>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is "Breaking Analysis" with Dave Vellante. >> A decade of big data investments combined with cloud scale, the rise of much more cost effective processing power. And the introduction of advanced tooling has catapulted machine intelligence to the forefront of technology investments. No matter what job you have, your operation will be AI powered within five years and machines may actually even be doing your job. Artificial intelligence is being infused into applications, infrastructure, equipment, and virtually every aspect of our lives. AI is proving to be extremely helpful at things like controlling vehicles, speeding up medical diagnoses, processing language, advancing science, and generally raising the stakes on what it means to apply technology for business advantage. But business value realization has been a challenge for most organizations due to lack of skills, complexity of programming models, immature technology integration, sizable upfront investments, ethical concerns, and lack of business alignment. Mastering AI technology will not be a requirement for success in our view. However, figuring out how and where to apply AI to your business will be crucial. That means understanding the business case, picking the right technology partner, experimenting in bite-sized chunks, and quickly identifying winners to double down on from an investment standpoint. Hello and welcome to this week's Wiki-bond CUBE Insights powered by ETR. In this breaking analysis, we update you on the state of AI and what it means for the competition. And to do so, we invite into our studios Andy Thurai of Constellation Research. Andy covers AI deeply. He knows the players, he knows the pitfalls of AI investment, and he's a collaborator. Andy, great to have you on the program. Thanks for coming into our CUBE studios. >> Thanks for having me on. >> You're very welcome. Okay, let's set the table with a premise and a series of assertions we want to test with Andy. I'm going to lay 'em out. And then Andy, I'd love for you to comment. So, first of all, according to McKinsey, AI adoption has more than doubled since 2017, but only 10% of organizations report seeing significant ROI. That's a BCG and MIT study. And part of that challenge of AI is it requires data, is requires good data, data proficiency, which is not trivial, as you know. Firms that can master both data and AI, we believe are going to have a competitive advantage this decade. Hyperscalers, as we show you dominate AI and ML. We'll show you some data on that. And having said that, there's plenty of room for specialists. They need to partner with the cloud vendors for go to market productivity. And finally, organizations increasingly have to put data and AI at the center of their enterprises. And to do that, most are going to rely on vendor R&D to leverage AI and ML. In other words, Andy, they're going to buy it and apply it as opposed to build it. What are your thoughts on that setup and that premise? >> Yeah, I see that a lot happening in the field, right? So first of all, the only 10% of realizing a return on investment. That's so true because we talked about this earlier, the most companies are still in the innovation cycle. So they're trying to innovate and see what they can do to apply. A lot of these times when you look at the solutions, what they come up with or the models they create, the experimentation they do, most times they don't even have a good business case to solve, right? So they just experiment and then they figure it out, "Oh my God, this model is working. Can we do something to solve it?" So it's like you found a hammer and then you're trying to find the needle kind of thing, right? That never works. >> 'Cause it's cool or whatever it is. >> It is, right? So that's why, I always advise, when they come to me and ask me things like, "Hey, what's the right way to do it? What is the secret sauce?" And, we talked about this. The first thing I tell them is, "Find out what is the business case that's having the most amount of problems, that that can be solved using some of the AI use cases," right? Not all of them can be solved. Even after you experiment, do the whole nine yards, spend millions of dollars on that, right? And later on you make it efficient only by saving maybe $50,000 for the company or a $100,000 for the company, is it really even worth the experiment, right? So you got to start with the saying that, you know, where's the base for this happening? Where's the need? What's a business use case? It doesn't have to be about cost efficient and saving money in the existing processes. It could be a new thing. You want to bring in a new revenue stream, but figure out what is a business use case, how much money potentially I can make off of that. The same way that start-ups go after. Right? >> Yeah. Pretty straightforward. All right, let's take a look at where ML and AI fit relative to the other hot sectors of the ETR dataset. This XY graph shows net score spending velocity in the vertical axis and presence in the survey, they call it sector perversion for the October survey, the January survey's in the field. Then that squiggly line on ML/AI represents the progression. Since the January 21 survey, you can see the downward trajectory. And we position ML and AI relative to the other big four hot sectors or big three, including, ML/AI is four. Containers, cloud and RPA. These have consistently performed above that magic 40% red dotted line for most of the past two years. Anything above 40%, we think is highly elevated. And we've just included analytics and big data for context and relevant adjacentness, if you will. Now note that green arrow moving toward, you know, the 40% mark on ML/AI. I got a glimpse of the January survey, which is in the field. It's got more than a thousand responses already, and it's trending up for the current survey. So Andy, what do you make of this downward trajectory over the past seven quarters and the presumed uptick in the coming months? >> So one of the things you have to keep in mind is when the pandemic happened, it's about survival mode, right? So when somebody's in a survival mode, what happens, the luxury and the innovations get cut. That's what happens. And this is exactly what happened in the situation. So as you can see in the last seven quarters, which is almost dating back close to pandemic, everybody was trying to keep their operations alive, especially digital operations. How do I keep the lights on? That's the most important thing for them. So while the numbers spent on AI, ML is less overall, I still think the AI ML to spend to sort of like a employee experience or the IT ops, AI ops, ML ops, as we talked about, some of those areas actually went up. There are companies, we talked about it, Atlassian had a lot of platform issues till the amount of money people are spending on that is exorbitant and simply because they are offering the solution that was not available other way. So there are companies out there, you can take AoPS or incident management for that matter, right? A lot of companies have a digital insurance, they don't know how to properly manage it. How do you find an intern solve it immediately? That's all using AI ML and some of those areas actually growing unbelievable, the companies in that area. >> So this is a really good point. If you can you bring up that chart again, what Andy's saying is a lot of the companies in the ETR taxonomy that are doing things with AI might not necessarily show up in a granular fashion. And I think the other point I would make is, these are still highly elevated numbers. If you put on like storage and servers, they would read way, way down the list. And, look in the pandemic, we had to deal with work from home, we had to re-architect the network, we had to worry about security. So those are really good points that you made there. Let's, unpack this a little bit and look at the ML AI sector and the ETR data and specifically at the players and get Andy to comment on this. This chart here shows the same x y dimensions, and it just notes some of the players that are specifically have services and products that people spend money on, that CIOs and IT buyers can comment on. So the table insert shows how the companies are plotted, it's net score, and then the ends in the survey. And Andy, the hyperscalers are dominant, as you can see. You see Databricks there showing strong as a specialist, and then you got to pack a six or seven in there. And then Oracle and IBM, kind of the big whales of yester year are in the mix. And to your point, companies like Salesforce that you mentioned to me offline aren't in that mix, but they do a lot in AI. But what are your takeaways from that data? >> If you could put the slide back on please. I want to make quick comments on a couple of those. So the first one is, it's surprising other hyperscalers, right? As you and I talked about this earlier, AWS is more about logo blocks. We discussed that, right? >> Like what? Like a SageMaker as an example. >> We'll give you all the components what do you need. Whether it's MLOps component or whether it's, CodeWhisperer that we talked about, or a oral platform or data or data, whatever you want. They'll give you the blocks and then you'll build things on top of it, right? But Google took a different way. Matter of fact, if we did those numbers a few years ago, Google would've been number one because they did a lot of work with their acquisition of DeepMind and other things. They're way ahead of the pack when it comes to AI for longest time. Now, I think Microsoft's move of partnering and taking a huge competitor out would open the eyes is unbelievable. You saw that everybody is talking about chat GPI, right? And the open AI tool and ChatGPT rather. Remember as Warren Buffet is saying that, when my laundry lady comes and talk to me about stock market, it's heated up. So that's how it's heated up. Everybody's using ChatGPT. What that means is at the end of the day is they're creating, it's still in beta, keep in mind. It's not fully... >> Can you play with it a little bit? >> I have a little bit. >> I have, but it's good and it's not good. You know what I mean? >> Look, so at the end of the day, you take the massive text of all the available text in the world today, mass them all together. And then you ask a question, it's going to basically search through that and figure it out and answer that back. Yes, it's good. But again, as we discussed, if there's no business use case of what problem you're going to solve. This is building hype. But then eventually they'll figure out, for example, all your chats, online chats, could be aided by your AI chat bots, which is already there, which is not there at that level. This could build help that, right? Or the other thing we talked about is one of the areas where I'm more concerned about is that it is able to produce equal enough original text at the level that humans can produce, for example, ChatGPT or the equal enough, the large language transformer can help you write stories as of Shakespeare wrote it. Pretty close to it. It'll learn from that. So when it comes down to it, talk about creating messages, articles, blogs, especially during political seasons, not necessarily just in US, but anywhere for that matter. If people are able to produce at the emission speed and throw it at the consumers and confuse them, the elections can be won, the governments can be toppled. >> Because to your point about chatbots is chatbots have obviously, reduced the number of bodies that you need to support chat. But they haven't solved the problem of serving consumers. Most of the chat bots are conditioned response, which of the following best describes your problem? >> The current chatbot. >> Yeah. Hey, did we solve your problem? No. Is the answer. So that has some real potential. But if you could bring up that slide again, Ken, I mean you've got the hyperscalers that are dominant. You talked about Google and Microsoft is ubiquitous, they seem to be dominant in every ETR category. But then you have these other specialists. How do those guys compete? And maybe you could even, cite some of the guys that you know, how do they compete with the hyperscalers? What's the key there for like a C3 ai or some of the others that are on there? >> So I've spoken with at least two of the CEOs of the smaller companies that you have on the list. One of the things they're worried about is that if they continue to operate independently without being part of hyperscaler, either the hyperscalers will develop something to compete against them full scale, or they'll become irrelevant. Because at the end of the day, look, cloud is dominant. Not many companies are going to do like AI modeling and training and deployment the whole nine yards by independent by themselves. They're going to depend on one of the clouds, right? So if they're already going to be in the cloud, by taking them out to come to you, it's going to be extremely difficult issue to solve. So all these companies are going and saying, "You know what? We need to be in hyperscalers." For example, you could have looked at DataRobot recently, they made announcements, Google and AWS, and they are all over the place. So you need to go where the customers are. Right? >> All right, before we go on, I want to share some other data from ETR and why people adopt AI and get your feedback. So the data historically shows that feature breadth and technical capabilities were the main decision points for AI adoption, historically. What says to me that it's too much focus on technology. In your view, is that changing? Does it have to change? Will it change? >> Yes. Simple answer is yes. So here's the thing. The data you're speaking from is from previous years. >> Yes >> I can guarantee you, if you look at the latest data that's coming in now, those two will be a secondary and tertiary points. The number one would be about ROI. And how do I achieve? I've spent ton of money on all of my experiments. This is the same thing theme I'm seeing across when talking to everybody who's spending money on AI. I've spent so much money on it. When can I get it live in production? How much, how can I quickly get it? Because you know, the board is breathing down their neck. You already spend this much money. Show me something that's valuable. So the ROI is going to become, take it from me, I'm predicting this for 2023, that's going to become number one. >> Yeah, and if people focus on it, they'll figure it out. Okay. Let's take a look at some of the top players that won, some of the names we just looked at and double click on that and break down their spending profile. So the chart here shows the net score, how net score is calculated. So pay attention to the second set of bars that Databricks, who was pretty prominent on the previous chart. And we've annotated the colors. The lime green is, we're bringing the platform in new. The forest green is, we're going to spend 6% or more relative to last year. And the gray is flat spending. The pinkish is our spending's going to be down on AI and ML, 6% or worse. And the red is churn. So you don't want big red. You subtract the reds from the greens and you get net score, which is shown by those blue dots that you see there. So AWS has the highest net score and very little churn. I mean, single low single digit churn. But notably, you see Databricks and DataRobot are next in line within Microsoft and Google also, they've got very low churn. Andy, what are your thoughts on this data? >> So a couple of things that stands out to me. Most of them are in line with my conversation with customers. Couple of them stood out to me on how bad IBM Watson is doing. >> Yeah, bring that back up if you would. Let's take a look at that. IBM Watson is the far right and the red, that bright red is churning and again, you want low red here. Why do you think that is? >> Well, so look, IBM has been in the forefront of innovating things for many, many years now, right? And over the course of years we talked about this, they moved from a product innovation centric company into more of a services company. And over the years they were making, as at one point, you know that they were making about majority of that money from services. Now things have changed Arvind has taken over, he came from research. So he's doing a great job of trying to reinvent themselves as a company. But it's going to have a long way to catch up. IBM Watson, if you think about it, that played what, jeopardy and chess years ago, like 15 years ago? >> It was jaw dropping when you first saw it. And then they weren't able to commercialize that. >> Yeah. >> And you're making a good point. When Gerstner took over IBM at the time, John Akers wanted to split the company up. He wanted to have a database company, he wanted to have a storage company. Because that's where the industry trend was, Gerstner said no, he came from AMEX, right? He came from American Express. He said, "No, we're going to have a single throat to choke for the customer." They bought PWC for relatively short money. I think it was $15 billion, completely transformed and I would argue saved IBM. But the trade off was, it sort of took them out of product leadership. And so from Gerstner to Palmisano to Remedi, it was really a services led company. And I think Arvind is really bringing it back to a product company with strong consulting. I mean, that's one of the pillars. And so I think that's, they've got a strong story in data and AI. They just got to sort of bring it together and better. Bring that chart up one more time. I want to, the other point is Oracle, Oracle sort of has the dominant lock-in for mission critical database and they're sort of applying AI there. But to your point, they're really not an AI company in the sense that they're taking unstructured data and doing sort of new things. It's really about how to make Oracle better, right? >> Well, you got to remember, Oracle is about database for the structure data. So in yesterday's world, they were dominant database. But you know, if you are to start storing like videos and texts and audio and other things, and then start doing search of vector search and all that, Oracle is not necessarily the database company of choice. And they're strongest thing being apps and building AI into the apps? They are kind of surviving in that area. But again, I wouldn't name them as an AI company, right? But the other thing that that surprised me in that list, what you showed me is yes, AWS is number one. >> Bring that back up if you would, Ken. >> AWS is number one as you, it should be. But what what actually caught me by surprise is how DataRobot is holding, you know? I mean, look at that. The either net new addition and or expansion, DataRobot seem to be doing equally well, even better than Microsoft and Google. That surprises me. >> DataRobot's, and again, this is a function of spending momentum. So remember from the previous chart that Microsoft and Google, much, much larger than DataRobot. DataRobot more niche. But with spending velocity and has always had strong spending velocity, despite some of the recent challenges, organizational challenges. And then you see these other specialists, H2O.ai, Anaconda, dataiku, little bit of red showing there C3.ai. But these again, to stress are the sort of specialists other than obviously the hyperscalers. These are the specialists in AI. All right, so we hit the bigger names in the sector. Now let's take a look at the emerging technology companies. And one of the gems of the ETR dataset is the emerging technology survey. It's called ETS. They used to just do it like twice a year. It's now run four times a year. I just discovered it kind of mid-2022. And it's exclusively focused on private companies that are potential disruptors, they might be M&A candidates and if they've raised enough money, they could be acquirers of companies as well. So Databricks would be an example. They've made a number of investments in companies. SNEAK would be another good example. Companies that are private, but they're buyers, they hope to go IPO at some point in time. So this chart here, shows the emerging companies in the ML AI sector of the ETR dataset. So the dimensions of this are similar, they're net sentiment on the Y axis and mind share on the X axis. Basically, the ETS study measures awareness on the x axis and intent to do something with, evaluate or implement or not, on that vertical axis. So it's like net score on the vertical where negatives are subtracted from the positives. And again, mind share is vendor awareness. That's the horizontal axis. Now that inserted table shows net sentiment and the ends in the survey, which informs the position of the dots. And you'll notice we're plotting TensorFlow as well. We know that's not a company, but it's there for reference as open source tooling is an option for customers. And ETR sometimes like to show that as a reference point. Now we've also drawn a line for Databricks to show how relatively dominant they've become in the past 10 ETS surveys and sort of mind share going back to late 2018. And you can see a dozen or so other emerging tech vendors. So Andy, I want you to share your thoughts on these players, who were the ones to watch, name some names. We'll bring that data back up as you as you comment. >> So Databricks, as you said, remember we talked about how Oracle is not necessarily the database of the choice, you know? So Databricks is kind of trying to solve some of the issue for AI/ML workloads, right? And the problem is also there is no one company that could solve all of the problems. For example, if you look at the names in here, some of them are database names, some of them are platform names, some of them are like MLOps companies like, DataRobot (indistinct) and others. And some of them are like future based companies like, you know, the Techton and stuff. >> So it's a mix of those sub sectors? >> It's a mix of those companies. >> We'll talk to ETR about that. They'd be interested in your input on how to make this more granular and these sub-sectors. You got Hugging Face in here, >> Which is NLP, yeah. >> Okay. So your take, are these companies going to get acquired? Are they going to go IPO? Are they going to merge? >> Well, most of them going to get acquired. My prediction would be most of them will get acquired because look, at the end of the day, hyperscalers need these capabilities, right? So they're going to either create their own, AWS is very good at doing that. They have done a lot of those things. But the other ones, like for particularly Azure, they're going to look at it and saying that, "You know what, it's going to take time for me to build this. Why don't I just go and buy you?" Right? Or or even the smaller players like Oracle or IBM Cloud, this will exist. They might even take a look at them, right? So at the end of the day, a lot of these companies are going to get acquired or merged with others. >> Yeah. All right, let's wrap with some final thoughts. I'm going to make some comments Andy, and then ask you to dig in here. Look, despite the challenge of leveraging AI, you know, Ken, if you could bring up the next chart. We're not repeating, we're not predicting the AI winter of the 1990s. Machine intelligence. It's a superpower that's going to permeate every aspect of the technology industry. AI and data strategies have to be connected. Leveraging first party data is going to increase AI competitiveness and shorten time to value. Andy, I'd love your thoughts on that. I know you've got some thoughts on governance and AI ethics. You know, we talked about ChatGBT, Deepfakes, help us unpack all these trends. >> So there's so much information packed up there, right? The AI and data strategy, that's very, very, very important. If you don't have a proper data, people don't realize that AI is, your AI is the morals that you built on, it's predominantly based on the data what you have. It's not, AI cannot predict something that's going to happen without knowing what it is. It need to be trained, it need to understand what is it you're talking about. So 99% of the time you got to have a good data for you to train. So this where I mentioned to you, the problem is a lot of these companies can't afford to collect the real world data because it takes too long, it's too expensive. So a lot of these companies are trying to do the synthetic data way. It has its own set of issues because you can't use all... >> What's that synthetic data? Explain that. >> Synthetic data is basically not a real world data, but it's a created or simulated data equal and based on real data. It looks, feels, smells, taste like a real data, but it's not exactly real data, right? This is particularly useful in the financial and healthcare industry for world. So you don't have to, at the end of the day, if you have real data about your and my medical history data, if you redact it, you can still reverse this. It's fairly easy, right? >> Yeah, yeah. >> So by creating a synthetic data, there is no correlation between the real data and the synthetic data. >> So that's part of AI ethics and privacy and, okay. >> So the synthetic data, the issue with that is that when you're trying to commingle that with that, you can't create models based on just on synthetic data because synthetic data, as I said is artificial data. So basically you're creating artificial models, so you got to blend in properly that that blend is the problem. And you know how much of real data, how much of synthetic data you could use. You got to use judgment between efficiency cost and the time duration stuff. So that's one-- >> And risk >> And the risk involved with that. And the secondary issues which we talked about is that when you're creating, okay, you take a business use case, okay, you think about investing things, you build the whole thing out and you're trying to put it out into the market. Most companies that I talk to don't have a proper governance in place. They don't have ethics standards in place. They don't worry about the biases in data, they just go on trying to solve a business case >> It's wild west. >> 'Cause that's what they start. It's a wild west! And then at the end of the day when they are close to some legal litigation action or something or something else happens and that's when the Oh Shit! moments happens, right? And then they come in and say, "You know what, how do I fix this?" The governance, security and all of those things, ethics bias, data bias, de-biasing, none of them can be an afterthought. It got to start with the, from the get-go. So you got to start at the beginning saying that, "You know what, I'm going to do all of those AI programs, but before we get into this, we got to set some framework for doing all these things properly." Right? And then the-- >> Yeah. So let's go back to the key points. I want to bring up the cloud again. Because you got to get cloud right. Getting that right matters in AI to the points that you were making earlier. You can't just be out on an island and hyperscalers, they're going to obviously continue to do well. They get more and more data's going into the cloud and they have the native tools. To your point, in the case of AWS, Microsoft's obviously ubiquitous. Google's got great capabilities here. They've got integrated ecosystems partners that are going to continue to strengthen through the decade. What are your thoughts here? >> So a couple of things. One is the last mile ML or last mile AI that nobody's talking about. So that need to be attended to. There are lot of players in the market that coming up, when I talk about last mile, I'm talking about after you're done with the experimentation of the model, how fast and quickly and efficiently can you get it to production? So that's production being-- >> Compressing that time is going to put dollars in your pocket. >> Exactly. Right. >> So once, >> If you got it right. >> If you get it right, of course. So there are, there are a couple of issues with that. Once you figure out that model is working, that's perfect. People don't realize, the moment you decide that moment when the decision is made, it's like a new car. After you purchase the value decreases on a minute basis. Same thing with the models. Once the model is created, you need to be in production right away because it starts losing it value on a seconds minute basis. So issue number one, how fast can I get it over there? So your deployment, you are inferencing efficiently at the edge locations, your optimization, your security, all of this is at issue. But you know what is more important than that in the last mile? You keep the model up, you continue to work on, again, going back to the car analogy, at one point you got to figure out your car is costing more than to operate. So you got to get a new car, right? And that's the same thing with the models as well. If your model has reached a stage, it is actually a potential risk for your operation. To give you an idea, if Uber has a model, the first time when you get a car from going from point A to B cost you $60. If the model decayed the next time I might give you a $40 rate, I would take it definitely. But it's lost for the company. The business risk associated with operating on a bad model, you should realize it immediately, pull the model out, retrain it, redeploy it. That's is key. >> And that's got to be huge in security model recency and security to the extent that you can get real time is big. I mean you, you see Palo Alto, CrowdStrike, a lot of other security companies are injecting AI. Again, they won't show up in the ETR ML/AI taxonomy per se as a pure play. But ServiceNow is another company that you have have mentioned to me, offline. AI is just getting embedded everywhere. >> Yep. >> And then I'm glad you brought up, kind of real-time inferencing 'cause a lot of the modeling, if we can go back to the last point that we're going to make, a lot of the AI today is modeling done in the cloud. The last point we wanted to make here, I'd love to get your thoughts on this, is real-time AI inferencing for instance at the edge is going to become increasingly important for us. It's going to usher in new economics, new types of silicon, particularly arm-based. We've covered that a lot on "Breaking Analysis", new tooling, new companies and that could disrupt the sort of cloud model if new economics emerge. 'Cause cloud obviously very centralized, they're trying to decentralize it. But over the course of this decade we could see some real disruption there. Andy, give us your final thoughts on that. >> Yes and no. I mean at the end of the day, cloud is kind of centralized now, but a lot of this companies including, AWS is kind of trying to decentralize that by putting their own sub-centers and edge locations. >> Local zones, outposts. >> Yeah, exactly. Particularly the outpost concept. And if it can even become like a micro center and stuff, it won't go to the localized level of, I go to a single IOT level. But again, the cloud extends itself to that level. So if there is an opportunity need for it, the hyperscalers will figure out a way to fit that model. So I wouldn't too much worry about that, about deployment and where to have it and what to do with that. But you know, figure out the right business use case, get the right data, get the ethics and governance place and make sure they get it to production and make sure you pull the model out when it's not operating well. >> Excellent advice. Andy, I got to thank you for coming into the studio today, helping us with this "Breaking Analysis" segment. Outstanding collaboration and insights and input in today's episode. Hope we can do more. >> Thank you. Thanks for having me. I appreciate it. >> You're very welcome. All right. I want to thank Alex Marson who's on production and manages the podcast. Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and our newsletters. And Rob Hoof is our editor-in-chief over at Silicon Angle. He does some great editing for us. Thank you all. Remember all these episodes are available as podcast. Wherever you listen, all you got to do is search "Breaking Analysis" podcast. I publish each week on wikibon.com and silicon angle.com or you can email me at david.vellante@siliconangle.com to get in touch, or DM me at dvellante or comment on our LinkedIn posts. Please check out ETR.AI for the best survey data and the enterprise tech business, Constellation Research. Andy publishes there some awesome information on AI and data. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching everybody and we'll see you next time on "Breaking Analysis". (gentle closing tune plays)

>> Announcer: From the CUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Welcome to this special CUBE conversation. I'm John Furrier here in Palo Alto, California. In our remote studio, we have a quarantine crew here during this COVID-19 crisis. Here talking about the crisis and the impact to business and overall work. Joined by a great guest Dustin Kirkland, CUBE alumni, who's now the chief product officer at Apex Clearing. This COVID-19 has really demonstrated to the mainstream world stage, not just inside the industry that we've been covering for many, many years, that the idea of at-scale means something completely different, and certainly DevOps and Agile is going mainstream to survive, and people are realizing that now. No better guest than have Dustin join us, who's had experiences in open source. He's worked across the industry from Ubuntu, Open Stack, Kubernetes, Google, Canonical. Dustin, welcome back to the CUBE here remotely. Looking good. >> Yeah, yeah, thanks, John. Last time we talked, I was in the studio, and here we are talking over the internet. This is a lot of fun. >> Well, I really appreciate it. I know you've been in your new role since September. A lot's changed, but one of the things why I wanted to talk with you is because you and I have talked many times around DevOps. This has been the industry conversation. We've been inside the ropes. Now you're starting to see, with this new scale of work-at-home forcing all kinds of new pressure points, giving people the realization that the entire life with digital and with technology can be different, doesn't have to be augmented with their existing life. It's a full-on technology driven impact, and I think a lot of people are learning that, and certainly, healthcare and finance are two areas, in particular, that are impacted heavily. Obviously, people are worried about the economy, and we're worried about people's lives. These are two major areas, but even outside that, there's new entrepreneurs right now that I know who are working on new ventures. You're seeing people working on new solutions. This is kind of bringing the DevOps concept to areas that quite frankly weren't there. I want to get your thoughts and reaction to that. >> Yeah, without a doubt, I mean, the whole world has changed in 30 short days. We knew something was amiss in China. We knew that there was a lot of danger for people. The danger for business, though, didn't become apparent until vast swathes of the work force got sent home. And there's a number of businesses and industries that are coping relatively well with this. Certainly those who have previously adopted, or have experienced, doing work remotely, doing business by video, teleconference, having resources in the cloud, having people and expertise who are able to continue working at nearly 100% capacity in 100% remote environments. There's a lot of technology behind that, and there are some industries, and in particular, some firms, some organizations, that were really adept and were able to make that shift almost overnight. Maybe there were a couple bumps along the way, some VPN settings needed to be tweaked, and Zoom settings needed to be changed a little bit, but for many, this was a relatively smooth transition, and we may be doing this for a very long time. >> Yeah, I want to get your thoughts, before we get into some of the product stuff that you guys are working on and some other things. What's your general reaction to people in your circles, inside industry and tech industry, and outside, what are you seeing a reaction to this new scale, work from home, social distancing, isolation, what are your observations? >> Yeah, you know, I think we're in for a long haul. This is going to be the new normal for quite some time. I think it's super important to check on the people you care about, and before we get into dev and tech, check on the people you care about, especially people who either aren't yet respecting the social distancing norms and impress upon them the importance that, hey, this is about you, this is about the people you care about, it's about people you don't even know, because there are plenty of people who can carry this and not even know. So definitely check on the people that you care about. And reach out to those people and stay in touch. We all need one another more than ever, right? I manage a team, and it's super important, I think, to understand how much stress everyone is under. I've got over a dozen people that report to me. Most of them have kids and families. We start out our weekly staff meeting now, and we bring the kids in. They're curious, they want to know what's going on. First five, 10 minutes of our meeting is meet the family. And that demystifies some of what we're doing, and actually keeps the other 50 minutes of the meeting pretty quiet in our experience. But it's really humanized an aspect of work from home that's always been a bit taboo. We laugh about the reporter in Korea whose kid and his wife came in during the middle of a live on-air interview. There's certainly, I've worked from home for almost 12 years, like, those are really uncomfortable situations. Until about a month ago, when that just became the norm. And from that perspective, I think there's a humanization that we're far more understanding of people who work from home now than ever before. >> It's funny, I've heard people say, you know, my wife didn't know what I did until I started working at home. And comments to seeing people's family, and saying, wow, that's awesome, and just bringing a personal connection, not just this software mechanism that connects people for some meeting, and we've all been on those meetings. They go long, and you're sitting there, and you're turning the camera off so you can sneeze. All those things are happening. But when you start to think about, beyond it being a software mechanism, that it's a social equation right now. People have shared experiences. It's been an interesting time. >> Yeah, and just sharing those experiences. We do a think internal on our Slack channel every day. We try to post a picture. We call it hashtag recess, and at recess we take a picture of walking the dogs, or playing with the kids, or gardening, or whatever it is, going for a run. Again, just trying to make the best of this, take advantage of, you know, it's hard working from home, but trying to take advantage of some of those once in a lifetime opportunities we have here. And my team has started pub quiz on Fridays, so we're mostly spread across, in the U.S., so we're able to do this at a reasonable hour, but the last couple of Fridays, we've jumped on a Zoom, downloaded a pub trivia game, most of us a crack a beer, or glass of wine, or a cocktail, and you know, it's just, it actually puts a punctuate mark on the end of the week, puts a period on the end of the week. Because that's the other thing about this, man, if you don't have some boundaries, it's easy to go from an eight or nine hour normal day to 10, 12, 14, 16 hour days, Saturday bleeds into Sunday bleeds into Monday, and then the rat race takes over. >> You got to get the exercise. You have a routine. That's my experience. What's your advice for people who are working at home for the first time? Do you have any best practices? >> I actually had a blog post on this about two weeks ago and put up almost a shopping list of some of the things that I've assembled here in the work from home environment. It's something I've been doing since 2008, so it's been there for a good long while. It's a little bit hard to accumulate all the technology that you need, but I would say, most important, have a space, some kind of space. Some people have more room or less, but even just a corner in a master bedroom with a standup desk, some space that is your own, that the family understands and respects. The other best practice is set some time boundaries. I like to start my day early. I'll try to break more a little bit for that recess, see the family some, and then knock off at a reasonable hour, so establish those boundaries. Yeah, I've got a bunch of tips in that blog post I can shoot you after this, but it's the sort of thing that, be a bit understanding, too, of other people in this situation for the first time, perhaps. So you know, offer whatever help and assistance you can, and be understanding that, man, things just aren't like they used to be. >> That's great advice. Thanks for the insights. Want to get to something that I see happening, and this always kind of happens when you see these waves where there's a downturn, or there's some sort of an event. In this case it's catastrophic in the way it vectored in like this and the impact that we just discussed. But what comes out of it is creativity around entrepreneurial activity, and certainly reinvention, businesses reforming, retrenching, resetting, whatever word, pivot, digital transformation, there's plenty of words for it. But this is the time where people can actually get a lot done. I always comment, in my last interview I did, you know, Shakespeare wrote Macbeth when he was sheltering in place, and Isaac Newton invented calculus, so you can actually get some work done. And you're starting to see people look at the new technology and start disrupting old incumbent markets, because now more than ever, things are exposed. The opportunity of recognition becomes clearer. So I wanted to get your thoughts on this. You're a product person, you've got a lot of product management skills, and you're currently taking this DevOps to financial market with fintech and your business, so you're applying known principles and software and tech and disrupting an existing industry. I think this is going to be a common trend for the next five years. >> Yeah, so on that first note, I think you're exactly right. There will be a reckoning, and there will be a ton of opportunities that come out of this for the already or the rapidly transformed digital native, digital focused business. There will be some that survive and thrive here. I think you're seeing a lot of this with the popularity of Zoom that has spiked recently. I think you're going to see technologies like DocuSign being used in places that, some of those places that still require wet signatures, but you just can't get to the notary and sign a, I don't know, a refi on your mortgage or something like that. And so I think you're going to see a bunch of those. The biggest opportunities are really around our education system. I've got two kids at home, and I'm in a pretty forward thinking school district in Austin, Texas, you know, but that's not the norm where our teachers are conducting classes and assignments over Zoom. I've got a kindergartener and a second grader. There's somewhat limits to what they can do with technology. I think you're going to see a lot of entrepreneurial solutions that develop in that space, and that's going to go from K through 12, and then into college. You think about how universities have had to shift and cancel classes, and what's happening with graduation. I've got a six and an eight year old, and I've been told I need to save $200,000 apiece for each of them to go to college, which is just an astounding number, especially to someone like me, who went to an inexpensive public university on a scholarship. Saving that kind of money for college, and just thinking about how much more efficient our education system might be with a lot more digital, a lot more digital education, digital testing and classes, while still maintaining the college experience, what that's going to look like in 10 years. I think we're going to see a lot of changes over these next 18 months to our educational system. >> Dustin, talk about the event dynamics. Physical events don't exist currently. Certainly, when they do come back, they should, and they will, the role of the virtual space is going to be highlighted and new opportunities will emerge. You mentioned education. People learn, not just for school, whether they're kids, whether they're professionals, learning and collaboration, work tools are going to reshape. What's your take on that marketplace, because we got to do virtual events. You can't just replicate a physical event and move it to digital. It's a complex system. >> Yeah, you're talking about an entire industry. We saw the Google Events, Google Next, Google IO, the Microsoft Events, just across the, I'm here in Austin, Texas, all of South by Southwest was canceled, which is just, it's breathtaking. When does that come back, and what does it look like? Is it a year or two or more from now? Events is where I spend my time, and when I get on a plane, and I fly somewhere, I'm usually going to a conference or trade show. Think about the sports industry. People who get on a plane, they go to an NFL game. John, I don't have all the answers, man, but I'm telling you, that entire industry is rapidly, rapidly going to evolve. I hope and pray that one day we're back to a, I can go back to a college football game again. I hope I can sit in a CUBE studio at a CUBE Con or an Open Stack or some other conference again. >> Hey, we should do a rerun, because I was watching the Patriots game last night, Tom Brady beating the Chiefs, October from last year. It was one of the best games of the season, went down to the wire, and I watched it, and I'm like, okay, that's Tom Brady, he's still in the Patriot uniform on the TV. Do we do reruns? This is the question. Right now, there's a big void for the next three months. What do we do? Do we replay the highlights from the CUBE? Do we have physical get togethers with Zoom? What's your take on how people should think about these events? >> Yeah, you know, the reruns only go so far, right? I'm a Texas Aggie, man. I could watch Johnny Football in his prime anytime. But I know what happened, and those games are just not as exciting as something that's a surprise. I'm actually curious about e-sports for the first time. What would it look like to watch a couple of kids who are really good at Madden Football on a Playstation go at it? What would other games that I've never seen look like? In our space, it's a lot more about, I think, podcasts and live content and staying connected and apprised of what's going on, making-- Oh, we locked up there for a second. It's, I think it's going to be really interesting. I'm still following you guys. I certainly see you active on social media. I'm sort of more addicted than ever to the live news, and in fact, I'm ready to start seeing some stuff that doesn't involve COVID-19, so from that perspective, man, keep churning out good content, and good content that's pertinent to the rest of our industry. >> That's great stuff. Well, Dustin, take a minute to explain what you're doing at Apex Clearing, your mission, and what are you guys excited about. >> Yeah, so Apex Clearing, we're a fintech. We're a very forward-focused, digitally-focused fintech. We are well positioned to continue servicing the needs of our clients in this environment. We went fully remote the first week of March, long before it was mandatory, and our business shifted pretty seamlessly. We worked through a couple of hiccups, provisioning extra VPN IP addresses, and upgrading a couple of service plans on some of the softwares, the service we buy, but besides that, our team has done just a marvelous job transitioning to remote. We are in the broker, dealer, and registered advisor space, so we provide the clearing services, which handles stock trades, equity trades, in the back end, and the custodial services. We actually hold, safeguard, the equities that our correspondents, we call our clients correspondents, their retail customers end up holding. So we've been around in our current form since about 2012. This was a retread of a previous company that was bought and retooled as Apex Clearing in 2012. Very shortly after that, we helped Robinhood, Wealthfront, Betterment, a whole bunch of really forward-looking companies reinvent what it meant to buy and sell and trade securities online, and to hold assets in a robo advisor like Betterment. Today, we are definitely well-known, well-respected for how quickly and seamlessly our APIs can be used by our correspondents in building really modern e-banking and e-brokerage experiences. >> So you guys-- >> So that went-- >> Are you guys like a DevOps platform-- >> We're more like software as a service for fintech and brokerage. So our products are largely APIs that our correspondents use their own credentials to interact with, and then using our APIs, they can open accounts, which means get an account number from the systems that allows them to then fund that account, connect via ACH and other bank connectivity platforms, transfer cash into those accounts, and then start conducting trades. Some of our correspondents have that down to a 60-second experience in a mobile app. From a mobile app, you can register for that account, if you need to, take a picture of an IED, have all of that imported, add your tax information, have that account number associated with your banking account, move a couple hundred dollars into that banking account, and then if the stock market's open, start buying and selling stock in that same window. >> Great, well, I wanted to talk about this, because to the earlier bigger picture, I think people are going to be applying DevOps principles, younger entrepreneurs, but also, reborn, if you will, professionals who are old school IT or whatever, moving faster. And you wrote a blog post I want to get your thoughts on. You wrote it on April second. How we've adapted Ubuntu's time-based release cycles to fintech and software as a service. What is that all about? What's the meaning behind this post? You guys are doing something new, unique, or-- >> To this industry and to many of the people around me, even our clients and customers around me, this is a whole new world. They've never seen anything like it. To those of us who have been around Linux, open source, certainly Ubuntu, Open Stack, Kubernetes, it's just standard operating procedures. There's nothing surprising about it, necessarily. But either it's some combination of the financial services world, just the nature of proprietary software, but also the concept of software as a service, SaaS, which is very different than Ubuntu or Kubernetes or Open Stack, which is released software, right. We ship software at the end of an Ubuntu cycle or a Kubernetes cycle. It's very different when you're a software as a service platform, and it's a matter of rolling out to production some changes, and those changes then going live. So, I wrote a post mainly to give some transparency, largely to our clients, our correspondents. We've got a couple hundred customers that use the Apex platform. I've met with many of them in a sort of one-on-many, one-to-one, one-on-many basis, where I'll show up and deliver the product road map, a couple of product managers will come and do a deep dive. Part of what we communicate to those customers is around, now, around our release cycles, and to many of them, it's a foreign concept that they've just never seen or heard before, and so I put together the blog post. We shared it internally, and educated the teams, and it was well-received. We shared it externally privately with a number of customers, and it was well-received, and a couple of them, actually a couple of the Silicon Valley based customers said, hey, why don't you just put this out there on Medium or on your blog or under an Apex banner, because this actually would be really well-received by others in the family, other partners in the family. So I'm happy to kind of dive into a couple of the key principles here, and we can sort of talk through it if you're interested, John. >> Well, I think the main point is you guys have a release cycle that is the speed of open source to SaaS, and fintech, which again, proprietary stuff is slower, monolithic. >> Yeah, the key principle is that we've taken this, and we've made it predictable and transparent, and we commit to these cycles. You know, most people maybe familiar with Ubuntu releasing twice a year, right, April and October, Ubuntu has released every April and October since 2004. I was involved with Ubuntu between 2008 and 2018 as an engineer, an engineering manager, and then a product manager, and eventually a VP of product at Canonical, and that was very much my life for 10 years, oriented around that. In that time, I spent a lot of time around Open Stack, which adopted a very similar model. Open Stack's released every six months, just after the Ubuntu release. A number of the members of the technical team and the committee that formed Open Stack came out of either Ubuntu or Canonical or both, and really helped influence that community. It's actually quite similar in Kubernetes, which developed independent, generally, of Ubuntu. Kubernetes releases on a quarterly basis, about every three months, and again, it's the sort of thing where it's just a cycle. It happens like clockwork every three months. So when I joined Apex and took a look at a number of the needs that we had, our correspondents had, our relationship managers, our sales team, the client-facing people in the organization, one of the biggest items that bubbled straight to the top is our customers wanted more transparency into our road maps, tighter commitments on when we're going to deliver things, and the ability to influence those. And you know what, that's not dissimilar from any product managers plight anywhere in the industry. But what I was able to do is take some of those principles that are common around Ubuntu and Kubernetes and Open Stack, which by the way, are quite familiar. We use a lot of Ubuntu and Kubernetes inside of Apex, and many of our correspondents are quite familiar with those cycles, but they'd never really seen or heard of a software as a service, a SaaS vendor, using something like that. So that's what's new. >> You've got some cycles going now. You've got schedules, so just looking here, just to get this out there, 'cause I think it's data. You did it last year in October, November, mid-cycle in January of this year. You've got a couple summits coming up? >> Yeah, that's right, we've broken it down into three cycles per year, three 16-week cycles per year. So it's a little bit more frequent than the twice a year Ubuntu, not quite as frenetic as the quarterly Kubernetes cycles. 16 weeks time three is 48. That leaves us four weeks of slack, really to handle Thanksgiving and Christmas and end of year holidays, Chinese New Year, whatever might come up. I'll tell you from experience, that's always been a struggle in the Ubuntu and Open Stack and Kubernetes world, it's hard to plan around those cycles, so what we've done here is we've actually just allocated four weeks of a slush fund to take care of that. We're at three 16-week cycles per year. We version them according to the year and then an iterator. So 20A, 20B, 20C are our three cycles in 2020, and we'll do 21A, B, and C next year. Each of those cycles has three summits. So to your point about we get together, back in the before everyone stopped traveling, we very much enjoyed twice a year getting together for CUBE con. We very much enjoyed the Open Stack summits and the various Ubuntu summits. Inside of a small company like ours, these were physical. We'd get together in Dallas or New York or Chicago or Portland, which is the four places we have offices. We were doing that basically every six weeks or so for one of these summits. Now they're all virtual. We handle them over Zoom. When they were physical, we'd do the summit in about three days of packed agendas, Tuesday, Wednesday, Thursday. Now that we've gone to virtual, we've actually spread it a little bit thinner across the week, and so we've done, we've poked some holes in the day, which has been an interesting learning experience, and I think we're all much happier with the most recent summit we did, spreading it over the course of the week, accounting for time zones, giving ourself, everyone, lunch breaks and stuff. >> Well, we'll have to keep checking in. I want to certainly collaborate with you on the virtual digital, check your progress. We're all learning, and iterating, if you will, on the value that you can do with these digital ones. Try to get that success with physical, not always easy. Appreciate, and you're looking good, looking good and safe. Stay safe, and great to check in with you, and congratulations on the new opportunity. >> Yeah, thanks, John. >> Appreciate it. Dustin Kirkland, chief product officer at Apex Clearing. I'm John Furrier with the CUBE, checking in with a remote interview during this time when we are getting all the information of best practices on how to deal with this new at-scale, the new shift that is digital, that is impacting, and opportunities are there, certainly a lot of challenges, and hopefully, the healthcare, the finance, and the business models of these companies can continue and get back to work soon. But certainly, the people are still sheltered in place, working hard, being creative, be the coverage here in the CUBE. I'm John Furrier, thanks for watching. (bright electronic music)

>> Narrator: From theCUBE studios in Palo Alto and Boston connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, welcome to this CUBE conversation here in the Palo Alto studios for theCUBE. I'm John Furrier, the host. We're here during this time where everyone's sheltering in place during the COVID-19 crisis. We're getting the interviews out and getting the stories that matter for you. It's theCUBE's mission just to share and extract the data from, signal from the noise, and share that with you. Of course the conversation here is about how the data analytics are being used. We have a great friend and CUBE alum, Bruno Kurtic, VP, founding VP of Product and Strategy for Sumo Logic, a leader in analytics. We've been following you guys, kind of going back I think many, many years, around big data, now with AI and machine learning. You guys are an industry leader. Bruno, thanks for spending the time to come on theCUBE, I know you're sheltering in place. Thanks for coming on. >> You're welcome, pleasure. >> Obviously with the crisis, the work at home has really highlighted the at-scale problem, right? We've been having many conversations on theCUBE of cybersecurity at scale, because now the endpoint protection business has been exploding, literally, a lot of pressure of malware. A convenient crime time for those hackers. You're starting to see cloud failure. Google had 18 hours of downtime. Azure's got some downtime. I think Amazon's the only one that haven't had any downtime. But everything is being at scale now, because the new work environment is actually putting pressure on the industry, not only just the financial pressure of people losing their jobs or the hiring freezes, but now the focus is staying in business and getting through this. But the pressure points of scale are starting to show. And working at home is one of them. Analytics has become a big part of it. Can you share your perspective of how people using analytics to get through this, because now the scale of the problem-solving is there with analytics. It's in charts on the virus, exponential curves, people want to know the impact of their business in all this. What's your view on this situation? >> Yeah. The world has changed so quickly. Analytics has always been important. But there are really two aspects of analytics that are important right now. A lot of our enterprises today, obviously, as you said, are switching to this sort of remote workforce. Everybody who was local is now remote, so, people are working from home. That is putting stress on the systems that support that working from home. It's putting stress on infrastructure, things like VPNs and networks and things like that because they're carrying more bits and bytes. It's putting stress on productivity tools, things like cloud provider tools, things like Office 365, and Google Drive, and Salesforce, and other things that are now being leveraged more and more as people are remote. Enterprises are leveraging analytics to optimize and to ensure that they can facilitate course of business, understand where their issues are, understand where their failures are, internal and external, route traffic appropriately to make sure that they can actually do the business they need. But that's only half of the problem. In fact, I think the other half of the problem is maybe even bigger. We as humans are no longer able to go out. We're not supposed to, and able to go shopping and doing things as we normally do, so all of these enterprises are not only working remotely, leveraging productivity tools and quote-unquote "digital technologies" to do work. They're also serving more customers through their digital properties. And so their sites, their apps, their retail stores online, and all of the digital aspects of enterprises today are under more load because consumers and customers are leveraging those channels more. People are getting groceries delivered at home, pharmaceuticals delivered at home. Everything is going through online systems rather than us going to Walgreens and other places to pick things up. Both of those aspects of scale and security are important. Analytics is important in both figuring out how do you serve your customers effectively, and how do you secure those sites. Because now that there's more load, there's more people, and it's a bigger honeypot. And then also, how do you actually do your own business to support that in a digital world? >> Bruno, that's a great point. I just want to reiterate that the role of data in all this is really fundamental and clear, the value that you can get out of the data. Now, you and I, we've had many conversations with you guys over the years. For all of us insiders, we all know this already. Data analytics, everyone's instrumenting their business. But now when you see real-life examples of death and destruction, I mean, I was reporting yesterday that leaked emails from the CDC in the United States showed that in January, they saw that people didn't have fevers with COVID-19. The system was lagging. There was no real-time notifications. This is our world. We've been living in this for this past decade, in the big data world. This is highlighting a global problem, that with notifications, with the right use of data, is a real game-changer. You couldn't get any more clear. I have to ask you, with all this kind of revelations, and I don't mean to be all gloom-and-doom, but that's the reality, highlights the fact that instrumenting and having the data analytics is a must-have. Can you share your reaction to that? >> Yeah, absolutely. You're right. Like you said, we are insiders here, and we've been espousing this world of what we internally in Sumo call the continuous intelligence, which essentially means to us and to our customers, that you collect and process all signals that are available to you as a business, as a government, as a whatever entity that is dealing with critical things. You need to process all of that data as quickly as you can. You need to mine it for insights. You need to, in an agile fashion, just like software development, you need to consume those insights, build them into your processes to improve, to react, to respond quickly, and then deliver better outcomes. The sooner you understand what the data is telling you, the sooner you can actually respond to whatever that data is telling you, and actually avoid bad outcomes, improve good outcomes, and overall, react to whatever is forcing you to react. >> I was just talking with Dave Vellante last week about this, my co-host, and also Jeff Frick, my general manager, who interviewed you in the past on theCUBE, about the transition and transformation that's happening. I want to just get your reaction to what we're seeing, and I wanted to get your thoughts on it. There's transitions and there's transformations. Yeah, we've been kind of in this data transition around analytics. You pointed out, as insiders, we've been pointing this out for years. But I think now there's more of a transformative component to this. I think it's becoming clear to everyone the role of data, and you've laid out some good things there. Now I want to ask you, on this transformation. Do you agree with it, and if you do, how does that change the roles? Because if I'm going to react to this as a business, whether small, medium, and large business, large enterprise or government, I now realize that the old world's over. I need to get to the new way. That means new roles, new responsibilities, new outcomes, new ways to measure. Can you share your thoughts on that? Do you agree with the transformation, and two, what are some of those new role changes? How should a business manager or technologist make that transformation? >> Yeah. If it was ever more clear, getting a switch, or a transformation as you say, from the old way we did business and we did technology to the new way, is only being highlighted by this crisis. If you are an enterprise, and you are trying to do everything yourself, running your own IT stacks and all of that, it is clear today that it is much more difficult to do that than if you were leveraging next generation technologies: clouds, SaaS, PaaS, and other things, because it is hard to get people even to work. I think if we have ever been in a place where this sort of transformation is a must, not a slow choice or an evolution, it is now. Because enterprises who have done that, who have done that already, are now at an advantage. I think this is a critical moment in time for us all as we all wake up to this new reality. It is not to say that enterprises are going to be switched over after this specific crisis, but what's going to happen, I believe, is that, I think the philosophies are going to change, enterprises are going to think of this as the new normal. They're going to think about, "Hey, if I don't have the data "about my business, about my customers, "about my infrastructure, about my systems, "I won't be able to respond to the next one." Because right now there's a lot of plugging the holes in the dam with fingers and toes, but we are going to need to be ready for this, because if you think about what this particular pandemic means, this isn't going to end in April or May. Because without a treatment, or without a vaccination, it's going to continue to resurface. Unless we eradicate the entire population of the virus, any new incident is going to start up like a small flare-up, and that is going to continue to bring us back into the situation. Over this time, we're going to have to continue to respond to this crisis as we are, and we need to plan for the future ones like this. That might not be a pandemic type of crisis. It could be a change in the business. It could be other types of world events, whatever it might be. But I think this is the time when enterprises are going to start adopting these types of procedures and technologies to be able to respond. >> It's interesting, Bruno, you bring up some good points. I think about all the conversations that I've had over the years with pros around "disaster recovery" and continuous operations. This is a different vector of what that means, because when you highlighted earlier, IT, it's not like a hurricane or a power outage. This is a different kind of disruption. We talked about scale. What are some of the things that you're seeing right now that businesses are being faced with, that you guys are seeing in the analytics, or use cases that have emerged from this new normal that is facing today's business with this crisis. What's changed? What is this new challenge? When you think about the business continuity and how continuous operations need to be sustained because, again, it's a different vector. It's not a blackout, it's not a hurricane. It's a different kind of disruption. It's one where the business needs to stay on more than ever. >> Yep. Correct. True. What's really interesting, and there are some relatively straightforward use cases that we're seeing. People are dealing with their authentication, VPN network issues, because everybody is low on bandwidth. Everybody is, all of these systems are at their breaking point because they're carrying more than they ever did. These are use cases that existed all along. The problem with the use cases that existed all along is that they've been slowly picking up and growing. This is the discontinuity right now. What's happened right now, all of a sudden you've got double, triple, quadruple the load, and you need to both scale up your infrastructure, scale up your monitoring, be much more vigilant about that monitoring, speed up your recovery because more is at stake, and all of those things. That's the generic use case that existed all along, but have not been in this disruptive type of operating environment. Second is, enterprises are now learning very quickly what they need to do in terms of scaling and monitoring their production, customer-facing infrastructure, what used to be in the data center, the three-tier world, adding a few notes to an application, to your website over time, worked. Right now everybody is realizing that this whole bent on building our microservices, building for scale, rearchitecting and all that stuff, so that you can respond to an instantaneous burst of traffic on your site. You want to capture that traffic, because it means revenue. If you don't capture it, you miss out on it, and then customers go elsewhere, and never come back, and all that stuff. A lot of the work loads are to ensure that the systems, the mission-critical systems, are up and running. It's all about monitoring real-time telemetry, accelerating root cause analysis across systems that are cloud systems, and so on. >> It's a great point. You actually were leading into my next question I wanted to ask you. You know, the old saying goes, "Preparation meets opportunity. Those are the lucky ones." Luck is never really there. You're prepared, and opportunity. Can you talk about those people that have been prepared, that are doing it right now, or who are actually getting through this? What does preparation look like? What's that opportunity? Who's not prepared? Who's hurting the most? Who's suffering, and what could they do differently? Are you seeing any patterns out there, that people, they did their work, they're cloud native, they're scaled out, or they have auto-scaling. What are some of the things where people were prepared, and could you describe that, and on the other side where people weren't prepared, and they're hurting. Can you describe those two environments? >> Sure. Yeah. You think about the spectrum of companies that are going through digital transformation. There are companies who are on the left side. I don't know whether I'm mirroring or not. Basically, on the left side are people who are just making that transformation and moving to serving customers digitally, and on the right side are the ones that are basically all in, already there, and have been building modern architectures to support that type of transformation. The ones that are already all the way on the right, companies like us, right? We've been in this business forever. We serve customers who are early adopters of digital, so we've had to deal with things like November 6th, primary elections, and all of our media and entertainment customers who were spiking. Or we have to deal with companies that do sporting events like World Cup or Super Bowl and things like that. We knew that our business was going to always demand of us to be able to respond to both scheduled and unscheduled disruptions, and we needed to build systems that can scale to that without many human interactions. And there are many of our customers, and companies who are in that position today, who are actually able to do business and are now thriving, because they are the ones capturing market share at this point in time. The people who are struggling are people who have not yet made it to that full transformation, people who, essentially, assume business as normal, who are maybe beginning that transformation, but don't have the know-how, or the architecture, or the technology yet to support it. Their customers are coming to them through their new digital channels, but those digital channels struggle. You'll see this, more often than not you're going to find these still running in a traditional data center than in the cloud. Sometimes they're running in the cloud where they've done just a regular lift-and-shift instead of rearchitecting and things like that. There's really a spectrum, and it's really funny and amazing how much it maps to the journey in digital transformation, and how this specific thing is essentially, what's happening right now, it looks like the business environment demands everybody to be fully digital, but not everybody is. Effectively, the ones that are not are struggling more than the ones that are. >> Yeah. Certainly, we're seeing with theCUBE, with the digital events happening on our side, all events are canceled, so they've got to move online. You can't just take a physical, old way of doing something, where there's content value, and moving it to digital. It's a whole different ball game. There's different roles, there's different responsibilities. It's a completely different set of things. That's putting pressure on all these teams, and that's just one use case. You're seeing it in IT, you're seeing it happen in marketing and sales, how people are doing business. This is going to be very, very key for these companies. The data will be, ultimately, the key. You guys are doing a great job. I do want to get to the news, and I want to get the plug in for Sumo Logic. I want to say congratulations to you guys. A press release went out today from Sumo Logic. You guys are offering free cloud-based data analytics to support work from home and online classroom environments. That's great news. Can you just share and give a plug for that, PSA? >> Sure! We basically have a lot of customers who, just like us, are now starting to work from home. As soon as this began, we got inbound demands saying, "Oh, could you get, do you have an application for this, "do you have some analytics for that, "things that support our work from home." We thought hey, why don't we just make this as a package, and actually build out-of-the-box solutions that can support people who have common working from home technologies that they used to use for 10% of their workforce, and now work for 100% of their workforce. Let's package those, let's push those out. Let's support educational institutions who are now struggling. I have two kids in here who are learning. Everything is online, right? We had to get another computer for them and all this stuff. They're younger, they're in fourth grade. They are doing this, I can see personally how the schools are struggling, how they're trying to learn this whole new model. They need to have their systems be reliable and resilient, and this is not just elementaries, but middle school, high schools, colleges have all expanded their on-premise teaching. So we said, "Okay. Let's do something to help the community "with what we do best." Which is, we can help them make sure that the things that they do, that they need to do for this remote workforce, remote learning, whatever it might be, is efficient, working, and secure. We packaged several bundles of these solutions and offered those for free for a while, so that both our customers, and non-customers, and educational institutions have something they can go and reach for when they are struggling to keep their systems up and running. >> Yeah, it's also a mindset change, too. They want comfort. They want to have a partner. I think that's great that you guys are doing for the community. Can you just give some color commentary on how this all went down? Did you guys have a huddle in your room, said, "Hey, this is a part of our business. "We could really package this up "and really push it out and help people." Is that how it all came together? Can you share some inside commentary on how this all went down and what happened? >> Yeah. Basically, we had a discussion, literally, I think, the first or the second day when we all were sent home. We got on our online meeting and sat down, and essentially learned about this inbound demand from our customers, and what they were looking to do. We were like, "Okay, why don't we, "why don't we just offer this? "Why don't we package it?" It was a cross-functional team that just sat there. It was a no-brainer. Nobody was agonizing over doing this for free or anything like that. We were just sitting there thinking, "What can we do? "Right now is the time for us to all "pull each other up and help each other. "It'll all sort itself out afterwards." >> You know, during the bubonic plague, Shakespeare wrote Macbeth during that time. You guys are being creative during this time, as the coronavirus, so props to you guys at Sumo Logic. Congratulations, and thanks for taking the time. Can you give some parting thoughts on it, for the folks who are working at home? Just some motivational inspiration from you guys? What's going to come next for you guys? >> Sure. And thank you for having me on this video. I would say that we have been making slow transition towards remote workforce as it is. In a lot of places around the world, it's not that easy to make it to an office. Traffic is getting worse, big centers are getting populated, real estate is getting more expensive, all of this stuff. I think, actually, this is an opportunity for enterprises, for companies, and for people to figure out how this is done. We can actually practice now. We're forced to practice. It might actually have positive impact on all industries. We are going to probably figure out how to travel less, probably figure out how to actually do this more effectively, the cost of doing business is going to go down, ability to actually find new jobs might broaden, because you might be able to actually find jobs at companies who never thought they could do this remotely, and now are willing to hire remote workforces and people. I think this is going to be all good for us in the end. Right now it feels painful, and everybody's scared, and all that stuff, but I think long term, both the transformation into digitally serving our customers and the transformation towards remote workforce is going to be good for business. >> Yeah. It takes a community, and we really appreciate the effort you guys make, making that free for people, the classrooms. Remember, Isaac Newton discovered gravity and calculus while sheltering in place. A lot of interesting, new things are going to happen. I appreciate it. >> Bruno: Absolutely. >> Bruno, thank you for taking the time and sharing your insights from your place, sheltering. I made a visit into the studio to get this interview and a variety of other interviews we're doing digitally here. Thanks for sharing. Appreciate your time. >> Thank you. Appreciate you as well. >> I'm John Furrier with theCUBE here. CUBE conversation with Bruno from Sumo Logic sharing his perspective on the COVID-19. The impact, the disruption and path to the future out of this, and the new normal that is going to change our lives. Thanks for watching.

>> From the Amazon Meeting Center, in downtown Seattle, it's theCUBE. Covering Imagine a Better World, a global education conference, sponsored by Amazon Web Services. >> Hey welcome back, everybody, Jeff Frick here with theCUBE. We're in downtown Seattle at AWS Imagine Education. About 900 people from 20 countries really coming together for the first ever AWS summit from public sector group, really focused just on education. We've got a little bit of a twist here, our next guest really coming from more of the artsy side of the house, which is always great to hear from. We've got Linda Drew, she's the Vice Chancellor, and Andy Cook, the Chief Operations Officer, both from Ravensbourne University in London. Welcome. >> We're really pleased >> Thank you. to be here, really excited. >> For the people that aren't familiar with Ravensbourne, give us a little overview of the school. >> We're in the center of London in Greenwich, which is right by the river. We have about two-and-a-half-thousand students and about 250 faculty. We specialize in design, media, and technology, and the interaction, and all that kind of stuff. >> Pretty fun space to be right now. >> Absolutely gorgeous place to be. >> There's so much talk about IT and the tech and IT in operations, but there's so much neat stuff happening really more on the creative side and in the arts. Leveraging technology in all different, new ways. >> Absolutely, it's kind of hand and glove, really. All the innovation that's happening is happening with the way that tech is disrupting what's happening in the creative workspace, and vice versa really. The two things are effecting each other. >> The channels of distribution now, being so open, there's no greater time to be an artist, a creator, because your path to publishing, your path to your audience is really, really short and direct, assuming you can get their attention. >> Absolutely, I think we recognize there's a huge opportunity there for us in terms of developing a competitive advantage in the sector using new, emerging technologies to forge a new path for the institution and help educate and bridge the skills gap for industry. >> What are the things you guys do, one of the classes is broadcast production, and we were talking to all of our guys behind the cameras that nobody can see, and that again is an evolving space and you guys, it's kind of an interesting play, on one hand you're talking about Shakespearian plays, on the other hand you're looking at the newest, latest, greatest way to get that out to consumers, to viewers, to schools, while training the people in the middle with the latest and greatest tools. You guys have started a AWS Elemental Experiment. I wonder if you can give us a little bit of color on that project. >> I can start, and I'll tell you about the impact that it has, and Andy might be able to follow up on some of the technical stuff. We've had a project going with the Royal Shakespeare Company in England, and it's one of their education programs where what we do is a three-way relationship between them, their plays being shot in Stratford-upon-Avon or in London, and one aspect of what happens is that what we do is host the live program that is shot in our TV production studio and jointly the recorded program and the live action is streamed to schools, several hundred schools at a time. Some of our recent shows have been reaching upwards of 85,000 school students at a time. >> 85,000? >> Absolutely. >> That is great reach. We'd been using the more traditional technology before and that was having some issues with school teachers and others that were saying they weren't getting a great service out of the live stream, and our students were a bit frustrated with what they were learning about the streaming technologies. Since having moved to AWS Elemental, that's really increased the satisfaction both of what our students are learning but also in what they're delivering in terms of the live streamed program and because they're streaming more than one thing, because we know that they're also streaming not just the content but also the British sign language. They're also streaming signed content as well. >> Great, great. Andy, you're on the hook for actually getting these systems up and working, right? >> (laughs) Well, I'm not sure about that, but I think Linda said it all, I think the previous stack of technology that we were using in this area were not reliable, we were getting a lot of jump outs with the streams, lots of complaints from our schools. This shift to Elemental has been transformational. Lots of really complimentary feedback from the schools that are taking part in this exercise. It's been really good. >> That's good, the story over and over with cloud basically anything is that the amount of scale and resources and expertise and hardware and software that Amazon can bring to bear on your behalf compared to what you can do on you own, it's just not the same and you're a relatively small school. It's that same scale delta whether it's a medium-size company, a big company, or multi-national. These guys have that massive scale across so many customers, and you get that delivered to your doorstep. >> As you well know, there's a massive shift taking place in the broadcast industry away from the, towards IP-driven technologies, so we see this as a real opportunity to develop our curriculum, add cloud technologies in to our existing courses and go on that journey away from the more traditional technologies to a cloud-based approach. >> I'm just curious if you've adopted cloud stuff in more your standard IT practices, or where are you on that journey? Or was the client satisfaction issue on these broadcasts what accelerated that adoption faster than your normal stuff? >> I think it's been quite closely related, in some ways. It's a bit kind of chicken and egg. We were already looking at ways of enhancing our infrastructure and this kind of stuff came along at the same time, so we just say how quickly can we get to move some of this stuff for our standard operational focus. >> I think most universities are in some sort of hybrid state running on premise services with some, putting their feet gently into the water of cloud technologies, but I think we're looking at really accelerating that journey towards AWS now for our infrastructure. >> I'm curious, were you here for the keynote this morning? >> Yeah, definitely. >> Did you see the Alexa movie with the kids in the dorm room? >> Yeah. >> Really exciting. Very exciting. >> I think one of the slides really sums up our journey and thoughts around working with Amazon. It's the IT transformation piece, then there's a adoption of machine learning in terms of improving the student experience, and then there's adopting cloud courses into our curriculum, so those three areas are really where we're looking to build a relationship with Amazon. >> It's interesting to see what defines this new education experience, because the kids have different expectations, they've all grown up with apps and mobile. To your point on the attention, if something's not working, they're used to flipping to another channel, switching to another input, so if it doesn't work, you only have their attention for a short period of time. I think it is really interesting to rethink what are the actual activities that define this new engagement and this new student experience while they're in your institution, and I thought that was a really pretty slick demo. >> That was a great example, really good demo. Some of the really exciting things that have come out of us adopting this technology thus far includes some students coming to us with ideas of setting up our very own television channel that we can broadcast on campus using this technology and a way of streaming it to students' phones and tablets so that they've got content about the university and it's activities on a regular basis. >> The ROI calculation for you to execute that when it's cloud-based is very, very different, right? >> Absolutely, yes >> It's pretty simple. (all laughing) Just buy a new rack of servers and the whole to-do. I'll give you the last word, what are you hoping to get out of these couple days here, what have you seen so far, any hallway conversations that are really getting your attention? >> Hopefully, not just a deeper relationship with AWS, but the traction to help us work towards innovating on creativity and technology into the future. >> Great. >> Brilliant. >> Andy goes I'm going to go with the Chancellor, smart man. (all laughing) >> Absolutely. >> Linda and Andy, thanks again for taking a few minutes-- >> Thank you very much. >> Absolute pleasure. and hope you enjoy the rest of your time here. >> Thank you. >> (mumbles) thank you. >> She's Linda, he's Andy, I'm Jeff, you're watching theCUBE, we're at AWS Imagine Education in downtown Seattle. Thanks for watching. (electronic tones)

>> From the Amazon Meeting Center in Downtown Seattle, it's the theCUBE. Covering, Imagine: A Better World, a global education conference, sponsored by Amazon Web Services. >> Hey welcome back everybody. Jeff Rick here with theCUBE. We're in Downtown Seattle Washington at the AWS Imagine Education Conference. First one they've ever done about 900 registrants. People from over 20 countries are here. Theresa Carlson gave the kickoff and it's a pretty exciting event. We've seen this movie before with Amazon. They get involved in a project, and it grows and grows and grows. So this is all about education. It's about education institutions. It's about students obviously, which are the core of education, and we're really excited to have our next guest. It was a big announcement that happened today. He's Marvin Martinez, the President of East Los Angeles College. Marvin great to see you. >> Thank you, pleasure to be here. >> So you're getting ready to go up on stage. it's a big announcement so tell about what it is. It's called the California Cloud... >> Computing. >> Computing Initiative. >> So this is what we've done. We've been developing for the last year a certificate where students can take a number of classes, which is basically a total of 15 units, and they're able to earn at the end of 15 units, a certificate in cloud computing. And the goal is to get them trained quickly to get them out to work quickly. Eventually we hope that the certificate evolves into a degree program, so then we're hoping that the students come back and they get their associate of our certificate and they're able to get even a better job, which is really the goal of this program is we want to get them started, want to get them excited, get them into an entry-level type of job, then they will know they like it. They're going to come back. They'll get that degree, you know do even better right. >> So let me, I just want to make sure I get this. This is a California Cloud Workforce Project. So it's really about the workforce and giving these kids the skills. So it's funny though Marvin where everybody says technology is taking away jobs. They forget yeah they take away some jobs, but there's new jobs created. >> All the time. >> All the time, there's a ton of openings especially in the engineering field and in the cloud, but so what are some of the cloud skills specifically that that kids are taking to get the certificate? >> Well you know the skills they're taking specifically so they could eventually work with some of the major industries in our area. Obviously from Amazon and other similar industries and similar businesses, and there's many of them. Los Angeles you know quickly is becoming the new Silicon Valley. So a lot of industries are moving. They call us all the time, they call me all the time, and say that you have trained students. We will hire them right now and we'll pay them a good salary. So no doubt it's a motivation for us because that's who we are as community colleges. We are here to serve students. We are here to get them trained, get them up there quickly and respond to the needs of industry, that area. >> So it's a really interesting planning that it's the community colleges that you guys have all come together. I think the number's 19 as part of this. So A, you know that you're doing it as a unified effort. So kids at a broad area can take advantage, and also you're also partnering with individual high schools. Each Community College is partners with an individual high school. So how does that work? How does that kind of come into fruition? >> Well you know, one thing that we want to do is that as we work with high schools, high schools today are also under pressure to ensure that their students are being trained well and that if they just get a high school diploma they can go and work somewhere. But also today high schools are getting smart. They're saying hey how do we work with a local college so that when students graduate, they graduate with a high school diploma and a degree from a college. So and why are they doing that because they know in order to be competitive, a young person needs to have these degrees. Today if you want to be competitive a high school diploma may not be enough. So we notice that motivation there. Secondly we're able to get students on a college campus, get them developed get them, they're mature, get them to take a college-level course and then they're able to go out and obviously and work once they complete this program. So the relationship is a natural one. It's one that high schools are seeking from us, which is great. That has not been the case all the time. Usually we've gone to them, but now they're coming to us and saying we need you help us out. >> The part I like about it too is the kids are smart. And they're like why am I taking philosophy? How am I going to use philosophy in my job, that or why am I taking this or why am I taking that? These are really concrete skills that A, they can go look in the newspaper today or I guess I don't know if they look in newspaper for jobs because couldn't find a newspaper if you threw it at them, but they could go seek the job listings at the Amazon sites and also they are working with this technology, they live in this technology, so it's not something foreign or something new. It's something they experience every day. So it's got to be a pretty easy sell I would imagine. >> It's an easy sell. Young people today are different than the way that we grew up. I grew up at a time where there were no cell phones, there was no bottled water. It was a whole different time. Young people today as you're seeing grow up with these technologies. It's part of the who they are. They more than just embraced it. So they welcome to use it in any way they can. So when we propose programs like these, guess what happens? They enroll en masse and that's because they understand it. They identify with it. Will they be willing to enroll in a Shakespeare class? They might but not as much as a class like this one. So no doubt the population today has changed, so part of my job is to introduce programs on the campus that I know will generate that kind of enrollment and interest. So we know that a program like this will do that and we just need to recognize the fact that the world has changed. Let me just add that we don't do that world's education institutions. As institutions we're some of the most conservative institutions in the history of this country. So for us to change it takes quite a lot. So what's forcing us to change, what was forcing us to change is that enrollment is down and not just in many of our colleges in LA but throughout the country. Enrollment is-- >> In Community colleges generally or colleges in general? Community colleges. >> Community colleges throughout the nation enrollment is down. And enrollment is down for a number of reasons. There's more jobs out there, so students are looking to go out and work, but also enrollment is down because of the curriculum and the courses that we have are just not interesting to them. So I think a program like this will help the campus. A program like this will get more students to come and take advantage of an incredible education that they can get at our campuses. >> I was just curious kind of what were the drivers of enrollment before that have kind of fallen away? Was it a particular type of skill set? Was it just that they don't want it generic anymore? They got to go get a job? I'm just curious if there was something that you had before that was appealing that you have now that's just not appealing anymore? >> Good questions. So the last time our economy was in bad shape when the employment was down. That was back around 2008-2009. Well guess what happened in our campuses? Enrollment was up. So when the economy is in bad shape people come back to school. When the economy is in great shape like it is today where there is a lot of jobs, enrollment is down. So we don't see the economy going down at all in a number of years. >> Anytime soon. >> So we have to develop programs that we think will be of interest to students first. Secondly we have to respond to the needs of the new economy. The new economy is now being dominated by these new technologies. We know about it, young people know about it. So when we develop a program like this and we know that it will generate interest. It will generate enrollment. And in many ways that's what drives the funding for a college. We're funded on the basis of how many people we enroll. So if we don't enroll a lot people, we have less money, so no doubt there's a motivation for us, a motivation for the entire system, to really partner with Amazon. And figure out a way for us to really get students train and to get them, hopefully get them a good job. >> So you segued perfectly. My last question was going to be kind of the role of Amazon in AWS, in terms of being a partner. I mean they obviously you know are thinking about things. Theresa's fantastic. She just talked about being from an education family, but at the same time you know they have their own reasons to do it. They need workers right? They need people to fill these jobs to fulfill Amazon's own growth beyond their ecosystem, their partners and customers etcetera. So what does it mean for you as an educator and part of this consortium of community colleges to have somebody like AWS come in and really help you codevelop and drive these types of new programs? >> Well it means everything. Number one we know that Amazon is a major employer. We know that the jobs that they have available are good-paying jobs. They have a career path and so we know it's a good direction for young people to take. So part of my job as an educator is many ways it's like a parent. You want to take care of your family, you want to take care of the kids and put them in the right path so they have the most success possible. Amazon offers that kind of path. So for us to partner with someone like Amazon is great. Secondly, students know who Amazon is. I don't have to sell them. They know who they are, and they know what Amazon can do and they know that it's a great career path for them. So now that I think it could be a great partnership for us but also it's an opportunity for Amazon to even continue further developing that workforce in Los Angeles in California. >> Alright Marvin, well thank you so much for spending a few minutes and I wish you nothing but the best with this California Cloud Workforce Project. Make sure I get it right? >> It's right. Thank you so much, I appreciate it. >> Thank you, alright he's Marvin, I'm Jeff. You're watching theCUBE. We're in Seattle at the Amazon Imagine Education event. First time ever, keep watching. It's going to grow and grow and grow. Thanks for watching. (electronic music)

>> Hi, this is George Gilbert. We have an extra-special guest today on our CUBEcast, Aman Naimat, Senior Vice President and CTO of Demandbase started with a five-person startup, Spiderbook. Almost like a reverse IPO, Demandbase bought Spiderbook, but it sounds like Spiderbook took over Demandbase. So Aman, welcome. >> Thank you, excited to be here. Always good to see you. >> So, um, Demandbase is a Next Gen CRM program. Let's talk about, just to set some context. >> Yes. >> For those who aren't intimately familiar with traditional CRM, what problems do they solve? And how did they start, and how did they evolve? >> Right, that's a really good question. So, for the audience, CRM really started as a contact manager, right? And it was replicating what a salesperson did in their own private notebook, writing contact phone numbers in an electronic version of it, right? So you had products that were really built for salespeople on an individual basis. But it slowly evolved, particularly with Siebel, into more of a different twist. It evolved into more of a management tool or reporting tool because Tom Siebel was himself a sales manager, ran a sales team at Oracle. And so, it actually turned from an individual-focused product to an organization management reporting product. And I've been building this stuff since I was 19. And so, it's interesting that, you know, the products today, we're going, actually pivoting back into products that help salespeople or help individual marketers and add value and not just focus on management reporting. >> That's an interesting perspective. So it's more now empowering as opposed to, sort of, reporting. >> Right, and I think some of it is cultural influence. You know, over the last decade, we have seen consumer apps actually take a much more, sort of predominant position rather than in the traditional, earlier in the 80s and 90s, the advanced applications were corporate applications, your large computers and companies. But over the last year, as consumer technology has taken off, and actually, I would argue has advanced more than even enterprise technology, so in essence, that's influencing the business. >> So, even ERP was a system of record, which is the state of the enterprise. And this is much more an organizational productivity tool. >> Right. >> So, tell us now, the mental leap, the conceptual leap that Demandbase made in terms of trying to solve a different problem. >> Right, so, you know, Demandbase started on the premise or around marketing automation and marketing application which was around identifying who you are. As we move towards more digital transaction and Web was becoming the predominant way of doing business, as people say that's 70 to 80 percent of all businesses start using online digital research, there was no way to know it, right? The majority of the Internet is this dark, unknown place. You don't know who's on your website, right? >> You're referring to the anonymity. >> Exactly. >> And not knowing who is interacting with you until very late. >> Exactly, and you can't do anything intelligent if you don't know somebody, right? So if you didn't know me, you couldn't really ask. What will you do? You'll ask me stupid questions around the weather. And really, as humans, I can only communicate if you know somebody. So the sort of innovation behind Demandbase was, and it still continues to be to actually bring around and identify who you're talking to, be it online on your website and now even off your website. And that allows you to have a much more sort of personalized conversation. Because ultimately in marketing and perhaps even in sales, it comes down to having a personal conversation. So that's really what, which if you could have a billion people who could talk to every person coming to your website in a personalized manner, that would be fantastic. But that's just not possible. >> So, how do you identify a person before they even get to a vendor's website so that you can start on a personalized level? >> Right, so Demandbase has been building this for a long time, but really, it's a hard problem. And it's harder now than ever before because of security and privacy, lots of hackers out there. People are actually trying to hide, or at least prevent this from leaking out. So, eight, nine years ago, we could buy registries or reverse DNS. But now with ISBs, and we are behind probably Comcast or Level 3. So how do you even know who this IP address is even registered to? So about eight years ago, we started mapping IP addresses, 'cause that's how you browse the Internet, to companies that they work at, right? But it turned out that was no longer effective. So we have built over the last eight years proprietary methods that know how companies relate to the IP addresses that they have. But we have gone to doing partnerships. So when you log into certain websites, we partner with them to identify you if you self-identify at Forbes.com, for example. So when you log in, we do a deal. And we have hundreds of partners and data providers. But now, the state of the art where we are is we are now looking at behavioral signals to identify who you are. >> In other words, not just touch points with partners where they collect an identity. >> Right. >> You have a signature of behavior. >> That's right. >> It's really interesting that humans are very unique. And based on what they're reading online and what they're reading about, you can actually identify a person and certainly identify enough things about them to know that this is an executive at Tesla who's interested in IOT manufacturing. >> Ah, so you don't need to resolve down to the name level. >> No. >> You need to know sort of the profile. >> Persona, exactly. >> The persona. >> The persona, and that's enough for marketing. So if I knew that this is a C-level supply chain executive from Tesla who lives in Palo Alto and has interests in these areas or problems, that's enough for Siemens to then have an intelligent conversation to this person, even if they're anonymous on their website or if they call on the phone or anything else. >> So, okay, tell us the next step. Once you have a persona, is it Demandbase that helps them put together a personalized? >> Profile. >> Profile, and lead it through the conversation? >> Yeah, so earlier, well, not earlier, but very recently, rebuilding this technology was just a very hard problem. To identify now hundreds of millions of people, I think around 700 are businesspeople globally which is majority of the business world. But we realize that in AI, making recommendations or giving you data in advanced analytics is just not good enough because you need a way to actually take action and have a personalized conversation because there are 100 thousand people on your website. Making recommendations, it's just overwhelming for humans to get that much data. So the better sort of idea now that we're working on is just take the action. So if somebody from Tesla visits your website, and they are an executive who will buy your product, take them to the right application. If they go back and leave your website, then display them the right message in a personalized ad. So it's all about taking actions. And then obviously, whenever possible, guiding humans towards a personalized conversation that will maximize your relationship. >> So, it sounds like sometimes it's anticipating and recommending a next best action. >> Yeah. >> And sometimes, it's your program taking the next best action. >> That's right, because it's just not possible to scale people to take actions. I mean, we have 30, 40 sales reps in Demandbase. We can't handle the volume. And it's difficult to create that personalized letter, right? So we make recommendations, but we've found that it's just too overwhelming. >> Ah, so in other words, when you're talking about recommendations, you're talking about recommendations for Demandbase for? >> Or our clients, employees, or salespeople, right? >> Okay. >> But whenever possible, we are looking to now build systems that in essence are in autopilot mode, and they take the action. They drive themselves. >> Give us some examples of the actions. >> That's right, so some actions could be if you know that a qualified person came to your website, notify the salesperson and open a chat window saying, "This is an executive. "This is similar to a person who will buy "a product from you. "They're looking for this thing. "Do you want to connect with a salesperson?" And obviously, only the people that will buy from you. Or, the action could be, send them an email automatically based on something they will be interested in, and in essence, have a conversation. Right? So it's all about conversation. An ad or an email or a person are just ways of having a conversation, different channels. >> So, it sounds like there was an intermediate marketing automation generation. >> Right. >> After traditional CRM which was reporting. >> Right, that's true. >> Where it was basically, it didn't work until you registered on the website. >> That's right. >> And then, they could email you. They could call you. The inside sales reps. >> That's right. >> You know, if you took a demo, >> That's right. >> you had to put an idea in there. >> And that's still, you know, so when Demandbase came around, that was the predominant between the CRM we were talking about. >> George: Right. >> There was a gap. There was a generation which started to be marketing. It was all about form fills. >> George: Yeah. >> And it was all about nurturing, but I think that's just spam. And today, their effectiveness is close to nothing. >> Because it's basically email or outbound calls. >> Yeah, it's email spam. Do you know we all have email boxes filled with this stuff? And why doesn't it work? Because, not only because it's becoming ineffective and that's one reason. Because they don't know me, right? And it boils down to if the email was really good and it related to what you're looking for or who you are, then it will be effective. But spam, or generic email is just not effective. So it's to some extent, we lost the intimacy. And with the new generation of what we call account-based marketing, we are trying to build intimacy at scale. >> Okay, so tell us more. Tell us first the philosophy behind account-based marketing and then the mechanics of how you do it. >> Sure, really, account-based marketing is nothing new. So if you walk into a corporation, they have these really sophisticated salespeople who understand their clients, and they focus on one-on-one, and it's very effective. So if you had Google as a client or Tesla as a client, and you are Siemens, you have two people working and keeping that relationship working 'cause you make millions of dollars. But that's not a scalable model. It's certainly not scalable for startups here to work with or to scale your organization, be more effective. So really, the idea behind account-based marketing is to scale that same efficacy, that same personalized conversation but at higher volume, right? And maximize, and the only way to really do that is using artificial intelligence. Because in essence, we are trying to replicate human behavior, human knowledge at scale. Right? And to be able to harvest and know what somebody who knows about pharma would know. >> So give me an example of, let's stay in pharma for a sec. >> Sure. >> And what are the decision points where based on what a customer does or responds to, you determine the next step or Demandbase determines what next step to take? >> Right. >> What are some of those options? Like a decision tree maybe? >> You can think of it, it's quite faddish in our industry now. It's reinforcement learning which is what Google used in the Go system. >> George: Yeah, AlphaGo. >> AlphaGo, right, and we were inspired by that. And in essence, what we are trying to do is predict not only what will keep you going but where you will win. So we give rewards at each point. And the ultimate goal is to convert you to a customer. So it looks at all your possible futures, and then it figures out in what possible futures you will be a customer. And then it works backwards to figure out where it should take you next. >> Wow, okay, so this is very different from >> They play six months ahead. So it's a planning system. >> Okay. >> Cause your sales cycles are six months ahead. >> So help us understand the difference between the traditional statistical machine learning that is a little more mainstream now. >> Sure. >> Then the deep learning, the neural nets, and then reinforcement learning. >> Right. >> Where are the sweet spots? What are the sweet spots for the problems they solve? >> Yeah, I mean, you know, there's a lot of fad and things out there. In my opinion, you can achieve a lot and solve real-world problems with simpler machine learning algorithms. In fact, for the data science team that I run, I always say, "Start with like the most simplest algorithm." Because if the data is there and you have the intuition, you can get to a 60% F-score or quality with the most naive implementation. >> George: 60% meaning? >> Like accuracy of the model. >> Confidence. >> Confidence. Sure, how good the model is, how precise it is. >> Okay. >> And sure, then you can make it better by using more advanced algorithms. The reinforcement learning, the interesting thing is that its ability to plan ahead. Most machine learning can only make a decision. They are classifiers of sorts, right? They say, is this good or bad? Or, is this blue? Or, is this a cat or not? They're mostly Boolean in nature or you can simulate that in multi-class classifiers. But reinforcement learning allows you to sort of plan ahead. And in CRM or as humans, we're always planning ahead. You know, a really good salesperson knows that for this stage opportunity or this person in pharma, I need to invite them to the dinner 'cause their friends are coming and they know that last year when they did that, then in the future, that person converted. Right, if they go to the next stage and they, so it plans ahead the possible futures and figures out what to do next. >> So, for those who are familiar with the term AB testing. >> Sure. >> And who are familiar with the notion that most machine learning models have to be trained on data where the answer exists, and they test it out, train it on one set of data >> Sure. >> Where they know the answers, then they hold some back and test it and see if it works. So, how does reinforcement learning change that? >> I mean, it's still testing on supervised models to know. It can be used to derive. You still need data to understand what the reward function would be. Right? And you still need to have historical data to understand what you should give it. And sure, have humans influence it as well, right? At some point, we always need data. Right? If you don't have the data, you're nowhere. And if you don't have, but it also turns out that most of the times, there is a way to either derive the data from some unsupervised method or have a proxy for the data that you really need. >> So pick a key feature in Demandbase and then where you can derive the data you need to make a decision, just as an example. >> Yeah, that's a really good question. We derive datas all the time, right? So, let me use something quite, quite interesting that I wish more companies and people used is the Internet data, right? The Internet today is the largest source of human knowledge, and it actually know more than you could imagine. And even simple queries, so we use the Bing API a lot. And to know, so one of the simple problems we ran into many years ago, and that's when we realized how we should be using Internet data which in academia has been used but not as used as it should be. So you know, you can buy APIs from Bing. And I wish Google would give their API, but they don't. So, that's our next best choice. We wanted to understand who people are. So there's their common names, right? So, George Gilbert is a common name or Alan Fletcher who's my co-founder. And, you know, is that a common name? And if you search that, just that name, you get that name in various contexts. Or co-occurring with other words, you can see that there are many Alan Fletchers, right? Or if you get, versus if you type in my name, Aman Naimat, you will always find the same kind of context. So you will know it's one person or it's a unique name. >> So, it sounds to me that reinforcement learning is online learning where you're using context. It's not perfectly labeled data. >> Right. I think there is no perfectly labeled data. So there's a misunderstanding of data scientists coming out of perfectly labeled data courses from Stanford, or whatever machine learning program. And we realized very quickly that the world doesn't have any perfect labeled data. We think we are going to crowdsource that data. And it turns out, we've tried it multiple times, and after a year, we realized that it's just a waste of time. You can't get, you know, 20 cents or 25 cents per item worker somewhere in wherever to hat and label data of any quality to you. So, it's much more effective to, and we were a startup, so we didn't have money like Google to pay. And even if you had the money, it generally never works out. We find it more effective to bootstrap or reuse unsupervised models to actually create data. >> Help us. Elaborate on that, the unsupervised and the bootstrapping where maybe it's sort of like a lawnmower where you give it that first. >> That's right. >> You know, tug. >> I mean, we've used it extensively. So let me give you an example. Let's say you wanted to create a list of cities, right? Or a list of the classic example actually was a paper written by Sergey Brin. I think he was trying to figure out the names of all authors in the world, and this is 1988. And basically if you search on Google, the term "has written the book," just the term "has written the book," these are called patterns, or hearse patterns, I think. Then you can imagine that it's also always preceded by a name of a person who's an author. So, "George Gilbert has written the book," and then the name of the book, right? Or "William Shakespeare has written the book X." And you seed it with William Shakespeare, and you get some books. Or you put Shakespeare and you get some authors, right? And then, you use it to learn other patterns that also co-occurred between William Shakespeare and the book. >> George: Ah. >> And then you learn more patterns and you use it to extract more authors. >> And in the case of Demandbase, that's how you go from learning, starting bootstrapping within, say, pharma terminology. >> Yes. >> And learning the rest of pharma terminology. >> And then, using generic terminology to enter an industry, and then learning terminology that we ourselves don't understand yet it means. For example, I always used this example where if we read a sentence like "Takeda has in-licensed "a molecule from Roche," it may mean nothing to us, but it means that they're partnered and bought a product, in pharma lingo. So we use it to learn new language. And it's a common technique. We use it extensively, both. So it goes down to, while we do use highly sophisticated algorithms for some problems, I think most problems can be solved with simple models and thinking through how to apply domain expertise and data intuition and having the data to do it. >> Okay, let's pause on that point and come back to it. >> Sure. >> Because that sounds like a rich vein to explore. So this is George Gilbert on the ground at Demandbase. We'll be right back in a few minutes.

>> Live from Boston, Massachusetts, this is the CUBE, covering Spark Summit East 2017 brought to you by databricks. Now, here are your hosts Dave Valante and George Gilbert. >> Welcome back to Boston everyone. It's snowing like crazy outside, it's a cold mid-winter day here in Boston but we're here with the CUBE, the world-wide leader in tech coverage. We are live covering Spark Summit. This is wall to wall coverage, this is our second day here. John Landry with us, he's the distinguished technologist for HP's personal systems data science group within Hewlett Packard. John, welcome. >> Thank you very much for having me here. >> So I was saying, I was joking, we do a lot of shows with HPE, it's nice to have HP back on the CUBE, it's been awhile. But I want to start there. The company split up just over a year ago and it's seemingly been successful for both sides but you were describing to us that you've gone through an IT transformation of sorts within HP. Can you describe that? >> In the past, we were basically a data warehousing type of approach with reporting and what have you coming out of data warehouses, using Vertica, but recently, we made an investment into more of a programming platform for analytics and so where transformation to the cloud is about that where we're basically instead of investing into our own data centers because really, with the split, our data centers went with Hewlett Packard Enterprise, is that we're building our software platform in the cloud and that software platform includes analytics and in this case, we're building big data on top of Spark and so that transformation is huge for us, but it's also enabled us to move a lot faster, the velocity of our business and to be able to match up to that better. Like I said, it's mainly around the software development really more than anything else. >> Describe your role in a little bit more detail inside of HP. >> My role is I'm the leader in our big data investments and so I've been leading teams internally and also collaborating across HP with our print group and what we've done is we've managed to put together a strategy around our cloud-based solution to that. One of the things that was important was we had a common platform because when you put a program platform in place, if it's not common, then we can't collaborate. Our investment could be fractured, we could have a lot of side little efforts going on and what have you so my role is to pry the leadership in the direction for that and also one of the reasons I'm here today is to get involved in the Spark community because our investment is in Spark so that's another part of my role is to get involved with the industry and to be able to connect with the experts in the industry so we can leverage off of that because we don't have that expertise internally. >> What are the strategic and tactical objectives of your analytics initiatives? Is it to get better predictive maintenance on your devices? Is it to create new services for customers? Can you describe that? >> It's two-fold, internal and external so internally, we got millions of dollars of opportunity to better our products with cost, also to optimize our business models and the way we can do that is by using the data that comes back from our products, our services, our customers, combining that together and creating models around that that are then automated and can be turned into apps that can be used internally by our organizations. The second part is to take the same approach, same data, but apply that back towards our customers and so with the split, our enterprise services group also went with Hewlett Packard Enterprise and so now, we have a dedicated effort towards creating manage services for the commercial environment. And that's both on the print size and on the personal system side so to basically fuel that, analytics is a big part of the story. So we've had different things that you'll see out there like touch point manager is one of our services we're delivering in personal systems. >> Dave: What is that? >> Touch point manager is aimed at providing management services for SMB and for commercial environments. So for instance, in touch point manager, we can provide predictive type of capabilities for support. A number of different services that companies are looking for when they buy our products. Another thing we're going after too is device as a service. So there's another thing that we've announced recently that basically we're invested into there and so this is obviously if you're delivering devices as a service, you want to do that as optimal as possible. Well, being able to understand the devices, what's happening with them, been able to predictive support on them, been able to optimize the usage of those devices, that's all important. >> Dave: A lot of data. >> The data really helps us out, right? So the data that we can collect back from our devices and to be able to take that and turn that around into applications that are delivering information inside or outside is huge for us, a huge opportunity. >> It's interesting where you talk about internal initiatives and manage services, which sound like they're most external, but on the internal ones, you were talking about taking customer data and internal data and turning those into live models. Can you elaborate on that? >> Sure, I can give you a great example is on our mobile products, they all have batteries. All of our batteries are instrumented as smart batteries and that's an industry standard but HP actually goes a step further on that, it's the information that we put into our batteries. So by monitoring those batteries and the usage in the field is we can tell how optimally they're performing, but also how they're being used and how we can better design batteries going forward. So in addition, we can actually provide information back into our supply chain. For instance, there's a cell supplier for the battery, there's a pack supplier, there's our unit manufacturer for the product, and so a lot of things that we've been able to uncover is that we can go and improve process. And so improving process alone helps to improve the quality of what we deliver and the quality of the experience to our customers. So that's one example of just using the data, turning that around into a model. >> Is there an advantage to having such high volume, such market share in getting not just more data, but sort of more of the bell curve, so you get the edge conditions? >> Absolutely, it's really interesting because when we started out on this, everybody's used to doing reporting which is absolute numbers and how much did you shift and all that kind of stuff. But, we're doing big data, right? So in big data, you just need a good sample population. Turn the data scientist into that and they've got their statistical algorithms against that. They give you the confidence factor based upon the data that you have so it's absolutely a good factor for us because we don't have to see all the platforms out there. Then, the other thing is, when you look at populations, we see variances in different customers so we're looking at, like one of our populations that's very valuable to us is our own, so we take the 60 thousand units that we have internally at HP and that's one of our sample populations. What a better way to get information on your own products? But, you take that and you take it to one of our other customers and their population's going to look slight different. Why? Because they use the products differently. So one of the things is just usage of the products, the environment they're used in, how they use them. Our sample populations are great in that respect. Of course, the other thing is, very important to point out, we only collect data under the rules and regulations that are out there, so we absolutely follow that and we absolutely keep our data secure and we absolutely keep everything and that's important. Sometimes, today they get a little bit spooked sometimes around that, but the case is that our services are provided based on customers signing up for them. >> I'm guessing you don't collect more data than Google. >> No, we're nowhere near Google. >> So, if you're not spooked at Google - >> That's what I tell people. I say if you got a smartphone, you're giving up a lot more data than we're collecting. >> Buy something from Amazon. Spark, where does Spark fit into all of this? >> Spark is great because we needed a programming platform that could scale in our data centers and in our previous approaches, we didn't have a programming platform. We started with a Hadoop, the Hadoop was very complex though. It really gets down to the hardware and you're programming and trying to distribute that load and getting clusters and you pick up Spark and immediately abstraction. The other thing is it allows me to hire people that can actually program on top of it. I don't have to get someone that knows Map Reduce. I can sit there and it's like what do you know? You know R, Scala, you know Python, it doesn't matter. I can run all of that on top of it. So that's huge for us. The other thing is flat out the speed because as you start getting going with this, we get this pull all of a sudden. It's like well I only need the data like once a month, it's like I need it once a week, I need it once a day, I need the output of this by the hour now. So, the scale and the speed of that is huge and then when you put that on the cloud platform, you know, Spark on a cloud platform like Amazon, now I've got access to all the compute instances. I can scale that, I can optimize it because I don't always need all the power. The flexibility of Spark and being able to deliver that is huge for our success. >> So, I've got to ask some columbo questions and George, maybe you can help me sort of frame it. So you mentioned you were using Hadoop. Like a lot of Hadoop practitioners, you found it very complex. Now, Hewlett Packard has resources. Many companies don't but so you mentioned people out doing Python and R and Scale and Map Reduce, are you basically saying okay, we're going to unify portions of our Hadoop complexity with Spark and that's going to simplify our efforts? >> No, what we actually did was we started on the Hadoop side of it. The first thing we did was try to move from a data warehouse to more of a data lake approach or repository and that was internal, right? >> Dave: And that was a cost reduction? >> That was a cost reduction but also, data accessibility. >> Dave: Yeah, okay. >> The other thing we did was ingesting the data. When you're starting to bring data in from millions of devices, we had a problem coming through the firewall type approach and you got to have something in front of that like a Kafka or something in front of it that can handle it. So when we moved to the cloud, we didn't even try to put up our own, we just used Kinesis and that we didn't have to spend any resources to go solve that problem. Well, the next thing was, when we got the data, you need to ingest the data in and our data's coming in, we want to split it out, we needed to clean it and what you, we actually started out running Java and then we ran Java on top of Hadoop, but then we came across Spark and we said that's it. For us to go to the next step of actually really get into Hadoop, we were going to have to get some more skills and to find the skills to actually program in Hadoop was going to be complex. And to train them organically was going to be complex. We got a lot of smart people, but- >> Dave: You got a lot of stuff to do, too. >> That's the thing, we wanted to spend more time getting information out of the data as opposed to the framework of getting it to run and everything. >> Dave: Okay, so there's a lot of questions coming out. You mentioned Kinesis, so you've replaced that? >> Yeah, when we went to the cloud, we used as many Amazon services as we can as opposed to growing something for ourselves so when we get onto Amazon, you know, getting data into an S3 bucket through Kineses was a no-brainer. When we transferred over to the cloud, it took us less than 30 days to point our devices at Kinesis and we had all our data flowing into S3. So that was like wow, let's go do something else. >> So I got to ask you something else. Again, I love when practitioners come on. So, one of the complaints that I hear sometimes from AWS users and I wonder if you see this is the data pipeline is getting more and more complex. I got an API for Kinesis, one for S3, one for DynamoDB, one for Elastic Plus. There must be 15 proprietary APIs that are primitive, and again, it gets complicated and sometimes it's hard to even figure out what's the right cost model to use. Is that increasingly becoming more complex or is it just so much simpler than what you had before and you're in nirvana right now? >> When you mentioned costs, just the cost of moving to the cloud was a major cost reduction for us. >> Reduction? >> So now it's - >> You had that HP corporate tax on you before - >> Yeah, now we're going from data centers and software licenses. >> So that was a big win for you? >> Yeah, huge, and that released us up to go spend dollars on resources to focus on the data science aspect. So when we start looking at it, we continually optimized, don't get me wrong. But, the point is, if we can bring it up real quickly, that's going to save us a lot of money even if you don't have to maintain it. So we want to focus on creating the code inside of Spark that's actually doing the real work as opposed to the infrastructure. So that cost savings was huge. Now, when you look at it over time, we could've over analyzed that and everything else, but what we did was we used a rapid prototyping approach and then from there, we continued to optimize. So what's really good about the cloud is you can predict the cost and with internal data centers and software licenses and everything else, you can't predict the cost because everybody's trying to figure out who's paying for what. But in the case of the cloud, it's all pretty much you get your bill and you understand what you're paying. So anyway - >> And then you can adjust accordingly? >> We continue to optimize so we use the services but if we have for some reason, it's going to deliver us an advantage, we'll go develop it. But right now, our advantage is we got umteen opportunities to create AI type code and applications to basically automate these services, we don't even have enough resources to do it right now. But, the common programming platform's going to help us. >> Can you drill into those umpteen examples? Just some of them because - >> I mentioned the battery one for instance. So take that across the whole system so now you've got your storage devices, you've got your software that's running on there, we've got built into our system security monitoring at the firmware level just basically connecting into that and adding AI around that is huge because now we can see a tax that may be happening upon your fleet and we can create services out of that. Anything that you can automate around that is money in our pocket or money in our customers' pocket so if we can save them money with these new services, they're going to be more willing to come to HP for products. >> It's actually more than just automation because it's the stuff you couldn't do with 1,000 monkeys trying to write Shakespeare. You have data that you could not get before. >> You're right, what we're doing, the automation is helping us uncover things that we would've never seen and you're right, the whole gorilla walking through the room, I could sit there and I could show you tons of examples of where we're missing the boat. Even when we brought up our first data sets, we started looking at them and some of the stuff we looked at, we thought this is just bad data and actually it wasn't, it was bad product. >> People talk about dark data - >> We had no data models, we had no data model to say is it good or bad? And now we have data models and we're continuing to create those data models around, you create the data model and then you can continue to teach it and that's where we create the apps around it. Our primitives are the data models that we're creating from the device data that we have. >> Are there some of these apps where some of the intelligence lives on the device and it can, like in a security attack, it's a big surface area, you want to lock it down right away. >> We do. The good example on the security is we built something into our products called Sure Start. What essentially it is is we have ability to monitor the firmware layer and so there's a local process that's running independent of everything else that's running that's monitoring what's happening at that firmware level. Well, if there's an attack, it's going to immediately prevent the attack or recover from the attack. Well, that's built into the product. >> But it has to have a model of what this anomalous behavior is. >> Well in our case, we're monitoring what the firmware should look like and if we see that the firmware, you know you take check sums from the firmware or the pattern - >> So the firmware does not change? >> Well basically we can take the characteristics of the firmware and monitor it. If we see that changing, then we know something's wrong. Now it can get corrupt through hardware failure maybe because glitches can happen maybe. I mean solar flares can cause problems sometimes. So, the point is we've found that customers had problems sometimes where basically their firmware would get corrupted and they couldn't start their system. So we're like are we getting attacked? Is this a hardware issue? Could it be bad Flash devices? There's always all kinds of things that could cause that. Well now we monitor it and we know what's going on. Now, the other cool thing is we create logs from that so when those events occur, we can collect those logs and we're monitoring those events so now we can have something monitor the logs that are monitoring all the units. So, if you've got millions of units out there, how are you going to do that manually? You can't and that's where the automation comes in. >> So the logs give you the ability up in the cloud or at HP to look at the ecosystem of devices, but there is intelligence down on the - >> There's intelligence to protect the device in an auto recover which is really cool. So in the past, you had to get your repair. Imagine if someone attacked your fleet of notebooks. Say you got 10 thousand of them and basically it brought every single one of them down one day. What would you do? >> Dave: Freak. >> And everything you got to replace. It was just an attack and it could happen so we basically protect against that with our products and at the same time, we can see that may be a current and then from the footprints of it, we can then do analysis on it and determine was that malicious, is this happening because of a hardware issue, is this happening because maybe we tried to update the firmware and something happened there? What caused that to happen? And so that's where collecting the data from the population then helps us do that and then mix that with other things like service events. Are we seeing service events being driven by this? Thermal, we can look at the thermal data. Maybe there's some kind of heat issue that's causing this to happen. So we starting mixing that. >> Did Samsung come calling to buy this? >> Well, actually what's funny is Samsung is actually a supplier of ours, is a battery supplier of ours. So, by monitoring the batteries, what's interesting is we're helping them out because we go back to them. One of the things I'm working on, is we want to create apps that can go back to them and they can see the performance of their product that they're delivering to us. So instead of us having to call a meeting and saying hey guys let's talk about this, we've got some problems here. Imagine how much time that takes. But if they can self-monitor, then they're going to want to keep supplying to us, then they're going to better their product. >> That's huge. What a productivity boost because you're like hey, we got a problem, let's meet and talk about it and then you take an action to go and figure out what it is. Now if you need a meeting, it's like let's look at the data. >> Yeah, you don't have enough people. >> But there's also potentially a shift in pricing power. I would imagine it shifts a little more in your favor if you have all the data that indicates the quality of their product. >> That's an interesting thing. I don't know that we've reached that point. I think that in the future, it would be something that could be included in the contracts. The fact that the world is the way it is today and data is a big part of that to where going forward, absolutely, the fact that you have that data helps you to better have a relationship with your suppliers. >> And your customers, I mean it used to be that the brand used to have all the information. The internet obviously changed all that, but this whole digital transformation and IOT and all those log data, that sort of levels the playing field back to the brand. >> John: It actually changes it. >> You can now add value for the consumer that you couldn't before. >> And that's what HP's trying to do. We're invested to exactly do that is to really improve or increase the value of our brand. We have a strong brand today but - >> What do you guys do with - we got to wrap - but what do you do with databricks? What's the relationship there? >> Databricks, again we decided that we didn't want to be the experts on managing the whole Spark thing. The other part was that we're going to be involved with Spark and help them drive the direction as far as our use cases and what have you. Databricks and Spark go hand in hand. They got the experts there and it's been huge, our relationship, being able to work with these guys. But I recognize the fact that, and going back to software development and everything else, we don't want to spare resources on that. We got too many other things to do and the less that I have to worry about my Spark code running and scaling and the cost of it and being able to put code in production, the better and so, having that layer there is saving us a ton of money and resources and a ton of time. Just imagine time to market, it's just huge. >> Alright, John, sorry we got to wrap. Awesome having you on, thanks for sharing your story. >> It's great to talk to you guys. >> Alright, keep it right there everybody. We'll be back with our next guest. This is the CUBE live from Spark Summit East, we'll be right back.