>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from the cube and ETR, this is Breaking Analysis with Dave Vellante. >> Organizations have considerable room to improve their performance without making expensive changes to their talent, their structure, or their fundamental business model. You don't need a slew of consultants to tell you what to do. You already know. What you need is to immediately ratchet up expectations, energy, urgency, and intensity. You have to fight mediocrity every step of the way. Amp it up and the results will follow. This is the fundamental premise of a hard-hitting new book written by Frank Slootman, CEO of Snowflake, and published earlier this year. It's called "Amp It Up, Leading for Hypergrowth "by Raising Expectations, Increasing Urgency, "and Elevating Intensity." Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. At Snowflake Summit last month, I was asked to interview Frank on stage about his new book. I've read it several times. And if you haven't read it, you should. Even if you have read it, in this Breaking Analysis, we'll dig deeper into the book and share some clarifying insights and nuances directly from Slootman himself from my one-on-one conversation with him. My first question to Slootman was why do you write this book? Okay, it's kind of a common throwaway question. And how the heck did you find time to do it? It's fairly well-known that a few years ago, Slootman put up a post on LinkedIn with the title Amp It Up. It generated so much buzz and so many requests for Frank's time that he decided that the best way to efficiently scale and share his thoughts on how to create high-performing companies and organizations was to publish a book. Now, he wrote the book during the pandemic. And I joked that they must not have Netflix in Montana where he resides. In a pretty funny moment, he said that writing the book was easier than promoting it. Take a listen. >> Denise, our CMO, you know, she just made sure that this process wasn't going to. It was more work for me to promote this book with all these damn podcasts and other crap, than actually writing the book, you know. And after a while, I was like I'm not doing another podcast. >> Now, the book gives a lot of interesting background information on Slootman's career and what he learned at various companies that he led and participated in. Now, I'm not going to go into most of that today, which is why you should read the book yourself. But Slootman, he's become somewhat of a business hero to many people, myself included. Leaders like Frank, Scott McNealy, Jayshree Ullal, and my old boss, Pat McGovern at IDG, have inspired me over the years. And each has applied his or her own approach to building cultures and companies. Now, when Slootman first took over the reins at Snowflake, I published a Breaking Analysis talking about Snowflake and what we could expect from the company now that Slootman and CFO Mike Scarpelli were back together. In that post, buried toward the end, I referenced the playbook that Frank used at Data Domain and ServiceNow, two companies that I followed quite closely as an analyst, and how it would be applied at Snowflake, that playbook if you will. Frank reached out to me afterwards and said something to the effect of, "I don't use playbooks. "I am a situational leader. "Playbooks, you know, they work in football games. "But in the military, they teach you "situational leadership." Pretty interesting learning moment for me. So I asked Frank on the stage about this. Here's what he said. >> The older you get, the more experience that you have, the more you become a prisoner of your own background because you sort of think in terms of what you know as opposed to, you know, getting outside of what you know and trying to sort of look at things like a five-year-old that has never seen this before. And then how would you, you know, deal with it? And I really try to force myself into I've never seen this before and how do I think about it? Because at least they're very different, you know, interpretations. And be open-minded, just really avoid that rinse and repeat mentality. And you know, I've brought people in from who have worked with me before. Some of them come with me from company to company. And they were falling prey to, you know, rinse and repeat. I would just literally go like that's not what we want. >> So think about that for a moment. I mean, imagine coming in to lead a new company and forcing yourself and your people to forget what they know that works and has worked in the past, put that aside and assess the current situation with an open mind, essentially start over. Now, that doesn't mean you don't apply what has worked in the past. Slootman talked to me about bringing back Scarpelli and the synergistic relationship that they have and how they build cultures and the no BS and hard truth mentality they bring to companies. But he bristles when people ask him, "What type of CEO are you?" He says, "Do we have to put a label on it? "It really depends on the situation." Now, one of the other really hard-hitting parts of the book was the way Frank deals with who to keep and who to let go. He uses the Volkswagen tagline of drivers wanted. He says in his book, in companies there are passengers and there are drivers, and we want drivers. He said, "You have to figure out really quickly "who the drivers are and basically throw the wrong people "off the bus, keep the right people, bring in new people "that fit the culture and put them "in the right seats on the bus." Now, these are not easy decisions to make. But as it pertains to getting rid of people, I'm reminded of the movie "Moneyball." Art Howe, the manager of the Oakland As, he refused to play Scott Hatteberg at first base. So the GM, Billy Bean played by Brad Pitt says to Peter Brand who was played by Jonah Hill, "You have to fire Carlos Pena." Don't learn how to fire people. Billy Bean says, "Just keep it quick. "Tell him he's been traded and that's it." So I asked Frank, "Okay, I get it. "Like the movie, when you have the wrong person "on the bus, you just have to make the decision, "be straightforward, and do it." But I asked him, "What if you're on the fence? "What if you're not completely sure if this person "is a driver or a passenger, if he or she "should be on the bus or not on the bus? "How do you handle that?" Listen to what he said. >> I have a very simple way to break ties. And when there's doubt, there's no doubt, okay? >> When there's doubt, there's no doubt. Slootman's philosophy is you have to be emphatic and have high conviction. You know, back to the baseball analogy, if you're thinking about taking the pitcher out of the game, take 'em out. Confrontation is the single hardest thing in business according to Slootman but you have to be intellectually honest and do what's best for the organization, period. Okay, so wow, that may sound harsh but that's how Slootman approaches it, very Belichickian if you will. But how can you amp it up on a daily basis? What's the approach that Slootman takes? We got into this conversation with a discussion about MBOs, management by objective. Slootman in his book says he's killed MBOs at every company he's led. And I asked him to explain why. His rationale was that individual MBOs invariably end up in a discussion about relief of the MBO if the person is not hitting his or her targets. And that detracts from the organizational alignment. He said at Snowflake everyone gets paid the same way, from the execs on down. It's a key way he creates focus and energy in an organization, by creating alignment, urgency, and putting more resources into the most important things. This is especially hard, Slootman says, as the organization gets bigger. But if you do approach it this way, everything gets easier. The cadence changes, the tempo accelerates, and it works. Now, and to emphasize that point, he said the following. Play the clip. >> Every meeting that you have, every email, every encounter in the hallway, whatever it is, is an opportunity to amp things up. That's why I use that title. But do you take that opportunity? >> And according to Slootman, if you don't take that opportunity, if you're not in the moment, amping it up, then you're thinking about your golf game or the tennis match that's going on this weekend or being out on your boat. And to the point, this approach is not for everyone. You're either built for it or you're not. But if you can bring people into the organization that can handle this type of dynamic, it creates energy. It becomes fun. Everything moves faster. The conversations are exciting. They're inspiring. And it becomes addictive. Now let's talk about priorities. I said to Frank that for me anyway, his book was an uncomfortable read. And he was somewhat surprised by that. "Really," he said. I said, "Yeah. "I mean, it was an easy read but uncomfortable "because over my career, I've managed thousands of people, "not tens of thousands but thousands, "enough to have to take this stuff very seriously." And I found myself throughout the book, oh, you know, on the one hand saying to myself, "Oh, I got that right, good job, Dave." And then other times, I was thinking to myself, "Oh wow, I probably need to rethink that. "I need to amp it up on that front." And the point is to Frank's leadership philosophy, there's no one correct way to approach all situations. You have to figure it out for yourself. But the one thing in the book that I found the hardest was Slootman challenged the reader. If you had to drop everything and focus on one thing, just one thing, for the rest of the year, what would that one thing be? Think about that for a moment. Were you able to come up with that one thing? What would happen to all the other things on your priority list? Are they all necessary? If so, how would you delegate those? Do you have someone in your organization who can take those off your plate? What would happen if you only focused on that one thing? These are hard questions. But Slootman really forces you to think about them and do that mental exercise. Look at Frank's body language in this screenshot. Imagine going into a management meeting with Frank and being prepared to share all the things you're working on that you're so proud of and all the priorities you have for the coming year. Listen to Frank in this clip and tell me it doesn't really make you think. >> I've been in, you know, on other boards and stuff. And I got a PowerPoint back from the CEO and there's like 15 things. They're our priorities for the year. I'm like you got 15, you got none, right? It's like you just can't decide, you know, what's important. So I'll tell you everything because I just can't figure out. And the thing is it's very hard to just say one thing. But it's really the mental exercise that matters. >> Going through that mental exercise is really important according to Slootman. Let's have a conversation about what really matters at this point in time. Why does it need to happen? And does it take priority over other things? Slootman says you have to pull apart the hairball and drive extraordinary clarity. You could be wrong, he says. And he admits he's been wrong on many things before. He, like everyone, is fearful of being wrong. But if you don't have the conversation according to Slootman, you're already defeated. And one of the most important things Slootman emphasizes in the book is execution. He said that's one of the reasons he wrote "Amp It Up." In our discussion, he referenced Pat Gelsinger, his former boss, who bought Data Domain when he was working for Joe Tucci at EMC. Listen to Frank describe the interaction with Gelsinger. >> Well, one of my prior bosses, you know, Pat Gelsinger, when they acquired Data Domain through EMC, Pat was CEO of Intel. And he quoted Andy Grove as saying, 'cause he was Intel for a long time when he was younger man. And he said no strategy is better than its execution, which if I find one of the most brilliant things. >> Now, before you go changing your strategy, says Slootman, you have to eliminate execution as a potential point of failure. All too often, he says, Silicon Valley wants to change strategy without really understanding whether the execution is right. All too often companies don't consider that maybe the product isn't that great. They will frequently, for example, make a change to sales leadership without questioning whether or not there's a product fit. According to Slootman, you have to drive hardcore intellectual honesty. And as uncomfortable as that may be, it's incredibly important and powerful. Okay, one of the other contrarian points in the book was whether or not to have a customer success department. Slootman says this became really fashionable in Silicon Valley with the SaaS craze. Everyone was following and pattern matching the lead of salesforce.com. He says he's eliminated the customer service department at every company he's led which had a customer success department. Listen to Frank Slootman in his own words talk about the customer success department. >> I view the whole company as a customer success function. Okay, I'm customer success, you know. I said it in my presentation yesterday. We're a customer-first organization. I don't need a department. >> Now, he went on to say that sales owns the commercial relationship with the customer. Engineering owns the technical relationship. And oh, by the way, he always puts support inside of the engineering department because engineering has to back up support. And rather than having a separate department for customer success, he focuses on making sure that the existing departments are functioning properly. Slootman also has always been big on net promoter score, NPS. And Snowflake's is very high at 72. And according to Slootman, it's not just the product. It's the people that drive that type of loyalty. Now, Slootman stresses amping up the big things and even the little things too. He told a story about someone who came into his office to ask his opinion about a tee shirt. And he turned it around on her and said, "Well, what do you think?" And she said, "Well, it's okay." So Frank made the point by flipping the situation. Why are you coming to me with something that's just okay? If we're going to do something, let's do it. Let's do it all out. Let's do it right and get excited about it, not just check the box and get something off your desk. Amp it up, all aspects of our business. Listen to Slootman talk about Steve Jobs and the relevance of demanding excellence and shunning mediocrity. >> He was incredibly intolerant of anything that he didn't think of as great. You know, he was immediately done with it and with the person. You know, I'm not that aggressive, you know, in that way. I'm a little bit nicer, you know, about it. But I still, you know, I don't want to give into expediency and mediocrity. I just don't, I'm just going to fight it, you know, every step of the way. >> Now, that story was about a little thing like some swag. But Slootman talked about some big things too. And one of the major ways Snowflake was making big, sweeping changes to amp up its business was reorganizing its go-to-market around industries like financial services, media, and healthcare. Here's some ETR data that shows Snowflake's net score or spending momentum for key industry segments over time. The red dotted line at 40% is an indicator of highly elevated spending momentum. And you can see for the key areas shown, Snowflake is well above that level. And we cut this data where responses were greater, the response numbers were greater than 15. So not huge ends but large enough to have meaning. Most were in the 20s. Now, it's relatively uncommon to see a company that's having the success of Snowflake make this kind of non-trivial change in the middle of steep S-curve growth. Why did they make this move? Well, I think it's because Snowflake realizes that its data cloud is going to increasingly have industry diversity and unique value by industry, that ecosystems and data marketplaces are forming around industries. So the more industry affinity Snowflake can create, the stronger its moat will be. It also aligns with how the largest and most prominent global system integrators, global SIs, go to market. This is important because as companies are transforming, they are radically changing their data architecture, how they think about data, how they approach data as a competitive advantage, and they're looking at data as specifically a monetization opportunity. So having industry expertise and knowledge and aligning with those customer objectives is going to serve Snowflake and its ecosystems well in my view. Slootman even said he joined the board of Instacart not because he needed another board seat but because he wanted to get out of his comfort zone and expose himself to other industries as a way to learn. So look, we're just barely scratching the surface of Slootman's book and I've pulled some highlights from our conversation. There's so much more that I can share just even from our conversation. And I will as the opportunity arises. But for now, I'll just give you the kind of bumper sticker of "Amp It Up." Raise your standards by taking every opportunity, every interaction, to increase your intensity. Get your people aligned and moving in the same direction. If it's the wrong direction, figure it out and course correct quickly. Prioritize and sharpen your focus on things that will really make a difference. If you do these things and increase the urgency in your organization, you'll naturally pick up the pace and accelerate your company. Do these things and you'll be able to transform, better identify adjacent opportunities and go attack them, and create a lasting and meaningful experience for your employees, customers, and partners. Okay, that's it for today. Thanks for watching. And thank you to Alex Myerson who's on production and he manages the podcast for Breaking Analysis. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters. And Rob Hove is our EIC over at Silicon Angle who does some wonderful and tremendous editing. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can email me at david.vellante@siliconangle.com or DM me @dvellante or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in enterprise tech. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. Be well. And we'll see you next time on Breaking Analysis. (upbeat music)

(bright music) >> Hey everyone, welcome back to theCUBE's continuing coverage of Snowflake Summit 22, live from Caesar's Forum in Las Vegas. I'm Lisa Martin here with about 7,000 plus folks, and this next Cube segment, two words, girl power. Please welcome one of our alumni back to the program, Colleen Kapase, SVP, Worldwide Partners and Alliances at Snowflake and Poornima Ramaswamy, EVP of Global Partnerships and Chief of Staff to the CEO. Ladies, welcome to the program! >> Thank you, very happy to be here, amazing event! >> Isn't it? It's so great to see this many people. Yesterday, the keynote, we got in barely, standing room only. I know there was at least one overflow room, maybe two. People are chomping at the bit to hear what Snowflake and its ecosystem has been up to the last three years, since 2019. >> It's been phenomenal! Since the last time we met together, as humans coming together, and then seeing the step function growth three years later, I don't think, we didn't grow gradually. We just jumped three years ahead, and people have just been hungry for the information and the sharing and the joint education, so it's been a phenomenal show. >> It has been, Poornima, talk to us about the Qlik partnership with Snowflake. What's it all about? What's your joint vision, your joint strategy? Give us all that good stuff. >> Sure, so speaking of three years, this relationship has been in existence for the last three years. We were at the last Snowflake Conference in 2019, and I liked what Frank said, even though we were not in-person in life the innovation has continued and our relationship has strengthened over the last three years as well. So it's interesting that everything that Frank and everything that was mentioned at the keynote yesterday is completely in alignment with Qlik's vision and strategy as well. We are focused on making data available for quick decision making, in a timely manner, for in the moment business decisions as such. The world has gone topsy-turvy in the last two years, so you want to know things that are changing as they happen and not one day late, one month late or one quarter late, because then the world's already passed you, that business moment has passed you. That's been our focus. We've got a dual product strategy and portfolio. We collaborate really strongly with Snowflake on both of those to make the most amount of data, made available on the Snowflake platform in the shortest amount of time, so that it's fresh, and it's timely for business decision makers to get access to it, to make decisions as they are dealing with supply chain challenges and people challenges and so on and can make those moments count as such. >> They have to, one of the things that we've learned in the pandemic is access to real-time data is no longer a, oh, that's great, nice to have. It's table stakes for businesses in every industry. Consumer expectations have risen to a level we've probably never seen, and let's face it, they're not going to go down. Nobody's going to want less data, slower. (laughs) Colleen, talk about the Qlik partnership from your Snowflake's perspective. >> Yeah, it's been fabulous, and we started on the BI side and keep evolving it, frankly with more technology, more solutions, making that real-time access, not just the the BI side of having the business intelligence and seeing the data but moving beyond that to the governance side, and that's such a huge piece of the relationship as well, and the trustworthy that executives have with the data, who's seeing it and how are we leveraging it, and we keep expanding that too and having some fun too. I know you guys have been making some acquisitions. >> Talk to us about what's going on at Qlik and some news today as well, acquisitions news, what's the deal? >> Yeah, so like I mentioned, we have a dual product strategy, a Qlik data integration platform and a Qlik analytics platform. And we are strengthening, making sure that we align with Snowflake's vision of all workloads, SaaS only and governed. So the announcement today was we do provide real-time data using our Qlik data integration platform into Snowflake, but that real-time data has to make its way into the hands of the business decision makers as well. So we launched what we call as direct query into Snowflake, so as and when data gets into the Snowflake platform, now customers for specific use cases can choose to access that data as it comes in by accessing it directly on Snowflake. And there are other use cases where the data's already been prepared and so on, and they'll continue using the Qlik analytics platform, but this direct query access will make a world of difference in terms of that active intelligence, in-the-moment decision making. The second announcement that we did was the SaaS first and going all into SaaS, so we are doing our data movement investments in our SaaS platform, and one of our first investments is on the Snowflake platform, going direct into Snowflake, and our data ingestion now, our data replication real-time is going to be available natively into the Snowflake platform through our SaaS data transformation investment that we've made. So those are the two big announcements, and governance has been the cornerstone for our platform end-to-end, right from the beginning, and that strength continues, and that's, again, completely in alignment with the vision that Snowflake has as well. >> I couldn't agree more, that native integration, we used to think about bringing the data to the work, and now it's bring the work to the data, because that's the secure environment, the governed environment, and that's what we're seeing with our product roadmaps together and where we're going, and it gives customers just peace of mind. When you're bringing the work to the data, it's more secure, it's more governed, and that real-time access, it's speed, because boy, so many executives have to make real-time decisions quickly. The world is moving faster than it ever has before, and I've never had an executive say, "Oh yeah, I'll just wait and get the data later." That's not a conversation they have. I need it, and I need it now, and I need it at my fingertips, and I need more of my entire organization to have access to that data, what I feel secure and safe to share with them. And so, having Qlik make that possible is just fantastic. >> The security piece is absolutely critical. We've seen such changes to the threat landscape in the last couple of years. It's no longer now a, if we get hit by a cyber attack, it's a matter of when. And the volume of data just keeps proliferating, proliferating, proliferating, which obviously is not going to slow down either. So having the governance factor, the ability to share data securely, leveraging powerful analytics across to customers and partners and ecosystem, it sounds like to me a pretty big differentiator of what Snowflake is delivering to its customers and the ecosystem. >> It is, and I would say one of the things that has held folks back from moving to the cloud before, was governance. Is this just going to be a free for all, Lisa? I'm not feeling secure with that. And so, having the ability to extend our ecosystem and work on that governance together gives executives peace of mind, that they can easily determine who's going to have access to what, which makes a transition to the cloud faster. And that's what we're looking for, because to have our customers experience the benefits of cloud and the moving up and moving down from a data perspective and really getting access to the data cloud, that's where the nirvana is, and so you guys are helping make that possible and provide that peace of mind, so it's amazing. >> You talk about peace of mind, and it's one of those things we think, oh, it's a marketing term or it's a soft term. It's actually not, it's completely measurable, and it's something that I talk to a lot of C-suite, and the statement of "I sleep better at night," is real. There's gravity with it, knowing that they can trust where the data is. The access is governed. It just keeps getting more and more critical every day. >> Colleen: Well, it's a newsworthy event, frankly- >> Absolutely, nobody wants don't to be a headline. >> If things don't go right, that's people's jobs on the line that's reputations, and that's careers, so that is so important, and I think with a lot of our customers that's our conversations directly of how can you ensure that this is going to be a secure experience? And it's Snowflake and some of our superpowers, and frankly, some of our partners superpowers too, together it's better. >> I can bring this home with a customer example, a couple of customer examples. So Urban Outfitters, I think they're a well-known brand. They've got about 650 stores, to your point on governed autonomy is what I call it. But then it's not just about helping with decision making at the top. You want to be able to make decision making at all levels, so we speak about data democratization. It's about not just strategic decisions that you make for a two-year timeframe or a five-year timeframe. It's about decisions that you want to make today in the first half of the day versus the second half of the day. So Urban Outfitters is a common customer, and during the pandemic they had to change their in-stores into distribution centers. They had to look at their supply chain landscape, because there were supply chain bottlenecks that are still happening today. So, with the power of both Qlik data integration and Qlik analytics, but then the combined power of Qlik and Snowflake, the customer actually was able to make insights available to their in-store managers, to their distribution centers, and from a time perspective, what used to take them days, or, in fact, sometimes even weeks, they're now able to get data in 15 minutes refresh time for their operational decision makers, their distribution centers and their order taking systems, so they're able to make decisions on which brands are moving, not moving. Do they need to change the product position in their stores? Do they need to change their suppliers today? Because, for what's going to be in their inventory one month later, because they are foreseeing, they're able to predict the supply chain bottlenecks that are coming in. They're able to do all of that today because that power of a governed autonomous environment that we've built but real-time data making fresh data available through Snowflake and easy-to-use dashboards and visualization through the analytics platform that we've got. And another customer ABB, 37 different SAP source systems being refreshed every two minutes, worldwide for B2B transactions to be able to make all of those decisions. >> And what you're talking about there, especially with their Urban Outfitters example, I think that's one that everybody as a consumer of clothing and apparel, what you just described, what Qlik and Snowflake enabled there, that could have very well saved that organization. We saw a lot of retailers that were not able to make that pivot. >> Poornima: Yep, no, and it did. >> You are exactly right. I think the differentiation on a lot of our core customers together of combing through, not just surviving but thriving through the pandemic, access to data and supply chain management, and it's these types of solutions that are game changing, and that's why Snowflake's not being sold just to the IT department, it's the business decision makers where they have to make decisions, and one of the things that surprised us the most was we had the star schema COVID data up on our data marketplace and the access to that, that we had our customers to determine supply chain management. What's open? What are the rules per state, per region? Where should we put supply? Where should we not? It was phenomenal. So when you have tools like what Qlik offers together with that data coming through the community, I think that's where a lot of executives experience the power of the data cloud, and that's what we want to see. And we're helping real businesses. We say we want to drive outcomes. Supply chain management was a massive outcome that we helped over the last two years. >> And that was critical, obviously we're still in that from a macro economic perspective. It's still a challenge for a lot of folks, but it was life and death. It was, initially, how do we survive this? And to your point, Colleen, now we've got this foundation, now we can thrive, and we can leave the competition who wasn't able to move this fast in the dust behind us. >> A foreseen function for change, really, and then that change wasn't just different, it was better. >> Yeah, it is better, and it now sets the foundation for the next stage of innovation, which is auto ML and AI ML. You're looking back, you're saying, "Okay this is all the data, "so these are the decisions I had to make in the moment." But then now they can start looking at what are the midterm and the long term strategic decisions I have to make, because I can now predict what are the interconnectedness or the second secondary level and the tertiary level impact for worldwide events. There's a pandemic. We are passed the pandemic. There's flood somewhere. There's fire somewhere. China shuts down every so often. You need new suppliers. How do you get out of your way in terms of making daily decisions, but start planning ahead? I think auto ML, AI ML, and data's going to be the foundation for that and real-time data at that. So what Snowflake's doing in terms of the investment in that space, and Qlik has acquired companies in the auto ML space and driving more automation, that time-to-business value and time-to-predictive insights is going to become very key. >> Absolutely key and also really a lifeline for organizations to be able to do that. >> And I have to say, it's a source of pride for us to see our partners growing and thriving in this environment too. Like some of these acquisitions they're making, Lisa, in the machine learning space, it's awesome. This is where customers want to go. They've got all this fabulous data. They now know how to access it real time. How do I use queries to make me smarter? How do I use this machine learning to look at a vast amount of data in a very real time fashion and make business decisions from? That's the future, that's where we're going. So to see you guys expand from BI, to governance, to machine learning, we're really, Lisa, watching companies in our ecosystem grow as we grow, and that's the piece I take a lot of personal pride in, and it's the fun part of the job, frankly. >> Yeah, as you should take part in that, and that's something too, that's been thematic the last... We were recovering this show yesterday and today that the growth and the substance of the Snowflake ecosystem. You see it, you feel it, and you hear it. >> Yeah, well in Frank Slootman's book, "Amp It Up," there's actually a section that he talks about, because I think he has some amazing lifelong advice on his journey of growth, and he tells us that, "Hey you can attach your company, "your personal career energy to an elevator going up "and a company and a high growth story "or a flat or declining." And it's harder in a flat and declining space, and Snowflake we certainly see as an elevator skyrocketing up and these organizations surrounding us with their technologies and capabilities to have joint outcomes, they're doing fantastic too. I've heard this story over and over again this week. I love seeing this story too with Qlik, and it's just amazing. >> I bet, Ladies, thank you so much for joining me, talking about the Snowflake-Qlik partnership, the better together power, and also, you're just scratching the surface. The future, the momentum, you can feel it. >> Yeah, I love it. >> We appreciate your insights and your time and good luck! >> Thank you, thank you. >> And let's let the girl bosses go! (laughs) >> Exactly! (laughs) For my girl boss guests, I'm Lisa Martin. You're watching theCUBE's coverage of Snowflake Summit 22, live from Caesar's Forum in Las Vegas. I'll be right back with my next guest. (bright music)

>> Announcer: Live from San Francisco, it's The Cube covering DevNet Create 2017 brought to you by Cisco. >> Welcome back here, we're live here in San Francisco for SiliconANGLE's the Cube's exclusive 2 days of coverage for Cisco's inaugural event DevNet Create, building on their 3 year old successful DevNet program which is Cisco core developer program now foraying out into the world of cloud native developers, open source, great move for Cisco. Our next guest, Wissam Ali-Ahmad, lead solutions architect with Splunk. Good to see you. >> Good to see you too, John. >> Here with Peter Burris of course, my co-host. >> Wissam: Hi, Peter. >> So Splunk being here is an important thing because you guys have been riding the wave for cloud, certainly your relationship with Amazon web service is well known, very successful. Splunk as a company went public, well known. You guys really, really hit a niche around big data and how cloud has helped you guys accelerate your business. So you've been transformed, but continuing to grow, so you're riding that wave, but now Cisco's on the wave, and Cisco's been involved in the wave. But from a relationship standpoint, oh yeah, we're the networking guys, we're going to come in and help Docker with this, we're going to come in and help Splunk with this, so they've been kind of a helper, not the main player. This is a new way to get back in and be really enabled for the cloud world. What's your reaction to this move by Cisco? >> I mean, we have a great partnership with Cisco for many years. And I think, you know, Splunk plays a good, as you said, we're a good player there. We integrate well. I mean, all the initiatives Cisco's involved with, we have integrations with Cisco on many levels with different technology. And also Splunk, the deal is with Splunk is that you need to bring invisibly to everything, and Splunk is that platform where you have access to all that data throughout all, all is like all that machine data so you have access to all that data, not only application data, not only network data. You need to look at everything these days. Especially when there's attacks. You know we heard recently, of course everybody heard about WannaCry, and to the tech, that attack, you need to look at everything, because you could someone bring in a laptop behind the firewall even, and they can be affected already, and if you don't have access to see what they're doing, not just from a network perspective, like what apps in the cloud they're accessing, you know, what other files on the locally, so, because you have access to all that data in Splunk, you should be able to get better visibility. >> And you guys have a unique position in the sense that you're close, again, to the machine. You know, logs and data We had Amanda on from Cisco, who was, in her tribe as a developer, she's not necessarily a network engineer, but she's brought on that mojo in from the developer community. When she was first day on the job, you know, they were doing some Python, some rest API stuff, you know, basic 101 stuff, but she didn't want to do an app that was showing hey, how many Twitter followers do I have? She had to go in and look at the devices. So now the opportunity with IOT is that for Cisco to make and expose the network for programmability >> Wissam: Right. >> And extend it. How are they going to do that? I mean you're closer to those guys in your relationship, but that's what everyone wants. They want the infrastructure to just go, that's DevOps >> Right. Yeah, they want the edge to come to them. They want data to be more accessible to all the users. And then so Cisco's on that path, definitely on that path, to get more infrastructure visibility in the data center and the networks, so they're definitely on that path of doing that. >> And let me build on this, so if we think about the various components associated with some of the things that Splunk does. A leader in the application of machine and AI and big data related technologies, to solving business problems. The algorithms for doing this have been around for a long time. The hardware couldn't do it, so you had to write really tight software to do it, and you were one of the first companies out there to really do that. And then it was, we'll point all that at sources of data, that you can apply these technologies, to create better business value. And there were two places where people did it. Web logs, for online marketing, and IT, since IT technology throws off an enormous amount of data. So as I think about it, the relationship with Cisco is especially interesting, because Cisco is going to be one of those companies that encourages people to create new sources of data and a lot of it, IOT and other places, and bring it back to companies and technologies that have a proven track record for generating value out of that data. So talk a bit about how Splunk intends to, going back to what John said, riding that wave. The algorithms are here, the hardware can do it, now we've got to get access to more of the data, and here comes Cisco being really serious about moving a lot of data around. What do you think? >> I mean, we like when people bring in a lot of data into Splunk. We also have been focusing a lot on the personas. On the, we call the Sherlock, the data Sherlock. Right, so that unique persona is where they need to look at, how do I make sense of my data? Not only just about bringing data, but how do I make sense of that data. What are solutions? What are use case I need to have better impact on the business? So we're actually helping solve real kind of business use cases. This morning, Yelp had a webinar about how they use Splunk driving all the web infrastructure for Yelp, the Yelp back end for all their-- >> Peter: This is still in the IT? >> Yeah. >> Peter: It's not Yelps marketing group, this is still in the IT? >> But they are correlating that with other business use cases, yes. >> Of course, it will start coming together. So where do you see some of these use cases popping up, now that Cisco is helping to create those new sources, and get people to, you know, acculturating people to the idea that these are sources of value, business value. Where do you see some of the new use cases? >> There's a lot of use cases now coming up around business analytics, around IOT as you mentioned. And an added element of machine learning across different data sources. So if I want to look at not just performance of one service, let's say my elevator, I want to see how that's going to affect other areas of my business, too. So you're able to see not only the power of correlating that data, but also be able to apply machine learning on that data. So there's a lot of use cases around business analytics. Security's always there, because security, as you know, attack vectors are getting complex every few months or so, so you need to also chase that, and you need to look at all the data, the behaviors in that data, to get better predictability, to get better prevention detection. >> So Splunk is emerging as a great software company for a lot of IT pros, but it still is more in the op side. How is this conference and the likelihood or the notion that developers are increasingly going to be part of that use case, it's utilizing data and data-related services to better understand operations, but find new ways of creating value out of the capabilities provided by that. What's the developer angle here for Splunk? >> Great question. We actually are focusing a lot on developer tools. So Splunk, being a platform. I always say Splunk is a full-feature platform for machine data and big data. So it's open in the sense that developers can develop their own content on Splunk. They can extend what we have. So an example of that is, the recent project called Mexico Contaro. So that's a project full that's looking at internet usage and coverage on Mexico, in Mexico City and across all the cities. And this was using Splunk to end Meraki API's, and bring all that data together, and network data to try to give exposure to kind of like government analytics. And that's a neat case because not necessarily only IT, but also helping all the goods out there. >> So Cisco, Meraki and other sources, plus Splunk to be able to get deep visibility into a number of ways, you know, a very complex system like Mexico City, which is about as complex as you get, actually operates. >> Wissam: Yes. That's one, yeah. >> Tell about the Splunk direction now, because everyone's been questioning about the public offering, because you're not putting numbers out there, active community, it's not that you guys aren't being transparent, but you've got to go to the next level of growth. Obviously Cisco's coming at the cloud native world. We see the cloud native compute foundation, really with great support of the Linux foundation. New open source stuff's going on all the time. How is Splunk looking at the future right now? What's next? I mean obviously security, we heard that at Dot Conf last year, but you guys have really a good position with the data. You have good account names. You've got great blue chip customers. What's next? What's the product solution look like for you guys? What's the new architecture? What's the new plan? >> I think more listening, looking at all the scale, and cloud and listen to the customers, making the data onboarding easier, making it more scalable, covering more use cases that we talked about. Innovate a lot of areas around machine learning, all that to cover more of the use cases, so we're definitely moving forward to go the next step beyond just-- >> So let's take another example. So DevOps, right, everyone loves the DevOps. It's not like a solution, you can't buy DevOps, you just got to do it, right? So that's pretty clear. You can't just write an Agile manifesto and say, "We're DevOps." You got to have a vision, maybe write a manifesto just to get the people motivated, but put the right people in place, let the things organically develop. So the question is, what is an ideal architecture, and what is a best practice, from your standpoint, where you've seen examples of people who've transformed into this DevOps world, where they really got the ball rolling, got some change happening, and then scaled it. Can you give us a kind of a pattern that you've seen the customers? >> I have not seen personally a lot of that, but definitely there's transformation happening. It's not easy to move into that DevOps switch. You cannot do it overnight. So you need as much as possible tools that would actually give exposure, how am I doing, right? Am I pushing my code at the speed it's expected to be? Do I have bugs addressed early on? So that kind of exposure you need a system that will give you basically to analyze all that data too, and then at Splunk we have a story on DevOps. DevOps and application exposure monitoring and that. And the unique thing about Splunk is that you don't only look at what's inside the application, which was AMP's that do application management, but you should look at everything, so we look outside the black box. Not inside the app, but look at outside too, so we're going to give you exposure of your whole DevOp process You know, from the beginning, the whole condis integration, so I see Splunk helping organizations moving into that kind of new process. >> But there's an interesting relationship between tools and process, or tools and skills, so John, you'll probably laugh at this. Many years ago I found myself sitting in a room with the CEO of a very, very large pharmaceutical, me and a group of other other consultants, and he said, the discussion was, are we going to buy SAP or not? And after two hours of people arguing about it, he finally said, "Screw it, we're doing it, "I'm sick and tired of these process arguments. "We're just going to do what SAP says in the process." There's a relationship between the practices suggested by Splunk and the types of things that a business actually does in a DevOps sense. What is this, how is Splunk changing the notion of DevOps, and how is now as Splunk extends itself, how is DevOps and new practices and new ways of thinking, altering the way that Splunk delivers capability? >> I mean, we always listen to our customers. And then we've actually been looking at addressing use cases, like on DevOps, from a persona aspect. Like as a DevOp engineer, I won't be able to address this kind of issues, and we listen to that, and we try to address those, not only just by a tool, but also by looking at best practices around that. And sometimes we manifest those through apps. So Splunk can actually, you can publish an app as a developer if you're not happy as a customer, you can modify, take one of our existing free apps, and then modify them cue on process, so we're not kind of specific rigid to certain way, and I know DevOps, and Agile Ward, is not even like a religion, you know, you're not supposed to follow, you're supposed to be flexible in certain areas, and even implementing DevOps comes in Agile way too. >> But it's still pedagogical, and John in many respects, there's your manifesto for DevOps, right? Is your choice of tools and how they come together, and degree to which they're integrated kind of take priority. >> Well, you got eight minutes until you have to go up on stage and do your talk. Here we're live in San Francisco. What are you going to be speaking about when you hit the stage in eight minutes? You have seven minutes to explain (laughs). >> (Laughs) Deliver pitch. So I'll be focusing a lot on the integrations that we have with various Cisco products, so we have, with Splunk you're able to bring in a lot of the API, data through API integrations, so I'm going to show how easy that process is to bring that data if you have an API like Meraki or ACI or Ice. And I'll also be focusing more on how the data you can do it from the cloud, easy, without having an agent involved, without having any software you need to install to collect the data, and we'll be talking more about the Mexico Contaro case, and then do some fun live demos also. >> But Cisco's got good API's, people might not know that, but they are API'd up pretty well on the equipment and the gear and the platform. >> Yes, of course. >> Just commentary on that, your reaction to share for people who are not fluent in Cisco, in terms of their enablement of getting data out? >> Yes, Cisco has a lot of good API's, capabilities around sharing that data, the openness of it has been great, and made easy for us, even for our customers to bring that data, the API, that data into Splunk, so it's a matter of a few minutes now to point to that API and bring that data into Splunk, and yeah, that's good. >> Wissam Ali-Ahmad, going on stage in seven minutes, you got it all done, congratulations. Thanks for coming on The Cube. I know you've got your big speech here to the packed house. Inaugural event here, Cisco's DevNet Create. Thanks for coming on The Cube. >> Thank you, John. >> More live coverage here in San Fransciso. This is The Cube, I'm John Furrier, with my co-host Peter Burris. Stay with us as we get down to wrapping up day two. Stay with us for more coverage after this short break. >> Hi, I'm April Mitchell, and I'm the senior directory of strategy and plan

>> [Announcer] Live from Boston Massachusetts. This is theCUBE. Covering Sparks Summit East 2017. Brought to you by Databricks. Now here are your hosts, Dave Vellante and George Gilbert. >> [Dave] Welcome back to Boston everybody, this is Spark Summit East #SparkSummit And this is theCUBE. Ion Stoica is here. He's Executive Chairman of Databricks and Professor of Computer Science at UCal Berkeley. The smarts is rubbing off on me. I always feel smart when I co-host with George. And now having you on is just a pleasure, so thanks very much for taking the time. >> [Ion] Thank you for having me. >> So loved the talk this morning, we learned about RISELabs, we're going to talk about that. Which is the son of AMP. You may be the father of those two, so. Again welcome. Give us the update, great keynote this morning. How's the vibe, how are you feeling? >> [Ion] I think it's great, you know, thank you and thank everyone for attending the summit. It's a lot of energy, a lot of interesting discussions, and a lot of ideas around. So I'm very happy about how things are going. >> [Dave] So let's start with RISELabs. Maybe take us back, to those who don't understand, so the birth of AMP and what you were trying to achieve there and what's next. >> Yeah, so the AMP was a six-year Project at Berkeley, and it involved around eight faculties and over the duration of the lab around 60 students and postdocs, And the mission of the AMPLab was to make sense of big data. AMPLab started in 2009, at the end of 2009, and the premise is that in order to make sense of this big data, we need a holistic approach, which involves algorithms, in particular machine-learning algorithms, machines, means systems, large-scale systems, and people, crowd sourcing. And more precisely the goal was to build a stack, a data analytic stack for interactive analytics, to be used across industry and academia. And, of course, being at Berkeley, it has to be open source. (laugh) So that's basically what was AMPLab and it was a birthplace for Apache Spark that's why you are all here today. And a few other open-source systems like Mesos, Apache Mesos, and Alluxio which was previously called Tachyon. And so AMPLab ended in December last year and in January, this January, we started a new lab which is called RISE. RISE stands for Real-time Intelligent Secure Execution. And the premise of the new lab is that actually the real value in the data is the decision you can make on the data. And you can see this more and more at almost every organization. They want to use their data to make some decision to improve their business processes, applications, services, or come up with new applications and services. But then if you think about that, what does it mean that the emphasis is on the decision? Then it means that you want the decision to be fast, because fast decisions are better than slower decisions. You want decisions to be on fresh data, on live data, because decisions on the data I have right now are original but those are decisions on the data from yesterday, or last week. And then you also want to make targeted, personalized decisions. Because the decisions on personal information are better than aggregate information. So that's the fundamental premise. So therefore you want to be on platforms, tools and algorithms to enable intelligent real-time decisions on live data with strong security. And the security is a big emphasis of the lab because it means to provide privacy, confidentiality and integrity, and as you hear about data breaches or things like that every day. So for an organization, it is extremely important to provide privacy and confidentiality to their users and it's not only because the users want that, but it also indirectly can help them to improve their service. Because if I guarantee your data is confidential with me, you are probably much more willing to share some of your data with me. And if you share some of the data with me, I can build and provide better services. So that's basically in a nutshell what the lab is and what the focus is. >> [Dave] Okay, so you said three things: fast, live and targeted. So fast means you can affect the outcome. >> Yes. Live data means it's better quality. And then targeted means it's relevant. >> Yes. >> Okay, and then my question on security, I felt like when cloud and Big Data came to fore, security became a do-over. (laughter) Is that a fair assessment? Are you doing it over? >> [George] Or as Bill Clinton would call it, a Mulligan. >> Yeah, if you get a Mulligan on security. >> I think security is, it's always a difficult topic because it means so many things for so many people. >> Hmm-mmm. >> So there are instances and actually cloud is quite secure. It's actually cloud can be more secure than some on-prem deployments. In fact, if you hear about these data leaks or security breaches, you don't hear them happening in the cloud. And there is some reason for that, right? It is because they have trained people, you know, they are paranoid about this, they do a specification maybe much more often and things like that. But still, you know, the state of security is not that great. Right? For instance, if I compromise your operating system, whether it's in cloud or in not in the cloud, I can't do anything. Right? Or your VM, right? On all this cloud you run on a VM. And now you are going to allow on some containers. Right? So it's a lot of attacks, or there are attacks, sophisticated attacks, which means your data is encrypted, but if I can look at the access patterns, how much data you transferred, or how much data you access from memory, then I can infer something about what you are doing about your queries, right? If it's more data, maybe it's a query on New York. If it's less data it's probably maybe something smaller, like maybe something at Berkeley. So you can infer from multiple queries just looking at the access. So it's a difficult problem. But fortunately again, there are some new technologies which are developed and some new algorithms which gives us some hope. One of the most interesting technologies which is happening today is hardware enclaves. So with hardware enclaves you can execute the code within this enclave which is hardware protected. And even if your operating system or VM is compromised, you cannot access your code which runs into this enclave. And Intel has Intell SGX and we are working and collaborating with them actively. ARM has TrustZone and AMB also announced they are going to have a similar technology in their chips. So that's kind of a very interesting and very promising development. I think the other aspect, it's a focus of the lab, is that even if you have the enclaves, it doesn't automatically solve the problem. Because the code itself has a vulnerability. Yes, I can run the code in hardware enclave, but the code can send out >> Right. >> data outside. >> Right, the enclave is a more granular perimeter. Right? >> Yeah. So yeah, so you are looking and the security expert is in your lab looking at this, maybe how to split the application so you run only a small part in the enclave, which is a critical part, and you can make sure that also the code is secure, and the rest of the code you run outside. But the rest of the code, it's only going to work on data which is encrypted. Right? So there is a lot of interesting research but that's good. >> And does Blockchain fit in there as well? >> Yeah, I think Blockchain it's a very interesting technology. And again it's real-time and the area is also very interesting directions. >> Yeah, right. >> Absolutely. >> So you guys, I want George, you've shared with me sort of what you were calling a new workload. So you had batch and you have interactive and now you've got continuous- >> Continuous, yes. >> And I know that's a topic that you want to discuss and I'd love to hear more about that. But George, tee it up. >> Well, okay. So we were talking earlier and the objective of RISE is fast and continuous-type decisions. And this is different from the traditional, you either do it batch or you do it interactive. So maybe tell us about some applications where that is one workload among the other traditional workloads. And then let's unpack that a little more. >> Yeah, so I'll give you a few applications. So it's more than continuously interacting with the environment continuously, but you also learn continuously. I'll give you some examples. So for instance in one example, think about you want to detect a network security attack, and respond and diagnose and defend in the real time. So what this means is that you need to continuously get logs from the network and from the more endpoints you can get the better. Right? Because more data will help you to detect things faster. But then you need to detect the new pattern and you need to learn the new patterns. Because new security attacks, which are the ones that are effective, are slightly different from the past one because you hope that you already have the defense in place for the past ones. So now you are going to learn that and then you are going to react. You may push patches in real time. You may push filters, installing new filters to firewalls. So that's kind of one application that's going in real time. Another application can be about self driving. Now self driving has made tremendous strides. And a lot of algorithms you know, very smart algorithms now they are implemented on the cars. Right? All the system is on the cars. But imagine now that you want to continuously get the information from this car, aggregate and learn and then send back the information you learned to the cars. Like for instance if it's an accident or a roadblock an object which is dropped on the highway, so you can learn from the other cars what they've done in that situation. It may mean in some cases the driver took an evasive action, right? Maybe you can monitor also the cars which are not self-driving, but driven by the humans. And then you learn that in real time and then the other cars which follow through the same, confronted with the same situation, they now know what to do. Right? So this is again, I want to emphasize this. Not only continuous sensing environment, and making the decisions, but a very important components about learning. >> Let me take you back to the security example as I sort of process the auto one. >> Yeah, yeah. >> So in the security example, it doesn't sound like, I mean if you have a vast network, you know, end points, software, infrastructure, you're not going to have one God model looking out at everything. >> Yes. >> So I assume that means there are models distributed everywhere and they don't know what a new, necessarily but an entirely new attack pattern looks like. So in other words, for that isolated model, it doesn't know what it doesn't know. I don't know if that's what Rumsfeld called it. >> Yes (laughs). >> How does it know what to pass back for retraining? >> Yes. Yes. Yes. So there are many aspects and there are many things you can look at. And it's again, it's a research problem, so I cannot give you the solution now, I can hypothesize and I give you some examples. But for instance, you can look about, and you correlate by observing the affect. Some of the affects of the attack are visible. In some cases, denial of service attack. That's pretty clear. Even the And so forth, they maybe cause computers to crash, right? So once you see some of this kind of anomaly, right, anomalies on the end devices, end host and things like that. Maybe reported by humans, right? Then you can try to correlate with what kind of traffic you've got. Right? And from there, from that correlation, probably you can, and hopefully, you can develop some models to identify what kind of traffic. Where it comes from. What is the content, and so forth, which causes behavior, anomalous behavior. >> And where is that correlation happening? >> I think it will happen everywhere, right? Because- >> At the edge and at the center. >> Absolutely. >> And then I assume that it sounds like the models both at the edge and at the center are ensemble models. >> Yes. >> Because you're tracking different behavior. >> Yes. You are going to track different behavior and you are going to, I think that's a good hypothesis. And then you are going to assemble them, assemble to come up with the best decision. >> Okay, so now let's wind forward to the car example. >> Yeah. >> So it sound like there's a mesh network, at least, Peter Levine's sort of talk was there's near-local compute resources and you can use bitcoin to pay for it or Blockchain or however it works. But that sort of topology, we haven't really encountered before in computing, have we? And how imminent is that sort of ... >> I think that some of the stuff you can do today in the cloud. I think if you're on super-low latency probably you need to have more computation towards the edges, but if I'm thinking that I want kind of reactions on tens, hundreds of milliseconds, in theory you can do it today with the cloud infrastructure we have. And if you think about in many cases, if you can't do it within a few hundredths of milliseconds, it's still super useful. Right? To avoid this object which has dropped on the highway. You know, if I have a few hundred milliseconds, many cars will effectively avoid that having that information. >> Let's have that conversation about the edge a little further. The one we were having off camera. So there's a debate in our community about how much data will stay at the edge, how much will go into the cloud, David Flores said 90% of it will stay at the edge. Your comment was, it depends on the value. What do you mean by that? >> I think that that depends who am I and how I perceive the value of the data. And, you know, what can be the value of the data? This is what I was saying. I think that value of the data is fundamentally what kind of decisions, what kind of actions it will enable me to take. Right? So here I'm not just talking about you know, credit card information or things like that, even exactly there is an action somebody's going to take on that. So if I do believe that the data can provide me with ability to take better actions or make better decisions I think that I want to keep it. And it's not, because why I want to keep it, because also it's not only the decision it enables me now, but everyone is going to continuously improve their algorithms. Develop new algorithms. And when you do that, how do you test them? You test on the old data. Right? So I think that for all these reasons, a lot of data, valuable data in this sense, is going to go to the cloud. Now, is there a lot of data that should remain on the edges? And I think that's fair. But it's, again, if a cloud provider, or someone who provides a service in the cloud, believes that the data is valuable. I do believe that eventually it is going to get to the cloud. >> So if it's valuable, it will be persisted and will eventually get to the cloud? And we talked about latency, but latency, the example of evasive action. You can't send the back to the cloud and make the decision, you have to make it real time. But eventually that data, if it's important, will go back to the cloud. The other question of all this data that we are now processing on a continuous basis, how much actually will get persisted, most of it, much of it probably does not get persisted. Right? Is that a fair assumption? >> Yeah, I think so. And probably all the data is not equal. All right? It's like you want to maybe, even if you take a continuous video, all right? On the cars, they continuously have videos from multiple cameras and radar and lidar, all of this stuff. This continuous. And if you think about this one, I would assume that you don't want to send all the data to the cloud. But the data around the interesting events, you may want to do, right? So before and after the car has a near-accident, or took an evasive action, or the human had to intervene. So in all these cases, probably I want to send the data to the cloud. But for the most cases, probably not. >> That's good. We have to leave it there, but I'll give you the last word on things that are exciting you, things you're working on, interesting projects. >> Yeah, so I think this is what really excites me is about how we are going to have this continuous application, you are going to continuously interact with the environment. You are going to continuously learn and improve. And here there are many challenges. And I just want to say a few more there, and which we haven't discussed. One, in general it's about explainability. Right? If these systems augment the human decision process, if these systems are going to make decisions which impact you as a human, you want to know why. Right? Like I gave this example, assuming you have machine-learning algorithms, you're making a diagnosis on your MRI, or x-ray. You want to know why. What is in this x-ray causes that decision? If you go to the doctor, they are going to point and show you. Okay, this is why you have this condition. So I think this is very important. Because as a human you want to understand. And you want to understand not only why the decision happens, but you want also to understand what you have to do, you want to understand what you need to do to do better in the future, right? Like if your mortgage application is turned down, I want to know why is that? Because next time when I apply to the mortgage, I want to have a higher chance to get it through. So I think that's a very important aspect. And the last thing I will say is that this is super important and information is about having algorithms which can say I don't know. Right? It's like, okay I never have seen this situation in the past. So I don't know what to do. This is much better than giving you just the wrong decision. Right? >> Right, or a low probability that you don't know what to do with. (laughs) >> Yeah. >> Excellent. Ion, thanks again for coming in theCUBE. It was really a pleasure having you. >> Thanks for having me. >> You're welcome. All right, keep it right there everybody. George and I will be back to do our wrap right after this short break. This is theCUBE. We're live from Spark Summit East. Right back. (techno music)