first is that AWS is, is really trying to simplify machine learning and use machine intelligence into all applications. And if you think about it, it's good news for organizations because they're not the become machine learning experts have invent machine learning. They can buy it from Amazon. I think the second is they're trying to simplify the data pipeline. The data pipeline today is characterized by a series of hyper specialized individuals. It engineers, data scientists, quality engineers, analysts, developers. These are folks that are largely live in their own swim lane. Uh, and while they collaborate, uh, there's still a fairly linear and complicated data pipeline, uh, that, that a business person or a data product builder has to go through Amazon making some moves to the front of simplify that they're expanding data access to the line of business. I think that's a key point. Is there, there increasingly as people build data products and data services that can monetize, you know, for their business, either cut costs or generate revenue, they can expand that into line of business where there's there's domain context. And I think the last thing is this theme that we talked about the other day, John of extending Amazon, AWS to the edge that we saw that as well in a number of machine learning tools that, uh, Swami talked about. >>Yeah, it was great by the way, we're live here, uh, in Palo Alto in Boston covering the analysis, tons of content on the cube, check out the cube.net and also check out at reinvent. There's a cube section as there's some links to so on demand videos with all the content we've had. Dave, I got to say one of the things that's apparent to me, and this came out of my one-on-one with Andy Jassy and Andy Jassy talked about in his keynote is he kind of teased out this idea of training versus a more value add machine learning. And you saw that today in today's announcement. To me, the big revelation was that the training aspect of machine learning, um, is what can be automated away. And it's under a lot of controversy around it. Recently, a Google paper came out and the person was essentially kind of, kind of let go for this. >>But the idea of doing these training algorithms, some are saying is causes more harm to the environment than it does good because of all the compute power it takes. So you start to see the positioning of training, which can be automated away and served up with, you know, high powered ships and that's, they consider that undifferentiated heavy lifting. In my opinion, they didn't say that, but that's clearly what I see coming out of this announcement. The other thing that I saw Dave that's notable is you saw them clearly taking a three lane approach to this machine, learning the advanced builders, the advanced coders and the developers, and then database and data analysts, three swim lanes of personas of target audience. Clearly that is in line with SageMaker and the embedded stuff. So two big revelations, more horsepower required to process training and modeling. Okay. And to the expansion of the personas that are going to be using machine learning. So clearly this is a, to me, a big trend wave that we're seeing that validates some of the startups and I'll see their SageMaker and some of their products. >>Well, as I was saying at the top, I think Amazon's really trying, working hard on simplifying the whole process. And you mentioned training and, and a lot of times people are starting from scratch when they have to train models and retrain models. And so what they're doing is they're trying to create reusable components, uh, and allow people to, as you pointed out to automate and streamline some of that heavy lifting, uh, and as well, they talked a lot about, uh, doing, doing AI inferencing at the edge. And you're seeing, you know, they, they, uh, Swami talked about several foundational premises and the first being a foundation of frameworks. And you think about that at the, at the lowest level of their S their ML stack. They've got, you know, GPU's different processors, inferential, all these alternative processes, processors, not just the, the Xav six. And so these are very expensive resources and Swami talked a lot about, uh, and his colleagues talked a lot about, well, a lot of times the alternative processor is sitting there, you know, waiting, waiting, waiting. And so they're really trying to drive efficiency and speed. They talked a lot about compressing the time that it takes to, to run these, these models, uh, from, from sometimes weeks down to days, sometimes days down to hours and minutes. >>Yeah. Let's, let's unpack these four areas. Let's stay on the firm foundation because that's their core competency infrastructure as a service. Clearly they're laying that down. You put the processors, but what's interesting is the TensorFlow 92% of tensor flows on Amazon. The other thing is that pie torch surprisingly is back up there, um, with massive adoption and the numbers on pie torch literally is on fire. I was coming in and joke on Twitter. Um, we, a PI torch is telling because that means that TensorFlow is originally part of Google is getting, is getting a little bit diluted with other frameworks, and then you've got MX net, some other things out there. So the fact that you've got PI torch 91% and then TensorFlow 92% on 80 bucks is a huge validation. That means that the majority of most machine learning development and deep learning is happening on AWS. Um, >>Yeah, cloud-based, by the way, just to clarify, that's the 90% of cloud-based cloud, uh, TensorFlow runs on and 91% of cloud-based PI torch runs on ADM is amazingly massive numbers. >>Yeah. And I think that the, the processor has to show that it's not trivial to do the machine learning, but, you know, that's where the infrared internship came in. That's kind of where they want to go lay down that foundation. And they had Tanium, they had trainee, um, they had, um, infrared chow was the chip. And then, you know, just true, you know, distributed training training on SageMaker. So you got the chip and then you've got Sage makers, the middleware games, almost like a machine learning stack. That's what they're putting out there >>And how bad a Gowdy, which was, which is, which is a patrol also for training, which is an Intel based chip. Uh, so that was kind of interesting. So a lot of new chips and, and specialized just, we've been talking about this for awhile, particularly as you get to the edge and do AI inferencing, you need, uh, you know, a different approach than we're used to with the general purpose microbes. >>So what gets your take on tenant? Number two? So tenant number one, clearly infrastructure, a lot of announcements we'll go through those, review them at the end, but tenant number two, that Swami put out there was creating the shortest path to success for builders or machine learning builders. And I think here you lays out the complexity, Dave butts, mostly around methodology, and, you know, the value activities required to execute. And again, this points to the complexity problem that they have. What's your take on this? >>Yeah. Well you think about, again, I'm talking about the pipeline, you collect data, you just data, you prepare that data, you analyze that data. You, you, you make sure that it's it's high quality and then you start the training and then you're iterating. And so they really trying to automate as much as possible and simplify as much as possible. What I really liked about that segment of foundation, number two, if you will, is the example, the customer example of the speaker from the NFL, you know, talked about, uh, you know, the AWS stats that we see in the commercials, uh, next gen stats. Uh, and, and she talked about the ways in which they've, well, we all know they've, they've rearchitected helmets. Uh, they've been, it's really a very much database. It was interesting to see they had the spectrum of the helmets that were, you know, the safest, most safe to the least safe and how they've migrated everybody in the NFL to those that they, she started a 24%. >>It was interesting how she wanted a 24% reduction in reported concussions. You know, you got to give the benefit of the doubt and assume some of that's through, through the data. But you know, some of that could be like, you know, Julian Edelman popping up off the ground. When, you know, we had a concussion, he doesn't want to come out of the game with the new protocol, but no doubt, they're collecting more data on this stuff, and it's not just head injuries. And she talked about ankle injuries, knee injuries. So all this comes from training models and reducing the time it takes to actually go from raw data to insights. >>Yeah. I mean, I think the NFL is a great example. You and I both know how hard it is to get the NFL to come on and do an interview. They're very coy. They don't really put their name on anything much because of the value of the NFL, this a meaningful partnership. You had the, the person onstage virtually really going into some real detail around the depth of the partnership. So to me, it's real, first of all, I love stat cast 11, anything to do with what they do with the stats is phenomenal at this point. So the real world example, Dave, that you starting to see sports as one metaphor, healthcare, and others are going to see those coming in to me, totally a tale sign that Amazon's continued to lead. The thing that got my attention was is that it is an IOT problem, and there's no reason why they shouldn't get to it. I mean, some say that, Oh, concussion, NFL is just covering their butt. They don't have to, this is actually really working. So you got the tech, why not use it? And they are. So that, to me, that's impressive. And I think that's, again, a digital transformation sign that, that, you know, in the NFL is doing it. It's real. Um, because it's just easier. >>I think, look, I think, I think it's easy to criticize the NFL, but the re the reality is, is there anything old days? It was like, Hey, you get your bell rung and get back out there. That's just the way it was a football players, you know, but Ted Johnson was one of the first and, you know, bill Bellacheck was, was, you know, the guy who sent him back out there with a concussion, but, but he was very much outspoken. You've got to give the NFL credit. Uh, it didn't just ignore the problem. Yeah. Maybe it, it took a little while, but you know, these things take some time because, you know, it's generally was generally accepted, you know, back in the day that, okay, Hey, you'd get right back out there, but, but the NFL has made big investments there. And you can say, you got to give him, give him props for that. And especially given that they're collecting all this data. That to me is the most interesting angle here is letting the data inform the actions. >>And next step, after the NFL, they had this data prep data Wrangler news, that they're now integrating snowflakes, Databricks, Mongo DB, into SageMaker, which is a theme there of Redshift S3 and Lake formation into not the other way around. So again, you've been following this pretty closely, uh, specifically the snowflake recent IPO and their success. Um, this is an ecosystem play for Amazon. What does it mean? >>Well, a couple of things, as we, as you well know, John, when you first called me up, I was in Dallas and I flew into New York and an ice storm to get to the one of the early Duke worlds. You know, and back then it was all batch. The big data was this big batch job. And today you want to combine that batch. There's still a lot of need for batch, but when people want real time inferencing and AWS is bringing that together and they're bringing in multiple data sources, you mentioned Databricks and snowflake Mongo. These are three platforms that are doing very well in the market and holding a lot of data in AWS and saying, okay, Hey, we want to be the brain in the middle. You can import data from any of those sources. And I'm sure they're going to add more over time. Uh, and so they talked about 300 pre-configured data transformations, uh, that now come with stage maker of SageMaker studio with essentially, I've talked about this a lot. It's essentially abstracting away the, it complexity, the whole it operations piece. I mean, it's the same old theme that AWS is just pointing. It's its platform and its cloud at non undifferentiated, heavy lifting. And it's moving it up the stack now into the data life cycle and data pipeline, which is one of the biggest blockers to monetizing data. >>Expand on that more. What does that actually mean? I'm an it person translate that into it. Speak. Yeah. >>So today, if you're, if you're a business person and you want, you want the answers, right, and you want say to adjust a new data source, so let's say you want to build a new, new product. Um, let me give an example. Let's say you're like a Spotify, make it up. And, and you do music today, but let's say you want to add, you know, movies, or you want to add podcasts and you want to start monetizing that you want to, you want to identify, who's watching what you want to create new metadata. Well, you need new data sources. So what you do as a business person that wants to create that new data product, let's say for podcasts, you have to knock on the door, get to the front of the data pipeline line and say, okay, Hey, can you please add this data source? >>And then everybody else down the line has to get in line and Hey, this becomes a new data source. And it's this linear process where very specialized individuals have to do their part. And then at the other end, you know, it comes to self-serve capability that somebody can use to either build dashboards or build a data product. In a lot of that middle part is our operational details around deploying infrastructure, deploying, you know, training machine learning models that a lot of Python coding. Yeah. There's SQL queries that have to be done. So a lot of very highly specialized activities, what Amazon is doing, my takeaway is they're really streamlining a lot of those activities, removing what they always call the non undifferentiated, heavy lifting abstracting away that it complexity to me, this is a real positive sign, because it's all about the technology serving the business, as opposed to historically, it's the business begging the technology department to please help me. The technology department obviously evolving from, you know, the, the glass house, if you will, to this new data, data pipeline data, life cycle. >>Yeah. I mean, it's classic agility to take down those. I mean, it's undifferentiated, I guess, but if it actually works, just create a differentiated product. So, but it's just log it's that it's, you can debate that kind of aspect of it, but I hear what you're saying, just get rid of it and make it simpler. Um, the impact of machine learning is Dave is one came out clear on this, uh, SageMaker clarify announcement, which is a bias decision algorithm. They had an expert, uh, nationally CFUs presented essentially how they're dealing with the, the, the bias piece of it. I thought that was very interesting. What'd you think? >>Well, so humans are biased and so humans build models or models are inherently biased. And so I thought it was, you know, this is a huge problem to big problems in artificial intelligence. One is the inherent bias in the models. And the second is the lack of transparency that, you know, they call it the black box problem, like, okay, I know there was an answer there, but how did it get to that answer and how do I trace it back? Uh, and so Amazon is really trying to attack those, uh, with, with, with clarify. I wasn't sure if it was clarity or clarified, I think it's clarity clarify, um, a lot of entirely certain how it works. So we really have to dig more into that, but it's essentially identifying situations where there is bias flagging those, and then, you know, I believe making recommendations as to how it can be stamped. >>Nope. Yeah. And also some other news deep profiling for debugger. So you could make a debugger, which is a deep profile on neural network training, um, which is very cool again on that same theme of profiling. The other thing that I found >>That remind me, John, if I may interrupt there reminded me of like grammar corrections and, you know, when you're typing, it's like, you know, bug code corrections and automated debugging, try this. >>It wasn't like a better debugger come on. We, first of all, it should be bug free code, but, um, you know, there's always biases of the data is critical. Um, the other news I thought was interesting and then Amazon's claiming this is the first SageMaker pipelines for purpose-built CIC D uh, for machine learning, bringing machine learning into a developer construct. And I think this started bringing in this idea of the edge manager where you have, you know, and they call it the about machine, uh, uh, SageMaker store storing your functions of this idea of managing and monitoring machine learning modules effectively is on the edge. And, and through the development process is interesting and really targeting that developer, Dave, >>Yeah, applying CIC D to the machine learning and machine intelligence has always been very challenging because again, there's so many piece parts. And so, you know, I said it the other day, it's like a lot of the innovations that Amazon comes out with are things that have problems that have come up given the pace of innovation that they're putting forth. And, and it's like the customers drinking from a fire hose. We've talked about this at previous reinvents and the, and the customers keep up with the pace of Amazon. So I see this as Amazon trying to reduce friction, you know, across its entire stack. Most, for example, >>Let me lay it out. A slide ahead, build machine learning, gurus developers, and then database and data analysts, clearly database developers and data analysts are on their radar. This is not the first time we've heard that. But we, as the kind of it is the first time we're starting to see products materialized where you have machine learning for databases, data warehouse, and data lakes, and then BI tools. So again, three different segments, the databases, the data warehouse and data lakes, and then the BI tools, three areas of machine learning, innovation, where you're seeing some product news, your, your take on this natural evolution. >>Well, well, it's what I'm saying up front is that the good news for, for, for our customers is you don't have to be a Google or Amazon or Facebook to be a super expert at AI. Uh, companies like Amazon are going to be providing products that you can then apply to your business. And, and it's allowed you to infuse AI across your entire application portfolio. Amazon Redshift ML was another, um, example of them, abstracting complexity. They're taking, they're taking S3 Redshift and SageMaker complexity and abstracting that and presenting it to the data analysts. So that, that, that individual can worry about, you know, again, getting to the insights, it's injecting ML into the database much in the same way, frankly, the big query has done that. And so that's a huge, huge positive. When you talk to customers, they, they love the fact that when, when ML can be embedded into the, into the database and it simplifies, uh, that, that all that, uh, uh, uh, complexity, they absolutely love it because they can focus on more important things. >>Clearly I'm this tenant, and this is part of the keynote. They were laying out all their announcements, quick excitement and ML insights out of the box, quick, quick site cue available in preview all the announcements. And then they moved on to the next, the fourth tenant day solving real problems end to end, kind of reminds me of the theme we heard at Dell technology worlds last year end to end it. So we are starting to see the, the, the land grab my opinion, Amazon really going after, beyond I, as in pass, they talked about contact content, contact centers, Kendra, uh, lookout for metrics, and that'll maintain men. Then Matt would came on, talk about all the massive disruption on the, in the industries. And he said, literally machine learning will disrupt every industry. They spent a lot of time on that and they went into the computer vision at the edge, which I'm a big fan of. I just loved that product. Clearly, every innovation, I mean, every vertical Dave is up for grabs. That's the key. Dr. Matt would message. >>Yeah. I mean, I totally agree. I mean, I see that machine intelligence as a top layer of, you know, the S the stack. And as I said, it's going to be infused into all areas. It's not some kind of separate thing, you know, like, Coobernetti's, we think it's some separate thing. It's not, it's going to be embedded everywhere. And I really like Amazon's edge strategy. It's this, you, you are the first to sort of write about it and your keynote preview, Andy Jassy said, we see, we see, we want to bring AWS to the edge. And we see data center as just another edge node. And so what they're doing is they're bringing SDKs. They've got a package of sensors. They're bringing appliances. I've said many, many times the developers are going to be, you know, the linchpin to the edge. And so Amazon is bringing its entire, you know, data plane is control plane, it's API APIs to the edge and giving builders or slash developers, the ability to innovate. And I really liked the strategy versus, Hey, here's a box it's, it's got an x86 processor inside on a, throw it over the edge, give it a cool name that has edge in it. And here you go, >>That sounds call it hyper edge. You know, I mean, the thing that's true is the data aspect at the edge. I mean, everything's got a database data warehouse and data lakes are involved in everything. And then, and some sort of BI or tools to get the data and work with the data or the data analyst, data feeds, machine learning, critical piece to all this, Dave, I mean, this is like databases used to be boring, like boring field. Like, you know, if you were a database, I have a degree in a database design, one of my degrees who do science degrees back then no one really cared. If you were a database person. Now it's like, man data, everything. This is a whole new field. This is an opportunity. But also, I mean, are there enough people out there to do all this? >>Well, it's a great point. And I think this is why Amazon is trying to extract some of the abstract. Some of the complexity I sat in on a private session around databases today and listened to a number of customers. And I will say this, you know, some of it I think was NDA. So I can't, I can't say too much, but I will say this Amazon's philosophy of the database. And you address this in your conversation with Andy Jassy across its entire portfolio is to have really, really fine grain access to the deep level API APIs across all their services. And he said, he said this to you. We don't necessarily want to be the abstraction layer per se, because when the market changes, that's harder for us to change. We want to have that fine-grained access. And so you're seeing that with database, whether it's, you know, no sequel, sequel, you know, the, the Aurora the different flavors of Aurora dynamo, DV, uh, red shift, uh, you know, already S on and on and on. There's just a number of data stores. And you're seeing, for instance, Oracle take a completely different approach. Yes, they have my SQL cause they know got that with the sun acquisition. But, but this is they're really about put, is putting as much capability into a single database as possible. Oh, you only need one database only different philosophy. >>Yeah. And then obviously a health Lake. And then that was pretty much the end of the, the announcements big impact to health care. Again, the theme of horizontal data, vertical specialization with data science and software playing out in real time. >>Yeah. Well, so I have asked this question many times in the cube, when is it that machines will be able to make better diagnoses than doctors and you know, that day is coming. If it's not here, uh, you know, I think helped like is really interesting. I've got an interview later on with one of the practitioners in that space. And so, you know, healthcare is something that is an industry that's ripe for disruption. It really hasn't been disruption disrupted. It's a very high, high risk obviously industry. Uh, but look at healthcare as we all know, it's too expensive. It's too slow. It's too cumbersome. It's too long sometimes to get to a diagnosis or be seen, Amazon's trying to attack with its partners, all of those problems. >>Well, Dave, let's, let's summarize our take on Amazon keynote with machine learning, I'll say pretty historic in the sense that there was so much content in first keynote last year with Andy Jassy, he spent like 75 minutes. He told me on machine learning, they had to kind of create their own category Swami, who we interviewed many times on the cube was awesome. But a lot of still a lot more stuff, more, 215 announcements this year, machine learning more capabilities than ever before. Um, moving faster, solving real problems, targeting the builders, um, fraud platform set of things is the Amazon cadence. What's your analysis of the keynote? >>Well, so I think a couple of things, one is, you know, we've said for a while now that the new innovation cocktail is cloud plus data, plus AI, it's really data machine intelligence or AI applied to that data. And the scale at cloud Amazon Naylor obviously has nailed the cloud infrastructure. It's got the data. That's why database is so important and it's gotta be a leader in machine intelligence. And you're seeing this in the, in the spending data, you know, with our partner ETR, you see that, uh, that AI and ML in terms of spending momentum is, is at the highest or, or at the highest, along with automation, uh, and containers. And so in. Why is that? It's because everybody is trying to infuse AI into their application portfolios. They're trying to automate as much as possible. They're trying to get insights that, that the systems can take action on. >>And, and, and actually it's really augmented intelligence in a big way, but, but really driving insights, speeding that time to insight and Amazon, they have to be a leader there that it's Amazon it's, it's, it's Google, it's the Facebook's, it's obviously Microsoft, you know, IBM's Tron trying to get in there. They were kind of first with, with Watson, but with they're far behind, I think, uh, the, the hyper hyper scale guys. Uh, but, but I guess like the key point is you're going to be buying this. Most companies are going to be buying this, not building it. And that's good news for organizations. >>Yeah. I mean, you get 80% there with the product. Why not go that way? The alternative is try to find some machine learning people to build it. They're hard to find. Um, so the seeing the scale of kind of replicating machine learning expertise with SageMaker, then ultimately into databases and tools, and then ultimately built into applications. I think, you know, this is the thing that I think they, my opinion is that Amazon continues to move up the stack, uh, with their capabilities. And I think machine learning is interesting because it's a whole new set of it's kind of its own little monster building block. That's just not one thing it's going to be super important. I think it's going to have an impact on the startup scene and innovation is going, gonna have an impact on incumbent companies that are currently leaders that are under threat from new entrance entering the business. >>So I think it's going to be a very entrepreneurial opportunity. And I think it's going to be interesting to see is how machine learning plays that role. Is it a defining feature that's core to the intellectual property, or is it enabling new intellectual property? So to me, I just don't see how that's going to fall yet. I would bet that today intellectual property will be built on top of Amazon's machine learning, where the new algorithms and the new things will be built separately. If you compete head to head with that scale, you could be on the wrong side of history. Again, this is a bet that the startups and the venture capitals will have to make is who's going to end up being on the right wave here. Because if you make the wrong design choice, you can have a very complex environment with IOT or whatever your app serving. If you can narrow it down and get a wedge in the marketplace, if you're a company, um, I think that's going to be an advantage. This could be great just to see how the impact of the ecosystem this will be. >>Well, I think something you said just now it gives a clue. You talked about, you know, the, the difficulty of finding the skills. And I think that's a big part of what Amazon and others who were innovating in machine learning are trying to do is the gap between those that are qualified to actually do this stuff. The data scientists, the quality engineers, the data engineers, et cetera. And so companies, you know, the last 10 years went out and tried to hire these people. They couldn't find them, they tried to train them. So it's taking too long. And now that I think they're looking toward machine intelligence to really solve that problem, because that scales, as we, as we know, outsourcing to services companies and just, you know, hardcore heavy lifting, does it doesn't scale that well, >>Well, you know what, give me some machine learning, give it to me faster. I want to take the 80% there and allow us to build certainly on the media cloud and the cube virtual that we're doing. Again, every vertical is going to impact a Dave. Great to see you, uh, great stuff. So far week two. So, you know, we're cube live, we're live covering the keynotes tomorrow. We'll be covering the keynotes for the public sector day. That should be chock-full action. That environment is going to impact the most by COVID a lot of innovation, a lot of coverage. I'm John Ferrari. And with Dave Alante, thanks for watching.

(upbeat music) >> Announcer: From the CUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE Conversation. >> Hey, welcome back everybody Jeffrey Frick with theCUBE. We are getting through the COVID crisis. It continues and impacting the summer. I can't believe the summer's almost over, but there's a whole lot of things going on in terms of privacy and contact tracing and this kind of this feeling that there's this conflict between kind of personal identification and your personal privacy versus the public good around things like contact tracing. And I was in a session last week with two really fantastic experts. I wanted to bring them on the show and we're really excited to have back for I don't even know how many times Michelle has been on Michelle Dennedy, She is the former chief privacy officer at Cisco and now she's running the CEO of Identity, Michelle great to see you. >> Good to see you always Jeff >> Yeah and for the first time Dr. Ann Cavoukian and she is the executive director Global Privacy & Security By Design Center. Joining us from Toronto, worked with the government and is not short on opinions about privacy. (laughing) Ann good to see you. >> Hi Jeff thank you >> Yes, so let's jump into it cause I think one of the fundamental issues that we keep hearing is this zero-sum game. And I know and it's a big topic for you that there seems to be this trade off this either or and specifically let's just go to contact tracing. Cause that's a hot topic right now with COVID. I hear that it's like you're telling everybody where I'm going and you're sharing that with all these other people. How is this even a conversation and where do I get to choose whether I want to participate or not? >> You can't have people traced and tracked and surveil. You simply can't have it and it can't be an either or win lose model. You have to get rid of that data. Zero-sum game where only one person can win and the other one loses and it sums to a total of zero. Get rid of that, that's so yesterday. You have to have both groups winning positive sum. Meaning yes, you need public health and public safety and you need privacy. It's not one versus the other. We can do both and that's what we insist upon. So the contact term tracing app that was developed in Canada was based on the Apple Google framework, which is actually called exposure notification. It's totally privacy protective individuals choose to voluntarily download this app. And no personal information is collected whatsoever. No names, no geolocation data, nothing. It's simply notifies you. If you've been exposed to someone who is COVID-19 positive, and then you can decide on what action you wish to take. Do you want to go get tested? Do you want to go to your family doctor, whatever the decision lies with you, you have total control and that's what privacy is all about. >> Jeffrey: But what about the person who was sick? Who's feeding the top into that process and is the sick person that you're no notifying they obviously their personal information is part of that transaction. >> what the COVID alerts that we developed based on the Apple Google framework. It builds on manual contact tracing, which also take place the two to compliment each other. So the manual contact tracing is when individuals go get to get tested and they're tested as positive. So healthcare nurses will speak to that individual and say, please tell us who you've been in contact with recently, family, friends, et cetera. So the two work together and by working together, we will combat this in a much more effective manner. >> Jeffrey: So shifting over to you Michelle, you know, there's PIN and a lot of conversations all the time about personal identifiable information but right. But then medical has this whole nother class of kind of privacy restrictions and level of care. And I find it really interesting that on one hand, you know, we were trying to do the contract tracing on another hand if you know, my wife works in a public school. If they find out that one of the kids in this class has been exposed to COVID somehow they can't necessarily tell the teacher because of HIPAA restriction. So I wonder if you could share your thoughts on this kind of crossover between privacy and health information when it gets into this kind of public crisis and this inherent conflict for the public right to know and should the teacher be able to be told and it's not a really clean line with a simple answer, I don't think. >> No and Jeff, and you're also layering, you know, when you're talking about student data, you layering another layer of legal restriction. And I think what you're putting your thumb on is something that's really critical. When you talk about privacy engineering, privacy by design and ethics engineering. You can't simply start with the legal premise. So is it lawful to share HIPAA covered data. A child telling mommy I don't feel well not HIPAA covered. A child seeing a doctor for medical services and finding some sort of infection or illness covered, right? So figuring out the origin of the exact same zero one. Am I ill or not, all depends on context. So you have to first figure out, first of all let's tackle the moral issues. Have we decided that it is a moral imperative to expose certain types of data. And I separate that from ethics intentionally and with apologies to true ethicists. The moral imperative is sort of the things we find are so wrong. We don't want a list of kids who are sick or conversely once the tipping point goes the list of kids who are well. So then they are called out that's the moral choice. The ethical choice is just because you can should you, and that's a much longer conversation. Then you get to the legal imperative. Are you allowed to based on the past mistakes that we made. That's what every piece of litigation or legislation is particularly in a common law construct in the US. It's very important to understand that civil law countries like the European theater. They try to prospectively legislate for things that might go wrong. The construct is thinner in a common law economy where you do, you use test cases in the courts of law. That's why we are such a litigious society has its own baggage. But you have to now look at is that legal structure attempting to cover past harms that are so bad that we've decided as a society to punish them, is this a preventative law? And then you finally get to what I say is stage four for every evaluation is isn't viable, are the protections that you have to put on top of these restrictions. So dire that they either cannot be maintained because of culture process or cash or it just doesn't make sense anymore. So does it, is it better to just feel someone's forehead for illness rather than giving a blood assay, having it sent away for three weeks and then maybe blah, blah, blah, blah, blah, blah. >> Right. >> You have to look at this as a system problem solving issue. >> So I want to look at it in the context of, again kind of this increased level of politicization and or, you know, kind of exposure outside of what's pretty closed. And I want to bring up AIDS and the porn industry very frankly right? Where people behaving in the behavior of the business risk a life threatening disease of which I still don't think it as a virus. So you know why, cause suddenly, you know, we can track for that and that's okay to track for that. And there's a legitimate reason to versus all of the other potential medical conditions that I may or may not have that are not necessarily brought to bear within coming to work. And we might be seeing this very soon. As you said, if people are wanting our temperatures, as we come in the door to check for symptoms. How does that play with privacy and healthcare? It's still fascinates me that certain things is kind of pop out into their own little bucket of regulation. I'm wondering if you could share your thoughts on that Ann. >> You know, whenever you make it privacy versus fill in the blank, especially in the context of healthcare. You end up turning it to a lose lose as opposed to even a win lose. Because you will have fewer people wanting to allow themselves to be tested, to be brought forward for fear of where that information may land. If it lands in the hands of your employer for example or your whoever owns your house if you're in renting, et cetera. It creates enormous problems. So regardless of what you may think of the benefits of that model. History has shown that it doesn't work well that people end up shying away from being tested or seeking treatment or any of those things. Even now with the contact tracing apps that have been developed. If you look globally the contact tracing apps for COVID-19. They have failed the ones that identify individuals in the UK, in Australia, in Western Canada that's how it started out. And they've completely dropped them because they don't work. People shy away from them. They don't use them. So they've gotten rid of that. They've replaced it with the, an app based on the Apple Google framework, which is the one that protects privacy and will encourage people to come forward and seek to be tested. If there's a problem in Germany. Germany is one of the largest privacy data protection countries in the world. Their privacy people are highly trusted in Germany. Germany based their app on the Apple Google framework. About a month ago they released it. And within 24 hours they had 6.5 million people download the app. >> Right. >> Because there is such trust there unlike the rest of the world where there's very little trust and we have to be very careful of the trust deficit. Because we want to encourage people to seek out these apps so they can attempt to be tested if there's a problem, but they're not going to use them. They're just going to shy away from them. If there is such a problem. And in fact I'll never forget. I did an interview about a month ago, three weeks ago in the US on a major major radio station that has like 54 million people followers. And I was telling them about the COVID alert the Canadian contact tracing app, actually it's called exposure notification app, which was built on the Apple Google framework. And people in hoard said they wouldn't trust anyone with it in the US. They just wouldn't trust it. So you see there's such a trust deficit. That's what we have to be careful to avoid. >> So I want to hold on the trust for just a second, but I want to go back to you Michelle and talk about the lessons that we can learn post 9/11. So the other thing right and keep going back to this over and over. It's not a zero-sum game. It's not a zero-sum game and yet that's the way it's often positioned as a way to break down existing barriers. So if you go back to 9/11 probably the highest profile thing being the Patriot Act, you know, where laws are put in place to protect us from terrorism that are going to do things that were not normally allowed to be done. I bet without checking real exhaustively that most of those things are still in place. You know, cause a lot of times laws are written. They don't go away for a long time. What can we learn from what happened after 9/11 and the Patriot Act and what should be really scared of, or careful of or wary of using that as a framework for what's happening now around COVID and privacy. >> It's a perfect, it's not even an analogy because we're feeling the shadows of the Patriot Act. Even now today, we had an agreement from the United States with the European community until recently called the Privacy Shield. And it was basically if companies and organizations that were, that fell under the Federal Trade Commissions jurisdiction, there's a bit of layering legal process here. But if they did and they agreed to supply enough protection to data about people who were present in the European Union to the same or better level than the Europeans would. Then that information could pass through this Privacy Shield unencumbered to and from the United States. That was challenged and taken down. I don't know if it's a month ago or if it's still March it's COVID time, but very recently on basis that the US government can overly and some would say indifferent nations, improperly look at European data based on some of these Patriot Act, FISA courts and other intrusive mechanisms that absolutely do apply if we were under the jurisdiction of the United States. So now companies and private actors are in the position of having to somehow prove that they will mechanize their systems and their processes to be immune from their own government intrusion before they can do digital trade with other parts of the world. We haven't yet seen the commercial disruption that will take place. So the unintended consequence of saying rather than owning the answers or the observations and the intelligence that we got out of the actual 9/11 report, which said we had the information we needed. We did not share enough between the agencies and we didn't have the decision making activity and will to take action in that particular instance. Rather than sticking to that knowledge. Instead we stuck to the Patriot Act, which was all but I believe to Congress people. When I mean, you see the hot mess. That is the US right now. When everyone but two people in the room vote for something on the quick. There's probably some sort of a psychological gun to your head. That's probably well thought out thing. We fight each other. That's part of being an American dammit. So I think having these laws that say, you've got to have this one solution because the boogeyman is coming or COVID is coming or terrorists or child pornographers are coming. There's not one solution. So you really have to break this down into an engineering problem and I don't mean technology when I say engineering. I mean looking at the culture, how much trust do you have? Who is the trusted entity? Do we trust Microsoft more than we trust the US government right now? Maybe that might be your contact. How you're going to build people, process and technology not to avoid a bad thing, but to achieve a positive objective because if you're not achieving that positive objective of understanding that safe to move about without masks on, for example, stop, just stop. >> Right, right. My favorite analogy Jeff, and I think I've said this to you in the past is we don't sit around and debate the merits of viscosity of water to protect concrete holes. We have to make sure that when you lead them to the concrete hole, there's enough water in the hole. No, you're building a swimming pool. What kind of a swimming pool do you want? Is it commercial, Is it toddlers? Is it (indistinct), then you build in correlation, protection and da da da da. But if you start looking at every problem as how to avoid hitting a concrete hole. You're really going to miss the opportunity to build and solve the problem that you want and avoid the risk that you do not want. >> Right right, and I want to go back to you on the trust thing. You got an interesting competent in that other show, talking about working for the government and not working directly for the people are voted in power, but for the kind of the larger bureaucracy and agency. I mean, the Edelman Trust Barometer is really interesting. They come out every year. I think it's their 20th year. And they break down kind of like media, government and business. And who do you trust and who do you not trust? What what's so fascinating about the time we're in today is even within the government, the direction that's coming out is completely diametrically opposed oftentimes between the Fed, the state and the local. So what does kind of this breakdown of trust when you're getting two different opinions from the same basic kind of authority due to people's ability or desire to want to participate and actually share the stuff that maybe or maybe not might get reshared. >> It leaves you with no confidence. Basically, you can't take confidence in any of this. And when I was privacy commissioner. I served for three terms, each term that was a different government, different political power in place. And before they had become the government, they were all for privacy and data protection believed in and all that. And then once they became the government all that changed and all of a sudden they wanted to control everyone's information and they wanted to be in power. No, I don't trust government. You know, people often point to the private sector as being the group you should distrust in terms of privacy. I say no, not at all. To me far worse is actually the government because everyone thinks they're there to do good job and trust them. You can't trust. You have to always look under the hood. I always say trust but verify. So unfortunately we have to be vigilant in terms of the protections we seek for privacy both with private sector and with the government, especially with the government and different levels of government. We need to ensure that people's privacy remains intact. It's preserved now and well into the future. You can't give up on it because there's some emergency a pandemic, a terrorist incident whatever of course we have to address those issues. But you have to insist upon people's privacy being preserved. Privacy forms the foundation of our freedom. You cannot have free and open societies without a solid foundation of privacy. So I'm just encouraging everyone. Don't take anything at face value, just because the government tells you something. It doesn't mean it's so always look under the hood and let us ensure the privacy is strongly protected. See emergencies come and go. The pandemic will end. What cannot end is our privacy and our freedom. >> So this is a little dark in here, but we're going to lighten it up a little bit because there's, as Michelle said, you know, if you think about building a pool versus putting up filling a hole, you know, you can take proactive steps. And there's a lot of conversation about proactive steps and I pulled Ann your thing Privacy by Design, The 7 Foundational Principles. I have the guys pull up a slide. But I think what's really interesting here is, is you're very, very specific prescriptive, proactive, right? Proactive, not reactive. Privacy is the default setting. You know, don't have to read the ULAs and I'm not going to read the, all the words we'll share it. People can find it. But what I wanted to focus on is there is an opportunity to get ahead of the curve, but you just have to be a little bit more thoughtful. >> That's right, and Privacy By Design it's a model of prevention, much like a medical model of prevention where you try to prevent the harms from arising, not just deal with them after the facts through regulatory compliance. Of course we have privacy laws and that's very important, but they usually kick in after there's been a data breach or privacy infraction. So when I was privacy commissioner obviously those laws were intact and we had to follow them, but I wanted something better. I wanted to prevent the privacy harms from arising, just like a medical model of prevention. So that's a Privacy By Design is intended to do is instantiate, embed much needed privacy protective measures into your policies, into your procedures bake it into the code so that it has a constant presence and can prevent the harms from arising. >> Jeffrey: Right right. One of the things I know you love to talk about Michelle is compliance, right? And is compliance enough. I know you like to talk about the law. And I think one of the topics that came up on your guys' prior conversation is, you know, will there be a national law, right? GDPR went through on the European side last year, the California Protection Act. A lot of people think that might become the model for more of a national type of rule. But I tell you, when you watch some of the hearings in DC, you know, I'm sure 90% of these people still print their emails and have their staff hand them to them. I mean, it's really scary that said, you know, regulation always does kind of lag probably when it needs to be put in place because people maybe abuse or go places they shouldn't go. So I wonder if you could share your thoughts on where you think legislation is going to going and how should people kind of see that kind of playing out over the next several years, I guess. >> Yeah, it's such a good question Jeff. And it's like, you know, I think even the guys in Vegas are having trouble with setting the high laws on this. Cameron said in I think it was December of 2019, which was like 15 years ago now that in the first quarter of 2020, we would see a federal law. And I participated in a hearing at the Senate banking committee, again, November, October and in the before times. I'm talking about the same thing and here we are. Will we have a comprehensive, reasonable, privacy law in the United States before the end of this president's term. No, we will not. I can say that with just such faith and fidelity. (laughing) But what does that mean? And I think Katie Porter who I'm starting to just love, she's the Congresswoman who's famous for pulling on her white board and just saying, stop fudging the numbers. Let's talk about the numbers. There's about a, what she calls the 20% legislative flip phone a caucus. So there are 20% or more on both sides of the aisle of people in the US who are in the position of writing our laws. who are still on flip phones and aren't using smart phones and other kinds of technologies. There's a generation gap. And as much as I can kind of chuckle at that a little bit and wink, wink, nudge, nudge, isn't that cute. Because you know, my dad, as you know, is very very technical and he's a senior citizen. This is hard. I hope he doesn't see that but... (laughing) But then it's not old versus young. It's not let's get a whole new group and crop and start over again. What it is instead and this is, you know, as my constant tome sort of anti compliance. I'm not anti compliance. You got to put your underwear on before your pants or it's just really hard. (laughing) And I would love to see anyone who is capable of putting their underwater on afterwards. After you've made the decision of following the process. That is so basic. It comes down to, do you want the data that describes or is donated or observed about human beings. Whether it's performance of your employees. People you would love to entice onto your show to be a guest. People you'd like to listen and consume your content. People you want to meet. People you want to marry. Private data as Ann says, does the form the foundation of our freedom, but it also forms the foundation of our commerce. So that compliance, if you have stacked the deck proactively with an ethics that people can understand and agree with and have a choice about and feel like they have some integrity. Then you will start to see the acceleration factor of privacy being something that belongs on your balance sheet. What kind of data is high quality, high nutrition in the right context. And once you've got that, you're in good shape. >> I'm laughing at privacy on the balance sheet. We just had a big conversation about data on the balance sheets. It's a whole, that's a whole another topic. So we can go for days. I have Pages and pages of notes here. But unfortunately I know we've got some time restrictions. And so, and I want to give you the last word as you look forward. You've been in this for a while. You've been in it from the private side, as well as the government side. And you mentioned lots of other scary things, kind of on the horizon. Like the kick of surveillance creep, which there's all kinds of interesting stuff. You know, what advice do you give to citizens. What advice do you give to leaders in the public sector about framing the privacy conversation >> I always want to start by telling them don't frame privacy as a negative. It's not a negative. It's something that can build so much. If you're a business, you can gain a competitive advantage by strongly protecting your customer's privacy because then it will build such loyalty and you'll gain a competitive advantage. You make it work for you. As a government you want your citizens to have faith in the government. You want to encourage them to understand that as a government you respect their privacy. Privacy is highly contextual. It's only the individual who can make determinations relating to the disclosure of his or her personal information. So make sure you build that trust both as a government and as a business, private sector entity and gain from that. It's not a negative at all, make it work for you, make it work for your citizens, for your customers, make it a plus a win win that will give you the best returns. >> Isn't it nice when doing the right thing actually provides better business outcomes too. It's like diversity of opinion and women on boards. And kind of things- >> I love that. we cover these days. >> Well ladies, thank you very very much for your time. I know you've got a hard stop, so I'm going to cut you loose or else we would go for probably another hour and a half, but thank you so much for your time. Thank you for continuing to beat the drum out there and look forward to our next conversation. Hopefully in the not too distant future. >> My pleasure Jeff. Thank you so much. >> Thank you. >> Thank you too. >> All right She's Michelle. >> She's Ann. I'm Jeff. You're watching theCUBE. Thanks for watching. We'll see you next time. (upbeat music)

>> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're having a Cube conversation in our Palo Alto studio, the conference season hasn't got to full swing yet, so we can have a little bit more relaxed atmosphere here in the studio and we're really excited, as part of our continuing coverage for the Data Makes Possible sponsored by Western Digital, looking at cool applications, really the impact of data and analytics, ultimately it gets stored usually on a Western Digital hard drive some place, and this is a great segment. Who doesn't like talking about sports, and football, and advanced analytics? And we're really excited, I have John Pollard here, he is the VP of Business Development for Zebra Sports, John, great to see you. >> Jeff, thanks for having me. >> Absolutely, so before we jump into the fun stuff, just a little bit of background on Zebra Sports and Zebra Technologies. >> Okay well, first, Zebra Technologies is a publicly traded company, we started in the late 1960s, and really what we do is we track enterprise assets in industries typically like healthcare, retail, travel and logistics, and transportation. And what we've done is take that heritage and bring that over into the world of sports, starting four years ago with our relationship with the NFL as the official player tracking technology. >> It's such a great story of an old-line company, right? based in Illinois-- >> Yeah, Lincolnshire. >> Outside of Chicago, right? RFID tags, and inventory management, and all this kind of old-school stuff. But then to take that into this really dynamic world, A, of sports, but even more, advanced analytics, which is relatively new. And we've been at it for a few years, but what a great move by the company to go into this space. How did they choose to do that? >> Well it was an opportunity that just came to them through an RFP, the NFL had investigated different technologies to track players including optical and a GPS-based technologies, and now of course with Zebra, our location and technologies are based on RFID. And so we just took the heritage and our capabilities of really working at the edge of enterprises in those traditional industries from transactional moments, to inventory control moments, to analytics at the end, and took that model and ported it over to football, and it's turned out to be a very good relationship for us in a couple of ways. We've matured as a sports business over the four years, we've developed more opportunities to take our solutions, not just in-game but moving them into the practice facilities for NFL teams, but it's also opened up the aperture for other industries to now appreciate how we can track minute types of information, like players moving around on the football field, and translating it into usable information. >> So, for the people that aren't familiar, they can do a little homework. But basically you have a little tag, a little sensor, that goes onto the shoulder pads, right? >> There's two chips. >> Two chips, and from that you can tell where that player is all the time and how they move, how they fast they move, acceleration and all the type of stuff, right? >> Correct, we put two chips inside of the shoulder pads for down linemen, or people who play with their hands on the ground, we put a third chip between the shoulder blades. Those chips communicate with receiver boxes that have been installed across the perimeter or around the perimeter of a stadium, and they blink 12 times per second. And that does tell you who's on the field, where they are on the field, and in proximity to other players on the field. And once the play starts itself, we can see how fast they're going, we can calculate change of direction, acceleration and deceleration metrics, we can also see, as you know with football, interesting information like separation from a wide receiver in defensive back, which is critical when you're evaluating players' capabilities. >> So, this started about four years ago, right? >> Yes, we started our relationship with the league in-game, four years ago. >> Okay, so I'd just love to kind of hear your take on how the evolution of the introduction of this data was received by the league, received by the teams, something they'd never had before, right? Kind of a look and feel and you can look at film, but not to the degree and the tightness of tolerances that you guys are able to deliver. >> Well, like any new technology and information resource, it takes time to first of all determine what you want to do with that information, you have an idea when you start, and then it evolves over time. And so what we started with was tagging the players themselves and during the time, what we've really enjoyed in working with the NFL is that the league has to be very pragmatic and thoughtful when introducing new technologies and information. So they studied and researched the information to determine how much of this information do they share with the clubs, how much do they share with the fans and the media, and then what type of information sharing, what does that mean in terms of impact of the integrity of the game and fair competition. So, for the first two years it was more of a research and testing type of process, and starting in 2016 you started to see more of an acceleration of that data being shared with the clubs. Each club would receive their own data for in-game, and then we would start to see some of that trickle out through the NFL's Next Gen Stats brand banner on their NFL.com site. And so then we start to see more of that and then what I think we've really seen pick up pace certainly in 2017 is more utilization of this information from a media perspective. We're seeing it more integrated into the broadcasts themselves, so you have like kind of a live tracking set of information that keeps you contextually involved in the game. >> Right. And you were involved in advanced analytics before you joined Zebra, so you've been kind of in this advanced stats world for a while. So how did it change when you actually had a real-time sensor on people's bodies? >> Yeah it does feel a bit like Groundhog Day, right? I started more in the stats and advanced analytics when I worked for STATS LLC. In 2007, I developed a piece of software for the New Orleans Saints that they used to track observational statistics to game video. And it was a similar type of experience in starting in 2009 and introducing that to teams where it took about three or four years where teams started to feel like that new information resource was not a nice to have but a need to have, a premium ingredient that they could use for game planning, and then player evaluation, and also the technology could provide them some efficiencies. We're seeing that now with the tracking data. We just returned from the NFL Combine a couple weeks ago, and what I felt in all the conversations that we had with clubs was that there was a high level of appreciation and a lot of interest in how tracking data can help facilitate their traditional scouting and player evaluation processes, the technology itself how can it make the teams more efficient in evaluating players and developing game plans, so there's a lot of excitement. We've kind of hit that tipping point, if I may, where there's general acceptance and excitement about the data and then it's incumbent upon us as a partner with the league and with the teams for our practice clients to teach them how to use the analytics and statistics effectively. >> So I'm just curious, some of the specific data points that you've seen evolve over time and also the uses. I think you were talking about a little bit off camera that originally it was really more the training staff and it was really more kind of the health of the player. Then I would imagine it evolved to now you can actually see what's going on in terms of better analysis, but I would imagine it's going to evolve where coaches are getting that feedback in real-time on a per-play basis and are making in-game adjustments based on this real-time data. >> Well technically that's feasible today but then there's the rules of engagement with the league itself, and so the teams themselves, and the coaches, and the sideline aren't seeing this tracking data live, whether it be in the booth or on the sidelines. Now in a practice environment, that's what teams are using our system for. With inside of three seconds they're seeing real-time information show up about players during practice. Let's take an example, a player during practice who's coming back from injury. You might want to monitor their output during the week as they come back and they make sure that they're ready for the game on a week to week basis. Trainers are now able to see that information and take that over to a position coach or a head coach and make them aware of the performance of the player during practice. And I think sometimes people think with tracking data it's all about managing in the health of the player and making sure they don't overwork. Where really, the antithesis of that is you can actually also identify players who aren't necessarily reaching their maximum output that will help them build throughout the week from peak performance during a game. And so a lot of teams like to say okay, I have a wide receiver, I know their max miles per hour, is, let's use an example, 20.5 miles an hour. He hasn't hit his max yet during the entire week, so let's get him into some drills and some sessions, where he can start hitting that max so that we reduce the potential for injury on game day. >> Right, another area that probably a lot of people would never think is you also put sensors on the refs. So you know not only where the refs are, but are they in the right positions technically and kind of from a best practices to make the calls for the areas that they're trying to cover. >> Right. >> There's got to be, was their a union pushback on this type of stuff? I mean there's got to be some interesting kind of dynamics going on. >> Yeah as far as the referees, I know that referees are tagged and the NFL uses that information and correlates that with the play calls themselves. We're not involved in that process but I know they're utilizing the information. In addition to the referees I should add, we also have a tag in the ball itself. >> [Jeff] That's right. >> 2017 season was the first year that we had every single game had a tagged ball. Now that tagged information in the ball was not shared with the clubs yet, the league is still researching the information, like they did with the players' stuff. A couple years of research, then they decide to distribute that to the teams and the media. So we are tracking a lot of assets, we also have tags in the first down markers and the pylons and I'll just cut to the chase, there are people who will say okay, does that mean you can use these chips and this technology to identify first down marks or when a ball might break the plane for a potential touchdown? Technically you can do that, and that's something the league may be researching, but right now that's not part of our charter with them. >> Right, so I'm just curious about the conversations about the data and the use of the data. 'Cause as you said there's a lot of raw data, and there's kind of governance issues and rules of engagement, and then there's also what types of analytics get applied on top of that data, and then of course also it's about context, what's the context of the analytics? So I wonder if you could speak to the kind of the evolution of that process, what were people looking at when you first introduced this four years ago, and how has it moved over time in terms of adding new analytics on top of that data set? >> That's one of my favorite topics to talk about, when we first started with the league and engaging teams for the practice solution or providing them analytics, they in essence got a large raw data file of XY coordinates, you can imagine (laughs) it was a gigantic hard drive-- >> Even better, XY coordinates. >> And put it into a spreadsheet and go. There was some of that early on and really what we had to do through the power of software, is develop and application platform that would help teams manage and organize this data appropriately, develop the appropriate reports, or interesting reports and analysis. And over the last two or three years I think we've really found our stride at Zebra in providing solutions to go along with the capabilities of the technology itself. So at first it was strength and conditioning coaches, plowing through this information in great detail or analytics staffs, and what we've seen over the last 24 months is director of analytics now, personnel staff, coaches as well, a broadening group of people inside of a football organization start to use this data because the software itself allows them to do so. I'll give an example, instead of just tabular information, and charts and graphs, we now take the data and we can plot them into a play field schematic, which as you know as we talked off camera you're very familiar with football, that just automates the process of what teams do today manually, is develop play cards so they can do self-study and advanced scouting techniques. That's all automated today, and not only that, it's animated because we have the tracking information and we can merge that to game video. So we're just trying to make the tools with the software more functional so everybody in the organization can utilize it beyond strength and conditioning, which is important, but now we're broadening the aperture and appealing to everybody in the organization. >> Do you do, I can just see you can do play development too, if you plug in everybody's speeds and feeds, you have a certain duration of time, you can probably AB test all types of routes, and timing on drops and now you know how hard the guy throws the ball to come up with a pretty wide array of options, I would imagine within the time window. >> Exactly, a couple of examples I could give, when we meet with teams we have every player, let's say on a team and we know all the routes they ran during an entire season. So you can imagine on a visualization tool, you can imagine, it's like a spaghetti chart of different routes and then you start breaking down the scenarios of context like we talked about earlier, it's third down, it's in the red zone, it's receptions. And so that becomes a smaller set of lines that you see on the chart. I'll tell you Jeff, when we start meeting with teams at the Combine and we start showing them their X or a primary receiver, or their slot receiver tendencies visually, they start leaning forward a bit, oh my goodness, we spend way too much time on the same route when we're targeting for touch down passes. Or we're right-handed too much, we have to change that up. That's the most gratifying thing, is that you're taking a picture and you're really illuminating and those coaches who intrinsically know that, but once they see a visual cue, it validates something in their head that either they have to change or evolve something in their game plan or their practice regimen. >> Well, that's what I was going to ask, and you lead right into it is, what are some of the things that get the old-school person or the people that just don't get that, they don't get it, they don't have the time, they don't believe it, or maybe believe it but they don't have the time, they're afraid to understand. What are some of those kind of light bulb moments when they go okay, I get it, as you said, most of the time if they're smart, it's going to be kind of a validation of something they've already felt, but they've never actually had the data in front of them. >> Right, that's exactly right. So that, the first thing is just quantifying, providing a quantifiable empirical set of evidence to support what they intrinsically know as professional evaluators or coaches. So we always say that they data itself and the technology isn't meant to be a silver bullet. It's now a new premium ingredient that can help support the processes that existed in the past and hopefully provide some efficiency. And so that's the first thing, I think the visual, the example I showed about the wide receiver tendencies when they're thrown to in the red zone, that always gets people leaning forward a little bit. Also with running backs, third down in three plus yards, or third down in short situations, and my right-hander to left-hander when I'm on a certain hash. Again the visualization just allows them to really mark something in their head-- >> Just in the phase. >> Where it makes them really understand. Another example that's interesting is players who play on special teams who are also wide receivers, so as we know, linebackers and tight ends tend to be, and quarterbacks tend to be involved in special teams. Well is there an effect when they've covered kick offs and punts, a large amount of those in a game, did that affect them on side a ball play, for instance? Think about Julian Edelman two Superbowls ago, he played 93 snaps against the Atlanta Falcons. and when you look at the route-- >> [Jeff] He played 93 snaps? >> Yeah, between special, because it went into overtime, right? It was an offensive game-- >> And he's on all the-- >> He played a lot of snaps, he played 93 snaps. how does that affect his route integrity? Not only the types and quality of the route, but the depth and speed he gets to those points, those change over time. So this type of information can give the experts just a little bit more information to find that edge. And I have a great mentor of mine, I have to bring him up, Gill Brant, former VP of Personnel to Dallas Cowboys, with Tex Schramm and Tom Landry, he looks at this type of information and he says, what would a team pay for one more victory? >> So as we know, all coaches and professional organizations and college are looking for an edge, and if we can provide that with our technology through efficiencies and some type of support information resource then we're doing our job. >> I just wanted to, before I let you go, just the human factors on that. I mean, football coaches are notoriously crazy workers and, right, you can always watch more films. So now you're adding a whole new category of data and information. How's that being received on their side? Is it, are they going to have to put new staff and resources against this? I mean, there's only so many hours in a day and I can't help but think of the second tier or third tier coaches who are going to be on the hook for going through this. Or can you automate so much of it so it's not necessarily this additional burden that they have to take on? 'Cause I would imagine if the Cowboys are doing it, the Eagles got to do it, the Giants got to do it, and the Washington Redskins got to do it, right? >> Right, right, well each team as you might expect, their cultures are different. And I would say two or three years ago you started to see more teams hire literally by title, director of analytics, or director of football information, instead of sharing that responsibility between two or three people that already existed in the organization. So that staffing I think occurred a couple, two or three years ago or over the last two or three years. This becomes another element for those staffs to work with. But also along that process over the last two or three years is, really, I always try to say in talking to teams and I'll be on the road again here soon talking to clubs after pro days conclude, is forget about staffs and analytics and that idea. Do you want to be information driven, and do you want to be efficient? And that's something everybody can grasp onto, whether you're the strength and conditioning coach, personnel staff or scout, or a position coach, or a head coach, or a coordinator. So we try to be information driven, and then that seems to ease the process of people thinking I have to hire more people. What I really need to do is ask my people that are already in place to maybe be more curious about this information, and if we're going to invest in a resource that can help support them and make them more efficient, make sure we leverage it. And so that's our process that we work with, it varies by team, some teams have large, large expansive staffs. That doesn't necessarily mean, in my opinion the most effective staff is using information. Sometimes it's the organizations that run very lean with a few set of people, but very focused and moving in one direction. >> I love it, data for efficiency, right? In God we trust, everybody else bring data. One of my favorite lines that we hear over and over and over at these shows. >> In fact, I might borrow that next week. >> You could take that one, alright. >> Thank you, Jeff. >> Well John, thanks for taking a few minutes and stopping by and participating in this Western Digital program, because it is all about the data and it is about efficiency, so it's not necessarily trying to kill people with more tools, but help them be better. >> That's what we're trying to do, I appreciate the opportunity and love to talk to you more. >> Absolutely, well hopefully we'll see you again. He's John Pollard, I'm Jeff Frick, you're watching theCUBE from Palo Alto studios, thanks for watching, we'll see you next time. (Upbeat music)

live from Los Angeles it's the queues covering open-source summit North America 2017 brought to you by the Linux Foundation and redhead he welcome back everyone here live in LA for the open source summit in North America I'm jumper with my co-host Jeff Fritz too many men he'll be back shortly is out scouring the hallways for all the news and analysis getting all the scuttlebutt are here we're here with our next guest brian behlendorf who is the executive director of the hyper ledger project for the Linux Foundation thanks for coming on thank ledger thanks for sharing we just talking before the camera started rolling about blockchain and the coolness around the hype around it but again the hype cycle is usually a pretext to the trend hyper ledger is one of those exciting projects that like AI everyone is jazzed about because it's the future right open source is getting bigger and bigger as Jim zemulon was saying 23 million developers and growing but there's still so much work to be done the global society's relying on open source it's shaping our culture - Ledger's one of those things where it is going to actually disrupt the culture and change it potentially and even this morning Chinese band virtual currencies and icos and all based upon doesn't mean it's time to invest yes and whatever China bands it's always been successful so your thoughts go first boy star let's get into hyper ledger project it's certainly super exciting probably people are talking about it heavily what's going on with the project give a quick update what's the purpose who's involved and when some of the milestones you guys have hyper ledger is less than two years old it was launched officially in December of 2015 I joined in main and it was founded on the principle that hey there's a lot of interesting stuff happening in the cryptocurrency world but there might be some more prosaic some more directly applicable applications of distributed ledger and smart contract technology to rebooting a lot of otherwise very thorny problems for industries in the world the main problem being you've got companies doing business with each other and the recording transactions and you know they'll have to go back and reconcile their systems to get audited bugs right and a lot of the systems out there depend upon processes at a very human processes that are prone to error prone to corruption right so the idea is the more that you can pull together you know information about transactions into a shared system of record which is really with the distributed ledger it's and then the more about of the governance and the and the business processes enclosed that you can automate by smart contract the more effective the more efficient a lot of these markets will be so that's what hyper ledger is about ok so certainly the the keynote was all about open sources being dependent upon and Jim's Emlyn as well as Christine Corbett said you know traditionally control we all know that open source but I love that the deployment changing the face of capitalism because hyper ledger is a term that you can almost apply to the notion of decentralize not just distributed but decentralized business so the notion of supply chain things in finance to moving Goods around the world this is interesting this is how about the impact of how you guys are seeing some of these applications we're now a decentralized architecture combined with distributed creates an opportunity for changing the face of capitalism flowing because the word distributed can be very loaded all right you know and even decentralized right it can be very loaded and what I what I tried to popularize is the idea of minimum viable centralization right you know football games and other sports games have referees right and when we play a game like this well sometimes you know sometimes we don't need a referee it's just us playing pick-up basketball but we want somebody on the periphery we all agree to who helps remind us what the rules are and throws a red flag from time to time all right and so you see in industries ranging from finance where you're building these transaction networks to you know supply chains where you need to track the flow of like food and to know when if food has gotten spoiled possibly where that came from or diamonds that have been involved in conflict time and you know other illegal activities right you want to know where that came for a minute and it involves that industry getting together and saying we all agree we have a big net interest in making our business actually follow certain rules and norms right and using a distributed ledger to to bring that about it's something that can just provide a lot of optimizations so most people think of like Bitcoin and ether a mezda with all this ICO buzz as de as the front end to really the underlying blockchain which you're talking about yeah and that's kind of like I get that fiat currency in this market developed to look crazed bubbles some people call it whatever but you're getting at something unique and this is that there's a real business value of hyper ledger I won't say boring but it's like meat and potatoes stuff it's like really kind of prosaic is the prosaic it's like so but it's disruptive so if you think about like the old days when we were growing up or I was growing up ERP was on mini computers and the prized resource planning relationship management software those were bloated monolithic software packages yeah still out there today and they handle the so called supply chain right so is the hypervisor a disruption to that is it an augmentation of that so some try to put it in context the cost of sending a shipping container from China to the United States right half of that is in paperwork half of that is because that container on average will go through 30 different organizations from the the you know the suppliers that you're assembling the goods into to all the different ports all the different regulatory authorities right out finally to where it's delivered and if you can optimize those business processes if you can make it so that the happen in a space where it's not about paper and facts which a lot of that world is still ruled by today or a bureaucrat sitting there reviewing stuff that's coming in and having to stamp it when really all that could be automated you could cut the cost of that and take the shipping industry from what is right now a money-losing industry to potentially being viable once again so optimization is really critical for them it's optimization but it but there's also some new capabilities here so I spent a year at Department of Health and Human Services trying to help make health care records more portable for patients right and we wrote it and got it I got the industry to write a ton of open source software implemented open standards to make these records shareable the problem was the patient wasn't involved right this was about trying to take two orgs do something that all of their bean counters told them not to do which was share patient records because no that's proprietary value and the HIPAA regulations all that not exactly blackens processes basically with blocking with blocking technology that we can reinvent that as a patient driven process right we could reinvent a lot of the other business processes out there that involve personally identifiable information like the Equifax disaster right we could reinvent how the credit markets assess risk in individuals through blockchain technology in a way that doesn't require us to build these big central anonymous third parties that Coover everybody's data and become these massive privacy titanic's right we can reinvent a lot of this through blockchain tech and that's a lot of what we're working on that Nagaraja because a analytics from that kind of a unique place because you're used to driving these big open-source projects there's a lot of people and they're trying to build the wrapper around the base core of blockchain to come up with their version or their kind of application if you will whether it be Bitcoin or whatever but you guys are in kind of a special place based on your roots we believe that I mean open standards are nice but what really matters is common code right and in a world like we envision where rather than saying you one big Network like Bitcoin or one big Network like aetherium you've got thousands or tens of thousands of these permission networks that cover different industries different geographies different regions what you need is common software so that when a developer goes to work on an application that touches one or multiple of these they've got familiar idioms to work they've got familiar technologies to work with like NGO or Java or JavaScript right but they've got a community of other technologies has been trained up on these technologies that can help them bootstrap and launch their project and maybe even become a contributor to the open source so what we've figured out at the Linux Foundation is how to make that virtuous cycle go right companies you know benefit commercially from it and then feed back into the project and that's what we're mentioning the word you get almost rethink and reimagine some of these things like the Equifax disaster yeah I think it's pretty man no breathing most tech people I really seen as as viable like absolutely it's gonna happen so there's a nice trajectory vision that people are buying into because it's somewhat you can see it hanging together playing out technically what are some of the things going on the project can you share with the folks watching about some things that you're doing to get there faster what's going on with the community with some of the issues with concerns how do people get involved take some time to go tobut deep words of the project so we're not a you know an RD kind of free thinking kind of thing we're about get writing code and shipping and getting into production right so hyper ledger fabric just hit a one dot oh that was a signal from the developers that this code is ready to be run in production systems and for you to track digital assets right doesn't by far does not mean it's the end of the road it's the end of chapter one right but at least it's a place where we you know the kind of the clear intent is let's make this actually usable by enterprises the other projects we've got eight different projects total at hyper ledger some of them even compete with each other right but we're driving all of them to get to a one dot oh and over time all of them talk about how they relate to each other in kind of complimentary ways what's some of the profile developers you're getting because some people always ask I know what should I get involved what can I sink my teeth into what are some of the meaty kind of things that people are doing with it who the persona that that are coming in these enterprise developers they more traditional full-stack developers can you give a range of some of the persona attributes because this is early code still I mean this whole space is still pretty early when it comes to understanding how to use these technologies especially at scale kind of at a DevOps scale a lot of the people first coming into the tech community now are fairly advanced right are kind of the whiz kids right but we're seeing that gradually broad broaden out we now are at a point where we could use developers coming in and writing sample applications right we could use people helping us with documentation we're developing training materials that will be creative commons-licensed so everybody will be able to deliver those and as they find bugs or add features to the training they can do that too we can really use anybody all right so folks watching get involved okay get any white spaces you might want to tease them out with that you see happening obviously mentioned tracking digital assets data is a stress that's cool anything that's going on with data probably is a digital asset but you'd agree what's some of the things that people could get motivated can you share any insight that you might have that would motivate someone to jump in I think any any industry has these challenges of weaving their systems together with other businesses and then trying to do that in a way that holds each other.you account right this is a system for building systems of record between organizations right and you know you running a database to me running a database we don't get there on our own we only get there by working with consortio by working in as a community to actually build these systems and so I'd say every every business has that challenge whether they're engineers have felt free to go in and try to tackle that extranet days when you see people building citizen networks similar concept where blockchain is one big happy family collaborative network all right final question for you kind of shooting for a little bit what do you expect to happen community any thoughts on some of the goals you have is executive director obviously you got some hackathons for good we'll see blockchain being applied to some real things with one dot out what do you see rolling out which some of your goals I massively grow the developer community both the well you know the one end of the spectrum which is the the whiz kids the hardcore developers to you know move forward on a kind of the leading edge of that but really we've got to bring you know hundred thousand developers into this space or the next couple years just to meet the demand that's there in the industry for that town alright so if I'm a now an executive as a hey I saw this great Cuban in friens awesome go get involved what how did someone get involved is just jump standard community model just jump in what advice would you give someone if they want to engage and participate for every one of our projects if you give gave it an hour you'd get to a running you know instance of that software right so fabric or sawtooth within an hour you should ever running for node instance that you can start writing chain code two which is the smart contract language right and and then from there getting involved in the community as a matter of joining mailing list joining our rocket chat channels rocket chats an alternative to slack that we actually prefer and I and I think you'll find a really welcoming community of other devs who want to tell you about what the projects are and want to help you kind of climb that learning curve one of the comments just enough good note here is that Christina gave him the key no she says code can shape culture you've been in the industry a long time you've seen the wave you've been on the shoulders of others and now as the open source goes to the next level how is code gonna shape the culture in your opinion actually people started working together to take that I would say that almost I'm not a moon shot but it's really more of an imperative that culture will be changed inclusion else is huge your thoughts on code shaping culture so we've we've had a decline in trust in institutions in the United States and worldwide not just in the last seven months since November but actually for the last 20 years there's Edelman does this survey every year where they ask you your trust in brands your trust in government your trust in the process the fairness of society and for 20 years that's been on a straight-line decline to the point where we ask ourselves like can you trust any level of government can you trust businesses to look out for your interest the answer almost generically is going to be no this is a technology that can save us from this is a technology that we I believe can help us define the rules of the game help us build society but then actually automate and implement that in a way that doesn't require us to have to bribe an official or curry favor with a school official to get our kid into that school or anything like that this is a way to try I think to make the world more accountable and more fair and open source has that inclusive and staying away from the gerrymander and I love the quote it's so confusing now it's like who do you ask where's the source of truth and it used to be RTFM and check the source code now it's not only there is no manual who is the source fake news all these bots means kind of crazy so this is that a call to arms the open source I think it is I think it really is the trust as a service ok Brian thanks so much for come on if you appreciate it Thank You director for the hyper ledger project super important project really a game changer changing the face of capitalism also continuing the trend accelerate open source I'm Shaun Frechette for more live coverage from the queue after this short break

>> Announcer: Live from Las Vegas, it's The Cube, covering Interconnect 2017, brought to you by IBM. >> Okay, welcome back everyone. Here, live in Las Vegas for IBM Interconnect 2017, this is SiliconANGLE's The Cube's coverage. Three days, a lot of great interviews, more in day two here. I'm John Furrier, my co-host Dave Vellante, our next guest is Tanya Seajay, founder and CEO of Orenda Software Solutions. Welcome to The Cube. >> Thank you so much. >> So, your company does a lot of cool things with data. One of the things, obviously, in the news, you can't read a story these days without hearing something about Trump, Uber, bad behavior. >> Dave: Fake news. >> Fake news, there's always scandal. It's the internet, for crying out loud. Everything's going on, but reputation now is measurable and data is out there and companies now as they go on to digital as a medium end to end for marketing and engaging customers, they got to be careful. What's your take? What's going on in this marketplace? >> There's a couple of things that are happening simultaneously. One is, we talked about this just briefly, the Edelman Trust Barometer. It's a global survey that's done every year, and it started I believe in 2010. In 2017, the findings were that we are in a trust crisis globally, and you would have heard that from Marc with Salesforce today. That's what he was referencing. At the same time, PricewaterhouseCooper came out with another survey across North America, and it was that we are in the midst of a trust economy and trust is growing. So, at one point, we used to make our buying decisions on whether or not a product was convenient or a good sale price, those kinds of things. Now, we want to know whether or not we trust the brand, whether or not we trust the CEO, and whether or not the companies have purpose. So, our buying decisions are changing, so not only are we in a trust crisis but we are also a trust economy. So, measuring trust is exceptionally important and a value to all brands globally. >> This purpose thing is interesting. We've been seeing the same thing, and we just had South by Southwest, Intel. We were headlining the Intel AI Lounge, and they had this program, AI for Social Good, which has got a great program. It's on our YouTube channel, youtube.com/siliconangle, folks that are watching, but there's a counterculture going on right now, we're seeing in this world. The younger audience is coming in, the new generation, the digital natives. They're living in a digital world 100%, so there seems to be a counterculture of anti-what it was, pre-now, internet, what it was before, trolling, all this stuff's been around for a while. But you're starting to see people really focus in on good and mission purpose. There's an element where there's a new generation saying, we want to apply tech for good, and you're seeing it with equality, they mentioned a lot of things on the stage today. But beyond that, it's kind of this post-9/11 generation where, like, hey what are you, all you old people bickering about? Just do social good. I mean, do you seeing that too? We're seeing a lot of it across the board. Can you share any stories in this area? >> Yeah, social good is really important in terms of giving back to your community, and in the communities where you do business, you want to have that connection. So, when we were creating Orenda, the software that measures trust, it also measures a few other things. We went back into about 30 years of research in social science and selected, there are six key factors to a healthy relationship, and what we were calling corporate social responsibility is now just more or less social good. So, you want to do things that are good to the communities that you do business in, and there's also the exchange of benefits. I do something for you, you do something for me, which brings in the more collaborative systems and partnership ecosystems that exist. >> It's a community model, too. With open source growing, connected internet, everyone's connected to each other. That's a community framework. >> That's right. >> And that's kind of the, seems to be the trend. >> It is a trend, and at one point, companies used to market to their customers. Now, you see something quite different. Customers are empowered and they're engaging through content so the exchange is continuous. One of the examples we have is with Apple. So, every time your heart beats, someone is talking about Apple. It is so huge. >> The velocity, you mean the velocity. >> Yeah, just the velocity. There's so much information coming out. We were following 25 different companies in December, and we pulled in five million data points. So, that's the amount of information that is coming at us and at brands at any particular time. What we need to do was turn that into insights in real time. If not, it's useless. >> It's interesting you mention Apple. So, we have a data science group within SiliconANGLE, The Cube. We call it our cognitive beta program. We haven't released it yet, but we're looking at all the Twitter data and we can actually see all the tweets. And then, we can extrapolate the users and obviously get all the data, which phone they're using, tweeting from. And that came out, you saw Trump was on an Android, an iPhone. And here at this show, based on the data that we have, 76% of this audience, here and online, is iPhone over Android. So, you say, okay, big deal, ho-hum. Actually, demographically, it matters. Now, some shows, the more geeky shows, you'll see Android over iPhone, so it's a small little data point. But you can almost, like that movie Contact, where you open up one door, you can get all those different insights. So, a small data point like that could add to other data. >> It could, and it's unlocking it, like you said, that is the most important part. You can get all this data. You can get it continuously. But unlocking it and telling everybody what it means to them, and it can mean something different depending on what kind of solution or problem that you're trying to overcome. But yeah. >> Yeah, and the other concepts we follow a lot in the big data world is data at rest and data in motion. And Dave and I were just at breakfast this morning, talking about content and motion brands and motion. So, your company really is measuring the brand in motion, right? >> That's right. >> So, this is kind of a cool new cutting edge coolness. >> It's really cool. >> Explain what's going on there. What's the cutting edge tech? What are some stories? Good, bad, and the ugly? >> One of the interesting things that we just did is we were following five of the biggest banks in Canada, and at the same time, CBC, which is the national broadcasting company, did this go public article and it was extremely negative. And we were tracking them, so we were able to show in real time the trust levels dropping. And in correlation to that, we looked at the stock prices of those companies, and they were also dropping. So, to be able to demonstrate that the brand itself, the reputation, particularly trust, was what the issue was, and that makes a lot of sense. It's money, it's banks, it's trust. That's what's going to be impacted the most. But being able to correlate that, it's a piece of information that we haven't been able to use before. >> So, that's insight. So now, the actionable insight is, wow we should send someone in there digitally, parachute into the virtual news cycle, and provide content or perspective. I'm saying, they can get in, stop that bleeding. >> Get in and stop the bleeding. And the other thing is that they were five national banks, but only one of them was taking the hit for it. They were the actual face of the issue. So, to be able to say, we're all being hit by this particular news story, yes, but you're being hit the most. >> It's a classic public relations problem. If you don't react, then it gets settled in, it becomes a matter of fact. >> Yeah, so you need to be able to deal with that escalation in real time. >> So, what do you guys do that's different than, a lot of sentiment analysis and it's kind of an overcrowded space. >> Tanya: It's a busy space, yeah. >> What's unique in what you guys do? >> What's unique is the actual social science on top of that. So, there is positive, negative, which gives you a little bit of information. What we did is just put on a whole other filter, and we use social science to do that. So, in order to show the brand momentum that needs to exist for a more resilient company, we said we need to know whether or not trust is increasing or decreasing, commitment with the brand or loyalty to the brand is increasing or decreasing. This is really important information. Positive, negative just doesn't tell you enough. So, when you are doing your messaging from a public relations point, you know to talk about integrity if there's a trust issue that you're dealing with. If it's satisfaction, then it's something that you want to do better in terms of a particular product. So, you get to focus on what the actual problem is, so that's how we're absolutely unique, is that we're able to measure emotion in a very different way, through social science and key factors that need to exist for a healthy brand. >> And the secret sauce behind the tech is what? Is it some cognitive, it's data science? >> We do a couple of things. So, one of the reasons why we partnered with IBM and are using Watson, the APIs, is that we built our own algorithms and we have it interact with a huge dictionary of words that we use. And we had to be able to customize that because the way we use language is always different. The way we talk about oil gas is different than we would talk about Coca-Cola, say. So, we had to be able to customize the dictionary so that if we use the word recall with a car manufacturer, that's extremely negative. But recall within the healthcare system is probably neutral. So, we had to be able to make those differences. So then, we also use AI. We use the Personality Insights tool within Watson, so we can take a whole customer buying group, look at them as an individual's huge amounts of data, millions and millions of data points, and say this is what this particular customer group or stakeholder group, this is what they need as a group, this is what they value, these are their key personalities. So again, you just get that deeper insight into who's buying your product. >> And the data sources? Talk about where the data comes from. >> The data comes from social media, and why that's really important is because within public relations and communications, there's always been focus groups, right, where you try to pull out insights into our brand from focus groups or surveys. >> Weeks and weeks and weeks of research. >> Right? Weeks and weeks of research. And you still have just a certain amount of data that you get to deal with. This, we treat social media as a huge focus group with tremendous amounts of data, tremendous amounts of insights, and we can pull it out in real time. So, if there's an issue that is escalating, we can say this is what your customer base is saying about you, this is how the impact is. We don't have to go through months of research to deal with an issue we need to deal with within 10 minutes, usually. >> So, Twitter's obviously a huge source of data, is that correct? >> Twitter's huge. >> 'Cause it's so real time and there's so much of it. What other sources? Is that the primary or a primary source? >> Facebook is interesting. You can get public information, but you can't private. Instagram is another. Blogs are a great source of information as well. Almost any online information where there's engagement, so there's a conversation that's taking place. If it's static, it doesn't, it doesn't really have an impact on you, right? >> Is there third party data sources that you use that other people use as well? Is it Twitter Firehose? Is it RSS feeds? Is there like a syndicate of data sources? >> We use GNIP, so that's owned by Twitter. Yeah, that's what we use. >> But for blogs, how do they get the blogs? >> You scrape them. >> So you scrape them. So RSS feeds and. >> Yeah, and I really enjoy the fact that a lot of governments are going into open source data, so the more we get, the better it is. We have a couple of relationships, partnerships with national media sources as well, so we're able to use that and go back into time, thankfully, from their end. >> Tanya, what's the coolest or weirdest discovery you've made with the data? Because as you get all this gesture data, I'm sure there's some things that just, whoa. >> One of the funnest for me, I'm a bit of a political nerd, and so I really, really enjoy politics. And when we were building out Orenda, we used the federal election in Canada, and yes we did do some with the US election too, but it was so much data, it was. (John and Dave laugh) >> John: Big tsunami. >> Yeah, thanks a lot, John. >> It's not stopping by the way either. It's continuing to go on. >> But yeah, the funniest moment, that one, just as an aside, was the whole, would you rather have Trump or a mozza stick as president, which was, really gained popularity. But for the federal election, what we did was follow the four federal candidates, and we were able to show when we stopped as a nation talking about Thomas Mulcair as the next leader and when we started talking about Justin Trudeau. And we were able to predict that Justin Trudeau's brand was building momentum, weeks before the polls came out and said that the machine changed. >> This year's contender. Alright. Well, Tanya, thanks so much for coming on The Cube. Really appreciate it. I love what you guys do. I think that's, you're on the cutting edge of really compelling social science, and as the culture deals with autonomous driving cars and smart cities, I think this is going to be an ongoing field of study of understanding the relationship between data and humans with respect to societal changes. So, again, this is I think one small use case of really an exploding area. So, thanks for sharing. It's The Cube here live in Las Vegas. For more Interconnect coverage, after this short break, I'm John Furrier with Dave Vellante. We'll be right back. Stay with us.