>>Hello everybody. I am Toshihiko Nishimura from Stanford. University is there to TTT out here, super aging, global OMIM global transportation group about infections, uh, or major point of concerns. In addition, this year, we have the COVID-19 pandemic. As you can see here, while the why the new COVID-19 patients are still increasing, meanwhile, case count per day in the United state, uh, beginning to decrease this pandemic has changed our daily life to digital transformation. Even today, the micro segmentation is being conducted online and doctor and the nurse care, uh, now increase to telemedicine. Likewise, the drug development process is in need of major change paradigm shift, especially in vaccine in drug development for COVID-19 is, should be safe, effective, and faster >>In the >>Anastasia department, which is the biggest department in school of medicine. We have Stanford, a love for drug device development, regulatory science. So cold. Say the DDT RDS chairman is Ron Paul and this love leaderships are long mysel and stable shaper. In the drug development. We have three major pains, one exceedingly long duration that just 20 years huge budget, very low success rate general overview in the drug development. There are Discoverly but clinical clinical stage, as you see here, Tang. Yes. In clinical stage where we sit, say, what are the programs in D D D R S in each stages or mix program? Single cell programs, big data machine learning, deep learning, AI mathematics, statistics programs, humanized animal, the program SNS program engineering program. And we have annual symposium. Today's the, my talk, I do like to explain limitation of my science significance of humanized. My science out of separate out a program. I focused on humanized program. I believe this program is potent game changer for drug development mouse. When we think of animal experiment, many people think of immediately mouse. We have more than 30 kinds of inbred while the type such as chief 57, black KK yarrow, barber C white and so on using QA QC defined. Why did the type mice 18 of them gave him only one intervention using mouse, genomics analyzed, computational genetics. And then we succeeded to pick up fish one single gene in a week. >>We have another category of gene manipulated, mice transgenic, no clout, no Kamal's group. So far registered 40,000 kind as over today. Pretty critical requirement. Wrong FDA PMDA negative three sites are based on arteries. Two kinds of animal models, showing safety efficacy, combination of two animals and motel our mouse and the swine mouse and non-human primate. And so on mouse. Oh, Barry popular. Why? Because mouse are small enough, easy to handle big database we had and cost effective. However, it calls that low success rate. Why >>It, this issue speculation, low success rate came from a gap between preclinical the POC and the POC couldn't stay. Father divided into phase one. Phase two has the city FDA unsolved to our question. Speculation in nature biology using 7,372 new submissions, they found a 68 significant cradle out crazy too, to study approved by the process. And in total 90 per cent Radia in the clinical stages. What we can surmise from this study, FDA confirmed is that the big discrepancy between POC and clinical POC in another ward, any amount of data well, Ms. Representative for human, this nature bio report impacted our work significantly. >>What is a solution for this discrepancy? FDA standards require the people data from two species. One species is usually mice, but if the reported 90% in a preclinical data, then huge discrepancy between pretty critical POC in clinical POC. Our interpretation is data from mice, sometime representative, actually mice, and the humor of different especially immune system and the diva mice liver enzyme are missing, which human Liba has. This is one huge issue to be taught to overcome this problem. We started humanized mice program. What kind of human animals? We created one humanized, immune mice. The other is human eyes, DBA, mice. What is the definition of a humanized mice? They should have human gene or human cells or human tissues or human organs. Well, let me share one preclinical stages. Example of a humanized mouse that is polio receptor mice. This problem led by who was my mentor? Polio virus. Well, polio virus vaccine usually required no human primate to test in 13 years, collaboration with the FDA w H O polio eradication program. Finally FDA well as w H O R Purdue due to the place no human primate test to transgenic PVL. This is three. Our principle led by loss around the botch >>To move before this humanized mouse program, we need two other bonds donut outside your science, as well as the CPN mouse science >>human hormone, like GM CSF, Whoah, GCSF producing or human cytokine. those producing emoji mice are required in the long run. Two maintain human cells in their body under generation here, South the generation here, Dr. already created more than 100 kinds based on Z. The 100 kinds of Noe mice, we succeeded to create the human immune mice led the blood. The cell quite about the cell platelets are beautifully constituted in an mice, human and rebar MAs also succeeded to create using deparent human base. We have AGN diva, humanized mouse, American African human nine-thirty by mice co-case kitchen, humanized mice. These are Hennessy humanized, the immune and rebar model. On the other hand, we created disease rebar human either must to one example, congenital Liba disease, our guidance Schindel on patient model. >>The other model, we have infectious DDS and Waddell council Modell and GVH Modell. And so on creature stage or phase can a human itemize apply. Our objective is any stage. Any phase would be to, to propose. We propose experiment, pose a compound, which showed a huge discrepancy between. If Y you show the huge discrepancy, if Y is lucrative analog and the potent anti hepatitis B candidate in that predict clinical stage, it didn't show any toxicity in mice got dark and no human primate. On the other hand, weighing into clinical stage and crazy to October 15, salvage, five of people died and other 10 the show to very severe condition. >>Is that the reason why Nicole traditional the mice model is that throughout this, another mice Modell did not predict this severe side outcome. Why Zack humanized mouse, the Debar Modell demonstrate itself? Yes. Within few days that chemistry data and the puzzle physiology data phase two and phase the city requires huge number of a human subject. For example, COVID-19 vaccine development by Pfizer, AstraZeneca Moderna today, they are sample size are Southeast thousand vaccine development for COVID-19. She Novak UConn in China books for the us Erica Jones on the Johnson in unite United Kingdom. Well, there are now no box us Osaka Osaka, university hundred Japan. They are already in phase two industry discovery and predict clinical and regulatory stage foster in-app. However, clinical stage is a studious role because that phases required hugely number or the human subject 9,000 to 30,000. Even my conclusion, a humanized mouse model shortens the duration of drug development humanize, and most Isabel, uh, can be increase the success rate of drug development. Thank you for Ron Paul and to Steven YALI pelt at Stanford and and his team and or other colleagues. Thank you for listening.

>>from around the globe. It's the Cube covering >>space and cybersecurity. Symposium 2020 hosted by Cal Poly >>Over On Welcome to this Special virtual conference. The Space and Cybersecurity Symposium 2020 put on by Cal Poly with support from the Cube. I'm John for your host and master of ceremonies. Got a great topic today in this session. Really? The intersection of space and cybersecurity. This topic and this conversation is the cybersecurity workforce development through public and private partnerships. And we've got a great lineup. We have Jeff Armstrong's the president of California Polytechnic State University, also known as Cal Poly Jeffrey. Thanks for jumping on and Bang. Go ahead. The second director of C four s R Division. And he's joining us from the office of the Under Secretary of Defense for the acquisition Sustainment Department of Defense, D O D. And, of course, Steve Jake's executive director, founder, National Security Space Association and managing partner at Bello's. Gentlemen, thank you for joining me for this session. We got an hour conversation. Thanks for coming on. >>Thank you. >>So we got a virtual event here. We've got an hour, have a great conversation and love for you guys do? In opening statement on how you see the development through public and private partnerships around cybersecurity in space, Jeff will start with you. >>Well, thanks very much, John. It's great to be on with all of you. Uh, on behalf Cal Poly Welcome, everyone. Educating the workforce of tomorrow is our mission to Cal Poly. Whether that means traditional undergraduates, master students are increasingly mid career professionals looking toe up, skill or re skill. Our signature pedagogy is learn by doing, which means that our graduates arrive at employers ready Day one with practical skills and experience. We have long thought of ourselves is lucky to be on California's beautiful central Coast. But in recent years, as we have developed closer relationships with Vandenberg Air Force Base, hopefully the future permanent headquarters of the United States Space Command with Vandenberg and other regional partners, we have discovered that our location is even more advantages than we thought. We're just 50 miles away from Vandenberg, a little closer than u C. Santa Barbara, and the base represents the southern border of what we have come to think of as the central coast region. Cal Poly and Vandenberg Air force base have partner to support regional economic development to encourage the development of a commercial spaceport toe advocate for the space Command headquarters coming to Vandenberg and other ventures. These partnerships have been possible because because both parties stand to benefit Vandenberg by securing new streams of revenue, workforce and local supply chain and Cal Poly by helping to grow local jobs for graduates, internship opportunities for students, and research and entrepreneurship opportunities for faculty and staff. Crucially, what's good for Vandenberg Air Force Base and for Cal Poly is also good for the Central Coast and the US, creating new head of household jobs, infrastructure and opportunity. Our goal is that these new jobs bring more diversity and sustainability for the region. This regional economic development has taken on a life of its own, spawning a new nonprofit called Reach, which coordinates development efforts from Vandenberg Air Force Base in the South to camp to Camp Roberts in the North. Another factor that is facilitated our relationship with Vandenberg Air Force Base is that we have some of the same friends. For example, Northrop Grumman has has long been an important defense contractor, an important partner to Cal poly funding scholarships and facilities that have allowed us to stay current with technology in it to attract highly qualified students for whom Cal Poly's costs would otherwise be prohibitive. For almost 20 years north of grimness funded scholarships for Cal Poly students this year, their funding 64 scholarships, some directly in our College of Engineering and most through our Cal Poly Scholars program, Cal Poly Scholars, a support both incoming freshman is transfer students. These air especially important because it allows us to provide additional support and opportunities to a group of students who are mostly first generation, low income and underrepresented and who otherwise might not choose to attend Cal Poly. They also allow us to recruit from partner high schools with large populations of underrepresented minority students, including the Fortune High School in Elk Grove, which we developed a deep and lasting connection. We know that the best work is done by balanced teams that include multiple and diverse perspectives. These scholarships help us achieve that goal, and I'm sure you know Northrop Grumman was recently awarded a very large contract to modernized the U. S. I. C B M Armory with some of the work being done at Vandenberg Air Force Base, thus supporting the local economy and protecting protecting our efforts in space requires partnerships in the digital realm. How Polly is partnered with many private companies, such as AWS. Our partnerships with Amazon Web services has enabled us to train our students with next generation cloud engineering skills, in part through our jointly created digital transformation hub. Another partnership example is among Cal Poly's California Cybersecurity Institute, College of Engineering and the California National Guard. This partnership is focused on preparing a cyber ready workforce by providing faculty and students with a hands on research and learning environment, side by side with military, law enforcement professionals and cyber experts. We also have a long standing partnership with PG and E, most recently focused on workforce development and redevelopment. Many of our graduates do indeed go on to careers in aerospace and defense industry as a rough approximation. More than 4500 Cal Poly graduates list aerospace and defense as their employment sector on linked in, and it's not just our engineers and computer sciences. When I was speaking to our fellow Panelists not too long ago, >>are >>speaking to bang, we learned that Rachel sins, one of our liberal arts arts majors, is working in his office. So shout out to you, Rachel. And then finally, of course, some of our graduates sword extraordinary heights such as Commander Victor Glover, who will be heading to the International space station later this year as I close. All of which is to say that we're deeply committed the workforce, development and redevelopment that we understand the value of public private partnerships and that were eager to find new ways in which to benefit everyone from this further cooperation. So we're committed to the region, the state in the nation and our past efforts in space, cybersecurity and links to our partners at as I indicated, aerospace industry and governmental partners provides a unique position for us to move forward in the interface of space and cybersecurity. Thank you so much, John. >>President, I'm sure thank you very much for the comments and congratulations to Cal Poly for being on the forefront of innovation and really taking a unique progressive. You and wanna tip your hat to you guys over there. Thank you very much for those comments. Appreciate it. Bahng. Department of Defense. Exciting you gotta defend the nation spaces Global. Your opening statement. >>Yes, sir. Thanks, John. Appreciate that day. Thank you, everybody. I'm honored to be this panel along with President Armstrong, Cal Poly in my long longtime friend and colleague Steve Jakes of the National Security Space Association, to discuss a very important topic of cybersecurity workforce development, as President Armstrong alluded to, I'll tell you both of these organizations, Cal Poly and the N S. A have done and continue to do an exceptional job at finding talent, recruiting them in training current and future leaders and technical professionals that we vitally need for our nation's growing space programs. A swell Asare collective National security Earlier today, during Session three high, along with my colleague Chris Hansen discussed space, cyber Security and how the space domain is changing the landscape of future conflicts. I discussed the rapid emergence of commercial space with the proliferations of hundreds, if not thousands, of satellites providing a variety of services, including communications allowing for global Internet connectivity. S one example within the O. D. We continue to look at how we can leverage this opportunity. I'll tell you one of the enabling technologies eyes the use of small satellites, which are inherently cheaper and perhaps more flexible than the traditional bigger systems that we have historically used unemployed for the U. D. Certainly not lost on Me is the fact that Cal Poly Pioneer Cube SATs 2020 some years ago, and they set the standard for the use of these systems today. So they saw the valiant benefit gained way ahead of everybody else, it seems, and Cal Poly's focus on training and education is commendable. I especially impressed by the efforts of another of Steve's I colleague, current CEO Mr Bill Britain, with his high energy push to attract the next generation of innovators. Uh, earlier this year, I had planned on participating in this year's Cyber Innovation Challenge. In June works Cal Poly host California Mill and high school students and challenge them with situations to test their cyber knowledge. I tell you, I wish I had that kind of opportunity when I was a kid. Unfortunately, the pandemic change the plan. Why I truly look forward. Thio feature events such as these Thio participating. Now I want to recognize my good friend Steve Jakes, whom I've known for perhaps too long of a time here over two decades or so, who was in acknowledge space expert and personally, I truly applaud him for having the foresight of years back to form the National Security Space Association to help the entire space enterprise navigate through not only technology but Polly policy issues and challenges and paved the way for operational izing space. Space is our newest horrifying domain. That's not a secret anymore. Uh, and while it is a unique area, it shares a lot of common traits with the other domains such as land, air and sea, obviously all of strategically important to the defense of the United States. In conflict they will need to be. They will all be contested and therefore they all need to be defended. One domain alone will not win future conflicts in a joint operation. We must succeed. All to defending space is critical as critical is defending our other operational domains. Funny space is no longer the sanctuary available only to the government. Increasingly, as I discussed in the previous session, commercial space is taking the lead a lot of different areas, including R and D, A so called new space, so cyber security threat is even more demanding and even more challenging. Three US considers and federal access to and freedom to operate in space vital to advancing security, economic prosperity, prosperity and scientific knowledge of the country. That's making cyberspace an inseparable component. America's financial, social government and political life. We stood up US Space force ah, year ago or so as the newest military service is like the other services. Its mission is to organize, train and equip space forces in order to protect us and allied interest in space and to provide space capabilities to the joint force. Imagine combining that US space force with the U. S. Cyber Command to unify the direction of space and cyberspace operation strengthened U D capabilities and integrate and bolster d o d cyber experience. Now, of course, to enable all of this requires had trained and professional cadre of cyber security experts, combining a good mix of policy as well as high technical skill set much like we're seeing in stem, we need to attract more people to this growing field. Now the D. O. D. Is recognized the importance of the cybersecurity workforce, and we have implemented policies to encourage his growth Back in 2013 the deputy secretary of defense signed the D. O d cyberspace workforce strategy to create a comprehensive, well equipped cyber security team to respond to national security concerns. Now this strategy also created a program that encourages collaboration between the D. O. D and private sector employees. We call this the Cyber Information Technology Exchange program or site up. It's an exchange programs, which is very interesting, in which a private sector employees can naturally work for the D. O. D. In a cyber security position that spans across multiple mission critical areas are important to the d. O. D. A key responsibility of cybersecurity community is military leaders on the related threats and cyber security actions we need to have to defeat these threats. We talk about rapid that position, agile business processes and practices to speed up innovation. Likewise, cybersecurity must keep up with this challenge to cyber security. Needs to be right there with the challenges and changes, and this requires exceptional personnel. We need to attract talent investing the people now to grow a robust cybersecurity, workforce, streets, future. I look forward to the panel discussion, John. Thank you. >>Thank you so much bomb for those comments and you know, new challenges and new opportunities and new possibilities and free freedom Operating space. Critical. Thank you for those comments. Looking forward. Toa chatting further. Steve Jakes, executive director of N. S. S. A Europe opening statement. >>Thank you, John. And echoing bangs thanks to Cal Poly for pulling these this important event together and frankly, for allowing the National Security Space Association be a part of it. Likewise, we on behalf the association delighted and honored Thio be on this panel with President Armstrong along with my friend and colleague Bonneau Glue Mahad Something for you all to know about Bomb. He spent the 1st 20 years of his career in the Air Force doing space programs. He then went into industry for several years and then came back into government to serve. Very few people do that. So bang on behalf of the space community, we thank you for your long life long devotion to service to our nation. We really appreciate that and I also echo a bang shot out to that guy Bill Britain, who has been a long time co conspirator of ours for a long time and you're doing great work there in the cyber program at Cal Poly Bill, keep it up. But professor arms trying to keep a close eye on him. Uh, I would like to offer a little extra context to the great comments made by by President Armstrong and bahng. Uh, in our view, the timing of this conference really could not be any better. Um, we all recently reflected again on that tragic 9 11 surprise attack on our homeland. And it's an appropriate time, we think, to take pause while the percentage of you in the audience here weren't even born or babies then For the most of us, it still feels like yesterday. And moreover, a tragedy like 9 11 has taught us a lot to include to be more vigilant, always keep our collective eyes and ears open to include those quote eyes and ears from space, making sure nothing like this ever happens again. So this conference is a key aspect. Protecting our nation requires we work in a cybersecurity environment at all times. But, you know, the fascinating thing about space systems is we can't see him. No, sir, We see Space launches man there's nothing more invigorating than that. But after launch, they become invisible. So what are they really doing up there? What are they doing to enable our quality of life in the United States and in the world? Well, to illustrate, I'd like to paraphrase elements of an article in Forbes magazine by Bonds and my good friend Chuck Beans. Chuck. It's a space guy, actually had Bonds job a fuse in the Pentagon. He is now chairman and chief strategy officer at York Space Systems, and in his spare time he's chairman of the small satellites. Chuck speaks in words that everyone can understand. So I'd like to give you some of his words out of his article. Uh, they're afraid somewhat. So these are Chuck's words. Let's talk about average Joe and playing Jane. Before heading to the airport for a business trip to New York City, Joe checks the weather forecast informed by Noah's weather satellites to see what pack for the trip. He then calls an uber that space app. Everybody uses it matches riders with drivers via GPS to take into the airport, So Joe has lunch of the airport. Unbeknownst to him, his organic lunch is made with the help of precision farming made possible through optimized irrigation and fertilization, with remote spectral sensing coming from space and GPS on the plane, the pilot navigates around weather, aided by GPS and nose weather satellites. And Joe makes his meeting on time to join his New York colleagues in a video call with a key customer in Singapore made possible by telecommunication satellites. Around to his next meeting, Joe receives notice changing the location of the meeting to another to the other side of town. So he calmly tells Syria to adjust the destination, and his satellite guided Google maps redirects him to the new location. That evening, Joe watches the news broadcast via satellite. The report details a meeting among world leaders discussing the developing crisis in Syria. As it turns out, various forms of quote remotely sensed. Information collected from satellites indicate that yet another band, chemical weapon, may have been used on its own people. Before going to bed, Joe decides to call his parents and congratulate them for their wedding anniversary as they cruise across the Atlantic, made possible again by communications satellites and Joe's parents can enjoy the call without even wondering how it happened the next morning. Back home, Joe's wife, Jane, is involved in a car accident. Her vehicle skids off the road. She's knocked unconscious, but because of her satellite equipped on star system, the crash is detected immediately and first responders show up on the scene. In time, Joe receives the news books. An early trip home sends flowers to his wife as he orders another uber to the airport. Over that 24 hours, Joe and Jane used space system applications for nearly every part of their day. Imagine the consequences if at any point they were somehow denied these services, whether they be by natural causes or a foreign hostility. And each of these satellite applications used in this case were initially developed for military purposes and continue to be, but also have remarkable application on our way of life. Just many people just don't know that. So, ladies and gentlemen, now you know, thanks to chuck beans, well, the United States has a proud heritage being the world's leading space faring nation, dating back to the Eisenhower and Kennedy years. Today we have mature and robust systems operating from space, providing overhead reconnaissance to quote, wash and listen, provide missile warning, communications, positioning, navigation and timing from our GPS system. Much of what you heard in Lieutenant General J. T. Thompson earlier speech. These systems are not only integral to our national security, but also our also to our quality of life is Chuck told us. We simply no longer could live without these systems as a nation and for that matter, as a world. But over the years, adversary like adversaries like China, Russia and other countries have come to realize the value of space systems and are aggressively playing ketchup while also pursuing capabilities that will challenge our systems. As many of you know, in 2000 and seven, China demonstrated it's a set system by actually shooting down is one of its own satellites and has been aggressively developing counter space systems to disrupt hours. So in a heavily congested space environment, our systems are now being contested like never before and will continue to bay well as Bond mentioned, the United States has responded to these changing threats. In addition to adding ways to protect our system, the administration and in Congress recently created the United States Space Force and the operational you United States Space Command, the latter of which you heard President Armstrong and other Californians hope is going to be located. Vandenberg Air Force Base Combined with our intelligence community today, we have focused military and civilian leadership now in space. And that's a very, very good thing. Commence, really. On the industry side, we did create the National Security Space Association devoted solely to supporting the national security Space Enterprise. We're based here in the D C area, but we have arms and legs across the country, and we are loaded with extraordinary talent. In scores of Forman, former government executives, So S s a is joined at the hip with our government customers to serve and to support. We're busy with a multitude of activities underway ranging from a number of thought provoking policy. Papers are recurring space time Webcast supporting Congress's Space Power Caucus and other main serious efforts. Check us out at NSS. A space dot org's One of our strategic priorities in central to today's events is to actively promote and nurture the workforce development. Just like cow calling. We will work with our U. S. Government customers, industry leaders and academia to attract and recruit students to join the space world, whether in government or industry and two assistant mentoring and training as their careers. Progress on that point, we're delighted. Be delighted to be working with Cal Poly as we hopefully will undertake a new pilot program with him very soon. So students stay tuned something I can tell you Space is really cool. While our nation's satellite systems are technical and complex, our nation's government and industry work force is highly diverse, with a combination of engineers, physicists, method and mathematicians, but also with a large non technical expertise as well. Think about how government gets things thes systems designed, manufactured, launching into orbit and operating. They do this via contracts with our aerospace industry, requiring talents across the board from cost estimating cost analysis, budgeting, procurement, legal and many other support. Tasker Integral to the mission. Many thousands of people work in the space workforce tens of billions of dollars every year. This is really cool stuff, no matter what your education background, a great career to be part of. When summary as bang had mentioned Aziz, well, there is a great deal of exciting challenges ahead we will see a new renaissance in space in the years ahead, and in some cases it's already begun. Billionaires like Jeff Bezos, Elon Musk, Sir Richard Richard Branson are in the game, stimulating new ideas in business models, other private investors and start up companies. Space companies are now coming in from all angles. The exponential advancement of technology and microelectronics now allows the potential for a plethora of small SAT systems to possibly replace older satellites the size of a Greyhound bus. It's getting better by the day and central to this conference, cybersecurity is paramount to our nation's critical infrastructure in space. So once again, thanks very much, and I look forward to the further conversation. >>Steve, thank you very much. Space is cool. It's relevant. But it's important, as you pointed out, and you're awesome story about how it impacts our life every day. So I really appreciate that great story. I'm glad you took the time Thio share that you forgot the part about the drone coming over in the crime scene and, you know, mapping it out for you. But that would add that to the story later. Great stuff. My first question is let's get into the conversations because I think this is super important. President Armstrong like you to talk about some of the points that was teased out by Bang and Steve. One in particular is the comment around how military research was important in developing all these capabilities, which is impacting all of our lives. Through that story. It was the military research that has enabled a generation and generation of value for consumers. This is kind of this workforce conversation. There are opportunities now with with research and grants, and this is, ah, funding of innovation that it's highly accelerate. It's happening very quickly. Can you comment on how research and the partnerships to get that funding into the universities is critical? >>Yeah, I really appreciate that And appreciate the comments of my colleagues on it really boils down to me to partnerships, public private partnerships. You mentioned Northrop Grumman, but we have partnerships with Lockie Martin, Boeing, Raytheon Space six JPL, also member of organization called Business Higher Education Forum, which brings together university presidents and CEOs of companies. There's been focused on cybersecurity and data science, and I hope that we can spill into cybersecurity in space but those partnerships in the past have really brought a lot forward at Cal Poly Aziz mentioned we've been involved with Cube set. Uh, we've have some secure work and we want to plan to do more of that in the future. Uh, those partnerships are essential not only for getting the r and d done, but also the students, the faculty, whether masters or undergraduate, can be involved with that work. Uh, they get that real life experience, whether it's on campus or virtually now during Covic or at the location with the partner, whether it may be governmental or our industry. Uh, and then they're even better equipped, uh, to hit the ground running. And of course, we'd love to see even more of our students graduate with clearance so that they could do some of that a secure work as well. So these partnerships are absolutely critical, and it's also in the context of trying to bring the best and the brightest and all demographics of California and the US into this field, uh, to really be successful. So these partnerships are essential, and our goal is to grow them just like I know other colleagues and C. S u and the U C are planning to dio, >>you know, just as my age I've seen I grew up in the eighties, in college and during that systems generation and that the generation before me, they really kind of pioneered the space that spawned the computer revolution. I mean, you look at these key inflection points in our lives. They were really funded through these kinds of real deep research. Bond talk about that because, you know, we're living in an age of cloud. And Bezos was mentioned. Elon Musk. Sir Richard Branson. You got new ideas coming in from the outside. You have an accelerated clock now on terms of the innovation cycles, and so you got to react differently. You guys have programs to go outside >>of >>the Defense Department. How important is this? Because the workforce that air in schools and our folks re skilling are out there and you've been on both sides of the table. So share your thoughts. >>No, thanks, John. Thanks for the opportunity responded. And that's what you hit on the notes back in the eighties, R and D in space especially, was dominated by my government funding. Uh, contracts and so on. But things have changed. As Steve pointed out, A lot of these commercial entities funded by billionaires are coming out of the woodwork funding R and D. So they're taking the lead. So what we can do within the deal, the in government is truly take advantage of the work they've done on. Uh, since they're they're, you know, paving the way to new new approaches and new way of doing things. And I think we can We could certainly learn from that. And leverage off of that saves us money from an R and D standpoint while benefiting from from the product that they deliver, you know, within the O D Talking about workforce development Way have prioritized we have policies now to attract and retain talent. We need I I had the folks do some research and and looks like from a cybersecurity workforce standpoint. A recent study done, I think, last year in 2019 found that the cybersecurity workforce gap in the U. S. Is nearing half a million people, even though it is a growing industry. So the pipeline needs to be strengthened off getting people through, you know, starting young and through college, like assess a professor Armstrong indicated, because we're gonna need them to be in place. Uh, you know, in a period of about maybe a decade or so, Uh, on top of that, of course, is the continuing issue we have with the gap with with stamps students, we can't afford not to have expertise in place to support all the things we're doing within the with the not only deal with the but the commercial side as well. Thank you. >>How's the gap? Get? Get filled. I mean, this is the this is again. You got cybersecurity. I mean, with space. It's a whole another kind of surface area, if you will, in early surface area. But it is. It is an I o t. Device if you think about it. But it does have the same challenges. That's kind of current and and progressive with cybersecurity. Where's the gap Get filled, Steve Or President Armstrong? I mean, how do you solve the problem and address this gap in the workforce? What is some solutions and what approaches do we need to put in place? >>Steve, go ahead. I'll follow up. >>Okay. Thanks. I'll let you correct. May, uh, it's a really good question, and it's the way I would. The way I would approach it is to focus on it holistically and to acknowledge it up front. And it comes with our teaching, etcetera across the board and from from an industry perspective, I mean, we see it. We've gotta have secure systems with everything we do and promoting this and getting students at early ages and mentoring them and throwing internships at them. Eyes is so paramount to the whole the whole cycle, and and that's kind of and it really takes focused attention. And we continue to use the word focus from an NSS, a perspective. We know the challenges that are out there. There are such talented people in the workforce on the government side, but not nearly enough of them. And likewise on industry side. We could use Maura's well, but when you get down to it, you know we can connect dots. You know that the the aspect That's a Professor Armstrong talked about earlier toe where you continue to work partnerships as much as you possibly can. We hope to be a part of that. That network at that ecosystem the will of taking common objectives and working together to kind of make these things happen and to bring the power not just of one or two companies, but our our entire membership to help out >>President >>Trump. Yeah, I would. I would also add it again. It's back to partnerships that I talked about earlier. One of our partners is high schools and schools fortune Margaret Fortune, who worked in a couple of, uh, administrations in California across party lines and education. Their fifth graders all visit Cal Poly and visit our learned by doing lab and you, you've got to get students interested in stem at a early age. We also need the partnerships, the scholarships, the financial aid so the students can graduate with minimal to no debt to really hit the ground running. And that's exacerbated and really stress. Now, with this covert induced recession, California supports higher education at a higher rate than most states in the nation. But that is that has dropped this year or reasons. We all understand, uh, due to Kobe, and so our partnerships, our creativity on making sure that we help those that need the most help financially uh, that's really key, because the gaps air huge eyes. My colleagues indicated, you know, half of half a million jobs and you need to look at the the students that are in the pipeline. We've got to enhance that. Uh, it's the in the placement rates are amazing. Once the students get to a place like Cal Poly or some of our other amazing CSU and UC campuses, uh, placement rates are like 94%. >>Many of our >>engineers, they have jobs lined up a year before they graduate. So it's just gonna take key partnerships working together. Uh, and that continued partnership with government, local, of course, our state of CSU on partners like we have here today, both Stephen Bang So partnerships the thing >>e could add, you know, the collaboration with universities one that we, uh, put a lot of emphasis, and it may not be well known fact, but as an example of national security agencies, uh, National Centers of Academic Excellence in Cyber, the Fast works with over 270 colleges and universities across the United States to educate its 45 future cyber first responders as an example, so that Zatz vibrant and healthy and something that we ought Teoh Teik, banjo >>off. Well, I got the brain trust here on this topic. I want to get your thoughts on this one point. I'd like to define what is a public private partnership because the theme that's coming out of the symposium is the script has been flipped. It's a modern error. Things air accelerated get you got security. So you get all these things kind of happen is a modern approach and you're seeing a digital transformation play out all over the world in business. Andi in the public sector. So >>what is what >>is a modern public private partnership? What does it look like today? Because people are learning differently, Covert has pointed out, which was that we're seeing right now. How people the progressions of knowledge and learning truth. It's all changing. How do you guys view the modern version of public private partnership and some some examples and improve points? Can you can you guys share that? We'll start with the Professor Armstrong. >>Yeah. A zai indicated earlier. We've had on guy could give other examples, but Northup Grumman, uh, they helped us with cyber lab. Many years ago. That is maintained, uh, directly the software, the connection outside its its own unit so that students can learn the hack, they can learn to penetrate defenses, and I know that that has already had some considerations of space. But that's a benefit to both parties. So a good public private partnership has benefits to both entities. Uh, in the common factor for universities with a lot of these partnerships is the is the talent, the talent that is, that is needed, what we've been working on for years of the, you know, that undergraduate or master's or PhD programs. But now it's also spilling into Skilling and re Skilling. As you know, Jobs. Uh, you know, folks were in jobs today that didn't exist two years, three years, five years ago. But it also spills into other aspects that can expand even mawr. We're very fortunate. We have land, there's opportunities. We have one tech part project. We're expanding our tech park. I think we'll see opportunities for that, and it'll it'll be adjusted thio, due to the virtual world that we're all learning more and more about it, which we were in before Cove it. But I also think that that person to person is going to be important. Um, I wanna make sure that I'm driving across the bridge. Or or that that satellites being launched by the engineer that's had at least some in person training, uh, to do that and that experience, especially as a first time freshman coming on a campus, getting that experience expanding and as adult. And we're gonna need those public private partnerships in order to continue to fund those at a level that is at the excellence we need for these stem and engineering fields. >>It's interesting People in technology can work together in these partnerships in a new way. Bank Steve Reaction Thio the modern version of what a public, successful private partnership looks like. >>If I could jump in John, I think, you know, historically, Dodi's has have had, ah, high bar thio, uh, to overcome, if you will, in terms of getting rapid pulling in your company. This is the fault, if you will and not rely heavily in are the usual suspects of vendors and like and I think the deal is done a good job over the last couple of years off trying to reduce the burden on working with us. You know, the Air Force. I think they're pioneering this idea around pitch days where companies come in, do a two hour pitch and immediately notified of a wooden award without having to wait a long time. Thio get feedback on on the quality of the product and so on. So I think we're trying to do our best. Thio strengthen that partnership with companies outside the main group of people that we typically use. >>Steve, any reaction? Comment to add? >>Yeah, I would add a couple of these air. Very excellent thoughts. Uh, it zits about taking a little gamble by coming out of your comfort zone. You know, the world that Bond and Bond lives in and I used to live in in the past has been quite structured. It's really about we know what the threat is. We need to go fix it, will design it says we go make it happen, we'll fly it. Um, life is so much more complicated than that. And so it's it's really to me. I mean, you take you take an example of the pitch days of bond talks about I think I think taking a gamble by attempting to just do a lot of pilot programs, uh, work the trust factor between government folks and the industry folks in academia. Because we are all in this together in a lot of ways, for example. I mean, we just sent the paper to the White House of their requests about, you know, what would we do from a workforce development perspective? And we hope Thio embellish on this over time once the the initiative matures. But we have a piece of it, for example, is the thing we call clear for success getting back Thio Uh, President Armstrong's comments at the collegiate level. You know, high, high, high quality folks are in high demand. So why don't we put together a program they grabbed kids in their their underclass years identifies folks that are interested in doing something like this. Get them scholarships. Um, um, I have a job waiting for them that their contract ID for before they graduate, and when they graduate, they walk with S C I clearance. We believe that could be done so, and that's an example of ways in which the public private partnerships can happen to where you now have a talented kid ready to go on Day one. We think those kind of things can happen. It just gets back down to being focused on specific initiatives, give them giving them a chance and run as many pilot programs as you can like these days. >>That's a great point, E. President. >>I just want to jump in and echo both the bank and Steve's comments. But Steve, that you know your point of, you know, our graduates. We consider them ready Day one. Well, they need to be ready Day one and ready to go secure. We totally support that and and love to follow up offline with you on that. That's that's exciting, uh, and needed very much needed mawr of it. Some of it's happening, but way certainly have been thinking a lot about that and making some plans, >>and that's a great example of good Segway. My next question. This kind of reimagining sees work flows, eyes kind of breaking down the old the old way and bringing in kind of a new way accelerated all kind of new things. There are creative ways to address this workforce issue, and this is the next topic. How can we employ new creative solutions? Because, let's face it, you know, it's not the days of get your engineering degree and and go interview for a job and then get slotted in and get the intern. You know the programs you get you particularly through the system. This is this is multiple disciplines. Cybersecurity points at that. You could be smart and math and have, ah, degree in anthropology and even the best cyber talents on the planet. So this is a new new world. What are some creative approaches that >>you know, we're >>in the workforce >>is quite good, John. One of the things I think that za challenge to us is you know, we got somehow we got me working for with the government, sexy, right? The part of the challenge we have is attracting the right right level of skill sets and personnel. But, you know, we're competing oftentimes with the commercial side, the gaming industry as examples of a big deal. And those are the same talents. We need to support a lot of programs we have in the U. D. So somehow we have to do a better job to Steve's point off, making the work within the U. D within the government something that they would be interested early on. So I tracked him early. I kind of talked about Cal Poly's, uh, challenge program that they were gonna have in June inviting high school kid. We're excited about the whole idea of space and cyber security, and so on those air something. So I think we have to do it. Continue to do what were the course the next several years. >>Awesome. Any other creative approaches that you guys see working or might be on idea, or just a kind of stoked the ideation out their internship. So obviously internships are known, but like there's gotta be new ways. >>I think you can take what Steve was talking about earlier getting students in high school, uh, and aligning them sometimes. Uh, that intern first internship, not just between the freshman sophomore year, but before they inter cal poly per se. And they're they're involved s So I think that's, uh, absolutely key. Getting them involved many other ways. Um, we have an example of of up Skilling a redeveloped work redevelopment here in the Central Coast. PG and e Diablo nuclear plant as going to decommission in around 2020 24. And so we have a ongoing partnership toe work on reposition those employees for for the future. So that's, you know, engineering and beyond. Uh, but think about that just in the manner that you were talking about. So the up skilling and re Skilling uh, on I think that's where you know, we were talking about that Purdue University. Other California universities have been dealing with online programs before cove it and now with co vid uh, so many more faculty or were pushed into that area. There's going to be much more going and talk about workforce development and up Skilling and Re Skilling The amount of training and education of our faculty across the country, uh, in in virtual, uh, and delivery has been huge. So there's always a silver linings in the cloud. >>I want to get your guys thoughts on one final question as we in the in the segment. And we've seen on the commercial side with cloud computing on these highly accelerated environments where you know, SAS business model subscription. That's on the business side. But >>one of The >>things that's clear in this trend is technology, and people work together and technology augments the people components. So I'd love to get your thoughts as we look at the world now we're living in co vid um, Cal Poly. You guys have remote learning Right now. It's a infancy. It's a whole new disruption, if you will, but also an opportunity to enable new ways to collaborate, Right? So if you look at people and technology, can you guys share your view and vision on how communities can be developed? How these digital technologies and people can work together faster to get to the truth or make a discovery higher to build the workforce? These air opportunities? How do you guys view this new digital transformation? >>Well, I think there's there's a huge opportunities and just what we're doing with this symposium. We're filming this on one day, and it's going to stream live, and then the three of us, the four of us, can participate and chat with participants while it's going on. That's amazing. And I appreciate you, John, you bringing that to this this symposium, I think there's more and more that we can do from a Cal poly perspective with our pedagogy. So you know, linked to learn by doing in person will always be important to us. But we see virtual. We see partnerships like this can expand and enhance our ability and minimize the in person time, decrease the time to degree enhanced graduation rate, eliminate opportunity gaps or students that don't have the same advantages. S so I think the technological aspect of this is tremendous. Then on the up Skilling and Re Skilling, where employees air all over, they can be reached virtually then maybe they come to a location or really advanced technology allows them to get hands on virtually, or they come to that location and get it in a hybrid format. Eso I'm I'm very excited about the future and what we can do, and it's gonna be different with every university with every partnership. It's one. Size does not fit all. >>It's so many possibilities. Bond. I could almost imagine a social network that has a verified, you know, secure clearance. I can jump in, have a little cloak of secrecy and collaborate with the d o. D. Possibly in the future. But >>these are the >>kind of kind of crazy ideas that are needed. Are your thoughts on this whole digital transformation cross policy? >>I think technology is gonna be revolutionary here, John. You know, we're focusing lately on what we call digital engineering to quicken the pace off, delivering capability to warfighter. As an example, I think a I machine language all that's gonna have a major play and how we operate in the future. We're embracing five G technologies writing ability Thio zero latency or I o t More automation off the supply chain. That sort of thing, I think, uh, the future ahead of us is is very encouraging. Thing is gonna do a lot for for national defense on certainly the security of the country. >>Steve, your final thoughts. Space systems are systems, and they're connected to other systems that are connected to people. Your thoughts on this digital transformation opportunity >>Such a great question in such a fun, great challenge ahead of us. Um echoing are my colleague's sentiments. I would add to it. You know, a lot of this has I think we should do some focusing on campaigning so that people can feel comfortable to include the Congress to do things a little bit differently. Um, you know, we're not attuned to doing things fast. Uh, but the dramatic You know, the way technology is just going like crazy right now. I think it ties back Thio hoping Thio, convince some of our senior leaders on what I call both sides of the Potomac River that it's worth taking these gamble. We do need to take some of these things very way. And I'm very confident, confident and excited and comfortable. They're just gonna be a great time ahead and all for the better. >>You know, e talk about D. C. Because I'm not a lawyer, and I'm not a political person, but I always say less lawyers, more techies in Congress and Senate. So I was getting job when I say that. Sorry. Presidential. Go ahead. >>Yeah, I know. Just one other point. Uh, and and Steve's alluded to this in bonded as well. I mean, we've got to be less risk averse in these partnerships. That doesn't mean reckless, but we have to be less risk averse. And I would also I have a zoo. You talk about technology. I have to reflect on something that happened in, uh, you both talked a bit about Bill Britton and his impact on Cal Poly and what we're doing. But we were faced a few years ago of replacing a traditional data a data warehouse, data storage data center, and we partner with a W S. And thank goodness we had that in progress on it enhanced our bandwidth on our campus before Cove. It hit on with this partnership with the digital transformation hub. So there is a great example where, uh, we we had that going. That's not something we could have started. Oh, covitz hit. Let's flip that switch. And so we have to be proactive on. We also have thio not be risk averse and do some things differently. Eyes that that is really salvage the experience for for students. Right now, as things are flowing, well, we only have about 12% of our courses in person. Uh, those essential courses, uh, and just grateful for those partnerships that have talked about today. >>Yeah, and it's a shining example of how being agile, continuous operations, these air themes that expand into space and the next workforce needs to be built. Gentlemen, thank you. very much for sharing your insights. I know. Bang, You're gonna go into the defense side of space and your other sessions. Thank you, gentlemen, for your time for great session. Appreciate it. >>Thank you. Thank you. >>Thank you. >>Thank you. Thank you. Thank you all. >>I'm John Furry with the Cube here in Palo Alto, California Covering and hosting with Cal Poly The Space and Cybersecurity Symposium 2020. Thanks for watching.

>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi I'm Peter Burris analyst at wiki bond welcome to another wiki bond the cube digital community event this one sponsored by HP and focusing on hybrid storage like all of our digital community events this one will feature about 25 minutes of video followed by a crowd chat which will be your opportunity to ask your questions share your experiences and push forward the community's thinking on the important issues facing business today so what are we talking about today again hybrid storage let's get going so what is hybrid storage in a lot of shops most people have associated the cloud with public cloud but as we gain experience with the challenges associated with transforming to digital business in which we use data as a singular value producing asset increasingly IT professionals are starting to realize this important relationship between data storage and cloud services and in many respects that's really what we're trying to master today is a better understanding of how the business is going to use data to affect significant changes in how it behaves in the marketplace and it's that question of behavior that question of action that question of location that is pushing business to think differently about how its cloud architectures are going to work we're going to keep data proximate to where it's created to where it's going to be used to where it's going to be able to generate value which demands that we have storage resources in place close to that data proximate to that activity near that value producing activity and that the cloud services will have to follow in many respects that's what we're talking about when we talk about hybrid cloud today we're talking about the increasing recognition that we're going to move cloud services to the data default and not move the data into the cloud public cloud specifically so it's this ongoing understanding as we gain experience with this powerful set of technologies that data architecture is going to be increasingly distributed that storage therefore will be increasingly distributed and that cloud services will flow to where the data is required utilizing storage technologies that can best serve that set of workload so it's a more complex world that demands new levels of simplicity ease of use and optimization so that's where we're going to start our conversation so these crucial questions of how data storage and cloud are going to come together to create hybrid architectures was the basis for a great cubed conversation between silicon angle wiki bonds david Volante and HPE sun dip aurora let's hear what they had to say talk about let's talk about the break down those three things cost efficiency ease of use and resource optimization let's start with cost efficiency so obviously there's TCO there's also the way in which I consume the people I presume are looking for a different pricing model is that are you hearing that yeah absolutely so as part of the cost of of running their business and being able to operate like a cloud everybody is looking at a variety of different procurement and utilization models one of the ways HPE provides utilization model that can map to their cloud journey a public cloud journey is through Greenlake the ability to use and consume data on-demand consume compute on demand across the entire portfolio of products HPE has essentially is what a Greenlake journey looks like and let's go into ease-of-use so what do you mean by that I mean people look they think cloud they think swipe the credit card and start you know deploying machines what do you mean by easy for us ease of use translates back to how do you map to a simpler operating and support model for us the support model is the is the key for customers to be able to to realize the benefits of going to that cloud to get to a simpler support model we use AI ops and for us a offs means using a product called info site info site is a product that is uses deep learning and machine learning algorithms to look at a wide net of call home data from physical resources out there and then be able to take that data and make it actionable and the action behind that is predictiveness the prescriptive nosov creating automated support tickets enclosing automated support tickets without anybody ever having to pick up a phone and call IT support that info site model now is being expanded across the board to all HP products it started with nimble now info site is available on three part it's available on synergy and a recent announcement said it's also available on pro alliance and we expect that info set becomes the glue the automation a I do that goes across the entire portfolio of HP products so this is a great example of applying AI to data so it's like call home taking to a whole new level isn't it yeah it absolutely is and in fact what it does is it uses the call home data that we've had for a long time with products like 3par which essentially was amazing data but not being auctioned on in an automated fashion it takes that data and creates an automation tasks around it and many times that automation task leads to much simpler support experience all right third item you mentioned was resource optimization let's let's drill down into that I infer from that there's there are performance implications is maybe governance compliance you know physical placement can you elaborate that's in color yes I think it's all of the above that he just talked about it's definitely about applying the right performance level to the right set of applications we call this application of air storage the ability to be able to understand which application is creating the data allows us to understand how that data needs to be accessed which in turn means we know where it needs to reside one of the things that HP is doing in the storage domain is creating a common storage fabric with the cloud we call that the fabric for the cloud the idea there is that we have a single layer between the on-premises and off premises resources that allows us to move data as needed depending on the application needs and depending on the user needs so this crucial new factors that have to be incorporated through everyone's thinking of cost efficiency ease of use and resource optimization it's going to place new types of stress on the storage hierarchy it's gonna require new technologies to better support digital transformation David Flor an analyst here in wiki bon has been a leading thinker of the relationship between the storage hierarchy and workloads and digital thinking for quite some time I had a great conversation with David not too long ago let's hear what he had to say about this new storage hierarchy and the new technologies they're gonna make possible these changes have you've been looking at this notion of modern storage architectures for 10 years now and you've been relatively prescient in understanding what's going to happen you were one of the first guys to predict well in advance of everybody else that the crossover between flash and HDD was gonna happen sooner rather than later so I'm not gonna spend a lot of time quizzing you what do you see as a modern storage architecture let's just let it rip ok well let's start with one simple observation the days of stand-alone systems for data have gone we're in a software-defined world and you want to be able to run those data architectures anywhere where you the data is and that means in your data center where you've is created or in the cloud or in a public cloud or at the edge you want to be able to be flexible enough to be able to do all of the data services where the best place is and that means everything has to be software German Software Defined is the first proposition of a modern day in a storage so so the second thing is that there are different types of technology you have the very fastest storage which is in the in in the DRAM itself you have env dim which is the next one down from that expensive but a lot cheaper than the dim and then you have different sorts of flash you have the high-performance flash and you have the 3d flash you know as many layers as you can which is much cheaper flash and then at the bottom you have HD DS and an even tape as storage devices so how the key question is how do you manage that sort of environment well let me start because it still sounds like we still have a storage hierarchy absolutely and it still sounds like that hierarchy is defined largely in terms of access speeds yep and price point size points yes those are the two mason and and bandwidth and latency as well with it which are tied into the richer tied into those yes so what you if you're gonna have this everywhere and you need services everywhere what you have to have is an architecture which takes away all of that complexity so that you all you see from an application point of view is data and how it gets there and how it's put away and how it's stored and how it's protected that's under the covers so the first thing is you need a virtualization of that data layer the physical layer the virtualization of that physical yes and secondly you need that physical layer to extend to all the places that may be using this data you you don't want to be constrained to this data set lives here you want to be able to say ok I want to move this piece of programming to the data as quickly as I can that's much much faster than moving the data to the to the processing so I want to be able to know where all the data is for this particular dataset or file or whatever it is where they all are how they connect together what the latency is between everything I want to understand that architecture and I want a virtualized view of that across that whole the nodes that make up my hybrid cloud so let me be clear here so so we are going to use a software-defined infrastructure that allows us to place the physical devices that have the right cost performance characteristics where they need to be based on the physical realities of latency of you know power availability hardening etc on the network and the network but we want to mask that complexity from the application the application developer an application administrator yes and Software Defined helps do that but doesn't completely do it No well you you want services which say exactly so their service is on top of all that apps that are that are recognizable by the developer by the you know the business person by the administrator as they think about how they use data towards those outcomes not use a storage or use a device but use the data to reach application outcomes that's absolutely right then that's what I call the data plane which is a series of services which enable that to happen and and driven by the application required so we've looked at this and some of the services include you know and and compression deduplication the backup restore security data protection so that's kind of that's kind of the services that now the enterprise by or needs to think about so that those services can be applied with you know by policy yes wherever they're required based on the utilization of the data correct where it's kind of where the event takes place and then you still have at the bottom of that you have the different types of devices you still have you still want of hamsters Mickey you still want hard disk they're not disappearing but if you're gonna use hard disks then you want to use it in the right way if you're using a hard disk you know you want to give it large box you to have it going sequentially in and out all the time so the storage administration and the day the physical schema and everything else is still important in all this but it's less important less the centerpiece of the buying decision correct increasingly it's how well does this stuff prove support the services that the business is using to achieve their outcomes and you want to use course the lowest cost that you can and there will be many different options over more more options open but but the automation of that is absolutely key and that automation from a vendor point of view one of the key things they have to do is to be able to learn from the usage by their customers across as broad a number of customers as they can learn what works what doesn't work learn so that they can put automation into their own software their own software services well sounds like we're talking four things we got we got software-defined still have a storage hierarchy defined by cost and performance but with mainly semiconductor stuff we've got great data services that are relevant to the business and automation that masks the complexity from the artificial AI there is also also made many things fantastic so David's thinking on the new storage hierarchy and how it's going to relate to new classes of workload is a baseline for a lot of the changes happening in the industry today but we still have to turn technology into services that deliver higher levels of value once again let's go back to Dave volantes conversation with Sun dip Arora and here what Sun dip has to say about some of the new digital services some of the new data services they're gonna be essential to supporting these new hybrid storage capabilities we have and what it does it it gives us the opportunity now not just you look at column data from storage but then also look at call home data from the compute side and then what we can do is correlate the data coming back to have better predictability and outcomes on your data center operations as opposed to doing it at the layer of infrastructure you also set out a vision of this this orchestration yeah lair can you talk more about that are we talking about across all clouds whether it's on pram or at the edge or in the public cloud yeah we are we're talking about making it as simple as possible where the customers are not necessarily picking and choosing it allows them to have a strategy that allows them to go across the data center whether it's a public cloud building their own private infrastructure or running on a traditional on-premises sand structure so this vision for us cloud fabric vision for us allows for customers to do that and what about software-defined storage yeah where does that fit into this whole equation yeah I'm glad you mentioned that because that was a third tenant of what HP truly brings to our customers software-defined is is something that allows us to maximize the utilization of the existing resources that our customers have so what we've done is we've partnered with a great deal of really strong software-defined vendors such as comm world cohesive accumulo de terre I know we work very closely with the likes of veeam Zotoh and and the goal there is to do to provide our customers with a whole range of options to drive building a software-defined infrastructure build off the Apollo series of products Apollo servers or storage products for us are extremely dense storage products that allow for both cost and resource optimization so Sunday I made some fantastic points about how new storage technologies are going to be turned into usable services that digital businesses will require as they conceived of their overall hybrid storage approach here's an opportunity hear a little bit more about what HPE thinks about some of these crucial areas let's hear what they have to say in this Chuck talk short take I'm gonna introduce you to HPE primary storage if you want the agility of the public cloud but need the resiliency and speed of high-end storage for mission-critical applications this force is a trade-off of agility for resiliency high-end storage is fast and reliable but falls short on agility and simplicity what if you could have it all what if you could have both agility and resiliency for your mission-critical apps introducing the world's most intelligent storage for mission-critical apps HP primary it delivers an on-demand experience so storage is instantly available Apple wear resiliency backed with a hundred percent availability guarantee predictive acceleration so apps aren't fast some of the time but fast all the time with embedded AI let me tell you more about HPE primarily was engineered to drive unique value in high-end storage there are four areas we focus on global intelligence powered with the most advanced AI for infrastructure info site an all active architecture with multiple nodes for higher resiliency and limitless parallelization a service centric OS that eliminates the risk and simplifies management and timeless storage with a new ownership experience that keeps getting better to learn more go to hp.com slash storage slash prime era so that's been a great series of conversations about hybrid storage and I want to thank Sun dip Arora of HPE David floor of wiki bonds to look at angle jim kanby lists of wiki bonds to look and angle and my colleague David Volante for helping out on the interview side I'm Peter Burris and this has been another wiki bond the cube digital community event sponsored by HPE now stay tuned for our Crouch at which will be your opportunity to ask your questions share your experiences and push for the community's thinking on hybrid storage once again thank you very much for watching let's crouch at

(upbeat pop music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CubeConversation. >> Hello, everyone welcome to this CUBE Conversation here in Palo Alto, California, I'm John Furrier, co-host of theCUBE. We are here forth Inhi Cho Suh General Manager of IBM Watson, Customer Engagement, Former Cube alumni, I think she's been on dozens of times. Great to see you again. Welcome to our Palo Alto Studios. >> Yeah, great being here, John. >> So, we haven't chatted in awhile. IBM thing just happened, a little bit of a rainy event, here in February. Interesting change over since we last talked, but first give an update on what you're up to these days, what group are you leading, what's new? >> Okay, well first of all, I'm here based in California, which I'm excited about, and I lead our Watson West office, which is our Watson headquarters, here on the west coast, in downtown San Francisco, and we hosted our Think Conference, and at Think I lead with, in IBM, what we call our Watson Customer Engagement Business Unit, which is really the business applications, of how we apply Watson and other disruptive tech to a line of business audiences, both SAS and on premise software, so really excited about the areas of applying AI and machine learning as well as Blockchain to things like supply chain, and logistics, to order management, to next generation of retail. A lot of new, exciting areas. >> Yeah, we've had many conversations over the years from big data to as your career spanned across IBM, and you have a much more horizontal view of things, now. You're horizontally scalable, as we say in the cloud world. What's your observation of the trends these days? Because there's a lot waves. Actually, the waves that you guys announced, was the IBM, Watson NE ware and the cloud private ware. Marvin and I had an amazing conversation that video went viral. This is now getting a big tailwind for IBM. What's your thoughts in general about the overall ecosystem, because you're here in Silicon Valley, you've seen the big waves, you've got another big data world, cloud is here, multi cloud. What's your thoughts on the big mega-trends? >> Yeah, that's a good question. I think the first chapter of cloud, everyone ran to public cloud. When you look at it through the lens of enterprise, though, the hot topic right now in the second chapter is really about not just public cloud, but multi-cloud, hybrid cloud. Meaning, whether it's a private, public, it's about thinking about the applications and the nature of the applications and regardless of where the data sits, what are the implications of actually getting work done? Through, kind of, new container services, new ways of microservices in the development, of how APIs are integrated, and so, the hot topic right now is definitely hybrid cloud, multi cloud. And the work we've done to certify, what we call, IBM cloud private really enables us to not just take any business application to any cloud in our cloud, as well, but actually to enable Watson and Watson based applications also across multi cloud environments. >> So, chapter two, Jenny mentioned that in her key notes, I want to dig into that because we've been talking a lot about multi cloud architecture, and one of the big debates has been, in the industry, oh, don't pick a soul cloud. I've been writing a bunch of content about that at this DOD jedi deal with Amazon and Oracle, fighting for it out there, but that's also happening at the enterprise, but the reality is, everyone has multiple clouds. If you've got a sales force or if you've got this and that and the other thing, you probably have multiple clouds, so it's not so much soul cloud vs. as it is, workloads having a cloud for the right job and that seems to be validated at IBM Think, in talking to the top technical people and in the industry. They all say, pick the right cloud for the job. And we've heard that before in Big Data. Pick the right tool for the job. So, given that, workloads seem to be driving the demand for cloud. Since you're on the app side, how are you seeing that? Because the world's flipped. It used to be infrastructure and software enable the app's capabilities. Now the workloads have infrastructure as code, made with cloud, they're driving the requirements. This is a change over. >> It is a big change and part of, I would say, when people first ran to the cloud, and a lot of the public cloud services were digital SaaS services, where people were wanting to stitch multiple applications across clouds, and that became a challenge, so in this next iteration, that I'm seeing is, really, a couple things. One is, data gravity. So, where does the data actually reside, for the workload that's actually happening? Whether it's the transactions, whether it's customer information, whether it's product information, that's one piece. The second piece is a lot more analytics, right? And the spectrum of analytics running from traditional warehouse capabilities, to more, let's say, larger scale big data projects to full blown advanced algorithms and AI applications, is, people are saying, look, not only do I want to stitch these applications across multiple clouds; I also want to make sure I can actually tap into the data to apply new types of analytics and derive new services and new values out of relationships, understanding of how products are consumed, and so forth. So, for us, when we think about it is, we want to be able to enable that fluid understanding of data across the clouds, as well as protect and be thoughtful about the data privacy rights around it, compliance around GDPR, as well as how we think about the security aspects as well, for the enterprise. >> That is a great point. I think I want to drill down on the data piece, your background on data obviously is going to be key in your job now obviously, it's pretty obvious with Watson, but David Floyd, a wiki bonds research analyst, just posted a taxonomy of hybrid cloud research report that laid out the different kinds of cloud you could have. There's edge clouds, there's all kinds of things from public to edge, so when you look at that, you're thinking, okay, the data plain is the critical nature of the cloud. Now, depending on which cloud architecture for the use case, the workload, whatever, the data plain seems to be this magical opportunity. AI is going to have a big part of that. Can you just talk about how you guys see that evolving? Because, obviously, AI is a killer part of your strategy. This data piece is inter-operating across the clouds. >> Yes. >> Data management governs you're smiling, cause there's a killer answer coming. >> Totally. This is such a great set up. Actually, Ginni even said it in her keynote at Think, which was, you can't have an AI strategy without an information architecture strategy, which is an IA strategy, and information architecture is all about what you said: it's data preparation; understanding the foundation of it, making sure you've got the right governance structure, the integration of it, and then actually how you apply the more advanced analytics on top. So, information architecture and thinking about the data aspects in all kinds of data. Majority of the data actually sits behind, what I would say, the traditional public firewall. So, it sits behind the firewalls of our enterprise clients, like 80 plus percent of it, and then, many of the clients, we actually recently did a study, with about 5,000 senior executives, across many, many thousands of organizations, and 85% of them want to apply AI to improve their customer service, to improve the way they engage their clients and their products and services, so this is a huge opportunity right now for pretty much every organization to think through; kind of their data strategy. Their information architecture strategy, as part of their overall AI strategy. >> So, a question a got on twitter comes up a lot, and, also on my notes here, I wanted to ask you is, how can companies increase transparency trust and mitigate bias in AI? Because this comes up a lot and that's the questions that come in from the community is, Hey, I got my site, my apps running in Germany. I've got users over there, I'm global. I have to manage compliance, I got all this governess now, I'm over my shoulders, kind of a pain in the butt, but also I don't want to have the software be skewed on bias and other things, and then, I also get this whole Facebook dynamic going on, where it's like, I don't trust people holding my data. This is a big, huge issue. >> It is enormous. >> You guys are in the middle of it, what's your thoughts, what's the update, what's the dynamic and what's the solution? >> So, this is a big topic. I think we could do a whole episode just on this topic alone. So, trust and developing trust and transparency in AI should be a fundamental requirement across many, many different types of institutions. So, first of all, the responsibility doesn't sit only with the technology vendors; it's a shared responsibility across government institutions, the consumers, as well as the business leaders, in terms of how they're thinking about it. The more important piece, though, is when you think about the population that's available, that really understands AI, and they're actually coding and developing on it, is that we have to think about the diverse population that's participating in the governance of it, because you don't want just one tribe or one group that's coding and developing the algorithms, or deciding the decision models. >> Like the nerds or the geeks; they're a social aspect, society aspect as well, right? Social science. >> Exactly. I actually just did a recent conversational series with Northwestern Kellogg's business school, around the importance of developing trust and transparency, not only in the algorithms themselves, but the methodology of how you think about culture and value and ethics come into play through different lens, depending on the country you live in, as you kind of referenced, depending on your different values and religious backgrounds. It may because of different institutional and/or policy positions, depending on the nature, and so there has to be a general awareness of this that's thoughtful. Now, why I'm so excited about the work we're doing at IBM is we've actually launched a couple new initiatives. One is, what we call, AI OpenScale, which is really a platform and an opportunity to have the ability to begin to apply AI, see how AI operations and models function in production. We have methodologies in terms of engaging understanding fairness, so there's a 360 degree fairness kit, which is actually available in the open source world, there's a set of tools to understand and train people on recognizing bias, so even just definitions of, what do you mean by bias? It could be things like, group think, it could be, you're just self selecting on certain data sets to reinforce your hypotheses, it could be unconscious levels and it's not just traditionally socially oriented, types of bias. >> It could be data bias, too. It could be data bias, right? >> Totally. Machine generated biases in IOT world, also. >> So, contextual and behavioral biases kind of kick into play here. >> Yeah, but it starts with transparency trust. It also starts with thoughtful governance, it starts with understanding in your position on policy around data privacy, and those things are things that should be educational conversations across the entire industry. >> How far along are we on the progress bar there? I mean, it seems like it's early and we seem to be talking for awhile, but it seems even more early than most people think. Still a lot more work. Your thoughts on where the progress bar is on this whole mash up of tech and social issues around bias and data? Where are we? >> We're really at the early stages, and part of the reason we're at the early stages is I think people have, so far, really applied AI in very simple task oriented applications. The more, what we call, broad AI, meaning multi task work flow applications are starting, and we're also starting seeing in the enterprise. Now, in the enterprise world, you can still have bias, so, for example, when you talked about data bias, one of the simple examples I use is, think about loan approvals. If one of the criteria may be based on gender, you may have a sensitivity around the lack of women owned business leaders, and that could be a scoring algorithm that says, hey, maybe it's a higher risk when in fact, it's not necessarily a higher risk, it's just that the sampling is off, right. So, that would be a detection to say, hey maybe you have sensitivity around that data set, because you actually have an insufficient amount of data. So, part of data detection and understanding biases; where you have sampling of data that's incorrect, where your segmentation could be rethought, where it may just require an additional supervision or like decision making criteria as part of your governance process. >> This is actually a great area for young people to get involved, whether at their universities or curriculum, this kind of seems to be, whether it's political science and/or data science kind of coming together, you kind of have a mash. What's your advice to people watching that might be either in high school, college, or rethinking their career, because this seems to be hot area. >> It is a hot area. I would recommend it for every student at every age, quite frankly and we're at such an early stage that it's not too late to join and you're not too young nor are you too old to actually get in the industry, so that's point one. This is a great time for everyone to get involved. The second piece is, I would just start with online courses that are available, as well as participate in communities and companies like IBM, where we actually make available on a number of our web based applications, that you can actually do some online training and courses to understand the services that we have, to begin to understand the taxonomy and the language, so a very simple set, would be like, learn the language of AI first, and then, as you're learning coding, if you're more technically inclined, there's just a myriad of classes available. >> Final question, before I move on to the topic around inclusion and diversity, machine learning is impacting all verticals. I was just in an interview, talking with Don En-ju-bin-ski, she's got a company where it's neuroscience and machine learning coming together. Machine learning's being impacted all over. We mentioned basic data bias, and machine learning can help there. Machine learning meets blank every vertical, every market, is being impacted machine learning, which will trigger some of the things you're seeing on the app side. Your thoughts, looking at where you've come from in your career at IBM to now, just the evolution of what machine learning has enabled, your thoughts on the impact of machine learning. >> Oh, it's exciting and I'll give you a real simple example, so one of the great things my own team actually did was apply machine learning to, everyone loves the holiday shopping period, right? Between Thanksgiving to New Years, so we actually develop, what we call, Watson Order Optimizer and one of my favorite brands is REI, so the recreational equipment incorporated company, they actually applied our Watson Order Optimizer to optimize in real time. The best place, let's say you want to order a kayak or a T-shirt or a hiking boot, but the best way to create the algorithms to ship from different stores, and shipping from stores, for most retailers, is a high cost variable, because you don't know what the inventory positions are, you don't necessarily know the movement of traffic into that store, you may not even know what the price promotions are, so what was exciting about putting machine learning algorithms to this was, we could actually curate things like shipping and tax information, inventory positions of products in stores, pricing, a movement of goods as part of that calculation. So, this is like a set of business rules that are automatically developed, using Watson, in a way that would be almost impossible for any human to actually come up with all of the possible business roles, right? Because this is such a complex situation, and then you're trying to do it at the peak time, which is, like Black Friday, Cyber Monday Weekend, so we were able to actually apply Watson Machine Learning to create the business roles for when it should be shipped from a warehouse or a particular store. In order to meet the customer requirement, which is the fulfillment of that brand experienced, or the product experienced, so my view is, there are so many different places across the industry, that we could actually apply machine learning to, and my team is really excited about what we've been doing, especially in the next generation of supply chain. >> And it's also causing students to be really attracted to computer science, both men and women. My daughter, who is a senior at Berkeley, is interested in it, so you're starting to see the impact of machine learning is hitting all main stream, which is a good segue to my next question, we've been very passionate, I know it's one of your passions is inclusion and diversity or diversity and inclusion, there's always debates: D before I or I before D? Some say inclusion and diversity or diversity and inclusion. It's all the same thing, there's just a lot of effort going on to bring the tech industry up to par with the reality of the world, and so you have a study out. I've got a copy here. Talk about this study: Women in Leadership and the Priority Paradox. Talk about the study; what was behind it and what were some of the findings? >> Sure, and I'm excited that your daughter, that's a senior in college, is going to be another woman that's entering the workforce, and especially being in tech, so the priority paradox is that we actually looked at over 2,300 organizations, these are some of the top institutions around the world, that are curating and attracting the best talent and skills. Now, when you look at that population, we were surprised to find out that you would think by 2019-2018 that only 18% of those organizations actually had women in senior leadership positions, and what I categorize as senior leading positions, are in the see-swee, as vice presidents, maybe senior executives or senior managers; director level folks. So, that's one piece, which is, wow, given the size and the state where we are in the industry, only 18%: we could do better. Now, why do we believe that? The second piece is, you want the full population of the human capacity to think and creatively solve. Some of the world's biggest complex problems; you don't want a small population of the world trying to do this, so, the second piece of the paradox, which was the most surprising, is that 79% of these companies actually said that formalizing or prioritizing gender, fostering that kind of inclusive culture, was not a business priority, and that they had a harder time actually mapping that gap. Now, in the study, what we actually discovered though, was those companies, that did make it a priority, actually had first mover advantage, and making it a priority is quite simple. It's about understanding how to create that inclusive culture, to allow different perspectives and different experiences to be allowed in the co-creation and development. >> So, first mover advantage, in terms of what? >> Performance, actual business performance, so even though 80% of the organizations that we interviewed actually said that they've not made it a business priority, the 20% that did, we actually saw higher performance in their outcomes, in terms of business performance. >> So, this is actually a business benefit, too. I think your point is, the first mover advantage is saying, those companies that actually brought in the leadership to create that different perspective, had higher performance. >> Absolutely. >> We've talked about this before; one of the things I always say is that, tech is now mainstream, and it's 18% of the target audience of tech isn't the market, it's 50/50 or 51. Some say 51% women/men, so who's building the products for half the audience? So, again, this doesn't make any sense, so this is a good statistic. >> It is, and if you think about the students that are actually graduating out of graduate school, recently, there's actually more women graduating out of grad school than men. When you think about that population that's now entering the workforce, and what's actually happening through the pipeline, I think there's got to be thoughtful focus and programmatic improvements across the industry, around how to develop talent and make sure that different companies and organizations can move. Like you said, problem solve for creating new products that actually serve the world, not just serve certain populations, but also do it in a way that's thoughtful about, kind of, the makeup. >> And the mainstream and prep of tech obviously makes it more attractive, I mean, you're seeing a lot more women thinking about machines, like my daughter, the question is, how do they come in and not lose their footing, mentor-ship? So, what are the priorities that you see the industry needs to do? What are some of the imperatives to keep the pipeline and keep all the mentoring, obviously mentoring is hot, we see the networking built. >> Yeah, mentoring is huge. >> What's your thoughts on the best practices that you've been involved in? >> Some of the best practices we've actually done a number with an IBM, we've done a program called, Tech Re-Entry, so women that have decided to come back into the tech workforce, we actually have a 12 week internship program to do that. Another is a big initiative that we have around P-TECH, which is the next generation of workers aren't just going to have a formal college and or PHD masters type degrees. The next generation, which we're calling, is not necessarily a white collar, blue collar, what we're calling it is, new collar, meaning these are students that are able to combine their equivalent of a high school degree and early college education in one to be kind of, if you think about it, next generation of technical vocational schools, right? That quickly enter the workforce, are able to do jobs in terms of web development, in terms of cloud management, cloud services, it could be next generation of-- >> It's a huge skill gap opportunity, this is a big opportunity for people. >> It is, and we're seeing great adoption. We've seen it on a number of states across the US, this is an effort that we partner with, the states and the governors of each state, because public education has got to be done in a systematic way that you can actually sustain it for many, many years and this is something that we were excited about championing in the state of New York first. >> The ReEntry program and other things, I always tell myself, the technology is so new now you could level up a lot faster than, there's not that linear school kind of mentality, you don't need eight years to learn something. You could literally learn something pretty quickly these days because the gap between you and someone else is so short now, because it's all new skills. >> It's true, it's true. We talk about digital disruption through the lens of businesses, but there's a huge digital disruption through the lens of what you're talking about, which is our individual development and talent, and the ability to learn through so many different channels that's available now, and the focus around micro degrees, micro skills, micro certifications, there's so many ways for everyone to get involved, but I really do encourage everyone across every industry to have some knowledge and basis and understanding of tech, because tech will redefine how services and products are delivered across every category. >> And that's not male or female: that's just everyone. Again, back to technology for good, we can solve technology problems, You guys have been doing it at IBM, solve technology problems, but now the people problem is about getting people empowered, all gender, races, et cetera, the people getting the skills, getting employed, working for clouds, this is an opportunity. >> This is a huge opportunity. I think this is an exciting time. We feel like we're entering this next phase of, what I call, chapter two of cloud, this is chapter two of digital reinvention, of the enterprise, digital reinvention of the individual, actually, and it's an opportunity for every country, every population group to get involved, in so many new and creative ways, and we're at the early foundation stages in terms of both AI development, as well as new capabilities like Blockchain. So, it's an exciting time for everybody. >> Well, that's a whole nother topic. We'll have to bring you back, Inhi. Great to see you, in fact, welcome to Palo Alto. First time in our studio. Let's co-host something together, me and you. We'll do a series: John and Inhi. >> I would love that. That would be fun. I'm excited to be here. >> You can drop by our studio anytime now that you live in Palo Alto, we're neighbors. Inhi Cho Suh here, general manager IBM Watson, customer engagement, friend of theCUBE, here inside our studios, Palo Alto. I'm John Furrier, thanks for watching. (upbeat music)

>>live from Las Vegas. It's the Cube covering. Inform Attica World 2018 Not you. Buy. Inform Attica. >>Welcome back and run. Live here in Las Vegas at the Venetian Cubes coverage of In From Attica, World 2018. I'm John for the coast to queue with by host the next two days. Peter Barrister, head of research for Wicked Bonds with an Angle and the Cube. Our next two guests from Deloitte. Leo Cabrera, who's senior manager. And Rajeev Krishna, who's the specialist leader on the engineering side. CDO side guys, Thanks for joining us. Thank you, John. Thank you, Lloyd. The leader in a lot of areas, absolutely doing a lot of cutting edge stuff from c'mon, the Blockchain crypto side tax side also in the I t side. You guys have been in a great top customers here in data in from Atticus, leading the charge, looking good with the trends. But the cloud is here. Cloud scale ecosystems developing. How do you guys see in from Attica? Evolving. Going forward, Mostly great messaging. But they still got customers out there that have sold stuff. They want to bring in cloud native new data. What's what's the prospects were in from Attica. >>Foreign Formica, Saudi lawyer. We have this nuanced article data advantage and basically would consider the inflection point between what we call in just 3.0, industry for point. And it's basically now we want to get value out of the data and our data advantage strategy Focus on three pillars. They have engineering wilderness and enable men for as Informatica Isa great component and a great supporter in each of these areas. Right, So, through these study we offer video service is we offer data governance. Studio chief did offer sheet state all of it. Yeah, on. And we partner with Informatica to profile the data to understand what will be the points in which we can find value over the data on off course with the new enterprise catalog to tool to do better governance for our clients. >>I want to get under the hood. I see the catalog is getting a lot of great reviews. Some people think that this is the next big wave in data management, similar to what we've seen in other ways like well, what? Relational databases and every way that comes on cap this catalogue New kind of catalogs emerging. What's your view on this? Is it away? Visit like recycled catalog, is it? >>So get a cataloguing and data. Curation has bean going on for decades, right? But it's never gained traction on, and it's never given Klein's the value because it was so manual takes tons of effort to get it right, right. So what inform Attica is done, which is absolute breakthrough? This embed a i into their enterprise data can log into which kind of accelerates the whole data. Cataloging on basically gives them gives climbs. The value in terms of cutting down on there are packed in terms of how many people, how many data students you need to put together >>So they modernize that. Basically, they exactly all the manual stuff put automation around and put some software to find around at machine learning. Is that kind of the secret to their success? >>Absolutely. And Down Delight has been partnering with Informatica for quite a while. In fact, we are one of the few companies that have a seat on the product advice report s o what we see from the marketplace we cannot feed into in from Attica to say, Hey, here's what you need to build into your products, right? So we be doing that with their MDM solution. For example, we have what we have. Articles indium, elevate. So we build machine learning into their MP and platform and offer. That's a solution similarly, and for America has built the clear platform into their E. D. C s. Oh, that's absolutely driving Valley for clients. And we have a lot of clients that are already leveraging >>a lot of risk and platforms tools, right? I see a lot of data stuff out there that's like like a feature, not a platform, that these guys got a platform, right? So But now the world's changing the cloud. How do you guys take that data advantage program or go to a CDO and saying, Look, you gotta think differently around the data, protect you explain your view on that. >>For us, data is now the center of everything, right? So any business who want to remain competitive in the future needs to get into entire end twin management of the data, getting the value of off data and also understanding what is the data coming from and what is the day they're going to write off course is studded with all the regulations. And now GDP are coming on Friday. It is a big, you know, pusher for companies to realize that over. If >>you have a big party on Friday, a big party or is this what you Katie was a big part. Nothing happened. So you're never mean GDP. Are you guys have a lot going on there? I mean, this is the center of the conversation. >>Yeah. I mean, we do have a lot of clients who need to be compliant on GDP are on informatica is one of the tools that have already pre established the policies, so you can quickly determine where is the data that GPR is gonna be monitoring and looking for compliance on So rather than doing it from a scratch, right? So it takes a lot of it >>for Let's build on this a little bit. So when we talk about different as John was saying, different generations of data management technology, we're coming out of a generation was focused on extract, transform and load where every single application or every single new analytics application wasn't you identify the source is uniquely you build extractions unique. You'd build transformations, you build load scripts. Uniquely all that stuff was done uniquely. Now what we're saying is catalog allows us to think to move into a re use world. We've been reusing code fragments and gets and all these other things for years. In many respects, what we're talking about is the ability to bring a reuse orientation inside the enterprise to data. Have I got that right? You got it >>right. Two minutes. But the most important parties how to get value out of that, right? Because they did >>manage to get value out of using >>it more exactly And understanding, You know, how can improve your operations or you know, the bottom line, or reduce the risk that you have in your data, which is basically CPR is about, >>and one other Salin point is on very scene for America bringing values their completeness of mission. Right. So when you talk about gdp are you need different aspects, right? You need your data integration. Whether it be through cloud around. Promise you need get a governor on top of what you're cataloging, right? You need security data security. Right? So it all comes together in the hole in dramatic solutions. And I think that's very see value is supposed to like pocket pockets >>of guys. I gotta ask you a question. We've seen many ways. I think it's a big way this whole date away. But you guys, you have a term called industry four point. Oh, is what is industry but the Deloitte term. But what is that? What is industry four point? Oh, me. Can you define that? >>You wanna take that door? >>Yeah, sure. So we've seen, you know, revolutions in terms off technology and data on. We've seen people going from kind of the industrial revolution to the dark. Amira, What? Three terms in the street? Four point off where data is annoying, right? So data is an acid that needs to be completely leverage. Not just you look a reactively and retrospectively like How did we do? Right? And not even just for predictive analytics. We've seen that for a few years now. It's also about using data to drive. This is value, right? So are there new ways to monetize data? Are there new ways to leverage data and grow your business? Right? So that's what Industry four. No, no is about. >>That's awesome. Well, we got a lot of things going on here. Thanks for coming on. The Cube had a couple of questions. Got a lot of dishes going on. That preparing for the big opening of the Solutions Expo Hall. We're in the middle of all the action. You're out in the open, accused. What we do. We go out in the open final question, eyes around the CDO. Who should the chief date officer report to the C I O board? What >>do you >>guys seeing? Because the CDO now picking a strategic role if Davis the new oil. That data is the fourth wave of innovation that we've seen over centuries. What does that mean? For the chief Data Officer? More power? Why'd you report to the C i o? Why is the CEO reported the Chief Data officer? What's your take? >>Traditionally our clients in the past, where the mandate for the studios were more in the data governess, right? As of today, it is going more into enablement the data, right? So more than Analytics case. Still, service is so well seen clients going from the studio moving from under the CEO in tow, the CEO and into the CMO in some cases, more about marketing. However, at the lawyer, our proposition is that companies should do a big shift and funded the new data function as a totally new vertical next to H. R next to finance right, which have his own funding and the CDO being the leader of that function, reporting directly to the CEO or >>enablement side CEO handling much of three things engineering, governance and enablement correct. So the CEO will handle Engineering Dept. Which not just its engineering, full stack developers, possibly our cloud native developers. Governance could come into policy, normal stuff. We've seen enablement more tooling, democratization of things. >>Yeah, yeah, >>yeah. I mean, what we've been seeing right in the real world, Liss, you have, for example, finance transformation that CIA full heads, right? So there's a lot of traction at that point to kind of bring the company together. But then that soon fizzles out. Sometimes you have, ah, the CMO bringing on and marketing campaign and, you know, analytics initiative, right? There's a lot of traction. Then it fizzes out. So you need somebody at the chief data officer of the C suite level to maintain that traction that moment, Um, in order freed value. >>But it seems the key issue is someone who is focused on data as an asset generating competitive returns on data as an asset because and the reason why it could be the CEO, it could be somebody else. Historically, an i t. The asset was the hardware on the argument here is that the asset is no longer the hardware now the data data. So whoever whatever you call it, someone and a group who's focused on generating returns out of data, >>Yes. But it has to have that executive level and that new talent mortal that we're proposing right where everybody knows a little bit of data in a sense. >>And the other thing is that I mean, think about this role that's dedicated to creating value of data, right? So you can understand you know how you create value in one function. Take it to the other function and tell them Hey, here's have helped finance right, get more value and then use the same thing marketing our sales. So it's also the cross pollination of ideas across different functions in an organization. S O n roll like that is helpful in terms of >>just to say, the data could very well become the next shared service's organization. That's because you don't want your salespeople to be great with data and your marketing people to be lousy with data. >>Correct. You're totally right on that. That's what we're proposing, right? So data being another vertical in entire business, >>the Lloyd bring all the action here on the Q. With all the data they're sharing here to you. It's the Cuban John for With Peter Burst, more live cover. Stay with us. We're here in Las Vegas. Live for in from Attica, World 2018 day. One of two days of wall to wall comes here out in the open. Bringing you all the data is Thank you. Stay with us.

[Music] hi I'm Peter burns and welcome to another cube conversation we are here in our Palo Alto studios with Chen C Wang Chen C is the founder and General Partner of rain capital and an old colleague Chen see you've been around for a long time we're very happy to have you here in a cube at least in my opinion one of the leading thinkers and security what's happening in ite data security digital business security we were colleagues at four store many years ago what are you doing now well I'm doing this new fund I just started rain capital it's an early-stage venture fund focusing on cyber security innovation so very excited about that and very specific yes I was security and AI as well but the core focus is cyber security so let's talk about what that actually means because there's a lot of new practices new processes new groups with NIT that are being spawned as a consequence of this memoriae agile devops one of the groups or one of the practices or expertise centers it's especially underrepresented in the security world somewhat surprisingly is DevOps what do we need to do to bring more security into DevOps right that's a really good question and that's one of the areas that I've been focusing on in the last two three years is looking at the impact of DevOps practices to IT or including security and it's a huge impact because initially or originally what we have is security is a practice that that is gatekeeper right so you got applications being developed and then you you test them and then you go through security tests at the end and before you could deploy DevOps practices disrupt all of that what DevOps set says is I'm a developer I can deploy my application directly onto a production productionserver without going through all those gates because business agility demands it right once you have developers or testers touching your production servers directly some of the old security practices go away right you cannot do that anymore because too heavy-handed there's also the notion of portability so today it's very common for companies to want move their workloads from the internal data center to AWS or maybe next month I want to move to Google Cloud or Azure and I don't want to go through all the testing and pre-implementation practices I want to do it right away and the portability also disrupts existing security practices right so if your security policy depends on you instrumenting the server to put some kind of module on there tomorrow the server is not not there anymore right so what do you do so it engenders hosts loads of issues and also is a catalyst for innovation so that's why I'm very very excited about that so when you talk to customers users because I know you still have work with a lot of relationships I'm sure that's going to be one of the distinctions that you bring to bear when you think about what rain capital does what are say the three things that you tell them you've already mentioned you got a you got to ensure that the practices are in place that portability is at least made more obvious and that you don't bind security down to a particular device because it device may not be there that's three what are some of the kind of organizational institutional things that DevOps folks have to do to make sure that everybody gets the security profile with the need right so what we're seeing in terms organizationally or culturally the change is that in a in a DevOps led organization the the boundaries are going away right so some of the companies that I'm seeing cloud native to start with they may not have an ops team right what they do is that IT there their infrastructure team is embedded with the applications team it really the application demand and knowing what the application wants to do and then works with the developers to establish policies and deployment practices as opposed to being arms and lands from the the developers which you know creates all kinds of tension so it's an organizational shift as well and mindset shift right so the mindset shift is that you're no longer somebody who enforces policies you actually enable business versus the policy enforcer and it's easy to say but they actually requires a very deep shift in thinking so I got another question on security and I won't move to something else but really quickly as IT organizations or as businesses source their IT capabilities more from public cloud or service providers that means that they also have to have a new approach to how they to institutionalizing the work the practice the process the certainty associated with good security in in your experience what are just a couple of things that businesses have to worry about as they negotiate and monitor and manage relationships with third parties as it pertains to security right that's a good question there possibly a long list of things right but I don't use the the top things is don't get locked in right all the platform providers want to give you all these enriched capabilities as long as you buy into our services right so what you want to do is I want to stay at the level that I can easily move right so then this may mean I have to do a little bit more things or have to compensate with third party technologies as opposed to buying into this vertically integrated notion of the platform providers and that's where you need to stay at because if otherwise you get locked it so that's one second is the the ability to do monitoring has to be real-time has and the the thing about DevOps is real-time visible of the real-time closed loop response right so you cannot like secure the analytics in the past has to usually is you get tons of logs put there and somebody chewing through logs and look for anomalies it's not fast enough it's not good enough anymore so what we want is monitoring capabilities that are able to do it platform in the platform independent way but able to give you real-time visibility and response capability right there and that's where the innovation comes from you know one thing I've noticed we've been talking I've been talking a couple customers and they're starting to discover that some of the cloud service providers are using security as a way to lock in right so so and security I think should be built in right it should be by default secure by default is the way we want to be you always know how to do yeah yeah and and you should be able to get out if if security is the differentiation then maybe it's not the right market rights group yeah all right so ring capital has in addition to looking at the whole DevOps world you bring in your security expertise to bear on potential investments it's got another distinction what's the other distinction about rain capital um it's a woman led venture fund which is a rarity in Silicon Valley right so oh is it I don't know you tell me yeah no it is a rarity there are not a lot of funds that one would associate as being a broadly representative of or very inclusive so as you are moving forward with recap we'll talk a bit about the evolving role of women in technology so so I would say that even though it's a woman led venture fund I don't think of ourselves as different just because woman led I think we are different because we have a deep deep understanding of the market and deep understanding of the technology and also very extensive relationship with end-users but in terms of women in technology and women in security I'm a big advocate um so for the past two years I was the program co-chair for the Grace Hopper conference I put together the security and privacy content for the conference and the the need for a an ecosystem that is inclusive that is enabling for underrepresented either gender or race it is huge right so people go to Grace Hopper conference and they come back and they so inspired because they see all these women representing them and I think in Silicon Valley we need that insecurity we need that even more because if you look at some statistics I think women in general IT is about 24% representative army representation in security is about 11% so we have a long way to go now I'm going to avoid making comments about that because it's smart not to but those are those are numbers that are distressing that's obvious clearly there's a lot of talent you're not the only one there's a lot of talent out there there's clearly got be brought to bear and so you might not be differentiated by the fact that as women you do things differently but it might nonetheless be a more comfortable home for a woman like yourself and have a great idea and want to turn it into a business failure that's one thing all right so Chauncey and by the way I got to say just a quick advertisement for the cube the cube has been a major supporter for for women in tech for a couple years now we've been at a number of these different conferences Jeff Frick who's the general manager john john fourier co co dave one co co put a lot of time and energy so we look forward and give us a point oh absolutely we look forward to more fruitful relationships like that in the future so once again I'm Peter Burris with Wicky bonds looking angle and we've been talking to Chen Zi Wang of rain Capital founder General Partner about a number of different topics Chauncey once again thank you for every much for being on the cube thank you for having me

>> Live from New York City, it's buck you. Here is your host, Jeff Frick. >> Hey, welcome back. I'm Jeff. Rick. We're here at the Cubes. Fifth birthday party. A big date in Icy in Manhattan is part of the big Date. A week. It's got Stratos cough, a dupe world. And, of course, big Aidan. I see. So now having our party, which is always good to have, and I'm joined department X gas. Kerry McFadden from Parodi Research. Carrie. Welcome. Well, thank you very much. So last last we saw he was actually a big data and twenty thirteen, So it's lots changing the year. >> Absolutely, Absolutely. I think the whole hoodoo thing is really taken off. And the thing that interests me the most about show or or the exhibitors at the show is that Bye. You could get a lot of data into Duke, but how do you get it out? How do you make it useful? What do you do with it when you get it out? You know, I said on structure data is structured. Date. Is that a combination? Is it ski Melis? >> All the above all the above, >> right? Exactly. So I think really, that's been on and actually have been Jeff to all the shows, right? Since the beginning, when it was just a new world. Okay, Cube started back. And I think two thousand ten two thousand filling our fifth birthday. Right? So at least at least at least twenty ten. So since then, you've seen, you know, progression off vendors coming in to provide services that actually enable Duke to do more than it does started is kind of a batch oriented type of solution that now, because of these other value added solutions can to really or near real time processing, you can take the data out of it a lot more easily. You can use do basically as a as a repository, right on DH. And a lot of the solutions out there are are evolving to the point where you can, uh, you could basically make a sense of the information, and I think that's a really important rights. Dated information information inside, right? That's where we want to go with this thing. Business decisions made in real time. Which way? Define as in time to do something about it. Right? Right. Yes. Some of the players, I mean, you've got the map. Our guys. You've got the act. Aeon folks that just bought pervasive software. So they've got the Predictive Analytics piece sort of covered. Obviously. That's stone breakers. Old company, you know, a variant of ing gris, right? You've got. Obviously, IBM is a player in this space. With their blue mix and their cloud capabilities and all of their information management pieces, every major vendor is got a piece of is part of the action, if you will. Trying to build something on top of a dupe to make it more useful and make it more valuable. Yeah, the floor was filled with little companies, big companies, and everyone is certainly jumping in. So let me get your prospectus that you've been coming for a lot of years on this thing. Where are we on the journey? How? How? You know, I think we're past the P E O C stage, right? People are getting stuff into production deployments, but it's still early days. You know, the Giants are playing tonight. Go Giants, are we? First inning, third inning, seventh inning. Where are we? I think we're probably in the second or third any second. I think we got a ways to go. And what's the next big hurdle to get us to the next inning. I think one of the problems is this storage issue, right? So you've got this issue of being able to scale out theoretically, exponentially, right? The nice thing about do piss If you need Teo, if you need more space, you just add No J had storage and whatnot, But what happens when you get too much information? You're into the pedal bike, multiple PETA right range now, and most of that data, you know you're not going to access. You may access only two percent of it overtime. I think they're a lot of figures around that. But actually, a wicked bon article that I read recently is very interesting, one called Flake Flake or what they were doing. Flake. I want to make sure he gets a slave by a herd where he said it to me off camera, right? It's a f L a P. It's a combination of flash and tape on DH. Basically, there's a great article on the Wicked Bond site by Wicked Bonds CTO, David's lawyer Okay, and his premises that at some point, relatively soon a cz thie as data grows exponentially into the multiple petabytes ranges and maybe even beyond The thing is gonna get squeezed is the traditional HDD or hardening is spinning disc, right? So tape has become much more, uh, much more resilient. Uh, tape last has a meat time failure of about twenty six or thirty years versus disc, which is about five. And obviously flash is much, much faster, right? Right in some cases don't get into all the nuances of almost feet feet, but flavor going to squeeze out disks and the men think so. And what that'll offer customers is a is a much lower TCO from managing those huge petabytes scale environments and also accessing it at a relatively quick speed. So I think that's that's a piece. It's interesting that the other part that's very interesting to me, Mr Cognitive Computing face. So I was at the no SQL event last week last month in in San Jose, and with that they had a cognitive computing component on DH. I think thie idea of trying to get machines to think more like people building neuro morphing chips to two. It's kind of mimic the way synapses or electricity, electricity in the brain, you know, works how neurons fire and so forth is very interesting. And I think once you Khun Get Dupe is the repository. You've got the data there. But how do you make use of it? And I think that's the challenge. That's going to be, well, paramount the next few years. Exciting days ahead. Well, Gary, thanks for taking a few minutes. We're at the fifth birthday party at the Cube. Were at Big Data and nice jefe. Rick, we're on the ground. Thanks for watching.

>>The cube at big data SV 2014 is brought to you by headline sponsors. When disco we make Hadoop invincible and Aptean accelerating big data, 2.0, >>Okay. We're back here, live in Silicon valley. This is big data. It has to be, this is Silicon England, Wiki bonds, the cube coverage of big data in Silicon valley and all around the world covering the strata conference. All the latest news analysis here in Silicon valley, the cube was our flagship program about the events extract the signal from noise. I'm John furrier, the founders of looking angle. So my co-host and co-founder of Wiki bond.org, Dave Volante, uh, George Matthew CEO, altruist on the cube again, back from big data NYC just a few months ago. Um, our two events, um, welcome back. Great to be here. So, um, what fruit is dropped into the blend or the change, the colors of the big data space this this time. So we were in new Yorkers. We saw what happened there. A lot of talk about financial services, you know, big business, Silicon valley Kool-Aid is more about innovation. Partnerships are being formed, channel expansion. Obviously the market's hot growth is still basing. Valuations are high. What's your take on the current state of the market? >>Yeah. Great question. So John, when we see this market today, I remember even a few years ago when I first visited the cave, particularly when it came to a deep world and strata a few years back, it was amazing that we talked about this early innings of a ballgame, right? We said it was like, man, we're probably in the second or third inning of this ball game. And what has progressed particularly this last few years has been how much the actual productionization, the actual industrialization of this activity, particularly from a big data analytics standpoint has merged. And that's amazing, right? And in a short span, two, three years, we're talking about technologies and capabilities that were kind of considered things that you play with. And now these are things that are keeping the lights on and running, you know, major portions of how better decision-making and analytics are done inside of organizations. So I think that industrialization is a big shift forward. In fact, if you've listened to guys like Narendra Mulani who runs most of analytics at Accenture, he'll actually highlight that as one of the key elements of how not only the transformation is occurring among organizations, but even the people that are servicing a large companies today are going through this big shift. And we're right in the middle of it. >>We saw, you mentioned a censure. We look at CSC, but service mesh and the cloud side, you seeing the consulting firms really seeing build-out mandates, not just POC, like let's go and lock down now for the vendors. That means is people looking for reference accounts right now? So to me, I'm kind of seeing the tea leaves say, okay, who's going to knock down the reference accounts and what is that going to look like? You know, how do you go in and say, I'm going to tune up this database against SAP or this against that incumbent legacy vendor with this new scale-out, all these things are on in play. So we're seeing that, that focus of okay, tire kicking is over real growth, real, real referenceable deployments, not, not like a, you know, POC on steroids, like full on game-changing deployments. Do you see that? And, and if you do, what versions of that do you seeing happening and what ending of that is that like the first pitch of the sixth inning? Uh, w what do you, how would you benchmark that? >>Yeah, so I, I would say we're, we're definitely in the fourth or fifth inning of a non ballgame now. And, and there's innings. What we're seeing is I describe this as a new analytic stack that's emerged, right? And that started years ago when particularly the major Hadoop distro vendors started to rethink how data management was effectively being delivered. And once that data management layer started to be re thought, particularly in terms of, you know, what the schema was on read what the ability to do MPP and scale-out was in terms of how much cheaper it is to bring storage and compute closer to data. What's now coming above that stack is, you know, how do I blend data? How do I be able to give solutions to data analysts who can make better decisions off of what's being stored inside of that petabyte scale infrastructure? So we're seeing this new stack emerge where, you know, Cloudera Hortonworks map are kind of that underpinning underlying infrastructure where now our based analytics that revolution provides Altrix for data blending for analytic work, that's in the hands of data analysts, Tableau for visual analysis and dashboarding. Those are basically the solutions that are moving forward as a capability that are package and product. >>Is that the game-changing feature right now, do you think that integration of the stack, or is that the big, game-changer this sheet, >>That's the hardening that's happening as we speak right now, if you think about the industrialization of big data analytics that, you know, as I think of it as the fourth or fifth inning of the ballgame, that hardening that ability to take solutions that either, you know, the Accentures, the KPMGs, the Deloitte of the world deliver to their clients, but also how people build stuff internally, right? They have much better solutions that work out of the box, as opposed to fumbling with, you know, things that aren't, you know, stitched as well together because of the bailing wire and bubblegum that was involved for the last few years. >>I got it. I got to ask you, uh, one of the big trends you saw in certainly in the tech world, you mentioned stacks, and that's the success of Amazon, the cloud. You're seeing integrated stacks being a key part of the, kind of the, kind of the formation of you said hardening of the stack, but the word horizontally scalable is a term that's used in a lot of these open source environments, where you have commodity hardware, you have open source software. So, you know, everything it's horizontally scalable. Now, that's, that's very easy to envision, but thinking about the implementation in an enterprise or a large organization, horizontally scalable is not a no brainer. What's your take on that. And how does that hyperscale infrastructure mindset of scale-out scalable, which is a big benefit of the current infrastructure? How does that fit into, into the big day? >>Well, I think it fits extremely well, right? Because when you look at the capabilities of the last, as we describe it stack, we almost think of it as vertical hardware and software that's factually built up, but right now, for anyone who's building scale in this world, it's all about scale-out and really being able to build that stack on a horizontal basis. So if you look at examples of this, right, say for instance, what a cloud era recently announced with their enterprise hub. And so when you look at that capability of the enterprise data hub, a lot of it is about taking what yarn has become as a resource manager. What HDFS has been ACOM as a scale-out storage infrastructure, what the new plugin engines have merged beyond MapReduce as a capability for engines to come into a deep. And that is a very horizontal description of how you can do scale out, particularly for data management. >>When we built a lot of the work that was announced at strata a few years ago, particularly around how the analytics architecture for Galerie, uh, emerged at Altryx. Now we have hundreds of, of apps, thousands of users in that infrastructure. And when we built that out was actually scaling out on Amazon where the worker nodes and the capability for us to manage workload was very horizontal built out. If you look at servers today of any layer of that stack, it is really about that horizontal. Scale-out less so about throwing more hardware, more, uh, you know, high-end infrastructure at it, but more about how commodity hardware can be leveraged and use up and down that stack very easily. So Georgia, >>I asked you a question, so why is analytics so hard for so many companies? Um, and you've been in this big data, we've been talking to you since the beginning, um, and when's it going to get easier? And what are you guys specifically doing? You know, >>So facilitate that. Sure. So a few things that we've seen to date is that a lot of the analytics work that many people do internal and external to organizations is very rote, hand driven coding, right? And I think that's been one of the biggest challenges because the two end points in analytics have been either you hard code stuff that you push into a, you know, a C plus plus or a Java function, and you push it into database, or you're doing lightweight analytics in Excel. And really there needs to be a middle ground where someone can do effective scale-out and have repeatability in what's been done and ease of use. And what's been done that you don't have to necessarily be a programmer and Java programmer in C plus plus to push an analytic function and database. And you certainly don't have to deal with the limitations of Excel today. >>And really that middle ground is what Altryx serves. We look at it as an opportunity for analysts to start work with a very repeatable re reasonable workflow of how they would build their initial constructs around an analytic function that they would want to deploy. And then the scale-out happens because all of the infrastructure works on that analyst behalf, whether that be the infrastructure on Hadoop, would that be the infrastructure of the scale out of how we would publish an analytic function? Would that be how the visualizations would occur inside of a product like Tableau? And so that, I think Dave is one of the biggest things that needs to shift over where you don't have the only options in front of you for analytics is either Excel or hard coding, a bunch of code in C plus plus, or Java and pushing it in database. Yeah. >>And you correct me if I'm wrong, but it seems to be building your partnerships and your ecosystem really around driving that solution and, and, and really driving a revolution in the way in which people think about analytics, >>Ease of use. The idea is that ultimately if you can't get data analysts to be able to not only create work, that they can actually self-describe deploy and deliver and deliver success inside of an organization. And scale that out at the petabyte scale information that exists inside of most organizations you fail. And that's the job of folks like ourselves to provide great software. >>Well, you mentioned Tableau, you guys have a strong partnership there, and Christian Chabot, I think has a good vision. And you talked about sort of, you know, the, the, the choices of the spectrum and neither are good. Can you talk a little bit more about that, that, that partnership and the relationship and what you guys are doing together? Yeah. >>Uh, I would say Tableau's our strongest and most strategic partner today. I mean, we were diamond sponsors of their conference. I think I was there at their conference when I was on the cube the time before, and they are diamond sponsors of our conference. So our customers and particular users are one in the same for Tablo. It really becomes a, an experience around how visual analysis and dashboard, and can be very easily delivered by data analysts. And we think of those same users, the same exact people that Tablo works with to be able to do data blending and advanced analytics. And so that's why the two software products, that's why the two companies, that's where our two customer bases are one in the same because of that integrated experience. So, you know, Tableau is basically replacing XL and that's the mission that thereafter. And we feel that anyone who wants to be able to do the first form of data blending, which I would think of as a V lookup in Excel, should look at Altryx as a solution for that one. >>So you mentioned your conference it's inspire, right? It >>Is inspiring was coming up in June, >>June. Yeah. Uh, how many years have you done inspire? >>Inspire is now in its fifth year. And you're gonna bring the >>Cube this year. Yeah. >>That would be great. You guys, yeah, that would be fun. >>You should do it. So talk about the conference a little bit. I don't know much about it, but I mean, I know of it. >>Yeah. It's very centered around business users, particularly data analysts and many organizations that cut across retail, financial services, communications, where companies like Walmart at and T sprint Verizon bring a lot of their underlying data problems, underlying analytic opportunities that they've wrestled with and bring a community together this year. We're expecting somewhere in the neighborhood of 550 600 folks attending. So largely to, uh, figure out how to bring this, this, uh, you know, game forward, really to build out this next rate analytic capability that's emerging for most organizations. And we think that that starts ultimately with data analysts. All right. We think that there are well over two and a half million data analysts that are underserved by the current big data tools that are in this space. And we've just been highly focused on targeting those users. And so far, it's been pretty good at us. >>It's moving, it's obviously moving to the casual user at some levels, but I ended up getting there not soon, but I want to, I want to ask you the role of the cloud and all this, because when you have underneath the hood is a lot of leverage. You mentioned integrates that's when to get your perspective on the data cloud, not data cloud is it's putting data in the cloud, but the role of cloud, the role of dev ops that intersection, but you're seeing dev ops, you know, fueling a lot of that growth, certainly under the hood. Now on the top of the stack, you have the, I guess, this middle layer for lack of a better description, I'm of use old, old metaphor developing. So that's the enablement piece. Ultimately the end game is fully turnkey, data science, personalization, all that's, that's the holy grail. We all know. So how do you see that collision with cloud and the big, the big data? >>Yeah. So cloud is basically become three things for a lot of folks in our space. One is what we talked about, which is scale up and scale out, uh, is something that is much more feasible when you can spin up and spin down infrastructure as needed, particularly on an elastic basis. And so many of us who built our solutions leverage Amazon being one of the most defacto solutions for cloud based deployment, that it just makes it easy to do the scale-out that's necessary. This is the second thing it actually enables us. Uh, and many of our friends and partners to do is to be able to bring a lower cost basis to how infrastructure stood up, right? Because at the end of the day, the challenge for the last generation of analytics and data warehousing that was in this space is your starting conversation is two to $3 million just in infrastructure alone before you even buy software and services. >>And so now if you can rent everything that's involved with the infrastructure and the software is actually working within days, hours of actually starting the effort, as opposed to a 14 month life cycle, it's really compressing the time to success and value that's involved. And so we see almost a similarity to how Salesforce really disrupted the market. 10 years ago, I happened to be at Salesforce when that disruption occurred and the analytics movement that is underway really impacted by cloud. And the ability to scale out in the cloud is really driving an economic basis. That's unheard of with that >>Developer market, that's robust, right? I mean, you have easy kind of turnkey development, right? Tapping >>It is right, because there's a robust, uh, economy that's surrounding the APIs that are now available for cloud services. So it's not even just at the starting point of infrastructure, but there's definite higher level services where all the way to software as industry, >>How much growth. And you'll see in those, in that, as that, that valley of wealth and opportunity that will be created from your costs, not only for the companies involved, but the company's customers, they have top line focus. And then the goal of the movement we've seen with analytics is you seeing the CIO kind of with less of a role, more of the CEO wants to the chief data officer wants most of the top line drivers to be app focused. So you seeing a big shift there. >>Yeah. I mean, one of the, one of the real proponents of the cloud is now the fact that there is an ability for a business analyst business users and the business line to make impacts on how decisions are done faster without the infrastructure underpinnings that were needed inside the four walls in our organization. So the decision maker and the buyer effectively has become to your point, the chief analytics officer, the chief marketing officer, right. Less so that the chief information officer of an organization. And so I think that that is accelerating in a tremendous, uh, pace, right? Because even if you look at the statistics that are out there today, the buying power of the CMO is now outstrip the buying power of the CIO, probably by 1.2 to 1.3 X. Right. And that used to be a whole different calculus that was in front of us before. So I would see that, uh, >>The faster, so yeah, so Natalie just kind of picked this out here real time. So you got it, which we all know, right. I went to the it world for a long time service, little catalog. Self-service, you know, Sarah's already architectures whatever you want to call it, evolve in modern era. That's good. But on the business side, there's still a need for this same kind of cataloguing of tooling platform analytics. So do you agree with that? I mean, do you see that kind of happening that way, where there's still some connection, but it's not a complete dependency. That's kind of what we're kind of rethinking real time you see that happen. >>Yeah. I think it's pretty spot on because when you look at what businesses are doing today, they're selecting software that enables them to be more self-reliant the reason why we have been growing as much among business analysts as we have is we deliver self-reliance software and in some way, uh, that's what tablet does. And so the, the winners in this space are going to be the ones that will really help users get to results faster for self-reliance. And that's, that's really what companies like Altrix Stanford today. >>So I want to ask you a follow up on that CMOs CIO discussion. Um, so given that, that, that CMOs are spending a lot more where's the, who owns the data, is that, is we, we talk, well, I don't know if I asked you this before, but do you see the role of a chief data officer emerging? And is that individual, is that individual part of the marketing organization? Is it part of it? Is it a separate parallel role? What are you, >>One of the things I will tell you is that as I've seen chief analytics and chief data officers emerge, and that is a real category entitled real deal of folks that have real responsibilities in the organization, the one place that's not is in it, which is interesting to see, right? Because oftentimes those individuals are reporting straight to the CEO, uh, or they have very close access to line of business owners, general managers, or the heads of marketing, the heads of sales. So I seeing that shift where wherever that chief data officer is, whether that's reporting to CEOs or line of business managers or general managers of, of, you know, large strategic business units, it's not in the information office, it's not in the CEO's, uh, purview anymore. And that, uh, is kind of telling for how people are thinking about their data, right? Data is becoming much more of an asset and a weapon for how companies grow and build their scale less. So about something that we just have to deal with. >>Yeah. And it's clearly emerging that role in certain industry sectors, you know, clearly financial services, government and healthcare, but slowly, but we have been saying that, >>Yeah, it's going to cross the board. Right. And one of the reasons why I wrote the article at the end of last year, I literally titled it. Uh, analytics is eating the world, is this exact idea, right? Because, uh, you have this, this notion that you no longer are locked down with data and infrastructure kind of holding you back, right? This is now much more in the hands of people who are responsible for making better decisions inside their organizations, using data to drive those decisions. And it doesn't matter the size and shape of the data that it's coming in. >>Yeah. Data is like the F the food that just spilled all over it spilled out from the truck and analytics is on the Pac-Man eating out. Sorry. >>Okay. Final question in this segment is, um, summarize big data SV for us this year, from your perspective, knowing what's going on now, what's the big game changer. What should the folks know who are watching and should take note of which they pay attention to? What's the big story here at this moment. >>There's definite swim lanes that are being created as you can see. I mean, and, and now that the bigger distribution providers, particularly on the Hadoop side of the world have started to call out what they all stand for. Right. You can tell that map are, is definitely about creating a fast, slightly proprietary Hadoop distro for enterprise. You can tell that the folks at cloud era are focusing themselves on enterprise scale and really building out that hub for enterprise scale. And you can tell Horton works is basically embedding, enabling an open source for anyone to be able to take advantage of. And certainly, you know, the previous announcements and some of the recent ones give you an indicator of that. So I see the sense swimlanes forming in that layer. And now what is going to happen is that focus and attention is going to move away from how that layer has evolved into what I would think of as advanced analytics, being able to do the visual analysis and blending of information. That's where the next, uh, you know, battle war turf is going to be in particularly, uh, the strata space. So we're, we're really looking forward to that because it basically puts us in a great position as a company and a market leader in particularly advanced analytics to really serve customers in how this new battleground is emerging. >>Well, we really appreciate you taking the time. You're an awesome guest on the queue biopsy. You know, you have a company that you're running and a great team, and you come and share your great knowledge with our fans and an audience. Appreciate it. Uh, what's next for you this year in the company with some of your goals, let's just share that. >>Yeah. We have a few things that are, we mentioned a person inspired coming up in June. There's a big product release. Most of our product team is actually here and we have a release coming up at the beginning of Q2, which is Altryx nine oh. So that has quite a bit involved in it, including expansion of connectivity, uh, being able to go and introduce a fair degree of modeling capability so that the AR based modeling that we do scales out very well with revolution and Cloudera in mind, as well as being able to package into play analytic apps very quickly from those data analysts in mind. So it's, uh, it's a release. That's been almost a year in the works, and we're very much looking forward to a big launch at the beginning of Q2. >>George, thanks so much. You got inspire coming out. A lot of great success as a growing market, valuations are high, and the good news is this is just the beginning, call it mid innings in the industry, but in the customers, I call the top of the first lot of build-out real deployment, real budgets, real deal, big data. It's going to collide with cloud again, and I'm going to start a load, get a lot of innovation all happening right here. Big data SV all the big data Silicon valley coverage here at the cube. I'm Jennifer with Dave Alonzo. We'll be right back with our next guest. After the short break.

>>Welcome back everyone. This is our final wrap-up of the Amazon web services. Reinvent conferences is SiliconANGLE and Wiki bonds. The cube is our flagship program. We go out to the events, extract the signal from the noise. I'm John furry or the founders to look an angle. And of course I'm joining my cohost partner in crime. Dave Volante, co-founder with you bond.org. Um, really exciting event, Dave, I got to say, this is our wrap up. Let's put a bow on this show. Let's put the bumper sticker on the car and let's see what, uh, what was this document? What happened day one enterprise day to infrastructure day three ties it all together with Kinesis. Amazon is doing two things. That's very, very rare in tech history, and that is a disrupting and innovating at the same time. The magic it's the magic formula. And to me, it's really two tactical executions, one ball moving the ball yard by yard first and 10, do it again to use the football analogy, moving the chains, moving the ball down the field, kind of a running game, ground game, whatever a call it. >>And then the big yardage passing play with Kinesis, I think really brings their success of an integrated stack. And I believe they're going to be the iPhone like model for the cloud they're they're light years ahead of everybody else on public cloud. Uh, they're winning the developers. And again, we just heard from Dr. Matt would kind of reiterating what we were saying in our previous segment about the diversity of the successes. It's not a one trick pony. They got diversity from startups to large enterprises to NASA. So Dave, I mean, I mean, who is going to take on Amazon, who is going to challenge Amazon? That's the question that we want to know right now. It's not looking good right now. They're got a commanding lead in the cloud space and it'd be really interesting to watch how the Kinesis, the enterprise movement, uh, with VDI announcement and workspaces and all the enhancements in the, in the performance is going to shift the sand in the industry. And you're already seeing Cisco down 12% VMware stocks down. I mean, game-changing, the sands are shifting. What's your >>Well, I think we're seeing history in the making here, John. I mean, I think last year at reinvent com leading up to we reinvent, we realized that this event was going to be big and not just the event. The event is a metaphor for the shift that's occurring in the industry. We're talking about a trillion plus dollar marketplace that Amazon is disrupting and believe it or not, they're tiny, even though there are three or 4 billion, they're tiny, it's a trillion dollar Tam that is absolutely getting flipped on its head. And what do we mean by that? Every premise about the it business is changing. We talk about a lot. Amazon has ch has turned the data center into an API. It's a very powerful concept. I think you're right on. It's the, it's the iPhone of the enterprise. Yes. That's. They're not like hall monitors checking about every application in the app store. >>That's not the point. The point is it's a consistent environment that is controlled by Amazon, very tightly controlled and it works. You know what you're getting, and it's innovating at a, at a breakneck speed. It's antithetical to everything we know about it. So, you know, you've been asking people all week what the bumper sticker is on the show. I can't wait to go back and see some of those, but I mean, this is the trend and the trend is your friend, or it might be your enemy. So when you say who's going to be able to compete with Amazon, I think Martin of eucalyptus set a set of best historically in economics. There's always people that will rent and there's always people that will buy. And the, the old guard is Amazon calls them is not going to take this lying down, but the old guard has to replicate an Amazon's model. How does it do that? It's got to create an open entry into its system. That's equivalent in terms of simplicity and power to the Amazon API. Number one, number two is it's got to be able to demonstrate to the developer community that you can inter-operate across those platforms in a way that you can get critical mass, the same way that you can with Amazon. And that's going to be the, the massive battle that's going to take place in the cloud Wars. >>I mean, I think one of the things that's interesting is that the word lock-in was something that we were talking on day one, especially in the enterprise, that's a word that gets kicked around. And you know, my feeling has always been lock-in is not necessarily a bad thing if it's, if you can, if you can have switching costs that aren't super high locking means, switching costs are so high that you can switch. I can switch from my iPhone to Android anytime I want. But the problem is that iPhone is a better product. It's integrated with the apps and I can buy all the same apps. So that's a very key thing. And I think the switching costs here are a lot higher and I, there are Amazon >>On the record. Amazon is the mother of all lock-ins. I mean, this is a beautiful business model and here's, what's so great about it is the customers. You heard them this week say if you took AWS away from me, I would burst out into tears. So Amazon's, I think brilliant challenge here is to how do they keep innovating? They're doing that, but how do they keep lowering prices? So people don't want to leave. So that that's, that's what I see as the disruptive piece. It, >>Well, being in this business all these years were, you know, a little bit older than some of the young guns were on the cube to me lock-in is moving right? You see, um, in the old days, huge capital outlays for, uh, for equipment, you had maintenance, all this stuff was locking. Now the lock-in shifting to OPEX and agility. So what's happening is Amazon is basically commoditizing the old way of how people would spend and shifting the lock-in to the op X side of the equation. I call it the heroin addiction where, Hey, it's so low cost and the agility is the lock-in. So the functionality of agility guarantees the lock. And I think that's what Amazon's betting the ranch on is that when can go to time to market, to value quicker, that is inherently a lock-in, that's a quote, user experience to use my iPhone example. >>If I'm going to have a good experience making money as an enterprise, that's good. That's good. Lock-in right. So it's all a relative term in that the lock-in has been around. I mean, they call it differentiation, but at the end of the day, I think Amazon's got a good, good play there. But like I said, I don't think Amazon has cracked the it nut yet. I think they're going to have some it penetration. And this is top of the first ending, as we were saying, the enterprise, it nut enterprise, it is not, has not. The nut has not been cracked. What >>Do you need to see to be convinced? Well, >>I just think the stack is going to be the, the same paradigm of having an integrated staff. I just want to see different levels of services because the table stakes for the enterprise are different. There's certain compliance issues and you know, they're checking the boxes right now. This is the ground game I was referring to earlier. Amazon is going to start checking the boxes. Oh, VDI, we got workspaces, I got this. I'm going to check the boxes. Ultimately the list is just too long to win everyone. Right? So I think, you know, so it's going to be an opportunity. I think OpenStack has a great hope. I think VMware and IBM and HP are big players. And I think OpenStack needs to step up its game and have a big player, pop down a billion dollars with like IBM David Linux and saying, look at OpenStack, we're behind it. And rally the troops. And that's all >>Sorry, go back to the lock-in comments because this is critical because to me, the definition of lock-in is it's, it's, it's less economically attractive to leave than it is to stay. And that's what Amazon is doing. They're making it, making it more economically attractive to stay than they are to leave. Here's why that's so important. The more people that they pull, and this is why Carlisle and back said, you know, we can't lose to the bookseller. And you said that because they know the old guard knows that if people go to Amazon, they're not going to leave. Cause it's going to be less attractive for them to leave than it is to stay. So there's a huge battle over that trillion dollar Tam. So the key is John that OpenStack and IBM and VMware and Oracle and all the others have to make it economically attractive to not go into Amazon. And that is the battle. >>One of the things that's very clear, Dave, that's coming out of the show for me. My bumper sticker is dev ops wins. And I think what that mean by that is, is that, and we refer to the cloud being in the top of the first inning, meaning really everything else was spring training. He used the baseball metaphor in the sense that this is all that this is all activation of a paradigm shift. That is so game-changing the dev ops concept of software developers. Writing code that trickles into a fully integrated stack is really amazing, right? This really replaces the pain of provisioning hardware cost of it, cost of the infrastructure. That stuff is that that is the real value of the crowd. So if you take the dev ops concepts, which to me is already a winner and put that into the enterprise market, that's going to be cloud ops. >>So to me, I think the opportunity right now for anyone who wants to with Amazon in my opinion, is to go out there and say, look it, you got to win the software developers, look at what a Mongo DB has done. We had Elliot the co-founder on, they made it good goodness for the developer. Whoever can do that for the enterprise will win. And I don't think that there's a direct one-to-one mapping of what dev ops is. It is in the Amazon world. And what dev ops is in the enterprise. I think that's more cloud ops because the guys that are provisioning EMC drives dealing with IBM and red hat a little bit slower, I would say in terms of deployment, they used to the big slow cycles. Dev ops guys are pushing code a little bit more, you know, nimble startup, clean sheet of paper, you know, Uber, Airbnb, those younger generations, but this is a generational shift and it's happening and it's all on the software. So to me, I think dev ops speaks to, >>I wanna, I wanna, uh, keep this thread going. So, so what's the playbook to, for the old guard to compete, you're saying you gotta, uh, attract developers, but that's not enough. You need a cloud platform, right? So take, for example, VMware, VMware announces, you know, hybrid cloud infrastructure as a service it's early days, they need a cloud platform. So what else do you need to compete? You need developers. You need, >>You gotta have, you gotta have trust and security, right? So here's the thing. Developers care about success of creativity for the solutions. And what Amazon's demonstrate is the time to value is the key thing. You hear people, whether they're startups or big company get to some value, double down on success, figure out how to be agile succeed. Fast, move on with the problem right now is that developers are like deer in the headlights. They go where the action is, right? And it's always been that way. I think OpenStack to me is an opportunity or whatever platform that is. Someone's got to get a big anchor tenant in that platform needs to step up and be the galvanizing force and create some solidarity around that approach for it. That is an opportunity for VMware. I think Pat Gelsinger is probably best positioned to do that. Pivotal is a, is a genius, but I think ultimately they might be biting off more than they can chew. So I worry about, you know, their car not being fast enough right now in, in the game. So, you know, worry about pivotal there. But I think VMware probably is a better position there. So they need, they need, they need infrastructure. They need this middleware, which is database queuing notifications. A lot of that, a lot of the stuff you see Amazon doing at the top of the stack managed services. So that's streaming data and all the goodness on them, >>Developers, you got to have a cloud platform at scale, you gotta have trust and security. I would add to that. You got to do things that Amazon's not going to do. So for instance, we heard all week, Amazon doesn't want to do one-offs. They don't like to do customization, whatever they do. They want everybody to benefit from that enterprise enterprises want customization. We've talked about this, John. That's why, for instance, you, you find that some of the customers won't go into Amazon, not because the security is bad, it's just different. And Amazon's not going to change the security profile. They're not going to change the policy. So enterprise, uh, players, the old guard, so to speak must continue to do custom stuff. One-off that Amazon won't do, but here's the bet that Amazon's making Amazon's that its ecosystem will over time be able to do those one-offs for the customer and put a buffer in between the Amazon platform and the customer. So that's, that's really interesting. >>Yeah. I would also add to that, that the main differentiation where Amazon and other potential people to compete with Amazon is scale, scale matters. Scale gives leverage. Amazon has proven that, and they're trying to use that leverage now to catapult into other markets for market expansion. So that's one thing. So, so, so the, so for the enterprise in particular, one area we watch heavily, I see two major trends. I see a cloud service that's similar to Amazon. It smells like an integrated stack, but it just has different feature sets tailored for the enterprise. That's more of that's the hybrid cloud clearly hybrid cloud is a winner. Amazon is not using that term hybrid cloud. And he's a hybrid ID, which is basically a head fake. It really means hybrid cloud. So that's hybrid cloud. The second thing is I think you're going to see data centers be Amazon in a box. >>So that's why I like io.com because io.com has essentially built pods and containers and essentially is cloud in a box. And I think shipping data centers is the future. And I think what I like about IO and here's why I'm interested in double clicking on that company is that they're basically shipping data centers. You've got Goldman Sachs, big companies. So IO IO has got, got that going on. And then you've got hybrid cloud. And then the third thing that's really relevant is that you started to see the vertical integration Dave of, of services. Look at CSC, CSC bought service mesh. We had, uh, this guy Jeff on earlier with, uh, that company is doing all the user experience they're offering full end-to-end full-stack developers for essentially web apps. Okay. That is a shift to what I call the dev ops world. Those two things. You're going to see these industries where it's ISV and integrators are kind of vertically integrated. They're going to actually build their own stuff. And that's going to be the, I think the innovation on the channel side. So the channel is up for grabs. Everything's being disrupted >>Battlefield. We've got developers, we've got cloud scale, we've got trust and security. We've got customization. And I'm going to add another one, which is the ecosystem, which is essentially your, you know, in part in your channel, but got to have a strong ecosystem, want to pick up this discussion with you and getting the hook. >>So the Dave wants to of what's the bumper sticker for the show. Give me the Dave Volante bumper sticker. You. We heard everyone said a story here. Um, >>What AWS, the, the trend is your friend, >>My bumper sticker. I'm going to throw a hashtag in there. The hashtag next generation computer revolution to me, this is the next generation computer revolution, total transformative hashtag next generation computer revolution. I think Amazon's leading the charge and I think they're going to shift the sands and everyone else is going to have to adjust. And that's good for everyone, Dave and the market wins a ruin murky on Hortonworks tweeted. Hey, we'd love it. Market expansion, rising tide floats all boats. And I think that's all >>Ultimately ultimately billion dollar Tam Gianna. I'm thrilled to a >>Part of covering that with the cube. I want to thank everyone for watching. Thanks. This is the day three wrap up this acute exclusive coverage from Amazon web services. Want to thank the crew here? All the guys back at the ranch. Kristen, Nicole art Lindsay, Mark Hopkins. Andrew, we got mic. We got Alex. Good job, Jeff Fricks do, uh, everyone. Jeff Kelly. We have the analysts. Come on. We've got this show covered, Dave. I think we fished this pond out. So look for us next to HP. Discover will be there. And, uh, December the week of the 10th or 11th and 12th, we'll be doing the OpenStack summit as well. Look for that. When that gets announced, um, my maybe doing the node node summit in December, we got also the spark summit and MIT event in January. The security event would be at Berkeley. We're going to all these great events tubes out of control. We've got storage, big data now cloud, we look for a lot of research. You can see a lot of cloud coverage coming out on the research. So I looked for that over the next few months, I will get bon.org. Thank you for watching. Well, that's a wrap day three exclusive coverage. This is the cube. I'm John fryer with Dave Volante here in Las Vegas until next time take care.

okay we're back here live at the IBM iod information on demand conference hashtag IBM iod this is the cube so looking the anglo Mookie bonds flagship program we go out for the events extracting from the noise i'm john furrier might join my co-host Davey lonte and we'd love to have analysts in here and in this case former analyst James Cole Beatles welcome to back to the cube thank you very much John thank you Dave pleasure see you again finger of being at IOD you're a thought leader you are an influencer you work at IBM so you you're out there the front lines doing some great work so thank you very much tell us explains the folks out there not about the show because we've had some people coming in last year you were private in but what does this fit what is this vector in context to what's relevant the market obviously big data and analytics is the hottest thing on the planet right now and you got social business now emerging categorically here but it has a couple different flavors to it right within IBM's context yeah but the messaging is simple right you got analytics that drives value outcomes social business is the preferred way of people going to operate their businesses engagement and all that is great stuff new channels marketing eccentric cetera explain to them how I OD is fitting into these megatrends into mega trends I think the hottest trends why our customers caring about what's going on here is a lot of a lot of activity around customers what is what does IOD fit into that a bigger picture yeah well you know the world has changed the world culture has changed radically and really in the last decade or so none is everywhere in the world everything is now online and digital increasingly it's streaming in terms of culture look what's happening to Hollywood is being deconstructed by the netflixs of the world you know movies and TV and music and everything is delivered online now all engagement more more engagements with your employer with your you know with merchants with your family everywhere is online things like streaming media so if you look at how the world culture has changed I yesterday I spoke here on a topic that's near and dear to my heart called big media it's the support of the ascendance of streaming media and not just the area as I laid out but in education like MOOCs distance learning we use it internally at IBM for our think fridays and Ginni Rometty and the executive team you know every Friday its cloud or its big data or whatever you know we need all need to get up to speed on the world culture has changed now analytics is fundamental to that whole proposition in terms of world culture analytics driving gagement analytics in terms of you know in a business context analytics a 360-degree view and you have data warehouses and the master data and you have predictive models to drive segmentation and target marketing and all that good stuff you know that's been in business for a long time that those set of practices they have become prevalent in most industries now not just in say retailing you know the Amazons of the world they're pervasive across all industries big data is fundamental to that you know engagement model its social social in the sense that social is one of many channels through which business is engaged with through which many people engage the social is assumed assuming a degree of importance in the fabric of modern life that goes beyond simple you know engagement with you know brands and whatnot social is how people create is how they declare who they are it's their identity and so social in your personal life we all know about Facebook and Twitter and everything else and YouTube but social has revolutionized enterprise cultures everywhere you know we use social internally of course we use our own Lotus connections most large and even many mid-sized firms now use social for interactions among employees or throughout their Val you chain so social business is about all of that it's the b2c it's the b2b it's the e2e and employ to employ all these different models of engagement they all demand a number of things obviously the social platform they demand the data of various sorts structured unstructured in shared repositories or cubes or Mars or whatnot they it demands the the big data platforms not only at respite in motion the streaming media to make it all happen in real time so at IOD if you see what the themes are this year and really it's been a building for several years cloud everything social is running in the cloud now more and more not just public Claus but Federation's of public and private clouds it's it's all about cognitive computing which is a relatively new term in the Sun sets achieved a certain amount of vogue in the last year or so which is really fundamentally as an evolutionary trend it's basically a I for the 21st century but leveraging unstructured data and and machine learning and so forth and predictive analytics and you know well the whole world learn what metadata was with the whole NSA yeah comments no it's like me and then just to wrap it up in memory real-time blu acceleration you know you need real-time you need streaming you need collaboration and social you know peer-to-peer user-generated content all of that to make this new world culture really take off and IBM provides all that we recognize that that's where the world's going we've been orienting reorienting all of our solutions around these models cloud social increasingly going forward and you know we provide solutions that enable our customers in all industries to go there and big data is fundamental to all of that as we say we're computer science meets social science that's always been Silicon angles kind of masthead view but to unpack what you just said from the market relevance you mentioned Netflix we saw Amazon coming out their own movie they're going to go direct with their own programming so so but that speaks to the direct business model of the web was originally pioneered as hey direct business model cut the middleman out but now that dimension has been explored so that kind of what you're saying there so that's cool the end user pieces interesting image is social so what's your take on the end user orientation what's the expectation because you got social you got a trash you got in motion you got learning machines providing great recommendations got the Watson kind of yeah reasoning for people so personalization recommendation engines the sea change attention time currency big days of all those buzzwords all right what is the expectation for users in the future right now we're moving into this new world where I can self serve myself monologue based the information from the web now it's all coming at everyone real time the alarms are going off as Jeff Jonas says what is that prefer user experience the direct business model people get that I think the business to see that but now the end users are now at the center of the value proposition how do what's the role of the user now they're participating in the media there are also consumers of the media yeah and they now have different devices so what's the sources of data so fundamentally yeah the role of the consumers expectations now is always everything is always on everything is always online everything is all digital everything is all real time and streaming everything is all self-service everything is all available in the palm of my hand and then the back-end infrastructure the cross-channel infrastructure users don't care about individual socials they really don't they don't really fundamentally care about Facebook or Twitter or whatever you have they just care that what their experience is seamless as they move from one channel to another they're not perceived as channels anymore they're simply perceived as places or communities that overlap too in a dizzying array of socials thus social is where we all live and thus social increasingly is mobile increasingly mobile is you know the user expects that the handoff from my smartphone to my tablet to my laptop to my digital TV sentence and so forth that it all happens through the magic of infrastructure that it's being taken care of and they don't have to worry about that handoff it all it's all part of one seamless experience yeah they always just say the search business it's the it's the it's the intersection of contextual and behavioral yeah and now you take that online behaviors community contextual is context to what people are interested at any given time yeah it's so many longtail distributions at any given time so do you see the the new media companies that the new brands that might emerge mean there's all the talk about Marissa Mayer kind of turning over yahoo and yeah she some say putting lipstick on a pig but but but is that they're just an old older branch trying to be cool but is that what users want just like media but just user experience me like we're small media but we got big ideas but the thing is the outcomes right small frying big blues go figure are the outcomes still the same company still want to drive sales for their business sell a product provide great value you just want to find great content and find people I mean the same concept of the old web search find out and run sumit give any vision on how that environment will evolve for a user like is it going to be pushed at me do you see it a new portal developing is mmm Facebook's kind of a walled garden humble don't care about that what's your take on that the future vision of a user experience online user experience online future vision in many ways I think let's talk about Internet of Things because that keeps coming more and more into the discussion it's it's not so much that the user wants a seamless experience across channel cross device all that but a big part of that experience is the user knows that increasingly they'll have some confidence that whatever environments physical environments there in our being obviously there's privacy implications that surveillance here are being monitored and tracked and optimized to meet their requirements to some degree in other words environmental monitoring internet of things in your smart home you want to configure so you smart home so that every room that you walk into is as you as you're moving there even before you get there has already been optimized to your needs that ideally there should prediction Oh Jim's walking into the bathroom so turn the light on and also start to heat up the water because it's ten o'clock at night Jim's usually takes his bath around this time you sort of want that experience to be handled by the internet of things like nest these new tools like nest oh yeah yeah so essentially then it's my user experience is not just me interacting with devices but me simply moving through environments that are continuously optimized to my knees and needs of my family you know the whole notion of autonomous vehicles your vehicle if it's your personal vehicle then you want to always autumn optimize the experience in terms of like you know the heat setting and and the entertainment justement saan the you know the media center and they're always to be tailored to your specific needs at any point in time but also let's say you take a zipcar you rent a zipcar and you've got an ID with that company or any of the other companies that provide those on-demand rental car services ideally in this scenario that whatever vehicle you you rent through them for a few hours or so when you enter it it becomes your vehicle is completely customized to your needs because you're a loyal customer of that firm and they've got your profile information this is just a hypothetical I'm not speaking to anything that I actually know about what they're doing but fundamentally you know ideally any on-demand vehicle or conveyance or other item that you you lease in this new economy is personalized to your needs while you're using it and then as it were depersonalized when you check it back in so the next person can have it personalized to their use as long as they need it that's the vision of a big part of the vision of customer experience management personalization not just of your personal devices but personalization of almost any device or environment in which you are operating so that's one kanodia wants this question no I would ask one more question on that on the user experience came on Twitter from a big data alex says while you're on the subject which a my Alex I don't great great friend of the cube but thanks for the tweet today we don't have our crowd shado-pan we can get the chat going there but why not talk about AR and I've been in reality I mean honestly Internet of Things is now not the palm of your hand it could be on your wrist or on your clothing the wearables on the glasses and just gave out three invites to google glass so this is again another edition augmented reality is software paradigm as well what is that what is it what does that fit into that what's your take on augmented reality augmented reality ok so augmented reality is that which I don't use myself I've just simply seen it demonstrated and plenty of places so augmented reality is all about layers of additional information overlaid on whatever visual video view or image view that you happen to be carrying with you or have available to you while you're walking around in your normal life so right now conceivably if this is an AR a setting that I would environment or enabled device I would be able to see for example that ok who's in this room in the sense that who is declared that they are in this area of Mandalay Bay right now and why specifically are they doing to the extent that they allow that information to be seen and o of these people here which of these people if any might be the person I'm going to be speaking with it for 30 so that if they happen to be in this environment i can see that i can see that they're to some degree they may have indicated status waiting for james could be a list to get done with the Wikibon people oh that's kind of cool so I'd see that overlay and I walk to other parts of the Convention Center I might also see overlays as I walk around like oh there's a course down as several rooms down that I actually put in my schedule it's going to start in about five minutes I'll just duck you into there because it reminds me through the overlay that's the whole notion of personalization of the environment in which you're walking around in real time dynamically and contextual in alignment with your needs or with your requirements are in alignment also with these whatever data those environment managers wish to share to anybody who's subscribing in that contact so that's a context-aware that theme have been talking about here on textual essentially it's a public space that's personalized to your needs in the sense that you have a personalized view in a dynamically update okay that sounds like crowd chat Oh are we running a trip crouched at right now crouch at San overlay so just as lovely overlay so look to the minute social network yeah tailored to the needs of the group yep that adds value on top of that data yeah so James I gotta get your take on something so we had Merv on yesterday great Adrian with my great Buy analyst day and he was on last week at Big Data NYC you know we did our own little vent there Don coincident with hadoop world so Murph said well we're just entering the trough of disillusionment for big data yeah you love those Gartner you know I love medications tools I mean they are genius and I get him but he said that's a good thing because it goes left to right so we're making progress here ok right but I'm getting nervous the internet of things I love the concept we don't we don't work on industrial internet and you know a smarter planet it's in there so I love it but I'm getting nervous here's why I look back at a lot of the promises that were made in the BI days 360-degree other business predictive analytics a lot of things that are now talking about in the hood sort of Hadoop big data movement that we're actually fulfilling with this new wave that the old wave really wasn't able to fill because the cousin sort of distracted doing sarbanes-oxley and reporting in and balanced scorecards so so I'm nervous he's old school now it when he when he referenced is something that was hot in the mid part of the two thousand decade okay go ahead okay we had a guy on today talking about balance core would you know we're just talking about crowd chat that's the hottest day in 2013 like five years or hurt anybody mentions sarbanes-oxley so what kind of saved that whole business Roy thank you and Ron but so heavy right so what I'm nervous about as we as I've seen a number of waves over the years where the the vendor community promises a vision great vision great marketing and then all of a sudden something hotter comes along like Internet of Things and says don't know this is really it so my question to you is will help us it'll help me in my mind you know close that dissonance gap is are these two initiatives the sort of big data analytics for everybody putting analytics in the hands of business users yeah or is that sort of complementary to the internet of thing his internet of things just the new big trillion dollar market that everybody's going to go after and forget about all those promises about analytics everywhere help me sure Jay through that my job is to clarify confusion hey um you know if you look at the convergence of various call them paradigms there's a lot of big data analytics is one of them right now clearly there's cloud clearly their social there's big data analytics in mobile and there's something called Internet of Things so some some talk about smack smac social mobile analytic a que a big data cloud if you add IOT of there it's smack yet I don't think it works or smash yet but fundamentally if you think about Internet of Things it's it's all about machines or automated devices of various sorts probes and you know your smartphone and whatever I know servers or even you know the autonomous vehicles those are things that do things and you know they might be sources of data they would are they might be consumers of data they might conceivably even be intermediaries or brokers or routers or data what I'm getting at is that if you look at big data analytics I always think of it as a pipeline all data it's like data sources and data consumers and then there's all these databases and other functions that operate between them to move data and analytics and insight from one end to the other of the pipe in a conceptual way think of the internet of things as well a new category of sources of data these devices whether they be probes or monitors or your smart phones and new consumers and they all those same things are probably going to be many of them consumers of data and there's message passing among them and then the data that they passed might be passed in real time through streaming like InfoSphere streams it might be cached or stored and various intermediate databases and various analytics performed on them so think of you know I like to think of the internet of persons places and things persons that's human endpoints consumers and and sources of data that's all of us that's social places that's geospatial you know you think about it the Internet of geospatial you know geo spatial coordinates of of data and analytics and then there's things there's you know automated endpoints or you know hardware even Nana from macro to nano devices so it's just a new range of sources and and consumers of data and new types of analytics that are performed in new functions that can be performed and outcomes enable when you as it were stack in and out of things with social with claw with mobile new possibilities in terms of optimization in real time it throughout the you know the smarter planet if you think about the smarter planet vision it's all about interconnected instrumented and intelligent instrumented you know instrumentation that traditionally it suggests hardware instrumentation that's what probes our sensors and actuators that's the Internet of Things it's a fundamental infrastructure within smarter planet I'd love that thank you for clarifying i could write a blog post out of that and i think i'm very well made so um now i want to follow up and bring it back to the users I know snack and I thought you were going to say a story no smack MapReduce analytics and query or sell smack on the cube so so I want bring it back to the users so we had a great conversation yesterday actually last week I'll be met it was on off you know ah be met and he said look why are there any any you know where all the big data apps he said you need three things to for big data apps you need domain expertise you need algorithms which are free and you need data scientists like oh we'll never get there all right oh so rules really free while there are that was this argument yeah it means a source if people charge him for algorithms big trouble was this point I think okay sure so and then we had a discussion yesterday about how in the early days of the automobile industry you know the forecast was this is problematic the gap to adoption is just aren't enough chauffeurs know the premise that we were putting forth in the discussion yesterday I don't know who that was with was that with Judith it was good was that look we've got to figure out a way to get analytics in the hands of the business user we can't have to go through a data scientist or some business analyst no that's not going to work and we'll never get adoption so what what's going to bridge that gap is it is it the things you talked about before all these you know cool solutions that you guys are developing the project neo that you announce today visualization yeah there's another piece of that what puts it in the hands of guys like me that I can actually use the data in new and productive ways yeah well self-service business intelligence and visualization tools that are embedded in the very experience of using apps for example on your smartphone democratization of data science down to all of us you need the right tools you need you need the tools that the new generation of people like my children's generation just adopt and they work in there just a tune from from the cradle to working with data and visualizations and creating visual you know analytics of various sorts though they may not perceive it as being analytics they miss may perceive it as working with shapes and patterns and stuff yeah you would stop yeah so playing around you know in a sandbox i love that terminology data scientists working you know sandboxes which is data that's martes that they build to do regression analysis and segmentation and decision trees and all you know all that good stuff you know the fact is your sandbox can conceivably be completely on your handheld device with all the visualizations built-in you're simply doing searches and queries you know you're asking natural language questions you're looking at the responses you're changing your queries you're changing your visualizations and so forth to see if anything pops out at you as being significant playing around it you know it's as simple a matter that that these kinds of tools such as IBM you know cognos and so forth enable everybody to become as it worried a data scientist without having to you know become a maquette their profession it's just a part of the fabric of living in modern society where data surrounds us people are going to start playing with data and they're going to start teaching themselves all these capabilities in the same way that when they invented automobiles and you know wasn't Henry 42 invented them it was in like the late 1800s by engineers in Europe and America you know it's like we didn't all become auto mechanics you know there are trained auto mechanics but I think most human beings in the modern world know that there's a thing called an automobile that has an engine that needs gasoline and oil and occasionally needs to be brought to a professional mechanic for a repair and so forth we have many of us have a rough idea of something called a carburetor blah blah blah you know in the same way that when computers came up after world war two and then gradually invaded our lives through PCs and everything we all didn't become computer scientist but most of us have an idea of what a hard disk is most of it no most of us know something about something called software and things are called operating systems in the same way now in this new world most of us will become big data analytics geeks practical into the extent that will learn enough of the basic terms of art and the relationships among the various components to live our lives and when the stuff breaks down we call the likes of IBM to come and fix it or better yet they just buy our products and they just work magically all the time without fail conversing and comfortable with the concepts to the point which you can leverage them and what about visualization where does that fit visualization visualization is where the rubber meets the road of analytics is it's where human beings how human beings extract meaning insight fundamentally maybe that's like yeah you extracted inside a lots of different ways you do searches and so forth but to play around it to actually see you know a heat map or a geospatial map or or or you know a pie chart or whatever you see things with your eyes that you may not have realized we're there and if you can play around and play with different visualizations against the same data set things will pop out that you know the statistical model just seek the raw output of a data mining our predictive model or statistical analysis those patterns may not suggest themselves and rows of numbers that would pop out to an average human being or to a data scientist they need the visualizations to see things that you know because in other words when you think about analytics it's all about the algorithms that are drilling through the data to find those patterns but it's also about the visualizations the algorithms and you need the visualizations and of course you need the data to really enable human beings of all levels of expertise to find meaning and fundamentally visualizations are a lingua franca between non-expert human beings and expert eamon beings between data scientists visualizations are a lingua franca Hey look what I saw what do you think you know that's the whole promise of tools like concert for example we demonstrated this this morning it's a collaborative environment as sharing of visualizations and data sets and so forth among business analysts and the normal knowledge worker you know it with it you know like what do you see here's what I see what do you think I don't see that here's another visualization what do you see there oh yeah I think I see what you mean and here's my annotation about what I have broader context I've you know here's what I oh this is great that's the whole notion of humans deriving insight we derive it in socials we derive it in teams of that some Dave might be adept at seeing things that Jim is just absolutely blind to or you know Nancy might see things that both of us are applying to but we're all looking at the same pictures and we're all working with the same data part art yeah it's all so let's talk about some plumbing conversations you know one of the things that we noticed we were at the splunk conference this year's blown came out of nowhere taking log files making them manageable saving time for people so the thing that comes out of the splunk conversation is that it's just so easy to use that their customer testimonials are overwhelmingly positive around the area hey I just dumped my data into this the splunk box and it grid good stuffs happening I can search it it can give me insight save me time so that's the kind of ease of use so so how does IBM getting to that scenario because you guys have some good products we've got on the platform side but you also have some older products legacy Lotus other environments collaborative software that's all coming together in converging so how do we get to that environment where it's just that he just dumped your data in and let it do its magic well Odin go that's the very proposition that we provide with our puresystems puredata systems portfolio tree data system and big insights right for Hadoop so forth big in size you know we have an appliance now yeah we have pdh so that's the whole create load and go scenario that because Bob pidgeotto unless wretched and others demonstrated on the main stage yesterday and today so we did we do that and we are simple and straight being easy to use and so forth that's our value prop that's the whole value prop of an appliance you know simple you don't need a ton of expertise we pre build all the expert in a expertise patterns that you can use to derive quick value from this deployment we provide industry solution accelerates from machine data analytics on top of big insights to do the kinds of things you're talking about with splunk offerings so fundamentally you know that's scenario we all we and we're you know we have many fine competitors we offer that capability now in terms of the broader context you're describing we're a well-established provider of solutions we go back more than a hundred years we have many different product portfolios we have lots and lots of customers who would invested in IBM for a long time they might have our older products our newer products in various combinations we support the older generations we strive to migrate our customers to the newer releases when they're ready we don't force them to migrate so we make very we're very careful in our row maps to provide them with a migration path and to make it worth their while to upgrade when the time comes to the newer feature ok so I got it don't change gears to the to the shiny new toy conversation which is you know you know we love that in Silicon Valley what's a shiny new toy there's always an emerging markets when you have see changes like this where there's a whole the new whole new wave comes in creates new wealth old gets destructed new tags over whatever the conversation goes but I got to ask you okay well Elsa to the IBM landscape that you that you're over overlooking with big data and under the under the hood with cloud etc there's always that one thing that kind of breaks out as the leader the leading toy a shiny object that that people gravitate to as as I'm honest I won't say lost later because you got you know it's not not about giving away free it's it's the product that goes well we this is the lead horse you know and in this game right yeah so what is that what is the IBM thing right now that you're doubling down on is it blu acceleration is it incites is it point2 with a few highlights right now that's really cutting through the new the new the new soil of yeah we're developing our own rip off version of google glass thank you know I'm saying it's always I mean I'm gonna say shiny too but there's always that sexy product well I want that I want L customers name I want that product which leads more you know how she lifts for other products is there one is there a few you can talk about that you've noticed anecdotally is going to be specific data but just observational a shiny toy for the consumer market or for the business business business mark okay yeah yeah is it Watson is Watson the draw is it what's the headline looking for the lead lead dog here what's the attack there's always one an emerging market well you can put your the spot here well you could say that the funny thing is the whole notion of a shiny new toy implies something tangible when the world is gone more and more intangible in the cloud so we are moving our entire portfolio beginning links the big data analytics solutions into the cloud cloud first development going forward our other core principles for the pure data systems portfolio and the light for the shiny the shiny new thing the new cons could be shiny new concept or new paradigm yeah but the shiny new thing is the cloud the cloud is something pervasive and the cloud is something that it really multi form factors that's not very sexy but customers want flexibility you know they want to acquire the same functionality either as a licensed software package and running on commodity hardware we offer that for our big data analytics offerings or as an appliance and one sort or another that specialized particular occurrence or as a SAS cloud offering or as a capability that they can deploy in a virtualization layer on top of IBM or non-ibm hardware or they want the abilities you can mix and match those various deployment form factors so in many ways the whole notion of multi form factor flexibility is the shiny new thing it's the hybrid model for deployment of these capabilities on Prem in the cloud combination thereof that's not terribly sexy because it's totally it's totally abstract but it's totally real I mean demand wise people can see them that drives my business because when you go to the cloud I mean that's where you can really begin to scale seriously beyond the petabytes the whole notion of big media it will exist entirely in the cloud big media I like to think is the next sexy thing because streaming is coming into every aspect of human existence where stream computing a lot of people who focus on Big Data think of volume as being like big headline oh god we'd go to petabytes and exabytes and all that yeah it's important some really fixate on variety all these disparate sources of data and now we have all the sensor data and that's very important we have all the social media and everything all those new sources that's extremely important but look at the velocity everybody is expecting real-time instantaneous continuous streaming you know everything we do all of our entertainment all of our education surveillance you know everything is completely streaming I think ubiquitous streaming to every device and everybody themselves continue to continuing to stream their very lives everywhere all the time is the sexy new thing Dave and I talk about running data we coined that term running data what four years ago so I got to get you got to get kind of a thought leader they're watching us and we're watching streaming data right now from these said these are your guys are streaming this is big media give us some wanna get your thought leader perspective here some thought leader mojo around um the hashtag data economy you know you need now you're moving into a conversation with c-level folks and they said James tell me what the hell is this data economy thing right so what is the data economy in your words kind of like I mean I'll say it's a mindset I'll everything else what's your take on that we've been discussing that internally and externally at IBM we're trying to get our heads around what that means here's my take as one IBM are one thought Leigh right by the way the trick of being a thought leader is just to let your own thoughts lead you where they will turn around where all my followers yeah hopefully they want to lead you to far astray where you're out in the wilderness too long that's an important type of people are talking about because people are trying to put the definition around at economy can you actually have a business construct around yeah data here is my taken on the layers of the meaning of data economy it's monetizing your data the whole notion of monetization of your data data becomes a product that you generate internally or that you source from externally but you repackage it up and then resell with value add the whole notion of data monetization and you know implies a marketplace for data based products you know when I say data I'm using it in the broader context of it could be streaming media as the kind of one is a very valuable category of you know data like you know whatever kollywood provides so there's a whole notion of monetizing your data or providing a marketplace for others to monetize their data and you take a transaction fee from that or it also means in more of a traditional big data or data warehousing bi sense it means that you drive superior outcomes for your your own business from your own data you know through the usual method of better decision if better decisions on trustworthy data and the like so if you look at data monetization in terms of those layers including the marketplace including you know data-driven okay in many ways the whole notion of a data economy hinges on everybody's realization now that the chief resource for betterment of humanity one of the chief resources going forward for us to get smarter as a species on this planet is to continue to harness the data that we ourselves generate you know people stop what data is being the new oil what oil was there before we ever evolved but data wasn't there before we we landed on earth or before we evolved we generate that so it's our own exhaust your own exhaust that's actually a renewable resource data exhaust from data from exhausted gold that's what we say data is the data exhaust it's good if you can harness it and put it together as Jeff Jones says the puzzle piece is the picture the big picture at the smarter picture the smarter planet so on the final question I want to wrap up here to our next guest but what's going on with you these days talk about what's up with you you know you're very active on Facebook will you give a good following I'll be coming up what's happening you know I'll make sure I said big birthday for you on your Facebook page what's going on in your life I'll see you're working at IBM one of the things are interesting what's on your mind these days when you're at leisure are you hanging out you think what are you thinking about the most what are you doing with your you know things with your family's cherith let's see what's going on well I hang out at home with my wife and drink beer and listen to music and tweet about it everybody knows that stuff kind of beer do you drink whatever is on sale I'm not going to say where we buy it but it's a very nice place that whose initials are TJ but fundamentally you know my my mind is an open book because I evangelize I put my thoughts and my work thoughts and love my personal thoughts out there on socials I lived completely ons but I completely unsocial I self-edit but fundamentally the thought leadership I produce that the blogs and whatnot I produce all the time I put them out there for general discussion and I get a lot of good sort of feedback the world and including from inside of IBM I just try to stretch people's minds what's going on with me I'm just enjoying what I'm doing for a living now people save Jim you're with IBM why aren't you an analyst I'm still doing very analyst style work in in a vendor context I'm a thought leader I was a thought leader as I try to be being a thought leader is like being a humorist it's like it's a statement of your ambition not your outcome or your results yeah you can write jokes too you're blue in the face but if nobody laughs then you're not a successful comedian likewise i can write thought leadership pieces till I'm blue in the face but if nobody responds that I'm not leaving anybody anywhere i'm just going around in circles so my my ambition and every single day is to say at least one thing that might stretch somebody's box a little bit wider yeah yeah I think I think IBM smart they've been in social for a while the content markings about you know marketing to individuals yeah with credibility so I love analysts I love all my buds like like Merv and everybody else and I'm you know sort of a similar cat but you know there's a role for X analysts inside of solution providers and we have any number John Hegarty we have we have Brian Hill another X forest to write you know it's it's a you know it's a big industry but it's a small industry we have smart people on both sides of the equation solution provider and influencer my line um under people 99 seats and you know I I suck up to my superiors at IBM i suck up to any analyst who says nice things about me and hosts be on their show and i was going out of my life i'm just a big suck up well we like we like to have been looking forward to doing some crowd chats with you our new crouch an application with you guys lock you into that immediately it's a thought leader haven that the Crouch as as it turns out Dave what's your take on the analyst role at IBM just do a little analysis of the analyst at IBM which you're taken well I think it's under situation I think that the role that they that IBM's put James in is precisely the way in which corporations vendors should use former analysts they should give you a wide latitude a platform and and not try to filter you you know and you're good like that and so guess what I do the usual marketing stuff to the traditional but I do the new generation of thought leadership marketing and there's a role for both of those to me marketing have said this is if I said it was I said a hundred times marketing should be a source of value to people and it's so easy to make marketing a source of value by writing great content or producing great content so yeah that's my take on a jonathan your your marketing is a great explainer you explain the value to the market and thereby hopefully for your company generate demand hopefully in the direction of your cut your customers buying your things but that's what analysts the influencers should be explainers it's you know probably Dave I mean has influenced as influences that we are with with a qu here's my take on it when you have social media of direct full transparency there's no you can't head fake anyone anymore that all those days are gone so analyst bloggers people who are head faking a journalist's head faking the house the audiences will find out everything so to me it's like it's the metaphor of when someone knocks on your door your house and you open it up and they want to sell you something you shut the door in their face when you come in there and they say hey I want to hang out I got you know I got some free beer and a big-screen TV you want to watch some football maybe you invite him in the living room so the idea of communities and direct marketing's about when if you let them into your living room yeah you're not selling right you are creating value see what i do i drop smart i try to drop smart ideas into every conversational contacts throughout socials and also at events like i od so you know a big part of what I do is I thought leadership marketer is not just right you know you're clever blogs and all that but I simply participate in all the relevant conversations where I want I want ideas to be introduced and oh by they want way I definitely want people to be aware that I am an IBM employee and my company's provides really good products and services and support you know that's really a chief role of an evangelist in a high-tech slider that's one of the reasons why we started crouched at because the hashtag get so difficult to go deep into so creates crowd chatter let's go deeper and have a conversation and add some value to it you know it's you thinking about earned media as parents been kicked around but in communities the endorsement of trust earning a position whether you work at IBM people don't care a he works at IBM or whatever if you're creating value and you maybe have some free beer you get an entry but you win on your own merits you know I'm saying at the end of the day the content is the own merits and I think that's the open source paradigm that is hitting the content business which is community marketing if your pain-in-the-ass think you're going to get bounced out right out of the community or if you're selling something you're on so you guys do a great job really am i awesome you thank you James I really love what you add to the iod experience here with this corner and all the interviews is great great material well thanks for having us here really appreciate it I learned a lot it's been great you guys are great to work with very professional the products got great great-looking luqman portfolio hidden all hitting all the buttons there so hitting all the Gulf box so this is the cube we'll be right back with our last interview coming up shortly with Jeff Jonas he's got some surprises for us so we'll we'll see what he brings brings to his a game apparently he told me last night is bring his a-game to the cube so I'm a huge Jeff Jonas fan he's a rock star we love them on the cube iza teka athlete like yourself we write back with our next guest after this short break

>>Hi everybody. We're back. This is Dave Volante from Wiki bond.org. We're live at strata in Santa Clara, California. This is Silicon angle TVs, continuous coverage of the strata conference. So Riley media or Raleigh media is a great partner of ours. And thanks to them for allowing us to be here. We've been going all week cause it's day three for us. I'm here with Jeff Kelly Wiki bonds that lead big data analysts. And we're here with Jack Norris. Who's the VP of marketing at Matt bar Jack. Welcome to the cube. Thank you, Dave. Thanks very much for coming on. And you know, we've been going all week. You guys are a great sponsor of ours. Thank you for the support. We really appreciate it. How's the show going for you? >>Great. A lot of attention, a lot of focus, a lot of discussion about Hadoop and big data. >>Yeah. So you guys getting a lot of traffic. I mean, it says I hear this 2,500 people here up from 1400 last year. So that's >>Yeah, we've had like five, six people deep in the, in the booth. So I think there's a lot of, a lot of interests. There's interesting. >>You know, when we were here last year, when you looked at the, the infrastructure and the competitive landscape, there wasn't a lot going on and just a very short time, that's completely changed. And you guys have had your hand in that. So, so that's good. Competition is a good thing, right? And, and obviously customers want choice, but so we want to talk about that a little bit. We want to talk about map bar, the kind of problems you're solving. So why don't we start there? What is map are all about? And you've got your own distribution of, of, of enterprise Hadoop. You make it Hadoop enterprise ready? Let's start there. >>Okay. Yeah, I mean, we invested heavily in creating a alternative distribution one that took the best of the open source community with the best of the map, our innovations, and really it's, it's about making Hadoop more applicable, broader use cases, more mission, critical support, you know, being able to sit in and work in a lights out data center environment. >>Okay. So what was the problem that you set out to solve? Why, why do, why do we need another distribution of Hadoop? Let me ask it that way. Get nice and close to. >>So there, there are some just big issues with, with the duke. >>One of those issues, let's talk about that. There's >>Some ease of use issues. There's some deep dependability issues. There's some, some performance. So, you know, let's take those in order right now. If you look at some of the distributions, Apache Hadoop, great technology, but it requires a programmer, right? To get access to the data it's through the Hadoop API, you can't really see the data. So there's a lot of focus of, you know, what do I do once the data's in there opening that up, providing a full file based access, right? So I can look at it and treat it like enterprise storage, see the data, use my standard tools, standard commands, you know, drag and drop from a file browser. You can do that with Matt bar. You can't do that with other districts >>Talking about mountain HDFS as a NFS correct >>Example. Correct. And then, and then just the underlying storage services. The fact that it's append only instead of full random read-write, you know, causes some, some issues. So, you know, that's some of the, the ease of use features. There's a whole lot. We could discuss there. Big picture for reliability. Dependability is there's a single point of failure, multiple single points of failure within Hadoop. So you risk data loss. So people have looked at Hadoop. Traditionally is, is batch oriented. Scratchpad right. We were out to solve that, right? We want to make sure that you can use it for mission critical data, that you don't have a risk of a data loss that you've got full high availability. You've got the full data protection in terms of snapshots and mirroring that you would expect with the enterprise products. >>It gets back to when you guys were, you know, thinking about doing this. I'm not even sure you were at the company at the time, but you, your DNA was there and you're familiar with it. So you guys saw this big data movement. You saw this at duke moon and you said, okay, this is cool. It's going to be big. And it's gonna take a long time for the community to fix all these problems. We can fix them. Now let's go do that. Is that the general discussion? Yeah. >>You know, I think, I think the what's different about this. This is the first open source package. The first open source project that's created a market. If you look at the other open source, you know, Linux, my SQL, et cetera, it was really late in the life cycle of a product. Everyone knew what the features were. It was about, you know, giving an alternative choice, better Unix. Your, your, the focus is on innovation and our founders, you know, have deep enterprise background or CTO was at Google and charge of big table, understands MapReduce at scale, spent time as chief software architect at Spinnaker, which was kind of the fastest clustered Nazanin on the planet. So recognize that the underlying layers of Hadoop needed some rearchitecture and needed some deep investment and to do that effectively and do that quickly required a whole lot of focus. And we thought that was the best way to go to market. >>Talk about the early validation from customers. Obviously you guys didn't just do this in a vacuum, I presume. So you went out and talked to some customers. Yeah. >>What sorts of conversations with customers, why we're in stealth mode? We're probably the loudest stealth >>As you were nodding. And I mean, what were they telling you at the time? Yeah, please go do this. >>The, what we address weren't secrets. I there've been gyrus for open for four or five years on, on these issues. >>Yeah. But at the same time, Jack, you've got this, you got this purist community out there that says, I don't want to, I don't want to rip out HDFS. You know, I want it to be pure. What'd you, what'd you say to those guys, you just say, okay, thank you. We, we understand you're not a prospect. >>And I think, I think that, you know, duke has a huge amount of momentum. And I think a lot of that momentum is that there isn't any risks to adopting Hadoop, right? It's not like the fractured no SQL market where there's 122 different entrance, which one's going to win. Hadoop's got the ecosystem. So when you say pure, it's about the API APIs, it's about making sure that if I create a MapReduce job, it's going to run an Apache. It's going to run a map bar. It's going to run on the other distributions. That's where I think that the heat and the focus is now to do that. You also have to have innovation occurring up and down the stack that that provides choice and alternatives for. >>So when I'm talking about purists, I don't, I agree with you the whole lock-in thing, which is the elephant in the room here. People will worry about lock-in >>Pun intended. >>No, no, but good one good catch. But so, but you're basically saying, Hey, where we're no more locked in than cloud era. Right. I mean, they've got their own >>Actually. I think we're less because it's so easy to get data in and out with our NFS. That there's probably less so, >>So, and I'm gonna come back to that. But so for instance, many, when I, when I say peers, I mean some users in ISV, some guys we've had on here, we had an Abby Mehta from Triceda on the other day, for instance, he's one who said, I just don't have time to mess with that stuff and figure out all that API integration. I mean, there are people out there that just don't want to go that route. Okay. But, but you're saying I'm, I'm inferring this plenty who do right. >>And the, and by the API route, I want to make sure I understand what you're saying. You >>Talked about, Hey, it's all about the API integration. It's not >>About, it's not the, it it's about the API APIs being consistent, a hundred percent compatible. Right. So if I, you know, write a program, that's, that's going after HDFS and the HDFS API, I want to make sure that that'll run on other distributions. Right. >>And that's your promise. Yeah. Okay. All right. So now where I was going with this was th again, there are some peers to say, oh, I just don't want to mess with all that. Now let's talk about what that means to mess with all that. So comScore was a big, high profile case study for you guys. They, they were cloud era customer. They basically, in my understanding is a couple of days migrated from Cloudera to Mapbox. And the impetus was, let's talk about that. Why'd they do that >>Performance data protection, ease of use >>License fee issues. There was some license issues there as well, right? The, the, your, your maintenance pricing was more attractive. Is that true? Or >>I read more mainly about price performance and reliability, and, you know, they tested our stuff at work real well in a test environment, they put it in production environment. Didn't actually tell all their users, they had one guys debug the software for half a day because something was wrong. It finished so quickly. >>So, so it took him a couple of days to migrate and then boom, >>Boom. And they've, they handle about 30 billion objects a day. So there, you know, the use of that really high performance support for, for streaming data flows, you know, they're talking about, they're doing forecasts and insights into web behavior, and, you know, they w the earlier they can do that, the better off they are. So >>Greg, >>So talk about the implications of, of your approach in terms of the customer base. So I'm, I'm imagining that your customers are more, perhaps advanced than a lot of your typical Hadoop users who are just getting started tinkering with Hadoop. Is it fair to say, you know, your customers know what they want and they want performance and they want it now. And they're a little more advanced than perhaps some of the typical early adopters. >>We've got people to go to our website and download the free version. And some of them are just starting off and getting used to Hadoop, but we did specifically target those very experienced Hadoop users that, you know, we're kind of, you know, stubbing their toes on, on the issues. And so they're very receptive to the message of we've made it faster. We've made it more reliable, you know, we've, we've added a lot of ease of use to the, to the Hindu. >>So I found this, let me interrupt, go back to what I was saying before is I found this comment that I found online from Mike Brown comScore. Skipio I presume you mean, he said comScore's map our direct access NFS feature, which exposes a duke distributed file system data as NFS files can then be easily mounted, modified, or overwritten. So that's a data access simplification. You also said we could capitalize on the purchase of map bar with an annual maintenance charge versus a yearly cost per node. NFS allowed our enterprise systems to easily access the data in the cluster. So does that make sense to you that, that enterprise of that annual maintenance charge versus yearly cost per node? I didn't get that. >>Oh, I think he's talking about some, some organizations prefer to do a perpetual license versus a subscription model that's >>Oh, okay. So the traditional way of licensing software >>And that, that you have to do it basically reinforces the fact that we've really invested in have kind of a, a product, you know, orientation rather than just services on top of, of some opensource. >>Okay. So you go in, you license it and then yeah. Perpetual license. >>Then you can also start with the free edition that does all the performance NFS support kick the tires >>Before you buy it. Sorry. Sorry, Jeff. Sorry to interrupt. No, no problem >>At all. So another topic, a lot of interest is security making a dupe enterprise ready. One of the pillars, there is security, making sure access controls, for instance, making sure let's talk about how you guys approach that and maybe how you differentiate from some of the other vendors out there, or the other >>Full Kerberos support. We Lincoln to enterprise standards for access eldap, et cetera. We leveraged the Linux, Pam security, and we also provide volume control. So, you know, right now in Hindu in Apache to dupe other distributions, you put policies at the file level or the entire cluster. And we see many organizations having separate physical clusters because of that limitation, right? And we'd provide volume. So you can define a volume. And in that volume control, access control, administrative privileges data protection class, and, you know, in a sense kind of segregate that content. And that provides a lot of, a lot of control and a lot more, you know, security and protection and separation of data. >>That scenario, the comScore scenario, common where somebody's moving off an existing distribution onto a map are, or, or you more going, going, seeing demand from new customers that are saying, Hey, what's this big data thing I really want to get into it. How's it shake out there >>Right now? There's this huge pent up demand for these features. And we're seeing a lot of people that have run on other distributions switched to map our >>A little bit of everything. How about, can you talk a little bit about your, your channel? You go to market strategy, maybe even some of your ecosystem and partnerships in the little time. >>Sure. So EMC is a big partner of the EMC Greenplum Mr. Edition is basically a map R you can start with any of our additions and upgrade to that. Greenplum with just a licensed key that gives us worldwide service and support. It's been a great partnership. >>We hear a lot of proof of concepts out there >>For, yeah. And then it just hit the news news today about EMC's distribution, Mr. Distribution being available with UCS Cisco's ECS gear. So now that's further expanded the, the footprint that we have about. >>Okay. So you're the EMC relationship. Anything else that you can share with us? >>We have other announcements coming out and >>Then you want to pre-announce in the queue. >>Oops. Did I let that slip >>It's alive? So be careful. And so, in terms of your, your channel strategy, you guys mostly selling direct indirect combination, >>It's it? It, it's kind of an indirect model through these, these large partners with a direct assist. >>Yeah. Okay. So you guys come in and help evangelize. Yep. Excellent. All right. Do you have anything else before we gotta got a roll here? >>Yeah, I did wonder if you could talk a little bit about, you mentioned EMC Greenplum so there's a lot of talk about the data warehouse market, the MPB data warehouses, versus a Hadoop based on that relationship. I'm assuming that Matt BARR thinks well, they're certainly complimentary. Can you just touch on that? And, you know, as opposed to some who think, well, Hadoop is going to be the platform where we go, >>Well, th th there's just, I mean, if you look at the typical organization, they're just really trying to get their, excuse me, their arms around a lot of this machine generated content, this, you know, unstructured data that just growing like wildfire. So there's a lot of Paducah specific use cases that are being rolled out. They're also kind of data lakes, data, oceans, whatever you want to call it, large pools where that information is then being extracted and loaded into data warehouses for further analysis. And I think the big pivot there is if it's well understood what the issue is, you define the schema, then there's a whole host of, of data warehouse applications out there that can be deployed. But there's many things where you don't really understand that yet having to dupe where you don't need to find a schema a is a, is a big value, >>Jack, I'm sorry. We have to go run a couple of minutes behind. Thank you very much for coming on the cube. Great story. Good luck with everything. And sounds like things are really going well and market's heating up and you're in the right place at the right time. So thank you again. Thank you to Jeff. And we'll be right back everybody to the strata conference live in Santa Clara, California, right after this word from our.