(bright music) >> Hello everyone, welcome today's session for the "AWS Startup Showcase" presented by theCUBE, featuring ThoughtSpot for this track and data and analytics. I'm John Furrier, your host. Today, we're joined by Victor Chang, VP of ThoughtSpot Everywhere and Corporate Development for ThoughtSpot. Victor, thanks for coming on and thanks for presenting. Talking about this building interactive data apps through ThoughtSpot Everywhere. Thanks for coming on. >> Thank you, it's my pleasure to be here. >> So digital transformation is reality. We're seeing it large-scale. More and more reports are being told fast. People are moving with modern application development and if you don't have AI, you don't have automation, you don't have the analytics, you're going to get slowed down by other forces and even inside companies. So data is driving everything, data is everywhere. What's the pitch to customers that you guys are doing as everyone realizes, "I got to go faster, I got to be more secure," (laughs) "And I don't want to get slowed down." What's the- >> Yeah, thank you John. No, it's true. I think with digital transformation, what we're seeing basically is everything is done in the cloud, everything gets done in applications, and everything has a lot of data. So basically what we're seeing is if you look at companies today, whether you are a SaaS emerging growth startup, or if you're a traditional company, the way you engage with your customers, first impression is usually through some kind of an application, right? And the application collects a lot of data from the users and the users have to engage with that. So for most of the companies out there, one of the key things that really have to do is find a way to make sense and get value for the users out of their data and create a delightful and engaging experience. And usually, that's pretty difficult these days. You know, if you are an application company, whether it doesn't really matter what you do, if you're hotel management, you're productivity application, analytics is not typically your strong suit, and where ThoughtSpot Everywhere comes in is instead of you having to build your own analytics and interactivity experience with a data, ThoughtSpot Everywhere helps deliver a really self-service interactive experience and transform your application into a data application. And with digital transformation these days, all applications have to engage, all applications have to delight, and all applications have to be self-service. And with analytics, ThoughtSpot Everywhere brings that for you to your customers and your users. >> So a lot of the mainstream enterprises and even businesses from SMB, small businesses that are in the cloud are scaling up, they're seeing the benefits. What's the problem that you guys are targeting? What's the use case? When does a potential customer or customer know they get that ThoughtSpot is needed to be called in and to work with? Is it that they want low code, no code? Is it more democratization? What's the problem statement and how do you guys turn that problem being solved into an opportunity and benefit? >> I think the key problem we're trying to solve is that most applications today, when they try to deliver analytics, really when they're delivering, is usually a static representation of some data, some answers, and some insights that are created by someone else. So usually the company would present, you know, if you think about it, if you go to your banking application, they usually show some pretty charts for you and then it sparks your curiosity about your credit card transactions or your banking transactions over the last month. Naturally, usually for me, I would then want to click in and ask the next question, which transactions fall into this category, what time, you know, change the categories a bit, usually you're stuck. So what happens with most applications? The challenge is because someone else is asking the questions and then the user is just consuming static insights, you wet their appetite and you don't satisfy it. So application users typically get stunted, they're not satisfied, and then leave application. Where ThoughtSpot comes in, ThoughtSpots through differentiation is our ability to create an interactive curiosity journey with the user. So ThoughtSpot in general, if you buy a standalone, that's the experience that we really stand by, now you can deliberate your application where the user, any user, business user, untrained, without the help of an analyst can ask their own questions. So if you see, going back to my example, if it's in your banking app, you see some kind of visualization around expense actions, you can dig in. What about last month? What about last week? Which transactions? Which merchant? You know, all those things you can continue your curiosity journey so that the business user and the app user ask their questions instead of an analyst who's sitting in the company behind a desk kind of asking your questions for you. >> And that's the outcome that everyone wants. I totally see that and everyone kind of acknowledges that, but I got to then ask you, okay, how do you make that happen? Because you've got the developers who have essentially make that happen and so, the cloud is essentially SaaS, right? So you got a SaaS kind of marketplace here. The apps can be deployed very quickly, but in order to do that, you kind of need self-service and you got to have good analytics, right? So self-service, you guys have that. Now on the analytics side, most people have to build their own or use an existing tool and tools become specialists, you know what I'm saying? So you're in this kind of like weird cycle of, "Okay, I got to deploy and spend resource to build my own, which could be long and tiresome." >> Yeah. >> "And or rely on other tools that could be good, but then I have too many tools but that creates specialism kind of silos." These seems to be trends. Do you agree with that? And if customers have this situation, you guys come in, can you help there? >> Absolutely, absolutely. So, you know, if you think about the two options that you just laid out, that you could either roll your own, kind of build your own, and that's really hard. If you think about analyst industry, where 20, $30 billion industry with a lot of companies that specialize in building analytics so it's a really tough thing to do. So it doesn't really matter how big of a company you are, even if you're a Microsoft or an Amazon, it's really hard for them to actually build analytics internally. So for a company to try to do it on their own, hire the talent and also to come up with that interactive experience, most companies fail. So what ends up happening is you deliver the budget and the time to market ends up taking much longer, and then the experience is engaging for the users and they still end up leaving your app, having a bad impression. Now you can also buy something. They are our competitors who offer embedded analytics options as well, but the mainstream paradigm today with analytics is delivering. We talked about earlier static visualizations of insights that are created by someone else. So that certainly is an option. You know, where ThoughtSpot Everywhere really stands out above everything else is our technology is fundamentally built for search and interactive and cloud-scale data kind of an experience that the static visualizations today can't really deliver. So you could deliver a static dashboard purchase from one of our competitors, or if you really want to engage your users again, today is all about self-service, it's all about interactivity, and only ThoughtSpot's architecture can deliver that embedded in a data app for you. >> You know, one of the things I'm really impressed with you guys at ThoughtSpot is that you see data as I see strategic advantage for companies and people say that it's kind of a cliche but, or a punchline, and some sort of like business statement. But when you start getting into new kinds of workflows, that's the intellectual property. If you can enable people to essentially with very little low-code, no-code, or just roll their own analysis and insights from a platform, you're then creating intellectual property for the company. So this is kind of a new paradigm. And so a lot of CIO's that I talked to, or even CSOs on the security side of like, they kind of want this but maybe can't get there overnight. So if I'm a CIO, Victor, who do I, how do I point to on my team to engage with you guys? Like, okay, you sold me on it, I love the vision. This is definitely where we want to go. Who do I bring into the meeting? >> I think that in any application, in any company actually, there's usually product leaders and developers that create applications. So, you know, if you are a SaaS company, obviously your core product, your core product team would be the right team we want to talk to. If you're a traditional enterprise, you'd be surprised actually, how many traditional enterprises that been around for 50, 100 years, you might think of them selling a different product but actually, they have a lot of visual applications and product teams within their company as well. For example, you know, we have customers like a big tractor company. You can probably imagine who they might be. They actually have visual applications that they use ThoughtSpot to offer to the dealers so that they can look at their businesses with the tractors. We also have a big telecom company, for example, that you would think about telecom as a whole service but they have a building application that they offer to their merchants to track their billing. So what I'm saying is really, whether you're a software company where that's your core product, or you're a traditional enterprise that has visual applications underneath to support your core product, there's usually product teams, product leaders, and developers. Those are the ones that we want to talk to and we can help them realize a better vision for the product that they're responsible for. >> I mean, the reality is all applications need analytics, right, at some level. >> Yes. >> Full instrumentation at a minimum log everything and then the ability to roll that up, that's where I see people always telling me like that's where the challenge seems to be. Okay, I can log everything, but now how do I have a... And then after the fact that they say, "Give me a report, what's happening?" >> That's right. >> They get stuck. >> They get stuck 'cause you get that report and you know, someone else asked that question for you and you're probably a curious person. I'm a curious person. You always have that next question, and then usually if you're in a company, let's just say, you're a CIO. You're probably used to having a team of analysts at your fingertip so at least if you have a question, you don't like the report, you can find two people, five people they'll respond to your request. But if you're a business application user, you're sitting there, I don't know about you, but I don't remember the last time I actually went through and really found a support ticket in my application, or I really read a detailed documentation describing features in application. Users like to be self-taught, self-service and they like to explore it on their own. And there's no analyst there, there's no IT guy that they can lean on so if they get a static report of the data, they'll naturally always want to ask more questions, then they're stuck. So it's that kind of unsatisfying where, "I have some curiosity, you sparked by questions, I can't answer them." That's where I think a lot of companies struggle with. That's why a lot of applications, they're data intensive but they don't deliver any insights. >> It's interesting and I like this anywhere idea because you think about like what you guys do, applications can be, they always start small, right? I mean, applications got to be built. So you guys, your solution really fits for small startups and business all the way up to large enterprises which in a large enterprise, they could have hundreds and thousands of applications which look like small startups. >> Absolutely, absolutely. You know, that's a great thing about the sort of ThoughtSpot Everywhere which takes the engine around ThoughtSpot that we built over the last eight or nine years and could deliver in any kind of a context. 'Cause nowadays, as opposed to 10, 15, 20 years ago, everything does run in applications these days. We talk about visual transformation at the beginning of the call. That's really what it means is today, the workflows of business are conducted in applications no matter who you're interacting with. And so we have all these applications. A lot of times, yes, if you have big analytical problems, you can take the data and put into a different context like ThoughtSpot's own UI and do a lot of analytics, but we also understand that a lot of times customers and users, they like to analyze in the context the workflow of the application they're actually working in. And so with that situation, actually having the analytics embedded within right next to their workflow is something that I think a lot of, especially business users that are less trained, they'd like to do that right in the context of their business productivity workflow. And so that's where ThoughtSpot Everywhere, I know the terminology is a little self-serving, but ThoughtSpot Everywhere, we think ThoughtSpot could actually be everywhere in your business workflow. >> That's great value proposition. I'm going to put my skeptic hat on challenge you and say, Okay, I don't want to... Prove it to me, what's in it for me? And how much is it going to cost me, how do I engage? So, you know- >> Yeah. >> What's in it for me as the buyer? If people want to buy this, I want to use it, I'm going to get engaged with ThoughtSpot and how much does it cost and what's the engagements look like? >> So, what's in it for you is easy. So if you have data in the cloud and you have an application, you should use ThoughtSpot Everywhere to deliver a much more valuable, interactive experience for your user's data. So that's clear. How do you engage? So we have a very flexible pricing models. If your data's in the cloud, we can either, you can purchase with us, we'll land small and then grow with your consumption. You know, that's always the kind of thing, "Hey, allow us to prove it to you, right?" We start, and then if a user starts to consume, you don't really have to pay a big bill until we see the consumption increase. So we have consumption and data capacity-based types of pricing models. And you know, one of the real advantages that we have for cloud applications is if you're a developer, often, even in the past for ThoughtSpot, we haven't always made that development experience very easy. You have to embed a relatively heavy product but the beauty for ThoughtSpot is from the beginning, we were designed with a modern API-based kind of architecture. Now, a lot of our BI competitors were designed and developed in the desktop server kind of era where everything you embed is very monolithic. But because we have an API driven architecture, we invest a lot of time now to wrap a seamless developer SDK, plus very easy to use REST APIs, plus an interactive kind of a portal to make that development experience also really simple. So if you're a developer, now you really can get from zero to an easy app for ThoughtSpot embedded in your data app in just often in less than 60 minutes. >> John: Yeah. >> So that's also a very great proposition where modern leaders is your data's in the cloud, you've got developers with an SDK, it can get you into an app very quickly. >> All right so bottom line, if you're in the cloud, you got to get the data embed in the apps, data everywhere with ThoughtSpot. >> Yes. >> All right, so let's unpack it a little bit because I think you just highlighted I think what I think is the critical factor for companies as they evaluate their plethora of tools that they have and figuring out how to streamline and be cloud native in scale. You mentioned static and old BI competitors to the cloud. They also have a team of analysts as well that just can make the executives feel like the all of the reports are dynamic but they're not, they're just static. But look at, I know you guys have a relation with Snowflake, and not to kind of bring them into this but to highlight this, Snowflake disrupted the data warehouse. >> Yes. >> Because they're in the cloud and then they refactored leveraging cloud scale to provide a really easy, fast type of value for their product and then the rest is history. They're public, they're worth a lot of money. That's kind of an example of what's coming for every category of companies. There's going to be that. In fact, Jerry Chen, who was just given the keynote here at the event, had just had a big talk called "Castles In The Cloud", you can build a moat in the cloud with your application if you have the right architecture. >> Absolutely. >> So this is kind of a new, this is a new thing and it's almost like beachfront property, whoever gets there first wins the category. >> Exactly, exactly. And we think the timing is right now. You know, Snowflake, and even earlier, obviously we had the best conference with Redshift, which really started the whole cloud data warehouse wave, and now you're seeing Databricks even with their Delta Lake and trying to get into that kind of swim lane as well. Right now, all of a sudden, all these things that have been brewing in the background in the data architecture has to becoming mainstream. We're now seeing even large financial institutions starting to always have to test and think about moving their data into cloud data warehouse. But once you're in the cloud data warehouse, all the benefits of its elasticity, performance, that can really get realized at the analytics layer. And what ThoughtSpot really can bring to the table is we've always, because we're a search-based paradigm and when you think about search. Search is all about, doesn't really matter what kind of search you're doing, it's about digging really deep into a lot of data and delivering interactive performance. Those things have always... Doesn't really matter what data architecture we sit on, I've always been really fundamental to how we build our product. And that translates extremely well when you have your data in a Snowflake or Redshift have billions of rows in the cloud. We're the only company, we think, that can deliver interactive performance on all the data you have in a cloud data warehouse. >> Well, I want to congratulate you, guys. I'm really a big fan of the company. I think a lot of companies are misunderstood until they become big and there was, "Why didn't everyone else do that search? Well, I thought they were a search engine?" Being search centric is an architectural philosophy. I know as a North Star for your company but that creates value, right? So if you look at like say, Snowflake, Redshift and Databricks, you mentioned a few of those, you have kind of a couple of things going on. You have multiple personas kind of living well together and the developers like the data people. Normally, they hated each other, right? (giggles) Or maybe they didn't hate each other but there's conflict, there's always cultural tension between the data people and the developers. Now, you have developers who are becoming data native, if you will, just by embedding that in. So what Snowflake, these guys, are doing is interesting. You can be a developer and program and get great results and have great performance. The developers love Snowflake, they love Databricks, they love Redshift. >> Absolutely. >> And it's not that hard and the results are powerful. This is a new dynamic. What's your reaction to that? >> Yeah, no, I absolutely believe that. I think, part of the beauty of the cloud is I like your kind of analogy of bringing people together. So being in the cloud, first of all, the data is accessible by everyone, everywhere. You just need a browser and the right permissions, you can get your data, and also different kind of roles. They all kind of come together. Things best of breed tools get blended together through APIs. Everything just becomes a lot more accessible and collaborative and I know that sounds kind of little kumbaya, but the great thing about the cloud is it does blur the lines between goals. Everyone can do a little bit of everything and everyone can access a little bit more of their data and get more value out of it. >> Yeah. >> So all of that, I think that's the... If you talk about digital transformation, you know, that's really at the crux of it. >> Yeah, and I think at the end of the day, speed and high quality applications is a result and I think, the speed game if automation being built in on data plays a big role in that, it's super valuable and people will get slowed down. People get kind of angry. Like I don't want to get, I want to go faster, because automations and AI is going to make things go faster on the dev side, certainly with DevOps, clouds proven that. But if you're like an old school IT department (giggles) or data department, you're talking to weeks not minutes for results. >> Yes. >> I mean, that's the powerful scale we're talking about here. >> Absolutely. And you know, if you think about it, you know, if it's days to minutes, it sounds like a lot but if you think about like also each question, 'cause usually when you're thinking about questions, they come in minutes. Every minute you have a new question and if each one then adds days to your journey, that over time is just amplified, it's just not sustainable. >> Okay- >> So now in the cloud world, you need to have things delivered on demand as you think about it. >> Yeah, and of course you need the data from a security standpoint as well and build that in. Chances is people shift left. I got to ask you if I'm a customer, I want to just run this by you. You mentioned you have an SDK and obviously talking to developers. So I'm working with ThoughtSpot, I'm the leader of the organization. I'm like, "Okay, what's the headroom? What's going to happen as a bridge, the future gets built so I'm going to ride with ThoughtSpot." You mentioned SDK, how much more can I do to build and wrap around ThoughtSpot? Because obviously, this kind of value proposition is enabling value. >> Yes. >> So I want to build around it. How do I get started and where does it go? >> Yeah, well, you can get started as easy as starting with our free trial and just play around with it. And you know, the beauty of SDK and when I talk about how ThoughtSpot is built with API-driven architecture is, hey, there's a lot of magic and features built into ThoughtSpot core pod. You could embed all of that into an application if you would like or you could also use our SDK and our APIs to say, "I just want to embed a couple of visualizations," start with that and allow the users to take into that. You could also embed the whole search feature and allow users to ask repetitive questions, or you can have different role-based kind of experiences. So all of that is very flexible and very dynamic and with SDK, it's low-code in the sense where it creates a JavaScript portal for you and even for me who's haven't coded in a long time. I can just copy and paste some JavaScript code and I can see my applications reflecting in real time. So it's really kind of a modern experience that developers in today's world appreciate, and because all the data's in the cloud and in the cloud, applications are built as services connected through APIs, we really think that this is the modern way that developers would get started. And analysts, even analysts who don't have strong developer training can get started with our developer portal. So really, it's a very easy experience and you can customize it in whichever way you want that suits your application's needs. >> Yeah, I think it's, you don't have to be a developer to really understand the basic value of reuse and discovery of services. I think that's one of these we hear from developers all the time, "I had no idea that Victor did that code. Why do I have to rewrite that?" So you see, reuse come up a lot around automation where code is building with code, right? So you have this new vibe and you need data to discover that search paradigm mindset. How prevalent is that on the minds of customers? Are they just trying to like hold on and survive through the pandemic? (giggles) >> Well, customers are definitely thinking about it. You know, the challenge is change is always hard, you know? So it takes time for people to see the possibilities and then have to go through especially in larger organizations, but even in smaller organizations, people think about, "Well, how do I change my workflow?" and then, "How do I change my data pipeline?" You know, those are the kinds of things where, you know, it takes time, and that's why Redshift has been around since 2012 or I believe, but it took years before enterprises really are now saying, "The benefits are so profound that we really have to change the workflows, change the data pipelines to make it work because we can't hold on to the old ways." So it takes time but when the benefits are so clear, it's really kind of a snowball effect, you know? Once you change a data warehouse, you got to think about, "Do I need to change my application architecture?" Then, "Do I need to change the analytics layer?" And then, "Do I need to change the workflow?" And then you start seeing new possibilities because it's all more flexible that you can add more features to your application and it's just kind of a virtuous cycle, but it starts with taking that first step to your point of considering migrating your data into the cloud and we're seeing that across all kinds of industries now. I think nobody's holding back anymore. It just takes time, sometimes some are slower and some are faster. >> Well, all apps or data apps and it's interesting, I wrote a blog post in 2017 called, "Data Is The New Developer Kit" meaning it was just like a vision statement around data will be part of how apps, like software, it'll be data as code. And you guys are doing that. You're allowing data to be a key ingredient for interactivity with analytics. This is really important. Can you just give us a use case example of how someone builds an interactive data app with ThoughtSpot Everywhere? >> Yeah, absolutely. So I think there are certain applications that when naturally things relates to data, you know, I talk about bending or those kinds of things. Like when you use it, you just kind of inherently know, "Hey, there's tons of data and then can I get some?" But a lot of times we're seeing, you know, for example, one of our customers is a very small company that provides software for personal trainers and small fitness studios. You know, you would think like, "Oh well, these are small businesses. They don't have a ton of data. A lot of them would probably just run on QuickBooks or Excel and all of that." But they could see the value is kind of, once a personal trainer conducts his business on a cloud software, then he'll realize, "Oh, I don't need to download any more data. I don't need to run Excel anymore, the data is already there in a software." And hey, on top of that, wouldn't it be great if you have an analytics layer that can analyze how your clients paid you, where your appointments are, and so forth? And that's even just for, again like I said, no disrespect to personal trainers, but even for one or two personal trainers, hey, they can be an analytics and they could be an analyst on their business data. >> Yeah, why not? Everyone's got a Fitbits and watches and they could have that built into their studio APIs for the trainers. They can get collaboration. >> That's right. So there's no application you can think that's too simple or you might think too traditional or whatnot for analytics. Every application now can become a very engaging data application. >> Well Victor, it's great to have you on. Obviously, great conversation around ThoughtSpot anywhere. And as someone who runs corp dev for ThoughtSpot, for the folks watching that aren't customers yet for ThoughtSpot, what should they know about you guys as a company that they might not know about or they should know about? And what are people talking about ThoughtsSpot, what are they saying about it? So what should they know that know that's not being talked about or they may not understand? And what are other people saying about ThoughtSpot? >> So a couple of things. One is there's a lot of fun out there. I think about search in general, search is generally a very broad term but I think it, you know, I go back to what I was saying earlier is really what differentiates ThoughtSpot is not just that we have a search bar that's put on some kind of analytics UI. Really, it's the fundamental technical architecture underlying that is from the ground up built for search large data, granular, and detailed exploration of your data. That makes us truly unique and nobody else can really do search if you're not built with a technical foundation. The second thing is, we're very much a cloud first company now, and a ton of our over the past few years because of the growth of these highly performing data warehouses like Snowflake and Redshift, we're able to really focus on what we do best which is the search and the query processing performance on the front end and we're fully engaged with cloud platforms now. So if you have data in the cloud, we are the best analytics front end for that. >> Awesome, well, thanks for coming on. Great the feature you guys here in the "Startup Showcase", great conversation, ThoughtSpot leading company, hot startup. We did their event with them with theCUBE a couple of months ago. Congratulations on all your success. Victor Chang, VP of ThoughtSpot Everywhere and Corporate Development here on theCUBE and "AWS Startup Showcase". Go to awsstartups.com and be part of the community, we're doing these quarterly featuring the hottest startups in the cloud. I'm John Furrier, thanks for watching. >> Victor: Thank you so much. (bright music)

>> Narrator: From the Cosmopolitan Hotel in Las Vegas, Nevada, it's theCUBE, covering Coupa Inspire 2019, brought to you by Coupa. >> Welcome to theCUBE, Lisa Martin on the ground at Coupa Inspire'19 from the Cosmopolitan in Vegas. And I'm pleased to be joined by one of Coupa's spend setters from Highmark Health, Gary Foster, VP of Procurement. Gary, welcome to theCUBE. >> Thank you, it's pleasure to be here. >> So we're here with about 2,300 folks or so I think this is the eighth Coupa Inspire. Lots of energy and excitement this morning in the general session as Rob kicked that off. There is some of the interesting things that I've learned about Coupa in the last short while including this morning was that there's now $1.2 trillion of spend going through being managed by the Coupa platform. Tremendous community of data. And so imperative as the role of Chief Procurement Officer is changing, the CFO is changing. You are a veteran in the procurement industry. Before we talk about Highmark Health, give me a little bit of an overview of some of the things that you've seen change in procurement and where you think we are today in terms of that role being not only very strategic, but very influential to the top line of a business. >> Okay, it's a great question. I have spent a little over three decades in procurement. We've come a long way from back then. There was a lot of carryover from the industrialization era, and post-World War II and Korean War era, et cetera. Where really wasn't even called procurement it was purchasing. And there was a bit of the darling in the manufacturing industry, because that had such a high impact on the cost of goods sold. And as you got into other organizations, it was kind of relegated to a back office function, very transactional, very administrative, very clerical. So it really took someone with a lot of guts and a lot of vision to say we can be more than that. We can provide insights, we can deliver efficient transaction work and free up people to do more advisory type of roles. So I'm pleased to say I experimented with that early on in my procurement career. And that has been the shift that I think is continuing on. The whole buzz around digitization is another enabler to free up the talent that we have, that we can put into providing insights and predictions and becoming true strategy advisors to the business. So when the most recent, I've had for teams that I've taken over to either completely transform or build from the ground up. And this most recent one, I've sort of mashed up a lot of things that I've learned over the past three decades, to try to prepare them for where I believe that the profession is going, where I believe the function is going. Back to your original question. It's really evolved a lot from that back office transactional, just focus on price, a little bit on supply reliability, if it was in manufacturing, to slowly but surely started evolving to, what can you do to help us with some business objectives? And do we trust you with some important strategic initiatives that we need to accomplish as a company or in my business? >> Right, so it sounds like early on that you had this awareness of, there's pockets, there's silos of spend and purchasing happening there that we don't have the visibility into, 'cause we're talking a lot about that today with, that's what today's CPO and CFO really need is that visibility and control. >> Gary: Right. >> Especially as all of these forcing functions or disruptors happen, the more regulatory requirements or companies growing organically or inorganically. And suddenly, there's many, many areas within a business that are buying and spending. >> Right. >> And if they don't have that awareness and visibility into it, not only is it obviously, it's a cost issue, but one of your points to the resource utilization perspective. There's a lot of opportunities miss. So it sounds like you kind of saw that early on in your career, that there are things going on, we need to get visibility into all of this. >> Yes, yes. And it's, that's probably the, that's one of the foundational building blocks is to get a good handle on where's the money going. So the financial side of the house understands it from their journal entries and from their cost centers. But procurement, really great world class procurement, brings a different lens that the business doesn't think of. And that the financial industry, financial segment of the business doesn't think of. So that's, but you're really kind of a chicken and egg thing, you can't really provide the insights, if you don't have your hands on the information. And the information is got to be usable, right? Data versus information-- >> Absolutely. >> Quandary. That's very much the case with procurement. But you can't get bogged down and going for perfection, because then you'll just, analysis paralysis. You won't get out of that cycle and you'll never be able to provide. So you have to know, you have to have a gut feel that this is enough, this is directionally correct. Let's take this to the next level. Let's start moving with, here are the patterns that we see, here's what we think is happening, here's where we think there are issues, right? So those are, I think, are some of the foundational pieces to the spend analysis question. >> So talk to us a little bit about Highmark Health. What you're doing there and how you guys are really focused on changing America's approach to healthcare? Which I think would be welcomed by a lot of people, by the way. >> (chuckles) Yes, we have a very, very ambitious goal. We believe we can be a catalyst to change healthcare in America. >> Lisa: How so? >> Well, first of all, we think that the model was wrong. If you think about the way that the healthcare industry has grown up in the US, you went to a hospital because you were either sick or injured. You had to go to those locations. You had to follow those procedures. You had to fill out those forms. You had to, you went to where the care was, and you had to bend to your schedule to whatever was available, right? We've all experienced trying to get an appointment with a doctor, and it's four months out, right? So we're doing, this was a year and a half ago, we introduced same-day appointments. So we have both a hospital system and an insurance company. So we can see the whole value chain-- >> Lisa: Okay. >> Through the healthcare experience. And one of the fundamentals that we're doing is, we're trying to bring a retail mindset to healthcare. >> Where the wellness comes to- >> You, as opposed to you having to go somewhere to access your health or to get connected with experts that can advise you or for checkups, et cetera. You're wearing an Apple Watch, that's only one of those Fitbits, et cetera. There's a multitude of wearables that are coming. The combination of IoT, and healthcare and big data is intersecting at a rapid rate where we will be, we are already able to look at millions of records, of chart information about patterns of diagnoses. And we know that the data tells us that if we can get people to engage in their health and make small changes, and just learn more, be educated and learn more about how, we know that the long-term costs of their healthcare will go down. So we are looking to partner, obviously, can't do this all on our own. >> Right. >> So this is not a David and Goliath kind of a thing. So we're looking actively to partner with breaking company, lead companies and breaking technology companies to be partners with us on this journey of how do we bring health to people and help improve their health, lower their disease rates, provide a better quality of life, lower their cost of health care, lower all the complications, you can see the graphs, right? It all runs, as you get as you get older, if you don't take care of yourself. >> Lisa: Right. >> The complications of healthcare issues just go exponentially up. And we know we can bend that curve down if we can transform the way that health is thought of and delivered to people in the country. >> Well, I'm already signed, you got me. So talk to me, though, about from a technology perspective. If we think about all the emerging technologies, you mentioned IoT, millions and millions of devices, we are sometimes overly connected. >> Gary: Yes. >> What is the opportunity that Highmark is working on with Coupa to be able to start changing that mindset and bringing that retail model to healthcare? How are they hoping to ignite that? >> Well, it's not on a direct connection with Coupa. Coupa is our procuring platform. So it enables us to provide efficient transactions and we get data insights. Coupa is very much an enabler for us in this process. What I would say is, and this goes back to the evolution of procurement as a profession, by having Coupa and other technologies at the fingertips of my team, it frees them to immerse themselves into their clients' business as well as their categories. So if they're, if I have someone who's a category manager of digital marketing, they can immerse themselves into that, and they can work that, my folks go, they attend senior level staff meetings, they have one on ones with executive VPs, they co-locate with the client on a regular basis. We really immerse ourselves into it. What Coupa is doing is it's allowing us to spend less time on transactions and process, and more time learning the business, more time understanding the industries that they operate in, looking for innovation, and bringing those innovative partners to the business that wouldn't necessarily have happened on its own. We have this incredible network, particularly if we have people that really, really have a passion for procurement, and really have a passion for being intimate with the customer. I know it's an overused phrase, but the trusted advisor status is definitely where we should be. That's an, the Coupa org, the Coupa platform, and tools enable my team to have, to bring those insights and those opportunities to the business. And we've gotten tremendous accolades from the CEO through the entire C-suite, about the level of business partnership that the procurement organization has, with all of the various areas of the Highmark organization. >> So you have this visibility now that you didn't have before with Coupa? >> Yeah. >> This control. Sounds like your resources and different parts of the organization are much better able to use their time to be strategic on other projects and to really start bringing that retail experience out there. Coupa kind of as, you mentioned, as an enabler is really foundational to that. I know you've actually won some awards. I think, Rob Bernstein actually mentioned this on stage this morning that you took top honors at the Procurement Leaders, Inaugural America's Procurement Awards. >> Gary: Yes. >> You've also been recognized as a Procurement Leader of the Year for transforming Highmark Health. What I love about the story is that showing how procurement, not only has it transitioned tremendously to be very strategic, but you're helping to transform an industry by getting this visibility on everywhere, where there's spend there, that operationally, Highmark Health seems to have a big leg up. >> Yes, yeah. No one could be everywhere at once. And if we can earn that trust, then the people in the business who are hired to play certain roles, strategy, development, or whatever, if they're, if they will, let us help them with our expertise, they can spend, they're more effective in their role. >> Right. >> Because they're not doing procurement work. They're not talking to suppliers. They're not negotiating deals. They're not looking, then let us provide that service, that professional service to them, really, as a consultant, as an advisor, and bring companies that, the more we get in depth into understanding the industries that we're buying in, the more we're learning about emerging companies. Who are the innovators? Who are the disruptors? Bringing those organizations because we're studying that in our markets, to our business partner, and making that introduction, which sparks an idea, which sparks an opportunity for the two to work together collaboratively on something new, or to resolve an issue that has not been addressed and no one found an answer to in the past. >> Well, you've put this really strong foundation in place that not only gives you the visibility and control, but it's going to allow Highmark Health on this ambitious goal, as you mentioned, about bringing wellness to us. And of course, there's the whole, there's the human in the way. So maybe tomorrow, Deepak Chopra, who's keynoting, will be able to give you guys some insight into how to help these people. And it's all of us people, right? Really embrace mindfulness, to be able to focus more on our passions. But what you guys are doing to transform healthcare is really inspirational so Gary, thank you-- >> Thank you very much. >> For joining me on theCUBE today. >> It was a pleasure. >> Likewise. For Gary Foster, I'm Lisa Martin. You're watching theCUBE from Coupa Inspire'19. Thanks for watching. (upbeat music)

>> Live from San Francisco, it's theCUBE! Covering IBM Think 2019. Brought to you by IBM. >> Welcome back, live here in San Francisco, it's theCUBE's coverage of IBM Think 2019. I'm John Furrier--Stu Miniman. Stu, four days, we're on our fourth day, the sun's shining, they've shut down Howard Street here at IBM. Big event for IBM, in San Francisco, not Las Vegas. Lot of great cloud action, lot of great AI data developers. Great story, good to see you again. Our next two guests, Julie Lockner, Director, Offering Management, Portfolio Operations at IBM, Data+AI, great to see you. >> Thank you, it's great to see you too, thank you. >> And Jay Limburn, Director of Offering Management, IBM Data+AI, thanks for coming on. >> Hey guys, great to be here. >> So, we've chatted many times at events, the role of data. So, we're religious about data, data flows through our blood, but IBM has put it all together now. All the reorgs are over, everyone's kind of, the table is set for IBM. The data path is clear, it's part of applications. It's feeding the apps. AI's the key workload inside the application. This is now a fully set-up group, give us the update, what's the focus? >> Yeah, it's really exciting because, if you think about it, before, we were called IBM Analytics, and that really is only a part of what we do. Now that we're Data+AI, that means that not only are we responsible for delivering data assets, and technology that supports those data assets to our customers, but infusing AI, not only in the technologies that we have, but also helping them build applications so they can fuse AI into their business processes. >> It's pretty broad, I mean, data's very much a broad swath of things. Analytics, you know, wrangling data, setting things up, cataloging them. Take me through how you guys set this up. How do you present it to the marketplace? How are clients engaged with it? Because it's pretty broad. But it could be, it needs to be specific. Take us through the methodology. >> So, you probably heard a lot of people today talk about the ladder to AI, right? This is IBM's view of how we explain our client's journey towards AI. It really starts at the bottom rung of the ladder, where we've got the collection of information. Collect your data. Once you've collected your data, you move up to the next rung, which is the Organize. And this is really where all the governance stuff comes in. This is how we can provide a view across that data, understand that data, provide trust to that data, and then serve that up to the consumers of that information, so they can actually use that in AI. That's where all the data science capabilities come in, allowing people to actually be able to consume that information. >> So, the bottom set is just really all the hard and heavy lifting that data scientists actually don't want to do. >> And writing algorithms, the collecting, the ingesting of data from any source, that's the bottom? And then, tell me about that next layer up, from the collection-- >> So, Collect is the physical assets or the collection of the data that you're going to be using for AI. If you don't get that foundation right, it doesn't really make sense. You have to have the data first. The piece in the middle that Jay was referring to, that's called Organize, our whole divisions are actually organized around these ladders to AI, so, Collect, Organize, Analyze, Infuse. On the Organize side, as Jay was mentioning, it's all about inventorying the data assets, knowing what data you have, then providing data quality rules, governance, compliance-type offerings, that allow organizations to not just know your data, trust your data, but then make it available so you can use your data, and the users are those data scientists, they're the analytics teams, they're the operation organizations that need to be able to build their solutions on top of trusted data. >> So, where does the Catalog fit in? Which level does that come into? >> Yeah, so, think of the Data Catalog as the DNS for data, all right? It's the way in which you can provide a full view of all of your information. Whether it's structured information, unstructured information, data you've got on PRAM and data you've got in a cloud somewhere. >> That's in the Organize layer, right? >> That's all in the Organize layer. So, if you can collect that information, you can then provide capabilities that allow you to understand the quality of that data, know where that data's come from, and then, finally, if you serve that up inside a compelling, business-friendly experience, so that a data scientist can go to one place, quickly make a decision on if that's the right data for them, and allow them to go and be productive by building a data science model, then we're really able to move the needle on making those data science organizations efficient, allowing us to build better models to transform their business. >> Yeah, and a big part of that is, if you think about what makes Amazon successful, it's because they know where all their products are, from the vendor, to when it shows up on the doorstep. What the Catalog provides is really the similar capability of, I would call it inventory management of your data assets, where we know where the data came from, its source--in that Collect layer-- who's transformed it, who's accessed it, if they're even allowed to see it, so, data privacy policies are part of that, and then being able to just serve up that data to those users. Being able to see that whole end-to-end lineage is a key point, critical point of the ladder to AI. Especially when you start to think about things like bias detection, which is a big part of the Analyze layer. >> But one of the things we've been digging into on theCUBE is, is data the next flywheel of innovation? You know, it used to be I just had my information, many years ago we started talking about, "Okay, I need to be able to access all that other information." We hear things like 80% of the data out there isn't really searchable today. So, how do you see data, data gravity, all those pieces, as the next flywheel of innovation? >> Yeah, I think it's key. I mean, we've talked a lot about how, you can't do AI without information architecture. And it's absolutely true. And getting that view of that data in a single location, so it is like the DNS of the internet. So you know exactly where to search, you can get hold of that data, and then you've got tools that give you self-service access to actually get hold of the data without any need of support from IT to get access to it. It's really a key-- >> Yeah, but to the point you were just asking about, data gravity? I mean, being able to do this where the data resides. So, for example, we have a lot of our customers that are mergers and acquisitions. Some teams have a lot of data assets that are on-premises, others have large data lakes in AWS or Azure. How do you inventory those assets and really have a view of what you have available across that landscape? Part of what we've been focusing on this year is making our technology work across all of those clouds. And having a single view of your assets but knowing where it resides. >> So, Julie, this environment is a bit more complicated than the old data warehousing, or even what we were looking at with big data and Hadoop and all those pieces. >> Isn't that the truth? >> Help explain why we're actually going to be able to get the information, leverage and drive new business value out of data today, when we've struggled so many times in the past. >> Well, I think the biggest thing that's changed is the adoption of DevOps, and when I say adoption of DevOps and things like containerization and Docker containers, Kubernetes, the ability to provision data assets very quickly, no matter where they are, build these very quick value-producing applications based on AI, Artificial Intelligence APIs, is what's allowing us to take advantage of this multi-cloud landscape. If you didn't have that DevOps foundation, you'd still be building ETL jobs in data warehouses, and that was 20 years ago. Today, it's much more about these microservices-based architecture, building up these AI-- >> Well, that's the key point, and the "Fuse" part of the stack, I think, or ladder. Stack? Ladder? >> Ladder. (laughs) >> Ladder to success! Is key, because you're seeing the applications that have data native into the app, where it has to have certain characteristics, whether it's a realtime healthcare app, or retail app, and we had the retail folks on earlier, it's like, oh my god, this now has to be addressable very fast, so, the old fenced-off data warehouse-- "Hey, give me that data!"--pull it over. You need a sub-second latency, or milliseconds. So, this is now a requirement. >> That's right. >> So, how are people getting there? What are some use cases? >> Sure. I'll start with the healthcare 'cause you brought that up. One of the big use cases for technology that we provide is really around taking information that might be realtime, or batch data, and providing the ability to analyze that data very quickly in realtime to the point where you can predict when someone might potentially have a cardiac arrest. And yesterday's keynote that Rob Thomas presented, a demonstration that showed the ability to take data from a wearable device, combine it with data that's sitting in an Amazon... MySQL database, be able to predict who is the most at-risk of having a potential cardiac arrest! >> That's me! >> And then present that to a call center of cardiologists. So, this company that we work with, iCure, really took that entire stack, Organize, Collect, Organize, Analyze, Infuse, and built an application in a matter of six weeks. Now, that's the most compelling part. We were able to build the solution, inventory their data assets, tie it to the industry model, healthcare industry model, and predict when someone might potentially-- >> Do you have that demo on you? The device? >> Of course I do. I know, I know. So, here is, this is called a BraveHeart Life Sensor. And essentially, it's a Bluetooth device. I know! If you put it on! (laughs) >> If I put it on, it'll track... Biometric? It'll start capturing information about your heart, ECG, and on Valentine's Day, right? My heart to yours, happy Valentine's Day to my husband, of course. The ability to be able to capture all this data here on the device, stream it to an AI engine that can then immediately classify whether or not someone has an anomaly in their ECG signal. You couldn't do that without having a complete ladder to AI capability. >> So, realtime telemetry from the heart. So, I see timing's important if you're about to have a heart attack. >> Yeah. >> Pretty important. >> And that's a great example of, you mentioned the speed. It's all about being able to capture that data in whatever form it's coming in, understand what that data is, know if you can trust that data, and then put it in the hands of the individuals that can do something valuable with the analysis from that data. >> Yeah, you have to able to trust it. Especially-- >> So, you brought up earlier bias in data. So, I want to bring that up in context of this. This is just one example of wearables, Fitbits, all kinds of things happening. >> New sources of tech, yeah. >> In healthcare, retail, all kinds of edge, realtime, is bias of data. And the other one's privacy because now you have a new kind of data source going into the cloud. And then, so, this fits into what part of the ladder? So, the ladder needs a secure piece. >> Tell me about that. >> Yeah, it does. So, that really falls into that Organize piece of that ladder, the governance aspects around it. If you're going to make data available for self-service, you've got to still make sure that that data's protected, and that you're not going to go and break any kind of regulatory law around that data. So, we actually can use technology now to understand what that data is, whether it contains sensitive information, credit card numbers, and expose that information out to those consumers, yet still masking the key elements that should be protected. And that's really important, because data science is a hugely inefficient business. Data scientists are spending too much time looking for information. And worse than that, they actually don't have all the information available that they need, because certain information needs to be protected. But what we can do now is expose information that wasn't previously available, but protect just the key parts of that information, so we're still ensuring it's safe. >> That's a really key point. It's the classic iceberg, right? What you see: "Oh, data science is going to "change the game of our business!" And then when they realize what's underneath the water, it's like, all this set-up, incompatible data, dirty data, data cleaning, and then all of a sudden it just doesn't work, right? This is the reality. Are you guys seeing this? Do you see that? >> Yeah, absolutely. I think we're only just really at the beginning of a crest of a wave, here. I think organizations know they want to get to AI, the ladder to AI really helps explain and it helps to understand how they can get there. And we're able then to solve that through our technology, and help them get there and drive those efficiencies that they need. >> And just to add to that, I mean, now that there's more data assets available, you can't manually classify, tag and inventory all that data, determine whether or not it contains sensitive data. And that's where infusing machine learning into our products has really allowed our customers to automate the process. I mentioned, the only way that we were able to deploy this application in six weeks, is because we used a lot of the embedded machine learning to identify the patient data that was considered sensitive, tag it as patient data, and then, when the data scientists were actually building the models in that same environment, it was masked. So, they knew that they had access to the data, but they weren't allowed to see it. It's perfectly--especially with HIMSS' conference this week as well! You were talking about this there. >> Great use case with healthcare. >> Love to hear you speak about the ecosystem being built around this. Everything, open APIs, I'm guessing? >> Oh, yeah. What kind of partners are-- >> Jay, talk a little bit-- >> Yeah, so, one of the key things we're doing is ensuring that we're able to keep this stuff open. We don't want to curate a proprietary system. We're already big supporters of open source, as you know, in IBM. One of the things that we're heavily-invested in is our open metadata strategy. Open metadata is part of the open source ODPi Foundation. Project Egeria defines a standard for common metadata interchange. And what that means is that, any of these metadata systems that adopt this standard can freely share and exchange metadata across that landscape, so that wherever your data is, whichever systems it's stored in, wherever that metadata is harvested, it can play part of that network and share that metadata across those systems. >> I'd like to get your thoughts on something, Julie. You've been on the analyst side, you're now at IBM. Jay, if you can weigh in on this too, that'd be great. We, here, we see all the trends and go to all the events and one of the things that's popping up that's clear within the IBM ecosystem because you guys have a lot of business customers, is that a new kind of business app developer's coming in. And we've seen data science highlight the citizen data scientist, so if data is code, part of the application, and all the ladder stuff kind of falls into place, that means we're going to see new kinds of applications. So, how are you guys looking at, this is kind of a, not like the cloud-native, hardcore DevOps developer. It's the person that says, "Hey, I can innovate "a business model." I see a business model innovation that's not so much about building technology, it's about using insight and a unique... Formula or algorithm, to tweak something. That's not a lot of programming involved. 'Cause with Cloud and Cloud Private, all these back end systems, that's an ecosystem partner opportunity for you guys, but it's not your classic ISV. So, there's a new breed of business apps that we see coming, your thoughts on this? >> Yeah, it's almost like taking business process optimization as a discipline, and turning it into micro-applications. You want to be able to leverage data that's available and accessible, be able to insert that particular Artificial Intelligence machine learning algorithm to optimize that business process, and then get out of the way. Because if you try to reinvent your entire business process, culture typically gets in the way of some of these things. >> I thought, as an application value, 'cause there's value creation here, right? >> Absolutely. >> You were talking about, so, is this a new kind of genre of developer, or-- >> It really is, I mean... If you take the citizen data scientist, an example that you mentioned earlier. It's really about lowering the entry point to that technology. How can you allow individuals with lower levels of skills to actually get in and be productive and create something valuable? It shouldn't be just a practice that's held away for the hardcore developer anymore. It's about lowering the entry point with the set of tools. One of the things we have in Watson Studio, for example, our data science platform, is just that. It's about providing wizards and walkthroughs to allow people to develop productive use models very easily, without needing hardcore coding skills. >> Yeah, I also think, though, that, in order for these value-added applications to be built, the data has to be business-ready. That's how you accelerate these application development life cycles. That's how you get the new class of application developers productive, is making sure that they start with a business-ready foundation. >> So, how are you guys going to go after this new market? What's the marketing strategy? Again, this is like, forward-pioneering kind of things happening. What's the strategy, how are you going to enable this, what's the plan? >> Well, there's two parts of it. One is, when Jay was mentioning the Open Metadata Repository Services, our key strategy is embedding Catalog everywhere and anywhere we can. We believe that having that open metadata exchange allows us to open up access to metadata across these applications. So, really, that's first and foremost, is making sure that we can catalog and inventory data assets that might not necessarily be in the IBM Cloud, or in IBM products. That's really the first step. >> Absolutely. The second step, I would say, is really taking all of our capabilities, making them, from the ground up, microservices-enabled, delivering them through Docker containers and making sure that they can port across whatever cloud deployment model our customers want to be able to execute on. And being able to optimize the runtime engines, whether it's data integration, data movement, data virtualization, based on data gravity, that you had mentioned-- >> So, something like a whole new developer program opportunity to bring to the market. >> Absolutely. I mean, there is, I think there is a huge opportunity for, from an education perspective, to help our customers build these applications. But it starts with understanding the data assets, understanding what they can do with it, and using self-service-type tools that Jay was referring to. >> And all of that underpinned with the trust. If you don't trust your data, the data scientist is not going to know whether or not they're using the right thing. >> So, the ladder's great. Great way for people to figure out where they are, it's like looking in the mirror, on the organization. How early is this? What inning are we in? How do you guys see the progression? How far along are we? Obviously, you have some data, examples, some people are doing it end-to-end. What's the maturity look like? What's the uptake? >> Go ahead, Jay. >> So, I think we're at the beginning of a crest of a wave. As I say, there's been a lot of discussion so far, even if you compare this year's conference to last year's. A lot of the discussion last year was, "What's possible with AI?" This year's conference is much more about, "What are we doing with AI?" And I think we're now getting to the point where people can actually start to be productive and really start to change their business through that. >> Yeah and, just to add to that, I mean, the ladder to AI was introduced last year, and it has gained so much adoption in the marketplace and our customers, they're actually organizing their business that way. So, the Collect divisions are the database teams, are now expanding to Hadoop and Cloudera, and Hortonworks and Mongo. They're organizing their data governance teams around the Organize pillar, where they're doing things like data integration, data replication. So, I feel like the maturity of this ladder to AI is really enabling our customers to achieve it much faster than-- >> I was talking to Dave Vellante about this, and we're seeing that, you know, we've been covering IBM since, it's the 10th year of theCUBE, all ten years. It's been, watching the progression. The past couple of years has been setting the table, everyone seems to be pumping, it makes sense, everything's hanging together, it's in one group. Data's not one, "This group, that group," it's all, Data, AI, all Analytics, all Watson. Smart, and the ladder just allows you to understand where a customer is, and then-- >> Well, and also, we mentioned the emphasis on open source. It allows our customers to take an inventory of, what do they have, internally, with IBM assets, externally, open source, so that they can actually start to architect their information architecture, using the same kind of analogy. >> And an opportunity for developers too, great. Julie, thanks for coming on. Jay, appreciate it. >> Thank you so much for the opportunity, happy Valentine's Day! Happy Valentine's Day, we're theCUBE. I'm John Furrier, Stu Miniman here, live in San Francisco at the Moscone Center, and the whole street's shut down, Howard Street. Huge event, 30,000 people, we'll be back with more Day Four coverage after this short break.

(spirited music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are in the Palo Alto Studios for a CUBE Conversation. Part of our Western Digital Data Makes Possible Series, really looking at a lot of cool applications. At the end of the day, data's underneath everything. There's infrastructure and storage that's holding that, but it's much more exciting to talk about the applications. We're excited to have somebody who's kind of on the cutting edge of a next chapter of something you're probably familiar with. He's Steven Webster, and he is the founder and CEO of Asensei. Steven, great to see you. >> Likewise, likewise. >> So, you guys are taking, I think everyone's familiar with Fitbits, as probably one of the earliest iterations of a biometric feedback, for getting more steps. At the end of the day, get more steps. And you guys are really taking it to the next level, which is, I think you call it connected coaching, so I wondered if you could give everyone a quick overview, and then we'll dig into it a little bit. >> Yeah, I think we're all very familiar now with connected fitness in hindsight, as a category that appeared and emerged, as, like you say, first it was activity trackers. We saw those trackers primarily move into smartwatches, and the category's got life in it, life in it left. I see companies like Flywheel and Peloton, we all know Peloton now. >> [Jeff] Right. >> We're starting to make the fitness equipment itself, the treadmill, the bike, connected. So, there's plenty of growth in that category. But our view is that tracking isn't teaching, and counting and cheering isn't coaching. And so we see this opportunity for this new category that's emerging alongside connected fitness, and that's what we call connected coaching. >> Connected coaching. So the biggest word, obviously, instead of fitness tracker, to the connected coaching, is coaching. >> Yeah. >> So, you guys really think that the coaching piece of it is core. And are you targeting high-end athletes, or is this for the person that just wants to take a step up from their fitness tracker? Where in the coaching spectrum are you guys targeting? >> I saw your shoe dog, Phil Knight, founder of Nike, a book on the shelf behind you there, and his co-founder, Bill Bowerman, has a great quote that's immortalized in Nike offices and stores around the world: "If you have a body, you're an athlete." So, that's how we think about our audience. Our customer base is anyone that wants to unlock their athletic potential. I think if you look at elite sports, and elite athletes, and Olympic athletes, they've had access to this kind of technology going back to the Sydney Olympics, so we're really trying to consumerize that technology and make it available to the people that want to be those athletes, but aren't those athletes yet. You might call it the weekend warrior, or just the committed athlete, that would identify, identify themselves according to a sport that they play. >> So, there's different parts of coaching, right? One, is kind of knowing the techniques, so that you've got the best practices by which to try to practice. >> [Steven] Yep. >> And then there's actually coaching to those techniques, so people practice, right? Practice doesn't make perfect. It's perfect practice that makes perfect. >> [Steven] You stole our line, which we stole from someone else. >> So, what are you doing? How do you observe the athlete? How do you communicate with the athlete? How do you make course corrections to the athlete to move it from simply tracking to coaching? >> [Steven] I mean, it starts with, you have to see everything and miss nothing. So, you need to have eyes on the athlete, and there's really two ways we think you can do that. One is, you're using cameras and computer vision. I think most of us are familiar with technologies like Microsoft Connect, where an external camera can allow you to see the skeleton and the biomechanics of the athlete. And that's a big thing for us. We talk about the from to being from just measuring biometrics: how's your heart rate, how much exertion are you making, how much power are you laying down. We need to move from biometrics to biomechanics, and that means looking at technique, and posture, and movement, and timing. So, we're all familiar with cameras, but we think the more important innovation is the emergence of smart clothing, or smart apparel, and the ability to take sensors that would have been discrete, hard components, and infuse those sensors into smart apparel. We've actually created a reference design for a motion capture sensor, and a network of those sensors infused in your apparel allows us to recover your skeleton, but as easily as pulling on a shirt or shorts. >> [Jeff] So you've actually come up with a reference design. So, obviously, begs a question: you're not working with any one particular apparel manufacturer. You really want to come up with a standard and publish the standard by which anyone could really define, capture, and record body movements, and to convert those movements from the clothing into a model. >> No, that's exactly it. We have no desire to be in the apparel industry. We have no desire to unseat Nike, Adidas, or Under Armour. We're actually licensing our technology royalty-free. We just want to accelerate the adoption of smart apparel. And I think the thing about smart apparel is, no one's going to walk into Niketown and say, "Where's the smart apparel department? "I don't want dumb apparel anymore." There needs to be a compelling reason to buy digitally enhanced apparel, and we think one of the most compelling reasons to buy that is so that we can be coached in the sport of our choice. >> [Jeff] So, then you're starting out with rowing, I believe, is your first sport, right? >> [Steven] That's correct, yeah. >> And so the other really important piece of it, is if people don't have smart apparel, or the smart apparel's not there yet, or maybe when they have smart apparel, there's a lot of opportunities to bring in other data sources beyond just that single set. >> [Steven] And that's absolutely key. When I think about biomechanics, that's what goes in, but there's also what comes out. Good form isn't just aesthetic. Good form is in any given sport. Good form and good technique is about organizing yourself so that you perform most efficiently and perform most effectively. Yeah, so you corrected a point in that we've chosen rowing as one of the sports. Rowing is all about technique. It's all about posture. It's all about form. If you've got two rowers who, essentially, have the same strength, the same cardiovascular capability, the one with the best technique will make the boat move faster. But for the sport of rowing, we also get a tremendous amount of telemetry coming off the rowing machine itself. A force curve weakened on every single pull of that handle. We can see how you're laying down that force, and we can read those force curves. We can look at them and tell things like, are you using your legs enough? Are you opening your back too late or too early? Are you dominant on your arms, where you shouldn't be? Is your technique breaking down at higher stroke rates, but is good at lower stroke rates? So it's a good place for us to start. We can take all of that knowledge and information and coach the athlete. And then when we get down to more marginal gains, we can start to look at their posture and form through that technology like smart apparel. >> There's the understanding what they're doing, and understanding the effort relative to best practices, but there's also, within their journey. Maybe today, they're working on cardio, and tomorrow, they're working on form. The next day, they're working on sprints. So the actual best practices in coaching a sport or particular activity, how are you addressing that? How are you bringing in that expertise beyond just the biometric information? >> [Steven] So yeah, we don't think technology is replacing coaches. We just think that coaches that use technology will replace coaches that don't. It's not an algorithm that's trying to coach you. We're taking the knowledge and the expertise of world-class coaches in the sport, that athletes want to follow, and we're taking that coaching, and essentially, think of it as putting it into a learning management system. And then for any given athlete, Just think of it the way a coach coaches. If you walked into a rowing club, I don't know if you've ever rowed before or not, but a coach will look at you, they'll sit you on a rowing machine or sit you on a boat, and just look at you and decide, what's the one next thing that I'm going to teach you that's going to make you better? And really, that's the art of coaching right there. It's looking for that next improvement, that next marginal gain. It's not just about being able to look at the athlete, but then decide where's the improvement that we want to coach the athlete? And then the whole sports psychology of, how do you coach his improvements? >> Because there's the whole hammer versus carrot. That's another thing. You need to learn how the individual athlete responds, what types of things do they respond better to? Do they like to get yelled? Do they like to be encouraged? Did they like it at the beginning? Did they like it at the end? So, do you guys incorporate some of these softer coaching techniques into the application? >> Our team have all coached sport at university-level typically. We care a lot and we think a lot about the role of the coach. The coach's job is to attach technique to the athlete's body. It's to take what's in your head and what you've seen done before, and give that to the athlete, so absolutely, we're thinking about how do you establish the correct coaching cues. How do you positively reinforce, not just negatively reinforce? Is that person a kinesthetic learner, where they need to feel how to do it correctly? Are they a more visual learner, where they respond better to metaphor? Now, one of the really interesting things with a digital coach is the more people we teach, the better we can get at teaching, because we can start to use some of the techniques of enlarged datasets, and looking at what's working and what's not working. In fact, it's the same technology we would use in marketing or advertising, to segment an audience, and target content. >> Right. >> [Steven] We can take that same technology and apply it how we think about coaching sports. >> So is your initial target to help active coaches that are looking for an edge? Or are you trying to go for the weakend warrior, if you will? Where's your initial market? >> For rowing, we've actually zeroed in on three athletes, where we have a point of view that Asensei can be of help. I'll tell you who the three are. First, is the high school athlete who wants to go to college and get recruited. So, we're selling to the parent as much as we're selling to the student. >> [Jeff] That's an easy one. Just show up and be tall. >> Well, show up, be tall, but also what's your 2k time? How fast can you row 2,000 meters? That's a pretty important benchmark. So for that high school athlete, that's a very specific audience where we're bringing very specific coaches. In fact, the coach that we're launching with to that market, his story is one of, high school to college to national team, and he just came back from the Olympics in Rio. The second athlete that we're looking at is the person who never wants to go on the water, but likes that indoor rowing machine, so it's that CrossFit athlete or it's an indoor rower. And again, we have a very specific coach who coaches indoor rowing. And then the third target customer is-- >> What's that person's motivation, just to get a better time? >> Interesting, in that community, there's a lot of competitiveness, so yeah, it's about I want to get good at this, I want to get better at this. Maybe enter local competitions, either inside your gym or your box. This weekend, in Boston, we have just had one of the largest indoor world, it was the World Indoor Rowing Championships, the C.R.A.S.H B's. There's these huge indoor rowing competitions, so that's a very competitive athlete. And then finally we have, what would be the master's rower or the person for whom rowing is. There's lots of people who don't identify themselves as a rower, but they'll get on a rowing machine two or three times a week, whether it's in their gym or whether it's at home. Your focus is strength, conditioning, working out, but staying injury-free, and just fun and fitness. I think Palaton validated the existence of that market, and we see a lot of people wanting to do that with a rowing machine, and not with a bike. >> I think most of these people will or will not have access to a primary coach, and this augments it, or does this become their primary coach based on where they are in their athletic life? >> [Steven] I think it's both, and certainly, and certainly, we're able to support both. I think when you're that high school rower that wants to make college, you're probably a member of either your school rowing crew or you're a member of a club, but you spend a tremendous amount of time on an erg, the indoor rowing machine, and your practice is unsupervised. Even though you know what you should be doing, there's nobody there in that moment watching you log those 10,000 meters. One of our advisors is, actually, a two-times Olympic world medalist from team Great Britain, Helen Glover. And Helen, I have a great quote from Helen, where she calculated for the Rio Olympics, in the final of the Rio Olympics, every stroke she took in the final, she'd taken 16,000 strokes in practice, which talks to the importance of the quality of that practice, and making sure it's supervised. >> The bigger take on the old 10,000 reps, right? 16,000 per stroke. >> Right? >> Kind of looking forward, right, what were some of the biggest challenges you had to overcome? And then, as you looked forward, right, since the beginning, were ubiquitous, and there's 3D goggles, and there'll be outside-in centers for that whole world. How do you see this world evolving in the immediate short-term for you guys to have success, and then, just down the road a year or two? >> That's a really good question. I think in the short-term, I think it's incumbent on us to just stay really focused in a single community, and get that product right for them. It's more about introducing people to the idea. This is a category creation exercise, so we need to go through that adoption curve of find the early adopters, find the early majority, and before we take that technology anywhere towards our mass market, we need to nail the experience for that early majority. And we think that it's largely going to be in the sport of rowing or with rowers. The cross participation studies in rowing are pretty strong for other sports. Typically, somewhere between 60-80% of rowers weight lift, bike, run, and take part in yoga, whether yoga for mobility and flexibility. There's immediately adjacent markets available to us where the rowers are already in those markets. We're going to stick there for awhile, and really just nail the experience down. >> And is it a big reach to go from tracking to coaching? I mean, these people are all super data focused, right? The beauty of rowing, as you mentioned, it's all about your 2k period. It's one single metric. And they're running, and they're biking, and they're doing all kinds of data-based things, but you're trying to get them to think really more on terms of the coaching versus just the tracking. Has that been hard for them to accept? Do you have any kind of feel for the adoption or the other thing, I would imagine, I spent all this money for these expensive clothing. Is this a killer app that I can now justify having? >> Right, right, right. >> Maybe fancier connected clothes, rather than just simply tracking my time? >> I mean, I think, talking about pricing in the first instance. What we're finding with consumers that we've been testing with, is if you can compare the price of a shirt to the price of shirt without sensors, it's really the wrong value proposition. The question we ask is, How much money are you spending on your CrossFit box membership or your Equinox gym membership? The cost of a personal trainer is easily upwards of $75-100 for an hour. Now, we can give you 24/7 access to that personal coaching. You'll pay the same in a year as you would pay in an hour for coaching. I think for price, it's someone who's already thinking about paying for personal coaching and personal training, that's really where the pricing market is. >> That's interesting, we see that time and time again. We did an interview with Knightscope, and they have security robots, and basically, it's the same thing. They're priced comparisons was the hourly rate for a human counterpart, or we can give it to you for a much less hourly rate. And now, you don't just get it for an hour, you get it for as long as you want to use it. Well, it's exciting times. You guys in the market in terms of when you're going G80? Have a feel for-- >> Any minute now. >> Any minute now? >> We have people using the product, giving us feedback. My phone's switched off. That's the quietest it's been for awhile. But we have people using the product right now, giving us feedback on the product. We're really excited. One in three people, when we ask, the metric that matters for us is net promoter score. How likely would someone recommend asensei to someone else? One in three athletes are giving us a 10 out of 10, so we feel really good about the experience. Now, we're just focused on making sure we have enough content in place from our coaches. General availability is anytime soon. >> [Jeff] Good. Very exciting. >> Yeah, we're excited. >> Thanks for taking a few minutes of your day, and I actually know some rowers, so we'll have to look into the application. >> Right, introduce us. Good stuff. >> He's Steven Webster, I'm Jeff Frick. You're watching theCUBE. We're having a CUBE Conversation in our Palo Alto Studios. Thanks for watching. (bright music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Veritas Vision 2017. Brought to you by Veritas. >> Welcome back to the Aria in Las Vegas, everybody. This is theCUBE, the leader in live tech coverage. We go out to the events and extract the signal from the noise. We're here at Veritas Vision 2017, #VtasVision. Jyothi Swaroop is here. He's the vice president of product and solutions marketing at Veritas. Jyothi, welcome to theCUBE. Good to see you. >> Thanks, Dave. I'm an officially an alum, now? >> A CUBE alum, absolutely! >> Two times! Three more times, we'll give you a little VIP badge, you know, we give you the smoking jacket, all that kind of stuff. >> Five or six times, you'll be doing the interviews. >> I'm going to be following you guys around, then, for the next three events. >> So, good keynote this morning. >> Jyothi: Thank you. >> Meaty. There was a lot going on. Wasn't just high-level concepts, it was a lot of high-level messaging, but then, here's what we've done behind it. >> No, it's actually the opposite. It's a lot of real products that customers are using. The world forgets that Veritas has only been out of Symantec, what, 20 months? Since we got out, we were kind of quiet the first year. That was because we were figuring our strategy out, investing in innovation and engineering, 'cause that's what Carlyle, our board, wants for us to do is invest in innovation and engineering, and build real products. So we took our time, 18 to 20 months to build these products out, and we launched them. And they're catching on like wildfire in the customer base. >> Jyothi, Bill came on and talked about, he made a lot of changes in the company. Focused it on culture, innovation, something he's want. What brought you? You know, a lot of places you could've gone. Why Veritas, why now? >> Well, Bill is one of the reasons, actually. I mean, if you look at his history and what he's done with different companies over the years, and how the journey of IT, as he put it during his keynote, he wants to make that disruption happen again at Veritas. That was one. Two was just the strategy that they had. Veritas has a Switzerland approach to doing business. Look, it's granted that most Fortune 500 or even midmarket customers have some sort of a Cloud project going on. But what intrigued me the most, especially with my background, coming from other larger companies is, Veritas was not looking to tie them down or become a data hoarder, you know what I mean? It's just charge this massive dollar per terabyte and just keep holding them, lock them into a storage or lock them into a cloud technology. But, we were facilitating their journey to whichever cloud they wanted to go. It was refreshing, and I still remember the first interview with Veritas, and they were talking about, "Oh, we want to help move customers' data "into Azure and AWS and Google," and my brain from previous storage vendors is going, "Hang on a minute. "How are you going to make money "if you're just going to move all of this data "to everyone else?" But that's what is right for the customer. >> Okay, so, how are you going to make money? >> Well, it's not just about the destination, right? Cloud's a journey, it's not just a destination. Most customers are asking us, "On average, we adopt three clouds," is what they're telling us. Whether it's public, private, on-prem, on average, they have about three separate clouds. What they say is, "Jyothi, our struggle is to move "an entire virtual business service "from on-prem to the Cloud." And once we've moved it, let's say Cloud A is suddenly expensive or is not working out for them. To get out of that cloud and move it to Cloud B is just so painful. It's going to cost me tons of money, and I lost all of the agility that I was expecting from Cloud A, anyway. If you have products like VRP from Veritas, for example, where we could move an entire cloud business service from Cloud A to Cloud B, and guess what. We can move it back onto on-prem on the fly. That's brilliant for the customers. Complete portability. >> Let's see. The portfolio is large. Help us boil it down. How should we think about it at a high level? We only have 20 minutes, so how do we think about that in 15, 20 minutes? >> I'll focus on three tenets. Our 360 data management wheel, if you saw at the keynote, has six tenets. The three tenets I'll focus on today are visibility, portability, and last, but definitely not the least, storage. You want to store it efficiently and cost-effectively. Visibility, most of our customers that are getting on their cloud journey are already in the Cloud, somewhere. They have zero visibility, almost. Like, "What applications should I move into the Cloud? "If I have moved these applications, "are they giving me the right value? "Because I've invested heavily in the Cloud "to move these applications." They don't know. 52% of our customers have dark data. We've surveyed them. All that dark data has now been moved into some cloud. Look, cloud is awesome. We have partnered up with every cloud vendor out there. But if we're not making it easy for customers to identify what is the right data to move to the Cloud, then they lost half the battle even before they moved to the Cloud. That's one. We're giving complete visibility with the Info Map connectors that we just announced earlier on in the keynote. >> That's matching the workload characteristics with the right sort of platform characteristics, is that right? >> Absolutely. You could be a Vmware user, you're only interested in VM-based data that you want to move, and you want role-based access into that data, and you want to protect only that data and back it up into the Cloud. We give you that granularity. It's one thing to provide visibility. It's quite another to give them the ability to have policy-driven actions on that data. >> Jyothi, just take us inside the customers for that. Who owns this kind of initiative? The problem in IT, it's very heterogeneous, very siloed. You take that multi-cloud environment, most customers we talk to, if they've got a cloud strategy, the ink's still drying. It's usually because, well, that group needed this, and somebody needed this, and it's very tactical. So, how do I focus on the information? Who drives that kind of need for visibility and manages across all of these environments? >> That's a great question, Stu. I mean, we pondered around the same question for about a year, because we were going both top-down and bottoms-up in the customer's organization, and trying to find where's our sweet spot. What we figured is, it's not a one-strategy thing, especially with the portfolio that we have. 80% of the time, we are talking to the CIOs, we are talking to the CXOs, and we're coming down with their digital transformation strategy or their cloud transformation strategy, they may call it whatever they want. We're coming top-down with our products, because when you talk visibility, a backup admin, he may not jump out of his seat the first thing. "Visibility's not what I care about, "the ease of use of this backup job "is what I care about, day one." But if you talk to the CIO, and I tell him, "I'll give you end-to-end visibility "of your entire infrastructure. "I don't care which cloud you're in." He'll be like, "I'm interested in that, "'cause I may not want to move 40% of this data "that I'm moving to Cloud A today. "I want to keep it back, or just delete it." 'Cause GDPR in Europe gives the citizens the right to delete their data. Doesn't matter which company the data's present in. The citizen can go to that company and say, "You have to delete my data." How will you delete the data if you just don't know where the data is? >> It's in 20 places in 15 different databases. Okay, so that's one. You had said there were three areas that you wanted to explore. >> The second one is, again, all about workload data and application portability. Over the years, we had storage lock-ins. I'm not going to name names, but historically, there are lots of storage vendors that tend to lock customers into a particular type of storage, or to the company, and they just get caught up in that stacked refresh every three years, and you just keep doing that over and over again. We're seeing more and more of cloud lock-in start to happen. You start migrating all of this into one cloud service provider, and you get familiar with the tools and widgets that they give you around that data, and then all of a sudden you realize this is not the right fit, or I'm moving too much data into this place and it's costing me a lot more. I want to not do this anymore, I want to move it to another local service provider, for example. It's going to cost you twice as much as it did just to move the data into the Cloud in the first place. With VRP, Veritas Resiliency Platform, we give our customers literally a few mouse clicks, if you watched the demo onstage. Literally, with a few mouse clicks, you identify the data that you want to move, including your virtual machines and your applications, and you move them as a business service, not just as random data. You move it as an entire business service from Cloud A to Cloud B. >> Jyothi, there's still physics involved in this. There's many reasons why with lock-in, you mentioned, kind of familiarity. But if I have a lot of data, moving it takes a lot of time as well as the money. How do we handle that? >> It goes back to the original talk track here about visibility. If you give the customer the right amount of visibility, they know exactly what to move. If the customer has 80 petabytes of data in their infrastructure, they don't have to move all 80 petabytes of it, if we are able to tell them, "These are the 10 petabytes that you need to move, "based on what Information Map is telling you." They'll only move those 10 petabytes, so the workload comes down drastically, because they're able to visualize what they need to move. >> Stu: Third piece of storage? >> Third piece of storage. A lot of people don't know this, but Veritas was the first vendor that launched the software to find storage solution. Back in the VOS days, Veritas, Oracle, and Sun Microsystems, we had the first file system that would be this paper over rocks, if you will, that was just a software layer. It would work with literally SAN/DAS, anything that's out there in the market, it would just be that file system that would work. And we've kept that DNA in our engineering team. Like, for example, Abhijit, who leads up our engineering, he wrote the first cluster file system. We are extending that beyond just a file system. We're going file, block, and object, just as any other storage vendor would. We are certifying on various commodity hardware, so the customers can choose the hardware of their choice. And not just that. The one thing we're doing very differently, though, is embedding intelligence close to the metadata. The reason we can do that is, unlike some of the classic storage vendors, we wrote the storage ground-up. We wrote the code ground-up. We could extract, if you look at an object, it has object data and metadata. So, metadata standard, it's about this long, right? It's got all these characters in it. It's hard to make sense of it unless you buy another tool to read that object and digest it for the customer. But what if you embed intelligence next to the metadata, so storage is not dumb anymore? It's intelligent, so you avoid the number of layers before you actually get to a BI product. I'll just give you a quick example in healthcare. We're all wearing Apple Watches and FitBits. The data is getting streamed into some object store, whether it's in the Cloud or on-prem. Billions of objects are getting stored even right now, with all the Apple Watches and FitBits out there. What if the storage could predictively, using machine learning and intelligence, tell you predictively you might be experiencing a stroke right on your watch, because your heartbeats are X and your pulse is Y? Combining all of the data and your history, based on the last month or last three months, I can tell you, "Jyothi, you should probably go see the doctor "or do something about it." So that's predictive, and it can happen at the storage layer. It doesn't have to be this other superficial intelligence layer that you paid millions of dollars for. >> So that analytic capability is really a feature of your platform, right? I mean, others, Stu, have tried it, and they tried to make it the product, and it really isn't a product, it's a byproduct. And so, is that something I could buy today? Is that something that's sort of roadmap, or, what's the reaction been from customers? >> The reaction has been great, both customers and analysts have just loved where we're going with this. Obviously, we have two products that are on the truck today, which are InfoScale and Access. InfoScale is a block-based product and Access is a file-based product. We also have HyperScale, which was designed specifically for modern workloads, containers, and OpenStack. That has its own roadmap. You know how OpenStack and containers work. We have to think like a developer for those products. Those are the products that are on the truck today. What you'll see announced tomorrow, I hope I'm not giving away too much, because Mike already announced it, is Veritas Cloud Storage. That's going to be announced tomorrow, and we're going to go deep into that. Veritas Cloud Storage will be this on-prem, object-based storage which will eventually become a platform that will also support file and block. It's just one single, software-defined, highly-intelligent storage system for all use cases. Throw whatever data you want at it. >> And the line on Veritas, the billboards, no hardware agenda. Ironic where that came from. Sometimes you'll announce appliances. What is that all about, and when do you decide to do that? >> Great question. You know, it's all about choice. It's the cliched thing to say, I know, but Veritas, most people don't know this, has a heavy channel revenue element to what we do. We love our partners and channel. Now, if you go to the channel that's catering to midmarket customers, or SMBs, they just want the easy button to storage. Their agility, I don't have five people sitting around trying to piece all of this together with your software and Seagate's hardware and whatever else, and piece this together. I just want a box, a pizza box that I can put in my infrastructure, turn it on, and it just works, and I call Veritas if something goes wrong. I don't call three different people. This is for those people. Those customers that just want the easy button to storage or easy button to back up. >> To follow up on the flip side, when you're only selling software, the knock on software of course is, I want it to be fast, I want it to be simple, I need to be agile. How come Veritas can deliver these kinds of solutions and not be behind all the people that have all the hardware and it's all fully baked-in to start with? >> Well, that's because we've written these from the ground up. When you write software code from the ground up, I mean, I'm an engineer, and I know how hard it is to take a piece of legacy code that's baked in for 10, 20 years. It's almost like adding lipstick, right? It just doesn't work, especially in today's cloud-first world, where people are in the DevOps situation, where apps are being delivered in five, 10, 15 minutes. Every day, my app almost gets updated on the phone every day? That just doesn't work. We wrote these systems from the ground up to be able to easily be placed onto any hardware possible. Now, again, I won't mention the vendor, but in my previous lives, there were a lot of hardware boxes and the software was written specifically for those hardware configurations. When they tried to software-define it forcefully, it became a huge challenge, 'cause it was never designed to do that. Whereas at Veritas, we write the software layer first. We test it on multiple hardware systems, and we keep fine-tuning it. Our ideal situation is to sell the software, and if the customer wants the hardware, we'll ship them the box. >> One of the things that struck me in the keynote this morning was what I'll call your compatibility matrix. Whether it was cloud, somebody's data store, that really is your focus, and that is a differentiator, I think. Knocking those down so you can, basically, it's a TAM expansion strategy. >> Oh, yeah, absolutely. I mean, TAM expansion strategy, as well as helping the customer choose what's best for them. We're not limiting their choices. We're literally saying, we go from the box and dropboxes of the world all the way to Dell EMC, even, with Info Map, for example. We'll cover end-to-end spectrum because we don't have a dollar-per-terabyte or dollar-per-petabyte agenda to store this data within our own cloud situation. >> All right, Jyothi, we got to leave it there. Thanks very much for coming back on theCUBE. It's good to see you again. >> Jyothi: No, it's great to be here. >> All right, keep it right there, everybody. We'll be back with our next guest. We're live from Veritas Vision 2017. This is theCUBE. (fast electronic music)

>> Announcer: Live from Austin, Texas it's the Cube. Covering South by Southwest 2017. Brought to you by Intel. Now here's John Furrier. Okay we're back live here at the South by Southwest Intel AI Lounge, this is The Cube's special coverage of South by Southwest with Intel, #IntelAI where amazing starts with Intel. Our next guest is Dr. Dawn Nafus who's with Intel and you are a senior research scientist. Welcome to The Cube. >> Thank you. >> So you've got a panel coming up and you also have a book AI For Everything. And looking at a democratization of AI we had a quote yesterday that, "AI is the bulldozer for data." What bulldozers were in the real world, AI will be that bulldozer for data, surfacing new experiences. >> Right. >> This is the subject of your book, kind of. What's your take on this and what's your premise? >> Right well the book actually takes a step way back, it's actually called Self Tracking, the panel is AI For Everyone. But the book is on self tracking. And it's really about actually getting some meaning out of data before we start talking about bulldozers. So right now we've got this situation where there's a lot of talk about AI's going to sort of solve all of our problems in health and there's a lot that can get accomplished, whoops. But the fact of the matter is is that people are still struggling with gees, like, "What does my Fitbit actually mean, right?" So there's this, there's a real big gap. And I think probably part of what the industry has to do is not just sort of build new great technologies which we've got to do but also start to fill that gap in sort of data education, data literacy, all that sort of stuff. >> So we're kind of in this first generation of AI data you mentioned wearable, Fitbits. >> Dawn: Yup. >> So people are now getting used to this, so that it sounds this integration into lifestyle becomes kind of a dynamic. >> Yeah. >> Why are people grappling >> John: with this, what's your research say about that? >> Well right now with wearables frankly we're in the classic trough of disillusionment. (laughs) You know for those of you listening I don't know if you have sort of wearables in drawers right now, right? But a lot of people do. And it turns out that folks tend to use it, you know maybe about three or four weeks and either they've learned something really interesting and helpful or they haven't. And so there's actually a lot of people who do really interesting stuff to kind of combine it with symptoms tracking, location, right other sorts of things to actually really reveal the sorts of triggers for medical issues that you can't find in a clinical setting. It's all about being out in the real world and figuring out what's going on with you. Right, so then when we start to think about adding more complexity into that, which is the thing that AI's good at, we've got this problem of there's only so many data sets that AI's any actually any good at handling. And so I think there's going to have to be a moment where sort of people themselves actually start to say, "Okay you know what? "This is how I define my problem. "This is what I'm going to choose to keep track of." And some of that's going to be on a sensor and some of it isn't. Right and sort of being really intervening a little bit more strongly in what this stuff's actually doing. >> You mentioned the Fitbit and you were seeing a lot of disruption in the areas, innovation and disruption, same thing good and bad potentially. But I'll see autonomous vehicles is pretty clear, and knows what Tesla's tracking with their hot trend. But you mentioned Fitbit, that's a healthcare kind of thing. AIs might seem to be a perfect fit into healthcare because there's always alarms going off and all this data flying around. Is that a low hanging fruit for AI? Healthcare? >> Well I don't know if there's any such thing as low hanging fruit (John laughs) in this space. (laughs) But certainly if you're talking about like actual human benefit, right? That absolutely comes the top of the list. And we can see that in both formal healthcare in clinical settings and sort of imaging for diagnosis. Again I think there's areas to be cautious about, right? You know making sure that there's also an appropriate human check and there's also mechanisms for transparency, right? So that doctors, when there is a discrepancy between what the doctor believes and what the machine says you can actually go back and figure out what's actually going on. The other thing I'm particularly excited about is, and this is why I'm so interested in democratization is that health is not just about, you know, what goes on in clinical care. There are right now environmental health groups who are looking at slew of air quality data that they don't know what to do with, right? And a certain amount of machine assistance to sort of figure out you know signatures of sort of point source polluters, for example, is a really great use of AI. It's not going to make anybody any money anytime soon, but that's the kind of society that we want to live in right? >> You are the social good angle for sure, but I'd like to get your thoughts 'cause you mentioned democratization and it's kind of a nuance depending upon what you're looking at. Democratization with news and media is what you saw with social media now you got healthcare. So how do you define democratization in your context and you're excited about.? Is that more of freedom of information and data is it getting around gatekeepers and siloed stacks? I mean how do you look at democratization? >> All of the above. (laughs) (John laughs) I'd say there are two real elements to that. The first is making sure that you know, people are going to use this for more than just business, have the ability to actually do it and have access to the right sorts of infrastructures to, whether it's the environmental health case or there are actually artists now who use natural language processing to create art work. And people ask them, "Why are you using deblurting?" I said, "Well there's a real access issue frankly." It's also on the side of if you're not the person who's going to be directly using data a kind of a sense of, you know... Democratization to me means being able to ask questions of how the stuff's actually behaving. So that means building in mechanisms for transparency, building in mechanisms to allow journalists to do the work that they do. >> Sharing potentially? >> I'm sorry? >> And sharing as well more data? >> Very, very good. Right absolutely, I mean frankly we still have a problem right now in the wearable base of people even getting access to their own data. There's a guy I work with named Hugo Campos who has an arterial defibrillator and he's still fighting to get access to the very data that's coming out of his heart. Right? (laughs) >> Is it on SSD, in the cloud? I mean where is it? >> It is in the cloud. It's going back to the manufacturer. And there are very robust conversations about where it should be. >> That's super sad. So this brings up the whole thing that we've been talking about yesterday when we had a mini segment on The Cube is that there are all these new societal use cases that are just springing up that we've never seen before. Self-driving cars with transportation, healthcare access to data, all these things. What are some of the things that you see emerging on that tools or approaches that could help either scientists or practitioners or citizens deal with these new critical problem solving that needs to apply technology to. I was talking just last week at Stanford with folks that are looking at gender bias and algorithms. >> Right, uh-huh it's real. >> Something I would never have thought of that's an outlier. Like hey, what? >> Oh no, it's happened. >> But it's one of those things were okay, let's put that on the table. There's all this new stuff coming on the table. >> Yeah, yeah absolutely. >> What do you see? >> So they're-- >> How do we solve that >> John: what approaches? >> Yeah there are a couple of mechanisms and I would encourage listeners and folks in the audience to have a look at a really great report that just came out from the Obama Administration and NYU School of Law. It's called AI Now and they actually propose a couple of pathways to sort of making sure we get this right. So you know a couple of things. You know one is frankly making sure that women and people of color are in the room when the stuff's getting built, right? That helps. You know as I said earlier you know making sure that you know things will go awry. Like it just will we can't predict how these things are going to work and catching it after the fact and building in mechanisms to be able to do that really matter. So there was a great effort by ProPublica to look at a system that was predicting criminal recidivism. And what they did was they said, "Look you know "it is true that "the thing has the same failure rate "for both blacks and whites." But some hefty data journalism and data scraping and all the rest of it actually revealed that it was producing false positives for blacks and false negatives for whites. Meaning that black people were predicted to create more crime than white people right? So you know, we can catch that, right? And when we build in more system of people who had the skills to do it, then we can build stuff that we can live with. >> This is exactly to your point of democratization I think that fascinates me that I get so excited about. It's almost intoxicating when you think about it technically and also societal that there's all these new things that are emerging and the community has to work together. Because it's one of those things where there's no, there may be a board of governors out there. I mean who is the board of governors for this stuff? It really has to be community driven. >> Yeah, yeah. >> And NYU's got one, any other examples of communities that are out there that people can participate in or? >> Yup, absolutely. So I think that you know, they're certainly collaborating on projects that you actually care about and sort of asking good questions about, is this appropriate for AI or not, right? Is a great place to start of reaching out to people who have those technical skills. There are also the Engineering Professional Association actually just came out a couple months ago with a set of guidelines for developers to be able to... The kinds of things you have to think about if you're going to build an ethical AI system. So they came out with some very high level principles. Operationalizing those principles is going to be a real tough job and we're all going to have to pitch in. And I'm certainly involved in that. But yeah, there are actually systems of governance that are cohering, but it's early days. >> It's great way to get involved. So I got to ask you the personal question. In your efforts with the research and the book and all of your travels, what's some of the most amazing things that you've seen with AI that are out there that people may know about or may not know about that they should know about? >> Oh gosh. I'm going to reserve judgment, I don't know yet. I think we're too early on the curve to be able to talk about, you know, sort of the magic of it. What I can say is that there is real power when ordinary people who have no coding skills whatsoever and frankly don't even know what the heck machine learning is, get their heads around data that is collected about them personally. That opens up, you can teach five year olds statistical concepts that are learned in college with a wearable because the data applies to them. So they know how it's been collected. >> It's personal. >> Yeah they know what it is already. You don't have to tell them what a outlier effect is because they know because they wear that outlier. You know what I mean. >> They're immersed in the data. >> Absolutely and I think that's where the real social change is going to come from. >> I love immersion as a great way to teach kids. But the data's key. So I got to ask you with the big pillars of change going on and at Mobile World Congress I saw you, Intel in particular, talking about autonomous vehicles heavily, smart cities, media entertainment and the smart home. I'm just trying to get a peg a comparable of how big this shift will be. These will be, I mean the '60s revolution when chips started coming out, the PC revolution and server revolution and now we're kind of in this new wave. How big is it? I mean in order of magnitude, is it super huge with all of the other ships combined? Are we going to see radical >> I don't know. >> configuration changes? >> You know. You know I'm an anthropologist, right? (John laughs) You know everything changes and nothing changes at the same time, right? We're still going to wake up, we're still going to put on our shoes in the morning, right? We're still going to have a lot of the same values and social structures and all the rest of it that we've always had, right. So I don't think in terms of plonk, here's a bunch of technology now. Now that's a revolution. There's like a dialogue. And we are just at the very, very baby steps of having that dialogue. But when we do people in my field call it domestication, right? These become tame, they become part of our lives, we shape them and they shape us. And that's not radical change, that's the change we always have. >> That's evolution. So I got to ask you a question because I have four kids and I have this conversation with my wife and friends all the time because we have kids, digital natives are growing up. And we see a lot of also work place domestication, people kind of getting domesticated with the new technologies. What's your advice whether it's parents to their kids, kids to growing up in this world, whether it's education? How should people approach the technology that's coming at them so heavily? In the age of social media where all our voices are equal right now, getting more filters are coming out. It's pretty intense. >> Yeah, yeah. I think it's an occasion where people have to think a lot more deliberately than they ever have about the sources of information that they want exposure to. The kinds of interaction, the mechanisms that actual do and don't matter. And thinking very clearly about what's noise and what's not is a fine thing to do. (laughs) (John laughs) so yeah, probably the filtering mechanisms has to get a bit stronger. I would say too there's a whole set of practices, there are ways that you can scrutinize new devices for, you know, where the data goes. And often, kind of the higher bar companies will give you access back, right? So if you can't get your data out again, I would start asking questions. >> All right final two questions for you. What's your experiences like so far at South by Southwest? >> Yup. >> And where is the world going to take you next in terms of your research and your focus? >> Well this is my second year at South by Southwest. It's hugely fun, I am so pleased to see just a rip roaring crowd here at the Intel facility which is just amazing. I think this is our first time as in Dell proper. I'm having a really good time. The Self Tracking book is in the book shelf over in the convention center if you're interested. And what's next is we are going to get real about how to make, how to make these ethical principles actually work at an engineering level. >> Computer science meets social science, happening right now. >> Absolutely. >> Intel powering amazing here at South by Southwest. I'm John Furrier you're watching The Cube. We've got a great set of people here on The Cube. Also great AI Lounge experience, great demos, great technologists all about AI for social change with Dr. Dawn Nafus with Intel. We'll be right back with more coverage after this short break. (upbeat digital beats)

>> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Twitter headquarters for Data Privacy Day. An interesting collection of people coming together here at Twitter to talk about privacy, the implications of privacy... And I can't help but think back to the classic Scott McNeely quote right, "Privacy is dead, get over it", and that was in 1999. Oh how the world has changed, most significantly obviously mobile phones with the release of the iPhone in 2007. So we're excited to really kind of have the spearhead of this event, Michael Kaiser. He's the executive director of the National Cyber Security Alliance in from Washington D.C.. Michael, great to see you. >> Thanks for having us in. >> For the folks that aren't here, what is kind of the agenda today? What's kind of the purpose, the mission? Why are we having this day? >> Well Data Privacy Day actually comes to us from Europe, from the EU which created privacy as a human right back in 1981. We've been doing it here in the United States since around 2008. NCSA took over the effort in 2011. The goal here really is just help educate people, people and businesses as well, about the importance of respecting privacy, the importance of safeguarding information, people's personal data. And then really hopefully with an end goal of building a lot more trust in the ecosystem around the handling of personal data which is so vital to the way the internet works right now. >> Right, and it seems like obviously companies figured out the value of this data long before individuals did and there's a trade for service. You use Google Maps, you use a lot of these services but does the value exchange necessarily, is it equal? Is it at the right level? And that seems to be kind of the theme of some of these privacy conversations. You're giving up a lot more value than you're getting back in exchange for some of these services. >> Yeah, and we actually have a very simple way that we talk about that. We like to say that personal information is like money and that you should value it and protect it. And so, trying to encourage people and educate people to understand that their personal information does have value and there is an exchange that's going on. They should make sure that those transactions are ones that they're comfortable in terms of giving their information and what they get back. >> Right, which sounds great Michael but then you know you get the EULA, you know you sign up for these things and they don't really give you the option. You can kind of read it but who reads it? Who goes through? You check the box and you move on. And or you get this announcement, we changed our policy, we changed our policy, we changed our policy. So, I don't know if realistic is the right word but how do people kind of navigate that? Because, let's face it my friends told me about Uber, I want to get an UBER. I download UBER. I'm stuck in a rainy corner in D.C. and I hit go and here comes the car. I don't really dig into the meat. Is there an option? I mean there's not really, I opt for privacy one, two, three and I'm opting out of five, six, seven. >> Yeah, I think we're seeing a little bit more granular controls for people on some of these things now but I think that's what we'd advocate for more. When we talk to consumers they tell us mostly that they want to have better clarity about what's being collected about them, better clarity about how that information's being used, or if it's, how it's being shared. Equally importantly, if there are controls where are they, how easy are they to use, and making them more prominent so people can engage in sort of making the services tailored to their own sort of privacy profile. I think we'd like to see more of that for sure, more companies being a little more forthcoming. Yeah you have the big privacy policy that's a long complicated legal document but there may be other way to create interfaces with your customers that make some of the key pieces more apparent. >> And do you see a trend where, because you mentioned in some of the notes that we prepared that privacy is good for business and potentially is a competitive differentiator. Are you starting to see where people are surfacing privacy more brightly so that they can potentially gain the customer, gain respect of the customer, the business of the customer over potentially a rival that's got that buried down? Is that really a competitive lever that you see? >> Well I think you see some extremes. So you see some companies that say we don't collect any information about you at all so that's part of, out there, and I think they're marketing to people who have extreme concerns about this. But I also think we're seeing again some places where there are more higher profile ability to control some of this data right. Even in you know places like the mobile setting where sometimes you'll just get a little warning saying oh this is about to use your location, is that okay, or your location is turned off you need to turn it back on in order to use this particular app. And I think those kinds of interfaces with the user of the technology are really important going forward. We don't want people overwhelmed like every time you turn on your phone you're going to have to answer 17 things in order to get to do x, y, and z but making people more aware of how the apps are using the information they collect about you I think is actually good for business. I think actually sometimes consumers get confused because they'll see a whole list of permissions that need to be provided and they don't understand how those permissions apply to what the app or service is really going to do. >> Right, right. >> Yeah, that's an interesting one. I was at a, we were at Grace Hopper in October and one of the keynote speakers was talking about how mobile data has really changed this thing right because once you're on your mobile phone it uses all the capabilities that are native in the phone in terms of geolocation, accelerometer, etc. All these things that a lot of people probably didn't know were different on the mobile Facebook app than were on the desktop Facebook app. Let's face it, most this stuff is mobile these days, certainly with the younger kids. As you said, and that's an interesting tack, why do you need access to my context? Why do you need access to my pictures? Why do you need access to my location? And then the piece that I'm curious to get your opinion, will some of the value come back to the consumer in terms of I'm not just selling your stuff, I'm not monetizing it via ads, I'm going to give some of that back to you? >> Yeah, I think there's a couple things there. One quick point on the other issue there, without naming names I was looking at an app and it said it had to have access to my phone, and I'm like why would this app need access to my phone? And then I realized later well it needs access to my phone because if the phone rings it needs to turn itself off so I can answer the phone. But that wasn't apparent right? And so I think it can be confusing to people like maybe it's innocuous in some ways. Some ways it might not be but in that case it was like okay yeah because if the phone rings I'd rather answer my phone than be looking at the app. >> Right, can I read it or can I just see it. You know the degree of the access too is very confusing. >> Yeah and I think in terms of the other issues that you're raising here about how the value exchange on data, I think the internet of things is really going to play a big role in this because it's really... You know in the current world it's about you know data, delivering ads, those kinds of things, making the experience more customized. But in IoT where you're talking about wearables or fitness or those kinds of things, or thermostats in your home, your data really drives that. So in order for those devices to really work well they have to have data about you. And that's where I think customers will really have to give great thought to. You know is that a good value proposition, right? I mean, do I want to share my data about when I come and leave every day just so my thermostat you know can turn on and off. And I think those are you know can be conscience decisions about when you're implementing that kind of technology. >> Right, so there's another interesting tack I'd love to get your opinion on. You know we see Flo from the Progressive commercials advertising to stick the USB in your cigarette lighter and we'll give you cheaper rates because now we know if you stop at stop signs or not. What's funny to me is that phone already knows whether you stop at stop signs or not and it already knows that you take 18 trips to 7-Eleven on a Saturday afternoon and you're sitting on your couch the balance of the time. As that information that's there somehow gets exposed and potentially runs into say healthcare mandated requirement from the company that you must wear Fitbits so now we know you're spending too much time at the 7-Eleven and on your couch and how that impacts your health insurance and stuff. And that's going to crash right into HIPAA. It just seems like there's this huge kind of collision coming from you know I can provide better service to people at the good end of the scale, and say aggregated risk models, but then what happens to the poor people at the other end? >> Well, I think that's why you have to have opt in, right? I think you can't make these things mandatory necessarily. And I think people have to be extremely aware of when their data is being collected and how it's being used. And so, you know the example of like the car insurance, I mean they can only, really should only be able to access that data about where you're going if you sign up to do that right? And if they want to say to you, hey Michael we might give you a better rate if we can track your, you know driving habits for a couple of weeks then that should be my choice right to give that data. Maybe my rates might be impacted if I don't but I can make that choice myself and should be allowed to make that choice myself. >> So it's funny, the opt in and opt out, so right now from your point of view what do you see in terms of the percentage of kind of opt in opt out on these privacy issues? Where is it and where should it be? >> Well I would like to see some more granular controls for the consumer in general right. I would like to see... And I said a little bit earlier a lot more transparency and ease of access to what's being collected about you and what's being used. You know outside of the formal legal process, obviously you know companies have to follow the law. They have to comply. They have to be, you know write these long EULAs or privacy policies in order to really reflect what they're doing. But they should be talking to their customers and understanding what's the most important thing that you want to know about my service before you sign up for it. And help people understand that and navigate their way through it. And I think in a lot of cases consumers will click yeah let's do it but they should do that really knowingly. If opting in is you're opting in it should be done with true consent right. >> Okay, so before I let you go just share some best practices, tips and tricks, you know kind of at least the top level what people should be thinking about, what they should be doing. >> Yeah, so we really, you know in this kind of space we look at a couple things. One, personal informations like money value and protect it. That really means being thoughtful about what information you share, when you share it, who you share it with. Own your online presence, this is really important. Consumers have an active role in how they interact with the internet. Use the settings that are there right. Use the safety and security or privacy and security settings that are in the services that you have. And then, actually a lot of this is behavioral. What you share is really important yourself so share with care right. I mean be thoughtful about the kinds of information that you put out there about yourself. Be thoughtful about the kind of information that you put about your friends and family. Realize that every single one of us in this digital world is entrusted with personal information about people much more than we used to be in the past. We have that responsibility to safeguard what other people give to us and that should be the common goal around the internet. >> I think we have to have you at the bullying and harassment convention down the road. Great insight Michael and really appreciate it. Have a great day today. I'm sure there's going to be a lot of terrific content that comes out. And for people to get more information go to the National Cyber Security Alliance. Thanks for stopping by. >> Thank you for having us. >> Absolutely. He's Michael Kaiser. I'm Jeff Frick. You're watching theCUBE, thanks for watching.