>> Narrator: From around the globe, it's theCUBE with digital coverage of AWS public sector online. (bright upbeat music) Brought to you by Amazon Web Services. >> Hi, and welcome back to theCUBE's coverage at AWS Public Sector Online, I'm your host Stu Miniman. Really excited always when we come to the AWS public sector show it's not only governments' but you've got nonprofits education, and lots of phenomenal use cases from the practitioners themselves. Really happy to welcome to the program, Dr. Sergio papa. He is a radio diagnostic specialist at the Centro Diagnostico Italiano, of course, in Italy, if you can't tell by the name there, and Dr. Papa, thank you so much for joining us. Why don't you start with a little bit, you know your role at CDI. >> Thank you for your invitation. And I am the director of the diagnostic imaging department and radiotherapy nuclear medicine. We are a very huge institution in the diagnostic area in the laboratory and diagnostic imaging, I think one of the biggest in Europe. >> Excellent. And, of course, one of the very relevant things to talk about, you have a project called Artificial Intelligent or AI for COVID. maybe explain to us a little bit about what led to this and how this what the goals are for this project. >> Yes, we, as you know, is you also are today we were imagining February, March, we're in the middle of the biggest bandemia we have ever experienced it in the past. So we were thinking about some new (mumbles) methodology to give an end for this portrait emergency (mumbles) is working from three or four years around (mumbles) in basic imaging. In particular, we are working very hard in radiomics. After if you need I can talk, I can speak about the radiomics methodology that we are using. So, we had the idea of fine radiomics method on diagnostic imagery and in particular chest X ray and with the with the purpose not to have the diagnosis for this patient, it doesn't matter for us. We were focusing on predicting the clinical outcome of this patient. And, I mean, all these, all the people already diagnosed with COVID-19 viewers where they had an X ray examination, then after we applied over this huge number of X ray examinations radiomic ometers to understand what could be the clear output of this patient. So, I mean, dividing the people going well and then people, otherwise, that we're going to averse of the illness, I mean, to critical therapy even to the death. So we were trying to we are trying to divide two groups of patients. >> Yeah, absolutely. It's so important, of course, one to have that diagnosis, to understand who needs the most treatment, making sure that, you know, hospitals can put the right resources in the right places. So really impressive to do something like machine learning on this on in a relatively short period of time from when this whole pandemic is started. Help us understand a little bit, you know, what are the underlying technologies? How does AWS have been into this whole discussion. >> Well the Support of AWS is in many different areas. The first is that we are really trying to develop a platform without AWS and that's useful for the hospitals, for the institution to store in unique imagery set, all the images can be from different institution, so we don't need any more to send the images in any way. This is the first thing that AWS can give to us. Second is the use of a machine learning all this to analyze this kind of images coming from x ray chest through AWS systems. The third I think could be an aid in the generating the structure of the reports for this patient and moreover, the identification of patterns, different patterns that we can find inside the images. This concerns the radiomics theory, I mean, inside the images, there are many, many more information than what radiologist can can get from. So, I think sector agents can help us, so, AWS can help us to detect all these kind of patterns that we want to collect for our study. And the fourth reason is for the AI that AWS can give us is to share this kind of modality with the other scientific centers of Research Sector and not only for this specific pandemia now but also in the future maybe we will have, we don't hope this but we could have the second wave of the pandemia, there are many signals so about this in China and also in Europe. So these will be useful in the future to find the circle and second wave of pandemia, and also the final reason is that we will share all our results of this study with all the scientific community, I mean, we will improve an open access model together with AWS to share this information with all the scientific community the world. >> It's wonderful that this information can be shared broadly across the community, so important for tackling, you know, this challenging pandemic. I'm curious has your unit or had you used artificial intelligence machine learning before, I'd love to just get a little bit of background on, you know, how much you've used this technology? how accessible it is to be able to leverage it for two use cases like this? >> Absolutely, because, I mean, I'm an radiologist. If I check an image in a CT scan or an X ray, I can see inside that image, the maximum that I can see is 10, 15 to the maximum different patterns, I mean, the volume, the dimension of growth, which is the way of taking contrast media or difference old wishy washout but with my eyes I can see 10, 15 different patterns. And if a machine system examine the same image, it can reach out hundreds of patterns that I cannot see. So, we can detect all these patterns in different images, we can collect this machine learning system can work on these and put together all the similar patterns. So it can divide even in different cluster and then the system has to compare all this difference casts or group or patterns with a very huge database that we built before comparing and try to understand which patterns are linked to different outcome. So we can say, okay, this image has 20, 30, 100 patterns that suggest to us the destiny of the nation will be in one specific while another lesion that for me is exactly the same with my eyes, systems will tell us there are the two lesions are really different, their destiny is really different. This is the radiomics theory and this is what we are applying in our study on X ray chest cavitation. As I said before we select only positive patient. I mean all people that is for sure they have positive to COVID and in the first X ray chest, entering the hospital, we try to evaluate from the first chest X ray what will be the real destiny of the patient better or worse, and then we can also predict, try to predict, obviously, how many intensive care beds are necessity in that institution, we can send the therapy and adjust the therapy for the the different kind, different group of patients, it could be a very big help to an institution, to an hospital especially in periods like in our March or April when every day in every hospital in northern Italy, they were entering 200 person per hospital. It was a dramatic situation. >> Excellent. One of the other things that this pandemic has done is really required some, you know, strong coordination between both public and private entities. If you could speak a little bit to that my understanding is that AWS also help support this with the donation of computational credits. I believe it's the AWS diagnostic development initiative. So help us understand, you know, how the finances and the partnerships between public and private help everyone really, you know, address this current challenge. >> Well, the support from AWS for us is very important because now we, in this way we can use a lot of computing systems much more than what you had in our institution. And moreover, I think that sharing our information without the scientific content at the end of our study, it would be very important thing to do. Now I know we are beginning to appear on our drawn as in our websites, some afford to share information about going. Our study could be really one of the most important of this. >> Great, final question I have for you, Dr. Papa, give us your ideal vision going forward. You talked a little bit about how you know the importance of this to be able to watch and be prepared for a potential wave two where else is this this research relevant and where do you see this project going forward? >> Well, our study is not as focused on pneumonia from COVID-19. But the methodology can be applied in every kind of interstitial pneumonia. I mean, this is one of the first to that, at least, this has made in radiomics to segment at one whole organ, usually in radiomics, we used to studies the single lesions or little areas, I mean, no deals or metastases or primary tumors. This is one of the first, very first important studies where the segmentation is dedicated to the whole organ, I mean, all the lung, both lung. In every patient we segmented to the right or left lung. And in order to study diffuse pathology, in this case, of pneumonia, interstitial pneumonia is very different from bacterial pneumonia. And this methodology at the end of the study will be shared with the scientific community and could be a very interesting advancement our job. >> Dr. Papa, thank you so much for joining us and thank you so much for the very important work that your organization is doing to help attack the global pandemic. >> Thank you too, thank you too. >> I'm Stu Miniman, thank you for watching theCUBE (bright upbeat music)

>>from around the globe. It's the queue with digital coverage of AWS public sector online brought to you by Amazon Web services. >>Everyone welcome back to the Cube's virtual coverage of AWS Public sector summit online. That's the virtual conference. Public Sector Summit is the big get together for Teresa Carlson and her team and Amazon Web services from the public sector, which includes all the government agencies as well as education state governments here in United States and also abroad for other governments and countries. So we're gonna do an analysis of Teresa's keynote and also summarize the event as well. I'm John Furrow, your host of the Cube. I'm joined with my co host of the Cube, Dave Volante Stew Minimum. We're gonna wrap this up and analyze the keynote summit a really awkward, weird situation going on with the Summit because of the virtual nature of it. This event really prides itself. Stew and Dave. We've all done this event. It's one of our favorites. It's a really good face to face environment, but this time is virtual. And so with the covert 19 that's the backdrop to all this. >>Yeah, so I mean, a couple of things, John. I think first of all, A Z, you've pointed out many times. The future has just been pulled forward. I think the second thing is with this whole work from home in this remote thing obviously was talking about how the cloud is a tailwind. But let's face it. I mean, everybody's business was affected in some way. I think the cloud ultimately gets a tail wind out of this, but but But I think the third thing is security. Public sector is always heavily focused on security, and the security model has really changed overnight to what we've been talking about for years that the moat that we've built the perimeter is no longer where organizations need to be spending money. It's really to secure remote locations. And that literally happened overnight. So things like a security cloud become much, much more important. And obviously endpoint security and other other things that we've talked about in the Cube now for last 100 days. >>Well, Steve, I want to get your thoughts cause you know, we all love space. Do we always want to go the best space events that they're gonna be virtual this year as well? Um, But the big news out of the keynote, which was really surprising to me, is Amazon's continued double down on their efforts around space, cyber security, public and within the public sector. And they're announcing here, and the big news is a new space business segment. So they announced an aerospace group to serve those customers because space to becoming a very important observation component to a lot of the stuff we've seen with ground station we've seen at reinvent public sector. These new kinds of services are coming out. It's the best, the cloud. It's the best of data, and it's the best of these new use cases. What's your thoughts? >>Yeah, interesting. John, of course. You know, the federal government has put together Space Forces, the newest arm of the military. It's really even though something it is a punchline. There's even a Netflix show that I believe got the trademark board because they registered for it first. But we've seen Amazon pushing into space. Not only there technology being used. I had the pleasure of attending the Amazon re Marcia last year, which brought together Jeff Bezos's blue origin as well as Amazon AWS in that ecosystem. So AWS has had a number of services, like ground Station that that that are being used to help the cloud technology extend to what's happening base. So it makes a lot of sense for for the govcloud to extend to that type of environment aside you mentioned at this show. One of the things we love always is. You know, there's some great practitioner stories, and I think so many over the years that we've been doing this show and we still got some of them. Theresa had some really good guests in her keynote, talking about transformation and actually, one of the ones that she mentioned but didn't have in the keynote was one that I got to interview. I was the CTO for the state of West Virginia. If you talk about one of those government services that is getting, you know, heavy usage, it's unemployment. So they had to go from Oh my gosh, we normally had people in, you know, physical answering. The phone call centers to wait. I need to have a cloud based contact center. And they literally did that, you know, over the weekend, spun it up and pulled people from other organizations to just say, Hey, you're working from home You know you can't do your normal job Well, we can train your own, we can get it to you securely And that's the kind of thing that the cloud was really built for >>and this new aerospace division day this really highlights a lot of not just the the coolness of space, but on Earth. The benefits of there and one of Amazon's ethos is to do the heavy lifting, Andy Jassy told us on the Cube. You know, it could be more cost effective to use satellites and leverage more of that space perimeter to push down and look at observation. Cal Poly is doing some really interesting work around space. Amazon's worked with NASA Jet Propulsion Labs. They have a lot of partnerships in aerospace and space, and as it all comes together because this is now an augmentation and the cost benefits are there, this is going to create more agility because you don't have to do all that provisioning to get this going spawned. All kinds of new creativity, both an academic and commercial, your thoughts >>Well, you know, I remember the first cloud first came out people talked a lot about while I can do things that I was never able to do before, you know, The New York Times pdf example comes to mind, but but I think what a lot of people forget is you know the point to a while. A lot of these mission critical applications Oracle databases aren't moving to the cloud. But this example that you're giving and aerospace and ground station. It's all about being able to do new things that you weren't able to do before and deliver them as a service. And so, to me, it shows a great example of tam expansion, and it also shows things that you never could do before. It's not just taking traditional enterprise APs and sticking them in the cloud. Yeah, that happens. But is re imagining what you can do with computing with this massive distributed network. And you know, I O. T. Is clearly coming into into play here. I would consider this a kind of I o t like, you know, application. And so I think there are many, many more to come. But this is a great example of something that you could really never even conceive in enterprise Tech before >>you, Dave the line on that you talked about i o t talk a lot about edge computing. Well, if you talk about going into space, that's a new frontier of the edge that we need to talk about >>the world. Glad it's round. So technically no edge if you're in space so again not to get nuance here and nerdy. But okay, let's get into the event. I want to hold on the analysis of the keynote because I think this really society impact public service, public sector, things to talk about. But let's do a quick review of kind of what's happened. We'll get to the event. But let's just review the guests that we interviewed on the Cube because we have the cube virtual. We're here in our studios. You guys were in yours. We get the quarantine cruise. We're still doing our job to get the stories out there. We talked to Teresa Carlson, Shannon Kellogg, Ken Eisner, Sandy Carter, Dr Papa Casey Coleman from Salesforce, Dr Shell Gentleman from the Paragon Institute, which is doing the fairground islands of researcher on space and weather data. Um, Joshua Spence math you can use with the Alliance for Digital Innovation Around some of this new innovation, we leave the Children's National Research Institute. So a lot of great guests on the cube dot net Check it out, guys. I had trouble getting into the event that using this in Toronto platform and it was just so hard to navigate. They've been doing it before. Um, there's some key notes on there. I thought that was a disappointment for me. I couldn't get to some of the sessions I wanted to, um, but overall, I thought the content was strong. Um, the online platform just kind of wasn't there for me. What's your reaction? >>Well, I mean, it's like a Z. That's the state of the art today. And so it's essentially a webinar like platforms, and that's what everybody's saying. A lot of people are frustrated with it. I know I as a user. Activity clicks to find stuff, but it is what it is. But I think the industry is can do better. >>Yeah, and just to comment. I'll make on it, John. One of things I always love about the Amazon show. It's not just what AWS is doing, But, you know, you walk the hallways and you walk the actual So in the virtual world, I walk the expo floor and its okay, Here's a couple of presentations links in an email address if you want to follow up, I felt even the A previous AWS online at a little bit more there. And I'm sure Amazon's listening, talking to all their partners and building out more there cause that's definitely a huge opportunity to enable both networking as well. As you know, having the ecosystem be able to participate more fully in the event >>and full disclosure. We're building our own platform. We have the platforms. We care about this guys. I think that on these virtual events that the discovery is critical having the available to find the sessions, find the people so it feels more like an event. I think you know, we hope that these solutions can get better. We're gonna try and do our best. Um, so, um well, keep plugging away, guys. I want to get your thoughts. They have you been doing a lot of breaking analysis on this do and your interviews as well in the technology side around the impact of Covert 19 with Teresa Carlson and her keynote. Her number one message that I heard was Covad 19 Crisis has caused a imperative for all agencies to move faster, and Amazon is kind of I won't say put things to the side because they got their business at scale. Have really been honing in on having deliverables for crisis solutions. Solving the problems and getting out to Steve mentioned the call centers is one of the key interviews. This is that they're job. They have to do this cove. It impacts the public services of the public sector that she's that they service. So what's your reaction? Because we've been covering on the commercial side. What's your thoughts of Teresa and Amazon's story today? >>Yeah, well, she said, You know, the agencies started making cloud migrations that they're at record pace that they'd never seen before. Having said that, you know it's hard, but Amazon doesn't break out its its revenue in public sector. But in the data, I look at the breaking analysis CTR data. I mean, it definitely suggests a couple of things. Things one is I mean, everybody in the enterprise was affected in some way by Kobe is they said before, it wouldn't surprise me if there wasn't a little bit of a pause and aws public sector business and then it's picking up again now, as we sort of exit this isolation economy. I think the second thing I would say is that AWS Public sector, based on the data that I see, is significantly outpacing the growth of AWS. Overall number one number two. It's also keeping pace with the growth of Microsoft Azure. Now we know that AWS, on balance is much bigger than Microsoft Azure and Infrastructures of Service. But we also know that Microsoft Azure is growing faster. That doesn't seem to be the case in public sector. It seems like the public sector business is is really right there from in terms of growth. So it really is a shining star inside of AWS. >>Still, speed is a startup game, and agility has been a dev ops ethos. You couldn't see more obvious example in public sector where speed is critical. What's your reaction to your interviews and your conversations and your observations? A keynote? >>Yeah, I mean something We've all been saying in the technology industry is Just imagine if this had happened under 15 years ago, where we would be So where in a couple of the interviews you mentioned, I've talked to some of the non profits and researchers working on covert 19. So the cloud really has been in the spotlight. Can I react? Bask scale. Can I share information fast while still maintaining the proper regulations that are needed in the security so that, you know, the cloud has been reacting fast when you talk about the financial resource is, it's really nice to see Amazon in some of these instances has been donating compute occasional resource is and the like, so that you know, critical universities that are looking at this when researchers get what they need and not have to worry about budgets, other agencies, if you talk about contact centers, are often they will get emergency funding where they have a way to be able to get that to scale, since they weren't necessarily planning for these expenses. So you know what we've been seeing is that Cloud really has had the stress test with everything that's been going on here, and it's reacting, so it's good to see that you know, the promise of cloud is meeting that scale for the most part, Amazon doing a really good job here and you know, their customers just, you know, feel The partnership with Amazon is what I've heard loud and clear. >>Well, Dave, one of these I want to get your reaction on because Amazon you can almost see what's going on with them. They don't want to do their own horn because they're the winners on the pandemic. They are doing financially well, their services. All the things that they do scale their their their position, too. Take advantage. Business wise of of the remote workers and the customers and agencies. They don't have the problems at scale that the customers have. So a lot of things going on here. These applications that have been in the i t world of public sector are old, outdated, antiquated, certainly summer modernize more than others. But clearly 80% of them need to be modernized. So when a pandemic hits like this, it becomes critical infrastructure. Because look at the look of the things unemployment checks, massive amount of filings going on. You got critical service from education remote workforces. >>these are >>all exposed. It's not just critical. Infrastructure is plumbing. It's The applications are critical. Legit problems need to be solved now. This is forcing an institutional mindset that's been there for years of, like, slow two. Gotta move fast. I mean, this is really your thoughts. >>Yeah. And well, well, with liquidity that the Fed put into the into the market, people had, You know, it's interesting when you look at, say, for instance, take a traditional infrastructure provider like an HP era Dell. Very clearly, their on Prem business deteriorated in the last 100 days. But you know HP Q and, well, HBO, you had some some supply chain problem. But Dell big uptick in this laptop business like Amazon doesn't have that problem. In fact, CEOs have told me I couldn't get a server into my data center was too much of a hassle to get too much time. It didn't have the people. So I just spun up instances on AWS at the same time. You know, Amazon's VD I business who has workspaces business, you know, no doubt, you know, saw an uptick from this. So it's got that broad portfolio, and I think you know, people ask. Okay, what remains permanent? Uh, and I just don't see this This productivity boom that we're now finally getting from work from home pivoting back Teoh, go into the office and it calls into question Stu, when If nobody is in the corporate office, you know the VP ends, you know, the Internet becomes the new private network. >>It's to start ups moving fast. The change has been in the past two months has been, like, two years. Huge challenges. >>Yeah, John, it's an interesting point. So, you know, when cloud first started, it was about developers. It was about smaller companies that the ones that were born in the cloud on The real opportunity we've been seeing in the last few months is, you know, large organizations. You talk about public sector, there's non profits. There's government agencies. They're not the ones that you necessarily think of as moving fast. A David just pointing out Also, many of these changes that we're putting into place are going to be with us for a while. So not only remote work, but you talk about telehealth and telemedicine. These type of things, you know, have been on our doorstep for many years, but this has been a forcing function toe. Have it be there. And while we will likely go back to kind of a hybrid world, I think we have accelerated what's going on. So you know, there is the silver lining in what's going on because, you know, Number one, we're not through this pandemic. And number two, you know, there's nothing saying that we might have another pandemic in the future. So if the technology can enable us to be more flexible, more distributed a xai I've heard online. People talk a lot. It's no longer work from home but really work from anywhere. So that's a promise we've had for a long time. And in every technology and vertical. There's a little bit of a reimagining on cloud, absolutely an enabler for thinking differently. >>John, I wonder if I could comment on that and maybe ask you a question. That's okay. I know your host. You don't mind. So, first of all, I think if you think about a framework for coming back, it's too said, You know, we're still not out of this thing yet, but if you look at three things how digital is an organization. How what's the feasibility of them actually doing physical distancing? And how essential is that business from a digital standpoint you have cloud. How digital are you? The government obviously, is a critical business. And so I think, you know, AWS, public Sector and other firms like that are in pretty good shape. And then there's just a lot of businesses that aren't essential that aren't digital, and those are gonna really, you know, see a deterioration. But you've been you've been interviewing a lot of people, John, in this event you've been watching for years. What's your take on AWS Public sector? >>Well, I'll give an answer that also wants to do away because he and I both talk to some of the guests and interview them. Had some conversations in the community is prep. But my take away looking at Amazon over the past, say, five or six years, um, a massive acceleration we saw coming in that match the commercial market on the enterprise side. So this almost blending of it's not just public sector anymore. It looks a lot like commercial cause, the the needs and the services and the APS have to be more agile. So you saw the same kind of questions in the same kind of crazy. It wasn't just a separate division or a separate industry sector. It has the same patterns as commercial. But I think to me my big takeaways, that Theresa Carlson hit this early on with Amazon, and that is they can do a lot of the heavy lifting things like fed ramp, which can cost a $1,000,000 for a company to go through. You going with Amazon? You onboard them? You're instantly. There's a fast track for you. It's less expensive, significantly less expensive. And next thing you know, you're selling to the government. If you're a start up or commercial business, that's a gold mine. I'm going with Amazon every time. Um, and the >>other >>thing is, is that the government has shifted. So now you have Covad 19 impact. That puts a huge premium on people who are already been setting up for digital transformation and or have been doing it. So those agencies and those stakeholders will be doing very, very well. And you know that Congress has got trillions of dollars day. We've covered this on the Cube. How much of that coverage is actually going for modernization of I T systems? Nothing. And, you know, one of things. Amazon saying. And rightfully so. Shannon Kellogg was pointing out. Congress needs to put some money aside for their own agencies because the citizens us, the taxpayers, we got to get the services. You got veterans, you've got unemployment. You've got these critical services that need to be turned on quicker. There's no money for that. So huge blind spot on the whole recovery bill. And then finally, I think that there's a huge entrepreneurial thinking that's going to be a public private partnership. Cal Poly, Other NASA JPL You're starting to see new applications, and this came out of my interviews on some of the ones I talked to. They're thinking differently, the doing things that have never been done before. And they're doing it in a clever, innovative way, and they're reinventing and delivering new things that are better. So everything's about okay. Modernize the old and make it better, and then think about something new and completely different and make it game changing. So to me, those were dynamics that are going on than seeing emerge, and it's coming out of the interviews. Loud and clear. Oh, my God, I never would have thought about that. You can only do that with Cloud Computing. A super computer in the Cloud Analytics at scale, Ocean Data from sale Drone using satellite over the top observation data. Oh, my God. Brilliant. Never possible before. So these are the new things that put the old guard in the Beltway bandits that check because they can't make up the old excuses. So I think Amazon and Microsoft, more than anyone else, can drive change fast. So whoever gets there first, well, we'll take most of the shares. So it's a huge shift and it's happening very fast more than ever before this year with Covert 19 and again, that's the the analysis. And Amazon is just trying to like, Okay, don't talk about us is we don't want to like we're over overtaking the world because outside and then look opportunistic. But the reality is we have the best solution. So >>what? They complain they don't want to be perceived as ambulance station. But to your point, the new work loads and new applications and the traditional enterprise folks they want to pay the cow path is really what they want to dio. And we're just now seeing a whole new set of applications and workloads emerging. What about the team you guys have been interviewing? A lot of people we've interviewed tons of people at AWS reinvent over the years. We know about Andy Jassy at all. You know, his his lieutenants, about the team in public sector. How do they compare, you know, relative to what we know about AWS and maybe even some of the competition. Where do you Where do you grade them? >>I give Amazon and, um, much stronger grade than Microsoft. Microsoft still has an old DNA. Um, you got something to tell them is bring some fresh brand there. I see the Jedi competition a lot of mud slinging there, and I think Microsoft clearly got in fear solution. So the whole stall tactic has worked, and we pointed out two years ago the number one goal of Jet I was for Amazon not to win. And Microsoft looks like they're gonna catch up, and we'll probably get that contract. And I don't think you're probably gonna win that out, right? I don't think Amazon is gonna win that back. We'll see. But still doesn't matter. Is gonna go multi cloud anyway. Um, Teresa Carlson has always had the right vision. The team is exceptional. Um, they're superb experience and their ecosystem partners Air second and NASA GPL Cal Poly. The list goes on and on, and they're attracting new talent. So you look at the benchmark new talent and unlimited capability again, they're providing the kinds of services. So if we wanted to sell the Cube virtual platform Dave, say the government to do do events, we did get fed ramp. We get all this approval process because Amazon customer, you can just skate right in and move up faster versus the slog of these certifications that everyone knows in every venture capitalists are. Investor knows it takes a lot of time. So to me, the team is awesome. I think that the best in the industry and they've got to balance the policy. I think that's gonna be a real big challenge. And it's complex with Amazon, you know, they own the post. You got the political climate and they're winning, right? They're doing well. And so they have an incentive to to be in there and shape policy. And I think the digital natives we are here. And I think it's a silent revolution going on where the young generation is like, Look at government served me better. And how can I get involved? So I think you're going to see new APS coming. We're gonna see a really, you know, integration of new blood coming into the public sector, young talent and new applications that might take >>you mentioned the political climate, of course. Pre Cove. It'll you heard this? All that we call it the Tech lash, right, The backlash into big tech. You wonder if that is going to now subside somewhat, but still is the point You're making it. Where would we be without without technology generally and big tech stepping up? Of course, now that you know who knows, right, Biden looks like he's, you know, in the catbird seat. But there's a lot of time left talking about Liz more on being the Treasury secretary. You know what she'll do? The big tech, but But nonetheless I think I think really it is time to look at big tech and look at the Tech for good, and you give them some points for that. Still, what do you think? >>Yeah, first of all, Dave, you know, in general, it felt like that tech lash has gone down a little bit when I look online. Facebook, of course, is still front and center about what they're doing and how they're reacting to the current state of what's happening around the country. Amazon, on the other hand, you know, a done mentioned, you know, they're absolutely winning in this, but there hasn't been, you know, too much push back if you talk culturally. There's a big difference between Amazon and AWS. There are some concerns around what Amazon is doing in their distribution facilities and the like. And, you know, there's been lots of spotlights set on that, um, but overall, there are questions. Should AWS and Amazon that they split. There's an interesting debate on that, Dave, you and I have had many conversations about that over the past couple of years, and it feels like it is coming more to a head on. And if it happens from a regulation standpoint, or would Amazon do it for business reason because, you know, one of Microsoft and Google's biggest attacks are, well, you don't want to put your infrastructure on AWS because Amazon, the parent company, is going to go after your business. I do want to pull in just one thread that John you and Dave were both talking about while today you know, Amazon's doing a good job of not trying todo ambulance case. What is different today than it was 10 or 20 years ago. It used to be that I t would do something and they didn't want to talk to their peers because that was their differentiation. But Amazon has done a good job of explaining that you don't want to have that undifferentiated heavy lifting. So now when an agency or a company find something that they really like from Amazon talking all their peers about it because they're like, Oh, you're using this Have you tried plugging in this other service or use this other piece of the ecosystem? So there is that flywheel effect from the cloud from customers. And of course, we've talked a lot about the flywheel of data, and one of the big takeaways from this show has been the ability for cloud to help unlock and get beyond those information silos for things like over 19 and beyond. >>Hey, John, if the government makes a ws spin out or Amazon spin out AWS, does that mean Microsoft and Google have to spin out their cloud businesses to? And, uh, you think that you think the Chinese government make Alibaba spin out its cloud business? >>Well, you know the thing about the Chinese and Facebook, I compare them together because this is where the tech lash problem comes in. The Chinese stolen local property, United States. That's well documented use as competitive advantage. Facebook stole all the notional property out of the humans in the world and broke democracy, Right? So the difference between those bad tech actors, um, is an Amazon and others is 11 enabling technology and one isn't Facebook really doesn't really enable anything. If you think about it, enables hate. It enables some friends to talk some emotional reactions, but the real societal benefit of historically if you look at society, things that we're enabling do well in free free societies. Closed systems don't work. So you got the country of China who's orchestrating all their actors to be state driven, have a competitive advantage that's subsidised. United States will never do that. I think it's a shame to break up any of the tech companies. So I'm against the tech lash breakup. I think we should get behind our American companies and do it in an open, transparent way. Think Amazon's clearly doing that? I think that's why Amazon's quiet is because they're not taking advantage of the system that do things faster and cheaper gets that's there. Ethos thinks benefits the consumer with If you think about it that way, and some will debate that, but in general Amazon's and enabling technology with cloud. So the benefits of the cloud for them to enable our far greater than the people taking advantage of it. So if I'm on agency trying to deliver unemployment checks, I'm benefiting the citizens at scale. Amazon takes a small portion of that fee, so when you have enabling technologies, that's how to me, The right capitalism model works Silicon Valley In the tech companies, they don't think this way. They think for profit, go big or go home and this has been an institutional thing with tech companies. They would have a policy team, and that's all they did. They didn't really do anything t impact society because it wasn't that big. Now, with networked economies, you're looking at something completely different to connected system. You can't handle dissidents differently is it's complex? The point is, the diverse team Facebook and Amazon is one's an enabling technology. AWS Facebook is just a walled garden portal. So you know, I mean, some tech is good, some text bad, and a lot of people just don't know the difference what we do. I would say that Amazon is not evil Amazon Web services particular because they enable people to do things. And I think the benefits far outweigh the criticisms. So >>anybody use AWS. Anybody can go in there and swipe the credit card and spin up compute storage AI database so they could sell the problems. >>The problems, whether it's covert problems on solving the unemployment checks going out, are serving veterans or getting people getting delivering services. Some entrepreneurs develop an app for that, right? So you know there's benefits, right? So this you know, there's not not Amazon saying Do it this way. They're saying, Here's this resource, do something creative and build something solve a problem. And that was the key message of the keynote. >>People get concerned about absolute power, you know, it's understandable. But if you know you start abusing absolute power, really, I've always believed the government should come in, >>but >>you know, the evidence of that is is pretty few and far between, so we'll see how this thing plays out. I mean, it's a very interesting dynamic. I point about why should. I don't understand why AWS, you know, gets all the microscopic discussion. But I've never heard anybody say that Microsoft should spend on Azure. I've never heard that. >>Well, the big secret is Azure is actually one of Amazon's biggest customers. That's another breaking analysis look into that we'll keep on making noted that Dave's do Thanks for coming to do great interviews. Love your conversations. Final words to I'll give you What's the big thing you took away from your conversations with your guests for this cube? Virtual coverage of public sector virtual summit >>so biggest take away from the users is being able to react to, you know, just ridiculously fast. You know it. Talk about something where you know I get a quote on Thursday on Friday and make a decision, and on Monday, on up and running this unparalleled that I wouldn't be able to do before. And if you talk about the response things like over nine, I mean enabling technology to be able to cut across organizations across countries and across domains. John, as you pointed out, that public private dynamic helping to make sure that you can react and get things done >>Awesome. We'll leave it there. Stew. Dave. Thanks for spending time to analyze the keynote. Also summarize the event. This is a does public sector virtual summit online Couldn't be face to face. Of course. We bring the Cube virtual coverage as well as content and our platform for people to consume. Go the cube dot net check it out and keep engaging. Hit us up on Twitter if any questions hit us up. Thanks for watching. >>Yeah, yeah, yeah, yeah, yeah, yeah

from the silicon angle media office in Boston Massachusetts it's the cube now here's your host David on tape the imperative to protect data has never been more pressing as companies transform themselves from businesses into digital businesses the intrinsic value of their data Rises exponentially the problem for infrastructure pros is that everything in IT is additive it seems like nothing ever dies which means more things to manage now think about that when you're protecting data you have bare metal VMs now containers you've got cloud you got to worry about the edge all this data needs to be protected not only does this increase complexity it expands the attack surface for adversaries wanting to steal or ransom your data at the heart of all this is a build out of a massively global distributed cloud we saw wave 1 of the cloud which was public wave 2 was really hybrid and that's evolving now in parallel you're seeing the emergence of multi cloud and as I said these earlier trends are additive they're not replacements and with me to discuss these important issues and how Dell EMC specifically is pivoting toward cloud data protection is Beth Phelan who is the president of Dell emcs Data Protection Division that's great to see you well good to be here again so we know the world is hybrid it's a fundamental the on-prem stuff is part of the fundamental digital digital transformations of these these companies and now you've got data protection for the cloud so what do you see happening in that world yeah let's start with what we're seeing in the market we recently remade on our global data protection index we've been doing it for many years and we've been really using that to help us understand the landscape and what our customers need and first not surprisingly it shows that continued trend of movement and reliance towards cloud environments for business applications continuing to increase on top of that the customers despite that are continuing to struggle with ensuring they have the right data protection for their cloud environments right so they're they're struggling you see that we see that as well what what's going on there well what is the data tell you yeah first of all more than half of the customers don't have a comprehensive data protection solution for their Salas cloud native and multi cloud environments more than two-thirds of the customers who may be relying on their cloud service providers for data protection say that they do not have a solution that covers all of their workloads so whether they're working with a cloud service provider or some other vendor they're being really clear that they do not have a comprehensive approach to cloud data protection yeah so I mean you see the cloud adoption is going like crazy but it seems like the data protection component is lagging how is that affecting the traction in your business yeah you know it's a double-edged sword right on one level customers see the advantages of moving to a cloud on the other hand you know they are really looking for vendors that they can partner with to still have the same confidence that the data is protected that they have on Prem and what we're seeing now is that customers are turning to us to help solve that problem we have over a thousand customers using Dell EMC for their Cloud Data Protection and we're narrowing in on three exabyte the data that we're currently protecting in the cloud so it's happening yeah that's pretty good traction so I want to talk about VMware obviously VMware is the linchpin of many customers hybrid strategy and it's a clearly an important component of Dell technologies talk a little bit about the relationship between Dell EMC data protection specifically and VMware I'm interested in you know they've announced project tenzou and there's kubernetes how are you guys working together to really deliver a value for customers so we are super excited about the opportunity to work so closely with VMware because as they're cut in their domain we're working directly with them and that's an advantage that comes with being part of the dell technologies family and so we were the first company to bring data protection for were kubernetes environments out to market it's available now so you'll see us bring that into the tenzou mission-critical has been moved forward partnering closely with with vmware and of course we're already fully certified for vmware cloud it's really an ongoing regular conversation about how we can work together to bring the best to our customers so Beth I gotta ask you so you're part of your role as the leader of the the division is obviously you gotta get a lot of mouths to feed big division you got to make your plan you got to deliver for customers but strategy is another key component of this how do all these cloud trends shape your strategy so core to our strategy is to be the essential provider of data protection for multi cloud environments so no matter where customers are choosing to deploy their applications they can have the same confidence that they always did that that data is protected and the way they can get it back so that's core and if you want three words to remember for our strategy think VMware cloud and cyber cloud is central to it and you're going to be hearing a lot more about it in the weeks and months ahead okay so I gotta ask you break out your binoculars maybe even the telescope what are the future what are the future's look like when you think about the division and the market so we've been talking about cloud for a long time but we are still in the middle of this journey customers are going to rely on the cloud even more for additional use cases and especially in the data protection space right now we're seeing backup to the cloud dr to the cloud but the future will include cyber resiliency that's leveraging cloud deployments you're also going to see more and more of an emphasis on people leveraging SAS for their software consumption and for us that means not only protecting SAS applications but it also means giving customers the option to consume data protection in a SAS model we already do that today with things like cloud snapshot manager with things like the power protect management and orchestration but you're going to see us do even more of that because they're just incredible benefits of people leveraging sass to consume their software data constantly evolving lamps landscape data protection has to evolve with it Beth thanks so much for thank you and thank you keep it right there we'll be right back right after this short break from world famous cloud Studios Dell Technologies presents the world's number one show on data protection solutions for today's organizations it's proven in modern magazine with Jake and Emmy hello everyone and welcome to the premiere of PM magazine where we cover the proven Dell technology solutions that you've come to rely on and the latest modern innovation driving powerful data protection for the future I recently spent some quality time with one of our customers and I learned a thing or two about Dell proven data protection solutions let's watch the clip we've always relied on tell performance efficiency and scale to help us keep pace with our data protection needs but there's so much more for example we've been crushing it with Dell cloud data protection for backup to the cloud in cloud backup cloud tearing cloud dr uh-huh look at the picture it's a huge business advantage how so our costs are down we spend less time on management we're meeting our service levels and we have peace of mind that all of our data is protected right awesome did you talk about how Dells agile development approach is accelerating the speed at which we deliver customer value yes and how cloud capabilities will continue to grow yes and about VMware protection yes and cyber recovery yes I mean we covered all of that as well as the mega trends that require data protection with a modern approach well modern is exactly what our guests today are here to discuss Jake he is Ken fatale a noted data protection expert and joining us from the field on her vacation in the Bahamas is Barbara Penner of the data management Institute thank you both for being here so Ken what should our viewers think about when they hear the phrase modern data protection they should think new requirements for modern applications cloud native workloads Cubana is multi-cloud and data services to name a few Barbara would you add anything to that list I would add business service recovery on premises or in the cloud autonomous protection to auto detect and protect workloads across edge core and cloud infrastructure and lastly all of this must operate at global scale thank you both this is exactly where we're heading with Dell power protect solutions well it's time for a break but when we come back we've got something special in store for you don't we Jake I was hoping you forgot oh no someone learned how to make cream puffs and it did not turn out well for him yeah my apologies in advance to my mother who tried to show me around the kitchen but as you can see we'll be right back [Music] we're back with Rob and Rob Emslie who's the director of product marketing for Delhi MCS data protection division Rob good to see you hi Dave good to be back so we just heard from Beth about some of the momentum that you guys have from your perspective from a product angle what is really driving this yeah well one of the things that we've you know definitely seen is that as we talk to our customers both existing and new customers cloud journeys is is top of mind for all of the CIOs it's being driven by either the desire to drive efficiency take out costs and data protection is one of the the most common use cases and one of the things that we find is that there's four use cases for data protection that we see long term retention of data cloud disaster recovery backup to the cloud and the emerging desire to stand up new applications in the cloud that need to be protected so backup in the cloud really completes the four major use cases well one of the things I think is really important this market is that you deliver optionality to your customers so how are our customers enabling these use cases yeah so the the first two UK's first two use cases of long term retention and cleitus recovery is is really driven by our software on our appliances both of those are really predicated based upon the assumption that customers are going to deploy data protection on premises to protect their on-premises workloads and then it's here to the cloud or which is becoming more common used to cloud as a disaster recovery target you know it's delivered by our data protection software and that's either in a software form factor or that software delivered in an integrated appliance form factor so let's talk about purpose-built backup appliances I think you know our friends at IDC I think you know coined that they tracked that market for a while you guys have been a leader there the acquisition of data domain obviously put you in a really strong position give us the update there is it's still a vibrant market is it growing what's the size it's it look like yeah so as we look at 2020 you know IDC forecasts the market size to be a little under five billion dollars so it's still a very large market the overall market is growing at a little over four percent but the interesting thing is that if you think about how the market is is made up it's made up of two different types of appliances one is a target appliance such as data domain and the new power protect dd and the other is integrated appliances where you integrate the target appliance architecture with data protection software and it's the integrated appliance part of the market that is really growing faster than the other part of the of the people being market it's actually growing at 8% in fact IBC's projection is that by 2022 half of the purpose-built back to appliance market will be made up of integrated appliance solutions so it's growing at twice the overall market rate but you guys have two integrated appliances what why - how should people think about those yeah so a little under three years ago we introduced a new integrated appliance the called the integrated data protection appliance it was really the combination of our backup software with our data domain appliance architecture and the integrated air protection appliance has been our workhorse for the last three years really allowing us to to support that that fastest-growing segment of the market in fact last year the integrated air protection appliance grew by over a hundred percent so triple digit growth was great you know it's something that you know allows us to address all market segments all the way down to SMB all the way to the enterprise but last year one of the things you may remember at Delta Nadi's world is we introduced our power to protect portfolio you know and that constituted power protect data manager our new software to find platform as well as the delivery of packet there in an integrated appliance form-factor with perfectly x400 so that's really our our new scale out data protection appliance we've never had a scale out appliance in the architecture before in the portfolio before and that gives us the ability to offer customers choice scale up or scale out integrated and target and with the X 400 it's available is a hybrid configuration or it's also our first or flash architecture so really we're providing customers with the existing software solutions that we've had in the market for a long time an integrated form factor with the integrator protection appliance as well as the brand-new software platform that will really be our innovation engine that will be where we'll be looking at supporting new workloads and certainly leaning into how we support cloud air protection and the hybrid cloud reality of the next decade okay so one of the other things I want to explore is we've heard a lot about your new agile development organization Beth has talked about that a lot and the benefit obviously is you're more you're able to get products out more quickly respond to market changes but ultimately the proof is in translating that development into product what can you tell us about how that's progressing yep so certainly with Papa Tech Data Manager and the X 400 that really is the the epicenter of our agile product development activities you know we've moved to a three-month cadence for software releases so working to deliver a small batch releases into the market much more rapidly than we've ever done before in fact since we introduced palpitate Denham manager where we we shipped the first release in July we're now at the third iteration of palpitate Data Manager and therefore the third iteration of the x100 appliance so there's three things that you know I'd like to highlight within the x100 appliance specifically first is really the the exciting news that we've introduced support for kubernetes so we're really the first you know large enterprise data protection vendor to to lean into providing kubernetes data protection so that becomes the vitally important especially with the developments over our partner in VMware with vSphere 7 with the introduction of tan zoo and the reality is that customers will have both these fear virtual machines and kubernetes containers working side-by-side and both of those environments need to be protected soap a patek denim algae and the x400 appliance has that support available now for customers to take advantage of second we talked about long-term retention of of data in the cloud the x100 appliance has just received the capabilities to also take part in long term retention to AWS so those are two very important cloud capabilities that are brand-new with the excellent appliance and then finally we introduced yet 400 appliance with a maximum configuration of four capacity cubes rough-and-tough that was 400 terabytes of usable capacity we've just introduced support of 12 capacity cubes so that gives the customers the ability to scale out the x100 appliance from 64 terabytes all the way to over a petabyte storage so now if you look at our two integrated appliances we now cover the landscape from small numbers of terabytes all the way through to a petabyte of capacity whether or not you pick a scale up architecture or a scale length architecture yeah so that really comes back to the point I was making about optionality and kubernetes is key it's gonna be a linchpin obviously a portability for multi cloud sets that up as we've said it's it's not the be-all end-all but it's a really necessary condition to enable multi cloud which is fundamental to your strategy absolutely alright Rob thanks very much for coming on the cube it's great to have you thanks Dave and thank you for watching everybody this is Dave Volante for the cube we'll see you next time [Music]

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Welcome to the cube at Lisa Martin with Dave Volante. This is our first day of covering AWS reinvent 2019 Dave, we have a jam packed three days here. The seventh time the cube has been at reinvent the super Superbowl. Here it is. I, I co I stole that from you but you just send it back to me. It is like the super bowl here. We're very pleased to welcome a couple of guests from refitted refinished tips, first time on the cube as well as our guest. Please welcome Hannah. We've got Hannah Helen, Helen's, our VP of cloud propositions and James Turk, the head of architecture and cloud from refinish. Guys, welcome to the cube. You. Thank you for having us. So here we are in the expo hall with thousands and thousands of folks, but I'd love for you guys to start a Hannibal. Start with you. Tell our audience about refinish if you're a data company, but really what is it that you guys do? What do you deliver to the community? Absolutely >>what we are, as I said, we are a data company, so we serve the global financial community. So we're looking at banks, asset managers, hedge funds, corporations with financial and risk data. That's a very powerful combination in these clouds. Environmental or we say without data flower is empty. So that's where we come in. >>And what type of data are we talking about? You know data as from a thematic perspective it is. There's, we know when every company knows on some level there's tremendous value in the data. The challenge is being able to access it and unlock the value. Give us a slice of and capital markets for example. What are some of the types of data services that you provide to your customers? >>So we have all sorts of data. So we obviously source the data from lots of different sources where it's coming from, from exchanges or from the, from the market data sources. And then our customers use that to analyze the data and really running the back testing for, for those data facts. They also commingled our data with alternative data sets as well, as well as they own internal data. So it's all about that, that analytical layer that they can add on top of our day. >>Okay. And estate as a service essentially. Is that right? We do have some data as a service. We also deliver the data to the client. People are interested in accessing data in all sorts of different ways, including increasingly on the cloud. So talk more about your cloud offering, your, your cloud and your title. Cloud architecture. >> So one of the things that we're doing is we have a combination, we're an interesting company in that we both have our own pieces of cloud infrastructure for our own purposes, but also increasingly we need to build and deliver solutions for our customers to be asked to consume data in the cloud. So that means being able to work with them to put it into the cloud that they want it to be going into, to be able to work out how we can keep that data up to date and to do it in a cost effective manner for our clients to be able to get the most out of it. >> How do you deal with the problems >>of data quality? You're getting data from different sources. How do you take care of that? >>So anyways, that's, that's really all our core strength and expertise that we have. We have been doing that for years and years. So again, coming from it from defense sources, we normalize the data on our side, we clean it up. And then so for our customers in a you our own information model, and we have created this app Poona permanent and unique, identify a post per ID. So we map all the datasets so it's very easy for our customers to consume that and then also map it back to they own data and third party data sets. Where does the global security come into play? Because that's a topic and thing that we talk about at every event when you're talking about all these different external data sources, quality. But security is, I imagine fundamental. How do you help deliver that? Absolutely. Obviously from that, from the cloud perspective, that has been a big theme in the, in the public cloud environment and I think we are seeing more and more feedback from our customers that as it comes down to public cloud, I think they are very comfortable actually now with uh, with the privacy and security of, of public cloud. >>So that has been, I think, big change past couple of years. I haven't personally seen those sponsors anymore coming, coming from customers the way that we saw a couple of years ago. >>Oh, one of the interesting things that we're seeing is an increasing move is that our clients want to be able to mix their data with that data. And so increasingly you're seeing interesting solutions coming to market, which allowed them to keep their data where their data is held on their cloud or even on their own premises and mix that with our data. And so we're trying to bring together those solutions where a customer doesn't have to put all of our data with theirs but all of their data with us. But keep that segregation as you say, because that PII data and all of those sorts of things are much more important these days for us to be able to be able to show that is how the data is being segregated and that things are being kept apart in an appropriate way. >>Who's responsible for that? Is that you guys, is it the cloud provider? Is it on customers? So it's a shared responsibility model. Where does, where do you leave off and where does the customer pick up? What do you advise customers in terms of, Hey, here's what we're going to do for you and now you have to be responsible for X. What does that line? >>Well, I quite often defining that service boundary is something that we continue to work on. So historically we've delivered data to clients and so we've had lines going into a client. It's a, um, premises. And then there's an obvious point at the end of that where this was us and that's you. As we get more into the cloud space, we have to define much more clearly what that service boundary is. So again, as we're developing out some of our cloud propositions, that's a key thing that we're working through as to what is it that the client wants to control and what is it that we need to control. >>It's very true, Hannah, I mean 10 years ago you talk to financial services companies and he said, we will never be in the cloud and now they're much more comfortable. Now you guys do this cloud survey each year. W w what are you seeing? I'll share some of our data. I wonder if it matches what, what do you, what are the big trends? >>Sure. Yeah. So we are doing this, it's almost becoming tradition for us to do this quota. They are on a yearly basis. So it's quite interesting to kind of compare the previous service and where we are today. So what we have found out on the survey this year is that the IOT, uh, investment is very much going to public cloud. So I think when we started the cloud survey a couple of years ago, we saw that about 32% of the ID investment went to public cloud. But then for next year that is increasing almost to two 50% so obviously public cloud is definitely here to stay. I think another, another key trend that we saw from the surveys that I think the testing that the companies have been doing, like they are learning more and more and they are really seeing the benefit from Papa now and I will highlight that especially our hedge fund customers, they were highlighting a face or so of course benefits with that, with the cloud. >>So about 92% so that actually when they moved to the cloud and do the project in the cloud environment, it really saves money for them, which is quite interesting. Payers also then at the same time to work many of the customer discussions. Like it can be also a challenge for, especially for large organizations as they move to the cloud environment, that how do you kind of manage that a traditional technology stack and when you move to the public cloud. So it's kind of two sided way there, but I think the general consensus as it comes down to out survey was that many of the organizations, they really saw that big transition that organizations are going for one that it can be very, very big impact for they own own business. So very, very positive message on that part. >>Let's dig into that a little bit more from a transition or we'll use Andy, Jesse's or a transformation. James, I'd love to get your perspective on what has changed in the last few years to see the numbers that Helen talked about. Um, really Hannah, excuse me, going up so significantly as we know that, you know, cloud one compute and storage and um, networking and maybe some data services. But what do you think has fundamentally changed across industries such that public cloud now is much more strategic? >>I think for a lot of firms and particularly in financial services, we spend a lot of time looking at analytics and being able to run those large analytical jobs and be able to scale them. I think that as people have become more comfortable about the data that they can put into the cloud and being able to get access to more data through companies like definitive, being able to run those machine learning jobs. And it was really interesting to see the keynote this morning to see Amazon really putting a lot of effort into democratizing the use of machine learning through Sage maker thought it was very exciting. Um, we think that that is going to be an increasing thing. So as you see in financial services, people are looking for those large workloads. They have really large data sets and so the only way that they can do that and it kind of realistic manner is being able to use public cloud. And then you see them taking a lot of the old traditional systems. And as we're seeing the risk appetite to be able to get onto cloud becoming more, they're going through the same of transformation, which we see many firms having gone through. You know, the developers are insisting that they're getting the best tools so that it can be, have the agility to deliver what their clients want. And again, one of the best ways of doing that is moving onto a public cloud infrastructure that really delivers those tools to >>what are, if you could talk about what you're seeing in terms of adoption of new tech. So I said we share some of our data at the macro, you know, spending slowing down, it's, it's reverting to pre 2018 levels. It's not falling off a cliff, but, but when you look at the spending data from ETR and others, it's slowing down. Financial services is a bellwether. You're seeing less experimentation and sort of more narrowing of their bets to the placing bets on things that they know are going to work. They've been experimenting with digital transformation for the last couple of years and now they're saying, Hey, we're now going to double down on the things that work. We're going to unplug the things, the legacy stuff so we can get rid of some of our technical debt. What are you seeing in terms of the trends of technology adoption for particularly for emerging tech within Fs? >>Yeah, and I think you've touched on this briefly, but I think what we are seeing is that the, when the, when we started co did the discussions with our customers, they all started with the kind of the backend technology I take on rotation at that time. But I in that trend as you say as well, so it's moving very much to the end users and end users. For example, data scientists speaking the analytical tools if they want to go into them. And I think that's a, that's a very big trend that we are seeing. So again, AI, ML analytics in general that you can add on top of the cloud environment and on top of the data, that will be the big thing happening. >>One of the things that Andy Jassy said this morning, James is in sort of these four kind of essentials for transformation to happen and he said the first one is you've got to get senior executive alignment and the second thing he said is has to be this, and I use the word aggressive, aggressive, top down approach. What are some of the changes that you're seeing with respect to, you know, where it comes to maybe what, what, what you said, Hannah, about the emerging technologies and the end users really in the data scientists needing to be able to get their hands wet with all this, but what are you seeing in terms of organizations that you work with? Where is that senior leadership really getting onboard where public cloud is a strategy that is driven top-down? >>Absolutely. I mean increasingly you're seeing that happen is that it really is going to be the top down strategy. There are a number of very large capital markets firms who have come out and said that they're going to adopt varying cloud providers. And increasingly that's because the level of trust has gone up and the level of maturity of the cloud providers. There's also increased. So a few years ago you would speak to the cloud providers and they really wouldn't understand the need to engage with the regulators. Now companies have large teams of people who go out and engage with the regulators and they will partner with the financial institutions to make sure that we're getting the right sort of level of engagement and the right level of permission to do these things. So that means that the senior management are there. And I think that also the senior management, you know, finally are starting to see some of the benefits flow through in terms of a combination of the agility, the different sort of cost controls and the elasticity. >>And if you think about some of the nature of the workloads that financial institution run, you've got a lot of this overnight processing, which still goes on for creating risk reports and all those sorts of things really well suited for elasticity. And in the last few years you've seen trust this massive increase in the regulatory requirement for those things. And certainly the institutions that I've worked with, you end up in a situation where you're saying, well, in order to be able to accommodate just working out what I need to do there, I'd need to build three different data centers clean. Nobody is doing that anymore. You're going to go out, you're going to partner with your cloud provider and they're going to provide you with that capability. That may not be something that you need in the longterm, but it'll be something that will help you work out what it is that you do need. And then you can turn that into a normal world. >>So AWS, AWS obviously is a cloud provider for you. There may be others as well, but you saw some of the announcements today. You mentioned some of the machine learning and AI stuff, Sage maker, you also saw a lot of activity around the data store, you know red shift and separating computer storage. Is that something that you care about is that your customers have to worry about that? Sometimes they ask you for the solution. >>We super care about this. In fact, one of the big things that we're looking at at the moment, and I was really interested in the announcements today, but exactly that is how do we get our data into people's data lakes? As I said, how do we do that in a way where we're making sure that the commitments that we have on digital rights management are being honored and how do we work with cloud providers like Amazon about how we do that. So we have very strong relationships with Amazon. We have very strong relationships with other providers as well. And so we are trying hard to work out what the best solution is because to be honest with you, we have to deliver where our clients want the data to be. So we're working with lots of different providers on this, but these are all really interesting times and this focus on the data and how you get the data into people's data lakes is really interesting to us and something where we're pushing very hard. >>Yeah. And then, and then how you act on it. It's a whole new layer of compute being driven and new workloads that are emerging as a result of that data. It's not just throw it in the data Lake anymore. It's I have to extract insights. Absolutely. Yeah. >>Talk to us about how on that front, how are you helping him? We'll start with you. How are you helping customers, maybe a large enterprise legacy organization actually start to use data for competitive advantage in business differentiation, especially where the enterprise is concerned, where they most likely have competitors that are born in the cloud, that have the agility and the speed and the appetite to take risks. How are you helping customers unlock this data and go, wow, this is a huge advantage in our business. Absolutely. So obviously as, as I said earlier though, because we are a data company, so our customers know know us from that perspective. So they come to us for, for both financial and risk data. That's kind of one >>go to place to get everything. And then we are obviously working very closely with our customers to also offer them new additional datasets. So things like alternative data obviously being one that you again want to go mingle your own data with a third party data with alternative data sets as well. So we, for example, formed a partnership with a company called Patal Finn earlier this year, which has this very nice technology to onboard different alternative data sets. And then we are onboarding those data sets for our customers. Again, combining that with our overall information model. But it's really, again, coming back to that flexible a question that we want to make sure that all our days are, can be served in the environment where our customers are. So whether they are in public cloud, private cloud, where they have their own prem solution, stale, obviously with, especially with a larger institution, they still have those, uh, as well as we, we hosting the offering for them as well as, or it's all about the flexibility that we will be offering. Excellent. >>Well, Hannah, James, thank you for joining David Mead, sharing with our audience who were fitted. It is what you do and really kind of this importance of data as we're in this new NextGen of cloud. We appreciate your time. Thank you so much for day. Volante I'm Lisa Martin. You're watching the queue from day one of our coverage of AWS reinvent 19. Thanks for watching.

>> Narrator: Live from New York, it's The Cube covering the IBM Machine Learning Launch Event brought to you by IBM. Here are your hosts, Dave Vellante and Stu Miniman. >> Good morning everybody, welcome to the Waldorf Astoria. Stu Miniman and I are here in New York City, the Big Apple, for IBM's Machine Learning Event #IBMML. We're fresh off Spark Summit, Stu, where we had The Cube, this by the way is The Cube, the worldwide leader in live tech coverage. We were at Spark Summit last week, George Gilbert and I, watching the evolution of so-called big data. Let me frame, Stu, where we're at and bring you into the conversation. The early days of big data were all about offloading the data warehouse and reducing the cost of the data warehouse. I often joke that the ROI of big data is reduction on investment, right? There's these big, expensive data warehouses. It was quite successful in that regard. What then happened is we started to throw all this data into the data warehouse. People would joke it became a data swamp, and you had a lot of tooling to try to clean the data warehouse and a lot of transforming and loading and the ETL vendors started to participate there in a bigger way. Then you saw the extension of these data pipelines to try to more with that data. The Cloud guys have now entered in a big way. We're now entering the Cognitive Era, as IBM likes to refer to it. Others talk about AI and machine learning and deep learning, and that's really the big topic here today. What we can tell you, that the news goes out at 9:00am this morning, and it was well known that IBM's bringing machine learning to its mainframe, z mainframe. Two years ago, Stu, IBM announced the z13, which was really designed to bring analytic and transaction processing together on a single platform. Clearly IBM is extending the useful life of the mainframe by bringing things like Spark, certainly what it did with Linux and now machine learning into z. I want to talk about Cloud, the importance of Cloud, and how that has really taken over the world of big data. Virtually every customer you talk to now is doing work on the Cloud. It's interesting to see now IBM unlocking its transaction base, its mission-critical data, to this machine learning world. What are you seeing around Cloud and big data? >> We've been digging into this big data space since before it was called big data. One of the early things that really got me interested and exciting about it is, from the infrastructure standpoint, storage has always been one of its costs that we had to have, and the massive amounts of data, the digital explosion we talked about, is keeping all that information or managing all that information was a huge challenge. Big data was really that bit flip. How do we take all that information and make it an opportunity? How do we get new revenue streams? Dave, IBM has been at the center of this and looking at the higher-level pieces of not just storing data, but leveraging it. Obviously huge in analytics, lots of focus on everything from Hadoop and Spark and newer technologies, but digging in to how they can leverage up the stack, which is where IBM has done a lot of acquisitions in that space and leveraging that and wants to make sure that they have a strong position both in Cloud, which was renamed. The soft layer is now IBM Bluemix with a lot of services including a machine learning service that leverages the Watson technology and of course OnPrem they've got the z and the power solutions that you and I have covered for many years at the IBM Med show. >> Machine learning obviously heavily leverages models. We've seen in the early days of the data, the data scientists would build models and machine learning allows those models to be perfected over time. So there's this continuous process. We're familiar with the world of Batch and then some mini computer brought in the world of interactive, so we're familiar with those types of workloads. Now we're talking about a new emergent workload which is continuous. Continuous apps where you're streaming data in, what Spark is all about. The models that data scientists are building can constantly be improved. The key is automation, right? Being able to automate that whole process, and being able to collaborate between the data scientist, the data quality engineers, even the application developers that's something that IBM really tried to address in its last big announcement in this area of which was in October of last year the Watson data platform, what they called at the time the DataWorks. So really trying to bring together those different personas in a way that they can collaborate together and improve models on a continuous basis. The use cases that you often hear in big data and certainly initially in machine learning are things like fraud detection. Obviously ad serving has been a big data application for quite some time. In financial services, identifying good targets, identifying risk. What I'm seeing, Stu, is that the phase that we're in now of this so-called big data and analytics world, and now bringing in machine learning and deep learning, is to really improve on some of those use cases. For example, fraud's gotten much, much better. Ten years ago, let's say, it took many, many months, if you ever detected fraud. Now you get it in seconds, or sometimes minutes, but you also get a lot of false positives. Oops, sorry, the transaction didn't go through. Did you do this transaction? Yes, I did. Oh, sorry, you're going to have to redo it because it didn't go through. It's very frustrating for a lot of users. That will get better and better and better. We've all experienced retargeting from ads, and we know how crappy they are. That will continue to get better. The big question that people have and it goes back to Jeff Hammerbacher, the best minds of my generation are trying to get people to click on ads. When will we see big data really start to affect our lives in different ways like patient outcomes? We're going to hear some of that today from folks in health care and pharma. Again, these are the things that people are waiting for. The other piece is, of course, IT. What you're seeing, in terms of IT, in the whole data flow? >> Yes, a big question we have, Dave, is where's the data? And therefore, where does it make sense to be able to do that processing? In big data we talked about you've got masses amounts of data, can we move the processing to that data? With IT, the day before, your RCTO talked that there's going to be massive amounts of data at the edge and I don't have the time or the bandwidth or the need necessarily to pull that back to some kind of central repository. I want to be able to work on it there. Therefore there's going to be a lot of data worked at the edge. Peter Levine did a whole video talking about how, "Oh, Public Cloud is dead, it's all going to the edge." A little bit hyperbolic to the statement we understand that there's plenty use cases for both Public Cloud and for the edge. In fact we see Google big pushing machine learning TensorFlow, it's got one of those machine learning frameworks out there that we expect a lot of people to be working on. Amazon is putting effort into the MXNet framework, which is once again an open-source effort. One of the things I'm looking at the space, and I think IBM can provide some leadership here is to what frameworks are going to become popular across multiple scenarios? How many winners can there be for these frameworks? We already have multiple programming languages, multiple Clouds. How much of it is just API compatibility? How much of work there, and where are the repositories of data going to be, and where does it make sense to do that predictive analytics, that advanced processing? >> You bring up a good point. Last year, last October, at Big Data CIV, we had a special segment of data scientists with a data scientist panel. It was great. We had some rockstar data scientists on there like Dee Blanchfield and Joe Caserta, and a number of others. They echoed what you always hear when you talk to data scientists. "We spend 80% of our time messing with the data, "trying to clean the data, figuring out the data quality, "and precious little time on the models "and proving the models "and actually getting outcomes from those models." So things like Spark have simplified that whole process and unified a lot of the tooling around so-called big data. We're seeing Spark adoption increase. George Gilbert in our part one and part two last week in the big data forecast from Wikibon showed that we're still not on the steep part of the Se-curve, in terms of Spark adoption. Generically, we're talking about streaming as well included in that forecast, but it's forecasting that increasingly those applications are going to become more and more important. It brings you back to what IBM's trying to do is bring machine learning into this critical transaction data. Again, to me, it's an extension of the vision that they put forth two years ago, bringing analytic and transaction data together, actually processing within that Private Cloud complex, which is what essentially this mainframe is, it's the original Private Cloud, right? You were saying off-camera, it's the original converged infrastructure. It's the original Private Cloud. >> The mainframe's still here, lots of Linux on it. We've covered for many years, you want your cool Linux docker, containerized, machine learning stuff, I can do that on the Zn-series. >> You want Python and Spark and Re and Papa Java, and all the popular programming languages. It makes sense. It's not like a huge growth platform, it's kind of flat, down, up in the product cycle but it's alive and well and a lot of companies run their businesses obviously on the Zn. We're going to be unpacking that all day. Some of the questions we have is, what about Cloud? Where does it fit? What about Hybrid Cloud? What are the specifics of this announcement? Where does it fit? Will it be extended? Where does it come from? How does it relate to other products within the IBM portfolio? And very importantly, how are customers going to be applying these capabilities to create business value? That's something that we'll be looking at with a number of the folks on today. >> Dave, another thing, it reminds me of two years ago you and I did an event with the MIT Sloan school on The Second Machine Age with Andy McAfee and Erik Brynjolfsson talking about as machines can help with some of these analytics, some of this advanced technology, what happens to the people? Talk about health care, it's doctors plus machines most of the time. As these two professors say, it's racing with the machines. What is the impact on people? What's the impact on jobs? And productivity going forward, really interesting hot space. They talk about everything from autonomous vehicles, advanced health care and the like. This is right at the core of where the next generation of the economy and jobs are going to go. >> It's a great point, and no doubt that's going to come up today and some of our segments will explore that. Keep it right there, everybody. We'll be here all day covering this announcement, talking to practitioners, talking to IBM executives and thought leaders and sharing some of the major trends that are going on in machine learning, the specifics of this announcement. Keep it right there, everybody. This is The Cube. We're live from the Waldorf Astoria. We'll be right back.

I from Las Vegas Nevada it's the cube covering IBM world of Watson 2016 brought to you by IBM now here are your hosts John furrier and Dave vellante hey welcome back everyone we're here live in Las Vegas for IBM's world of Watson at the mandalay bay here this is the cube SiliconANGLE media's flagship program we go out to the events and extract the signal-to-noise I'm John Ford SiliconANGLE i'm here with dave vellante my co-host chief researcher red Wikibon calm and our next guest is inderpal bhandari who's the chief global chief data officer for IBM welcome to the cube welcome back thank you thank you meet you you have in common with Dave at the last event 10 years Papa John was just honest we just talked about the ten year anniversary of I OD information on demand and Dave's joke why thought was telling we'll set up the says that ten years ago different data conversation how do you get rid of it is I don't want the compliance and liability now it shifted to a much more organic innovative exciting yeah I need a value add what's the shift what's the big change in 10 years what besides the obvious of the Watson vision how did what it move so fast or too slow what's your take on this ya know so David used to be viewed as exhaust right the tribe is something to get rid of like you pointed out and now it's much more to an asset and in fact you know people are even talking about about quantifying it as an asset so that you can reflect it on the balance sheet and stuff like that so it certainly moved a long long way and I think part of it has to do with the fact that we are inundated with data and data does contain valuable information and to the extent that you're able to glean it and act on it efficiently and quickly and accurately it leads to a competitive advantage what's the landscape for architects out there because a lot of things that we hear is that ok i buy the day they I got a digital transformation ok but now I got to get put the data to work so I need to have it all categorized what's the setup is there a general architecture philosophy that you could share with companies that are trying to set themselves up for some baseline foundational sets of building blocks I mean I think they buy the Watson dream that's a little Headroom I just want to start in kindergarten or in little league or whatever metaphor we want to use any to baseline what's today what's the building blocks approach the building blocks approach I mean from a if you're talking about a pure technical architectural that kind of approach that's one thing if you're really going after a methodology that's going to allow you to create value from data I would back you up further I would say that you want to start with the business itself and gaining an understanding of how the business is going to go about monetizing itself not its data but you know what is the businesses monetization strategy how does the business plan to make money over the next few years not how it makes money today but over the next few years how it plans to make money that's the right starting point once you've understood that then it's basically reflecting on how data is best used in service of that and then that leads you down to the architecture the technologies the people you need the skills makes the process Tanner intuitive the way it used to be the ivory tower or we would convene and dictate policy and schemas on databases and say this is how you do it you're saying the opposite business you is going to go in and own the road map if you will the business it's a business roadmap and then figure it out yeah go back then go back well that's that's really the better way to address it than my way so the framework that we talked about in in Boston and now and just you're like the professor I'm the student so and I've been out speaking to other cheap date officers about it it's spot on this framework so let me briefly summarize it and we can I heard you not rebuilding it to me babe I'm saying this is Allah Falls framework I've stolen it but with no shame no kidding and so again we're doing a live TV it's you know he can source your head I will give him credit so but you have said they're there are two parallel and three sequential activities that have to take place for data opposite of chief data officer the two parallel our partnership with the line of business and get the skill sets right the three sequential are the thing you just mentioned how you going to monetize data access to data data sources and Trust trust the data okay so great framework and I'd say I've tested it some CEOs have said to me well I geeza that's actually better than the framework I had so they've sort of evolved as I said you're welcome and oh okay but now so let's drill into that a little bit maybe starting with the monetization piece in the early days Jonna when people are talking about Big Data it was the the mistake people made was I got to sell the data monetize the data itself not necessarily it's what you're saying yes yes I think that's the common pitfall with that when you start thinking about monetization and you're the chief data officer your brain naturally goes to well how do I monetize the data that's the wrong question the question really is how is the business planning to monetize itself what is the monetization strategy for the overall business and once you understand that then you kind of back into what data is needed to support it and that's really kind of the sets the staff the strategy in place and then the next two steps off well then how do you govern that data so it's fit for the purpose of that business lead that you just identified and finally what data is so critical that you want to centralize it and make sure that it's completely trusted so you back into those three those three steps so thinking about data sources you know people always say well should you start with internal should you start with external and the answer presumably is it depends it depends on the business so how do you how do you actually go through that decision tree what's that process like yeah I mean if you know you start with the monetization strategy of the company so for example I'll use IBM a banana and the case of IBM took me the first few months to understand that our monetization strategy was around cognitive business specifically making enterprises into cognitive businesses and so then the strategy that we have internally for IBM's data is to enable cognition within within IBM the enterprise and move forward with that and then that becomes a showcase for our customers because it is after all such a good example of a complex enterprise and so backing you know backing in from that strategy it becomes clear what are some of the critical data elements that you need to master that you need to trust that you need to centralize and you need to govern very very rigorously so that's basically how I approached it did I answer your question daivam do you get so so you touched on the on the second part I want to drill into the the third sequential activities which which is sources so i did so you did we just talk about this well the sources i mean if you had something add to that yes in terms of the i think you mentioned the internal versus external so one thing else i'll mention especially if you kind of take that 10-year outlook that we were talking about 10 years ago serials had very internal outlook in terms of the data was all internal business data today it's much more external as well there's a lot more exogenous data that we have to handle and validity and that's because we're making use of a lot more unstructured data so things like news feeds press releases articles that have just been written all our fair game to amplify the view that you have about some entity so for example if we're dealing with a new supplier you know previously we might gather some information by talking with them now we'd also be able to look at essentially everything that's out there about them and factor that in so it is a there's an element of the exogenous data that's brought to bear and then that obviously becomes part of the realm of the CDO as well to make sure that that data is available and you unusable by the business is John Kelly said something go ahead sorry well Jeff Jonas would say that's the observation space right that you want to have the news feeds it's extra metadata that could change the alchemy if you will of whatever the mix of the data is that kind of well yeah I would say you might even go further than just metadata i would say that in some some sense it's part of your intrinsic data set because you know it gives you additional information about the entities that you're collecting data on and that measuring the John Kelly in the keynote this morning he made two statements he said one is in three to five years every health care practitioners going to going to want to consult Watson and then he also said same thing for MA because watch is going to know every public piece of data about every single company right so it's would seem that within the three to five year time frame that the shift is going to be increasingly toward external data sources not necessarily the value in the lever points but in terms of the volume certainly of data is that fair I think it's a it's a fair statement I mean I think if you think of it in the healthcare context if you know a patient comes in and there's a doctor or a practitioner that's examining the patient right there they're generating some data based on their interaction but then if you think about the exogenous data that's relevant and pertinent to that case that could involve you know thousands of journals and articles and so you know your example of essentially saying that the external data could be far greater than the internal data out say we're already there okay and then the third sequential piece is trust are you gonna be able to trust the trust we talk a lot about we were down to Big Data NYC the same week you guys made your big announcement the data works everybody talks about data Lakes we joke gets the data swamp and can't really trust the data yeah we further away from a single version of the truth than we ever were so how are you dealing with that problem internally at IBM and what's the focus is it more on reporting is it more on supporting lines of business in product yeah the focus internal within IBM is in terms of driving cognition at the way I would describe it is at points where today we have significant human judgment being exercised to make decisions and that's you know thousands of points in our enterprise or complicated enterprise like IBM's and each of those decision points is actually an opportunity to inject cognitive technology and play and then bring to bear and augmented intelligence to those decisions that you know a factors in the exogenous data so leaving a much better informed decision but also them a much more accurate decision okay the two parallel activities let's start with the first one line of business you know relationships sounds like bromide why is it not just sort of a trite throwaway statement what where's the detail behind that so the detail behind that if you go back to the very first and the most important step and this whole thing with regard to the monetization strategy of the company understanding that if you don't have those deep relationships with the lines of business there's no way that you'll be able to understand the monetization strategy of the business so that's why that's a concurrent activity that has to start on day one otherwise you won't even get past the you know that that very first first base in terms of understanding what the monetization strategies are for the business and that can only really come by working directly with the business units meeting with their leadership understanding their business so you have to do that due diligence and that's where that partnership becomes critical then as you move on as you progress to that sequence you need them again so for instance once you understood the strategy and now you understood what data you need to follow that strategy and to govern it you need their help in governing the business because in many cases the businesses may be the ones collecting the data or at least controlling the source systems for that data so that partnership then just gets deeper and deeper and deeper as you move forward in that program I love the conscience of monetizing earlier and this some tweets going around you know what's holding it back cost of building it obviously and manageability but I want to bring that back and bring a developer perspective here because a lot of emphasis is on developing apps where the data is now part of the development process I wrote a blog post in 2008 saying that dated some new development kit radical at the time but reality it came out to be true and that they're looking at data as library of value to tap into so if stuffs annandale they could be sitting there for years but I could pull something out and be very relevant in context in real time and change the game on some insight and the insight economy is bob was saying so what is your strategy for IBM 21 on board more developer goodness and to how do you talk to customers were really trying to figure out a developer strategy so they can build apps and not to go back and rewrite it make it certainly mobile first etc but what's how does a date of first appt get built and I should developers be programming with you I'll give you a way to think about it right i mean and going back again to that ten-year paradigm shift right so ten years ago if somebody wanted to write an application and put it on the internet and it was based on data the hardest part was getting hold of the data because it was just very very difficult for them to get all of it to access the data and then those who did manage to get all of the data they were very successful in being able to utilize it so now with the the paradigm shift that's happened now is the approaches that you make the data available to developers and so they don't have to go through that work both in terms of accessing collecting finding that data then cleaning it it's also significant and so time consuming that it could put put back there their whole process of eventually getting to the app so to the extent that you have large stores of data that are ready to go and you can then make that available to a body of developers it just unleashes it's like having a library of code available is it all the hard work and I think that's a good way to look at it I mean that's think that's a very good way to look at it because you've also got technologies like the deep learning technologies where you can essentially train them with data so you don't need to write the code they get trained to later so I see a DevOps of data means like an agile meets I'm again you're right a lot of the cleaning and this is where you no more noise we all know that problem or data creates more noise better cleaning tools so however you can automate that yes seems to be the secret differentiator it's an accelerator it's amazing accelerator for development if you have good sets of data that are available for them to used so I want to round out my my little framework here your frame working with my my learnings for the fifth one being skills yes so this is complicated because it involves organization skills changes as pepper going through the lava here we try to get her on the cube Dave home to think the pamper okay babe yeah so should I take over pepper you want to go see pepper I want to see pepper on the cube hey sorry exact dress but so a lot of issues there there's reporting structures so what do you mean when you talk about sort of the skill sets and rescaling so and I'll describe to you a little bit about the organization that I have at IBM as an example some of that carries over and some of that doesn't the reason I say that is again I mean the skills piece there are some generic skill sets that you need for to be achieved data officer to be a successful chief data officer in an enterprise there is one pillar that I have in my organization is around data science data engineering DevOps deep learning and these are the folks who are adept at those technologies and approaches and methodologies and they can take those and apply them to the enterprise so in a sense these are the more technical people then another pillar that's again pretty generic and you have to have it is the information and data governance pillow so that anything that's flowing any data that's flowing through the data platform that I spoke off in the first pillar that those that that data is governed and fit for purpose so they have to worry about that as soon as any data is you even think of introducing that into the platform these folks have to be on that and they're essentially governing it making sure that people have the right access security the quality is good its improving there's a path to improving it and so forth I think those are some fairly generic you know skill sets that we have to get in the case of the first pillar what's difficult is that there aren't that many people with those skills and so it's hard to find that talent and so the sooner you get on it so that would that's the biggest barrier in the case of the second pillar what's the most difficult piece there is you need people who can walk the balance between monetization and governance too much governance and you essentially slow everything down and nothing moved a cuff and you're handcuffed and then you know if it's too much monetization you might run aground because you you ignored some major regulation so walking that loss of market value yeah that's what you have to really get ahead of your skis as they say and have a faceplant you'll try too hard to live boost mobile web startups like Twitter that's big cock rock concert with Twitter Facebook if you try to monetize too early yes you lose the flywheel effect of value absolutely so walking that balance is critical so that's that that's really finding the skill set to be able to do that that's that's what what's at play in that second or the third one is if you are applying it to an enterprise you have to integrate these you know this platform into the workflow off the enterprise itself otherwise you're not going to create any impact because that's where the impact gets created right that's basically where the data is that the tip of the spear to so to speak so you it's going to create value and in a large enterprise which has legacy systems which are silos which is acquiring companies and so on and so forth that's enough itself a significant job and that skill set is that's a handicapped because if you have that kind of siloed mentality you don't get the benefits of the data sharing right so what's that what's said how much how much effort would it take I'm just kind of painting that picture kind of like out there like well a lot of massively hard ya know that that's you know a lot of you know a lot of people think that data mining is all about my data you know this is my data I'm not going to give it to you the one of the functions of the chief data office is to change that mindset yeah and to stop making use of the data in a broader context than just a departmental siloed type of approach and now some data can legitimately be used only departmentally but the moment you need two or more department start using that data I mean it's essentially corporate data so are those roles a shared service everybody see that works it maybe varies but is it a shared service that reports into the chief data officer or is it embedded into the business those those skill sets that you talked about I think those skill sets are definitely part of the chief data officer you know organization now it's interesting you mentioned that about embedding them and the business units now in a in a large enterprise a complicated enterprise like IBM the different business units and that potentially have different business objectives and so forth you know you you do need a chief data officer role for each of these business units and that's something that I've been advocating that's my fault pillar and we are setting that up and then within the context of IBM so that they serve the business unit but they essentially reporting to me so that they can make use of the overall corporate structure you do their performance review the performance review is done by the business unit it is ok but the functional direction is given by me ok so I get back to still go either way oh yes that's a balance loon yeah absolutely under a lot of time for sure i'll get back to this data mining because you bring up a good point we can maybe continue on our next time we talk but data monies were all the cutting edge kind of best practices are were arsed work what we're relations are still there technically if you're here but that the dynamic of data mining is is that you're assuming no new data so with if you have a lot of data coming in most of the best data mining techniques are like a corpus you attack it and learned but if the pile of data is getting bigger faster that you could date a mine it what good is against or initial circular hole I'm going to again you know just take you back 10 years from now and now right and the differences between the two so it's very interesting points that you bring up I'll give you an example from 10 years ago this data mining example not ten years ago actually my first go-around at IBM so it's like 94 yeah one of the things I've done was we had a program a computer program that every team in the National Basketball Association started using and this was a classic data mining program it would look at the data and find insights and present them and one of the insights that it came up with and this was for a critical playoff game it told the coach you got to play your backup point guard and your backup forward now think about that which same coach would actually go with that so it's very hard for them to believe that they don't know if it's right or wrong in my own insurance and the way we got around that was we essentially pointed back to the snippets of video where those circumstances occurred and now the coach could see what is going on make a you know an informed decision flash forward to now the systems we have now can actually look at all that context all at once what's happening in the video what's happening in the audio also the data can piece together the context so data mining is very different today than what it was them now it's all about weaving the context and the story together and serving it up yeah what happened what's happening and what's going to happen kinda is the theaters of yes there are in sight writing what happened it's easy just yeah look at the data and spit out some insight what's happening now is a bit harder in memory I think that's the difference between cognition as it away versus data mining as you know we understood a few years ago great cartridge we can go for another hour but do we ever get enough love to follow up on some of the deep learning maybe come down to armonk next time we're in this certainly on the sports data we have a whole program on sports data so we love the sports with the ESPN of tech and bringing you all the action right here yes I did Doug before Moneyball you know my mistake was letting right yeah yeah right the next algorithm but that's okay you know we put a little foot mark on the cube notes for that thank you very much thank you appreciate okay live in Mandalay Bay we're right back with more live coverage I'm Sean for a table on thing great back today I am helping people

>> live from Boston, Massachusetts. >> It's the Cube >> covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day villain Day and >> stew minimum. Welcome back to Boston, everybody. This is the IBM Chief Data Officer Summit. And this is the Cube, the worldwide leader in live tech coverage. Caitlin Lepic is here. She's an executive within the chief data officer office at IBM. And she's joined by Dave Shoot Mel, who's a research director at, uh D. C. And he covers cognitive systems and content analytics. Folks, welcome to the Cube. Good to see you. Thank you. Can't. Then we'll start with you. You were You kicked off the morning and I referenced the Forbes article or CDOs. Miracle workers. That's great. I hadn't read that article. You put up their scanned it very quickly, but you set up the event. It started yesterday afternoon at noon. You're going through, uh, this afternoon? What's it all about? This is evolved. Since, what, 2014 >> it has, um, we started our first CDO summit back in 2014. And at that time, we estimated there were maybe 200 or so CDOs worldwide, give or take and we had 30, 30 people at our first event. and we joked that we had one small corner of the conference room and we were really quite excited to start the event in 30 2014. And we've really grown. So this year we have about 170 folks joining us, 70 of which are CEOs, more acting, the studios in the organization. And so we've really been able to grow the community over the last two years and are really excited to see to see how we can continue to do that moving forward. >> And IBM has always had a big presence at the conference that we've covered the CDO event. So that's nice that you can leverage that community and continue to cultivate it. Didn't want to ask you, so it used that we were talking when we first met this morning. It used to be dated was such a wonky topic, you know, data was data value. People would try to put a value on data, and but it was just a really kind of boring but important topic. Now it's front and center with cognitive with analytics. What are you seeing in the marketplace. >> Yeah, I think. Well, what we're seeing in the market is this emphasis on predictive applications, predictive analytics, cognitive applications, artificial intelligence of deep learning. All of those those types of applications are derived and really run by data. So unless you have really good authoritative data to actually make these models work, you know, the systems aren't going to be effective. So we're seeing an emerging marketplace in both people looking at how they can leverage their first party data, which, you know, IBM is really talking about what you know, Bob Picciotto talked about this morning. But also, we're seeing thie emergency of a second party and third party data market to help build these models out even further so that I think that's what we're really seeing is the combination of the third party data along with the first party data really being the instrument for building these kind of predictive models, you know, they're going to take us hopefully, you know, far into the future. >> Okay, so, Caitlin square the circle for us. So the CDO roll generally is not perceived. Is it technology role? Correct. Yet as Davis to saying, we're talking about machine learning cognitive. Aye, aye. These air like heavy technical topics. So how does the miracle worker deal with all this stuff generally? And how does IBM deal with it inside the CDO office? Specifically? >> Sure. So it is. It's a very good point, you know, Traditionally, Seo's really have a business background, and we find that the most successful CDO sit in the business organization. So they report somewhere in a line of business. Um, and there are certainly some that have a technical background, but far more come from business background and sit in the business. I can't tell you how we are setting up our studio office at IBM. Um, so are new. And our first global chief date officer joined in December of last year. Interpol Bhandari, um and I started working for him shortly thereafter, and the way he's setting up his office is really three pillars. So first and foremost, we focused on the data engineering data sign. So getting that team in place next, it's information, governance and policy. How are we going to govern access, manage, work with data, both data that we own within our organization as well as the long list of of external data sources that that we bring in and then third is the business integration filler. So the idea is CDOs are going to be most successful when they deliver those data Science data engineering. Um, they manage and govern the data, but they pull it through the business, so ensuring that were really, you know, grounded in business unit and doing this. And so those there are three primary pillars at this point. So prior >> to formalizing the CDO role at I b m e mean remnants of these roles existed. There was a date, equality, you know, function. There was certainly governance in policy, and somebody was responsible to integrate between, you know, from the i t. To the applications, tow the business. Were those part of I t where they sort of, you know, by committee and and how did you bring all those pieces together? That couldn't have been trivial, >> and I would say it's filling. It's still going filling ongoing process. But absolutely, I would say they typically resided within particular business units, um, and so certainly have mature functions within the unit. But when we're looking for enterprise wide answers to questions about certain customers, certain business opportunities. That's where I think the role the studio really comes in and what we're What we're doing now is we are partnering very closely with business units. One example is IBM analytic. Seen it. So we're here with Bob Luciano and other business units to ensure that, as they provide us, you know, their data were able to create the single trusted source of data across the organization across the enterprise. And so I agree with you, I think, ah, lot of those capabilities and functions quite mature, they, you know, existed within units. And now it's about pulling that up to the enterprise level and then our next step. The next vision is starting to make that cognitive and starting to add some of those capabilities in particular data science, engineering, the deep learning on starting to move toward cognitive. >> Dave, I think Caitlin brought up something really interesting. We've been digging into the last couple of years is you know, there's that governance peace, but a lot of CEOs are put into that role with a mandate for innovation on. That's something that you know a lot of times it has been accused of not being all that innovative. Is that what you're seeing? You know what? Because some of the kind of is it project based or, you know, best initiatives that air driving forward with CEOs. I think what we're seeing is that enterprises they're beginning to recognize that it's not just enough to be a manufacturer. It's not just enough to be a retail organization. You need to be the one of the best one of the top two or the top three. And the only way to get to that top two or top three is to have that innovation that you're talking about and that innovation relies on having accurate data for decision making. It also relies on having accurate data for operations. So we're seeing a lot of organizations that are really, you know, looking at how data and predictive models and innovation all become part of the operational fabric of a company. Uh, you know, and if you think about the companies that are there, you know, just beating it together. You know Amazon, for example. I mean, Amazon is a completely data driven company. When you get your recommendations for, you know what to buy, or that's all coming from the data when they set up these logistics centers where they're, you know, shipping the latest supplies. They're doing that because they know where their customers are. You know, they have all this data, so they're they're integrating data into their day to day decision making. And I think that's what we're seeing, You know, throughout industry is this this idea of integrating decision data into the decision making process and elevating it? And I think that's why the CDO rule has become so much more important over the last 2 to 3 years. >> We heard this morning at 88% percent of data is dark data. Papa Geno talked about that. So thinking about the CEOs scope roll agenda, you've got data sources. You've gotto identify those. You gotta deal with data quality and then Dave, with some of the things you've been talking about, you've got predictive models that out of the box they may not be the best predictive models in the world. You've got iterated them. So how does an organization, because not every organizations like Amazon with virtually unlimited resource is capital? How does an organization balance What are you seeing in terms of getting new data sources? Refining those data source is putting my emphasis on the data vs refining and calibrating the predictive models. How organizations balancing that Maybe we start with how IBM is doing. It's what you're seeing in the field. >> So So I would say, from what we're doing from a setting up the chief data office role, we've taken a step back to say, What's the company's monitor monetization strategy? Not how your mind monetizing data. How are how are you? What's your strategy? Moving forward, Um, for Mance station. And so with IBM we've talked about it is moved to enabling cognition throughout the enterprise. And so we've really talked about taking all of your standard business processes, whether they be procurement HR finance and infusing those with cognitive and figuring out how to make those smarter. We talking examples with contracts, for example. Every organization has a lot of contracts, and right now it's, you know, quite a manual process to go through and try and discern the sorts of information you need to make better decisions and optimize the contract process. And so the idea is, you start with that strategy for us. IBM, it's cognitive. And that then dictates what sort of data sources you need. Because that's the problem you're trying to solve in the opportunity you're chasing down. And so then we talk about Okay, we've got some of that data currently residing today internally, typically in silos, typically in business units, you know, some different databases. And then what? What are longer term vision is, is we want to build the intelligence that pulls in that internal data and then really does pull in the external data that we've that we've all talked about. You know, the social data, the sentiment analysis, analysis, the weather. You know, all of that sort of external data to help us. Ultimately, in our value proposition, our mission is, you know, data driven enablement cognition. So helps us achieve our our strategy there. >> Thank you, Dad, to that. Yeah, >> I mean, I think I mean, you could take a number of examples. I mean, there's there's ah, uh, small insurance company in Florida, for example. Uh, and what they've done is they have organized their emergency situation, their emergency processing to be able to deal with tweets and to be able to deal with, you know, SMS messages and things like that. They're using sentiment analysis. They're using Tex analytics to identify where problems are occurring when hurricane happens. So they're what they're doing is they're they're organizing that kind of data and >> there and there were >> relatively small insurance company. And a lot of this is being done to the cloud, but they're basically getting that kind of sentiment analysis being ableto interpret that and add that to their decision making process. About where should I land a person? Where should I land? You know, an insurance adjuster and agent, you know, based on the tweets, that air coming in rather than than just the phone calls that air coming into the into the organization, you know? So that's a That's a simple example. And you were talking about Not everybody has the resources of an Amazon, but, you know, certainly small insurance companies, small manufacturers, small retail organizations, you, Khun get started by, you know, analyzing your You know what people are saying about you. You know, what are people saying about me on Twitter? What are people saying about me on Facebook? You know how can I use that to improve my customer service? Uh, you know, we're seeing ah whole range of solutions coming out, and and IBM actually has a broad range of solutions for things like that. But, you know, they're not the only points out there. There's there's a lot of folks do it that kind of thing, you know, in terms of the dark data analysis and barely providing that, you know, as part of the solution to help people make better decisions. >> So the answers to the questions both You're doing both new sources of data and trying to improve the the the analytics and the models. But it's a balancing act, and you could come back to the E. R. A. Y question. It sounds like IBM strategies to supercharge your existing businesses by infusing them with new data and new insights. Is >> that correctly? I would say that is correct. >> Okay, where is in many cases, the R A. Y of analytics projects that date have been a reduction on investment? You know, I'm going to move stuff from my traditional W two. A dupe is cheaper, and we feels like Dave, we're entering a new wave now maybe could talk about that a little bit. >> Yeah. I mean, I think I think there's a desk in the traditional way of measuring ROI. And I think what people are trying to do now is look at how you mentioned disruption, for example. You know what I think? Disruption is a huge opportunity. How can I increase my sales? How can I increase my revenue? How can I find new customers, you know, through these mechanisms? And I think that's what we're starting to see in the organization. And we're starting to see start ups that are dedicated to providing this level of disruption and helping address new markets. You know, by using these kinds of technologies, uh, in in new and interesting ways. I mean, everybody uses the airbnb example. Everybody uses uber example. You know that these are people who don't own cars. They don't know what hotel rooms. But, you know, they provide analytics to disrupt the hotel industry and disrupt the taxi industry. It's not just limited to those two industries. It's, you know, virtually everything you know. And I think that's what we're starting to see is this height of, uh, virtual disruption based on the dark data, uh, that people can actually begin to analyze >> within IBM. Uh, the chief data officer reports to whom. >> So the way we've set up in our organization is our CBO reports to our senior vice president of transformation and operations, who then reports to our CEO our recommendation as we talked with clients. I mean, we see this as a CEO level reporting relationship, and and oftentimes we advocate, you know, for that is where we're talking with customers and clients. It fits nicely in our organization within transformation operations, because this line is really responsible for transforming IBM. And so they're really charged with a number of initiatives throughout the organization to have better skills alignment with some of the new opportunities. To really improve process is to bring new folks on board s. So it made sense to fit within, uh, organization that the mandate is really transformation of the company of the >> and the CDO was a peer of the CIA. Is that right? Yes. >> Yes, that's right. That's right. Um, and then in our organization, the role of split and that we have a chief data officer as well as a chief analytics officer. Um, but, you know, we often see one person serving both of those roles as well. So that's kind of, you know, depend on the organizational structure of the company. >> So you can't run the business. So to grow the business, which I guess is the P and L manager's role and transformed the business, which is where the CDO comes. >> Right? Right, right. Exactly. >> I can't give you the last word. Sort of Put a bumper sticker on this event. Where do you want to see it go? In the future? >> Yes. Eso last word. You know, we try Tio, we tried a couple new things. Uh, this this year we had our deep dive breakout sessions yesterday. And the feedback I've been hearing from folks is the opportunity to talk about certain topics they really care about. Is their governance or is innovation being able to talk? How do you get started in the 1st 90 days? What? What do you do first? You know, we we have sort of a five steps that we talk through around, you know, getting your data strategy and your plan together and how you execute against that. Um And I have to tell you, those topics continue to be of interest to our to our participants every year. So we're going to continue to have those, um, and I just I love to see the community grow. I saw the first Chief data officer University, you know, announced earlier this year. I did notice a lot of PR and media around. Role of studio is miracle workers, As you mentioned, doing a lot of great work. So, you know, we're really supportive. Were big supporters of the role we'll continue to host in person events. Uh, do virtual events continue to support studios? To be successful on our big plug is will be world of Watson. Eyes are big IBM Analytics event in October, last week of October in Vegas. So we certainly invite folks to join us. There >> will be, >> and he'll be there. Right? >> Get still, try to get Jimmy on. So, Jenny, if you're watching, talking to come on the Q. >> So we do a second interview >> and we'll see. We get Teo, And I saw Hillary Mason is going to be the oh so fantastic to see her so well. Excellent. Congratulations. on being ahead of the curve with the chief date officer can theme. And I really appreciate you coming to Cube, Dave. Thank you. Thank you. All right, Keep right there. Everybody stew and I were back with our next guest. We're live from the Chief Data Officers Summit. IBM sze event in Boston Right back. My name is Dave Volante on DH. I'm a longtime industry analysts.

from Las Vegas accepting the signal from the noise it's the kue coverage interconnect 2016 brought to you by IBM now your host John hurry and Dave vellante okay welcome back around we are here live in Las Vegas for IBM interconnect 2016 special presentation of the cube our flagship program would go out to the events and extract the signal from the noise I'm John forreal echoes gave a lot they are next guest pricing Kohler who's the chief information technology officer and I'm saying this for the first time on the cube the weather company and IBM business welcome back to the cube thank you very much glad to be back last time you weren't an IBM business we were just the weather company were just the weather company so congratulations on your success want to say we really big fans of it but what Papa Chiana the team have done is visionary bold and very relevant so congratulations hey how's it feel it is grateful din we are really excited the opportunity with the IBM platform and you know the reach and the capabilities I mean it it really helps accelerate what we were trying to get done as the weather company you know as our own standalone business um and you know as you try to prepare and protect the entire planet all of its people and all of its businesses prepare and protect them for tomorrow which is really what the weather is company is all about finding that intersection of consumer behavior helping prepare and protect you as a in your personal life and your family but also you as a business owner how do we prepare and protect you to do better tomorrow because of the weather and the insights that we can provide fit straight into the work the Bob picciano in team have been doing with the insights you know economy with Watson and analytics with insights as a service all of that just kind of plugs together in it it really is a natural fit it's interesting to see IBM's move we were asked to guess on from IBM earlier and Jamie Thomas said it's all open source we want to get in early so this is an early bet for IBM certainly a bold move with the weather company but it's interesting the scuttlebutt as we talk to our sources inside the company close to the company have telling us that the weather companies is infiltrating and affecting the DNA IBM in a good way and you guys have always been a large scale data company and that is what all businesses are striving to digitize everything yes and so take us through that I mean one I think it's fair to say that you guys are kind of infecting I play in a positive way the mindset of being large-scale data yeah well why is that so compelling and how did you guys get here obviously whether the big data problem share some commentary around where it all came from well i think you know it's in my DNA first of all and it's in our company's DNA it's are no teams DNA you know I'm a change agent you would not want to hire me to maintain something good if you want to hire me to you know to break something and rebuild it better that's I'm your guy so you know I think when you look at the movement from you know the kind of the movement over time of IBM and you know the constant evolution that IBM goes through time is ripe when you take the cloud capabilities and you take data and you take analytics and the whole concept and capabilities of Watson Watson gets smarter as it learns more Watson can only be as smart as the data you feed it and so for Watson to continue to learn and continue to solve new problems and continue to expand its capability set we do have to feed it more data and and so you know looking at whether whether it was the original big data problem ever since the first mainframe the first you know application ever written on a mainframe was a weather forecast and ever since then everybody's been trying to figure out how to make the forecast more accurate and a lot of that comes from more data the more data you have the more accurate your forecast is going to be so we've been trying to solve this big data problem Walt and Dave talks about it was saw earlier in the opening about digital assets and in this digital transformation companies have to create more digital assets that's just dating yeah in this new model so when you look at the data aspect you say whether also is a use case where people are familiar with we were talking before we went on camera that people can understand the geekiness of whether it's different they're familiar with it but also highlights a real-life use case and the IOT Internet of Things wearables we heard you have sports guys on here tracking sensors this brings up that digital digitizing is going to be everything not just IT right it makes it real right if I think about my parents right we've been talking about IOT hey dad you're gonna have a connected refrigerator why does he care what do I need a connected refrigerator for but as you start to bring these insights to life and you make them real and you say you know what if I actually understand the humidity levels in your house and I can get that off the sensor on the air intake of your refrigerator I can now correlate that the humidity level outside of your house and I might be able to actually tweak your HVAC and I can make that run efficiently and I can now you know cut thirty percent of your cooling costs and all of these you know examples they're integrated they become real yeah and and I think weather is great because everybody checks their weather app the weather channel app or the weather underground app every day they're always looking at it and you know we get it right seventy-eight percent of the time we'd get it wrong sometimes we're constantly working to maintain our number-one position and data accuracy on weather forecasting and you know the more data we have the more accurate we can make it and so we've got any safer to you think just think about the use cases of people's lives slippery rose you know events correct I mean it's all tied in no goes back to another you know if I understand what's going on with the anti-lock braking system of a car and I already have a communication vehicle into everybody in that car which is our appt in their pocket I can alert them if the car is up ahead are having here are their abs activated and if all of the cars up ahead are having their abs activated I could alert them two miles back and say hey get ready slow down it's real it's not forecasted it's real data I'm giving you a real alert you should really take action and you know as we move from you know weather-alerts that we're looking out forward in time many hours as we're now doing rain alerts where we tell you it's going to start raining in the next seven minutes ten minutes people love those because it's right now and I can make a decision right now lightning strikes are always fascinating oh god because I gotta see crisis so last fall at IBM insight we interviewed David Kinney death your CEO and then right after I think was the week after I was watching some you know I was in Boston watching some sports program and there's bill belichick complaining about the in accuracy of whether i'll try that whether some reporter asked him about you know you factor in the weather i don't even pay attention i look at the weather forecast they're always wrong as a wait a minute I just I just interviewed David Kennedy he was bragging on the weather is the accuracy and how much it's improved so helping you mentioned seventy-eight percent of the time it's it's gotten better over time it has it still got rooms we're not perfect so so talk about that progression it is the data but how much better are you over time where is that better is it just short term or is it longer term at so color to that it's a great question and it's a fair point I think one of the biggest changes we've made in the last three years that the weather company is we've taken our forecast from what was roughly 2 million locations where we would do a forecast two million locations around the globe and today we we create a forecast for 2.2 billion locations around the globe because the weather is different at Fenway then Boston Logan it's just different than the the start time of rain the start time of a thunderstorm you know that's gonna be different now maybe five minutes but it's different the temperature the wind it's different and so as we've increased the accuracy and granularity of ours are our locations we've also done that from a time perspective as well so we used to produce a forecast every four to six hours depending upon how fast the models ran and did they run and complete successfully we now update our forecast every 15 minutes and so we we've increased the the you know all aspects of that and when you when you now think about getting your weather forecast you can no longer just type in BOS for your airport code and say i want to know what the weather is at boston logan if you're you know if you're in cambridge the boston logan forecast is not accurate for you you know five years ago every that was fine for everybody right right and so we have to retrain people to think about and make sure that when they're looking for a forecast and they're using our apps they can get a very specific forecast for where they are whatever point on the globe they are and and don't have you know Boston you know Logan as your you know favorite for your city if you're sitting in Cambridge or your you know you know it in Andover further outside where I am now where you gonna be my guess I gotta get so different you leverage the gps capabilities get that pinpoint location it will improve what the forecast is telling so I feel like this is one of those omni headed acquisition monsters for lack of a better term because when the acquisition was first announced is huh wow really interesting remember my line Dell's by an emc IBM is buying the weather company oh how intriguing it's a contrast it's all about the data the Dane is a service and then somebody whispered in my ear well you know there's like 800 Rockstar data scientists that come along with that act like wow it's all about the data scientists and then on IBM's earnings call i hear the weather company will provide the basis for our IOT platform like okay there's another one so we're take uh uh well i think IBM made a very smart move i'm slightly biased on that opinion but I think I be made a very smart move at very forward-looking move and one built on a cloud foundation not kind of a legacy foundation and when you think about IOT data sets we ingest 100 terabytes of data a day i ingest 62 different types of data at the weather company i ingest this data and then i distributed it massive volumes so what we had fundamentally built was the world's you know largest cloud-based iot data platform and you know IBM has many capabilities of their own and as we bring these things together and create a true next-gen cloud-based IOT data engine the ability for IBM to become smarter for Watson to become smarter than all of IBM's customers and clients to to become smarter with better applications better alerts better triggers and that alerts if you think about alerting my capability to alert hundreds of millions of people weather-alerts whether that's a lightning alert a rain alert a tornado warning whatever it is that's not really any different than me being able to alert a store clerk a night stock clerk at the local you know warehouse club that they need a stock you know aisle three differently put a different in cap on because we now have a new insight we have a new insight for what demand is going to be tomorrow and how do we shift what's going on that alert going down to a handheld device on the guy driving the four club yeah it's no different skoda tato yeah the capability to ingest transform store do analytics lon provide alerting on and then distribute data at massive scale that's what we do we talk about is what happened when Home Depot gets a big truck comes in a bunch of fans and say we know where this know the weather company did for you yeah we don't understand you'll understand you'll fake it later they file a big on the top of it so I OT as well as markets where people don't can't understand that some people don't know it means being like what's IOT Internet of Things I don't get it explain to them some little use cases that you guys are involved in today and some of these new areas that you're highlighting with with learning somehow see real life examples for for businesses and users there is a smarter planet kind of you know safe society kind of angle to it but it's also there's a nuts-and-bolts kind of practical if business value saving money saving lives changing you know maintenance what are some of the things share the IOT so there's there's only two things there so one is what is IOT and IOT really is is sensor data at the end of the day computers sensors electronic equipment has a sensor in it usually that sensor is there to do its job it's there to make a decision for what if it's a thermostat it has a sensor in it what's the temperature you know and so there are sensors in everything today things have become digitized and so those sensors are there as next as those next evolutions have come online those those sensors got connected to the Internet why because it was easier than to manage and monitor you know you know here we are at the mandalay bay how many thermostat sensors do you think this hotel casino complex has thousands and so you can't walk around and look at each one to understand well how's the temperature doing they all needed to be shipped back to a central room so that the in a building manager could actually do his job more efficiently those things then got connected so you could look at it on a smartphone those things they continued to get connected to make those jobs easier that first version of all of those things it was siloed that data SAT within just this hotel but now as we move forward we have the ability to take that data and merge it with other data sets there's actually a personal a Weather Underground personal weather station on the roof of the Mandalay Bay and it's actually collecting weather data every three seconds sending it back to us we have a very accurate understanding of the state of the Earth's atmosphere right atop this building having those throws is very good for the weather data but now how does the weather data impact a business that cares about the weather that has there we understand what the Sun load is on the top of this building and so we can go ahead and pre-heat your pre cool rooms get ahead of what's changing out sign that will have an impact here inside we have sensors on aircraft today that are collecting telemetry from aircraft turbulence data that helps us understand exactly what's going on with that airplane and as that's fed in real-time back down to the earth we process that and then send it back to the plane behind it and let that plane behind it know that it needs to alter it course change its flight plan automatically and update the pilots that they need to change course to a smoother altitude so gone are the days of the pilot having to radio down and fall around his body it's bumpy to get these through there anywhere machines can can can do this in real time collected and synthesize it from hundreds of aircraft that have been flying in that same route now we can actually take that and produce a better you know in flight plan for those for those machines we do that with with advertising so you know when you think about advertising you be easy the easy example is hey we know that you're going to sell more of X product when y weather condition happens that's easy but what if I also help you know when not to run an ad how do I help save you money you know if I know that there's no way for me to actually impact demand of your product up or down because we know over the course of time looking at your skew data and weather data that no matter what what we do weathers gonna have this impact on your product save your money don't run an ad tomorrow because it doesn't matter what you do you're not going to actually move your product more that's great and it's much business intelligence it's all the above its contextual data help people get insights in subjective and prescriptive analytics all rolled into one in a tool that alerts the actual person may explain to people out they were predictive versus prescriptive means a lot people get those confused what's your how would you prescriptive is you know where we want data that just tell us what to do based upon historic looking trends so i can take ten years of weather data and I can marry that up with ten years of some other data set and I can come up with you know a trend based upon the past and with that then I could prescribe what you should do in the future hey looks like general trend bring an umbrella tomorrow it's good it might rain but if I get into predictive analytics now I can start to understand by looking at forward-looking data things that haven't happened yet or new data sets that I'm merging in in real time oh wait a minute we thought that every time it rained more people went to this gas station to fill up but wait a minute today there's an accident on the road and people no matter what we do they're not going to go to that gas station because they're not even going to drive by it so being able to predict based upon feet of our real-time data but also forward-looking data the predictive analytics is really around the insights that we want to guess I got to ask you one question about the IBM situation and I want you to kind of reflect get him get you know all right philosophical for a second what's the learning that you've had over the past few weeks months post-acquisition inside IBM is there a learning that you to kind of hit you that you didn't expect there's something you'd expect what sure what was your big takeaway from this experience personally and you had some great success in the business now integrated into IBM what's the learning that cuz that's comes out of this for you I am really proud of the team at the weather company you know I I think what we have been able to accomplish as a small company you know comparative to my four hundred and sixty-eight thousand colleagues at IBM yeah what we've been able to accomplish what we've been able to do is really you know it's impressive and I've been proud of my team I'm proud of our company I'm proud of what we were able to get done as a company and you know the reflection really is as you bring that into IBM how do you make sure that you can you can now scale that to benefit such a large organization and and so while we were great at doing it for ourselves and we built an amazing business with amazing growth you know attracted lots of people that looked at buying us and obviously IBM executing on that I think that's amazing and I'm proud of that but I think my biggest reflection is that doesn't necessarily equate to success at IBM and we now have to retool and retrans form ourselves again to be able to take what we know how to do really well which is build great capabilities build big data platforms build analytics engines and inside engines and then armed a sea of developers to use our API we can't just take what we've done and go mate rest on your laurels you gotta go reinvent so I think my biggest you know real learning and take away from the kind of integration process is well we have a lot to learn and we have a lot of change we need to do so that we can actually now adapt and and continue to be us but do it in a way that works as an IBM ER and and that's that's there's there's going to be an art to this and we've got a ways to learn so I'm going in while eyes wide open around what I have to learn but I also am very reflective on on how proud I am as a leader of the team that you know has created you know such an amazing capability acquisition is done you savor it you come in you get blue washed and I hope I had a Saturday afternoon where I say okay got all like what is this gonna think so and then okay so you you wake up in the morning and you sort of described at a high level you know what you're doing but top three things that you're focused on the next you know 12 12 months so so you know the biggest thing that I'm focused on number one is making sure that we protect the weather company culture and how we know how to do and build great things and so I've got to lead us through obviously becoming integrated with IBM but not losing who we are and IBM is very supportive of that you know Bob picciano his team have been awesome and you know John Kelly and team have been awesome everybody that we have worked with has been so supportive of Bryson please make sure you find the right way through this we don't want to break you and I think that's natural for any acquisition for any yeah but you guys aren't dogmatic you were very candid saying we're gonna transform ourselves and adapt absolutely and so and so so we've got that on wrestling on my mind how do we go find immediate wins there's there's a a million different ways for us to win there's thousands of IBM sales teams that are out in front of clients it's just today with new problems how do we quickly adapt what we've been good at doing and help solve new problems very quickly so that's on my mind and then you know wrapping that in a way that becomes self service we can't I don't want to scale my team through people to solve all these problems I want to find a way to make sure that all these capabilities new data sets new insights new capabilities that we bring the life I want to do that in a self-service way I want to make sure that our technology the way we interact with developers the developer community that we bring in to kind of work on our behalf to make this happen I don't want to solve all these problems I want to enable others to solve the problems and so we're very focused on the self service aspect which i think is very new prices thank you so much taking the time out of your busy schedule to see with us in the queue good to see you again or any congratulations IOT everything's a sensor that we're a sense are here in the cube and we sense that it's time to go to SiliconANGLE DV and check out all the videos we have a purpose our sensor is to get the data to share that out with you thanks for the commentary and insight appreciate it whether company great success weather effects of song could affect stock prices all kinds of things in the real world so we had a lot of a lot of big data thank you very much look you here live in Las Vegas right back more coverage at this short break

from galvanized San Francisco extraction signal from the noise it's the kue cover the apache spark community event brought you IBM now your host John free George okay welcome back everyone we are live in San Francisco for this special q presentation with the IBM sparkman the event here live at galvanized in San Francisco workspace incubator great place for developer education IBM's big announcement today their commitment to spark they didn't see any numbers but I'm counting in the hundreds of millions of years to quote Papa Chiana on my call with him on Friday with rod $17 fuck yeah holler last for hundreds of millions yeah hundred millions of dollars getting late in the day going to be your coming rod Smith's our next guest rod welcome to the cube thank you very much with a catalyst behind spark at IBM worked hard on it yeah you guys tell a story what's the story well we worked on big data and I have a group of folks that go out and work with customers all the time and what we were doing Hadoop we would do these cool applications that sometimes you know small clusters 20 minutes you get a result and a customer would say can you do that in a couple seconds kind of look around and go what changed it means it did the business problem and they couldn't tell us but it's one of those data points in your head that go something's not quite right you know what's what's changing or what are they trying to tell me that they can't and that's when we started learning you know customers were looking for technology that they could iterate on quickly you know open-ended questions it wasn't the give me a problem do the game pew pew output I'm done this was oh gee there's the journey I now see some interesting insights I have other questions was it was something not right the data that they got didn't match their hypothesis or was it the expectation that if I can do it fast on google and find a Thai restaurant down the block well so I can it went that way something doesn't right what was with me that said why can't you tell me what you're really trying to accomplish what I learned is that as we go through these kind of digital transfer mation real real time they were thinking about how their business is going to change so fast and so the problems always been for technologists and vendors like IBM tell us the problem we pick out the technology and you're pretty well stuck with it it stays that way and they wanted more flexibility open-ended questions lots of different data sources on demand when they had to have it on this they wanted to see results along the way and they would rather have analytics be approximation that they could use quickly rather than after the fact and more accurate okay so you know when you went through that it wasn't they couldn't find a bi person to talk bad about and I couldn't find a data person so you know it was fun to try to put piece puzzles together and that's where spark came into this so I see a lot of other trends are kind of vectoring into that convergence which is in-memory databases you know the community flash for persistence store on the storage side so this you as a close to all that action what was the aha moment for for within IBM is han hey you know what this spark thing is the next Linux me we got to get out in front of this and help the community go faster and then kind of rising tide floats elbows what was that flash point flow we we had two of them one was that in our commerce group there's ways that they work on online pricing and there's a vendor stander which takes about a week when you get data off of a site or retail site they analyze that they correct the analytics they put it back up again takes about a week but we showed them a spark we could do it in about four hours a week down to four hours and now they started to think oh you know what do we offer customers now we have ways to have not just one product many products let's bring in other data location data traffic data weather data social data so that kind of exploded internally on this is a big change this is something that we can relate to cus of multiple data source of the need for unification and speed and and speed speed first because be first that's a heck all the speed i want to bring other data sets and it's time to value i mean if you're going to be a digital business and look at real time where it's going Netflix others have really set the standard on ok so then i'm a so let's take a next level so rod you're crazy we can't do that it would disrupt all these other businesses we have so how does that conversation happen within IBM the way that happens in IBM is rod you are crazy and you're going to cause me odds it up so please go away and I don't go away easily but you keep pushing on this and part of my job is to work with customers can I show value so I can take the product team saying you need to take this more seriously I've got currency now and then as you just said the marketplace starts to light up spark is on the front page as people are talking about how they're using it well Hadoop is growing too at the same time so it loop does it seeds the market seats the Mars you see you're playing ahead do but if you see the customer challenges and you're like you guys just connect the dots and and then it's back to the customer is talking about what their problems they want to use or the solutions are looking for so yeah it takes time because it's it's risky meaning that all of us have quarterly is what we're doing but how do we now make it safer for people in IBM jump in the water so that eventually they don't hate me so what's your what's your comment when a friend says hey rod you know linux was great but it's a different era oh you know here with cloud and mobile open source with the patch he's evolved to the point where it's very manageable for vendors to be contributed as well with with non company contributors how do you guys see the difference between those two worlds because really this is a Linux moment but there's no big bad main many many computer companies name frames out there but their specialized for like the Z systems are great but like this is scale out commodity hardware a dupe now that's growing how do you how do you describe that because there is a Linux correlation what linux was for open source then operating systems now this is kind of distributed analytics I think you're you're you know the the part of this is kind of real-time digital business transformations and while there is not a you know bad company out there you know amazon and others have shown how they can be online businesses and use analytics and be very effective but i'm a brick and mortar company and an online business how do i do the same thing and spark starts to really show that no they don't have a corner on the market we can compete so that's the big factor on this is well it's not one company doing this it's I need to be able to compete at the speed the businesses that didn't have to see that Amazon started kind of post recession or you know Dom bubble bursting you know web services was just kind of kicking through if we remember our history lessons and what happened was they really had no traction they built some building blocks right they made a good decision to integrate to core building blocks compute and storage and they built from there so in a way you guys can enable companies to have their own amazon like extensive experience because it's a fresh clean cute paper right it is and I think we're spark it's interesting is like you said in two verticals what do i do to retail what do I do in health care what are we doing finance right very specialized I we've shown in Watson you can do Watson for cancer research you can do Watson for cooking right but they're very vertical now so specialized domain expertise becomes really interesting right that's the big part and that's the part I really liked about spark they were the community really thought about solution developers you know they stayed away kind of middle ground I you don't have to be a deep dated person or a deep analytics API person what's the problem you want to solve how can I help you do that I think that's a you know that's interesting is that that's because most people go Jay this is speeds and feeds software we look at the solutions more holistic but then you're really talk about customer problems right the so-called outcomes that go on well that's what and I think that's the part that I've enjoyed is I want to talk to you you know about what your problem is I don't want to talk technology I you know I don't want to have to make a technology choice from stay one spark helps me with that I don't notify programming while all those things come together so I can concentrate we can concentrate on talking to the customer but you know learn from them what are you trying to accomplish so you watch the next things on your list good I just gonna say you know looking at your LinkedIn page i love this at BP emerging technologies for 20 some odd years so you see here you've seen a lot of technology's come a lot of emerging technologies and the acceleration of these technologies is only going more right you have a whole lot more in your portfolio you have to look at today then then you did yesterday or five years ago yeah why is sparks a special in the cornucopia of technologies that you've seen coming over the years it's a good question and and as I've done merging technologies I've learned that I have to you know listen to customers very carefully on it and when I hear those kind of repeatable business patterns do I see an economic change a transformation that really sticks with me and sometimes the old things have start really big you know they start out good and then they fade away but I always look for technologies that seem to have lots of dimensions to them from a business value standpoint that's what attracted me to spark and my team working with some customers on pocs we could do them quickly you know I really like to get to the point where you know we an industry we with notebooks and others we can do solutions in less than four hours for a customer what better thing to take your you know employee to lunch and spat them on the back for you know something that you didn't expect for weeks well one of the exciting things that you guys have done is you shine the spotlight on spark and you opened up the conversation globally around IBM is making a big move spark was a little bit of an outlier and the mainstream press I mean the press we're picking up spark oh yeah berkeley some credibility of great people behind it but now it's like wow it's going to get the attention of CX cxos out there and they're going to be like hmm if ibm's looking at it must be relevant because of the history you guys have with innovation but they're going to ask you the question I'm going to ask you which is it's not baked out yet where are we with this what are you guys going to do how does IBM work with the community to continue to bake out spark because a lot of people are using it bringing it in but it's evolving super fast and that's going to be the question is it baked and how does it get baked faster so I think there is lots of areas that if we just talked about if I'm doing retail or health care or fine it's going to be lots of specialized analytics because that's what spark for me is is enabling custom analytics on this second part is as you think about how you want to look at bigger problems I think that many times are learning is to try to you know once we got a technology lets make everything fit it rather than starting to separate it by business problems and I think we can do that now or we can bring to the table technology learning best practices around this and solutions I think you know at the end of the day it's house part can be integrated into a business solution and our customers very quickly and hopefully those customers see it broadly from interoperability standpoint of what they're going to do so the final question I have for you is what was the biggest learning that you've taken away from this process that was magnified through this whole journey of a taking IBM from being a participant in the as a citizen in the community early on as a founding member of spark this is back in two thousand nine so it wasn't like no one knew he was going on and you know we bird cover on Hadoop from the beginning so we'd love to watch these ecosystems grow but from from the early days to now today mmm what was the biggest thing that you learned that was magnified out of all the reactions all the feedback all the customers what can you share I I think for me when we did a spark hack you know our hackathon piece when 28,000 IBM ER showed up with ideas that told us twenty eight thousand 28,000 so now you stopped and 28,000 people who were focused on the customer so they had a thought of how this could be relevant this is great I mean this isn't like back talking for this isn't one little vein with a little stream it's big and it big was what we can do for our customer when was that um about two months ago how did you pull that off just out an email blast all the IBM's put on the message board to a crowd chat what did you do well when you put out an email blast the second one is you put on a webcam to explain to people what you're going to do with it what you'd like them to do and I'll we're setting it up and and then you step back and you know kind of cross your fingers hope people show up and then when you know you invite ten thousand and twenty eight thousand show up you kind of know that we're turning a corner as a company on understanding how we can use that for this this also highlights this whole connectedness apps internet of things and people are things to so their mobile device when you have that kind of people close to the action the creativity is there right there on the front lines and they don't feel like that the work they do is going to be taken by the machinery in the old days I got to go back all these hurdles I gotta jump now they could instantly be there with some solutions so that's that's super compelling the next question is security and how does how do you see that leaving in because now one of the things that came up will first meeting let me back up but I get this you think about security question for a second last week ahead dupe summit we were talking with the Hadoop ecosystem Hortonworks ODP conversations etc but when you looked at kind of like reading the tea leaves it was sparked that was kind of stealing the show the subtext was smart all the spark sessions were packed the developers had was salivating over sparks like to hear that I did why why is that why are the Hadoop developers salivating over spark is it because they wanted to go faster do they see extensions any thoughts I think that I've say it two ways one is I think there was and since I did who do for quite a while I think people thought for a while Hadoop was going to be an analytics platform and it it kind of went down the path of being immoral generalized platform so you can do more than MapReduce jobs so there's been this pent-up demand for really analytics focus and spark offered that focus and the performance side I think that's the parts in Hadoop sold kind of a false dream or it didn't materialize fast but I don't think of material out of false treaty I'm saying if they promise them around yeah it well and people set those you know well the fresh maybe yeah I don't think the vendors all I think was more than well vendors you know it did to unstructured data does that unstructured data does that storing data and I didn't be able to act on it creates some interesting dynamics I mean I've worked with customers who you know started to put data in Hadoop but to have put data dupe you know we're only going to do a year's worth of data and then putting three years of data because they want to do monte pucker up my Carlo simulations against a Monty Python it's time you threw water on us and we love yours we on the cube but the problem says we're talking about before like you know our internal use we can produce you know interesting innovations in days that's going to attract audiences because now they can show their you know business people what they can do for them that's what's really driving this I mean if you gotta see XO you know CMO says you know show me what you can do you know do segmentation on my population for these products they want it in in minutes not so you know going to run it in different jobs and the over a certain period of time I was just talking with the CEOs of docusign box 18 1018 Syrian kinky was executive director and then EVP a platform that Salesforce the common thread amongst those executives was the new digital transformation has such a dynamic or impactful economic impact yes I mean dr. Sanyal using examples how literally Deutsche Telekom saved 230 million dollars on one process yes one process yes with analytics and yeah process improvements extreme it sounds funny but it's extremely low hanging fruit they haven't had technology and the economics and be able support it now we do and now you're seeing the solution developer go I think I can make a business result faster yeah and if they can show it then businesses react and I think that's the beautiful thing about what Hadoop is done I mean I brought that up earlier trying to tease that out with reality we're seeing is that that mark is continuing to grow but there's a world beyond Hadoop yep I mean Hortonworks this public company I mean IBM is massive so you got Hadoop and then sparks a beautiful extension to that that enables so much more well I think spark will go further because it's more to me is another dimension it's an integration technology so i can have sparked up to legacy systems without hadoop you know in there doing analytics in there being an avenue for doing joins on data doing analytics on unstructured and transactional data whether data pulling it all together and I think that's the again talking about multi-dimensional that's what that was hard even five years ago so any relational database that's a nightmare yeah and you're asked about security so you want to touch on yeah okay go ahead so part of the things that I like about spark is the technology is called resilient distributed data sets r dds so I read data from a source and I make it into this r DD I can work on it that gives me a great data point or a great interaction with a Cassandra datastax did a really great job of a spark driver so you think about this in businesses for a db2 or something now I know where I can put my security and my governance I can put those at certain endpoints now as i'm reading in my application writing these things out so again back to my point of an integration it's not something that i'm trying to get around a business i'm at integrating extending their life and/or capabilities that's right so I got to ask you the internal IBM question my last question is it what's the vibe like at IBM because you know I've been you know I worked at IBM way back in the day back in the 80s and the cultures changed right so much mm-hmm but there's still a huge technical group of people at IBM so I got to ask you the question with all this new cloud innovation all this new capabilities to do stuff differently what's it like for all the technical guys at IBM right now because they got to be like Hayden we can now do this we can so new capabilities are emerging what's the what's the vibe like and what are some of the things that that are low-hanging fruit that are that our game change because low-hanging fruit is game-changing today oh yes I what's the vibe eternally at idea I've internally is very hot I mean the guys and gals at this you look at cloud computing look we've done with bluemix it got is getting you know great recent press it's getting great results with customers back to this time to value piece it's new to us I mean there's only a small group that started that so now the rest of the IBM arts are going this is really cool how do we do it now you've got analytics that you know we're starting you've been you know competencies are on this now you can take the real-time aspect so yeah the five is really all those little silos you know identity system here I got to build all the software now you can gotta go horizontal yeah so you know that's kind of a new thing that's kind of exciting it's gonna be fun to watch my final question I guess is my final final question is have you been keeping track this is the sixth and final time analytics well rods great to have you on the cube you're awesome great great commentary great great insight spark in the cloud is what data bricks announce what about an on-premise i'm a customer i want i want on prem I don't necessarily want to do what's next I 40 s or other stuff oh I think you're going to see you know like hybrid models for cloud where spark as a service is there on prem i think one of the really exciting parts to me is that one the unified program model to the portability of the analytic models so let's say I start on prom because I'm worried about security and other things and then I want to move it to a cloud service well I don't have to go rewrite it I can just move the analytics over from a model standpoint so I think you're going to see this evolved very fast as people want to do either on prem or hybrid or you know dedicated cuz of the integration capabilities and the distributed nature of it that's the point yep awesome well I'll let you get the last word on the segment share what the folks who's not or aren't watching what is this all about today why is in San Francisco today IBM's announcement what's so groundbreaking about it I know you're part of it a little bit biased but share the folks why what why now what's this all about what's what's what's going on here well we think that the kind of epicenter for spark innovation is here in San Francisco amp lab with data bricks and others are doing here and we want to be a part of that and I think spark technology senator setting up is about how we can contribute and learn and you know help the community grow we think this is gonna you brought some food to the party I mean you are I said earlier beer right you bring a you know the ml yeah you got them back other wine napa valley of course you got to go to wine well craft beers good north north bay thanks so much for coming on the cube really appreciate the insight because it is a great color from an expert IBM here we're on the ground this is the cube special presentation live in San Ruby back with more with live coverage of the breakouts in the event tonight IBM spark community event here in san fran at the galvanized workspace education center we write back