>>Mhm. >>The company Nutanix was founded as the world was coming out of the financial crisis in 2009 Cop Computing was still in its infancy but had shown the way for what was possible with automation and simplification of infrastructure provisioning and management at scale. Now what Nutanix did is it brought cloud concepts to data centers and created the market for hyper converged infrastructure, a software defined architecture that eliminated stovepipes in the heavy lifting Associated with traditional compute networking in storage management. Now in the first part of the next decade, Nutanix essentially set the standard for this new world, building a loyal customer base, reaching escape velocity and successfully going public in 2016. Fast forward to 2021 and much has changed. Cloud is no longer knew rather it's become a staple of the digital economy as we exit the isolation economy. The cloud is much different today. It's expanding to on prem and out to the edge. New connections are being made in hybrid and across cloud models and as such, connecting and managing infrastructure across these new clouds to create a facile experience for users irrespective of where the data lives. Has become a major priority for organizations. They don't want to waste time and money on making the plumbing work. But that's easier said than done as the market is evolving. So is Nutanix to meet these new customer challenges and opportunities and with me ahead of dot next the major event of the year for Nutanix customers is Monica Monica Kumar who is the senior vice president of marketing and cloud go to market for the company Monica always great to see you welcome back to the cube. >>Thank you so much. Dave I'm so happy to be here again. >>Okay, so you heard my little narrative upfront, what's your perspective on the cloud market and where your customers are in their journey? >>Well, as you said, Dave Cloud is a critical enabler for rapid growth for organizations now, it's no longer just uh you know, nice to have, it's become a must have for organizations to survive and thrive in this digital economy. Uh In fact I follow a lot of um surveys that are happening around cloud adoption and one of the key trends that's coming out is it's no longer just about I. T. Practitioners adopting cloud. In fact, 78% of C. X. O. S are looking to cloud to speed up transformation of the entire businesses. You know, 80% of business executives are looking to cloud to mitigate their risks of their companies and 87% of the executives view cloud as critical to achieving their corporate growth goals. So what we are now realizing is that hybrid multi cloud is becoming the preferred model Which means there is no one cloud that customers are using, they're using the right cloud for the right workload. In fact, according to Gartner Group, 81% of public cloud users are using more than two providers. So what's happening is increasingly businesses are relying on multiple public clouds and on premises to meet their needs and are looking for that flexibility and that's delivered by different cloud providers. Um We've done our own survey called Nutanix Enterprise cloud and that we do it every year and 86% of respondents in the last service said hybrid cloud is the ideal operating model. So the Net Net that we're hearing from our customers is cloud is not a destination, it's an operating model. Customers want the right cloud for the right workload and the right applications. >>Okay, awesome. So the world, great setup. Thank you. So the world is moving to multi cloud. I think there's not no debate on that and that is really the mainstream. That's the norm. Talk about where Nutanix fits into this new world. >>Absolutely. So we're at an inflection point as organizations are grappling with this complexity. Now, obviously you can imagine the more computing environments to use this complexity in running and managing those hybrid solutions across multiple clouds. When Nutanix is focused singularly on is making that cloud complexity invisible. So our customers can focus on their business outcomes. We are solving the complexity of running and managing multiple clouds, just like we did for infrastructure and data centers a decade ago when we first started as a company. Now with the Nutanix start platform enabling our customers to seamlessly connect their private and public clouds simply move applications, data licenses across any cloud, optimize the work replacement and costs all while leveraging a consistent set of services, tools and processes. So for us it's really, really crucial that we give customers the choice to pick the hardware. Of the choice, the cloud of their choice, the virtual machines, they want to deploy the containers and data and help them realize their entire hybrid multi cloud strategy. It's all about giving our customers that peace of mind to deploy and operate the apps and data across multiple clouds with ease and flexibility. >>All right, let's talk about dot next my I think I'm pretty sure my first dot next was the first one ever, which I think was 2015. It was pre I P O. The focus is obviously evolving what's the focus this year? >>Well, dot next has evolved to become the industry's leading hybrid multi cloud conference. It's almost here. It's taking place next week, september 28th, 23rd and this year's event will bring together it and cloud professionals from around the globe to explore the latest trends, solutions, best practices and hybrid, multi cloud technology. Now we're obviously gonna, you know, future a lot of thought leaders from within the industry as well as in general, you know, people that impact our lives in a positive manner. And we're going to really focus on topics around hybrid multi cloud hyper converged infrastructure, private cloud ABM organization, you know, kubernetes containers, how do you figure out which after deploy where? So you're gonna see a lot of focus on hybrid multi colored solutions this year we're going to have lots of real world stories, hands on labs, best practices for practitioners. And again as I said all the tools that attendees need to go back and then put to practice some of the hybrid multi cloud strategies that they would learn and dark next >>talk a little bit more Monica about the what's in it for me for for attendees, what can they expect? What are they going to be able to take away from from this conference? >>Well, so as I said, a conferences both for business leaders and I. T. Leaders and practitioners. So for the business leaders, as I said, they'll get to hear from the latest industry visionaries around where the world of cloud is moving to, what are the latest and greatest innovations and hybrid multi cloud technologies uh and how can they make the businesses more competitive? How can they, you know, create more business value for the organization by using these technologies. For the IOT practitioners, they will go away as I said, learning from their peers in how they are adopting cloud, what are some of the myths around cloud computing. Get some information on deployment details and the benefits some of the piers are realizing since they moved to new tenants for example, in general, since they've adopted, you know, hybrid multi cloud solutions, they will also be able to connect with their industry peers, access democrat pounds. Uh in fact one of the major uh spotlights and not next will be the test drive live uh practitioners can get hands on our technology and really test drive it during the event itself and learn how to create a hybrid cloud within an hour, learn how to deploy databases with a click of a button for example, so lots of great goodies there and oh by the way we have some amazing external speakers as well besides our own, you know engineers, executives and so on. We have a whole roster of third party speakers too. >>That's awesome. Now, you know, one of the other things too is one of the ways you were able to reach escape velocity as a company is you had a strong partner ecosystem I presume is going to be a partner network participating as well. >>Yes, absolutely, thank you for reminding me about that partnerships is very, very, very, very important in Nutanix. You know, it does take a village, we have a full day dedicated to our partners and partner technology and solutions. It's called the part exchange. It's on Monday September 20, so again we hope that you all will participate but also you'll see partners are embedded uh in our september 21st and 22nd agenda and programme as well which is the main two days of dot next. So partners are in our life and blood, they're part of our ecosystem. >>That's great. What's next for Nutanix as you head into 20, >>Well before I go there, I do want to focus on a couple more featured speakers. So for those of you who are interested in cybersecurity, we will have Theresa Patton, who is the first female white house C I O and a leading cybersecurity expert. She'll be speaking. I'm actually interviewing her as well. We have Rachel, so johnny who is the founder of Girls who code and marshall plan for moms. We have Gary Vaynerchuk who's the ceo of Winner Media who is an author and entrepreneur. So I do hope that folks will plan to join if not for the core hybrid, multi colored content but also for these amazing speakers and last but not least. Hey, if none of this excites you then we do have some amazing entertainment. We have john taylor of Duran, Duran and the electric fondue coke, Romeo also headlining our day to keynote. >>So fantastic. I love it. Okay, go ahead please. >>Well I was gonna say so now let me talk about So what's next? Well for us, what's next is really helping customers realize their full hybrid, multi cloud strategy and empower them to make the right cloud decisions. So in fact one of the things you're gonna see us launch next week is also a new brand campaign. It's called cloud on your terms and you'll see that all over plastered all over dot next and so on. We are fully invested in our customer success to help them build, run and operate anywhere to help them easily migrate to public cloud or stay on premises if they choose to. And ultimately to make cloud complexity invisible for our customers, >>you know uh cloud your way kind of thing. I love that. And I and I failed to mention one of the first conferences I went to next, I met some developers and I was like whoa, cool. Because you guys one of the first that really truly do infrastructure as a code and bring that on prem and now it's going across clouds. So September 20 you kick off the partner day, is that right? And then the big keynote start the 21st right >>And go through the 20 >>third. Yes, >>yes. And we have a lot of on demand content as well around the keynote. So it's gonna be a packed packed set of agenda and days and you can choose whatever content you want to attend and participate in. >>Excellent. You guys always put under great program so go there register, we'll see you there, Monica. Always a pleasure. Thanks so much. >>Thank you so much for having me. I really appreciate it. >>All right. And we'll see you at dot next. This is Dave Volonte for the cube. >>Mhm mm

(upbeat music) >> Welcome back to HPE Discover 2021, theCube's virtual coverage, continuous coverage of HPE's annual customer event. My name is Dave Vellante and we're going to dive into the intersection of high-performance computing, data and AI with Dr. Eng Lim Goh who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Dr. Goh, great to see you again. Welcome back to theCube. >> Hey, hello, Dave. Great to talk to you again. >> You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the Day 2 keynotes here at Discover. And you talked about thriving in the age of insights and how to craft a data-centric strategy and you addressed some of the biggest problems I think organizations face with data. And that's, you got to look, data is plentiful, but insights, they're harder to come by and you really dug into some great examples in retail, banking, and medicine and healthcare and media. But stepping back a little bit we'll zoom out on Discover '21, you know, what do you make of the events so far and some of your big takeaways? >> Hmm, well, you started with the insightful question. Data is everywhere then but we lack the insight. That's also part of the reason why that's a main reason why, Antonio on Day 1 focused and talked about that, the fact that we are in the now in the age of insight and how to thrive in this new age. What I then did on the Day 2 keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Very pertinent question, Dave. You know, the two challenges I spoke about how to, that we need to overcome in order to thrive in this new age, the first one is the current challenge. And that current challenge is, you know state of this, you know, barriers to insight, when we are awash with data. So that's a statement. How to overcome those barriers. One of the barriers to insight when we are awash in data, in the Day 2 keynote, I spoke about three main things, three main areas that receive from customers. The first one, the first barrier is with many of our customers, data is siloed. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing, and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a Federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know, barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And it's tough to get to value out of them. In that case I use the example of the May 6, 2010 event where the stock market dropped a trillion dollars in tens of minutes. We all know those who are financially attuned with, know about this incident. But that this is not the only incident. There are many of them out there. And for that particular May 6, event, you know it took a long time to get insight, months, yeah, before we, for months we had no insight as to what happened, why it happened. And there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road to go with the tough data. Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road and analyze that data took a long time to assemble. And he discovered that there was quote stuffing. That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees had the rule in there that says all trades less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 100 shares trades to fly under the radar to do this manipulation. So here is, here the second barrier. Data could be raw and disperse. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah. Here we have three quick examples of customers. One was a great example where they were trying to build a language translator a machine language translator between two languages. But in order to do that they need to get hundreds of millions of word pairs of one language compare with the corresponding other hundreds of millions of them. They say, "Where I'm going to get all these word pairs?" Someone creative thought of a willing source and huge source, it was a United Nations. You see, so sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data. The second one has to do with, there was the, sometimes you may just have to generate that data. Interesting one. We had an autonomous car customer that collects all these data from their cars. Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hail because that's a rare occurrence. So instead of waiting for a time where the car can drive in hail, they build a simulation by having the car collected in snow and simulated hail. So these are some of the examples where we have customers working to overcome barriers. You have barriers that is associated with the fact, that data silo, if federated barriers associated with data that's tough to get at. They just took the hard road. And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow, I tell you, I have about 100 questions based on what you just said. And as a great example, the flash crash in fact Michael Lewis wrote about this in his book, the "Flash Boys" and essentially. It was high frequency traders trying to front run the market and sending in small block trades trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. Can I guess my question is can technology help us get get out of the problem? And that maybe is where AI fits in. >> Yes. Yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, assemble them to see if you can find a material trend. You can see lots of trends. Like, no, we, if humans at things we tend to see patterns in clouds. So sometimes you need to apply statistical analysis, math to be sure that what the model is seeing is real. And that required work. That's one area. The second area is, you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. And in this case before the change in the rules. By the way, after the discovery, the authorities changed the rules and all shares all trades of different, any sizes it has to be reported. Not, yeah. But the rule was applied to to say earlier that shares under 100, trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't, wanted for various reasons not to put everything in there so that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such there are times we just need to go back to the raw data. >> I want to ask you-- Or be it that it's going to be tough there. >> Yeah, so I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about and I'm going to make a statement. You tell me if it's on point or off point. Seems that most of the AI going on in the enterprise is modeling data science applied to troves of data. But there's also a lot of AI going on in consumer, whether it's fingerprint technology or facial recognition or natural language processing. Will, to two-part question, will the consumer market, let's say as it has so often in the enterprise sort of inform us is sort of first part. And then will there be a shift from sort of modeling, if you will, to more, you mentioned autonomous vehicles more AI inferencing in real-time, especially with the Edge. I think you can help us understand that better. >> Yeah, this is a great question. There are three stages to just simplify, I mean, you know, it's probably more sophisticated than that, but let's just simplify there're three stages to building an AI system that ultimately can predict, make a prediction. Or to assist you in decision-making, have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data. And the machine starts to evolve a model based on all the data is seeing it starts to evolve. To a point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model, you know after you're trained it with all that data to see whether his prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision and that's the inference. So a lot of times depending on what we are focusing on. We in data science are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that a robust, it is good, but then it is not consistent. Now what you do is you try another model. So sometimes you just keep trying different models until you get the right kind, yeah, that gives you a good robust decision-making and prediction. Now, after which, if it's tested well, Q8 you will then take that model and deploy it at the Edge, yeah. And then at the Edge is essentially just looking at new data applying it to the model that you have trained and then that model will give you a prediction or a decision. So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful, can you also do learning at the Edge? That's the reason why we spoke about swarm learning the last time, learning at the Edge as a swarm. Because maybe individually they may not have enough power to do so, but as a swarm, they may. >> Is that learning from the Edge or learning at the Edge. In other words, is it-- >> Yes. >> Yeah, you don't understand my question, yeah. >> That's a great question. That's a great question. So answer is learning at the Edge, and also from the Edge, but the main goal, the goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. So that you don't need to have to send all that data back and assemble it back from all the different Edge devices assemble it back to the Cloud side to do the learning. With swarm learning, you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send the autonomous vehicle example you gave is great 'cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front and then maybe they do that and then they send that smaller data set back and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming to, let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah, well, today full of these insightful questions that actually touches on the second challenge. How do we, to in order to thrive in this new age of insight. The second challenge is our future challenge. What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talk about what to collect, and when to organize it when you collect, and then where will your data be going forward that you are collecting from? So what, when, and where. For the what data, for what data to collect that was the question you asked. It's a question that different industries have to ask themselves because it will vary. Let me give you the, you use the autonomous car example. Let me use that and you have this customer collecting massive amounts of data. You know, we talking about 10 petabytes a day from a fleet of their cars and these are not production autonomous cars. These are training autonomous cars, collecting data so they can train and eventually deploy a commercial cars. Also these data collection cars, they collect 10 as a fleet of them collect 10 petabytes a day. And then when it came to us, building a storage system to store all of that data they realize they don't want to afford to store all of it. Now here comes the dilemma. What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma. Now in working with them on this process of trimming down what they collected. I'm constantly reminded of the 60s and 70s. To remind myself 60s and 70s, we call a large part of our DNA, junk DNA. Today we realized that a large part of that, what we call junk has function has valuable function. They are not genes but they regulate the function of genes. So what's junk in yesterday could be valuable today, or what's junk today could be valuable tomorrow. So there's this tension going on between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you know you worry, you ignore the wrong ones. You can see this tension in our customers. And then it depends on industry here. In healthcare they say, I have no choice. I want it all, why? One very insightful point brought up by one healthcare provider that really touched me was you know, we are not, we don't only care. Of course we care a lot. We care a lot about the people we are caring for. But we also care for the people we are not caring for. How do we find them? And therefore, they did not just need to collect data that they have with, from their patients they also need to reach out to outside data so that they can figure out who they are not caring for. So they want it all. So I asked them, "So what do you do with funding if you want it all?" They say they have no choice but they'll figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us, rightfully that you know, we have to then work out a way to to help them build a system. So that healthcare. And if you go to other industries like banking, they say they can afford to keep them all. But they are regulated same like healthcare. They are regulated as to privacy and such like. So many examples, different industries having different needs but different approaches to how, what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can store. But on the other hand you know, if you kind of don't want to afford it and decide not to store some, maybe those some become highly valuable in the future. You worry. >> Well, we can make some assumptions about the future, can't we? I mean we know there's going to be a lot more data than we've ever seen before, we know that. We know, well not withstanding supply constraints and things like NAND. We know the price of storage is going to continue to decline. We also know and not a lot of people are really talking about this but the processing power, everybody says, Moore's Law is dead. Okay, it's waning but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth, actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again another insightful question that we touched on, on our keynote and that goes up to the why, I'll do the where. Where will your data be? We have one estimate that says that by next year, there will be 55 billion connected devices out there. 55 billion. What's the population of the world? Well, off the order of 10 billion, but this thing is 55 billion. And many of them, most of them can collect data. So what do you do? So the amount of data that's going to come in is going to way exceed our drop in storage costs our increasing compute power. So what's the answer? The answer must be knowing that we don't and even a drop in price and increase in bandwidth, it will overwhelm the 5G, it'll will overwhelm 5G, given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all that data from the 55 billion devices of the data back out to a central, as a bunch of central cost because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you still be too expensive given the number of devices out there. You know given storage costs dropping it'll still be too expensive to try and install them all. So the answer must be to start at least to mitigate the problem to some leave most a lot of the data out there. And only send back the pertinent ones, as you said before. But then if you did that then, how are we going to do machine learning at the core and the Cloud side, if you don't have all the data you want rich data to train with. Sometimes you want to a mix of the positive type data, and the negative type data. So you can train the machine in a more balanced way. So the answer must be you eventually, as we move forward with these huge number of devices are at the Edge to do machine learning at the Edge. Today we don't even have power. The Edge typically is characterized by a lower energy capability and therefore, lower compute power. But soon, you know, even with low energy, they can do more with compute power, improving in energy efficiency. So learning at the Edge today we do inference at the Edge. So we data, model, deploy and you do inference at age. That's what we do today. But more and more, I believe given a massive amount of data at the Edge you have to have to start doing machine learning at the Edge. And if when you don't have enough power then you aggregate multiple devices' compute power into a swarm and learn as a swarm. >> Oh, interesting, so now of course, if I were sitting in a flyer flying the wall on HPE Board meeting I said, "Okay, HPE is a leading provider of compute." How do you take advantage that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products, but there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for your customers? >> The wall will have to have a balance. Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud side. And it has to be hybrid. And then we need capability on the Edge side. Yeah that we need to build systems that on one hand is Edge-adapted. Meaning they environmentally-adapted because the Edge differently are on it. A lot of times on the outside, they need to be packaging-adapted and also power-adapted. Because typically many of these devices are battery-powered. So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insightful for that. Antonio announced in 2018 for the next four years from 2018, $4 billion invested to strengthen our Edge portfolio our Edge product lines, Edge solutions. >> Dr. Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers. The diversity of customers and the diversity in the way they have approached their different problems with data strategy. So the excitement is around data strategy. Just like, you know, the statement made for us was so, was profound. And Antonio said we are in the age of insight powered by data. That's the first line. The line that comes after that is as such we are becoming more and more data-centric with data the currency. Now the next step is even more profound. That is, you know, we are going as far as saying that data should not be treated as cost anymore, no. But instead, as an investment in a new asset class called data with value on our balance sheet. This is a step change in thinking that is going to change the way we look at data, the way we value it. So that's a statement. So this is the exciting thing, because for me a CTO of AI, a machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So that's why when the people start to value data and say that it is an investment when we collect it it is very positive for AI because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. So it'd be great if the community values data. >> Well, are you certainly see it in the valuations of many companies these days? And I think increasingly you see it on the income statement, you know data products and people monetizing data services, and yeah, maybe eventually you'll see it in the balance sheet, I know. Doug Laney when he was at Gartner Group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr. Goh. >> Yeah, yeah, yeah. Your question is the process and methods in valuation. But I believe we'll get there. We need to get started and then we'll get there, I believe, yeah. >> Dr. Goh it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh yeah, no doubt. People will better understand how to align some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCube. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (both chuckling) >> Well, excellent, we'll leave it there. Thank you for spending some time with us so keep it right there for more great interviews from HPE Discover '21. This is Dave Vellante for theCube, the leader in enterprise tech coverage. We'll be right back (upbeat music)

(upbeat music) >> Welcome back to HPE Discover 2021, theCUBE's virtual coverage, continuous coverage of HPE's Annual Customer Event. My name is Dave Vellante, and we're going to dive into the intersection of high-performance computing, data and AI with Doctor Eng Lim Goh, who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Doctor Goh, great to see you again. Welcome back to theCUBE. >> Hello, Dave, great to talk to you again. >> You might remember last year we talked a lot about Swarm intelligence and how AI is evolving. Of course, you hosted the Day 2 Keynotes here at Discover. And you talked about thriving in the age of insights, and how to craft a data-centric strategy. And you addressed some of the biggest problems, I think organizations face with data. That's, you've got a, data is plentiful, but insights, they're harder to come by. >> Yeah. >> And you really dug into some great examples in retail, banking, in medicine, healthcare and media. But stepping back a little bit we zoomed out on Discover '21. What do you make of the events so far and some of your big takeaways? >> Hmm, well, we started with the insightful question, right, yeah? Data is everywhere then, but we lack the insight. That's also part of the reason why, that's a main reason why Antonio on day one focused and talked about the fact that we are in the now in the age of insight, right? And how to try thrive in that age, in this new age? What I then did on a Day 2 Keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So, maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights. You know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Oh, very pertinent question, Dave. You know the two challenges I spoke about, that we need to overcome in order to thrive in this new age. The first one is the current challenge. And that current challenge is, you know, stated is now barriers to insight, when we are awash with data. So that's a statement on how do you overcome those barriers? What are the barriers to insight when we are awash in data? In the Day 2 Keynote, I spoke about three main things. Three main areas that we receive from customers. The first one, the first barrier is in many, with many of our customers, data is siloed, all right. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above, they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know? Barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And you know, it's tough to get at, to tough to get a value out of them, right? And in that case, I use the example of, you know, the May 6, 2010 event where the stock market dropped a trillion dollars in terms of minutes. We all know those who are financially attuned with know about this incident but that this is not the only incident. There are many of them out there. And for that particular May 6 event, you know, it took a long time to get insight. Months, yeah, before we, for months we had no insight as to what happened. Why it happened? Right, and there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road they go with the tough data, right? Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road. And analyze that data took a long time to assemble. And they discovered that there was caught stuffing, right? That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees, the rule in there that says, all trades less than a hundred shares don't need to report in there. And so what people did was sending a lot of less than a hundred shares trades to fly under the radar to do this manipulation. So here is the second barrier, right? Data could be raw and dispersed. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah? Here we have three quick examples of customers. One was a great example, right? Where they were trying to build a language translator or machine language translator between two languages, right? By not do that, they need to get hundreds of millions of word pairs. You know of one language compare with the corresponding other. Hundreds of millions of them. They say, well, I'm going to get all these word pairs. Someone creative thought of a willing source and a huge, it was a United Nations. You see? So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data, right? The second one has to do with, there was the sometimes you may just have to generate that data. Interesting one, we had an autonomous car customer that collects all these data from their their cars, right? Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can drive in hill, they build a simulation by having the car collected in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated. In fact, that data silo, they federated it. Virus associated with data, that's tough to get at. They just took the hard road, right? And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow! I tell you, I have about a hundred questions based on what you just said, you know? (Dave chuckles) And as a great example, the Flash Crash. In fact, Michael Lewis, wrote about this in his book, the Flash Boys. And essentially, right, it was high frequency traders trying to front run the market and sending into small block trades (Dave chuckles) trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. (Dave chuckles) I guess my question is can technology help us get out of the problem? And that maybe is where AI fits in? >> Yes, yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, right? Assembled them to see if you can find a material trend, right? You can see lots of trends, right? Like, no, we, if humans look at things that we tend to see patterns in Clouds, right? So sometimes you need to apply statistical analysis math to be sure that what the model is seeing is real, right? And that required, well, that's one area. The second area is you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. Now, in this case, before the change in the rules, right? But by the way, after the discovery, the authorities changed the rules and all shares, all trades of different any sizes it has to be reported. >> Right. >> Right, yeah? But the rule was applied, you know, I say earlier that shares under a hundred, trades under a hundred shares need not be reported. So, sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't wanted a various reasons not to put everything in there. So that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such, there are times we just need to go back to the raw data. >> I want to ask you... >> Oh, it could be, that it's going to be tough, yeah. >> Yeah, I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about but. And I'm going to make a statement, you tell me if it's on point or off point. So seems that most of the AI going on in the enterprise is modeling data science applied to, you know, troves of data. But there's also a lot of AI going on in consumer. Whether it's, you know, fingerprint technology or facial recognition or natural language processing. Well, two part question will the consumer market, as it has so often in the enterprise sort of inform us is sort of first part. And then, there'll be a shift from sort of modeling if you will to more, you mentioned the autonomous vehicles, more AI inferencing in real time, especially with the Edge. Could you help us understand that better? >> Yeah, this is a great question, right? There are three stages to just simplify. I mean, you know, it's probably more sophisticated than that. But let's just simplify that three stages, right? To building an AI system that ultimately can predict, make a prediction, right? Or to assist you in decision-making. I have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data, and the machine starts to evolve a model based on all the data it's seeing. It starts to evolve, right? To a point that using a test set of data that you have separately kept aside that you know the answer for. Then you test the model, you know? After you've trained it with all that data to see whether its prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision. And that's the inference, right? So a lot of times, depending on what we are focusing on, we in data science are, are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you need to make. You pick the right models. And then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that robust. It is good, but then it is not consistent, right? Now what you do is you try another model. So sometimes it gets keep trying different models until you get the right kind, yeah? That gives you a good robust decision-making and prediction. Now, after which, if it's tested well, QA, you will then take that model and deploy it at the Edge. Yeah, and then at the Edge is essentially just looking at new data, applying it to the model that you have trained. And then that model will give you a prediction or a decision, right? So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful. Can you also do learning at the Edge? >> Right. >> That's the reason why we spoke about Swarm Learning the last time. Learning at the Edge as a Swarm, right? Because maybe individually, they may not have enough power to do so. But as a Swarm, they may. >> Is that learning from the Edge or learning at the Edge? In other words, is that... >> Yes. >> Yeah. You do understand my question. >> Yes. >> Yeah. (Dave chuckles) >> That's a great question. That's a great question, right? So the quick answer is learning at the Edge, right? And also from the Edge, but the main goal, right? The goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the Call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. Right? So that you don't need to have to send all that data back and assemble it back from all the different Edge devices. Assemble it back to the Cloud Site to do the learning, right? Some on you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send. >> Yeah. >> The autonomous vehicle, example you gave is great. 'Cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front. And then maybe they do that and then they send that smaller data setback and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming through. Let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah. Well, today full of these insightful questions. (Dr. Eng chuckles) That actually touches on the the second challenge, right? How do we, in order to thrive in this new age of insight? The second challenge is our future challenge, right? What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talked about what to collect, right? When to organize it when you collect? And then where will your data be going forward that you are collecting from? So what, when, and where? For what data to collect? That was the question you asked, it's a question that different industries have to ask themselves because it will vary, right? Let me give you the, you use the autonomous car example. Let me use that. And we do have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from a fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars, right? Also this data collection cars, they collect 10, as a fleet of them collect 10 petabytes a day. And then when they came to us, building a storage system you know, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma, right? Now in working with them on this process of trimming down what they collected, you know, I'm constantly reminded of the 60s and 70s, right? To remind myself 60s and 70s, we called a large part of our DNA, junk DNA. >> Yeah. (Dave chuckles) >> Ah! Today, we realized that a large part of that what we call junk has function as valuable function. They are not genes but they regulate the function of genes. You know? So what's junk in yesterday could be valuable today. Or what's junk today could be valuable tomorrow, right? So, there's this tension going on, right? Between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you worry, you ignore the wrong ones, right? You can see this tension in our customers, right? And then it depends on industry here, right? In healthcare they say, I have no choice. I want it all, right? Oh, one very insightful point brought up by one healthcare provider that really touched me was you know, we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But who also care for the people we are not caring for? How do we find them? >> Uh-huh. >> Right, and that definitely, they did not just need to collect data that they have with from their patients. They also need to reach out, right? To outside data so that they can figure out who they are not caring for, right? So they want it all. So I asked them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us rightfully, that you know we have to then work out a way to help them build a system, you know? So that's healthcare, right? And if you go to other industries like banking, they say they can afford to keep them all. >> Yeah. >> But they are regulated, seemed like healthcare, they are regulated as to privacy and such like. So many examples different industries having different needs but different approaches to what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can install, right? But on the other hand, you know if you kind of don't want to afford it and decide not to start some. Maybe those some become highly valuable in the future, right? (Dr. Eng chuckles) You worry. >> Well, we can make some assumptions about the future. Can't we? I mean, we know there's going to be a lot more data than we've ever seen before. We know that. We know, well, not withstanding supply constraints and things like NAND. We know the prices of storage is going to continue to decline. We also know and not a lot of people are really talking about this, but the processing power, but the says, Moore's law is dead. Okay, it's waning, but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again, another insightful question that we touched on our Keynote. And that goes up to the why, uh, to the where? Where will your data be? Right? We have one estimate that says that by next year there will be 55 billion connected devices out there, right? 55 billion, right? What's the population of the world? Well, of the other 10 billion? But this thing is 55 billion. (Dave chuckles) Right? And many of them, most of them can collect data. So what do you do? Right? So the amount of data that's going to come in, it's going to way exceed, right? Drop in storage costs are increasing compute power. >> Right. >> Right. So what's the answer, right? So the answer must be knowing that we don't, and even a drop in price and increase in bandwidth, it will overwhelm the, 5G, it will overwhelm 5G, right? Given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all of that data from the 55 billion devices of the data back to a central, as a bunch of central cost. Because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you'll still be too expensive given the number of devices out there. You know given storage costs dropping is still be too expensive to try and install them all. So the answer must be to start, at least to mitigate from to, some leave most a lot of the data out there, right? And only send back the pertinent ones, as you said before. But then if you did that then how are we going to do machine learning at the Core and the Cloud Site, if you don't have all the data? You want rich data to train with, right? Sometimes you want to mix up the positive type data and the negative type data. So you can train the machine in a more balanced way. So the answer must be eventually, right? As we move forward with these huge number of devices all at the Edge to do machine learning at the Edge. Today we don't even have power, right? The Edge typically is characterized by a lower energy capability and therefore lower compute power. But soon, you know? Even with low energy, they can do more with compute power improving in energy efficiency, right? So learning at the Edge, today we do inference at the Edge. So we data, model, deploy and you do inference there is. That's what we do today. But more and more, I believe given a massive amount of data at the Edge, you have to start doing machine learning at the Edge. And when you don't have enough power then you aggregate multiple devices, compute power into a Swarm and learn as a Swarm, yeah. >> Oh, interesting. So now of course, if I were sitting and fly on the wall and the HPE board meeting I said, okay, HPE is a leading provider of compute. How do you take advantage of that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products. But there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for the customers? >> Hmm, the wall will have to have a balance, right? Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud Site, right? And it has to be hybrid. And then we need capability on the Edge side that we need to build systems that on one hand is an Edge adapter, right? Meaning they environmentally adapted because the Edge differently are on it, a lot of times on the outside. They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. It must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insight for that Antonio announced in 2018. For the next four years from 2018, right? $4 billion invested to strengthen our Edge portfolio. >> Uh-huh. >> Edge product lines. >> Right. >> Uh-huh, Edge solutions. >> I could, Doctor Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of, certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers, right? The diversity of customers and the diversity in the way they have approached different problems of data strategy. So the excitement is around data strategy, right? Just like, you know, the statement made for us was so was profound, right? And Antonio said, we are in the age of insight powered by data. That's the first line, right? The line that comes after that is as such we are becoming more and more data centric with data that currency. Now the next step is even more profound. That is, you know, we are going as far as saying that, you know, data should not be treated as cost anymore. No, right? But instead as an investment in a new asset class called data with value on our balance sheet. This is a step change, right? Right, in thinking that is going to change the way we look at data, the way we value it. So that's a statement. (Dr. Eng chuckles) This is the exciting thing, because for me a CTO of AI, right? A machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. Right? (Dr. Eng chuckles) So, that's why when the people start to value data, right? And say that it is an investment when we collect it it is very positive for AI. Because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. >> Yeah. >> So it'd be great, if the community values data. >> Well, you certainly see it in the valuations of many companies these days. And I think increasingly you see it on the income statement. You know data products and people monetizing data services. And yeah, maybe eventually you'll see it in the balance sheet. I know Doug Laney, when he was at Gartner Group, wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? >> Yeah, yeah. >> Dr. Goh... (Dave chuckles) >> The question is the process and methods in valuation. Right? >> Yeah, right. >> But I believe we will get there. We need to get started. And then we'll get there. I believe, yeah. >> Doctor Goh, it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh, yeah, no doubt. People will better understand how to align, you know some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCUBE. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (Dave chuckles) (Dr. Eng laughs) >> Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HPE Discover 21. This is Dave Vellante for theCUBE, the leader in Enterprise Tech Coverage. We'll be right back. (upbeat music)

(upbeat electronic music) >> Announcer: Live from Las Vegas, it's theCUBE, covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. (crowd chattering) >> Hello everybody and welcome to theCUBE's live coverage of ServiceNow. We are here in Las Vegas, Nevada at The Venetian. I'm your host, Rebecca Knight. Co-hosting with Dave Vellante and Jeff Frick. It's great to be here with you-- >> Hey, Rebecca. >> doing the show. >> Busy week. >> Very busy week and we are only-- >> Busy month. (laughs) >> And it's only day one. So we just heard John Donahoe who is the new CEO, he's been CEO for a year, he was at eBay for a decade. He got up on stage and he said, "When I came "to this job I could barely spell IT." But I want to talk to you first, Dave, and say how's John doing, how's the company doing? What's your take on this? >> Well, the company's doing great. It's the fastest growing software company over a billion dollars. It's got consistent growth. 35-40% growth each quarter, year over year. It's growing sequentially, it's throwing off, it's free cash flow is actually growing faster than it's revenue, which is quite impressive. Company's got a 29 billion dollar market cap. Couple years ago ServiceNow, when Frank Slootman was running the company said, we're going to put the stake in the ground and we're going to be a four billion dollar company, I think this company's going to do four billion dollars in its sleep. I think the next milestone is how they get to 10 billion. And beyond that, how they get to 15 billion, how they take their market value from where it is today in the high 20's, low 30's, up to 100 billion. This company wants to be the next great enterprise software company. Basically automating manual tasks you wouldn't think there's that many manual left, but when you think about whether it's scheduling meetings, or scheduling travel or keeping track of medical leave, and all this other stuff that's manual, they want to automate that process. >> Right, exactly, that's what he talked, the tagline this year and really for the brand identity is making more work work better for people. He said that people are at the heart of this brand. Jeff, does this strike you as a new idea? Is this going to work for ServiceNow? >> It's not really a new idea but their kind of changing their shift. It's interesting when we saw Frank Slootman on he was always, the IT guys are my homies, right? He was very specifically focused on going after IT. And Fred's great kind of early intro was, remember the copier room with all the colored pieces of paper. (Rebecca laughs) Vacation requests, new laptop request, etc. How does he make that automated. And more importantly how does he let the people responsible for that be able to code and build a workflow. So I think the vision is consistent, they're obviously expanding beyond just, the IT are my homies, 'cause it's still ultimately workflow. And I think at the end of the day it's competition for how do you work. What screen or what app is on your screen as you go through your day to day workflow. And they're obviously trying to grab more of those processes so that you're doing them inside of ServiceNow versus one of the many other applications that you might be trying to do. >> Just to follow up on that, when Jeff and I first started covering this show it was 2013, less than 5% of ServiceNow's business was outside of the IT department. Today it's about 35% is outside the IT department. So they have their strategy of, they call it, land and expand. Christian Chabot from Tableau I think was the first I heard use that term. These guys are executing on that. Starting with IT and then moving into HR, moving into maybe facilities, moving into marketing, other parts of the organization, customer service management, security, I don't know if they count that as IT, but cohort businesses. So if you look at their financials their up-selling is phenomenal. Huge percentage of their business comes from existing customers. If you look at the anatomy of a typical ServiceNow customer, they might start with a 50 or 75 thousand dollar deal. That quickly jumps to a multi-hundred thousand dollar deal, then up to a multi-million dollar deal. And then up into the high eight figures. So it's really a tremendous story and the reason is, and Jeff you and I have talked about this a lot, is because when Fred Luddy started the company he developed a platform. He took that platform to the venture capital community and they said well what do you do with this? He said you can do anything with it. They said, yeah, get out. So he said all right I'm going to write an app. He worked at Peregrine so he wrote and IT service management app. And when ServiceNow went public, I remember Gartner Group came out and said, eh, it's a tiny little market, help desk is a dying market, flat, billion dollar TAM. Well this company's TAM, it's almost immeasurable. I mean it's, the TAM is literally in the half a trillion dollars in my view. I mean it's enormous. >> It's workflow, right, so again it's just that competition for the screen. And as everyone goes from their specialty and tries to expand, right? Sales force is trying to expand more into marketing. You've got Zendesk and other kinds of help desk platforms that are trying to get into more workflow. What they were smart is they went into IT 'cause IT controls the applications that are in shop. And so to use that as a basis, and IT touches whether it's an HR process where I need to get the person a new laptop. Or it's facilities where I need to open up a new building or etc., IT touches it all. So a really interesting way to try to grab that screen and application space via the IT systems. >> And that's where John Donahoe comes is. As you said Jeff, Frank Slootman, Data Domain, EMC, you know, IT guy. And now John Donahoe, not an IT guy, came from the consumer world, he's trying to take the ServiceNow brand into the C suite. So we have him on a little later, we're going to talk to him about sort of how he's doing that. But this is a company that's transforming, they're constantly transforming. Really trying to become a brand name, the next great enterprise software company. >> I think another thing that really came out in the keynote and also just on the main stage this morning is this idea of change is not just about the technology. In fact, the technology is the easy part. One of the things he kept saying, and he brought up other people and customers and partners to talk about his too, is that it really is a culture shift. And it really is about a different way of leading. It's a different way of bringing in the right kind of talent who are not just these IT guys, let's be honest. >> Right. >> But they are data scientists, they are creative people, they integrate design thinking into the way they do their jobs, with this over-arching goal of how do I make the employee experience better and how do I make the candidate experience better too. Because that's another part of this. It's not just the people who are already working for you. In the period where there is a war for talent-- >> Jeff: Right, right. >> you also have to be thinking about okay, how do the people that we want to get-- >> Jeff: Right. >> What's their experience like when we're trying to attract them. >> So question for you, Rebecca, 'cause you cover this space-- >> Rebecca: I do, yes. >> a lot, right, and you write for MIT and-- >> Rebecca: HBR. >> HBR and the new way to work and the good, I'm trying to remember-- >> Rebecca: It's called Best Practices, yeah. >> book that you did, that interview. So as it is competition for talent, how did it strike you? 'Cause at the end of the day that's really what it's all about. How do you get and retain the best people when there just aren't enough people for all the jobs that are out there. >> It's interesting because I do feel as though, obviously, you want to be able to enjoy your workday and that's what Andrew Wilson at Accenture was talking about, really it's about having fun. And it's about having it be a great experience. At the same time I do think the human part of work is so essential. As we've talked about before, you don't quit jobs you quit bosses. And it really is about who is your manager and who is the person who is leading this change. >> Jeff: Right. And how are they interacting with employees and with you personally. >> But should it be fun, I mean, they're still paying you to show up. (Rebecca laughs) >> And I think sometimes we get confused. Clearly the mundane still takes-- >> Yes. >> a ridiculously too high percentage-- >> Rebecca: True. >> of time to do the routine, where there's this automation opportunity. But the other piece is the purpose piece and they brought up purpose early on in the keynote, right? >> Rebecca: Yes. >> People want to work for purpose driven organizations and the millennial workers have said they want to be involved in that. It's not just about shareholders and stakeholders and customers. So there is a bigger calling that they need to deliver on to attract and maintain the best people. >> A couple words about the show. So we do a lot of shows. This is a legit 18,000 person show, we're at the Sands Convention Center. It's crowded, the line at the Starbucks coffee the morning-- >> Rebecca: (laughs) Around the block. >> was about 60 to 65 deep, I mean that's a lot of people waiting for coffee. The other thing I want to stress is the ecosystem. When Jeff and I first started this show the ecosystem was very thin, Jeff, as you recall, and that's one of the things we said is watch the ecosystem as an indicator of progress. Well the ecosystem's exploding. You've seen acquisitions where companies like CXC and Accenture have got into the business big time. You see E&Y, Deloitte coming in as big partners now of ServiceNow and as we've often joked, the system integrators like to eat at the trough. So there's a lot of business going on in this ecosystem. >> Right, and that was part of the keynote too. The software's the easy part. It's are you investing in the change management for your people, are you investing in best practices. And if you're not then you're probably wasting some of your money. >> Great. Well it's going to be a great show, this is just segment one, we've got a lot of great guests so I'm excited to get going with both of you. >> Jeff: All right. >> Dave: All-righty. >> I'm Rebecca Knight for Dave Allante and Jeff Frick, we will have more from ServiceNow Knowledge18 coming up just after this. (electronic music)