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

>> Hi, I'm Peter Burris, and welcome to another Wikibon Action Item. (funky electronic music) This was a very interesting week in the tech industry, specifically because IBM's Think Conference aggregated in a large number of people. Now, The CUBE was there. Dave Vellante, John Furrier, and myself all participated in somewhere in the vicinity of 60 or 70 interviews with thought leaders in the industry, including a number of very senior IBM executives. The reason why this becomes so important is because IBM made a proposal to the industry about how some of the digital disruption that the market faces is likely to unfold. The normal approach or the normal mindset that people have used is that startups, digital native companies were going to change the way that everything was going to operate, and the dinosaurs were going to go by the wayside. IBM's interesting proposal is that the dinosaurs actually are going to learn to dance, utilizing or playing on a book title from a number of years ago. And the specific argument was laid out by Ginni Rometty in her keynote, when she said that there are number of factors that are especially important here. Factor number one is that increasingly, businesses are going to recognize that the role that their data plays in competition is on the ascending. It's getting more important. Now, this is something that Wikibon's been arguing for quite some time. In fact, we have said that the whole key to digital disruption and digital business is to acknowledge the difference between business and digital business, is the role that data and data assets play in your business. So we have strong agreement there. But on top of that, Ginni Rometty made the observation that 80% of the data that could be accessed and put the work in business has not yet been made available to the new activities, the new processes that are essential to changing the way customers are engaged, businesses operate, and overall change and disruption occurs. So her suggestion is that that 80%, that vast amount of data that could be applied that's not being tapped, is embedded deep within the incumbents. And so the core argument from IBM is that the incumbent companies, not the digital natives, not the startups, but the incumbent companies are poised to make a significant, to have a significant role in disrupting how markets operate, because of the value of their data that hasn't currently been put to work and made available to new types of work. That was the thesis that we heard this week, and that's what we're going to talk about today. Are the incumbent really going to strike back? So Dave Vellante, let me start with you. You were at Think, you heard the same type of argument. What did you walk away with? >> So when I first heard the term incumbent disruptors, I was very skeptical, and I still am. But I like the concept and I like it a lot. So let me explain why I like it and why I think there's some real challenges. If I'm a large incumbent global 2,000, I'm not going to just roll over because the world is changing and software is eating my world. Rather what I'm going to do is I'm going to use my considerable assets to compete, and so that includes my customers, my employees, my ecosystem, the partnerships that I have there, et cetera. The reason why I'm skeptical is because incumbents aren't organized around their data assets. Their data assets are stovepipe, they're all over the place. And the skills to leverage that data value, monetize that data, understand the contribution that data makes toward monetization, those skills are limited. They're bespoke and they're very narrow. They're within lines of business or divisions. So there's a huge AI gap between the true digital business and an incumbent business. Now, I don't think all is lost. I think a lot of strategies can work, from M&A to transformation projects, joint ventures, spin-offs. Yeah, IBM gave some examples. They put up Verizon and American Airlines. I don't see them yet as the incumbent disruptors. But then there was another example of IBM Maersk doing some very interesting and disrupting things, Royal Bank of Canada doing some pretty interesting things. >> But in a joint venture forum, Dave, to your point, they specifically set up a joint venture that would be organized around this data, didn't they? >> Yes, and that's really the point I'm trying to make. All is not lost. There are certain things that you can do, many things that you can do as an incumbent. And it's really game on for the next wave of innovation. >> So we agree as a general principle that data is really important, David Floyer. And that's been our thesis for quite some time. But Ginni put something out there, Ginni Rometty put something out there. My good friend, Ginni Rometty, put something out there that 80% of the data that could be applied to disruption, better customer engagement, better operations, new markets, is not being utilized. What do we think about that? Is that number real? >> If you look at the data inside any organization, there's a lot of structured data. And that has better ability to move through an organization. Equally, there's a huge amount of unstructured data that goes in emails. It goes in voicemails, it goes in shared documents. It goes in diagrams, PowerPoints, et cetera, that also is data which is very much locked up in the way that Dave Vellante was talking about, locked up in a particular process or in a particular area. So is there a large amount of data that could be used inside an organization? Is it private, is it theirs? Yes, there is. The question is, how do you tap that data? How do you organize around that data to release it? >> So this is kind of a chicken and egg kind of a problem. Neil Raden, I'm going to turn to you. When we think about this chicken and egg problem, the question is do we organize in anticipation of creating these assets? Do we establish new processes in anticipation of creating these data assets? Or do we create the data assets first and then re-institutionalize the work? And the reason why it's a chicken and egg kind of problem is because it takes an enormous amount of leadership will to affect the way a business works before the asset's in place. But it's unclear that we're going to get the asset that we want unless we affect the reorganization, institutionalization. Neil, is it going to be a chicken? Is it going to be the egg? Or is this one of the biggest problems that these guys are going to have? >> Well, I'm a little skeptical about this 80% number. I need some convincing before I comment on that. But I would rather see, when David mentioned the PowerPoint slides or email or that sort of thing, I would rather see that information curated by the application itself, rather than dragged out in broad data and reinterpreted in something. I think that's very dangerous. I think we saw that in data warehousing. (mumbling) But when you look at building data lakes, you throw all this stuff into a data lake. And then after the fact, somebody has to say, "Well, what does this data mean?" So I find it kind of a problem. >> So Jim Kobielus, a couple weeks ago Microsoft actually introduced a technology or a toolkit that could in fact be applied to move this kind of advance processing for dragging value out of a PowerPoint or a Word document or something else, close and proximate to the application. Is that, I mean, what Neil just suggested I think is a very, very good point. Are we going to see these kinds of new technologies directly embedded within applications to help users narrowly, but businesses more broadly, lift that information out of these applications so it can be freed up for other uses? >> I think yeah, on some level, Peter, this is a topic called dark data. It's been discussed in data management circles for a long time. The vast majority, I think 75 to 80% is the number that I see in the research, is locked up in terms of it's not searchable, it's not easily discoverable. It's not mashupable, I'm making up a word. But the term mashup hasn't been used in years, but I think it's a good one. What it's all about is if we want to make the most out of our incumbent's data, then we need to give the business, the business people, the tools to find the data where it is, to mash it up into new forms and analytics and so forth, in order to monetize it and sell it, make money off of it. So there are a wide range of data discovery and other tools that support a fairly self-service combination and composition of composite data object. I don't know that, however, that the culture of monetizing existing dataset and pulling dark data into productized forms, I don't think that's taken root in any organization anywhere. I think that's just something that consultants talk about as something that gee, should be done, but I don't think it's happening in the real world. >> And I think you're probably correct about that, but I still think Neil raised a great point. And I would expect, and I think we all believe, that increasingly this is not going to come as a result of massive changes in adoption of new data science, like practices everywhere, but an embedding of these technologies. Machine learning algorithms, approaches to finding patterns within application data, in the applications themselves, which is exactly what Neil was saying. So I think that what we're going to see, and I wanted some validation from you guys about this, is increasingly tools being used by application providers to reveal data that's in applications, and not open source, independent tool chains that then ex-post-facto get applied to all kinds of different data sources in an attempt for the organization to pull the stuff out. David Floyer, what do you think? >> I agree with you. I think there's a great opportunity for the IT industry in this area to put together solutions which can go and fit in. On the basis of existing applications, there's a huge amount of potential, for example, of ERP systems to link in with IOT systems, for example, and provide a data across an organization. Rather than designing your own IOT system, I think people are going to buy-in pre-made ones. They're going to put the devices in, the data's going to come in, and the AI work will be done as part of that, as part of implementing that. And right across the board, there is tremendous opportunity to improve the applications that currently exist, or put in new versions of applications to address this question of data sharing across an organization. >> Yeah, I think that's going to be a big piece of what happens. And it also says, Neil Raden, something about whether or not enormous machine learning deities in the sky, some of which might start with the letter W, are going to be the best and only way to unlock this data. Is this going to be something that, we're suggesting now that it's something that's going to be increasingly-distributed closer to applications, less invasive and disruptive to people, more invasive and disruptive to the applications and the systems that are in place. And what do you think, Neil? Is that a better way of thinking about this? >> Yeah, let me give you an example. Data science the way it's been practiced is a mess. You have one person who's trying to find the data, trying to understand the data, complete your selection, designing experiments, doing runs, and so forth, coming up with formulas and then putting them in the cluster with funny names so they can try to remember which one was which. And now what you have are a number of software companies who've come up with brilliant ways of managing that process, of really helping the data science to create a work process in curating the data and so forth. So if you want to know something about this particular model, you don't have to go to the person and say, "Why did you do that model? "What exactly were you thinking?" That information would be available right there in the workbench. And I think that's a good model for, frankly, everything. >> So let's-- >> Development pipeline toolkits. That's a hot theme. >> Yeah, it's a very hot theme. But Jim, I don't think you think but I'm going to test it. I don't think we're going to see AI pipeline toolkits be immediately or be accessed by your average end user who's putting together a contract, so that that toolkit or so that data is automatically munched and ingested or ingested and munched by some AI pipeline. This is going to happen in an application. So the person's going to continue to do their work, and then the tooling will or will not grab that information and then combine it with other things through the application itself into the pipeline. We got that right? >> Yeah, but I think this is all being, everything you described is being embedded in applications that are making calls to backend cloud services that have themselves been built by data scientists and exposed through rest APIs. Steve, Peter, everything you're describing is coming to applications fairly rapidly. >> I think that's a good point, but I want to test it. I want to test that. So Ralph Finos, you've been paying a lot of attention during reporting season to what some of the big guys are saying on some of their calls and in some of their public statements. One company in particular, Oracle, has been finessing a transformation, shall we say? What are they saying about how this is going as we think about their customer base, the transformation of their customer base, and the degree to which applications are or are not playing a role in those transformations? >> Yeah, I think in their last earnings call a couple days ago that the point that they were making around the decline and the-- >> Again, this is Oracle. So in Oracle's last earnings call, yeah. >> Yeah, I'm sorry, yeah. And the decline and the revenue growth rate in the public cloud, the SAS end of their business, was a function really of a slowdown of the original acquisitions they made to kind of show up as being a transformative cloud vendor, and that are basically beginning to run out of gas. And I think if you're looking at marketing applications and sales-related applications and content-type of applications, those are kind of hitting a natural high of growth. And I think what they were saying is that from a migration perspective on ERP, that that's going to take a while to get done. They were saying something like 10 or 15% of their customer base had just begun doing some sort of migration. And that's a data around ERP and those kinds of applications. So it's a long slog ahead of them, but I'd rather be in their shoes, I think, for the long run than trying to kind of jazz up in the near-term some kind of pseudo-SAS cloud growth based on acquisition and low-lying fruit. >> Yeah, because they have a public cloud, right? I mean, at least they're in the game. >> Yeah, and they have to show they're in the game. >> Yeah, and specifically they're talking about their applications as clouds themselves. So they're not just saying here's a set of resources that you can build, too. They're saying here's a set of SAS-based applications that you can build around. >> Dave: Right. Go ahead, Ralph, sorry. >> Yeah, yeah. And I think the notion there is the migration to their ERP and their systems of record applications that they're saying, this is going to take a long time for people to do that migration because of complexity in process. >> So the last point, or Dave Vellante, did you have a point you want to make before I jump into a new thought here? >> I just compare and contrast IBM and Oracle. They have public clouds, they have SAS. Many others don't. I think this is a major different point of differentiation. >> Alright, so we've talked about whether or not this notion of data as a source of value's important, and we agree it is. We still don't know whether or not 80% is the right number, but it is some large number that's currently not being utilized and applied to work differently than the data currently is. And that likely creates some significant opportunities for transformation. Do we ultimately think that the incumbents, again, I mention the chicken and the egg problem. Do we ultimately think that the incumbents are... Is this going to be a test of whether or not the incumbents are going to be around in 10 years? The degree to which they enact the types of transformation we thought about. Dave Vellante, you said you were skeptical. You heard the story. We've had the conversation. Will incumbents who do this in fact be in a better position? >> Well, incumbents that do take action absolutely will be in a better position. But I think that's the real question. I personally believe that every industry is going to get disrupted by digital, and I think a lot of companies are not prepared for this and are going to be in deep trouble. >> Alright, so one more thought, because we're talking about industries overall. There's so many elements we haven't gotten to, but there's one absolute thing I want to talk about. Specifically the difference between B2C and B2B companies. Clearly the B2C industries have been disrupted, many of them pretty significantly, over the last few years. Not too long ago, I have multiple not-necessarily-good memories of running the aisles of Toys R Us sometime after 10 o'clock at night, right around December 24th. I can't do that anymore, and it's not because my kids are grown. Or I won't be able to do that soon anymore. So B2C industries seem to have been moved faster, because the digital natives are able to take advantage of the fact that a lot of these B2C industries did not have direct and strong relationships with those customers. I would posit that a lot of the B2B industries are really where the action's going to take. And the kind of way I would think about it, and David Floyer, I'll turn to you first. The way I would think about it is that in the B2C world, it's new markets and new ways of doing things, which is where the disruption's going to take place. So more of a substitution as opposed to a churn. But in the B2B markets, it's disrupting greater efficiencies, greater automation, greater engagement with existing customers, as well as finding new businesses and opportunities. What do you think about that? >> I think the B2B market is much more stable. Relationships, business relationships, very, very important. They take a long time to change. >> Peter: But much of that isn't digital. >> A lot of that is not digital. I agree with that. However, I think that the underlying change that's happening is one of automation. B2B are struggling to put into place automation with robots, automation everywhere. What you see, for example, in Amazon is a dedication to automation, to making things more efficient. And I think that's, to me, the biggest challenges, owning up to the fact that they have to change their automation, get themselves far more efficient. And if they don't succeed in doing that, then their ability to survive or their likelihood of being taken over with a reverse takeover becomes higher and higher and higher. So how do you go about that level, huge increase in automation that is needed to survive, I think is the biggest question for B2B players. >> And when we think about automation, David Floyer, we're not talking about the manufacturing arms or only talking about the manufacturing arms. We're talking about a lot of new software automation. Dave Vellante, Jim Kobielus, RPA is kind of a new thing. Dave, we saw some interesting things at Think. Bring us up to speed quickly on what the community at Think was talking about with RPA. >> Well, I tell you. There were a lot of people in financial services, which is IBM's stronghold. And they're using software robots to automate a lot of the backend stuff that humans were doing. That's a major, major use case. I would say 25 to 30% of the financial services organizations that I talked to had active RPA projects ongoing at the moment. I don't know. Jim, what are your thoughts? >> Yeah, I think backend automation is where B2B disruption is happening. As the organizations are able to automate more of their backend, digitize more of their backend functions and accelerate them and improve the throughput of transactions, are those that will clean up. I think for the B2C space, it's the frontend automation of the digitalization of the engagement channels. But RPA is essentially a key that's unlocking backend automation for everybody, because it allows more of the frontend business analysts and those who are not traditionally BPM, or business process re-engineering professionals, to begin to take standard administrative processes and begin to automate them from, as it were, the outside-in in a greater way. So I think RPA is a secret key for that. I think we'll see some of the more disruptive organizations, businesses, take RPA and use it to essentially just reverse-engineer, as it were, existing processes, but in an automated fashion, and drive that improvement but in the backend by AI. >> I just love the term software robots. I just think that that's, I think that so strongly evokes what's going to happen here. >> If I could add, I think there's a huge need to simplify that space. The other thing I witnessed at IBM Think is it's still pretty complicated. It's still a heavy lift. There's a lot of big services component to this, which is probably why IBM loves it. But there's a massive market, I think, to simplify the adoption or RPA. >> I completely agree. We have to open the aperture as well. Again, the goal is not to train people new things, new data science, new automation stuff, but to provide tools and increasingly embed those tools into stuff that people are already using, so that the disruption and the changes happen more as a consequence of continuing to do what the people do. Alright, so let's hit the action item we're on, guys. It's been a great conversation. Again, we haven't talked about GDPR. We haven't talked about a wide array of different factors that are going to be an issue. I think this is something we're going to talk about. But on the narrow issue of can the disruptors strike back? Neil Raden, let's start with you. Neil Raden, action item. >> I've been saying since 1975 that I should be hanging around with a better class of people, but I do spend a lot of time in the insurance industry. And I have been getting a consensus that in the next five to 10 years, there will no longer be underwriters for claims adjustments. That business is ready for massive, massive change. >> And those are disruptors, largely. Jim Kobielus, action item. >> Action item. In terms of business disruption, is just not to imagine that because you were the incumbent in the past era in some solution category that's declining, that that automatically guarantees you, that makes your data fit for seizing opportunities in the future. As we've learned from Blockbuster Video, the fact that they had all this customer data didn't give them any defenses against Netflix coming along and cleaning their coffin, putting them out of business. So the next generation of disruptor will not have any legacy data to work from, and they'll be able to work miracles because they made a strategic bet on some frontend digital channel that made all the difference. >> Ralph Finos, action item. >> Yeah, I think there's a notion here of siege mentality. And I think the incumbents are in the castle walls, and the disruptors are outside the castle walls. And sometimes the disruptors, you know, scale the walls. Sometimes they don't. But I think being inside the walls is a long-run tougher thing to be at. >> Dave Vellante, action item. >> I want to pick up on something Neil said. I think it's alluring for some of these industries, like insurance and financial services and healthcare, even parts of government, that really haven't been disrupted in a huge way yet to say, "Well, I'll wait and I'll see what happens." I think that's a huge mistake. I think you have to start immediately thinking about strategies, particularly around your data, as we talked about earlier. Maybe it's M&A, maybe it's joint ventures, maybe it's spinning out new companies. But the time is past where you should be acting. >> David Floyer, action item. >> I think that it's easier to focus on something that you can actually do. So my action item is that the focus of most B2B companies should be looking at all of their processes and incrementally automating them, taking out the people cost, taking out the cost, other costs, automating those processes as much as possible. That, in my opinion, is the most likely path to being in the position that you can continue to be competitive. Without that focus, it's likely that you're going to be disrupted. >> Alright. So the one thing I'll say about that, David, is when I think you say people cost I think you mean the administrative cost associated with people. >> And people doing things, automating jobs. >> Alright, so we have been talking here in today's Wikibon Action Item about the question, will the incumbents be able to strike back? The argument we heard at IBM Think this past week, and this is the third week of March, was that data is an asset that can be applied to significantly disrupt industries, and that incumbents have a lot of data that hasn't been bought into play in the disruptive flow. And IBM's argument is that we're going to see a lot of incumbents start putting their data into play, more of their data assets into play. And that's going to have a significant impact ultimately on industry structure, customer engagement, the nature of the products and services that are available over the course of the next decade. We agree. We generally agree. We might nitpick about whether it's 80%, whether it's 60%. But in general, the observation is an enormous amount of data that exists within a large company, that's related to how they conduct business, is siloed and locked away and is used once and is not made available, is dark and is not made available for derivative uses. That could, in fact, lead to significant consequential improvements in how a business's transaction costs are ultimately distributed. Automation's going to be a big deal. David Floyer's mentioned this in the past. I'm also of the opinion that there's going to be a lot of new opportunities for revenue enhancement and products. I think that's going to be as big, but it's very clear that to start it makes an enormous amount of sense to take a look at where your existing transaction costs are, where existing information asymmetries exist, and see what you can do to unlock that data, make it available to other processes, and start to do a better job of automating local and specific to those activities. And we generally ask our clients to take a look at what is your value proposition? What are the outcomes that are necessary for that value proposition? What activities are most important to creating those outcomes? And then find those that, by doing a better job of unlocking new data, you can better automate those activities. In general, our belief is that there's a significant difference between B2C and B2B businesses. Why? Because a lot of B2C businesses never really had that direct connection, therefore never really had as much of the market and customer data about what was going on. A lot of point-of-sale perhaps, but not a lot of other types of data. And then the disruptors stepped in and created direct relationships, gathered that data and were able to rapidly innovate products and services that served consumers differently. Where a lot of that new opportunity exists is in the B2B world. And here's where the real incumbents are going to start flexing their muscles over the course of the next decade, as they find those opportunities to engage differently, to automate existing practices and activities, change their cost model, and introduce new approaches to operating that are cloud-based, blockchain-based, data-based, based on data, and find new ways to utilize their people. If there's one big caution we have about this, it's this. Ultimately, the tooling is not broadly mature. The people necessary to build a lot of these tools are increasingly moving into the traditional disruptors, the legacy disruptors if we will. AWS, Netflix, Microsoft, companies more along those lines. That talent is very dear still in the industry, and it's going to require an enormous effort to bring those new types of technologies that can in fact liberate some of this data. We looked at things like RPA, robot process automation. We look at the big application providers to increasingly imbue their products and services with some of these new technologies. And ultimately, paradoxically perhaps, we look for the incumbent disruptors to find ways to disrupt without disrupting their own employees and customers. So embedding more of these new technologies in an ethical way directly into the systems and applications that serve people, so that the people face minimal changes to learning new tricks, because the systems themselves have gotten much more automated and much more... Are able to learn and evolve and adjust much more rapidly in a way that still corresponds to the way people do work. So our action item. Any company in the B2B space that is waiting for data to emerge as an asset in their business, so that they can then do all the institutional, re-institutionalizing of work and reorganizing of work and new types of investment, is not going to be in business in 10 years. Or it's going to have a very tough time with it. The big challenge for the board and the CIO, and it's not successfully been done in the past, at least not too often, is to start the process today without necessarily having access to the data, of starting to think about how the work's going to change, think about the way their organization's going to have to be set up. This is not business process re-engineering. This is organizing around future value of data, the options that data can create, and employ that approach to start doing local automation, serve customers, and change the way partnerships work, and ultimately plan out for an extended period of time how their digital business is going to evolve. Once again, I want to thank David Floyer here in the studio with me. Neil Raden, Dave Vellante, Ralph Finos, Jim Kobielus remote. Thanks very much guys. For all of our clients, once again this has been a Wikibon Action Item. We'll talk to you again. Thanks for watching. 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>> Narrator: From Las Vegas, it's The Cube. Covering IBM Think 2018. Brought to you by IBM. >> Welcome back to IBM Think, everybody. My name's Dave Vallante, I'm here with Peter Burris. You're watching The Cube, the leader in live tech coverage. This is our day three. We're wrapping up wall to wall coverage of IBM's inaugural Think Conference. Thirty or forty thousand people, too many people to count, I've been joking all week. Sam Werner is here, he's the VP of Offering Management for Software Defined Storage, Sam, good to see you again. And Steve Kenniston is joining him otherwise known as the storage alchemist. Steven, great to see you again. >> Steven: Thanks, Dave. >> Dave: Alright, Sam. Let's get right into it. >> Sam: Alright. >> Dave: What is the state of data protection today and what's IBM's point of view? >> Sam: Well, I think anybody who's been following the conference and saw Jenny's key note, which was fantastic, I think you walked away knowing how important data is in the future, right? The way you get a competitive edge is to unlock insights from data. So if data's so important you got to be able to protect that data, but you're forced to protect all this data. It's very expensive to back up all this data. You have to do it. You got to keep it safe. How can you actually use that back-up data to, you know, perform analytics and gain some insights of that data that's sitting still behind the scenes. So that's what it's really all about. It's about making sure your data's safe, you're not going to lose it, that big big competitive advantage you have and that data, this is the year of the incumbent because the incumbent can start unlocking valuable data, so - >> Dave: So, Steve, we've talked about this many times. We've talked about the state of data protection, the challenges of sort of bolting on data protection as an afterthought. The sort of one size fits all problem, where you're either under protected or spending too much and being over protected, so have we solved that problem? You know, what is next generation data protection? What does it look like? >> [Steve} Yeah, I think that's a great Question, Dave. I think what you end up seeing a lot of... (audio cuts out) We talk at IBM about the modernize and transform, a lot. Right? And what I've started to try to do is boil it down almost at a product level. WhY - or at least an industry level - why modernize your data protection environment, right? Well if you look at a lot of the new technologies that are out there, costs have come way down, right? Performance is way up. And by performance around data protection we talk RPO's and RTO's. Management has become a lot simpler, a lot of design thinking put in the interfaces, making the Op Ec's job a lot easier around protecting information. A lot of the newer technologies are connected to the cloud, right? A lot simpler. And then you also have the ability to do what Sam just mentioned, which is unlock, now unlock that business value, right? How do I take the data that I'm protecting, and we talk a lot about data reuse and how do I use that data for multiple business purposes. And kind of unhinge the IT organization from being the people that stumble in trying to provide that data out there to the line of business but actually automate that a little bit more with some of the new solutions. So, that's what it means to me for a next generation protection environment. >> Dave: So it used to be this sort of, okay, I got an application, I got to install it on a server - we were talking about this earlier - get a database, put some middleware on - uh! Oh, yeah! I got to back it up. And then you had sort of these silos emerge. Virtualization came in, that obviously change the whole back up paradigm. Now you've got the cloud. What do you guys, what's your point of view on Cloud, everybody's going after this multi-cloud thing, protecting SAS data on prem, hybrid, off-prem, what are you guys doing there? >> Sam: So, uh, and I believe you spoke to Ed Walsh earlier this we very much believe in the multi-cloud strategy. We are very excited on Monday to go live with a Spectrum Protect Plus on IBM's cloud, so it's now available to back up workloads on IBM Cloud. And what's even more exciting about it is if you're running Spectrum Protect Plus on premises, you can actually replicate that data to the version running in the IBM cloud. So now you have the ability not only to back up your data to IBM cloud, back up your data IN IBM cloud where you're running applications there, but also be able to migrate work loads back and forth using this capability. And our plan is to continue to expand that to other clouds following our multi-cloud strategy. >> Dave: What's the plus? >> Sam: Laughs >> Dave: Why the plus? >> Kevin: That's the magic thing, they can't tell you. >> Group: (laughing) >> Dave: It's like AI, it's a black box. >> Sam: Well, I will answer that question seriously, though. IBM's been a leader in data protection for many years. We've been in the Gardeners Leaders Quadrant for 11 years straight with Spectrum Protect, and Spectrum Protect Plus is and extension of that, bringing this new modern approach to back up so it extends the value of our core capability, which you know, enterprises all over the world are using today to keep their data safe. So it's what we do so well, plus more! (laughing) >> Dave: Plus more! - [Sam] Plus more. >> Dave: So, Steve, I wonder if you could talk about the heat in the data protection space, we were at VM World last year, I mean, it was, that was all the buzz. I mean, it was probably the most trafficked booth area, you see tons of VC money that have poured in several years ago that's starting to take shape. It seems like some of these upstarts are taking share, growing, you know, a lot of money in, big valuations, um, what are your thoughts on What's that trend? What's happening there? How do you guys compete with these upstarts? >> Steve: Yeah, so I think that is another really good question. So I think even Ed talks a little bit about a third of the technology money in 2017 went to data protection, so there's a lot of money being poured in. There's a lot of interest, a lot of renewed interest in it. I think what you're seeing, because it cut - it's now from that next generation topic we just talked about, it's now evolving. And that evolution is it's not, it's no longer just about back up. It's about data reuse, data access, and the ability to extract value from that data. Now all of a sudden, if you're doing data protection right, you're backing up a hundred percent of your data. So somewhere in the repository, all my data is sitting. Now, what are the tools I can use to extract the value of that data. So there used to be a lot of different point products, and now what folks are saying is, well now, look, I'm already backing it up and putting it in this data silo, so to speak. How do I get the value out of it? And so, what we've done with Plus, and why we've kind of leap frogged ourselves here with - from going from Protect to Protect Plus, is to be able to now take that repository - what we're seeing from customers is there's a definitely a need for back up, but now we're seeing customers lead with this operational recovery. I want operational recovery and I want data access. So now, what Spectrum Protect Plus does is provides that access. We can do automation, we can provide self service, it's all rest API driven, and then what we still do is we can off load that data to Spectrum Protect, our great product, and then what ends up happening is the long term retention capabilities about corporate compliance or corporate governance, I have that, I'm protecting my business, I feel safe, but now I'm actually getting a lot more value out of that silo of data now. >> Peter: Well, one of the challenges, especially as we start moving into an AI analytics world, is that it's becoming increasingly clear that backing up the data, a hundred percent of the data, may not be capturing all of the value because we're increasingly creating new models, new relationships amongst data that aren't necessarily defined by an application. They're transient, then temporal, they're, they come up they come down, how does a protection plane handle, not only, you know, the data that's known, from sources that are known, but also identifying patterns of how data relationships are being created, staging it to the appropriate place, it seems as though this is going to become an increasingly important feature of any protection scheme? >> Steve: I think, I think a lot - you bring up a good topic here - I think a lot of the new protection solutions that are all rest API driven now have the capability to actually reach out to these other API's, and of course we have our whole Watson platform, our analytics platform that can now analyze that information, but the core part, and the reason why I think - back to your previous question about this investment in some of these newer technologies, the legacy technologies didn't have the metadata plane, for example, the catalog. Of course you had a back up catalog , but did you have an intelligent back up catalog. With the Spectrum Protect Plus catalog, we now have all of this metadata information about the data that you're backing up. Now if I create a snapshot, or reuse situation where to your point being, I want to spin something back up, that catalog keeps track of it now. We have full knowledge of what's going. You might not have chosen to again back that new snap up, but we know it's out there. Now we can understand how people are using the data, what are they using the data for, what is the longevity of how we need to keep that data? Now all of a sudden there's a lot more intelligence in the back up and again to your earlier question, I think that's why there's this renewed interest in kind of the evolution. >> Dave: Well, they say at this point you really can't do that multi-cloud without that capability. I wanted to ask you about something else, because you basically put forth this scenario or premise that it's not just about back up, it's not just about insurance, my words, there's other value that you could extract. Um, I want to bring up ransomware. Everybody talks about air gaps - David Foyer brings that up a lot and then I watch, like certain shows like, I don't know if you saw the Zero Days documentary where they said, you know, we laugh at air gaps, like, oh! Really? Yeah, we get through air gaps, no problem. You know, I'm sure they put physical humans in and they're going to infect. So, so there's - the point I'm getting to is there's other ways to protect against ransomware, and part of that is analytics around the data and all the data's - in theory anyway - in the backup store. So, what's going on with ransomware, how are you guys approaching that problem, where do analytics fit? You know, a big chewy question, but, have at it. >> Sam: Yeah, no I'm actually very glad you asked that question. We just actually released a new version of our core Spectrum Protect product and we actually introduced ransomware detection. So if you think about it, we bring in all of your data constantly, we do change block updates, so every time you change files it updates our database, and we can actually detect things that have changed in the pattern. So for example, if you're D-Dup rate starts going down, we can't D-Dup data that's encrypted. So if all of a sudden the rate of D-Duplication starts going down that would indicate the data's starting to be encrypted, and we'll actually alert the user that something's happening. Another example would be, all the sudden a significant amount of changes start happening to a data set, much higher than the normal rate of change, we will alert a user. It doesn't have to be ransomware, it could be ransomware. It could be some other kind of malicious activity, it could be an employee doing something they shouldn't be - accessing data that's not supposed to be accessed. So we'll alert the users. So this kind of intelligence, uh, you know is what we'll continue to try to build in. IBM's the leader in analytics, and we're bringing those skills and applying it to all of our different software. >> Dave: Oh, okay. You're inspecting that corpus of backup data, looking for anomalus behavior, you're say you're bringing in IBM analytics and also presumably some security capabilities from IBM, is that right? >> Sam: That's right. Absolutely. We work very closely with our security team to ensure that all the solutions we provide tie in very well with the rest of our capabilities at IBM. One other thing though, I'll mention is our cloud object storage, getting a little bit away from our backup software for a second, but object storage is used often - >> Kevin: But it's exciting! >> Sam: It is exciting! It's one of my favorite parts of the portfolio. It's a place where a lot of people are storing backup and archive data and we recently introduced worm capability, which mean Write Once Read Many. So once it's been written it can't be changed. It's usually used for compliance purposes but it's also being used as an air gap capability. If the data can't be changed, then essentially it can't be you know encrypted or attacked by ransomware. And we have certification on this as well, so we're SEC compliant, we can be used in regulated industries, so as we're able to in our data protection software off load data into a object store, which we have the capability, you can actually give it this worm protection, so that you know your backup data is always safe and can always be recovered. We can still do this live detection, and we can also ensure your backup is safe. >> Dave: That's great. I'm glad to hear that, cause I feel like in the old days, that I asked you that question about ransomware, and well, we're working on that - and two years later you've come up with a solution. What's the vibe inside of IBM in the storage group? I mean it seems like there's this renewed energy, obviously growth helps, it's like winning, you know, brings in the fans, but, what's your take Steve? And I'll close with Sam. >> Steve: I would almost want to ask you the same question. You've been interviewing a lot of the folks from the storage division that have come up here today and talked to you. I mean you must hear the enthusiasm and the excitement. Right? >> Dave: Yeah, definitely. People are pumped up. >> Steve: And I've rejoined IBM, Sam has rejoined IBM, right? And I think what we're finding inside is there used to be a lot of this, eh yeah, we'll eventually get there. In other words, it's like you said, next year, next year. Next, next quarter. Next third quarter, right? And now its, how do we get it done? People are excited, they want to, they see all the changes going on, we've done a lot to - I don't want to say sort out the portfolio, I think the portfolio's always been good - but now there's like a clean crisp clear story around the portfolio, how they fit together, why they're supposed to - and people are rallying behind that. And we're seeing customer - we're voted by IDCE, number one in the storage software business this year. I think people are really getting behind, you want to work for a winning team, and we're winning and people are getting excited about it. >> Dave: Yeah, I think there's a sense of urgency, a little startup mojo, it's back. So, love that, but Sam I'll give you the last word, before we wrap. Just on Think? Just on the Market? >> Sam: I got to tell you, Think has been crazy. It's been a lot of fun so far. I got to tell you, I have never seen so much excitement around our storage portfolio from customers. These were the easiest customer discussions I've ever had at one of these conferences, so they're really excited about what they're doing and they're excited about the direction we're moving in. So, yeah. >> Dave: Guy, awesome seeing you. Thanks for coming back on The Cube, both of you, and, uh, really a pleasure. Alright. Thank you for watching. Uh, this is a wrap from IBM Think 2018. Guys, thanks for helping us close that up. Peter, thank you for helping - >> Peter: Absolutely. >> Dave: me co-host this week. John Furie was unbelievable with the pop up cube, really phenomenal job, John and the crew. Guys, great great job. Really appreciate you guys coming in from wherever you were Puerto Rico or the Bahamas, I can't keep track of you anymore. Go to siliconangle.com, check out all the news. TheCube.net is where all these videos will be and wikibon.com for all the research, which Peter's group has been doing great work there. We're out! We'll see you next time. (lively tech music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering IBM Think 2018. (upbeat music) Brought to you by IBM. >> Welcome back to IBM Think 2018 everybody. My name is Dave Vellante and I'm with my co-host Peter Burris. You're watching theCUBE, the leader in live tech coverage. This is day three of our wall to wall coverage of IBM Think. The inaugural Think conference. Good friend Eric Herzog is here. He runs marketing for IBM storage. They're kicking butt. You've been in three years, making a difference, looking great, new Hawaiian shirt. (laughter) Welcome back my friend. >> Thank you, thank you. >> Good to see you. >> Always love being on theCUBE. >> So this is crazy. I mean, I miss Edge, I loved that show, but you know, one stop shopping. >> Well, a couple things. One when you look at other shows in the tech industry, they tend to be for the whole company so we had a lot of small shows and that was great and it allowed focus, but the one thing it didn't do is every division, including storage, we have all kinds of IBM customers who are not IBM storage customers. So this allows us to do some cross pollination and go and talk to those IBM customers who are not IBM storage customers which we can always do at a third party show like a VM World or Oracle World, but you know those guys tend to have a show that's focused on every division they have. So it could be a real advantage for IBM to do it that way, give us more mass. And it also, you know, helps us spend more on third party shows to go after a whole bunch of new prospects and new clients in other venues. >> You, you've attracted some good storage DNA. Yourself and some others, Ed Walsh was on yesterday. He said Joe Tucci made a comment years ago Somebody asked him what's your biggest fear. If IBM wakes up and figures it out in storage. Looks like you guys are figuring it out. >> Whipping it up and figuring it out. >> Four quarters of consistent growth, you know redefining your portfolio towards software defined. One of the things we've talked about a lot, and I know you brought this was the discipline around you know communicating, getting products to market, faster cycles, because people buy products and solutions right? So you guys have really done a good job there, but what's your perspective on how you guys have been winning in the last year or so? >> Well I think there's a couple of things. One is pure accident, okay. Which is not just us, is one of the leaders in the industry, where I used to work and Ed used to work has clearly stubbed its toe and has lost its way and that has benefited not only IBM but actually even some of our other competitors have grown at the expense of, you know, EMC. And they're not doing as well as they used to do and they've been cutting head count and you know, there's a big difference in the engineering spend of what EMC does versus what Michael Dell likes to spend on engineering. We have been continuing to invest. Sales resources, marketing resources, tech support resources in the field, technical resources from a development perspective. The other thing we did as Ed came back was rationalize the portfolio. Make sure that you don't have 27 products that overlap, you have one. And maybe it has a slight overlap with the product next to it, but you don't have to have three things that do the same thing and quite honestly, IBM, before I showed up, we did have that. So that's benefited us and then I think the third thing is we've gone to a solution-oriented focus. So can we talk about, as nerdy as tracks per sector and TPI and BPI and, I mean all the way down to the hard drive or to the flash layer? Sure we can. You know what, have you ever... You guys have been doing this forever. Ever met a CIO who was a storage guy? >> No, no. CIOs don't care about storage. >> Exactly, so you've got to... >> We've had quite a couple of ex-CIOs who were storage guys. (laughter) >> So you've really got to talk about applications, workloads, and use cases. How you solve the business problems. We've created a whole set of sales tools that we call the conversations available to the IBM sales team and our business partners which is how to talk to a CIO, how to talk to a line of business owner, how to talk to the VP of software development in a global enterprise who doesn't care at all, and also to get people to understand that it's not... Storage is a critical foundation for cloud, for AI, for other workloads, but if you talk latency right off the top, especially with a CIO or the senior executive, it's like what are you talking about? What you have to say is we can make your cloud sing, we can make your cloud never go down. We can make sure that the response time on the web browser is in a second. Whereas you know Google did that test about if you click and it takes more than two and a half seconds, they go away. Well even if that's your own private cloud, guess what they do the same thing. So you've got to be able to show them how the storage enables cloud and AI and other workloads. >> Let's talk about that for a second. Because I was having a thought here. It's maybe my only interesting thought here at Think, being pretty much overwhelmed. But the thought that I had was if you think about all the things that IBM is talking about, block chain, analytics, cloud, go on down the list, none of them would have been possible if we were still working at 10, 20, 30 milliseconds of wait time on a disc head. The fundamental change that made all of this possible is the move from disc to flash. >> Eric: Right. >> Storage is the fundamental change in this industry that has made all of this possible. What do you think about that? >> So I would agree with that. There is no doubt and that's part of the reason I had said storage is a critical foundation for cloud or AI workloads. Whether you're talking not just pure performance but availability and reliability. So we have a public reference Medicat. They deliver healthcare services as a service, so it's a software as a service model. Well guess what? They provide patient records into hospitals and clinics that tend to be focused at the university level like the University of California Health Center for the students. Well guess what? If not only does it need to be fast, if it's not available then you can't get the healthcare records can you? So, and while it's a cloud model, you have to be able to have that availability characteristic, reliability. So storage is, again, that critical foundation. If you build a building in a major city and the foundation isn't very good, the building falls over. And storage is that critical foundation for any cloud, any AI, and even for the older workloads like an SAP Hana or a Oracle workload, right? If, again if the storage is not resilient, oh well you can't access the shipping database or the payroll database or the accounts receivable database cause the storage is down and then obviously if it's not fast, it takes forever to get Dave Vellante's bill, right. And that's a waste of time. >> So it's plumbing, but the plumbing's getting more intelligent isn't it? >> Well that's the other thing we've done is we are automating everything. We are imbuing our software, and we announced this, that our range are going to be having an intelligent infrastructure software plane if you will that is going to help do diagnostics. For example, in one of the coming releases, if a customer allows access, if a power supply is going bad, we will tell them it's going bad and it'll automatically send a PO to IBM with a serial number, the address, and say please send me a new power supply before the power supply actually fails. But it also means they don't have to stock a power supply on their shelf which means they have a higher cost of cap ex. And for a big shop there's a bunch of power supplies, a bunch of flash modules, maybe hard drives if they're still dinosauric in how they behave. And they have those things and they buy them from us and our competitors. So imbuing it with intelligence, automating everything we can automate. So automatically tiering data, moving data around from tier to tier, moving it out to the cloud, what we do with the reuse of backup sets. Instead of doing it the old way of back up. And I know you've got Sam Warner coming on later today and he'll talk about modern data protection, how that is revolutionizing what dev ops and other guys can do with their, essentially, what we would've called in the old days back up data. >> Let's talk about your spectrum launch. Spectrum NAS, give us some plugs for that. What's the update there? >> So we announced on the 20th of February a whole set of changes regarding the Spectrum family. We have things around Spectrum PROTECT, with GDPR, Spectrum PROTECT Plus as a service as well as some additional granularity features and I know Sam Warner's going to come on later today. Spectrum NAS software defined network attached storage. Okay, we're not going to sell any infrastructure with it. We have for big data analytics our Spectrum scale, but think of Spectrum NAS as traditional network attached storage workloads. Home directories. Things like that. Small file service where Spectrum scale has one of our public references, and they were here actually at Edge a couple of years ago, one of the largest banks in the world, their entire fraud detection system is based on Spectrum scale. That's not what you would use Spectrum NAS for. So, and it's often common as you know in the file world to have sort of a traditional file system and then a big one that does big data, analytics and AI and is very focused on that and so that's what we've done. Spectrum NAS is a software only, software defined, rounds out our block, now gives a traditional file. We had scale out file already and IBM cloud object storage is also software defined. >> Well how about the get put world. What's happening there? I mean we've been waiting for it to explode. >> Ah so the get put world is all about NVME. NVME, new storage protocol as you know it's been scuzzy forever. Scuzzy and/or SATA. And it's been that way for years and years and years and years, but now you've got flash. As Peter pointed out spinning disc is really slow. Flash is really fast and the protocol of Scuzzy was not keeping up with the performance so NVME is coming out. We announced an NVME over InfiniBand Fabric solution. We announced that we will be adding a fiber channel. NVME fabric based and also in ethernet. Those will come and one of the key things we're doing is our hardware, our infrastructure's all ready to go so all you have to do is a non-disruptive software upgrade and for anyone who's bought today, it'll be free. So you can start off with fiber channel or ethernet fabrics today or InfiniBand fabric now that we can ship, but on the ethernet and fiber channel side, they buy the array today and then later this year in the second half software upgrade and then they'll have NVME over fiber channel or NVME over ethernet. >> Explain why NVME and NVME over fabric is so important generally but in particular for this sort of new class of applications that's emerging. >> Well the key thing with the new class of applications is they're incredibly performance and latency sensitive. So we're trying to do real artificial intelligence and they're trying to, for example, I just did a presentation and one of our partners, Mark III has created a manufacturing system using AI and Watson. So you use cameras all over, which has been common, but it actually will learn. So it'll tell you whether cans are bad. Another one of our customers is in the healthcare space and they're working on a genomic process for breast cancer along with radiology and they've collected over 20 million radiological samples of breast cancer analysis. So guess what, how are you going to sort through that? Are you or I could sort through 20 million images? Well guess what, AI can do that, narrow it down, and say whether it's this type of breast cancer or that type of breast cancer. And then the doctor can decide what to do about it. And that's all empowered by AI and that requires incredible performance which is what NVME delivers. Again, that underlying foundation of AI, in this case going from flash with Scuzzy, flash to NVME, increasing the power that AI can deliver because of its storage foundation. >> But even those are human time transactions. What about when we start taking the output of that AI and put it directly into operational transactions that have to run like a bat out of hell. >> Which is where NVME will come in as well. You cannot have the performance that we've had these last almost 30 years with Scuzzy and even slower when you talk about SATA. That's just not going to cut it with flash. And by the way, you know there's going to be things beyond flash that will be faster than flash. So flash two, flash three, it's just the way it was with the hard drive world, right? It was 2400 RPM then 36 then 54 then 72 then 10k then 15/5. >> More size, more speed, lower energy. >> Which is what NVME will help you do and you can do it as a fabric infrastructure or you can do it in the array itself. You dual in box and out of box connectivity with NVME increasing the performance within your array and increasing the performance outside of the array as you go out to your host and out into your switching infrastructure. >> So I'm loving Think. It's too many people to count, I've been joking all week. 30,000 40,000. We're still tallying up. I'm going to miss Edge for sure. I'm going to miss the updates in the you know, late spring. But so let's get 'em now. What can we expect? What are you trying to accomplish in the next six to nine months? What should we be looking for without giving any confidential information. >> Well we've already publicly announced that we'll be fleshing out NVME across the board. >> Dave: Right. >> So we already publicly announced that. That will be a big to-do. The other thing we're looking at is continuing to imbue what we do with additional solution sets. So that's something we have a wide set of software. For example, we publicly announced this week that the Versa stack, all flash array will be available with IBM cloud private with a CYSCO validated design in May. So again, in this case taking a very powerful system, the Versa Stack all flash, which already delivers ROI and TCO, but still is if you will a box. Now that box is a converge box with compute with switching with all flash array and with a virtual environment. But now we're putting, again as a bundle, IBM cloud private on there. So you'll see more and more of those types of solutions both with the rest of IBM but also from third parties. So if that offers the right solution set to cut capx/opx, automate processes, and again, for the cloud workloads, AI workloads and any workloads, storage is that foundation. The critical foundation. So we will make sure that we'll have solutions wrapped around that throughout the rest of this year. >> So it's great to see the performance in the storage division. Great people. We're under counting it. We're not even counting all the cloud storage that goes and counts somewhere else. You guys are doing a great job. You know, best of luck and really keep it up Eric, thanks very much for coming back on theCUBE. >> Great thank you very much. >> We really appreciate it. >> Thanks again Peter. >> Alright keep it right there everybody we'll be back with our next segment right after this short break. You're watching theCUBE live from Think 2018. (upbeat music)

>> Narrator: From Las Vegas, it's the Cube, covering IBM Think 2018, brought to you by IBM. >> Welcome back to Las Vegas, everybody. You're watching the Cube, the leader in live tech coverage. My name is Dave Vellante, and this is day three of our wall-to-wall coverage of IBM's inaugural Think conference. Mary Glackin's here, she's the vice president of weather business solutions, public, private partnerships, IBM Watson, and she's joined by Sheri Bachstein as the global head of consumer business at the Weather Company, an IBM company. Ladies, welcome to the Cube, thanks so much for coming on. >> Thank you, you're welcome. >> Thanks. >> Alright, Mary, going to start with the Weather Company. When IBM acquired the Weather Company, a lot of people were like, "What?", and they said, "Okay, data science, I get that.", and then, there was an IoT spin on that. Obviously, you have a lot of data, but, I got to ask you, what business are you in? >> So, what we like to say is we're in, not in the weather business, we're in the decision business. We're really dedicated, everyday, to help businesses, make the best decisions possible, and Sheri works on the consumer end of the business to do exactly the same thing. >> So, talk about your respective roles. Sheri, you're on the consumer side, as Mary just said, what does that entail? >> So, the consumer side is any touchpoint where we're bringing weather and weather insights to our consumers, whether it's on our weather channel app, whether it's on our web platform, mobile web, on wearables, so, it's anywhere where we're connecting with consumers, and, as Mary said, it's really about helping consumers make decisions. In our field, the forecast and some of the weather data has become a commodity almost, and we've actually shared our weather data with a lot of partners, and, so, now, we're using machine learning and data science to really come up with weather insights to help consumers make decisions, and it could be something just as simple as what to wear today, what's going to happen for a big event, or it can be around how do I keep people safe during severe weather. >> Yeah, I mean, we all look at the weather. I mean, I look at it everyday. >> Yeah. >> Of course, when you travel, like, what do I bring, what do I wear? Living in the East Coast these days, a lot of storms that we've >> That's right. >> encountered in the East Coast. I wonder if you could talk about life at IBM. I mean, again, it was a curious acquisition to a lot of people. Have you guys assimilated, how has it changed your business? >> I would say pretty dramatically. So, coming back to IBM acquiring us, they acquired us, really, for two reasons. One is we had some underlying technology that was really of interest to them that they're leveraging today, but the other part was because weather impacts so many businesses. So, as we've come into IBM, we've had alliances with IBM research. We're working on a pretty exciting project in bringing the next generation weather model to market, using high performance computing there. We've had alliances, definitely, through Watson in bringing AI into our products, and then, our product lines marry up with a lot of IBM product lines. So, we've rolled out a really exciting offering in closed captioning, and it really works well with some of the classical media business, weather media business that we have been providing. >> So, how do you guys make money? Maybe we could talk about the consumer side and the business side. A lot of people must ask that question. >> Yeah. >> They're advertising, okay, fine, >> Yeah. >> but that's not the core of what you guys do. >> Yeah, so, on the consumer side, a big majority of our revenue is drive by advertising, but we had to look at that business as well, 'cause as programmatic advertising has kind of taken up the landscape, how did we pivot to really generate more revenue, and, so, we've done that by creating Watson advertising, and that was one of the first implementations of Watson after the acquisition on the consumer side, and what we've done is we've created an open, scalable environment that, now, we can not only sell meaningful insights on our platform, but we can now give that to our partners, that they can go off our property and use the weather insights, we can use different data around location and media to help our partners really have a better experience, not only on our platform, but on any publisher's platform. >> So, that's your customers using Watson for advertising to drive their business. >> That's right. >> It's not like IBM is getting into the advertising business, per se, directly, is that right? >> Right, well, we're leveraging the power of Watson to create these insights. One of the products we created is called Weather FX, and, really, what it's doing, it's taking predictive analytics on the retail side, which is really an underused technology for retailers, but taking our historical weather data, mixing it with their retail data' to come up with insights so we can come up with interesting things that, say, in the northeast, like right now, during the winter, soda sells tremendously during very snowy or rainy winters. We can look at, you know, strawberry Pop-Tarts sell fairly well right before a hurricane, and, so, these are insights that we can bring to retailers, but it helps them with their supply chain, it helps them with their inventory, it can actually even help them with pricing, and, so, this is one of the ways we're taking our weather technology and marrying it with the advertising world to help provide those insights. >> For real, with the strawberry Pop-Tarts? >> For real, yeah, I guess, you know, you don't have to cook 'em or something. I don't know, so, yeah. >> Right, yeah, it's simple if the lights go out, okay. I mean, we want to ask you about your title, public and private partnerships. It's interesting, what is that all about? >> So, it's really about the fact that weather has really been something that's been shared globally around the world for hundreds of years at this point, and, so, the Weather Company and IBM take it very seriously that we be good partners in that community of weather providers. So, one of the things that we feel passionately about is we have a shared safety mission with national meteorological services globally. So, here in the US, we transmit, Sheri's team does, the warnings that come from the National Weather Service unaltered with attribution to the National Weather Service. We feel that it's really important that there's a sole authoritative voice when there's really danger. So, we share that safety mission, and then, we're trying to help in other parts of the world. We've had some partnerships to try to increase the observing in Africa which is really a part of the world that's under-observed. So, some of IBM's philanthropic efforts have been helping to fill in there and work with those national met services. So, it's really one of the really fun parts of my job. >> You know, we talk a lot about digital transformation, and Ginni Rometty was talking about the incumbent disruptors, and we've been riffing on that all week. We've made the observation that companies that are digital have data at their core, and they've organized, sort of, human expertise around that data. Most companies, Fortune 1000, are built around human expertise and built around other assets, the bottling plant or the factory, et cetera. I look at the Weather Company as a data company, that's probably fair. Did you evolve into that data is clearly at your core? Has it always been, and it's very interesting that IBM has acquired this company as it changes its DNA. I wonder if you could address that. >> Go ahead (laughs). >> So, I think there's a couple aspects around our data. There's obviously the weather data which is really powerful, but then, there's also location data. We're one of the largest location data providers besides Google and some of the others, because our weather accuracy starts with location which is really important. We have 250 million users that use our application, and we want to give them the most accurate forecast, and that starts with location. Because we add value, users will opt in to give us that data which is really important to us that we do keep their data private and opt in to that to get that location data. So, that's really powerful, because, now we can deliver products based on time and location and weather, and it just makes for better weather insights for, not only our consumers, but for our businesses. >> Yeah, yeah. >> Do you use, I mean, how do you use social? I mean, you know how Waze tells you where the traffic is and you report back. Do you guys rely heavily on that, or do you more rely on machines to help you with your forecast? Is it a combination? >> So, I could talk a little bit. One of our new market areas we've been going into is ground transportation. So, we do have a partner that's providing us some transportation, traffic information, but what we bring to it is being able to do, the predictive thing, is to take the weather piece and how that's going to influence that traffic. So, as the storm comes through, we know by looking at past events what that will mean and we bring that piece to the table. So, it's an example of how we go, not just giving you a weather forecast, but really forecasting the impacts and giving you insights, so that if you're running a large trucking operation, you can reroute fleets around it and avoid weather like that and keep people safe. >> Talk about, oh, go ahead, please. >> One of the brands within our portfolio is Weather Underground, and what they brought to the table for us is a personal weather station that works. So, we have about 270,000 around the world, and these are people that just really love the weather. They have a personal weather station in their backyard and they provide that data that then goes into Mary's team in helping looking at the forecast. So, that's one of the ways that we're using kind of a social network in sensoring to influence some of the work that we're doing. >> I mean, the weather forecast, for years, have been the butt of many jokes. You guys are data science oriented, data scientists, the data doesn't lie. We just keep iterating >> Yeah. >> and make it better and better and better. What could you tell us about the improvements of the forecast over the last decade? Maybe Bill Belichick makes jokes about the weather and you hear it, you say, "You know, actually "the weather's predictions have gotten much better." You guys measure it, what can you share with us? >> Oh, it's gotten so much better over the course of my career, it's pretty dramatic and it's getting better still. You're going to see some real breakthroughs coming up. So, one of the things that we've really put a lot of bets on in IBM is the internet of things, >> Dave: Right. >> and, so, we are, today, pulling off of cellphones atmospheric pressure data and that's going into our next generation model. So, this'll be more data than anybody has powering that model. So, you're able to augment traditional data sources like, you may or may not know, we still launch weather balloons twice a day to measure through the atmosphere, but, in our technology, we take data off of airplanes, we take data off of cellphones, we'll soon be taking data off of cars which will tell us when the windshield wipers are moving, is it raining or not, when the anti-lock brakes things lock, that roads are icy, all of that. So, all of that will come in to improve forecasting. >> So, this requires partnerships with all that and amazing supply chain. >> Absolutely. >> I presume IBM helps there as well, but did you have a lot of that in motion prior to the acquisition, how does that all work? >> I think we've really been empowered by IBM. >> Yep, absolutely. >> Yeah. >> There's no question about that, and it's about finding the win-win. When we work with car manufacturers they're looking to have safe experiences for their drivers and we can help in that regard, and, as we move into autonomous vehicles, there's just going to be even more demand for very high resolution, accurate weather information. >> Am I correct at all, the weather data from all these devices actually goes back to the IBM cloud, is that right, and that's where the models are iterated and developed, is that correct, or does some of it stay out in the network? >> It's all a cloud-based operation that's here. We do do some, I mentioned before that we're working with IBM research on next generation high-performance computing which is actually, it can be cloud-based, but it's also on Prim-based, because of the very large cores we need for computing these models. We're going to run a very high-resolution model globally at a very high frequency. >> So, thinking about some of the industries that you're helping, I mean, you mentioned retail before. Obviously, government's very interested in this. I would imagine investors are interested in the weather in a big way. >> Yeah. >> Maybe you could talk about some of the more interesting industries, use cases, business models. >> Yeah, there's a lot out there, there's traditional ones we've served for years like energy traders that are very interested in, you know, because they're trying to make decisions about that. The financial services sector is also very interested. When they can get some additional insights through footfall traffic, if they know certain stores are seeing more footfall traffic, that will give them some indication, a little edge up in the marketplace for that. So, we see those kind of things, and other traditional areas as well, agriculture, what you would expect there. >> So people, you know, you hear a lot of talk in the press about artificial intelligence and Elon Musk predictions and the like, but here's an example where machine intelligence, everybody welcomes, keeps getting better and better and better. How far could we take AI and weather? Where do you see this going in the next 10 years? >> So, on the consumer side, I think it's really about transforming the way that we're delivering weather on the digital platform, the new age of the weather app will say, and, really, users want a personalized experience. They want to know how the weather's going to impact me, but they don't want to personalize, right? So, that's where machine learning is coming in, that we can be able to provide those insights. We'll know that, maybe, you're an allergy sufferer or migraine sufferer, and we're going to tell you that the conditions are right for that you might have symptoms related to that around health. So, there's a lot of ways, on the consumer side, more personalized experience, giving you more assurance that you don't have to, necessarily, go to the app to find information. We're going to send it to you more proactively, and, so, machine learning is helping us do that cognitive science as well. So, it's a pretty exciting time to be part of the weather. >> Yeah, that bum knee I have, you know, you might want to get ahead of the pain. >> That's right, with the arthritis, yes, yes, so, definitely. >> Alright, Mary, we'll give you last word on IBM Think and, you know, the whole trend of AI and weather. >> So, I think it's really exciting. I think Ginni says it really well. It's about AI and the person as well. You know, AI doesn't take over. It's really finding the way to AI to really assist decision makers and that's we're going on the business end of things is really sorting through tons and tons of data to really provide the insights that people can make, businesses can make really great decisions. >> Well, it's always been a really fascinating acquisition to me, and, now, just to see how it's evolving is really amazing. So, Sheri and Mary, thanks very much for coming on the Cube >> Thank you. >> and sharing your experiences. >> Thanks so much. >> Great, thank you. >> You're welcome, alright, keep it right there, everybody, you're watching the Cube. We're live from Think 2018 and we'll be right back. (techno beat)

>> Narrator: Live from Las Vegas, it's theCUBE covering IBM Think 2018 brought to you by IBM. >> We're back at IBM Think 2018. This is theCUBE the leader and live tech coverage. My name is Dave Vellante and I'm here with my cohost Peter Burst. Day two of our wall to wall coverage of IBM's inaugural Think conference. Rajesh Nambiar is here, he's the general manager of global business services for application services within IBM. Thanks for coming on theCUBE. >> Thank you for having me here. >> So how's this event going for you? You're in from Singapore. We were saying you must love the fact that IBM's consolidated a lot of it's major events in one place. You get a lot done in a week. >> Absolutely, I think this is four or five days that we're going to be here. Phenomenal amount of energy, I mean when you go around you can see that. I think as you said, combining some of the events it's made it even more interesting for us. I'll be meeting more people, more clients, more productive session for sure. >> So let's talk about what you do with application services and then we really want to get in to one of the themes that Jenny hit on today which is incumbent disruptors and competing from the core of your proprietary data. So let's get in to it. Start with your organization and what you guys do. >> Absolutely, so as you mentioned and I do the application services for GBS in IBM. Within application services I think we focus on application development and management which is a large area for us and as you know that IBM has been managing applications for many, many large clients over the period of time and it's a very large portfolio for IBM. What we see truly is enterprises as you saw and they are going to have enormous amount of issues with the new age companies if you may, all of the new companies which are sort of coming out. You can call them bond digital or bond in cloud or uberization of the organizations, whatever. So you'd find that the enterprises are going to have significant issues maintaining the company advantage over a period of time. And one of the ways they could sort of regain the leadership or company advantage would be by ensuring that they are digitally reinventing themselves. The problem is, I think as Forbes said recently in an article around saying that, about 80 percent of the digital transformation projects really fail with multiple reasons as to why they fail. I want to argue saying that one of the, and you heard from Jenny this morning, that you know you have the business architecture and the technology architecture. If we focus on the technology for second, you would find that many of these new incumbents that you mentioned will, they try to compete purely on the digital side of the equation. They will have a harder chance or they might not even get where they want to go. And we want to argue saying that if they kind of pay attention to the core they have, and I want to sort of define what this core and digital is going to be, so think of it this way, I mean core is what, if any company has been around for awhile, then they would of had a significant amount of core systems, systems of records if you want to call it where they have the business process embedded into that. They have the customer data embedded into that. Now what's happening on the other side, of course everybody wants to get there, the whole digital dimension, on the digital side of the equation you do have systems of engagement, where you truly understand and engage, I want to say customers but then again also have employees of your organization. So you're going to engage them in the last mile if you may. What touches the customer, touches your employees, that's what we call systems of engagement in the digital. Now, organizations tend to see these two as two different things and if you do not build your digital eco system, leveraging what you have in core, I believe that the chances of you failing in your digital transformation are very, very high. Why is that? Because I believe the intersection of these two worlds if you may, the core and the digital, is not that easy for people to leverage and I believe that we as a company, we help our clients sort of leverage that intersection if you may. >> Okay so, where do you start? Is it application modernization? Is it allowing them to develop applications that are more sort of more native as you say? When you talk to customers where do you see the starting point? >> Okay so, when you look at these two fundamentally there are synergies between these two worlds and they are discouraged. Synergies are natural why? Because as I mentioned before, in the core or in the systems of records you'll find business processes getting embedded, customers data getting embedded. And then on the other side of the digital system, you always have the user experience which is what we all want to try. I mean the user experience is all about everything. I was talking to a bank recently (foreign name). So they said, we built this phenomenally wonderful user friendly mobile app for our customers and what happened was the app was fantastic and it was great user experience and everything was fine, he just add for every transaction it took like a minute for the balance to show up on the mobile app. That's not what he wanted because why is that? Because your focusing on the digital only. The fact that it just go to your core systems, get the customer data and bring it back to the watch app or the mobile app or whatever, that wasn't the plan that I weighed and hence my point being that if you look at the synergies which is great, there are a lot of discordance because the way that all systems are being built is very different. Maybe you're using a waterfall. The new systems are getting built in a different way. If you leverage the synergies, manage the discordant in a nicer way. A great example would be, so do you have micro services coming out of your core systems to enable your digital systems. You have the right API's getting built from the core systems to enable your digital systems. If you're able to manage this intersection well, then I think you have a play and that's how I believe that we should. So again to your point, do you modernize? I believe you do the three things to get the synergy right. One would be you are to optimize your core systems for efficiency because more and more the systems get older and older. You're going to have challenges in maintaining them, more expensive to maintain them, so you optimize those systems for efficiency. Then you modernize them to build in or enable new capability. So second, as I said modernize, what you really do, you're making sure that it is easier for the digital systems to get to you, to understand what you're doing, to get the customer data, so that is a modernized space. The third is that you have to innovate sort of co create if you may and make sure that you're able to build those newer systems, digital systems using the core and enabling the core for growth. So if you had an organization, if you want growth, you're not going to get it if you don't do these three things in my opinion. >> So Rajesh, many years ago I did a research project for a client and we looked very closely at the consequences of increasing the functionality and automation in systems of engagement and how that drove work back in the core and we found that every success of generation of enhancement on the systems of engagement, drove the number of transactions back at the core sevenfold. Are you seeing relationships like that? Is there rules of thumbs that people should use now as their systems of engagement get even more powerful, more human friendly? What is the new kind of expectation these days? >> So the issue is definitely what you said. I mean for every about seven or eight times is what you want to drive the, for every single transaction which is rising out systems of engagement. However, one of the way to make it more efficient when the systems of records, which is the core systems if you may, is by using the modern, stuff we'll be talking about, if you have designed your core systems and enabled micro services in the right way, maybe instead of having seven or eight transactions, you could be able to do that in two or three. Similarly >> Peter: Unstage them. >> Unstage them, yeah in a certain way so that you're not getting into the performance issue which I talked about in this banking example as you know, you don't want to build a wonderful digital app but having that to go through a significant performance issue over the period of time. So that is one of the things. The other important element of what he just now said is also the talent piece of it. We underestimate, I think, as we said, one of the reasons why many of these engagements fail is also because people don't think talent is a big deal in a lot of this. Because when you really see, if you're a big company, been around for awhile, you have a very strong core, and your people in the IT organization are going to be wired somewhat to the processes which are going to be sort of the ordeal if you may. And how do you move to this new world of digital? So there is a fundamental difference from the talent point of view. Two things, as an organization you're moving from process centric to user centric. Now you want to build something for your customer, for your employee. When you do that the talent base, of course their minds have changed, but also a simple example, we always hire people for skills. Maybe still, some companies still do, for skills. But I believe that's a passe because you know what do you now need is a tenacity for learnability or tenacity for a life long learning for the people whom you're hiring. Not necessarily a skill that you value today because what happens in today's world, after six months that skill is no longer valuable for you. So what do you do with them? But if you have a tenacity for life long learning, the ability for you to pick up new skills and then transform yourself will be so high that you're not stuck with people who are all skills for a long period of time. >> I was talking to a senior, a guy who owns development and he said one of the biggest impacts of open source over the last few years was that it brought the notion of responsibility, recognition, reputation, and change the way that the evolvers talk about collaboration with each other, not just in the open source world but overall. I think collaboration and new collaboration agile also has to be part of the equation. What do you think? >> Well without a question. In fact, I was about to say, collaboration's very, very key because again, when you move from process interviews as intrigue, you also find, traditionally organizations are very role based, so everybody had a role. I'm a developer, I'm a tester, I'm a architect. But in the new world, this is going to be changing into maybe parts of people who are sort of working on a garage metal. Everybody does everything. You have a smaller group of people who are able to evaluate something very, very quick and in an agile fashion as opposed to the traditional way of saying, I'm sort of role based, I have an organization and that's how they operate. So I think there's significant difference and again, I would probably say to leverage the talent for the newer market. Again there are about two or three things that one could potentially do. One would clearly be this learnability. Skills are no longer what is valued, it's the learnability. The ability for you to sort of quickly move from one to another would be valued. Second would be diversity of skills. Today we hire more people with user experience, with psychology major. You would of never thought of this 10 years ago. We never hired anybody from art school but we do that today because of... >> I was really happy. My son's a music major. >> My son is a psychology major, I was just telling you in the University of Colorado. So they get hired probably as well as already the STEM students are going to be, so that's good. (laughing) And the last one is of course, I have this notion called digital label . I don't know if you've heard this before. And Jenny talked about it today. So you're going to have man and machine, when you do that, automation is a great influencer in all of this. I think there are going to be the digital label and the human label are going to co exist. So we're calling it hybrid label. So any task that you're going to do, we will have people which is sort of high capability now, leveraging watts, which is a digital label. So that's another important thing in the talent market. >> And the laborer increasingly requires sort of multi tool skills not only domain expertise but also digital skills. >> Rajesh: Absolutely >> At least being able to understand how the leverage, the machine intelligence. I want to ask you, and I know Peter you got to go soon. But this trend with IOT, Blockchain, we saw the IV and Maersk example today where they're attacking inefficiencies where there's a third party trust involved and it's creating a trustless system. Do you see a trend toward sort of putting token economics embedded inside of applications, things like Blockchain, increasingly going into core applications? Is that a trend you're seeing yet? >> Yes, yes, I think not as much as we would like to see but I think it's beginning to sort of level up in a period of time. I think Blockchain is still, as I said, there's a more in the experimentation phase, and there are a few companies who have leveraged fully. Great example is as you saw this morning with what we're doing with the APMM Maersk, the fact that we're able to do the distribution systems within shipping. And any radio finding that there's going to be a significant amount of paperwork or transactional arrangements that are being done outside of the normal systems. I think Blockchain would be a great way to solve this issue. >> I want to tease your session a little bit. You're going talk, you got a CIO panel, what is that? >> Well the talk is actually going to be I think unlocking the value of the core system. So there's going to be something similar to what we talked about. We've got great session with three CIO's who are going to be on the panel. We're going to have the Carhartt CIO John Hill is going to be on the panel, and they've done a lot of good work in terms of truly making sure that they understood that if they don't level the core they can't really get to the digital. >> Was that CarHartt? >> Carhartt, yes. >> The only brand I wear. >> Really? (laughing) >> They'll be interesting, then KLM with (mumbling) with their history of the core that they've had for several years and how they're really moving into the new digital era and then being sort of a customer friendly airline if you may, so he's going to talk about some of that. And then we also have the TPX which is the communications organization which they've done gone through about 12 acquisitions over the last 12 years, so one a year pretty much. How are they integrating all of those companies and how are they really putting them together into sort of one system. >> Peter: And when is that session? >> That session's on Thursday morning at 11:30, I hope you guys are there to watch that. I'm worried because it's the last day. >> It's a getaway day but listen, a good day to go down and check it out because that notion of what incumbents should be doing and competing from the core is very, very important idea. So Rajesh thanks for coming on theCUBE and explaining that. Best of luck to you tomorrow and great to see you. >> Thank you so much, thank you. >> Alright, keep it right there buddy. We'll be back with our next guest. This is CUBE, you're watching live from IBM Think 2018. We'll be right back. (upbeat music)

>> Voiceover: Live from San Francisco, it's The Cube. Covering Samsung Developer Conference 2017. Brought to you by Samsung. >> Okay, welcome back, everyone. Live here in San Francisco, this is The Cube's exclusive coverage of Samsung Developer Conference, SDC 2017. I'm John Furrier, co-founder of SiliconANGLE Media, and co-host of The Cube. My next guest is Dominic Venuto, who is the General Manager of the consumer division of The Weather Channel, and Watson Advertising, which is part of The Weather Company. Welcome to The Cube. >> Thank you for having me. >> Finally, I got the consumer guy on. I've interviewed The Weather Company folks from the IBM side, two different brands. One's the data, big data science operation going on, the whole Weather Company. But Weather Channel, the consumer stuff, Weather Underground, that's your product. >> Yes, you saved the best for last. We touch the consumer. >> So, weather content is good. So obviously, the hurricanes have been in the news over the years. Out here in California, the fires. People are interested in whether the impact, it used to be a unique thing on cable, go to the Weather Channel, check the forecast, read the paper. Now with online apps, weather is constantly a utility for users. So it's not a long-tail editorial product. It's pretty fundamental. >> Yeah, we want to be where our consumers are. Fundamentally we want to help people make better decisions and propel the world. And since weather touches everything, we need to be where the consumers are. So now, with all the digital touchpoints, whether that's your phone, or its a watch, your television, desktop if you still have one and you're still using it, as some of us do. We want to be there, for that very reason. And in fact, what we're aiming for, is to move from a utility, because if we are going to help people make better decisions, a utility only goes so far, would be a platform to anticipate behavior and drive decisions. >> So tell me about the Weather Underground and the weather.com consumer product. They're all one in the same now? Obviously one was very successful, with user generated content. This is not going away. Explain the product side of The Weather Channel consumer division. >> Yeah, so we have two brands in our portfolio, Weather Underground, which is more of a challenger brand. It's very data rich, and visualizes data in a number of different ways, that a certain user group really loves. So if you're a weather geek, as we call them, an avid aficionado of weather, and you really want to really get in there and understand what's happening, and look at the data, then Weather Underground is a platform. >> So for users to tie into, to put up weather stations, and other things that might be relevant. >> Exactly so, we started out in 2001, originally the first IOT implementation at the consumer level, connected devices. Where you could connect a personal weather station, put one in your back yard, and connect it to our platform, and feed hyper-local data into our network. And then we feed that into our forecast, to improve that, and actually validate whether the forecast is right or not, based on what people have at home. And we've hit a recent milestone. We've got over 250,000 personal weather stations connected to the network, which we are super thrilled about. And now, what we are doing is, we are extending that network to other connected devices, and air quality is a big topic right now, in other parts of the world, especially in Asia, where air quality is not always where it should be, that's a big thing we think we can... >> That's a big innovation opportunity for you, I mean, you point out the underground product was part of maker-culture, people do-it-yourself weather stations, evolve now into really strong products. That same dynamic could be used for air control, not just micro-climates. >> Exactly, yeah. >> In California, we had a problem this week. >> Exactly, California is a good example, really topical, where cities may have had great air quality, and all of the sudden the environment changes, and you want to know, what is it like? What is the breathing quality like outside right now? And you can come to our network and see that. And we're growing the air quality sensors every month, it's only been up a few months right now, so that's expanding quite well. >> So for the folks that don't know, The Weather Channel back end, has a huge data-driven product. I don't want to get into that piece, because we've talked about it. Go to youtube.com/siliconangle, search Weather Company. You'll see all our great videos from the IBM events, that are out, if you want the detail. But I do want to ask you, what's really happening with you guys, there's two things. One is, it's an app and content for devices, like Samsung is using. And two, essentially you're an IOT network. Sensors are sensors, whether they're user-generated, or user-populated, you guys are deploying a serious IOT capability. >> Absolutely, it's one of the reasons that IBM acquired The Weather Company, which houses the brands of Weather Underground and The Weather Channel, is that we have this fantastic infrastructure, this IOT infrastructure, ingesting large amounts of data, processing it, and then serving it back out to consumers at scale globally. >> What are you guys doing there with Samsung? Anything just particular in the IOT side, or? >> We've got a couple of initiatives going on with Samsung, a few I can't mention right now, but stay tuned. Some really cool things in the connect-at-home, that we're excited about, that builds on some of the work... >> Nest competitor? >> Not exactly a Nest competitor. Think more kitchen. >> Kitchen, okay. >> Think more kitchen. >> We had the goods, cooking in the kitchen, from our previous guest. So the question is, IOT personal, I get that. What else is going on with IOT, with you guys, that you can share? Lifestyle, in the home is great, but... >> So again, going back to how do we help people make better decisions, now that we are collecting data from not just personal weather stations, but air quality monitors, we are collecting it from cars, we are collecting it from the cell phone. We are really able to ingest data at scale, and when you're doing that, we've got hundreds of thousands of data sets that we are feeding into our models, when you do that, we've solved the computing challenge, now we are applying machine-learning and artificial intelligence to process this and extract insights. To validate data sets, in our forecast, and then deliver that back to the end user. >> One of the tech geek themes we talk about all of the time is policy-based something. Programming, setting the policy. So, connecting the dots from what you're saying is, I'm driving my car, and I want to know if it's hot, or the road temperature. I might want to know if I'm running too fast, and my sensor device on me wants to impact the weather, for comfortable breathing for me, for instance. The lifestyle impacts, the content of data, is not just watching a video on The Weather Channel. >> No, it's not. >> So this is a new user experience. It's immersive, it's lifestyle-oriented, it's relevant. What are some of the products you're doing with Samsung, that can enable this new user expectation? >> One of the products that we have right now, we we're one of the initial partners for the Made for Samsung program, is, we've got calendar integration in our app. So now we know, if you've got a meeting coming up, and you need to travel to get there, maybe there's a car trip involved, we know, obviously, the forecast. We know what traffic might be, and we can give you heads up, an alert, that says, hey you might want to leave 15 minutes early for that meeting coming up. That's in the Samsung product right now, which is really, again, helping people make better decisions. So we've got a lot of examples like that. But again, the calendar integration in the Made for Samsung app is really exciting. We recently announced, in fact I think it was this morning, we announced integration with Trip Advisor. So similarly, if we see time on your calendar, and the weather is fine for the weekend, we might suggest outdoor activities for you to go and explore, using Trip Advisor's almost one-billion library of events that they have. >> What's the coolest thing you guys are working on right now? >> Oh, that's a very long list. I say that I'm probably the luckiest guy in IBM right now, because I get to work with millions of consumers, we reach 250 million consumers a month, and I'm also bringing Watson to consumers, and artificial intelligence, which is a unique challenge to solve. Introducing consumers to a new paradigm of user interaction and abilities. So, I think the most exciting thing is taking artificial intelligence and machine-learning, and bringing that to consumers at scale, and solving some of the challenges there. >> Well contratulations. I'm a big fan of IBM, what they're doing with weather data, The Weather Company, The Weather Channel. Bringing that data and immersing it into these new networks that are being created, new capabilities, really helps the consumer, so. Hope to see you at the Think conference coming up next year. >> Yes, we are excited about that, and stay tuned, we may have some more exciting stuff to unveil. >> Make sure our writers get ahold of it, break the stories. It's The Cube, bringing you the data. The weather's fine in San Francisco today. I'm John Farrier with The Cube. More live from San Francisco, from the SDC Samsung Developer Conference, after this short break. (electronic music)