>> Live from San Jose, in the heart of Silicon Valley, it's theCube. Covering Datawork Summit 2018. Brought to you by Hortonworks. >> Welcome back to theCube's live coverage of Dataworks here in San Jose, California. I'm your host, Rebecca Knight along with my cohost James Kobielus. We have with us Vimal Endiran. He is the Global Business Data Group Ecosystem Lead, at Accenture. He's coming to us straight from the Motor City. So, welcome Vimal. >> Thank you, thank you Rebecca. Thank you Jim. Looking forward to talk to you for the next ten minutes. >> So, before the cameras were rolling we were talking about how data veracity and how managers can actually know that the data that they're getting, that they're seeing, is trustworthy. What's your take on that right now? >> So, in the today's age the data is coming at you in a velocity that you never thought about, right. So today, the organizations are gathering data probably in the magnitude of petabytes. This is a new normal. We used to talk about gigs and now it's in petabytes. And the data coming in the form of images, video files, from the edge, you know edge devices, sensors, social media and everything. So, the amount of data, this is becoming the fuel for the new economy, right. So that companies, who can find a way to take advantage and figure out a way to use this data going to have a competitive advantage over their competitors. So, for that purpose, even though it's coming at that volume and velocity doesn't mean it's useful. So the thing is if they can find a way to make the data can be trustworthy, by the organization, and at the same time it's governed and secured. That's what's going to happen. It used to be it's called data quality, we call it when the structure it's okay, everything is maintained in SAP or some system. It's good it's coming to you. But now, you need to take advantage of the tools like machine learning, artificial intelligence, combining these algorithms and tool sets and abilities of people's mind, putting that in there and making it somewhat... Things can happen to itself at the same time it's trustworthy, we have offerings around that Accenture is developing place... It differs from industry to industry. Given the fact if the data coming in is something it's only worth for 15 seconds. After that it has no use other than understanding how to prevent something, from a sense of data. So, we have our offerings putting into place to make the data in a trustworthy, governed, secured, for an organization to use it and help the organization to get there. That's what we are doing. >> The standard user of your tools is it a data steward in the traditional sense or is it a data scientist or data engineer who's trying to, for example, compile a body of training data for use in building and training machine learning models? Do you see those kinds of customers for your data veracity offerings, that customer segment growing? >> Yes. We see both sides pretty much all walk of customers in our life. So, you hit the nail on the head, yes. We do see that type of aspects and also becoming, the data scientists you're also getting another set of people, the citizen data scientist. The people--- >> What is that? That's a controversial term. I've used that term on a number of occasions and a lot of my colleagues and peers in terms of other analysts bat me down and say, "No, that demeans the profession of data science by calling it..." But you tell me what how Accenture's defining that. >> The thing is, it's not demeaning. The fact is to become a citizen data scientist you need the help of data scientists. Basically, every time you need to build a model. And then you feed some data to learn. And then have an outcome to put that out. So you have a data scientist creating algorithms. What a citizen data scientist means, say if I'm not a data scientist, I should be able to take advantage of a model built for my business scenario, feed something data in, whatever I need to feed in, get an output and that program, that tool's going to tell me, go do this or don't do this, kind of things. So I become a data scientist by using a predefined model that's developed by an expert. Minds of many experts together. But rather than me going and hiring hundred experts, I go and buy a model and able to have one person maintain or tweak this model continuously. So, how can I enable that large volume of people by using more models. That's what-- >> If a predictive analytics tool that you would license from whatever vendor. If that includes prebuilt machine learning models for a particular tasks in it does that... Do you as a user of that tool, do you become automatically a citizen data scientist or do you need to do some actual active work with that model or data to live up to the notion of being a citizen data scientist? >> It's a good question. In my mind, I don't want to do it, my job is something else. To make something for the company. So, my job is not creating a model and doing that. My job is, I know my sets of data, I want to feed it in. I want to get the outcome that I can go and say increase my profit, increase my sales. That's what I want to do. So I may become a citizen data scientist without me knowing. I won't even be told that I'm using a model. I will take this set of data, feed it in here, it's going to tell you something. So, our data veracity point of view, we have these models built into some of platforms. That can be a tool from foreign works, taking advantage of the data storage tool or any other... In our own algorithms put in that helps you to create and maintain the data veracity to a scale of, if you say one to five, one is being low, five is being bad, to maintain at the five level. So that's the objective of that. >> So you're democratizing the tools of data science for the rest of us to solve real business problems. >> Right. >> So the data veracity aside, you're saying the user of these tools is doing something to manage, to correct or enhance or augment the data that's used to feed into these prebuilt models to achieve these outcomes? >> Yes. The augmented data, the feed data and the training data it comes out with an outcome to say, go do something. It tells you to perform something or do not perform. It's still an action. Comes out with an action to achieve a target. That's what it's going to be. >> You mention Hortonworks and since we are here at Dataworks and the Hortonworks show, tell us a little bit about your relationship with that company. >> Definitely. So Hortonworks is one of our premiere strategic partners. We've been the number one implementers, the partners for last two years in a row, implementing their technology across many of our clients. From partnership point of view, we have jointly developed offerings. What Accenture is best at, we're very good at industry knowledge. So with our industry knowledge and with their technology together what we're doing is we're creating some offerings that you can take to market. For example, we used to have data warehouses like using Teradata and older technology data warehouses. They're still good but at the same time, people also want to take the structured, unstructured data, images files and able to incorporate into the existing data warehouses. And how I can get the value out of the whole thing together. That's where Hortonworks' type of tools comes to play. So we have developed offerings called Modern Data Warehouse, taking advantage of your legacy systems you have plus this new data coming together and immediately you can create an analytics case, used case to do something. So, we have prebuilt programs and different scripts that take in different types of data. Moving into a data lake, Hortonworks data lake and then use your existing legacy data and all those together help you to create analytics use cases. So we have that called data modernization offering, we have one of that. Then we have-- >> So that's a prebuilt model for a specific vertical industry requirements or a specific business function, predictive analytics, anomaly detection and natural language processing, am I understanding correctly? >> Yes. We have industry based solutions as well but also to begin with, the data supply chain itself. To bring the data into the lake to use it. That's one of the offerings we play-- >> ...Pipeline and prepackaged models and rules and so forth. >> Right, prepackaged data ingestion, transformation, that prepackaged to take advantage with the new data sets along with your legacy data. That's one offering called data modernization offering. That to cloud. So, we can take to cloud. Hortonworks in a cloud it can be a joure, WS, HP, any cloud plus moving data. So that's one type of offering. Today actually we announced another offering jointly with Hortonworks, Atlas and Grainger Tool to help GDPR compliance. >> Will you explain what that tool does specifically to help customers with GDPR points. Does it work out of the box with Hortonworks data stewards studio? >> Well, to me I can get your answers from my colleagues who are much more technical on that but the fact is I can tell you functionally what the tool does is. >> Okay, please. >> So you, today the GDPR is basically, there's account regulations about you need to know about your personal data and you have your own destiny about your personal data. You can call the company and say, "Forget about me." If you are an EU resident. Or say, "Modify my data." They have to do it within certain time frame. If not they get fined. The fine can be up to 4% of the company's... So it's going to be a very large fine. >> Total revenue, yeah. >> So what we do is, basically take this tool. Put it in, working with Hortonworks this Atlas and Granger tool, we can go in and scan your data leak and we can scan at the metadata level and come into showcase. Then you know where is your personal data information about a consumer lies and now I know everything. Because what used to be in a legacy situation, the data originated someplace, somebody takes it and puts a system then somebody else downloads to an X file, somebody will put in an access data base and this kind of things. So now your data's pulling it across, you don't know where that lies. In this case, in the lake we can scan it, put this information, the meta data and the lineage information. Now, you immediately know where the data lies when somebody calls. Rebecca calls and says, "No longer use my information." I exactly know it's stored in this place in this table, in this column, let me go and take it out from here so that Rebecca doesn't exist anymore. Or whoever doesn't exist anymore. So that's the idea behind it. Also, we can catalog the entire data lake and we know not just personal information, other information, everything about other dimensions as well. And we can use it for our business advantage. So that's what we announced today. >> We're almost out of time but I want to finally ask you about talent because this is a pressing issue in Silicon Valley and beyond in really the tech industry, finding the right people, putting them in the right jobs and then keeping them happy there. So recruiting, retaining, what's Accenture's approach? >> This area, talent is the hardest one. >> Yes! >> Thanks to Hortonworks and Hortonworks point of view >> Send them to Detroit where the housing is far less expensive. >> Not a bad idea. >> Exactly! But the fact is-- >> We're both for Detroiters. >> What we did was, Hortonworks, Accenture has access to Hortonworks University, all their educational aspects. So we decided we're going to take that advantage and we going to enhance our talent by bringing the people from our... Retraining the people, taking the people to the new. People who know the legacy data aspects. So take them to see how we take the new world. So then we have a plan to use Hortonworks together the University, the materials and the people help, together we going to train about 500 people in different geos, 500 per piece and also our the development centers in India, Philippines, these places, so we have a larger plan to retrain the legacy into new. So, let's go and get people from out of the college and stuff, start building them from there, from an analyst to a consultant to a technical level and so that's the best way we are doing and actually the group I work with. Our group technology officer Sanjiv Vohra, he's basically in charge of training about 90,000 people on different technologies in and around that space. So the magnet is high but that's our approach to go and try and people and take it to that. >> Are you training them to be well rounded professionals in all things data or are you training them for specific specialties? >> Very, very good question. We do have this call master data architect program, so basically in the different levels after these trainings people go through specially you have to do so many projects, come back have an interview with a panel of people and you get certified, within the company, at certain level. At the master architect level you go and help a customer transform their data transformation, architecture vision where do you want to go to, that level. So we have the program with a university and that's the way we've taken it step by step to people to that level. >> Great. Vimal, thank you so much for coming on theCube. >> Thank you. >> It was really fun talking to you. >> Thank you so much, thank you for having me. Thank you. >> I'm Rebecca Knight for James Kobielus we will have more, well we actually will not be having any more coming up from Dataworks. This has been the Dataworks show. Thank you for tuning in. >> Signing off for now. >> And we'll see you next time.

>> Announcer: From around the globe, it's theCUBE! With digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back everyone to theCUBE's coverage of IBM Think 2021. I'm John Furrier, host of theCUBE. We're here with Mark Foster, Senior Vice President of IBM Services and IBM's Global Business Services. It's a global landscape, the world's changing, it's going hybrid. Mark, thanks for coming on theCUBE. >> Great to see you, John, good to be with you. >> You know, the theme this year is all about hybrid cloud. Global transformation is the innovation at scale. That's the discussion, that's the way I see it. The question I have for you to start right away is how has the last year in particular changed businesses as they're leveraging the tech? You know, they want to solve their critical problems and transform themselves, the pandemic has forced them to look at this. How has this last year changed the way businesses are leveraging tech? >> Well, there's definitely been an acceleration in the digital transformations across all of our clients around the world. They have been compelled to leverage technology to connect with their customers in these unique times. They've been forced to use technology tools to allow their teams to connect and operate around the world. And all of this has reinforced also the opportunity to leverage things like extreme automation, AI, and the leverage of things like the cloud to deal with the virtual and more remote nature of working around the world. >> How much of the change last year do you think's going to to be temporary or long lasting. What's not going to be given up? (laughs) What are people realizing? Is it temporary or is it long lasting? What's your take? >> Well, I think we have to recognize that we are moving into a genuinely hybrid world, well, hybrid insofar as I think that some of the lessons we've learned over this past period are going to durably change the way we work, but we're also going to have a certain amount of back to the future, as well, as we try and put back some of the aspects of physical interaction, the ways of actually bringing empathy, creativity together through being together in groups. But I do think also we're going to take a number of these areas of acceleration and they're going to be extrapolated out to genuinely lead to an acceleration of what might've taken place over over five years taking place over a lot shorter period. >> You know, I think that group dynamic is really a big deal. I think that's going to be something that, to me, jumps out at this transformation. People want to work together. They want to be part of something, totally right on. With that, I got to ask you, now that we have this kind of new virtual experience, we're remote, we're not in person, wish we were, but even when we are in personal, it'll still be hybrid virtual experience events means we're still going to act as a group. This kind of brings up the idea of a virtual enterprise. You kind of mentioned that. What you mean and how do you define a virtual enterprise? >> Well, I think a virtual enterprise for us is an extension of the thought process we've had before around how technology is transforming the way all businesses operate. If you do apply, you know, the power of technology to build new business platforms and think about new ways of applying technology to transform your business processes, you think about the way that all of us are reinventing the relationship between people and technology in our organizations, the virtual enterprise just takes that to the next level. It recognizes that if you are able to take a location out of the equation, if you're able to leverage ecosystems more completely through connecting through networks of organizations, all of this extends the vision that we have of how the cognitive enterprise of the past comes to life. And we create this even more connected, even more expansive vision of business which is of course able to leverage technology within its own four walls. It's able to leverage it powerfully with its business partners. But then finally, it's about how you create the platforms upon which you create new ecosystems for competition and new markets that can be created in that way. >> That's really compelling insight right there. I think that's right on the money. I have to ask you, what do you think the differentiating characteristics are for this enterprise? What's going to be the differentiator, what's going to make it work? What do you need- >> I think what's going to make it work first of all, I think we think there's going to be a sort of a golden thread of what you might call an extended intelligent workflow that runs through the enterprise and its partners. And the power of that sort of thread of core processes and core differentiation to be brought to life by the mutual leverage of technology through partnerships is going to be a hugely powerful. So therefore all the partners' ability to embrace those technologies to embrace the vision for how those workflows come together is going to be very important. I think it's going to be very important that actually the ecosystem and its success becomes the strategy of the of the participants as opposed to being something that they happened to be going along with. So it becomes the strategy of the organization. And I think finally, there's a huge amount around here around how you leverage and think about the power of your people, the culture that you create to be inclusive and expansive in terms of applying new talent, building new talent, to allow this virtual enterprise to thrive. >> That's actually brilliant. You know, ecosystem is part of it, not an afterthought or a marketing gimmick. It's got to be part of it, that's awesome. Let's bring that to the next level. The role of the ecosystems are taking a bigger role for you, as you said, what specifically can you point to that has a change that's made in the ecosystem that you can point to, says that's an impactful change, this is a table stake, this is a guaranteed continuing practice. Can you give an example? >> Well, I think what we can see around the world in terms of how the world has solved for something like you're getting vaccines created and distributed on the back of the COVID crisis, that's taken an ecosystem coming together to work in completely different ways in an accelerated way to deliver on very big outcomes. Well, we can also see, you know, clients who are developing their strategies to try and connect the dots across different players to position their business as a platform upon which others bring their parts, their organizations to bear. And I think that we can see therefore that this idea of ecosystems is being used to solve really big problems, but it's also potentially a model that can be used to actually define really big market opportunities as well. And when you can connect the dots and you can expand your market footprint by combining with other key players at scale and also create a way that smaller organizations can come and sit upon the platforms that you create and leverage those capabilities, then the opportunity to actually use that to really expand your horizons of where your business can go are very real. >> You know, that's a really interesting, mind blowing concept. You think about the idea of a network effect or ecosystems, and integration, and collective intelligence. These are paradigms that have been around for awhile, at least past 10 years. It was the Holy Grail, let's hope we get to that. It seems like that's happening right now. And I think more than ever, it can be harnessed. And so I think you starting to see that with the hybrid cloud and it's not just tech, it's societal impact, it's impacting people, their jobs, and their ability to contribute and work. So this is a huge concept. So really excited this conversation. I guess the next question I have for you, Mark, is how do you bring clients this value? How do they create value? And how do they take this and transform their business with it? What's the playbook? >> Well, I think for clients, the first thing for them to recognize is to understand that this is the world that they are operating in. And I think that from a playbook point of view, the first thing I would say is you do need to think about which ecosystems do I want to play in? How do I think I could win by being a part of, or shaping an ecosystem? I think, secondly, there's the opportunity to think about how you use all the data that's out there in the world to be a stronger source of innovation across an ecosystem, to think about how your products and services could be modernized to succeed in that world. How you build those innovations into this new vision of an extended workflow or process view that binds the players of your ecosystem together. And you're really thinking about how to reinvent the way work gets done. Apply automation, apply AI, apply blockchain, apply IoT to transform those workflows is a massive, massive opportunity. To recognize that actually by the power of that, you're able to have significantly more impact than before. So make sure you're setting your ambitions high enough around the impact the change you're trying to drive can bring, and then I think also just making sure you're thinking all the time about what this means for the culture of your organizations, the workforce you want to connect with, how you want to access talent and bring it to bear across this new extended value chain? You know, who do you need to employ, versus who do you need to contract with, versus who do you need to make sure are participating in the processes that you're driving? And then finally, how do you make sure that you have the infrastructure, and the systems, and the applications that are open enough to allow you to really bring this vision to life? So the underlying hybrid cloud, hybrid architectures that you have and the networks you have that bind you together become fundamental. >> That's awesome. Great insight there. I guess my final question is how has your personal outlook changed in the past year when everyone is working from home? And now we're starting to see the pandemic, you know, light at the end of the tunnel from this pandemic, once we emerge out of it, people want to have a growth strategy, want to get back to real life. Any words of advice for our viewers on your personal outlook and as we come out of the pandemic and they can participate- >> Well, I think the first thing to recognize is we all have a collective wish around the world, probably for the first time for a long time, I think pretty much most people in humanity are sharing a shared view about a desire to have a more expansive horizon than the one that's outside the window of their kitchen, which I'm looking out of right now, and being able to get out and about, and engage in some more aspects that of normal life. And I do think that we're all looking forward to that opportunity. I think we're going to have to recognize that we're probably all going to also adapt our behaviors, going forward, but there's almost an enormous amount of exciting things that we've all got pent up we want to go and do, and I think, you know, the critical thing for us all is to hopefully approach that world safely. But at the same time, recognize that there is hope, we are working our way through this as a world. And as long as we try and make sure we do that in a way that is actually equitable, and that we do make sure that all boats are lifted as we return here, then I think that's a really positive view of how the future will be for all of us. So we should all look forward to that. >> Mark, it's great to have you on theCUBE. I love the insight, I love your message. It's right on, it's relevant, and super cool because that's what people want. They want to collaborate and be with people. I guess with the final minute we have left, share an observation from the past year and a half. Something that surprised you that happened in the industry, something that you didn't expect or something that you did expect that's positive that we can look to and say, "That's a good thing, we want to double down on that." >> Well, I think the positive thing that I think we can double down on is that we have all actually learned to be perhaps more open to interacting with people who we wouldn't otherwise have interacted with through this medium, that actually I have found that I've broadened my network of people that I've been engaging with through the fact that it has been actually relatively easy to connect even at high levels with people, but all the people have been able to connect in a strange way with a bigger group of connections than you would have done through the normal physical constraints of flying somewhere, seeing someone, meeting someone, and how you use your time to do that. So I would say one of the positive things is actually how open people have been to start new relationships over this virtual medium. Of course, the trick is going to be, can we build on those virtual relationships we've created and make them more sustainable once we're back to a more normal life and they become, you know, the new friendships, the new business relationships and networks that we can thrive on for the future? >> That's genius, love it. I agree. CUBE Virtual's here doing it. We're trying content, community, collaboration, connection, friendships, new things, touch someone with a click and engage. Mark Foster here, clicking into our CUBE Virtual for IBM Think. I'm John Furrier, thanks for watching. (upbeat music) ♪ Dah, deeah ♪ ♪ Dah, dee ♪ (chimes ringing)

>> Narrator: From around the globe, it's theCUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome back to IBM Think 2021. This is the cubes ongoing coverage, where we go out to the events, we extract the signal from the noise, of course virtually in this case, now we're going to talk about ecosystems, partnerships and the flywheel they deliver in the technology business. And with me are Jason Kelly, he's the general manager global strategic partnerships, IBM global business services and Mani Dasgupta, who's the vice president of marketing for IBM global business services. Folks it's great to see you again. I wish we were face-to-face, but this'll have to do. >> Good to see you Dave and same, I wish we were face to face, but we'll, we'll go with this. >> Soon. We're being patient. Jason, let's start with you. You, you have a partner strategy. I wonder if you could sort of summarize that and tell us more about it. >> So it's interesting that we start with the strategy because you said, we have a partner strategy Dave and I'd say that the market has dictated back to us, a partner strategy. Something that we it's not new, we didn't start it yesterday. It's something that we continue to evolve in and build even stronger. This thought of a, a partner strategy is it... Nothing's better than the thought of a partnership and people say, "Oh, well, you know you got to work together as one team and as a partner." And it sounds almost as a one to one type relationship. Our strategy is much different than that Dave and our execution is even better. And that, that execution is focused on now the requirement that the market, our clients are showing to us and our strategic partners, that one... One player, can't deliver all their needs. They can't design solution and deliver that from one place. It does take an ecosystem to the word that you called out, this thought of an ecosystem. And our strategy and execution is focused on that. And the reason why I say it evolves is because the market will continue to evolve and this thought of being able to look at a client's, let's call it a workflow, let's call it a value chain from one end to the other, wherever they start their process to wherever it ultimately hits that end user, it's going to take many players to cover that. And then we as IBM want to make sure that we are the general contractor of that capability with the ability to convene the right strategic partners, bring out the best value for that outcome, not just technology for technology's sake, but the outcome that the end client is looking for so that we bring value to our strategic partners and that end client. >> I think about when you talk about the, the value chain, you know, I'm imagining, you know the business books years ago where you see the conceptual value chain, you could certainly understand that and you could put processes together to connect them and now, you've got technology. I think of APIs. It's, it's, it really supports that everything gets accelerated and, and Mani, I wonder if you could address sort of the the go to market, how this notion of ecosystem which is so important is impacting the way in which you go to market. >> Absolutely. So modern business, you know demands a new approach to working. The ecosystem thought that Jason was just alluding to, it's a mutual benefit of all these companies working together in the market. It's a mutual halo of the brands. So as responsible, you know, for the championship of, of the IBM and the Global Business Services brand, I am very, very interested in this mutual working together. It should be a win, win, win as we say in the market. It should be a win for, our clients first and foremost, it should be a win for our partners and it should be a win for IBM, and we are working together right now on an approach to bring this go-to-market market strategy to life. >> So I wonder if we can maybe talk about, how this actually works and, and pulling some examples. You must have some favorites that we can touch on. Is that, is that fair? Can we, can we name some names? >> Sure. Names always work in debut writing. It's always in context of reality that we can talk about, as I said, this execution and not just a strategy and I'll, I'll start with probably what's right in the front of many people's minds. As we're doing this virtually because of what, because of an unfortunate pandemic. Just disastrous loss of life and things that have taken us down a path we go, whoa! (clears throat) How do we, how do we address that? Well, anytime there's a tough task IBM raises its hand first. You know, whether it was putting a person on the moon and bringing them home safely, or standing up a system behind the current social security administration, you know during the depression, you pick it. Well here we are now and why not start with that as an example because I think it calls out just what we mentioned here. First, Dave, this thought of, of an ecosystem because the first challenge, how do we create and address the biggest data puzzle of our lives which is, how do we get this vaccine created in record time? Which it was. The fastest before that was four years. This was a matter of months. So Pfizer created the first one out and then had to get it out to distribution. Behind that is a wonderful partner of ours, SAP trying to work with that. So us working with SAP, along with Pfizer in order to figure out, how to get that value chain and some would say supply chain, but I'll, I'll address that in a second, but there's many players there. And, and so we were in the middle of that with Pfizer committed to saying, how do we do that with SAP? So now you see players working together as one ecosystem. But then think about the ecosystem that that's happening where you have a federal government agency. You have Ms. State, Alocal, you have healthcare life science industry, you have consumer industry. Oh, wait a second Dave, this is getting very complicated, right? Well, this is the thought of convening in the ecosystem. And this is what I'm telling you is, is our execution and it, it has worked well and so it's, it's it's happening now and we see it still developing and being, being, you know very productive in real time. But then, I said there was a another example and that's with me, you, Mani, whomever. You pick the consumer. Ultimately we are that outcome of, of the value chain. That's why I said I don't want to just call it a supply chain because at the end is, is, is someone consuming and in this case we need a shot. And so we partnered with Salesforce, IBM and Salesforce saying, wait a minute that's not a small task. It's not just get, get the content there and put it in someone's arm. Instead there's scheduling that must be done. There's follow up, and entire case management like system. Salesforce is a master at this. So work.com team with IBM we said now, let's get that part done for the right type of UI UX capability, that user experience, user interaction interface and then also, in bringing another player in the ecosystem. One of ours, Watson health, along with our blockchain team, we brought together something called a digital health pass. So, I've just talked about two ecosystems where multiple ecosystems working together. So you think of an ecosystem of ecosystems. I call it out blockchain technology and obviously supply chain, but there's also AI, IOT. So you start to see where, look, this is truly an orchestration effort that has to happen with very well designed capability and so of course we master in design and tying that, that entire ecosystem together and convening it so that we get to the right outcome. You, me, Mani are all getting the shot, being healthy. That's a real-time example of us working with an ecosystem and teaming with key strategic partners. >> You know Mani, I, I, I mean, Jason you're right. I mean this pandemic's been horrible. I have to say, I'm really thankful it didn't happen 20 years ago because it would have been like, okay here's some big PCs and a modem and go ahead and figure it out. So, at least, the tech industry has saved the business. I mean, with, and earlier we mentioned AI, automation, data, you know, even things basic things like, security at the end point. I mean so many things and you're right. I mean, IBM in particular, other large companies, you mentioned, SAP who have taken the lead and it's really, I, I don't, I Mani I don't think the tech industry gets enough credit but I wonder if there's some of your favorite partnerships that you can talk about. >> Yeah. So I'm going to, I'm going to build on what you just said, Dave. IBM is in this unique position amongst this ecosystem. Not only the fact that we have the world's leading most innovative technologies to bring to bear, but we also have the consulting capabilities that go with it. Now to make any of these technologies work towards the solution that Jason was referring to in this digital health pass, it could be any other solution, you would need to connect these disparate systems sometimes make them work towards a common outcome to provide value to the clients. So I think our role as IBM within this ecosystem is pretty unique in that we are able to bring both of these capabilities to bear. In terms of, you know, you asked about favorites. There are, this is really a co-opetition market where everybody has products, everybody has services. The most important thing is how are we, how are we bringing them all together to serve the need or the need of the hour in this case? I would say one important thing in this, as you observe how these stories are panning out. In an ecosystem, in a partnership, it is about the value that we provide to our clients together. So it's almost like a "sell with" model from, from a go-to-market perspective. There is also a question of our products and services being delivered through our partners, right? So think about this, the span and scope or what we do here and so that's the sell through, and then of course we have our products running within our partner companies and our partner products for example, Salesforce, running within IBM. So this is a very interesting and a new way of doing business. I would say it's almost like the, the modern way of doing business with modern IT. >> Well, and you mentioned co-opetition. I mean, I look at it, you're, you're, you're part of IBM that will work with anybody 'cause you're your customer first. Whether it's AWS, Microsoft, I mean, Oracle is a, is a, is a really tough competitor but your customers are using Oracle and they're using IBM. So I mean, as a, those are some, you know good examples I think of your point about co-opetition. >> Absolutely. If you pick on any other client, I'll mention in this case, Delta. Delta was working with us on moving, being more agile and now this pandemic has impacted the airline sector particularly hard, right? With travel stopping and anything. So they are trying to get to a model which will help them scale up, scale down be more agile, be more secure be closer to their customers to try and understand how they can provide value to their customers and customers better. So we are working with Delta on moving them to cloud, on the journey to cloud. Now that public cloud could be anything. The, the beauty of this model in a hybrid cloud approach is that you're able to put them on red hat openshift, you're able to do and package the, the services into microservices kind of a model. You want to make sure all the applications are running on a... On a portable almost a platform agnostic kind of a model. This is the beauty of this ecosystem that we are discussing as the ability, to do what's right for the end customer at the end of the day. >> How about some of the like SaaS players? Like some of the more prominent ones. And we, we, we watched the ascendancy of ServiceNow and Workday, you mentioned Salesforce. How do you work with those guys? Obviously there's an AI opportunity but maybe you could add some color there. >> So I like the fact Dave that you call out the different hyperscalers, for example whether it's AWS, whether it's Microsoft, knowing that they have their own cloud instances, for example. And when you, when you mentioned, hey, had this happened a long time ago, you know you started talking about the, the heft of the technology. I started thinking of all the, the the truck loads of servers or whatever they, you know they'd have to pull up, we don't need that now because it can happen in the cloud. And you don't have to pick one cloud or the other. And so when people say hybrid cloud, that's what comes out. You start to think of what I call, I call, you know, a hybrid of hybrids because I told you before, you know these roles are changing. People aren't just buyers or suppliers. They're both. And then you start to say, what are, what are different people supplying? Well, in that ecosystem, we know there's not going to be one player. There's going to be multiple. So we partner by doing just what Mani called out as this thought of integrating in hybrid environments on hybrid platforms with hybrid clouds, multi-clouds. Maybe I want something on my premises, something somewhere else. So in giving that capability, that flexibility, we empower and this is what it's doing is that co-opetition. We empower our partners, our strategic partners. We want them to be better with us and this is just the thought of, you know, being able to actually bring more together and move faster. Which is almost counter-intuitive. You're like, wait a minute, you're adding more players but you're moving faster. Exactly. Because we have the capability to integrate those, those technologies and get that outcome that Mani mentioned. >> I would add to one Jason, you mentioned something very, very interesting. I think if you want to go just fast, you go alone. But if you want to go further, you go together. And that is the core of our point of view, in this case is that we want to go further and we want to create value that is long lasting. >> What about like, so I get the technology players and there's maybe things that you do, that others don't or vice versa so the gap fillers, et cetera. But what about, how, maybe customers do they get involved? Perhaps government agencies, maybe they be, they they be customer or an NGO as another example. Are they part of this value chain part of this ecosystem? >> Absolutely. I'll give you... I'll stick with the same example when I mentioned a digital health pass. That digital health pass, is something that we have as IBM and it's a credential. Think of it as a health credential, not a vaccine passport cause it could be used for a test for, a negative test on COVID, it could be used for antibodies. So if you have this credential it's something that we as IBM created years back and we were using it for learning. When you think of, you know getting people certifications versus a four-year diploma. How do we get people into the workforce? That was what was original. That was a Jenny Rometty thought. Let's focus on new collar workers. So we had this asset that we'd already created and then said wait, here's a place for it to work with, with health, with validation verification on someone's option, it's optional. They choose it. Hey, I want to do it this way. Well, the state of New York said that they want it to do it that way and they said, listen we are going to have a digital health pass for all of our, all of our New York citizens and we want to make sure that it's equitable. It could be printed or on a screen and we want it to be designed in this way and we want it to work on this platform and we want to be able to, to work with these strategic partners, like Salesforce and SAP, Alocal. I mean, I can just keep going. And we said, "Okay, let's do this." And this is this thought of collaboration and doing it by design. So we haven't lost that Dave. This only brings it to the forefront just as you said. Yes, that is what we want. We want to make sure that in this ecosystem, we have a way to ensure that we are bringing together, convening not just point products or different service providers but taking them together and getting the best outcomes so that that end user can have it configured in the way that they, they want it. >> Guys, we've got to leave it there but it's clear you're helping your customers and your partners on this, this digital transformation journey that we already, we all talk about. You get this massive portfolio of capabilities, deep, deep expertise. I love the hybrid cloud and AI focus. Jason and Mani, really appreciate you coming back in the cubes. Great to see you both. >> Thank you so much, Dave. Fantastic. >> Thank you Dave. Great to be with you. >> All right, and thank you for watching everybody. Dave Vellante, for the cube and in continuous coverage of IBM Think 2021, the virtual edition. Keep it right there. (poignant music) (bright uplifting music)

>> (lively music) >> Narrator: From around the globe it's theCUBE, with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome to theCUBE's coverage of IBM Think 2021, the digital event experience. I'm your host, Lisa Martin. I've got an alumni joining me and a brand new guest to the CUBE please welcome Paul Papas, the Global Managing Partner, for IBM Global Business Services, this is transformation services. Paul, welcome back to the virtual CUBE. >> Thanks Lisa great to be here with you today. And Dominique Dubois is here as well. She is the Global Strategy and Offerings Leader in business transformation services or BTS at IBM. Dominique, welcome to the program. >> Thanks Lisa, great to be here. So, we're going to be talking about accelerating business transformation with intelligent workflows. We're going to break through all that, but Paul we're going to start with you. Since we last got together with IBM, a lot has changed so much transformation, so much acceleration of transformation. Talk to me from your perspective, how have you seen the way that businesses running change and what some of the changes in the future are going to be? >> Well, you hit on two key words there Lisa and thanks so much for that question. Two key words that you hit on were change and acceleration. And that's exactly what we see. We were seeing this before the pandemic and if anything, with the pandemic did when things started started kind of spreading around the world late or early last year, around January, February timeframe we saw that word acceleration really take hold. Every one of our clients were looking for new ways to accelerate the change that they had already planned to adapt to this new, this new normal or this new abnormal, depending on how you view it. In fact, we did a study recently, an IBV study that's our Institute of Business Value and found that six out of 10 organizations were accelerating all of their transformation initiatives they had already planned. And that's exactly what we're seeing happening right now in all parts of the world and across all industries. This acceleration to transform. >> So, one of the things that we've talked about for years, Paul, before the pandemic was even a thing, is that there was a lot of perceived technical barriers in terms of like the tech maturity for organizations and employees being opposed to change. People obviously it can be a challenge. They're used to doing things the way they are. But as you just said, in that IBV survey, nearly 60% of businesses say we have to accelerate our transformation due to COVID, probably initially to survive and then thrive. Talk to me about some of those, those barriers that were there a little over a year ago and how businesses 60 plus percent of them have moved those out of the way. >> You know at IBM we've got a 109 year history of being a technology innovation company. And the rate of pace of technical change is always increasing. It's something that we love and that we're comfortable with. But the rate and pace of change is always unsettling. And there's always a human element for change. And the human element is always the rate, the rate setter in terms of the amount of change that you can have in an organization. Our former chairman Ginni Rometty, used to say that growth and comfort cannot co-exist. And it's so true because changing is uncomfortable. It's unsettling. It can be, it can be nerve-racking. It can instill fear and fear can be paralyzing in terms of driving change. And what we also see is there's a disconnect, a lot of times and that IBV study that I was referring to before, we saw results coming back where 78% of executives feel that they have provided the training and enablement to help their employees transform to new required skills and new ways of working but only half of the people surveyed felt the same way. Similarly, we saw a disconnect in terms of companies feeling that they're providing the right level of health and wellness support during the pandemic. And only half of the employees responded back they feel that they're getting that level of support. So, the people change aspect of doing a transformation or adapting to new circumstances is always the most critical component and always the hardest component. And when we talk about helping our clients do that in IBM that's our service as organization. That's the organization that Dominique Dubois is representing here today. I'm responsible for business transformation services within our organization. We help our clients adapt using new technologies, transforming the way they work, but also addressing the people change elements that could be so difficult and hitting them head on so that they can make sure that they can survive and thrive in a meaningful and lasting way in this new world. >> One of the hardest things is that cultural transformation regardless of a pandemic. So, I can't imagine I'd love to get one more thing, Paul from you before we head over to Dominique. IBM is on 109 year old organization. Talk to me about the IBM pledge. This is something that came up last year, huge organization massive changes last year, not just the work from home that the mental concerns and issues that people had. What did IBM do like as a grassroots effort that went viral? >> Yeah, so, it's really great. So, when the pandemic started, we all have to shift it, We all have to shift to working from home. And as you mentioned, IBM's 109 year old company, we have over 300,000 employees working in 170 countries. So, we had to move this entire workforce. It's 370,000 humans to working in a new way that many of which have never done before. And when we started experiencing, the minute we did that, within a few weeks, my team and I were talking Dominique is on my team and we were having conversations where we were feeling really exhausted. Just a few weeks into this and it was because we were constantly on Webex, we were constantly connected and we're all used to working really hard. We travel a lot, we're always with our clients. So, it wasn't that, you have a team that is adapting to like working more hours or longer hours, but this was fundamentally different. And we saw that with schools shutting down and lock downs happening in different of the world the home life balance was getting immediately difficult to impossible to deal with. We have people that are taking care of elderly parents, people that are homeschooling children, other personal life situations that everyone had to navigate in the middle of a pandemic locked at home with different restrictions on when you can go out and get things done. So, we got together as a group and we just started talking about how can we help? How can we help make life just a little bit easier for all of our people? And we started writing down some things that we would, we would commit to doing with each other. How we would address each other. And when that gave birth to was what we call the IBM Work From Home Pledge. And it's a set of principles, all grounded in the belief that, if we act this way, we might just be able to make life just a little bit easier for each other and it's grounded in empathy. And there are parts of the Plex that are pledging to be kind. Recognizing that in this new digital world that we're showing up on camera inside of everyone's home. We're guests in each other's homes. So, let's make sure that we act appropriately as guests at each other's home. So, if children run into the frame during the middle of a meeting or dog started barking during the middle of a meeting, just roll with it. Don't call out attention to it. Don't make people feel self-conscious about it. Pledged the support so your fellow IBM by making time for personal needs. So, if someone has to, do homeschooling in the middle of the day, like Dominique's got triplets she's got to do homeschooling in the middle of the day. Block that time off and we will respect that time on your calendar. And just work around it and just deal with it. There are other things like respecting that camera ready time. As someone who's now been on camera every day it feels like for the last 14 months we want to respect the time that people when they have their cameras off. And not pressure them to put their cameras on saying things like, Hey, I can't see you. There's no reason to add more pressure to everyone's life, if someone's camera's off, it's all for a reason. And then other things like pledging to checking on each other, pledging to set boundaries and tend to our own self-care. So, we published that as a group, we just again and we put it on a Slack channel. So it's kind of our communication method inside the company. It was just intended to be for my organization but it started going viral and tens of thousands of IBM members started taking, started taking the pledge and ultimately caught the attention of our CEO and he loved it, shared it with his leadership team, which I'm a part of. And then also then went on LinkedIn and publicly took the pledge as well. Which then also got more excitement and interaction with other companies as well. So, grassroots effort all grounded in showing empathy and helping to make life just a little bit easier for everyone. >> So important, I'm going to look that up and I'm going to tell you as a person who speaks with many tech companies a week. A lot of businesses could take a lead from that and it gets really important and we are inviting each other into our homes and I see you're a big Broadway fan I'll have to ask you that after we wrap (giggles) Dominique I don't know how you're doing any of this with triplets. I only have two dogs (Dominique laughs) but I'd love to know this sense of urgency, that is everywhere you're living it. Paul talked about it with respect to the acceleration of transformation. How from your lens is IBM and IBM helping customers address the urgency, the need to pivot, the need to accelerate, the need to survive and thrive with respect to digital transformation actually getting it done? >> Right, thanks Lisa, so true our clients are really needing to and ready to move with haste. That that sense of urgency can be felt I think across every country, every market, every industry. And so we're really helping our clients accelerate their digital transformations and we do that through something that we call intelligent workflows. And so workflows in and of themselves are basically how organizations get work done. But intelligent workflows are how we infuse; predictive properties, automation, transparency, agility, end to end across a workflow. So, pulling those processes together so they're not solid anymore and infusing. So, simply put we bring intelligent workflows to our clients and it fundamentally reinvents how they're getting work done from a digital perspective, from a predictive perspective, from a transparency perspective. And I think what really stands apart when we deliver this with our clients in partnership with our clients is how it not only delivers value to the bottom line, to the top line it also actually delivers greater value to their employees, to the customers, to the partner to their broader ecosystem. And intelligent workflows are really made up of three core elements. The first is around better utilizing data. So, aggregating, analyzing, getting deeper insight out of data, and then using that insight not just for employees to make better decisions, but actually to support for emerging technologies to leverage. So we talked about AI, automation, IOT, blockchain, all of these technologies require vast amounts of data. And what we're able to bring both on the internal and external source from a data perspective really underpins what these emerging technologies can do. And then the third area is skills. Our skills that we bring to the table, but also our clients deep, deep expertise, partner expertise, expertise from the ecosystem at large and pulling all of that together, is how we're really able to help our clients accelerate their digital transformations because we're helping them shift, from a set of siloed static processes to an end-to-end workflow. We're helping them make fewer predictions based on the past historical data and actually taking more real-time action with real time insights. So, it really is a fundamental shift and how your work is getting done to really being able to provide that emerging technologies, data, deep skills-based end to end workflow. >> That word fundamental has such gravity. and I know we say data has gravity being fundamental in such an incredibly dynamic time is really challenging but I was looking through some of the notes that you guys provided me with. And in terms of what you just talked about, Dominique versus making a change to a silo, the benefits and making changes to a spectrum of integrated processes the values can be huge. In fact, I was reading that changing a single process like billing, for example might deliver up to 20% improved results. But integrating across multiple processes, like billing, collections, organizations can achieve double that up to 40%. And then there's more taking the intelligent workflow across all lead to cash. This was huge. Clients can get 50 to 70% more value from that. So that just shows that fundamental impact that intelligent workflows can make. >> Right, I mean, it really is when we see it really is about unlocking exponential value. So, when you think about crossing end to end workflow but also, really enhancing what clients are doing and what companies are doing today with those exponential technologies from kind of single use the automation POC here and AI application POC here, actually integrating those technologies together and applying them at scale. When I think intelligent workflows I think acceleration. I think exponential value. But I also really think about at scale. Because it's really the ability to apply these technologies the expertise at scale that allows us to start to unlock a lot of that value. >> So let's go over Paul, in the last few minutes that we have here I want to talk about IBM garage and how this is helping clients to really transform those workflows. Talk to me a little bit about what IBM garage is. I know it's not IBM garage band and I know it's been around since before the pandemic but help us understand what that is and how it's delivering value to customers. >> Well, first I'm going to be the first to invite you to join the IBM garage band, Lisa so we'd love to have you >> I'm in. no musical experience required... >> I like to sing, all right I mean (laughs) We're ready, we're ready for. So, let me talk to you about IBM garage and I do want to key on two words that Dominique was mentioning speed and scale. Because that's what our clients are really looking for when they're doing transformations around intelligent workflows. How can you transform at scale, but do that with speed. And that really becomes the critical issue. As Dominique mentioned, there's a lot of companies that can help you do a proof of concept do something in a few weeks that you can test an idea out and have something that's kind of like a throw away piece of work that maybe proves a point or just proves a point. But even if it does prove the point at that point you'd have to restart a new, to try to get something that you could actually scale either in the production technology environment or scale as a change across an organization. And that's where IBM garage comes in. It's all a way of helping our clients co-create, co-execute and then cooperate, innovating at scale. So, we use methods like design thinking inside of IBM we've trained several hundred thousand people on design thinking methods. We use technologies like neural and other things that help our clients co-create in a dynamic environment. And what's amazing for me is that, the cause of the way we were, we were doing work with clients in a garage with using IBM garage in a garage environment before the pandemic. And one of our clients Frito-Lay of North America, is an example where we've helped them innovate at scale and speed using IBM garage over a long period of time. And when the pandemic hit, we in fact were running 11 garages across 11 different workflow areas for them the pandemic hit and everyone was sent home. So, we all instantly overnight had to work from home together with relay. And what was great is that we were able to quickly adapt the garage method to working in a virtual world. To being able to run that same type of innovation and then use that innovation at scale in a virtual world, we did that overnight. And since that time which happened, that happened back in March of last year throughout the pandemic, we've run over 1500 different garage engagements with all of our clients all around the world in a virtual, in a virtual environment. It's just an incredible way, like I said to help our clients innovate at scale. >> That's fantastic, go ahead Dominique. >> Oh, sorry, was just said it's a great example, we partnered with FlightSafety International, they train pilots. And I think a great example of that speed and scale right is in less than 12 weeks due to the garage methodology and the partnership with FlightSafety, we created with them and launched an adaptive learning solution. So, a platform as well as a complete change to their training workflow such that they had personalized kind of real-time next best training for how they train their pilots for simulators. So, reducing their cycle time but also improving the training that their pilots get, which as people who normally travel, it's really important to us and everyone else. So, just a really good example, less than 12 weeks start to start to finish. >> Right, talk about acceleration. Paul, last question for you, we've got about 30 seconds left I know this is an ecosystem effort of IBM, it's ecosystem partners, it's Alliance partners. How are you helping align right partner with the right customer, the right use case? >> Yeah, it's great. And our CEO Arvind Krishna has really ushered in this era where we are all about the open ecosystem here at IBM and working with our ecosystem partners. In our services business we have partnerships with all the major, all the major technology players. We have a 45 year relationship with SAP. We've done more SAP S 400 implementations than anyone in the world. We've got the longest standing consulting relationship with Salesforce, we've got a unique relationship with Adobe, they're only services and technology partner in the ecosystem. And we just recently won three, procedures Partner Awards, with them and most recently we announced a partnership with Celonis which is an incredible process execution software company, process mining software company that's going to help us transform intelligent workflows in an accelerated way, embedded in our garage environment. So, ecosystem is critical to our success but more importantly, it's critical to our client success. We know that no one alone has the answers and no one alone can help anyone change. So, with this open ecosystem approach that we take and global business services and our business transformation services organization, we're able to make sure that we bring our clients the best of everyone's capabilities. Whether it's our technology, partners, our services IBM's own technology capabilities, all in the mix, all orchestrated in service to our client's needs all with the goal of driving superior business outcomes for them. >> And helping those customers in any industry to accelerate their business transformation with those intelligent workloads and a very dynamic time. This is a topic we could keep talking about unfortunately, we are out of time but thank you both for stopping by and sharing with me what's going on with respect to intelligent workflows. How the incremental exponential value it's helping organizations to deliver and all the work that IBM is doing to enable its customers to be thrivers of tomorrow. We appreciate talking to you >> Paul: Thanks Lisa. >> Dominique: Thank you >> For Paul Papas and Dominique Dubois I'm Lisa Martin. You're watching the CUBE's coverage of IBM Think the digital event experience. (gentle music)

♪ Dah, deeah ♪ ♪ Dah, dee ♪ (chimes ringing) >> Announcer: From around the globe, it's theCUBE! With digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back everyone to theCUBE's coverage of IBM Think 2021. I'm John Furrier, host of theCUBE. We're here with Mark Foster, Senior Vice President of IBM Services and IBM's Global Business Services. It's a global landscape, the world's changing, it's going hybrid. Mark, thanks for coming on theCUBE. >> Great to see you, John, good to be with you. >> You know, the theme this year is all about hybrid cloud. Global transformation is the innovation at scale. That's the discussion, that's the way I see it. The question I have for you to start right away is how has the last year in particular changed businesses as they're leveraging the tech? You know, they want to solve their critical problems and transform themselves, the pandemic has forced them to look at this. How has this last year changed the way businesses are leveraging tech? >> Well, there's definitely been an acceleration in the digital transformations across all of our clients around the world. They have been compelled to leverage technology to connect with their customers in these unique times. They've been forced to use technology tools to allow their teams to connect and operate around the world. And all of this has reinforced also the opportunity to leverage things like extreme automation, AI, and the leverage of things like the cloud to deal with the virtual and more remote nature of working around the world. >> How much of the change last year do you think's going to to be temporary or long lasting. What's not going to be given up? (laughs) What are people realizing? Is it temporary or is it long lasting? What's your take? >> Well, I think we have to recognize that we are moving into a genuinely hybrid world, well, hybrid insofar as I think that some of the lessons we've learned over this past period are going to durably change the way we work, but we're also going to have a certain amount of back to the future, as well, as we try and put back some of the aspects of physical interaction, the ways of actually bringing empathy, creativity together through being together in groups. But I do think also we're going to take a number of these areas of acceleration and they're going to be extrapolated out to genuinely lead to an acceleration of what might've taken place over over five years taking place over a lot shorter period. >> You know, I think that group dynamic is really a big deal. I think that's going to be something that, to me, jumps out at this transformation. People want to work together. They want to be part of something, totally right on. With that, I got to ask you, now that we have this kind of new virtual experience, we're remote, we're not in person, wish we were, but even when we are in personal, it'll still be hybrid virtual experience events means we're still going to act as a group. This kind of brings up the idea of a virtual enterprise. You kind of mentioned that. What you mean and how do you define a virtual enterprise? >> Well, I think a virtual enterprise for us is an extension of the thought process we've had before around how technology is transforming the way all businesses operate. If you do apply, you know, the power of technology to build new business platforms and think about new ways of applying technology to transform your business processes, you think about the way that all of us are reinventing the relationship between people and technology in our organizations, the virtual enterprise just takes that to the next level. It recognizes that if you are able to take a location out of the equation, if you're able to leverage ecosystems more completely through connecting through networks of organizations, all of this extends the vision that we have of how the cognitive enterprise of the past comes to life. And we create this even more connected, even more expansive vision of business which is of course able to leverage technology within its own four walls. It's able to leverage it powerfully with its business partners. But then finally, it's about how you create the platforms upon which you create new ecosystems for competition and new markets that can be created in that way. >> That's really compelling insight right there. I think that's right on the money. I have to ask you, what do you think the differentiating characteristics are for this enterprise? What's going to be the differentiator, what's going to make it work? What do you need- >> I think what's going to make it work first of all, I think we think there's going to be a sort of a golden thread of what you might call an extended intelligent workflow that runs through the enterprise and its partners. And the power of that sort of thread of core processes and core differentiation to be brought to life by the mutual leverage of technology through partnerships is going to be a hugely powerful. So therefore all the partners' ability to embrace those technologies to embrace the vision for how those workflows come together is going to be very important. I think it's going to be very important that actually the ecosystem and its success becomes the strategy of the of the participants as opposed to being something that they happened to be going along with. So it becomes the strategy of the organization. And I think finally, there's a huge amount around here around how you leverage and think about the power of your people, the culture that you create to be inclusive and expansive in terms of applying new talent, building new talent, to allow this virtual enterprise to thrive. >> That's actually brilliant. You know, ecosystem is part of it, not an afterthought or a marketing gimmick. It's got to be part of it, that's awesome. Let's bring that to the next level. The role of the ecosystems are taking a bigger role for you, as you said, what specifically can you point to that has a change that's made in the ecosystem that you can point to, says that's an impactful change, this is a table stake, this is a guaranteed continuing practice. Can you give an example? >> Well, I think what we can see around the world in terms of how the world has solved for something like you're getting vaccines created and distributed on the back of the COVID crisis, that's taken an ecosystem coming together to work in completely different ways in an accelerated way to deliver on very big outcomes. Well, we can also see, you know, clients who are developing their strategies to try and connect the dots across different players to position their business as a platform upon which others bring their parts, their organizations to bear. And I think that we can see therefore that this idea of ecosystems is being used to solve really big problems, but it's also potentially a model that can be used to actually define really big market opportunities as well. And when you can connect the dots and you can expand your market footprint by combining with other key players at scale and also create a way that smaller organizations can come and sit upon the platforms that you create and leverage those capabilities, then the opportunity to actually use that to really expand your horizons of where your business can go are very real. >> You know, that's a really interesting, mind blowing concept. You think about the idea of a network effect or ecosystems, and integration, and collective intelligence. These are paradigms that have been around for awhile, at least past 10 years. It was the Holy Grail, let's hope we get to that. It seems like that's happening right now. And I think more than ever, it can be harnessed. And so I think you starting to see that with the hybrid cloud and it's not just tech, it's societal impact, it's impacting people, their jobs, and their ability to contribute and work. So this is a huge concept. So really excited this conversation. I guess the next question I have for you, Mark, is how do you bring clients this value? How do they create value? And how do they take this and transform their business with it? What's the playbook? >> Well, I think for clients, the first thing for them to recognize is to understand that this is the world that they are operating in. And I think that from a playbook point of view, the first thing I would say is you do need to think about which ecosystems do I want to play in? How do I think I could win by being a part of, or shaping an ecosystem? I think, secondly, there's the opportunity to think about how you use all the data that's out there in the world to be a stronger source of innovation across an ecosystem, to think about how your products and services could be modernized to succeed in that world. How you build those innovations into this new vision of an extended workflow or process view that binds the players of your ecosystem together. And you're really thinking about how to reinvent the way work gets done. Apply automation, apply AI, apply blockchain, apply IoT to transform those workflows is a massive, massive opportunity. To recognize that actually by the power of that, you're able to have significantly more impact than before. So make sure you're setting your ambitions high enough around the impact the change you're trying to drive can bring, and then I think also just making sure you're thinking all the time about what this means for the culture of your organizations, the workforce you want to connect with, how you want to access talent and bring it to bear across this new extended value chain? You know, who do you need to employ, versus who do you need to contract with, versus who do you need to make sure are participating in the processes that you're driving? And then finally, how do you make sure that you have the infrastructure, and the systems, and the applications that are open enough to allow you to really bring this vision to life? So the underlying hybrid cloud, hybrid architectures that you have and the networks you have that bind you together become fundamental. >> That's awesome. Great insight there. I guess my final question is how has your personal outlook changed in the past year when everyone is working from home? And now we're starting to see the pandemic, you know, light at the end of the tunnel from this pandemic, once we emerge out of it, people want to have a growth strategy, want to get back to real life. Any words of advice for our viewers on your personal outlook and as we come out of the pandemic and they can participate- >> Well, I think the first thing to recognize is we all have a collective wish around the world, probably for the first time for a long time, I think pretty much most people in humanity are sharing a shared view about a desire to have a more expansive horizon than the one that's outside the window of their kitchen, which I'm looking out of right now, and being able to get out and about, and engage in some more aspects that of normal life. And I do think that we're all looking forward to that opportunity. I think we're going to have to recognize that we're probably all going to also adapt our behaviors, going forward, but there's almost an enormous amount of exciting things that we've all got pent up we want to go and do, and I think, you know, the critical thing for us all is to hopefully approach that world safely. But at the same time, recognize that there is hope, we are working our way through this as a world. And as long as we try and make sure we do that in a way that is actually equitable, and that we do make sure that all boats are lifted as we return here, then I think that's a really positive view of how the future will be for all of us. So we should all look forward to that. >> Mark, it's great to have you on theCUBE. I love the insight, I love your message. It's right on, it's relevant, and super cool because that's what people want. They want to collaborate and be with people. I guess with the final minute we have left, share an observation from the past year and a half. Something that surprised you that happened in the industry, something that you didn't expect or something that you did expect that's positive that we can look to and say, "That's a good thing, we want to double down on that." >> Well, I think the positive thing that I think we can double down on is that we have all actually learned to be perhaps more open to interacting with people who we wouldn't otherwise have interacted with through this medium, that actually I have found that I've broadened my network of people that I've been engaging with through the fact that it has been actually relatively easy to connect even at high levels with people, but all the people have been able to connect in a strange way with a bigger group of connections than you would have done through the normal physical constraints of flying somewhere, seeing someone, meeting someone, and how you use your time to do that. So I would say one of the positive things is actually how open people have been to start new relationships over this virtual medium. Of course, the trick is going to be, can we build on those virtual relationships we've created and make them more sustainable once we're back to a more normal life and they become, you know, the new friendships, the new business relationships and networks that we can thrive on for the future? >> That's genius, love it. I agree. CUBE Virtual's here doing it. We're trying content, community, collaboration, connection, friendships, new things, touch someone with a click and engage. Mark Foster here, clicking into our CUBE Virtual for IBM Think. I'm John Furrier, thanks for watching. (upbeat music) ♪ Dah, deeah ♪ ♪ Dah, dee ♪ (chimes ringing)

(lively music) >> From around the globe it's theCUBE, with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome to theCUBE's coverage of IBM Think 2021, the digital event experience. I'm your host, Lisa Martin. I've got an alumni joining me and a brand new guest to the CUBE please welcome Paul Papas, the Global Managing Partner, for IBM Global Business Services, this is transformation services. Paul, welcome back to the virtual CUBE. >> Thanks Lisa great to be here with you today. And Dominique Dubois is here as well. She is the Global Strategy and Offerings Leader in business transformation services or BTS at IBM. Dominique, welcome to the program. >> Thanks Lisa, great to be here. So, we're going to be talking about accelerating business transformation with intelligent workflows. We're going to break through all that, but Paul we're going to start with you. Since we last got together with IBM, a lot has changed so much transformation, so much acceleration of transformation. Talk to me from your perspective, how have you seen the way that businesses running change and what some of the changes in the future are going to be? >> Well, you hit on two key words there Lisa and thanks so much for that question. Two key words that you hit on were change and acceleration. And that's exactly what we see. We were seeing this before the pandemic and if anything, with the pandemic did when things started started kind of spreading around the world late or early last year, around January, February timeframe we saw that word acceleration really take hold. Every one of our clients were looking for new ways to accelerate the change that they had already planned to adapt to this new, this new normal or this new abnormal, depending on how you view it. In fact, we did a study recently, an IBV study that's our Institute of Business Value and found that six out of 10 organizations were accelerating all of their transformation initiatives they had already planned. And that's exactly what we're seeing happening right now in all parts of the world and across all industries. This acceleration to transform. >> So, one of the things that we've talked about for years, Paul, before the pandemic was even a thing, is that there was a lot of perceived technical barriers in terms of like the tech maturity for organizations and employees being opposed to change. People obviously it can be a challenge. They're used to doing things the way they are. But as you just said, in that IBV survey, nearly 60% of businesses say we have to accelerate our transformation due to COVID, probably initially to survive and then thrive. Talk to me about some of those, those barriers that were there a little over a year ago and how businesses 60 plus percent of them have moved those out of the way. >> You know at IBM we've got 109 year history of being a technology innovation company. And the rate of pace of technical change is always increasing. It's something that we love and that we're comfortable with. But the rate and pace of change is always unsettling. And there's always a human element for change. And the human element is always the rate, the rate setter in terms of the amount of change that you can have in an organization. Our former chairman Ginni Rometty, used to say that growth and comfort cannot co-exist. And it's so true because changing is uncomfortable. It's unsettling. It can be, it can be nerve-racking. It can instill fear and fear can be paralyzing in terms of driving change. And what we also see is there's a disconnect, a lot of times and that IBV study that I was referring to before, we saw results coming back where 78% of executives feel that they have provided the training and enablement to help their employees transform to new required skills and new ways of working but only half of the people surveyed felt the same way. Similarly, we saw a disconnect in terms of companies feeling that they're providing the right level of health and wellness support during the pandemic. And only half of the employees responded back they feel that they're getting that level of support. So, the people change aspect of may doing a transformation or adapting to new circumstances is always the most critical component and always the hardest component. And when we talk about helping our clients do that in IBM that's our service as organization. That's the organization that Dominique Dubois are representing here today. I'm responsible for business transformation services within our organization. We help our clients adapt using new technologies, transforming the way they work, but also addressing the people change elements that could be so difficult and hitting them head on so that they can make sure that they can survive and thrive in a meaningful and lasting way in this new world. >> One of the hardest things is that cultural transformation regardless of a pandemic. So, I can't imagine I'd love to get one more thing, Paul from you before we head over to Dominique. IBM is on 109 year old organization. Talk to me about the IBM pledge. This is something that came up last year, huge organization massive changes last year, not just the work from home that the mental concerns and issues that people had. What did IBM do like as a grassroots effort that went viral? >> Yeah, so, it's really great. So, when the pandemic started, we all have to shift it, We all have to shift to working from home. And as you mentioned, IBM's 109 year old company, we have over 300,000 employees working in 170 countries. So, we had to move this entire workforce. It's 370,000 humans to working in a new way that many of which have never done before. And when we started experiencing, the minute we did that, within a few weeks, my team and I were talking Dominique is on my team and we were having conversations where we were feeling really exhausted. Just a few weeks into this and it was because we were constantly on Webex, we were constantly connected and we're all used to working really hard. We travel a lot, we're always with our clients. So, it wasn't that, you have a team that is adapting to like working more hours or longer hours, but this was fundamentally different. And we saw that with schools shutting down and lock downs happening in different of the world the home life balance was getting immediately difficult to impossible to deal with. We have people that are taking care of elderly parents, people that are homeschooling children, other personal life situations that everyone had to navigate in the middle of a pandemic locked at home with different restrictions on when you can go out and get things done. So, we got together as a group and we just started talking about how can we help? How can we help make life just a little bit easier for all of our people? And we started writing down some things that we would, we would commit to doing with each other. How we would address each other. And when that gave birth to was what we call the IBM Work From Home Pledge. And it's a set of principles, all grounded in the belief that, if we act this way, we might just be able to make life just a little bit easier for each other and it's grounded in empathy. And there are parts of the Plex that are pledging to be kind. Recognizing that in this new digital world that we're showing up on camera inside of everyone's home. We're guests in each other's homes. So, let's make sure that we act appropriately as guests at each other's home. So, if children run into the frame during the middle of a meeting or dog started barking during the middle of a meeting, just roll with it. Don't call out attention to it. Don't make people feel self-conscious about it. Pledged the support so your fellow IBM by making time for personal needs. So, if someone has to, do homeschooling in the middle of the day, like Dominique's got triplets she's got to do homeschooling in the middle of the day. Block that time off and we will respect that time on your calendar. And just work around it and just deal with it. There are other things like respecting that camera ready time. As someone who's now been on camera every day it feels like for the last 14 months we want to respect the time that people when they have their cameras off. And not pressure them to put their cameras on saying things like, Hey, I can't see you. There's no reason to add more pressure to everyone's life, if someone's camera's off, it's all for a reason. And then other things like pledging to checking on each other, pledging to set boundaries and tend to our own self-care. So, we published that as a group, we just again and we put it on a Slack channel. So it's kind of our communication method inside the company. It was just intended to be for my organization but it started going viral and tens of thousands of IBM members started taking, started taking the pledge and ultimately caught the attention of our CEO and he loved it, shared it with his leadership team, which I'm a part of. And then also then went on LinkedIn and publicly took the pledge as well. Which then also got more excitement and interaction with other companies as well. So, grassroots effort all grounded in showing empathy and helping to make life just a little bit easier for everyone. >> So important, I'm going to look that up and I'm going to tell you as a person who speaks with many tech companies a week. A lot of businesses could take a lead from that and it gets really important and we are inviting each other into our homes and I see you're a big Broadway fan I'll have to ask you that after we wrap (giggles) Dominique I don't know how you're doing any of this with triplets. I only have two dogs (Dominique laughs) but I'd love to know this sense of urgency, that is everywhere you're living it. Paul talked about it with respect to the acceleration of transformation. How from your lens is IBM and IBM helping customers address the urgency, the need to pivot, the need to accelerate, the need to survive and thrive with respect to digital transformation actually getting it done? >> Right, thanks Lisa, so true our clients are really needing to and ready to move with haste. That that sense of urgency can be felt I think across every country, every market, every industry. And so we're really helping our clients accelerate their digital transformations and we do that through something that we call intelligent workflows. And so workflows in and of themselves are basically how organizations get work done. But intelligent workflows are how we infuse; predictive properties, automation, transparency, agility, end to end across a workflow. So, pulling those processes together so they're not solid anymore and infusing. So, simply put we bring intelligent workflows to our clients and it fundamentally reinvents how they're getting work done from a digital perspective, from a predictive perspective, from a transparency perspective. And I think what really stands apart when we deliver this with our clients in partnership with our clients is how it not only delivers value to the bottom line, to the top line it also actually delivers greater value to their employees, to the customers, to the partner to their broader ecosystem. And intelligent workflows are really made up of three core elements. The first is around better utilizing data. So, aggregating, analyzing, getting deeper insight out of data, and then using that insight not just for employees to make better decisions, but actually to support for emerging technologies to leverage. So we talked about AI, automation, IOT, blockchain, all of these technologies require vast amounts of data. And what we're able to bring both on the internal and external source from a data perspective really underpins what these emerging technologies can do. And then the third area is skills. Our skills that we bring to the table, but also our clients deep, deep expertise, partner expertise, expertise from the ecosystem at large and pulling all of that together, is how we're really able to help our clients accelerate their digital transformations because we're helping them shift, from a set of siloed static processes to an end-to-end workflow. We're helping them make fewer predictions based on the past historical data and actually taking more real-time action with real time insights. So, it really is a fundamental shift and how your work is getting done to really being able to provide that emerging technologies, data, deep skills-based end to end workflow. >> That word fundamental has such gravity. and I know we say data has gravity being fundamental in such an incredibly dynamic time is really challenging but I was looking through some of the notes that you guys provided me with. And in terms of what you just talked about, Dominique versus making a change to a silo, the benefits and making changes to a spectrum of integrated processes the values can be huge. In fact, I was reading that changing a single process like billing, for example might deliver up to 20% improved results. But integrating across multiple processes, like billing, collections, organizations can achieve double that up to 40%. And then there's more taking the intelligent workflow across all lead to cash. This was huge. Clients can get 50 to 70% more value from that. So that just shows that fundamental impact that intelligent workflows can make. >> Right, I mean, it really is when we see it really is about unlocking exponential value. So, when you think about crossing end to end workflow but also, really enhancing what clients are doing and what companies are doing today with those exponential technologies from kind of single use the automation POC here and AI application POC here, actually integrating those technologies together and applying them at scale. When I think intelligent workflows I think acceleration. I think exponential value. But I also really think about at scale. Because it's really the ability to apply these technologies the expertise at scale that allows us to start to unlock a lot of that value. >> So let's go over Paul, in the last few minutes that we have here I want to talk about IBM garage and how this is helping clients to really transform those workflows. Talk to me a little bit about what IBM garage is. I know it's not IBM garage band and I know it's been around since before the pandemic but help us understand what that is and how it's delivering value to customers. >> Well, first I'm going to be the first to invite you to join the IBM garage band, Lisa so we'd love to have you >> I'm in. no musical experience required... >> I like to sing, all right I mean (laughs) We're ready, we're ready for. So, let me talk to you about IBM garage and I do want to key on two words that Dominique was mentioning speed and scale. Because that's what our clients are really looking for when they're doing transformations around intelligent workflows. How can you transform at scale, but do that with speed. And that really becomes the critical issue. As Dominique mentioned, there's a lot of companies that can help you do a proof of concept do something in a few weeks that you can test an idea out and have something that's kind of like a throw away piece of work that maybe proves a point or just proves a point. But even if it does prove the point at that point you'd have to restart a new, to try to get something that you could actually scale either in the production technology environment or scale as a change across an organization. And that's where IBM garage comes in. It's all a way of helping our clients co-create, co-execute and then cooperate, innovating at scale. So, we use methods like design thinking inside of IBM we've trained several hundred thousand people on design thinking methods. We use technologies like neural and other things that help our clients co-create in a dynamic environment. And what's amazing for me is that, the cause of the way we were, we were doing work with clients in a garage with using IBM garage in a garage environment before the pandemic. And one of our clients Frito-Lay of North America, is an example where we've helped them innovate at scale and speed using IBM garage over a long period of time. And when the pandemic hit, we in fact were running 11 garages across 11 different workflow areas for them the pandemic hit and everyone was sent home. So, we all instantly overnight had to work from home together with relay. And what was great is that we were able to quickly adapt the garage method to working in a virtual world. To being able to run that same type of innovation and then use that innovation at scale in a virtual world, we did that overnight. And since that time which happened, that happened back in March of last year throughout the pandemic, we've run over 1500 different garage engagements with all of our clients all around the world in a virtual, in a virtual environment. It's just an incredible way, like I said to help our clients innovate at scale. >> That's fantastic, go ahead Dominique. >> Oh, sorry, was just said it's a great example, we partnered with FlightSafety International, they train pilots. And I think a great example of that speed and scale right is in less than 12 weeks due to the garage methodology and the partnership with FlightSafety, we created with them and launched an adaptive learning solution. So, a platform as well as a complete change to their training workflow such that they had personalized kind of real-time next best training for how they train their pilots for simulators. So, reducing their cycle time but also improving the training that their pilots get, which as people who normally travel, it's really important to us and everyone else. So, just a really good example, less than 12 weeks start to start to finish. >> Right, talk about acceleration. Paul, last question for you, we've got about 30 seconds left I know this is an ecosystem effort of IBM, it's ecosystem partners, it's Alliance partners. How are you helping align right partner with the right customer, the right use case? >> Yeah, it's great. And our CEO Arvind Krishna has really ushered in this era where we are all about the open ecosystem here at IBM and working with our ecosystem partners. In our services business we have partnerships with all the major, all the major technology players. We have a 45 year relationship with SAP. We've done more SAP S 400 implementations than anyone in the world. We've got the longest standing consulting relationship with Salesforce, we've got a unique relationship with Adobe, they're only services and technology partner in the ecosystem. And we just recently won three, procedures Partner Awards, with them and most recently we announced a partnership with Celonis which is an incredible process execution software company, process mining software company that's going to help us transform intelligent workflows in an accelerated way, embedded in our garage environment. So, ecosystem is critical to our success but more importantly, it's critical to our client success. We know that no one alone has the answers and no one alone can help anyone change. So, with this open ecosystem approach that we take and global business services and our business transformation services organization, we're able to make sure that we bring our clients the best of everyone's capabilities. Whether it's our technology, partners, our services IBM's own technology capabilities, all in the mix, all orchestrated in service to our client's needs all with the goal of driving superior business outcomes for them. >> And helping those customers in any industry to accelerate their business transformation with those intelligent workloads and a very dynamic time. This is a topic we could keep talking about unfortunately, we are out of time but thank you both for stopping by and sharing with me what's going on with respect to intelligent workflows. How the incremental exponential value it's helping organizations to deliver and all the work that IBM is doing to enable its customers to be thrivers of tomorrow. We appreciate talking to you >> Thanks Lisa. >> Thank you >> For Paul Papas and Dominique Dubois I'm Lisa Martin. You're watching the CUBE's coverage of IBM Think the digital event experience. (gentle music)

>>From around the globe. It's the Cube with digital coverage of IBM think 2021 brought to you by >>IBM. Welcome back to IBM Think 2021. This is the cubes ongoing coverage where we go out to the events, we extract the signal from the noise of course, virtually in this case now we're going to talk about ecosystems, partnerships in the flywheel, they deliver in the technology business and with me or Jason kelly, general manager, global strategic partnerships, IBM global business services and Mani Das Gupta, who is the vice president of marketing for IBM Global Business services folks. It's great to see you again in which we're face to face. But this will have to do >>good to see you Dave and uh same, I wish we were face to face but uh we'll we'll go with this >>soon. We're being patient, Jason. Let's start with you. You have a partner strategy. I wonder if you could sort of summarize that and tell us more about it. >>So it's interesting that we start with the strategy because you said we have a partner strategy dave and I'd say that the market has dictated back to us a partner strategy something that we it's not new and we didn't start it yesterday. It's something that we continue to evolve and build even stronger. This thought of a partner strategy is it nothing is better than the thought of a partner ship. And people say oh well you know you got to work together as one team and as a partner And it sounds almost as a 1-1 type relationship. Our strategies is much different than that. David our execution is even better and that that execution is focused on now. The requirement that the market our clients are showing to us and our strategic partners that one player can't deliver all their needs, they can't Design solution and deliver that from one place. It does take an ecosystem to the word that you called out. This thought of an ecosystem and our strategy and execution is focused on that. And the reason why I say it evolves is because the market will continue to evolve and this thought of being able to look at a client's let's call it a a workflow, let's call it a value chain from one end to the other, wherever they start their process to wherever it ultimately hits that end user. It's going to take many players to cover that. And then we, as IBM want to make sure that we are the general contractor of that capability with the ability to convene the right strategic partners, bring out the best value for that outcome, not just technology for technology's sake, but the outcome that the incline is looking for so that we bring value to our strategic partners and that in client. >>I think about when you talk about the value chain, you know, I'm imagining, you know, the business books years ago you see the conceptual value chain, you can certainly understand that you can put processes together to connect them and now you've got technology, I think of a P. I. S. It's it's really supports that everything gets accelerated and and uh money. I wonder if you could address some of the the go to market how this notion of of ecosystem which is so important, is impacting the way in which you go to market. >>Absolutely. So modern business, you know, demands a new approach to working the ecosystem. Thought that Jason was just alluding to, it's a mutual benefit of all these companies working together in the market, it's a mutual halo of the brands, so as responsible for the championship of the IBM and the global business services brand. I am very, very interested in this mutual working together. It should be a win win win, as we say in the market, it should be a win for our clients, first and foremost, it should be a win for our partners and it should be a win for IBM and we are working together right now on an approach to bring this, go to market strategy to life. >>So I wonder if we could maybe talk about how this actually works and and pull in some examples, uh you must have some favorites that that we can touch on. Uh is that, is that fair? Can we, can we name some names, >>sure names, always working debut, right. And it's always in context of reality that we can talk about, as I said, this execution and not just a strategy. And I'll start with probably what's right in the front of many people's minds as we're doing this virtually because of what because of an unfortunate pandemic, um, this disastrous loss of life and things that have taken us down a path. We go well, how do we, how do we address that? Well, any time there's a tough task, IBM raises its hand first. You know, whether it was putting a person on the moon and bringing them home safely or standing up a system behind the current Social Security Administration, you know, during the Depression, you pick it well here we are now. And why not start with that as an example? Because I think it calls out just what we mentioned here first day, this thought of a, of an ecosystem because the first challenge, how do we create uh and address the biggest data puzzle of our lives, which is how do we get this vaccine created in record time, which it was the fastest before that was four years. This was a matter of months. Visor created the first one out and then had to get it out to distribution. Behind. That is a wonderful partner of R. S. A. P. Trying to work with that. So us working with S. A. P. Along with Pfizer in order to figure out how to get that value chain. And some would say supply chain, but I'll address that in a second. But there's many players there. And so we were in the middle of that with fires are committed to saying, how do we do that with S. A. P. So now you see players working together as one ecosystem. But then think about the ecosystem that that's happening where you have a federal government agency, a state, a local, you have healthcare, life science industry, you have consumer industry. Oh wait a second day. This is getting very complicated, Right? Well, this is the thought of convening an ecosystem and this is what I'm telling you is our execution and it has worked well. And so it's it's it's happening now. We still it's we see it's still developing and being, being, you know, very productive in real time. But then I said there was another example and that's with me, you mani whomever you pick the consumer. Ultimately we are that outcome of of the value chain. That's why I said, I don't want to just call it a supply chain because at the end is a someone consuming and in this case we need a shot. And so we partnered with Salesforce, IBM and Salesforce saying, wait a minute, that's not a small task. It's not just get the content there and put it in someone's arm instead they're scheduling that must be done. There's follow up an entire case management like system sells force is a master at this, so work dot com team with IBM, we sit now let's get that part done for the right type of UI UX capability that the user experience, user interaction interface and then also in bringing another player in the ecosystem, one of ours Watson health along with our block changing, we brought together something called a Digital Health pass. So I've just talked about two ecosystems work multiple ecosystems working together. So you think of an ecosystem of ecosystems. I called out Blockchain technology and obviously supply chain but there's also a I I O T. So you start to see where look this is truly an orchestration effort. It has to happen with very well designed capability and so of course we master and design and tying that that entire ecosystem together and convening it so that we get to the right outcome you me money all getting into shot being healthy. That's a real time example of us working with an ecosystem and teeming with key strategic partners, >>you know, money, I mean Jason you're right. I mean pandemics been horrible, I have to say. I'm really thankful it didn't happen 20 years ago because it would have been like okay here's some big pcs and a modem and go ahead and figure it out. So I mean the tech industry has saved business. I mean with not only we mentioned ai automation data, uh even things basic things like security at the end point. I mean so many things and you're right, I mean IBM in particular, other large companies you mentioned ASAP you have taken the lead and it's really I don't money, I don't think the tech industry gets enough credit, but I wonder if there's some of your favorite, you know, partnerships that you can talk about. >>Yeah, so I'm gonna I'm gonna build on what you just said. Dave IBM is in this unique position amongst this ecosystem. Not only the fact that we have the world leading most innovative technologies to bring to bear, but we also have the consulting capabilities that go with it now to make any of these technologies work towards the solution that Jason was referring to in this digital health pass, it could be any other solution you would need to connect these disparate systems, sometimes make them work towards a common outcome to provide value to the client. So I think our role as IBM within this ecosystem is pretty unique in that we are able to bring both of these capabilities to bear. In terms of you know, you asked about favorite there are this is really a coop petition market where everybody has products, everybody has service is the most important thing is how how are we bringing them all together to serve the need or the need of the hour in this case, I would say one important thing in this. As you observe how these stories are panning out in an ecosystem in in part in a partnership, it is about the value that we provide to our clients together. So it's almost like a cell with model from from a go to market perspective, there is also a question of our products and services being delivered through our partners. Right? So think about the span and scope of what we do here. And so that's the sell through. And then of course we have our products running within our partner companies and our partner products, for example. Salesforce running within IBM. So this is a very interesting and a new way of doing business. I would say it's almost like the modern way of doing business with modernity. >>Well. And you mentioned cooperation. I mean you're you're part of IBM that will work with anybody because your customer first, whether it's a W. S. Microsoft oracle is a is a is a really tough competitor. But your customers are using oracle and they're using IBM. So I mean as a those are some good examples. I think of your point about cooper Titian. >>Absolutely. If you pick on any other client, I'll mention in this case. Delta, Delta was working with us on moving, being more agile. Now this pandemic has impacted the airline sector particularly hard, right With travel stopping and anything. So they are trying to get to a model which will help them scale up, scale down, be more agile will be more secure, be closer to their customers, try and understand how they can provide value to their customers and customers better. So we are working with Delta on moving them to cloud on the journey to cloud. Now that public cloud could be anything. The beauty of this model and a hybrid cloud approach is that you are able to put them on red hat open shift, you're able to do and package the services into a microservices kind of a model. You want to make sure all the applications are running on a portable, almost platform. Agnostic kind of a model. This is the beauty of this ecosystem that we are discussing is the ability to do what's right for the end customer at the end of the day, >>how about some of the like sass players, like some of the more prominent ones and we watched the ascendancy of service now and and, and work day, you mentioned Salesforce. How do you work with those guys? Obviously there's an Ai opportunity, but maybe you could add some, you know, color there. >>So I like the fact that you call out the different hyper scholars for example, uh whether it's a W. S, whether it's Microsoft, knowing that they have their own cloud instances, for example. And when you, when you mentioned, he had this happened a long time ago, you know, you start talking about the heft of the technology, I started thinking of all the truckloads of servers or whatever they have to pull up. We don't need that now because it can happen in the cloud and you don't have to pick one cloud or the other. And so when people say hybrid cloud, that's what comes out, you start to think of what I I call, you know, a hybrid of hybrids because I told you before, you know, these roles are changing. People aren't just buyers or suppliers, they're both. And then you start to say what we're different people supplying well in that ecosystem, we know there's not gonna be one player, there's gonna be multiple. So we partner by doing just what monty called out is this thought of integrating in hybrid environments on hybrid platforms with hybrid clouds, Multi clouds, maybe I want something on my premises, something somewhere else. So in giving that capability that flexibility we empower and this is what's doing that cooperation, we empower our partners are strategic partners, we want them to be better with us. And this is this thought of being able to actually bring more together and move faster which is almost counterintuitive. You're like wait a minute you're adding more players but you're moving faster. Exactly because we have the capability to integrate those those technologies and get that outcome that monty mentioned, >>I would add to this one. Jason you mentioned something very very interesting. I think if you want to go just fast you go alone but if you want to go further, you go together. And that is the core of our point of view in this case is that we want to go further and we want to create value that is long lasting. >>What about like so I get the technology players and there may be things that you do that others don't or vice versa. So the gap fillers etcetera. But what about how to maybe customers that they get involved? Perhaps government agencies, may they be they be customer or an N. G. O. As another example, Are they part of this value chain? Part of this ecosystem? >>Absolutely. I'll give you I'll stick with the same example when I mentioned a digital health past that Digital Health Pass is something that we have as IBM and it's a credential Think of it as a health credential not a vaccine passport because it could be used for a test for a negative test on Covid, it could be used for antibiotics. So if you have this credential, it's something that we, as IBM created years back and we were using it for learning. When you think of getting people uh certifications versus a four year diploma, how do we get people into the workforce? That was what was original. That was a jenny Rometty thought, let's focus on new collar workers. So we had this asset that we'd already created and then it's wait, there's a place for it to work with, with health, with validation verification on someone's option, it's optional. They choose it. Hey, I want to do it this way. Well, the state of new york said that they wanted to do it that way and they said, listen, we are going to have a digital health pass for all of our, all of our new york citizens and we want to make sure that it's equitable, it could be printed or on a screen and we want it to be designed in this way and we wanted to work on this platform and we want to be able to, to work with the strategic Partners, a Salesforce and ASAP and work. I mean, I can just keep and we said okay let's do this. And this is the start of collaboration and doing it by design. So we haven't lost that day but this only brings it to the forefront just as you said, yes, that is what we want. We want to make sure that in this ecosystem we have a way to ensure that we are bringing together convening not just point products or different service providers but taking them together and getting the best outcome so that that end user can have it configured in the way that they want it >>guys, we got to leave it there but it's clear you're helping your customers and your partners on this this digital transformation journey that we already we all talk about. You get this massive portfolio of capabilities, deep, deep expertise, I love the hybrid cloud and AI Focus, Jason and money really appreciate you coming back in the cubes. Great to see you both. >>Thank you so much. Dave Fantastic. All >>Right. And thank you for watching everybody's day Vigilante for the Cuban. Our continuous coverage of IBM, think 2021, the virtual edition. Keep it right there. Yeah. Mhm. Mhm. >>Mhm.

>> Narrator: From theCUBE studios in Palo Alto in Boston, it's theCUBE, covering IBM Think. Brought to you by IBM. >> Hi everybody, welcome back. This is Dave Vellante, and you're watching theCUBE's coverage of IBM Think 2020, the digital version of IBM Think and theCUBE is pleased to be providing the wall-to-wall coverage as we have physically for so many years at big IBM events. Jesus Mantas is here, he's the managing partner for Global Strategy for IBM and the Global Business Services. Jesus, great to see you, thanks for coming on. >> Great to be here, Dave. >> So, every guest that we've talked to this week, really, we've talked about COVID but just briefly. Here, we're going to do a bigger drill down and really try to get, Jesus your perspectives as an IBM's point of view on what's going on here. So let me start with, we've never seen anything like this before, obviously. I mean, there are some examples you got to go back to 1918, try to get some similarities, but 1918 is a long long time ago, so, what's different about this? What are the similarities? >> Yeah, it's, you know what Mark Twain used to say that history doesn't repeat, but it often rhymes. I think there are similarities of what we are experiencing right now in this pandemic with other pandemics like Spanish flu. I think the situation is unique in terms of the impact, and the synchronicity of that impact, right? So we can go back, whether if you want, economic crisis, or our society crisis, where you have either one country or one aspect being disrupted. But this is really society being interrupted, on a global scale. So its impact is unprecedented in that perspective in modern time, and I think all of us are adjusting to it. >> I want to ask you about digital transformation, because I've made the point that, while a lot of people talk digital transformation, there's been a lot of complacency, people say, not my lifetime, we're a bank, we're making a lot of money, we're doing okay. How do you think COVID-19 will sort of change that complacency and really accelerate digital transformation as a mindset and actually turn it into action? >> Yeah, I think the best way to put it is digital transformation five months ago was about obtaining competitive advantage and digital transformation today in many industries is about survival. That is how big of a change it is. The need for efficiency and cost savings, the need for resiliency that we have talked about, the need to be able to drive agility, to be able to switch and adapt, the need to make hyper local decisions, right, to use data, none of that can be done unless you have fully digitized processes, you are consuming local data and you have trained the people to really operate in those new, more intelligent processes. So, it has gone from optionality is okay, you can do okay but if you digitize you're going to do better to unless you digitize your business may not exist next year. I think that's the change, the change is, I think now is widely understood that the majority of our digitization processes have to be accelerated, and I would say there is a great statistic that when we go back in history, and there has been many, as I mentioned, of this crisis. You can look back at the two behaviors that businesses have, one is, to play defense and then what happens two years later, and the other one is, okay, you defense but you immediately switch to offense and then what happens two year later. Those companies that use this time to just defend and hunker down, history said in a couple of years later, 21% of them outperform. But those businesses that they shift from defense to offense and actually accelerate in these cases, programs like digitalization, 37% outperform. So, there is a premium for businesses that right now actually immediately switch to offense, focus on this set of digitalization and empowering cloud, managing data, ensuring the skills of the people, they're more likely not only to survive, but thrive in the next few years than those that just use this time to defend. >> To your point, it's about survival, it's not about, not getting disrupted, 'cause you're going to get disrupted, it's almost a certainty, and so in order to survive, you've got to digitally transform. Your final thoughts on digital transformation, then I want to ask you if there's a silver lining in all this. >> I think, what we do, we can't change the context. But we cannot let the context define who we are either as individuals or as company. What we can do, is to choose how do we act on that context. I would say, those organizations and those individuals that take advantage of the situation to understand that some of these behaviors are going to change, understand that the more that we shift technology to the cloud, the more that we shift work close to the cloud, the more that we use technologies like artificial intelligence and drive nonlinear decisions that massively optimize everything we do from the way that we deliver health care, to the way that we manage supply chains, to the way that we secure food, frankly, to the way that we protect the environment, there is a silver lining that technology it is one of those solutions that can help in all of these areas, and the silver lining of this is, hopefully, let's use this time to get better prepared for the next pandemia, to get better prepared for the next crisis, to implement technologies that drive efficiency faster, that create new jobs, that protect the environment, and while we cannot change the fact that we have COVID-19, we can change what happens after COVID-19, so what we return to is something better that what we enter before COVID-19. >> Very thoughtful commentary, Jesus. Thank you so much for coming on theCUBE, blessings to your family and yourself. >> Appreciated Dave, thank you, and thank you for everything you do to keep everybody informed. >> Really a pleasure. And thank you for watching everybody. This is Dave Vellante. You're watching theCUBE's coverage of IBM Think 2020 the digital event, to be right back right after the short break. (upbeat music)

>> From the KIPP studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> IBM's new CEO has an opportunity to reset the direction of the company. Outgoing CEO Ginni Rometty, inherited a strategy that was put in place over two decades. It became fossilized in a lower-margin services-led model that she helped architect. Ginni spent a large portion of her tenure, shrinking the company so it could grow. But unfortunately, she ran out of time. For decades, IBM has missed opportunities to aggressively invest in the key waves that are now powering the tech economy. Instead, IBM really tried to balance investing innovation with placating Wall Street. We believe IBM has an opportunity to return to the Big Blue status that set the standard for the tech industry. But several things have to change, some quite dramatically. So we're going to talk about what it's going to take for IBM to succeed in this endeavor. Welcome to this special Wikibon CUBE Insights powered by ETR. In this breaking analysis, we're going to address our view of the future of IBM and try to accomplish three things. First, I want to review IBM's most recent earnings, the very first one under new CEO Arvind Krishna, and we'll discuss IBM's near-term prospects. Next, we'll look at how IBM got to where we are today. We want to review some of the epic decisions that it has made over the past several years and even decades. Finally, we'll look at some of the opportunities that we see for IBM to essentially remake itself and return to that tech titan that was revered by customers and feared by competitors. First, I want to look at the comments from new CEO Arvind Krishna. And let's try to decode them a bit. Arvind in the first earnings call that he held, and in interviews as well, and also internal memos, he's given some clues as to how he's thinking. This slide addresses a few of the key points. Arvind has clearly stated that he's committed to growing the IBM company, and of course, increasing its value. This is no surprise, as you know, every IBM CEO has been under pressure to do the same. And we'll look at that further a little later on in the segment. Arvind, also stated that he wants the company, he said it this way, "To lead with a technical approach." Now as we reported in January when Krishna was appointed to CEO. We're actually very encouraged that the IBM board chose a technical visionary to lead the company. Arvind's predecessors did not have the technical vision needed to make the bold decisions that we believe are now needed to power the company's future. As a technologist, we believe his decisions will be more focused on bigger tactical bets that can pay bigger returns, potentially with more risk. Now, as a point of just tactical commentary, I want to point out that IBM noted that it was doing well coming into the March month, but software deals especially came to a halt as customers focused on managing the pandemic and other parts of the business were okay. Now, this chart pulls some of the data from IBM's quarter. And let me make a few comments here. Now, what was weird here, IBM cited modest revenue growth on this chart, this was pulled from their slides. But revenue was down 2% for the quarter relative to last year. So I guess that's modest growth. Cloud revenue for the past 12 months, the trailing 12 months, was 22 billion and grew 23%. We're going to unpack that in a minute. Red Hat showed good growth, Stu Miniman and I talked about this last week. And IBM continues to generate a solid free cash flow. Now IBM, like many companies, they prudently suspended forward guidance. Some investors bristled at that, but I really have no problem with it. I mean, just way too much uncertainty right now. So I think that was a smart move by IBM. And basically, everybody's doing it. Now, let's take a look at IBM's business segments and break those down and make a few comments there. As you can see, in this graph, IBM's 17 plus billion dollar quarter comprises their four reporting segments. Cloud and cognitive software, which is, of course, its highest margin and highest growth business at 7%. You can see its gross margin is really, really nice. But it only comprises 30% of the pie. Services, the Global Business Services and GTS global technology services are low-growth or no growth businesses that are relatively low margin operations. But together they comprise more than 60% of IBM's revenue in the quarter and consistently throughout the last several years. Systems, by the way, grew nicely on the strength of the Z15 product cycles, it was up by 60% and dragged storage with it. But unfortunately power had a terrible quarter and hence the 4% growth. But decent margins compared to services of 50%. IBM's balance sheet looks pretty good. It took an advantage of some low rates recently and took out another $4 billion in corporate debt. So it's okay, I'm not too concerned about its debt related to the Red Hat acquisition. Now, welcome back to cloud at 22 billion for the past 12 months and growing at 23%. What, you say? That sounds very large, I don't understand. It's understandable that you don't understand. But let me explain with this next graphic. What this shows is the breakdown of IBM's cloud revenue by segment from fiscal year 19. As you can see, the cloud and cognitive segments, or segment which includes Red Hat comprises only 20% of IBM's cloud business. I know, kind of strange. Professional services accounts for 2/3 of IBM's Cloud revenue with systems at 14%. So look, IBM is defining cloud differently than most people. I mean, actually, that's 1% of the cloud business of AWS, Azure and Google Cloud come from professional services and on-prem hardware. This just doesn't have real meaning. And I think frankly, it hurts IBM's credibility as it hides the ball on cloud. Nobody really believes this number. So, I mean, it's really not much else I can say there. But look, why don't we bring in the customer angle, and let's look at some ETR data. So what this chart shows is the results of an ETR survey. That survey ran, we've been reporting on this, ran from mid March to early April. And more than 1200 respondents and almost 800 IBM customers are in there. If this chart shows the percentage of customers spending more on IBM products by various product segments that we chose with three survey samples April last year, January 2020, and the most recent April 2020 survey. So the good news here is the container platforms, OpenShift, Ansible, the Staples of Red Hat are showing strength, even though they're notably down from previous surveys. But that's the part of IBM's business that really is promising. AI and machine learning and cloud, they're right there in the mix, and even outsourcing and consulting and really across the board, you can see a pretty meaningful and respectable number or percent of customers are actually planning on spending more. So that's good, especially considering that the survey was taken right during the middle of the COVID-19 pandemic. But, if you look at the next chart, the net scores across IBM's portfolio, they're not so rosy. Remember, net score is a measure of spending momentum. It's derived by essentially subtracting the percent of customers that are spending less from those that are spending more. It's a nice simple metric. Kind of like NPS and ETR surveys, every quarter with the exact same methodology for consistency so we can do some comparisons over time series, it's quite nice. And you can see here that Red Hat remains the strongest part of IBM's portfolio. But generally in my experience as net scores starts to dip below 25% and kind of get into the red zone, that so called danger zone. And you can see many parts of IBM's portfolio are showing softness as we measure in net score. And even though you see here, the outsourcing and consulting businesses are up relative to last year, if you slice the data by large companies, as we showed you with Sagar Kadakia last week, that services business is showing deceleration, same thing we saw for Accenture, EY, Deloitte, etc. So here's the takeaway. Red Hat, of course, is where all the action is, and that's where IBM is going to invest in our opinion, and we'll talk a little bit more about that and drill into that kind of investment scenario a bit later. But what I want to do now is I want to come back to Arvind Krishna. Because he has a chance to pull off a Satya Nadella like move. Maybe it's different, but there are definite similarities. I mean, you have an iconic brand, a great company, that's in many technology sectors, and yes, there are differences, IBM doesn't have the recurring software revenue that Microsoft had, it didn't have the monopoly and PCs. But let's move on. Arvind has cited four enduring platforms for IBM, mainframes, services, middleware, and the newest hybrid cloud. He says that IBM must win the architectural battle for hybrid cloud. Now, I'm going to really share later what we think that means. There's a lot in that statement, including the role of AI in the edge. Both of which we'll address later on in this breaking analysis. But before we get there, I want to understand from a historical perspective where we think Arvind is going to take IBM. And to do that, we want to look back over the modern history of IBM, modern meaning of the post mainframe dominance era, which really started in 1993 when Louis Gerstner took over. Look, it's been well documented how Louis Gerstner pivoted into services. He wrote his own narrative with the book, "Who Says Elephants Can't Dance". And you know, look, you can't argue with his results. The graphic here shows IBM's rank in the fortune 500, that's the green line over time. IBM was sixth under Gerstner, today it's number 38. The blue area chart on the Insert, it shows IBM's market cap. Now, look, Gerstner was a hero to Wall Street. And IBM's performance under his tenure was pretty stellar. But his decision to pivot to services set IBM on a path that to this day marks company's greatest strength, and in my view, its greatest vulnerability. Name a product under the mainframes in which IBM leads. Again, middleware, I guess WebSphere, okay. But you know, IBM used to be the leader in the all important database market, semiconductors, storage servers, even PCs back in the day. So, I don't want to beat on this too much, I can say it's been well documented. And I said earlier, Ginni essentially inherited a portfolio that she had to unwind, and hence the steep revenue declines as you see here, and it's 'cause she had to jettison the so called non-strategic businesses. But the real issue is R&D, and how IBM has used it's free cash. And this chart shows IBM's breakdown of cash use between 2007 and 2019. Blue is cash return to shareholders, orange is research and development, and gray is CapEx. Now I chose these years because I think we can all agree that this was the period of tech defined by cloud. And you can see, during those critical early formative years, IBM consistently returned well over 50%, and often 60% plus of its free cash flow to shareholders in the form of dividends and stock buybacks. Now, while the orange appears to grow, it's because of what you see in this chart. The point is the absolute R&D spend really didn't change too much. It pretty much hovered, if you look back around 5 1/2 to $6 billion annually, the percentage grew because IBM's revenue declined. Meanwhile, IBM's competitors were spending on R&D and CapEx, what were they doing? Well, they were building up the cloud. Now, let me give you some perspective on this. In 2007 IBM spent $6.2 billion on R&D, Microsoft spent 7 billion that same year, Intel 5.8 billion, Amazon spent 800 million, that's it. Google spent 2.1 billion that year. And that same year, IBM returned nearly $21 billion to shareholders. In 2012 IBM spent $6.3 billion on R&D, Microsoft that year 9.8 billion, Intel 10 billion, Amazon 4.6 billion, less than IBM, Google 6.1 billion, about the same as IBM. That year IBM returned almost $16 billion to shareholders. Today, IBM spends about the same 6 billion on R&D, about the same as Cisco and Oracle. Meanwhile, Microsoft and Amazon are spending nearly $17 billion each. Sorry, Amazon 23 billion, and IBM could only return $7 billion to shareholders last year. So while IBM was returning cash to its shareholders, its competitors were investing in the future and are now reaping the rewards. Now IBM suspended its stock buybacks after the Red Hat deal, which is good, in my opinion. Buybacks have been a poor use of cash for IBM, in my view. Recently, IBM raised its dividend by a penny. It did this so it could say that it has increased its dividend 25 years in a row. Okay, great, not expensive. So I'm glad that that investors were disappointed with that move. But since 2007, IBM has returned more than $175 billion to shareholders. And somehow Arvind has to figure out how to tell Wall Street to expect less while he invests in the future. So let's talk about that a little bit. Now, as I've reported before, here is the opportunity. This chart shows data from ETR. It plots cloud landscape and is a proxy for multi-cloud and hybrid cloud. It plots net score or spending momentum on the y-axis, and market share, which really isn't market share, as we've talked about, it's a measure of pervasiveness in the data set, that's plotted on the x-axis. So, the point is, IBM has presence, it's pervasive in the marketplace, Red Hat and OpenShift, they have relevance, they have momentum with higher net scores. Arvind's opportunity is to really plug OpenShift into IBM's, large install base, and increase Red Hat's pervasiveness, while at the same time lifting IBM momentum. This, in my view, as Stu Miniman and I reported last week at the Red Hat Summit, puts IBM in a leading position to go after multi and hybrid cloud and the edge. So let's break that down a little bit further. When Arvind talks about winning the architectural battle for hybrid cloud, what does he mean by that? Here's our interpretation. We think IBM can create the de facto standard for cloud and hybrid cloud. And this includes on-prem, public cloud, cross clouds, or multi cloud, and importantly, the edge. Here's the opportunity, is to have OpenShift run natively, natively everywhere, on-premises in the AWS cloud, in the Azure Cloud, GCP, Alibaba, and the IBM Cloud and the Oracle Cloud, everywhere natively, so we can take advantage of the respective services within all those clouds. Same thing for on-prem, same thing for edge opportunities. Now I'll talk a little bit more about that in a moment. But what we're talking about here is the entire IT stack running natively, if I haven't made that point on OpenShift. The control plane, the security plane, the transport, the data management plane, the network plane, the recovery plane, every plane, a Red Hat lead stack with a management of resources is 100% identical, everywhere the same cloud experience. That's how IBM is defining cloud. Okay, I'll give them a mulligan on that one. IBM can be the independent broker of this open source standard covering as many use cases and workloads as possible. Here's the rub, this is going to require an enormous amount of R&D. Just think about all the startups that are building cloud native services and imagine IBM building or buying to fill out that IT stack. Now I don't have enough time to go in too deep to all other areas, but I do want to address the edge, the opportunity there and weave in AI. Beyond what I said above, which I want to stress, the points I made above about hybrid, multi-cloud include edge, the edge is a huge opportunity. But IBM and in many other, if not most other traditional players, we think are kind of missing the boat on that. I'll talk about that in a minute. Here's the opportunity, AI inference is going to run at the edge in real-time. This is going to be incredibly challenging. We think about this, a car running inference AI generates a billion pixels per second today, in five years, it'll be 15 times that. The pressure for real-time analysis at the edge is going to be enormous, and will require a new architecture with new processing models that are likely going to be ARM-based in our opinion. IBM has the opportunity to build end-to-end solutions powered by Red Hat to automate the data pipeline from factory to data center to cloud and everywhere. Anywhere there's instruments, IBM has an opportunity to automate them. Now rather than toss traditional Intel-based IT hardware over the fence to the edge, which is what IBM and most people are doing right now, IBM can develop specialized systems and make new silicon investments that can power the edge with very low cost and efficient systems that process data in real-time. Hey look, I'm out of time, but some other things I want you to consider, IBM transitioning to a recurring revenue model. Interestingly, Back to the Future, right? IBM used to have a massive rental revenue stream before it converted that base to sales. But if Arvind can recreate a culture of innovation and win the day with developers via its Red Hat relationships, as I said recently, he will be CEO of the decade. But he has to transform the portfolio by investing more in R&D. He's got to convince the board to stop pouring money back to investors for a number of years, not just a couple of quarters and do Whatever they have to do to protect the company from corporate raiders. This is not easy, but with the right leader, IBM, a company that has shown resilience through the decades, I think it can be done. All right, well, thanks for watching this episode of the Wikibon CUBE Insights powered by ETR. This is Dave Vellante. And don't forget, these episodes are available as podcasts, wherever you listen, I publish weekly on siliconangle.com, where you'll find all the news, I publish on wikibon.com which is our research site. Please comment on my LinkedIn posts, check out etr.plus, that's where all the data lives. And thanks for watching everybody. This is Dave Vellante for Breaking Analysis, we'll see you next time. (soft music)

>>Yeah, >>from The Cube Studios in Palo Alto and Boston. It's the Cube covering IBM. Think brought to you by IBM. >>Everybody welcome back. This is Dave Vellante, and you're watching the Cube's coverage of IBM. Think 2020 the digital version of IBM, thinking the Cube is pleased to be providing the wall to wall coverage, as we have physically. You know, so many years at big IBM events Spaces. Man test is here. He's the managing partner for global strategy for IBM and the Global Business Services. Jesus, great to see you. Thanks for coming on. >>Great to be here, Dave. >>So every guest that we've talked to this week really? We've talked about Cove it, but just briefly Ah, here. We're going to do a big drill down and really try to get Jesus, your perspectives and IBMs Point of view on what's going on here. So let me start with We've never seen anything like this before, Obviously. I mean, there are some examples going back to 1918. Try to get some similarities. But in 1918 was a long, long time ago. So So what's different about this? What are the similarities? >>Yeah, it's Ah, >>you know what Mark Point used to say? That history doesn't repeat, but it often rights, I think that are similar interests off what we're experiencing right now in this pandemic with Father from that makes like Spanish blue. I think the situation is unique in terms off the I'm the synchronicity of that impact. Right? So we can go back while they're everyone economic crisis or our society crisis where you have either one country of one aspect being disrupted. But this is really a society being disrupted, you know, on a global scale. So it impact is unprecedented in that in that perspective, in more than time. And I think all of us are adjusting to it. We upgrade 170 countries, so we've been able to see if you want every element of the curve off this convenient right? So from from getting back to a new normal, that is happening already in China and some of the countries in Asia Pacific to being just kind of like coming over the peat in Europe, North America, to some of the emerging countries where they're coming up. So so it gives the thing, gives us up what business continuity means and the importance of being prepared for this. I think it gives us a perspective on the health aspect of it as well as the economic impact. And most importantly, we've been very focused on aside the end. I don't have a lot of clients is figuring out what are they going to return back to when we say we want to return to work? I think is, what are we returning to? What are going to be the permanent changes? Where's the adaptation that is gonna be systemic and permanent? >>I want to ask you about digital transformation because I've made the point that, you know, while a lot of people talk digital transformation, there's been a lot of complacency. >>Digital transformation five months ago was about obtaining our competitive advantage on digital transformation. Today, in many industries, it's about survival, That is, that is how big of a change it is, the the need for efficiency and cost savings. We need local resiliency that we have talked about. They need to be able to drive agility, to be able to switch and about. They need to make hyper local decisions right to use data none of that can be done unless you have fully digitize processes. You are consuming local data and you have to train the people to really, um, operate in those new, more intelligent processes. So it has gone from Optionality is okay. You can do okay, But if you digitize your going to do better to, unless you digitize >>your business may not access next year. >>I've been just change the changes. I think now is widely understood that the majority of our digital digitization processes have to be accelerated. And I would say that is ah, great statistic that when we go back in history, Andi has been many. As I mentioned off this crisis, you can look back. The two >>behaviors that businesses have one is to play defense funding. What happens two years later on the other one is okay, you defense. But you immediately switched to offense and then what happens? Uh, two years later, those companies that use this time to just defend and hunker down history said in a couple of years later, 21% of them out there, But those businesses that they shift from defense to offense and actually accelerate in this case is, um, uh, programs like digitalization. 37% outperform. So very sad screaming for businesses that right now actually immediately switched to offense. Focus on this set of digitalization and empowering cloud managing data, ensuring the skills of the people. They're more likely not only to survive but thrive in the next three years. That don't just use this time >>to your point. It's about survival. It's not about, you know, not getting disrupted because you're going to get disrupted. It's almost a certainty. And so, in order to survive, you've got to digitally transform your final thoughts on digital transformation that I want to ask you. If there's a silver lining and all this, >>I think what we do, we can't change the conference. Um, but we cannot let the conflicts define who we are. Either It's individuals for this company. Well, we can do is to choose How do we act on that? I would say, um, those organizations and those individuals that take advantage of inspiration to understand that some of these behaviors are going to change, understand that the more that we should technology to the cloud, the more that we should workloads to the cloud, the more that we use technologies like artificial intelligence on Dr Nonlinear decisions that massively optimize everything we do from the way that we deliver healthcare to the way that we have managed supply change to the way that we secure food, frankly, to the way that we protect the environment that is a silver lining, that technology. It is one of those solutions that help in all of these areas, and the silver lining of this is is hopefully, let's use this time to get better, prepare for the next academia to get better, prepare for the next crisis. To implement technologies that drive efficiency faster. They create new jobs, they protect the environment. And what we cannot change The fact that we have 19 we can change. What happens after the 19. So we return to is something better than what we enter before. >>Very thoughtful commentary. Jesus, Thank you so much for coming on the cube. A blessing steer to your family and yourself. >>Appreciate it, Dave. Thank you. And thank you for everything You do too Well. Kept everybody informed. >>Really? Our pleasure. And thank you for watching everybody. This is Dave Vellante. you're watching the Cube's coverage of IBM. Think 2020. The digital event Right back. Right after this. Short break. >>Yeah, yeah, yeah, yeah.

>>Yeah, >>from The Cube Studios in Palo Alto and Boston. It's the Cube covering IBM. Think brought to you by IBM. >>Everybody welcome back. This is Dave Vellante, and you're watching the Cube's coverage of IBM. Think 2020 the digital version of IBM, thinking the Cube is pleased to be providing the wall to wall coverage, as we have physically. You know, so many years at big IBM events Spaces. Man test is here. He's the managing partner for global strategy for IBM and the Global Business Services. Jesus, great to see you. Thanks for coming on. >>Great to be here, Dave. >>So every guest that we've talked to this week really? We've talked about Cove it, but just briefly Ah, here. We're going to do a big drill down and really try to get Jesus, your perspectives and IBMs Point of view on what's going on here. So let me start with We've never seen anything like this before, Obviously. I mean, there are some examples going back to 1918. Try to get some similarities. But in 1918 was a long, long time ago. So So what's different about this? What are the similarities? >>Yeah, it's Ah, >>you know what Mark Point used to say? That history doesn't repeat, but it often rights, I think that are similar interests off what we're experiencing right now >>in this pandemic with father from that makes like Spanish blue. I think the situation is unique in terms off the I'm the synchronicity of that impact. Right? So we can go back while they're everyone economic crisis or our society crisis >>where you have either one country of one aspect being disrupted. But this is really a society being disrupted, you know, on a global scale. So it impact is unprecedented in that in that perspective, in more than time. And I think all of us are adjusting to it. Um, one of the points of view. Um, we've been able to if you want curate this. IBM is we upgrade 170 countries, so we've been able to see if you want every element of the curve off these convenient, right? So from from getting back to a new normal, that is happening already in China and some of the countries in Asia Pacific to being just kind of like coming over the peat in Europe, North America to some of the emerging countries where they're coming up. So so it gives thing gives us up what business continuity means and the importance of being prepared for this. I think it gives us a perspective on the health aspect of it as well as the economic impact. And most importantly, we've been very focused. Nous ous IBM. I don't have a lot of clients is figuring out What are they going to return back to when we say we want to return to work? I think is, what are we returning to? What are going to be the permanent changes? Where's the adaptation that is gonna be systemic and permanent? I'm not going to be just like, you know, we're going to get through this way. >>Whenever we had a CIO tell us that they weren't ready from a business resiliency business continuity standpoint, they said, we're we were all d are focused, very narrow. Uh, and wow, we really need to rethink that kind of another myth. Your thoughts on that? >>Correct. I think you go back the last three years. Business continuity in most of the decisions that binds would do, I would say I check on the box, right? So he was assumed that you needed to have a business. Continuity was heavily focused on disaster recovery that it was. He must evaluate it under the expectation that it decides there really happens that has now changed permanently, or at least for the foreseeable future, where business continuity is not a check on the box is actually a differentiating feature. And the differentiating feature comes from the fund that on now every one of our fines were prepared. Now every one of our competitors was prepared to the changes, the agility, the efficiency on if you want. The cloud ification of your business is the ability to provide the services in the face of a crisis that forces people to be socially distant, forces people to have to deliver from home. So that has actually become a much >>more important criterion buying decision. So So we, uh, fortunately, we have support very well in that we have 95% of IBM employees. I work from home in 99% of our global delivery centres on network is work from home. And we made that shit even before some of the country's declare that it was a stay at home or so. So that has Bean a feature that now our clients are appreciated. We've been able to to deliver those services so our clients could continue to deliver their services and going forward. I think that's gonna be a much more important if you want. A feature is how cloud ified is your delivery. How prepare are you? Not for just one crisis, but to make, probably subsequent crisis something normal, that one disrupt your operation? You just adapt. >>It's interesting. I had a conversation earlier with Ed Walsh, was one of your GM, said one of your hardware divisions. And and And he was explaining to me that, you know, across your 170 plus countries, you know, it was really the local supply chain. And he actually made the point detection really good quarter in Italy, which surprised me and he said, but they were sort of micro managing at the local level to your to your point. I want I want to ask you about digital transformation because I've made the point that, you know, while a lot of people talk digital transformation, there's been a lot of complacency because they're not in my lifetime where we're a bank. We're making a lot of money. We're doing okay. How do you think over 19 will sort of change that complacency and really accelerate digital transformation is a mindset and actually turn it into action? >>Yeah, I think the best way to put it is, um, digital transformation. Five months ago, it was about obtaining a competitive advantage on digital transformation. Today, in many industries, it's about survival. That is, that is how big of a change car it is. The the need for efficiency and cost savings, the need for resiliency that we have talked about. They need to be able to, um, to drive agility, to be able to switch and about. They need to make hyper local decisions right to use data that none of that can be done unless you have fully digitize processes. You are consuming local data and you have to train the people to really, um, operate in those new, more intelligent processes. So it has gone from Optionality is okay. You can do okay, but if you digitize, you're gonna do better to. Unless you digitize your business may not access next year. I think just change the changes. I think now is widely understood that the majority of our digital digitization processes have to be accelerated. And I would say that is, ah, great statistic that when we go back in history, Andi has been many. As I mentioned off this crisis, you can look back. The two behaviors >>that businesses have one is to play defense funding. What happens two years later on the other one is you defense. But you immediately switched to offense. And then what happens two years later? Those companies that use this time to just defend and hunker down history said in a couple of years later, 21% of them out there for But those businesses that they shift from defense to offense and actually accelerate in this case is, um uh, programs like digitalization. 37% of perform so very sad premium for businesses that right now actually immediately switched to offense. Focus on this set of digitalization and empowering cloud managing data, ensuring the skills of the people. They're more likely not only to survive but thrive in the next three years that don't just use this thanks >>to your point. It's about survival. It's not about, you know, not getting disrupted because you're going to get disrupted. It's almost a certainty. And so, in order to survive, you've got to digitally transform your final thoughts on digital transformation that I want to ask you if there's a silver lining and all this. >>No, I think I mean, um, I'd say the final thoughts is this a sigh said is, I don't think anybody anybody would say that government in and the Christ World crisis that that comes, is is anything that anybody would wish for or would help for. But we can change. I mean, that is the reality it is here. It's impact. It's devastation. Um, I think the human toll that comes from that many families that are being impacted for that, um, you know, I think my my my heart goes to roll those families, my own family. It's in the Spain, one of the worst countries in the world that is being impacted for this. Um so it's ah, it's clearly a tragedy. I think what we do we can't change the company. Um, but we cannot let the conflicts define who we are. Either it's individuals for this company. Well, we can do is to choose How do we act on that pump it? I would say, um, those organizations and those individuals that take advantage of inspiration to understand that some of these behaviors are going to change on this time, that the more that we should technology to the cloud, the more that we should Workloads to the cloud the more that we use technologies like artificial intelligence on Dr Nonlinear decisions that massively optimize everything we do from the way that we deliver healthcare. So the way that we manage supply change to the way that we secure food, frankly, to the way that we protect the environment that is a silver lining, that technology. It is one of those solutions that help in all of these areas, and the silver lining of this is is hopefully, let's use this time to get better, prepare for the next academia to get better, prepare for the next crisis, to implement technologies that drive efficiency faster, they create new jobs, they protect the environment and what we cannot change the fund that we have 19. We can change what happens after the 19 So we return to there's something better than what we enter before. >>Very thoughtful commentary. Jesus. Thank you so much for coming on the cube. A blessing Steer to your family and yourself. >>Appreciate it, Dave. Thank you. And thank you for everything. You do too well, Keep everybody informed. >>Really? Our pleasure. And thank you for watching everybody. This is Dave Volante. You're watching the Cube's coverage of IBM. Think 2020. The digital event. Right back. Right after this. Short break. >>Yeah, yeah, yeah.

>> Live from San Francisco. It's theCUBE covering IBM Think 2019 brought to you by IBM. >> Welcome back everyone, this is the CUBE's live coverage in San Francisco at Moscone Center for IBM Think 2019. I'm John Furrier with Dave. Volante Dave it's been in AI, it's been cloud, it's been in data changing the game. We've got two great guests here Murali Nemani, CMO of ScienceLogic, your CEO has been on the CUBE before and Joe Damassa who is the VP of strategy and offerings for hybrid cloud service at IBM. Thanks for joining us. >> Welcome. >> Appreciate it. >> Thank you guys. >> Welcome to CUBE. So day four of four days coverage, yes, you can see the messaging settling the feedback settling, AI clearly front and center, role of data in that and then cloud scale across multiple capabilities. Obviously on premise multi cloud is existing already. Software's changing all this. >> Right. >> And so AI impacting operations is key. So how do you guys work together? What's the relationships in ScienceLogic and IBM? Could you just take a minute to explain that? >> I think I mean, clearly, as you talked about the hybrid nature of what we're dealing with, with the complexity of it, it's all going to be about the data. You know, software is great, but it's about software that collects the data, analyzes the data, and gives you the insights so you can actually automate and create value for our clients. So it's really this marriage, it's a technology but it's a technology that allows us to get access to the data so we can make change, it's all about the data. >> And so a lot of what IBM has been doing is building the analytics engines and Watson it's for them. Our partnership has been really building the data and the data lake and the real time aspects of collecting and preparing that data so that you can really get interesting outcomes out of it. So when you think about predictive models, when you think about the the way that data can be applied to doing things like anomaly detection that ultimately accelerate and automate operations. That's where the relationship really starts taking hold. >> So you guys are specialized in AIops and IT apparatus as that transforms with scale and data which you need machine running, you need a kind of gave it automation. >> Yes. >> And which is the devops use of operations is don't go down, right, up and running, high availability. >> Yeah. >> So on the cloud services side, talk about where the rubber is meeting the road from a customer standpoint, because the cultural shift from IT Service Management, IT operations has been this manual, some software here and there, but it's been a process. Older processes change a little bit, but this is a new game. Talk about how you guys are engaging the customers. >> Well, a part of it I mean, it's interesting when you step back and you stop breathing, you're on exhaust in terms of pushing what you're trying to sell and you listen to your customers what we're hearing is that they all understand the destination. They understand they're moving to the cloud, they understand the value that's going to bring, they're having a hard time getting started. It's how do I start the journey ? I've got all of this estate and traditional IT operations capabilities it's kind of move. How do I modernize it? How do I make it so it's portable across different environments. And so when you step back, you know, we basically said, hey, you need the portability of the platform. So what we're doing with Red Hat, what we're doing with IBM, cloud private, it creates that portable containerizing ability to take our existing workloads and start moving them, right. And then the other thing that the clients need are the services. Who's going to help me advise me on what workloads should move, which one shouldn't, most of the staff fails because you move the wrong things. How do you manage that? How do you build it? And then when you're done, and you've got this hybrid complex environment, how do we actually get insights to it and the data I need to operationalize it? How do I do IT apps, when I don't own everything within the four walls of my data set. >> Now, are you guys going to market together? You guys sell each other products, the relationship with ScienceLogic and IBM is it a partnership, is it a joint development? Can you explain a little bit more on how you guys work together? >> Well, we're one of the largest sort of services provider in the industry. So as we bring, our products, our technologies and our capabilities to market, we bring ScienceLogic into those deals, we use ScienceLogic in our services so that we can actually deliver the value to our clients. So it is sort of a co development, co joint partnership plus also our goal to market. >> So you use that as a tool to do discovery and identify the data that's in and the data that we're talking about is everything I need to know about my IT operations, my applications, the dependencies. Maybe you could describe a little bit more. >> Sure if you think about one of the things that Joe was mentioning is, today, the workloads are shifting, you're going from, let's say management performance monitoring and management platforms that you need to evolve from, to incorporate new technologies like containers and microservices and server-less architectures. That's one area of how did the tool sets fundamentally evolve to support the latest technologies that are being deployed? So think about that. Second is, how do you consolidate those set of tools now you're managing? Because you're adopting cloud based technologies or new capabilities, and so get consolidation there. And the third is, these workloads that are now migrating out of your private cloud or private data center into public clouds, right? And then that workload migration, I think it is Forrester level saying, about 20% of the total workloads are currently in some sort of a public cloud environments. So there's a lot of work to do in terms of getting to that tipping point of where workloads are now truly in a multi cloud hybrid cloud. So as IBM accelerates that transition and their core competencies in helping these large enterprises make that transition, you need a common manageable environment, that the common visibility across those workloads. So that's at the heart of what we're pulling, and then the data sets happened to be data sets that are coming either from the application layer, data coming from the log management systems, it could be data coming from a service desk in terms of the kind of CMDB based data sets, and we're building a data lake that ultimately allows you to see across these heterogeneous system. >> It could be service request to get that really touches the business process so you can now start to sort of map the value and how change is going to affect that value, right? >> Yeah, exactly. >> Yeah. >> I mean, what's interesting about ScienceLogic as a partner, it's the breadth of their platform in terms of the different things they can monitor, the depth, the ability to go into containers, and kind of understand what the applications are doing in them and the scale in terms of the types of devices. So when you think about, the types of devices, we're going to have to manage everything from, sensors in an Internet of Things, environment to routers, to sophisticated servers and applications that can be running anywhere, you need the flexibility of the platform that they have in order to be able to deliver that. >> And I think that's a key point when you talking about containers and Kubernetes, we heard your CEO Jeannie remitting mentioned Kubernetes, onstage like, that's great, good time(mumbles) I know no one like Kubernetes now it's mainstream. >> Yeah. >> So this is showing them what's going on the industry which is the on premise decomposition of on premise with cloud private, you guys have. >> Yes. >> Is giving them the ability to use containers to manage their existing stuff and do that work and then have the extension to cloud, public cloud or whatever public cloud. This gives them more mount modern capabilities. So the question is, this change the game we know that but how has it changed AIOps and what does it mean? So I guess the first question is, what is AIOps? And what is this new on premise with cloud private and full public cloud architecture look like in AIOps 2.0? >> So for me, it's a very simple definition. It's really using algorithmic mechanisms, right? Towards automating operations, right? It's a very simple way, simplistic way of looking at it. But ultimately, the end game is to automate operations because you need to move at the pace of business and machine speed. And if you want to go, move in machine speed, you can have, I mean, you can't throw enough humans at this problems, right? Because of the pace of change, the familiarity of the workloads spinning up and sitting down. We have a bank as a customer who turns up containers for every 90 seconds and then turn them down. Just can't keep that in that real time state of change and being able to understand the topological relationships between the application layer and the underlying infrastructure so that you can truly understand the service health because when an application degrades in performance, the biggest issue is a war room's scenario where everyone's saying, it's not me, it's not me and because everyone's green on their front, but it's now how do you get that connective tissue all the way running-- >> Well it's also not only the change, it's also the velocity of data coming off that exhaust or the changes and services is thrown off tons of data that you need machines now I mean, that's kind of the thing. >> Exactly, yeah. And I would add to that, I think part of the definition of AIOps is evolving. We know where we're coming from is more fit for purpose analytics, right? I have this problem, I'm the collect this data, I'm going to put these automations in place too address it. We need to kind of take it data Model approach that says, how do I ingest all of this data? You know, even at the start, when you're looking at which workloads and you're doing discovery and assessment of workloads, that data should go into a data lake that can be used later when you're actually doing the operations and management of those workloads. So what data do we collect at every stage of the migration and the transformation of it, and including the operational data? And then how do we put a form analytics on it, and then get the true insights? I think we're just scratching the surface of applying to AI, because it's all been very narrow cast, narrow focus, I have this problem, I collect this data, I can automate this server, it needs to move much beyond that to it... >> And services are turning up and on and off so fast as a non deterministic angle here, and you got state, non deterministic, I mean, those are hard technical computer science problems to solve >> Yeah. >> That's you don't just put a processor around say, oh, yeah. >> Well, let's back to the the scalability of the platform, the ability in real time to be monitoring and looking at that data and then doing something right. >> All right now, humans aren't completely removed from the equation, right? And so I'm interested in how the humans are digesting and visualizing all this data, especially at this speed there a visualization component? How does that all evolving? >> Yeah, I think that to me I mean, that's part of the biggest challenges. You humans are a, they have to be the ones that kind of analyze what's coming and say, what does this mean when you haven't already algorithmically built it into your automation technology, right? And then they also don't have to be the one to train, the system is doing to actually do it. So one of the things that were are that struggling with not struggling with, we're experimenting with is, how best to visualize this, right? We do some things now, we've got a hybrid cloud management platform, we're teaming with the product guys, and it's the ability to have four consoles. One from a consumption, how do I consume services from Amazon, IBM Cloud on premise, how do I deploy it? So in a Dev apps model, how do I fulfill that very quickly and operational councils, right, and then cost on asset management so you can actually have at glance say, oh, you know, I've got a big Hadoop cluster which been spun up, I'm paying $100,000 for it and it has zero utilization. So how do you visualize that so you can say oh, I'm need to put a rule in that if somebody's spinning something up on, you know, IBM Cloud and they're not using it, I either shut it down, or I sent messages out, right, for governance in top of it. So it's putting business rules and logic in terms, in addition to visualization to help automate. >> And Jeannie talked about this at our keynote efficiency versus innovation around how to manage and this is where the scale comes in. Because if you know that something's working, you want to to double down on it, you can then, kind of automate that away and then you just move someone, the humans to something else. This is where the AIOps I think it's going to be, I think, going to change the category. I mean, it's a Gartner Magic Quadrant for the IT operations. >> Right. >> AI potentially decimates that, I mean... >> Yeah, there's this argument that you know, you have these nice quadrants or let's say nicely defined market segments. You have the NPMD, the ITSM, the ITOM, you know, you have APM and so what's happening is in this world of AIOps, none of those D marks really fit anymore because you're seeing the convergence of that. And then the other transition that's happening is this movement from, you know, classic ops or Dev and a dev to Ops, Dev Ops and now dev sec Ops, you know, you're trying to get worlds to converge. And so when we talk about the data and being able to build data models, those data models need to converge across those domains. So a lot of the work we do is collect data sets from log management, from service desk and service management, from APM etc, and then build that data model in real time. So you can.... >> It kind of building an Uber or CMDB or I mean, right? (loud laughter) I mean, do most of your clients have a single CMDB? Probably not, right? >> Yeah. So this is sort of a new guidepost, isn't it? >> Yeah, a part of it is. There are these data puddles if you will, all right data exist in a lot of different places How do you bring them together so you can federate different data sources, different catalogs into a common platform because if a user is trying to decide, okay, should I spin this up on, you know, this environment or that one, you want the full catalog of capabilities that are on premise in your CMDB system with the legacy environment out of the catalogs that may exist on Amazon or Azure, etc and you want data across all that. >> It seems that everything's a data problem now. And datas are being embedded into the applications which are then the workflows are defining infrastructure, architecture, or are sole cloud, multi cloud, whatever the resource is, so we had JPMorgan Chase on top data geek on and she was talking about, we have models for the models and IBM has been talking about this concept of reasoning around the data. This is why I always like the cognition kind of angle of cognitive, because that's not just math, math is math, you do math on, you know, supervised machine learning and knowing processes to be efficient, but the cognition and the reasoning really helps get at that data set, right. So can you guys react to that? I mean, is everything a data problem? Is that how you should look at it and how does reasoning fit into all this? >> Well, I mean, that's back to your point about what is the humans role in this, right. So we're moving in a services business from primarily labor base with tools to make them more efficient to the technology doing the work. But the humans have to then say, when the technology get stumped, what does that mean? So should I build a new, how do I train it better? How do I, you know, take my domain expertise? How do I do the deep analytics to tell me all right, how do I solve those problems in the future? So the role changes I think Jenny talks about in terms of new collar workers. I mean, these are data scientists, these are people that understand the dynamics of the inner relationship of the different data, the data models that need to get built and they are guiding in effect the automation. >> I thought your CTO was on theCUBE talking about, Paul was talking about, you know, take the heavy and Rob Thomas was also on, the GM of the data plus AI team. I think he really nailed it. If you guys to take away the heavy lifting of the setup work then the data science who're actually there to do the reasoning or help assist in managing what's going on and putting guard rails around whatever business policy is. >> Today, I mean, we talked to in this about 79 percent I think it's a gardener stat of 79 percent of the data scientists. And these are these PhDs, they're highly valuable, spend their time collecting, preparing, cleansing those data models, right? So, you're now really applying that PhD level knowledge base towards solving a problem, you're just trying to make sense of the data. So one, do you have a holistic and a few? Two, is there a way to automate those things so you can then apply the human aspects towards the things that Joe was talking about. So that's a big part of what we're trying to come together in terms of the market for. >> Well guys thanks for the insight, thanks for coming on, great job. I think we talked for you know, an hour and on cultural shift because you mentioned the sets in here Ops and devs. It's a melting pot and it's a cultural shifts. I think that topic is worth following up on. But I'll let you guys just get a quick plug for you. I know you going to an event coming up and you got some work. You can talk about what you guys are doing. You got an event coming up, what your pitch, give a quick flag. >> Yeah, so we've got our symposium, which is our big user conference. It's in April. It's right in, it's on April 22 to 23rd to the 25th. It's in downtown Washington DC, Cherry Blossom festival season at the Ritz Carlton. And so a lot of that, we'll have theCUBE there as well. >> Yeah of course. >> So, we're looking forward to it. A lot of great energy to be carried over. >> We love going to the District. (laughs loudly) >> What don't we say, you guys are great, great to visit. So give the plugs with a service you're doing. Just give an update on what you guys are up to. >> Yeah, I think I mean, we're also we're investing the technology when we're full on board with the containerization, as we talked about, we're putting together a services portfolio. I think Jenny mentioned that we're taking a whole bunch of capability across IBM Global Technology Services, Global Business Services, and really coalescing into about, you know, 23 offerings to help customers advise on cloud, move to cloud build for cloud and manage on cloud and then you've seen the announcements here about what we're doing around the multi cloud management system. Those four console I talked about how do we help, you know, put a gearbox in place to manage the complexity of the hybrid nature that our customers are dealing with. >> It seems IBM got clear visibility on what's happening with cloud, cloud private, I think a really big announcement. I think it's not talked about in the show and I'll always kind of mentioned the key linchpin but you see cloud, multi cloud, hybrid cloud, you got AI and you got partnerships, ecosystem now its execution time, right? >> Yeah, exactly and, and frankly, that's the challenge, right? So we used to be able to manage it all on the four runs, right? Your SAP instances was in the data center, your servers were in the data center, your middleware is in the data center. Now I got my applications running in Salesforce.com often software as a service. I've got three or four different infrastructures of service providers. But I still have the legacy that I got to deal with. I mean the integration problems are just tremendous. >> Chairman VP of strategy at IBM hybrid cloud and Murali Nemani, CMO ScienceLogic, AI operations, bringing in hybrid clouds to theCUBE bringing all the coverage day four. I'm with Dave Volante, it's all about cloud AI developers all happening here in San Francisco this week. Stay with us from this short break. (upbeat music)

>> Live from Boston, it's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back, everyone, to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillin. We have two guests for this session, we have Rebecca Shockley, she is executive consultant and IBM Global Business Services, and Alfred Essa, vice president analytics and R&D at McGraw-Hill Education. Rebecca and Alfred, thanks so much for coming on theCUBE. >> Thanks for having us. >> So I'm going to start with you, Rebecca. You're giving a speech tomorrow about the AI ladder, I know you haven't finished writing it-- >> Shh, don't tell. >> You're giving a speech about the AI ladder, what is the AI ladder? >> So, when we think about artificial intelligence, or augmented intelligence, it's very pervasive, we're starting to see it a lot more in organizations. But the AI ladder basically says that you need to build on a foundation of data, so that data and information architecture's your first rung, and with that data, then you can do analytics, next rung, move into machine learning once you're getting more comfortable, and that opens up the whole world of AI. And part of what we're seeing is organizations trying to jump to the top of the ladder or scramble up the ladder really quickly and then realize they need to come back down and do some foundational work with their data. I've been doing data and analytics with IBM for 21 years, and data governance is never fun. It's hard. And people would just as soon go do something else than do data governance, data security, data stewardship. Especially as we're seeing more business-side use of data. When I started my career, data was very much an IT thing, right. And part of my early career was basically just getting IT and business to communicate in a way that they were saying the same things. Well now you have a lot more self-service analytics, and business leaders, business executives, making software decisions and various decisions that impact the data, without necessarily understanding the ripples that their decisions can have throughout the data infrastructure, because that's not their forte. >> So what's the outcome, what's the result of this? >> Well, you start to see organizations, it's similar to what we saw when organizations first started making data lakes, right? The whole concept of a data lake, very exciting, interesting, getting all the data in together, whether it's virtual or physical. What ended up happening is without proper governance, without proper measures in place, you ended up with a data swamp instead of a data lake. Things got very messy very quickly, and instead of creating opportunities you were essentially creating problems. And so what we're advising clients, is you really have to make sure that you're focused on taking care of that first rung, right? Your data architecture, your information architecture, and treating the data with the respect as a strategic asset that it is, and making sure that you're dealing with that data in a proper manner, right? So, basically telling them, yes we understand that's fun up there, but come back down and deal with your foundation. And for a lot of organizations, they've never really stepped into data governance, because again, data isn't what they think makes the company run, right? So banks are bankers, not data people, but at the same time, how do you run a bank without data? >> Well exactly. And I want to bring you into this conversation, Alfred, as McGraw-Hill, a company that is climbing the ladder, in a more steady fashion. What's your approach? How do you think about bringing your teams of data scientists together to work to improve the company's bottom line, to enhance the customer experience? >> First I'd sort of like to start with laying some of the context of what we do. McGraw-Hill Education has been traditionally a textbook publisher, we've been around for over a hundred years, I started with the company over a hundred years ago. (all laughing) >> You've aged well. >> But we no longer think of ourselves as a textbook publisher. We're in the midst of a massive digital transformation. We started that journey over five years ago. So we think of ourselves as a software company. We're trying to create intelligent software based on smart data. But it's not just about software and AI and data, when it comes to education it's a tale of two cities. This is not just the U.S., but internationally. Used to be, we were born, went to school, got a job, raised a family, retired, and then we die. Well now, education is not episodic. People need to be educated, it's life-long learning. It's survival, but also flourishing. So that's created a massive problem and a challenge. It's a tale of two cities, by that I mean there's an incredible opportunity to apply technology, AI, we see a lot of potential in the new technologies. In that sense, it's the best of times. The worst of times is, we're faced with massive problems. There's a lot of inequity, we need to educate a people who have largely been neglected. That's the context. So I think in now answering your question about data science teams, first and foremost, we like to get people on the teams excited about the mission. It's like, what are we trying to achieve? What's the problem that we're trying to achieve? And I think the best employees, including data scientists, they like solving hard problems. And so, first thing that we try to do is, it's not what skills you have, but do you like solving really, really hard problems. And then taking it next step, I think the exciting thing about data science is it's an interdisciplinary field. It's not one skill, but you need to bring together a combination of skills. And then you also have to excel and have the ability to work in teams. >> You said that the AI has potential to improve the education process. Now, people have only so much capacity to learn, how can AI accelerate that process? >> Yeah, so if we stand back a little bit and look at the traditional model of education, there's nothing wrong with it but it was successful for a certain period of years, and it works for some people. But now the need for education is universal, and life long. So what our basic model, current model of education is lecture mode and testing. Now from a learning perspective, learning science perspective, all the research indicates that that doesn't work. It might work for a small group of people, but it's not universally applicable. What we're trying to do, and this is the promise of AI, it's not AI alone, but I think this is a big part of AI. What we can do is begin to customize and tailor the education to each individual's specific needs. And just to give you one quick example of that, different students come in with different levels of prior knowledge. Not everyone comes into a class, or a learning experience, knowing the same things. So what we can do with AI is determine, very, very precisely, just think of it as a brain scan, of what is it each student need to know at every given point in time, and then based on that we can determine also, this is where the models and algorithms are, what are you ready to learn next. And what you might be ready to learn next and what I might be ready to learn next is going to be very different. So our algorithms also help route delivery of information and knowledge at the right time to the right person, and so on. >> I mean, you're talking about these massive social challenges. Education as solving global inequity, and not every company has maybe such a high-minded purpose. But does it take that kind of mission, that kind of purpose, to unite employees? Both of you, I'm interested in your perspectives here. >> I don't think it takes, you know, a mission of solving global education. I do firmly agree with what Al said about people need a mission, they need to understand the outcome, and helping organizations see that outcome as being possible, gives them that rally point. So I don't disagree, I think everybody needs a mission to work towards but it doesn't have to be solving-- >> You want to extract that mission to a higher level, then. >> Exactly. >> Making the world a better place. >> Exactly, or at least your little corner of the world. Again what we're seeing, the difficulty is helping business leaders or consumers or whomever understand how data plays into that. You may have a goal of, we want better relationship with our customer, right? And at least folks of my age think that's a personal one-on-one kind of thing. Understanding who you are, I can find that much more quickly by looking at all your past transactions, and all of your past behaviors, and whether you clicked this or that. And you should expect that I remember things from one conversation to the next. And helping people understand that, you know, helping the folks who are doing the work, understand that the outcome will be that we can actually treat our customers the way that you want to be treated as a person, gives them that sense of purpose, and helps them connect the dots better. >> One of the big challenges that we hear CDOs face is getting buy-in, and what you're proposing about this new model really appending the old sage on the stage model, I mean, is there a lot of pushback? Is it difficult to get the buy-in and all stakeholders to be on the same page? >> Yeah, it is, I think it's doubly difficult. The way I think about it is, it's like a shift change in hockey, where you have one shift that's on the ice and another one that's about to come on the ice, that's a period of maximum vulnerability. That's where a lot of goals are scored, people get upset, start fighting. (all laughing) That's hockey. >> That's what you do. >> Organizations and companies are faced with the same challenge. It's not that they're resisting change. Many companies have been successful with one business model, while they're trying to bring in a new business model. Now you can't jettison the old business model because often that's paying the bills. That's the source of the revenue. So the real challenge is how are you going to balance out these two things at the same time? So that's doubly difficult, right. >> I want to ask you quickly, 'cause we have to end here, but there's a terrible shortage of cybersecurity professionals, data science professionals, the universities are simply not able to keep up with demand. Do you see the potential for AI to step in and fill that role? >> I don't think technology by itself will fill that role. I think there is a deficit of talented people. I think what's going to help fill that is getting people excited about really large problems that can be solved with this technology. I think, actually I think the talent is there, what I see is, I think we need to do a better job of bringing more women, other diverse groups, into the mix. There are a lot of barriers in diversity in bringing talented people. I think they're out there, I think we could do a much better job with that. >> Recruiting them, right. Alfred, Rebecca, thanks so much for coming on theCUBE, it was a pleasure. >> Thank you so much for having us. >> I'm Rebecca Knight, for Paul Gillin, we will have more from theCUBE's live coverage of the IBM CDO Summit here in Boston coming up in just a little bit.

(dynamic music) >> Hi, I'm Peter Burris, and welcome to another CUBEConversation. This is another very special CUBEConversation in that it's part of a product launch. Today, BMC has come on to theCUBE to launch Helix, a new approach to thinking about cognitive services management. And we're, over the course of the next 20 minutes or so, gonna present some of the salient features of Helix and how it solves critical business problems. And at the end of the segment, at the end of this video segment, we're gonna then go into a CrowdChat and give you, the community, an opportunity to express your thoughts, ask your questions, and get the information that you need from us analysts, from BMC, and also from your peers about what you need to do to exploit cognitive systems management in your business. Now this is a very real problem, this is not something that's being made up. The reality is we're looking at a lot of data-first technologies that are transforming the way business works. Technologies like AI, and machine learning, and deep learning, technologies like big data, having an enormous impact about how businesses behave. These technologies invoke much greater complexity at the application at the systems level and Wikibon strongly believes that we do not understand how businesses can pursue these technologies and these richer applications without finding ways to apply elements of them directly into the IT service management stack. And the reason why is if you don't have high-quality, lower-cost, speedy automation inside how you run your service management overall platform, then it's going to create uncertainty up hiring stack and that's awful for digital business. So to better understand and take us through this launch today, we've got some great guests. And it starts, obviously, with the esteemed Nayaki Nayyar who is the President of the Digital Services Management business unit at BMC, CUBE alum. Nayaki, thanks very much for being here. >> Thank you, Peter, really excited to be here and look forward to our conversation. We are too excited about the launch of BMC Helix and happy to share the details with you. >> So let's start with the why. Obviously, there's a... You know, I've articulated kind of a generalization of some of the challenges that businesses face but it goes deeper than that. Take us through some of the key issues that your customers are facing as they think about this transition to a new way of running their business. >> So, let's put ourselves in the customers' shoes. Then you look at what their journey looks like. Customers are evolving from the online world into the digital world and what we see is, what we call, cognitive world. And the way their journey looks like, especially as customers are entering into the digital world, there are proliferation of clouds. They don't have just one cloud, they have private clouds, hybrid clouds, managed clouds, we call it multi-cloud. So they're entering into a multi-cloud world. In addition, there's also proliferation of devices. It's not just phones that we have to worry about now. As IoT's getting more and more relevant and prevalent, how you help customers manage all the devices and how you provide the service through not just one channel but channel of our customers' or consumers' preference. It could be a Slack as a channel, SMS as a channel, Skype as a channel. So across this multi-cloud, multi-device, and multi-channel, this explosion of technology that is happening in every customer's landscape, and to address this explosion, is where AIML, chatbots, and virtual agents really play a role for them to handle the complexities. So the automation that AIML, chatbots, and virtual agents bring to help customers address these multi-cloud, multi-channel, multi-device world is what we call how we have them evolve from ITSM to cognitive services management. >> Let's talk about that a little bit. We'll get into exactly what you're announcing in a second but historically when we thought about service management we thought about devices. What you're really describing, this transition is, again that notion of how all of these different elements come together in, sometimes, very unique ways and that's what's driving the need for the cognitive. It's not just, you can do multiple clouds, multi-devices, multiple channels, it's your business can put them together in ways that serve your business' needs the best. And now we need a service management capability that can attend to those resources. >> Absolutely. So if you go 10, 15 years back, BMC had a great portfolio. We had Remedy Service Management Suite. We also had Discovery to help customers discover the on-prem assets and provide its service to remedy service management. That's what we had, we were very successful. ITSM, as a category, was created for that whole space. But in this new world of multi-cloud, right, where customers have private clouds, managed clouds, hybrid clouds, multi-devices where IoT is becoming more and more relevant, and multi-channel, customers now have to discover these assets. We call it Discovery as-a-Service but now they can discover the assets across AWS, Azure, OpenStack, and Cloud Foundry and evolve into providing service from reactive to proactive service, and that's what we call Remedy as-a-Service, and then extend that service beyond IT to also lines of business. Now you wanna also provide that service to HR, and procurement, and also various lines of business. And the most important thing is how you provide that experience to your end-users and your end-customers is what we call Digital Workplace-as-a-Service where now customers can consume that service in channel of their preference. They can consume that service through mobile device, of course through web, but also Slack, SMS, chatbots, and virtual agents. So that's what we are combining all of that, that entire suite, we are containerizing that suite using Dockers and Kubernetes so that now customers can run in their choice of cloud. They can run it in AWS cloud, Azure cloud, or in BMC cloud. This whole suite is what we call BMC Helix and helps our customers evolve from ITSM to what we call cognitive services management. >> So that's what BMC's announcing today. >> Yes. >> It's this notion of BMC Helix. >> Yes. >> And it's predicated on the idea, if I can, also of, not only you're going to use these technologies to manage new stuff, we have to bring the old stuff forward. Additionally, we're gonna see a mix of labor, or people, and automation as companies find the right mix for them. >> Right. >> And so we wanna bring and sustain these practices and these approaches forward. Nobody likes a forced migration, especially not in an IT organization. >> Right. >> So that's how we see Helix. if I got this right. >> Yes. >> Helix is gonna help customers bring their existing assets, existing practices, modernize them using some of the new technologies and that's how we get to this new cognitive vision. >> Absolutely. The investments customers have already made in their on-prem assets, in their managing their IT assets, that same concepts come into this new multi-cloud, multi-device, and multi-channel world but now it extends beyond that. It extends beyond just IT to also lines of business and also all these, what we call, omni-channel experiences that you can provide. And this whole suite is, what we call, 3 C's, Helix stands for 3 C's. Everything as a service, Remedy as-a-Service, Discovery as-a-Service, Business Workplace as-a-Service, containerized so that customers can run this in the choice of their cloud, they can run in AWS cloud, Azure cloud, or our cloud with cognitive capabilities, with AIML, and chatbots. And that's how we help them evolve from that existing implementations to this whole new world as they enter into the cognitive world. >> Exciting stuff. >> Absolutely. We are very excited about it. We've been working with a lot of customers already, and we have made really, really good traction. >> So let's do this, Nayaki, let's take a look at a product video that kinda describes how this all comes together in a relatively simple, straightforward way. >> Absolutely. (upbeat music) >> Hi, Peter Burris again, welcome back. We're talking more about BMC's Helix announcement. Great product video. Once again, we're here with Nayaki Nayyar, but we're also being joined by Vidhya Srinivasan who's in Marketing within the Digital Services Management unit at BMC. Thank you very much for joining us in theCUBE. >> Great to be here, thank you. >> So we've heard a lot about the problems, we've heard a lot about BMC Helix as a solution, but obviously it's more than just the technology. There's things that customers have to think about, about how these technologies, how service management, cognitive service management's going to be impacting the business. As businesses become more digital, technology and related services get dragged more deeply into functions. So, Nayaki, tell us a little bit more about how the outcomes within business, the capabilities of businesses are gonna change as a consequence of applying these technologies. >> Absolutely, Peter. So if you look at, traditionally, IT service management was a very reactive process. Every ticket that came in was manually created, assigned, and routed. That was a very reactive process. But as we enter into this cognitive world and you apply intelligence, AIML, you evolve into what we call a proactive and predictive. Before an issue actually happens, you want to resolve that issue. And that's what we call the cognitive services management. And the real business outcomes, you put yourself in a customer's shoes who's providing this service and evolving into this proactive, predictive, and cognitive world, they wanna provide that service at the highest accuracy, at the highest speed, and the lowest cost. That's what is gonna become competitive advantage for every company indifferent of the industry. They could be in a telco, they could be in high-tech, or pharmaceutical. It doesn't matter which industry they are in, how they provide this service at the highest accuracy, highest speed, and lowest cost is gonna be fundamentally a competitive advantage for these customers. >> And when we talk about accuracy, again we're not just talking about accuracy in a technology context. We're talking about accuracy in terms of a brand promise, perhaps. >> Absolutely. >> Or a service promise, or a product promise. >> Yes. >> That's the context. We wanna make sure that the customer is getting what they expect fast, with accuracy, and at low cost. >> Right, every time you tweet or you're SMS-ing your service provider, you expect that response to be at the highest accuracy, at the speed, and the cost. >> So when we start talking about multi-channel, Vidhya, what we're really saying is that this is not just your, you know, this is not just service management for the traditional technology service desk. We're talking about service management for other personas, other individuals, other consumers as well. Take us through that a little bit. >> Yeah, that's right. So we actually take a very holistic approach, right, across the enterprise. So we have end-users who are, at the end of the day, the key subscribers or consumers of our service and we wanna make sure they're very happy with what we provide. We have the agents which kinda goes to the IT persona that people know about in the service desk. But then, as Nayaki said earlier, it's also about extending to a lines of business so you have HR agents, right, people who support HR requests, people who support facilities or procurement request. So making sure that the agent persona is able to do everything that they need to do at the most efficiency level that they can so that they can meet their SLAs to their end consumers is a big part of what Helix, BMC Helix and cognitive service management can provide. And ultimately, when you think about this transformation and where they wanna go, there's a lot of custom applications and custom needs that businesses have. So really thinking about the developer persona and how you actually embed and build intelligent applications through our cognitive microservices that BMC Helix provides is a big part of that value proposition we provide. So as you navigate through this journey and become a cognitive enterprise, how do you make sure that all of these personas throughout your enterprise is able to deliver and get value out of this is what BMC Helix provides for the whole enterprise. >> So the whole concept of incorporating these cognitive capabilities into a service management stack allows us to not only envision, in a traditional way, more complex applications but actually extend this out to new classes of users because we are masking a lot of the complexity and a lot of the uncertainty associated with how this stuff works from that customer. >> That's correct. >> For end-users, for agents, and for developers, and consumers, and customers too. >> Great. >> That's good. >> So you know what... Great conversation. But let's hear what a customer has to say about it, shall we? >> Absolutely, okay. >> My name is Marco Jongen. I work for a company called DSM. And I'm the Director for Service Management within the Global Business Services department. Royal DSM is a global science-based company active in health, nutrition, and materials. And by connecting our unique competencies in life science and in material sciences, DSM is driving economic prosperity, environmental progress, and social advance to create sustainable value for all stakeholders simultaneously. The Global Business Service department is serving the 20,000 employees of DSM spread over 200 locations globally. We are handling, annually, about 600,000 tickets, and we are supporting four business functions: finance, HR, procurement, and IT. We started together with BMC on a shared services transformation across IT, HR, finance, and procurement. And we created a unified ticketing system and a self-service portal using the Remedy system and the Digital Workplace environment. And with this, we are now able to handle all functions in one unified ticketing tool and giving visibility to all our employees with questions related to finance, HR, purchasing, and IT. We were still have and involved with BMC in bringing this product to the next level and we are very excited in the work we have done with BMC so far. >> That was great to hear Royal DSM is transforming its shared services organization with cognitive services management. But, Nayaki, there's no such thing as an easy transformation especially one of this magnitude. We're talking about digital business which is, we're using data assets differently, it's affecting virtually every feature of business today. And now we've got a technology set that's gonna have potentially an enormous impact on IT but everything that IT is being, or everywhere that IT is being employed. That kind of a transformation is not something that people do lightly. They expect their suppliers to help them out. So what is BMC gonna do to ensure that customers are successful as they go through this transformation to cognitive services management? >> Absolutely, Peter. I always say these transformations are not one-month, two-month transformations. These are multi-year transformations and it's a journey that customers go through. We partner very closely with customers in this journey, assessing their requirements, understanding what their future looks like, and helping them every step of the way. Especially in service management, this change, this transformation that is happening, is gonna be very disruptive to their end-to-end processes. Today, all service desks are manned by individuals. Every ticket that comes in gets manually created, assigned, and routed. But if you fast forward into the future world in the next two to three years, that service desk function, which is especially level zero, level one, level two, service desk function, will completely get replaced by bots or virtual agents. It could be 50-50, 70-30, you can pick what the percentage-- >> Whatever the business needs. >> Right? But it is coming. And it is very important for customers to see that change and that transformation that is happening and to be ready for it. And that's where we are working very closely with them in making sure it's not just a system transformation. It's also the people side and the process that have to change. And companies who can do that, what we call cognitive service management using bots and virtual agents at the highest accuracy, highest speed, and the lowest cost, I keep coming back to that because that is what is gonna give them the highest competitive advantage. >> Lot to think about. >> Absolutely. >> Exciting future, crucial for IT if it's gonna succeed moving forward, but even if the business choose to use cloud, you're going to need to be able to discover and sustain service management at a very, very high level. >> Absolutely. How we discover, how we help them discover, how we help them provide that service proactively, predictively, and provide that experience through omni-channel experiences, what this whole thing brings together for our customers. >> Excellent, this has been a great conversation. Nayaki Nayyar, President of BMC's Digital Services Management business unit. Thank you very much for being here on theCUBE and working with us to help announce Helix. Now don't forget folks, that immediately after this, we'll be running the CrowdChat. And in that CrowdChat, your peers, BMC experts, us analysts will be participating to help answer your questions, share experience, identify simpler ways of doing more complex things. So join us in the CrowdChat. Once again, Nayaki, thank you very much. >> Thank you, Peter, and thank you everyone. Thank you all.

(upbeat music) >> Hi we're at the Palo Alto studio of SiliconANGLE Media and theCUBE. My name is George Gilbert, we have a special guest with us this week, Veeru Ramaswamy who is VP IBM Watson IoT platform and he's here to fill us in on the incredible amount of innovation and growth that's going on in that sector of the world and we're going to talk more broadly about IoT and digital twins as a broad new construct that we're seeing in how to build enterprise systems. So Veeru, good to have you. Why don't you introduce yourself and tell us a little bit about your background. >> Thanks George, thanks for having me. I've been in the technology space for a long time and if you look at what's happening in the IoT, in the digital space, it's pretty interesting the amount of growth, the amount of productivity and efficiency the companies are trying to achieve. It is just phenomenal and I think we're now turning off the hype cycle and getting into real actions in a lot of businesses. Prior to joining IBM, I was junior offiicer and senior VP of data science with Cable Vision where I led the data strategy for the entire company and prior to that I was the GE of one of the first two guys who actually built the Cyamon digital center. GE digital center, it's a center of excellence. Looking at different kinds of IoT related projects and products along with leading some of the UX and the analytics and the club ration or the social integration. So that's the background. >> So just to set context 'cause this is as we were talking before, there was another era when Steve Jobs was talking about the next work station and he talked about objectory imitation and then everything was sprinkled with fairy dust about objects. So help us distinguish between IoT and digital twins which GE was brilliant in marketing 'cause that concept everyone could grasp. Help us understand where they fit. >> The idea of digital twin is, how do you abstract the actual physical entity out there in the world, and create an object model out of it. So it's very similar in that sense, what happened in the 90s for Steve Jobs and if you look at that object abstraction, is what is now happening in the digital twin space from the IoT angle. The way we look at IoT is we look at every center which is out there which can actually produce a metric on every device which produces a metric we consider as a sense so it could be as simple as the pressure, temperature, humidity sensors or it could be as complicated as cardio sensors and your healthcare and so on and so forth. The concept of bringing these sensors into the to the digital world, the data from that physical world to the digital world is what is making it even more abstract from a programming perspective. >> Help us understand, so it sounds like we're going to have these fire hoses of data. How do we organize that into something that someone who's going to work on that data, someone is going to program to it. How do they make sense out of it the way a normal person looks at a physical object? >> That's a great question. We're looking at sensors as a device that we can measure out of and that we call it a device twin. Taking the data that's coming from the device, we call that as a device twin and then your physical asset, the physical thing itself, which could be elevators, jet engines anything, physical asset that we have what we call the asset twin and there's hierarchical model that we believe that will have to be existing for the digital twin to be actually constructed from an IoT perspective. The asset twins will basically encompass some of the device twins and then we actually take that and represent the digital twin on a physical world of that particular asset. >> So that would be sort of like as we were talking about earlier like an elevator might be the asset but the devices within it might be the bricks and the pulleys and the panels for operating it. >> Veeru: Exactly. >> And it's then the hierarchy of these or in manufacturing terms, the building materials that becomes a critical part of the twin. What are some other components of this digital twin? >> When we talk about digital twin, we don't just take the blueprint as schematics. We also think about the system, the process, the operation that goes along with that physical asset and when we capture that and be able to model that, in the digital world, then that gives you the ability to do a lot of things where you don't have to do it in the physical world. For instance, you don't have to train your people but on the physical world, if it is periodical systems and so on and so forth, you could actually train them in the digital world and then be able to allow them to operate on the physical world whenever it's needed. Or if you want to increase your productivity or efficiency doing predictive models and so forth, you can test all the models in your digital world and then you actually deploy it in your physical world. >> That's great for context setting. How would you think of, this digital twins is more than just a representation of the structure, but it's also got the behavior in there. So in a sense it's a sensor and an actuator in that you could program the real world. What would that look like? What things can you do with that sort of approach? >> So when you actually have the data coming this humongous amount of terabyte data that comes from the sensors, once you model it and you get the insights out of that, based on the insight, you can take an actionable outcome that could be turning off an actuator or turning on an actuator and simple thngs like in the elevator case, open the door, shut the door, move the elevator up, move the elevator down etc. etc All of these things can be done from a digital world. That's where it makes a humongous difference. >> Okay, so it's a structured way of interacting with the highly structured world around us. >> Veeru: That's right. >> Okay, so it's not the narrow definition that many of us have been used to like an airplane engine or the autonomous driving capability of a car. It's more general than that. >> Yeah, it is more general than that. >> Now let's talk about having sort of set context with the definition so everyone knows we're talking about a broader sense that's going on. What are some of the business impacts in terms of operational efficiency, maybe just the first-order impact. But what about the ability to change products into more customizable services that have SLAs or entirely new business models including engineered order instead of make to stock. Tell us something about that hierarchy of value. >> That's a great question. You're talking about things like operations optimization and predicament and all of that which you can actually do from the digital world it's all on digital twin. You also can look into various kinds of business models now instead of a product, you can actually have a service out of the product and then be able to have different business models like powered by the hour, pay per use and kinds of things. So these kinds of models, business models can be tried out. Think about what's happening in the world of Air BnB and Uber, nobody owns any asset but still be able to make revenue by pay per use or power by the hour. I think that's an interesting model. I don't think it's being tested out so much in the physical asset world but I think that could be interesting model that you could actually try. >> One thing that I picked up at the Genius of Things event in Munich in February was that we really have to rethink about software markets in the sense that IBM's customers become in the way your channel, sometimes because they sell to their customers. Almost like a supply chain master or something similar and also pricing changes from potentially we've already migrated or are migrating from perpetual licenses to service softwares or service but now we could do unit pricing or SLA-based pricing, in which case you as a vendor have to start getting very smart about, you owe your customers the risk in meeting an SLA so it's almost more like insurance, actuarial modeling. >> Correct so the way we want think about is, how can we make our customers more, what do you call, monetizable. Their products to be monetizable with their customers and then in that case, when we enter into a service level agreement with our customers, there's always that risk of what we deliver to make their products and services more successful? There's always a risk component which we will have to work with the customers to make sure that combined model of what our customers are going to deliver is going to be more beneficial, more contributing to both bottom line and top line. >> That implies that your modeling, someone's modeling and risk from you the supplier to your customer as vendor to their customer. >> Right. >> That sounds tricky. >> I'm pretty sure we have a lot of financial risk modeling entered into our SLAs when we actually go to our customers. >> So that's a new business model for IBM, for IBM's sort of supply chain master type customers if that's the right word. As this capability, this technology pervades more industries, customers become software vendors or if not software vendors, services vendors for software enhanced products or service enhanced products. >> Exactly, exactly. >> Another thing, I'd listened to a briefing by IBM Global Services where they thought, ultimately, this might end up where there's far more industries are engineered to order instead of make to stock. How would this enable that? >> I think the way we want think about it is that most of the IoT based services will actually start by co-designing and co-developing with your customers. And that's where you're going to start. That's how you're going to start. You're not going to say, here's my 100 data centers and you bring your billion devices and connect and it's going to happen. We are going to start that way and then our customers are going to say, hey by the way, I have these used cases that we want to start doing, so that's why platform becomes so imortant. Once you have the platform, now you can scale, into a scale, individual silos as a vertical use case for them. We provide the platform and the use cases start driving on top of the platform. So the scale becomes much easier for the customers. >> So this sounds like the traditional application. The traditional way an application vendor might turn into a platform vendor which is a difficult transition in itself but you take a few use cases and then generalize into a platform. >> We call that a zone application services. The zone application service is basically, is drawing on perfectly cold platform service which actually provides you the abilities. So for instance like an asset management. An asset management can be done in an oil and gas rig, you can look at asset management in power tub vine, you can can look at asset management in a jet engine. You can do asset management across any different vertical but that is a common horizontal application so most of the time you get 80% of your asset management API's if you will. Then you can be able to scale across multiple different vertical applications and solutions. >> Hold that thought 'cause we're going to come back to joint development and leveraging expertise from vendor and customer and sharing that. Let's talk just at a high level one of the things that I keep hearing is that in Europe industry 4.0 is sort of the hot topic and in the states, it's more digital twins. Help parse that out for us. >> So the way we believe how digital twin should be viewed is a component view. What we mean the component view is that we have your knowledge graph representation of the real assets in the digital world and then you bring in your IoT sensors and connections to the models then you have your functional, logical, physical models that you want to bring into your knowledge graph and then you also want to be able to give the ability of search visualize allies. Kind of an intelligent experience for the end consumer and then you want to bring your similation models when you do the actual similation models in digital to bring it in there and then your enterprise asset management, your ERP systems, all of that and then when you connect, when you're able to build a knowledge graph, that's when the digital twin really connects with your enterprise systems. Sort of bring the OT and the IT together. >> So this is sort of to try and summarize 'cause there are a lot of moving parts in there. You've got you've got the product hierarchy which, in product Kaiser call it building materials, sort of the explosion of parts in an assembly, sub-assembly and then that provides like a structure, a data model then the machine learning models in the different types of models that they could be represent behavior and then when you put a knowledge graph across that structure and behavior, is that what makes it simulation ready? >> Yes, so you're talking about entities and connecting these entities with the actual relationship between these entities. That's the graph that holds the relation between nodes and your links. >> And then integrating the enterprise systems that maybe the lower level operation systems. That's how you effect business processes. >> Correct. >> For efficiency or optimization, automation. >> Yes, take a look at what you can do with like a shop floor optimization. You have all the building materials, you need to know from your existing ERP systems and then you will actually have the actual real parts that's coming to your shop floors to manage them and now base supposing, depending on whether you want to repair, you want to replace, you want an overall, you want to modify whatever that is, you want to look at your existing building materials and see, okay do I first have it do we need more? Do we need to order more? So your auditing system naturally gets integrated into that and then you have to integrate the data that's coming from these models and the availability of the existing assets with you. You can integrate it and say how fast can you actually start moving these out of your shop, into the. >> Okay that's where you translate essentially what's more like intelligent about an object or a rich object into sort of operational implications. >> Veeru: Yes. >> Okay operational process. Let's talk about customer engagement so far. There's intense interest in this. I remember in the Munich event, they were like they had to shut off attendance because they couldn't find a big enough venue. >> Veeru: That's true. >> So what are the characteristics of some of the most successful engagements or the ones that are promising. Maybe it's a little early to say successful. >> So, I think the way you can definitely see success from customer engagement are two fold. One is show what's possible. Show what's possible with after all desire to connect, collection of data, all of that so that one part of it. The second part is understand the customer. The customer has certain requirements in their existing processes and operations. Understand that and then deliver based on what solutions they are expecting, what applications they want to build. How you bring them together is what is, so we're thinking about. That Munich center you talked about. We are actually bringing in chip manufacturers, sensor manufacturers, device manufacturers. We are binging in network providers. We are bringing in SIs, system integrators all of them into the fold and show what is possible and then your partners enable you to get to market faster. That's how we see the engagement with customer should happen in a much more foster manner and show them what's possible. >> It sounds like in the chip industry Moore's law for many years it wasn't deterministic that you we would do double things every 18 months or two years, it was actually an incredibly complex ecosystem web where everyone's sort of product release cycles were synchronized so as to enable that. And it sounds like you're synchronizing the ecosystem to keep up. >> Exactly The saxel of a particular organization IoT efforts is going to depend on how do you build this ecosystem and how do you establish that ecosystem to get to market faster. That's going to be extremely key for all your integration efforts with your customer. >> Let's start narrowly with you. IBM what are the key skills that you feel you need to own starting from sort of the base rocket scientists you know who not only work on machine learning models but they come up with new algorithms on top of say tons of flow work or something like that. And all the way up to the guys who are going to work in conjunction with the customer to apply that science to a particular industry. How does that hold together? >> So it all starts on the platform. On the platform side we have all the developers, the engineers who build these platform all the video connection and all of that to make the connections. So you need the highest software development engineers to build these on the platform and then you also need the solution builders so who is in front of the customer understanding what kind of solutions you want to build. Solutions could be anything. It could be predictive maintenance, it could be as simple as management, it could be remote monitoring and diagnostics. It could be any of these solutions that you want to build and then the solution builders and the platform builders work together to make sure that it's the holistic approach for the customer at the final deployment. >> And how much is the solution builder typically in the early stages IBM or is there some expertise that the customer has to contribute almost like agile development, but not two programmers but like 500 and 500 from different companies. >> 500 is a bit too much. (laughs) I would say this is the concept of co-designing and co-development. We definitely want the ultimate, the developer, the engineers form, the subject exports from our customers and we also need our analytics experts and software developers to come and sit together and understand what's the use case. How do we actually bring in those optimized solution for the customer. >> What level of expertise or what type of expertise are the developers who are contributing to this effort in terms of do they have to, if you're working with manufacturing let's say auto manufacturing. Do they have to have automotive software development expertise or are they more generically analytics and the automotive customer brings in the specific industry expertise. >> It depends. In some cases we have RGB for instance. We have dedicated servers, that particular vertical service provider. We understand some of this industry knowledge. In some cases we don't, in some cases it actually comes from the customer. But it has to be an aggregation of the subject matter experts with our platform developers and solution developers sitting together, finding what's the solution. Literally going through, think about how we actually bring in the UX. What does a typical day of a persona look like? We always by the way believe it's an augmented allegiance which means the human and the machine work together rather than a complete. It gives you the answer for everything you ask for. >> It's a debate that keeps coming up Doug Anglebad sort of had his own answer like 50 years ago which was he sort of set the path for modern computing by saying we're not going to replace people, we're going to augment them and this is just a continuation of that. >> It's a continuation of that. >> Like UX design sounds like someone on the IBM side might be talking to the domain expert and the customer to say how does this workflow work. >> Exactly. So have this design thinking, design sessions with our customers and then based on that we take that knowledge, take it back, we build our mark ups, we build our wire frames, visual designs and the analytics and software that goes behind it and then we provide on top of platform. So most of the platform work, the standard what do you call table state connections, collection of data. All of that as they are already existing then it's one level above as to what the particular solution a customer wants. That's when we actually. >> In terms of getting the customer organization aligned to make this project successful, what are some of the different configurations? Who needs to be a sponsor? Where does budget typically come from? How long are the pilots? That sort of stuff so to set expectations. >> We believe in all the agile thinking, agile development and we believe in all of that. It's almost given now. So depending on where the customer comes from so the customer could actually directly come and sign up to our platform on the existing cloud infrastructure and then they will say, okay we want to build applications then there are some customers really big customers, large enterprises who want to say, give me the platform, we have our solution folks. We will want to work on board with you but we also want somebody who understands building solutions. We integrate with our solution developers and then we build on top of that. They build on top of that actually. So you have that model as well and then you have a GBS which actually does this, has been doing this for years, decades. >> George: Almost like from the silicon. >> All the way up to the application level. >> When the customer is not outsourcing completely, The custom app that they need to build in other words when when they need to go to GBS Global Business Services, whereas if they want a semi-packaged app, can they go to the industry solutions group? >> Yes. >> I assume it's the IoT, Industry Solutions Group. >> Solutions group, yes. >> They then take a it's almost maybe a framework or an existing application that needs customization. >> Exactly so we have IoT-4. IoT for manufacturing, IoT for retail, IoT for insurance IoT for you name it. We have all these industry solutions so there would be some amount of template which is already existing in some fashion so when GBS gets a request to say here is customer X coming and asking for a particular solution. They would come back to IoT solutions group to say, they already have some template solutions from where we can start from rather than building it from scratch. You speed to market again is much faster and then based on that, if it's something that is to be customizable, both of them work together with the customer and then make that happen, and they leverage our platform underneath to do all the connection collection data analytics and so on and so forth that goes along with that. >> Tell me this from everything we hear. There's a huge talent shortage. Tell me in which roles is there the greatest shortage and then how do different members of the ecosystem platform vendors, solution vendors sort of a supply-chain master customers and their customers. How do they attract and retain and train? >> It's a fantastic question. One of the difficulties both in the valley and everywhere across is that three is a skill gap. You want advanced data scientists you want advances machinery experts, you want advanced AI specialists to actually come in. Luckily for us, we have about 1000 data scientists and AI specialists distributed across the globe. >> When you say 1000 data scientists and AI specialists, help us understand which layer are they-- >> It could be all the way from like a BI person all the way to people who can build advanced AI models. >> On top of an engine or a framework. >> We have our Watson APIs from which we build then we have our data signs experience which actually has some of the models then built on top of what's in the data platform so we take that as well. There are many different ways by which we can actually bring the AM model missionary models to build. >> Where do you find those people? Not just the sort of band strengths that's been with IBM for years but to grow that skill space and then where are they also attracted to? >> It's a great question. The valley definitely has a lot of talent, then we also go outside. We have multiple centers of excellence in Israel, in India, in China. So we have multiple centers of excellence we gather from them. It's difficult to get all the talent just from US or just from one country so it's naturally that talent has to be much more improvement and enhanced all the wat fom fresh graduates from colleges to more experienced folks in the in the actual profession. >> What about when you say enhancing the pool talent you have. Could it also include productivity improvements, qualitative productivity improvements in the tools that makes machine learning more accessible at any level? The old story of rising obstruction layers where deep learning might help design statistical models by doing future engineering and optimizing the search for the best model, that sort of stuff. >> Tools are very, very hopeful. There are so many. We have from our tools to python tools to psychic and all of that which can help the data scientist. The key part is the knowledge of the data scientist so data science, you need the algorithm, the statistical background, then you need your applications software development background and then you also need the domestics for engineering background. You have to bring all of them together. >> We don't have too many Michaelangelos who are these all around geniuses. There's the issue of, how do you to get them to work more effectively together and then assuming even each of those are in short supply, how do you make them more productive? >> So making them more productive is by giving them the right tools and resources to work with. I think that's the best way to do it, and in some cases in my organization, we just say, okay we know that a particular person is skilled is up skilled in certain technologies and certain skill sets and then give them all the tools and resources for them to go on build. There's a constant education training process that goes through that we in fact, we have our entire Watson ED platform that can be learned on Kosera today. >> George: Interesting. >> So people can go and learn how to build a platform from a Kosera. >> When we start talking with clients and with vendors, things we hear is that and we were kind of I think early that calling foul but in the open source infrastructure big data infrastructure this notion of mix-and-match and roll your own pipeline sounded so alluring, but in the end it was only the big Internet companies and maybe some big banks and telcos that had the people to operate that stuff and probably even fewer who could build stuff on it. Do we do we need to up level or simplify some of those roles because mainstream companies can't have enough or won't will have enough data scientists or other roles needed to make that whole team work >> I think it will be a combination of both one is we need to up school our existing students with the stem background, that's one thing and the other aspect is, how do you up scale your existing folks in your companies with the latest tools and how can you automate more things so that people who may not be schooled will still be able to use the tool to deliver other things but they don't have to go to a rigorous curriculum to actually be able to deal with it. >> So what does that look like? Give us an example. >> Think of tools like today. There are a lot of BI folks who can actually build. BI is usually your trends and graphs and charts that comes out of the data which are simple things. So they understand the distribution and so on and so forth but they may not know what is the random model. If you look at tools today, that actually gives you to build them, once you give the data to that model, it actually gives you the outputs so they don't really have to go dig deep I have to understand the decision tree model and so on and so forth. They have the data, they can give the data, tools like that. There are so many different tools which would actually give you the outputs and then they can actually start building app, the analytics application on top of that rather than being worried about how do I write 1000 line code or 2000 line code to actually build that model itself. >> The inbuilt machine learning models in and intend, integrated to like pentaho or what's another example. I'm trying to think, I lost my, I having a senior moment. These happen too often now. >> We do have it in our own data science tools. We already have those models supported. You can actually go and call those in your web portal and be able to call the data and then call the model and then you'll get all that. >> George: Splank has something like that. >> Splank does, yes. >> I don't know how functional it is but it seems to be oriented towards like someone who built a dashboard can sort of wire up a model, it gives you an example of what type of predictions or what type of data you need. >> True, in the Splank case, I think it is more of BI tool actually supporting a level of data science moral support on the back. I do not know, maybe I have to look at this but in our case we have a complete data science experience where you actually start from the minute the data gets ingested, you can actually start the storage, the transformation, the analytics and all of that can be done in less than 10 lines of coding. You can just actually do the whole thing. You just call those functions then it will the right there in front of you. So in twin you can do that. That I think is much more powerful and there are tools, there are many many tools today. >> So you're saying that data science experience is an enter in pipeline and therefore can integrate what were boundaries between separate products. >> The boundary is becoming narrower and narrower in some sense. You can go all the way from data ingestion to the analytics in just few clicks or few lines of course. That's what's happening today. Integrated experience if you will. >> That's different from the specialized skills where you might have a tri-factor, prexada or something similar as for the wrangling and then something else for sort of the the visualizations like Altracks or Tavlo and then into modeling. >> A year or so ago, most of data scientists try to spend a lot of time doing data wrangling because some of the models, they can actually call very directly but the wrangling is actually where they spend their time. How do you get the data crawl the data, cleanse the data, etc. That is all now part of our data platform. It is already integrated into the platform so you don't have to go through some of these things. >> Where are you finding the first success for that tool suite? >> Today it is almost integrated with, for instance, I had a case where we exchange the data we integrate that into what's in the Watson data platform and the Watson APIs is a layer above us in the platform where we actually use the analytics tools, more advanced AI tools but the simple machinery models and so on and so forth is already integrated into as part of the Watson data platform. It is going to become an integrated experience through and through. >> To connect data science experience into eWatson IoT platform and maybe a little higher at this quasi-solution layer. >> Correct, exactly. >> Okay, interesting. >> We are doing that today and given the fact that we have so much happening on the edge side of things which means mission critical systems today are expecting stream analysts to get to get insights right there and then be able to provide the outcomes at the edge rather than pushing all the data up to your cloud and then bringing it back down. >> Let's talk about edge versus cloud. Obviously, we can't for latency and band width reasons we can't forward all the data to the cloud, but there's different use cases. We were talking to Matasa Harry at Sparks Summit and one of the use cases he talked about was video. You can't send obviously all the video back and you typically on an edge device wouldn't have heavy-duty machine learning, but for video camera, you might want to learn what is anomalous or behavior call out for that camera. Help us understand some of the different use cases and how much data do you bring back and how frequently do retrain the models? >> In the case of video, it's so true that you want to do a lot of any object ignition and so on and so forth in the video itself. We have tools today, we have cameras outside where if a van goes it detect the particular object in the video live. Realtime streaming analytics so we can do that today. What I'm seeing today in the market is, in the transaction between the edge and the cloud. We believe edge is an extension of the cloud, closer to the asset or device and we believe that models are going to get pushed from the cloud, closer to the edge because the compute capacity and storage and the networking capacity are all improving. We are pushing more and more computing to their devices. >> When you talk about pushing more of the processing. you're talking more about predicts and inferencing then the training. >> Correct. >> Okay. >> I don't think I see so much of the training needs to be done at the edge. >> George: You don't see it. >> No, not yet at least. We see the training happening in the cloud and then once a train, the model has been trained, then you come to a steady, steady model and then that is the model you want to push. When you say model, it could be a bunch of coefficients. That could be pushed onto the edge and then when a new data comes in, you evaluate, make decisions on that, create insights and push it back as actions to the asset and then that data can be pushed back into the cloud once a day or once in a week, whatever that is. Whatever the capacity of the device you have and we believe that edge can go across multiple scales. We believe it could be as small with 128 MB it could be one or two which I see sitting in your local data center on the premise. >> I've had to hear examples of 32 megs in elevators. >> Exactly. >> There might be more like a sort of bandwidth and latency oriented platform at the edge and then throughput and an volume in the cloud for training. And then there's the issue of do you have a model at the edge that corresponds to that instance of a physical asset and then do you have an ensemble meaning, the model that maps to that instance, plus a master canonical model. Does that work for? >> In some cases, I think it'll be I think they have master canonical model and other subsidiary models based on what the asset, it could be a fleet so you in the fleet of assets which you have, you can have, does one asset in the fleet behave similar to another asset in the fleet then you could build similarity models in that. But then there will also be a model to look at now that I have to manage this fleet of assets which will be a different model compared to action similarity model, in terms of operations, in terms of optimization if I want to make certain operations of that asset work more efficiently, that model could be completely different with when compared to when you look at similarity of one model or one asset with another. >> That's interesting and then that model might fit into the information technology systems, the enterprise systems. Let's talk, I want to go get a little lower level now about the issue of intellectual property, joint development and sharing and ownership. IBM it's a nuanced subject. So we get different sort of answers, definitive answers from different execs, but at this high level, IBM says unlike Google and Facebook we will not take your customer data and make use of it but there's more to it than that. It's not as black-and-white. Help explain that for so us. >> The way you want to think is I would definitely paired back what our chairman always says customers' data is customers' data, customer insights is customer insights so they way we look at it is if you look at a black box engine, that could be your analytics engine, whatever it is. The data is your inputs and the insights are our outputs so the insights and outputs belong to them. we don't take their data and marry it with somebody else's data and so forth but we use the data to train the models and the model which is an abstract version of what that engine should be and then more we train the more better the model becomes. And then we can then use across many different customers and as we improve the models, we might go back to the same customers and hey we have an improved model you want to deploy this version rather than the previous version of the model we have. We can go to customer Y and say, here is a model which we believe it can take more of your data and fine tune that model again and then give it back to them. It is true that we don't actually take their data and share the data or the insights from one customer X to another customer Y but the models that make it better. How do you make that model more intelligent is what out job is and that's what we do. >> If we go with precise terminology, it sounds like when we talk about the black box having learned from the customer data and the insights also belonging to the customer. Let's say one of the examples we've heard was architecture engineering consulting for large capital projects has a model that's coming obviously across that vertical but also large capital projects like oil and gas exploration, something like that. There, the model sounds like it's going to get richer with each engagement. And let's pin down so what in the model is sort of not exposed to the next customer and what part of the model that has gotten richer does the next customer get the balance of? >> When we actually build a model, when we pass the data, in some cases, customer X data, the model is built out of customer X data may not sometimes work with the customer Y's data so in which case you actually build it from scratch again. Sometimes it doesn't. In some case it does help because of the similarity of the data in some instance because if the data from company X in oil gas is similar to company Y in oil gas, sometimes the data could be similar so in which case when you train that model, it becomes more efficient and the efficiency goes back to both customers. we will do that but there are places where it would really not work. What we are trying to do is. We are in fact trying to build some kind of knowledge bundles where we can actually what used to be a long process to train the model can ow shortened using that knowledge bundle of what we have actually gained. >> George: Tell me more about how it works. >> In retail for instance, when we actually provide analytics, from any kind of IoT sense, whatever sense of data this comes in we train the model, we get analytics used for ads, pushing coupons, whatever it is. That knowledge, what you have gained off that retail, it could be models of models, it could be metamodels, whatever you built. That can actually serve many different customers but the first customer who is trying to engage with us, you don't have any data to the model. It's almost starting from ground zero and so that would actually take a longer time when you are starting with a new industry and you don't have the data, it'll take you a longer time to understand what is that saturation point or optimization point where you think the model cannot go any further. In some cases, once you do that, you can take that saturated model or near saturated model and improve it based on more data that actually comes from different other segments. >> When you have a model that has gotten better with engagements and we've talked about the black box which produces the insights after taking in the customer data. Inside that black box there's like at the highest level we might call it the digital twin with the broad definition that we started with, then there's a data model which a data model which I guess could also be incorporated into the knowledge graft for the structure and then would it be fair to call the operational model the behavior? >> Yes, how does the system perform or behave with respect the data and the asset itself. >> And then underpinning that, the different models that correspond to the behaviors of different parts of this overall asset. So if we were to be really precise about this black box, what can move from one customer to the next and what what won't? >> The overall model, supposing I'm using a random data retrieval model, that remains but actual the coefficients are the feature rector, or whatever I use, that could be totally different for customers, depending on what kind of data they actually provide us. In data science or in analytics you have a whole platora of all the way from simple classification algorithms to very advanced predictive modeling algorithms. If you take the whole class when you start with a customer, you don't know which model is really going to work for a specific user case because the customer might come and can say, you might get some idea but you will not know exactly this is the model that will work. How you test it with one customer, that model could remain the same kind of use case for some of other customer, but that actual the coefficients the degree of the digital in some cases it might be two level decision trees, in others case it might be a six level decision tree. >> It is not like you take the model and the features and then just let different customers tweak the coefficients for the features. >> If you can do that, that will be great but I don't know whether you can really do it the data is going to change. The data is definitely going to change at some point of time but in certain cases it might be directly correlated where it can help, in certain cases it might not help. >> What I'm taking away is this is fundamentally different from traditional enterprise applications where you could standardize business processes and the transactional data that they were producing. Here it's going to be much more bespoke because I guess the processes, the analytic processes are not standardized. >> Correct, every business processes is unique for a business. >> The accentures of the world we're trying to tell people that when SAP shipped packaged processes, which were pretty much good enough, but that convince them to spend 10 times as much as the license fee on customization. But is there a qualitative difference between the processes here and the processes in the old ERP era? I think it's kind of different in the ERP era and the processes, we are more talking about just data management. Here we're talking about data science which means in the data management world, you're just moving data or transforming data and things like that, that's what you're doing. You're taking the data. transforming to some other form and then you're doing basic SQL queries to get some response, blah blah blah. That is a standard process that is not much of intelligence attached to it but now you are trying to see from the data what kind of intelligence can you derive by modeling the characteristics of the data. That becomes a much tougher problem so it now becomes one level higher of intelligence that you need to capture from the data itself that you want to serve a particular outcome from the insights you get from is model. >> This sounds like the differences are based on one different business objectives and perhaps data that's not as uniform that you would in enterprise applications, you would standardize the data here, if it's not standardized. >> I think because of the varied the disparity of the businesses and the kinds of verticals and things like that you're looking at, to get complete unified business model, is going to be extremely difficult. >> Last question, back-office systems the highest level they got to were maybe the CFO 'cause you had a sign off on a lot of the budget for the license and a much much bigger budget for the SI but he was getting something that was like close you quarter in three days or something instead of two weeks. It was a control function. Who do you sell to now for these different systems and what's the message, how much more strategic how do you sell the business impact differently? >> The platforms we directly interact with the CIO and CTOs or the head of engineering. And the actual solutions or the insights, we usually sell it to the COOs or the operational folks. So because the COO is responsible for showing you productivity, efficiency, how much of savings can you do on the bottom line top line. So the insights would actually go through the COOs or in some sense go through their CTOs to COOs but the actual platform itself will go to the enterprise IT folks in that order. >> This sounds like it's a platform and a solution sell which requires, is that different from the sales motions of other IBM technologies or is this a new approach? >> IBM is transforming on its way. The days where we believe that all the strategies and predictives that we are aligned towards, that actually needs to be the key goal because that's where the world is going. There are folks who, like Jeff Boaz talks about in the olden days you need 70 people to sell or 70% of the people to sell a 30% product. Today it's a 70% product and you need 30% to actually sell the product. The model is completely changing the way we interact with customers. So I think that's what's going to drive. We are transforming that in that area. We are becoming more conscious about all the strategy operations that we want to deliver to the market we want to be able to enable our customers with a much broader value proposition. >> With the industry solutions group and the Global Business Services teams work on these solutions. They've already been selling, line of business CXO type solutions. So is this more of the same, it's just better or is this really higher level than IBM's ever gotten in terms of strategic value? >> This is possibly in decades I would say a high level of value which come from a strategic perspective. >> Okay, on that note Veeru, we'll call it a day. This is great discussion and we look forward to writing it up and clipping all the videos and showering the internet with highlights. >> Thank you George. Appreciate it. >> Hopefully I will get you back soon. >> I was a pleasure, absolutely. >> With that, this George Gilbert. We're in our Palo Alto studio for wiki bond and theCUBE and we've been talking to Veeru Ramaswamy who's VP of Watson IoT platform and we look forward to coming back with Veeru sometime soon. (upbeat music)

(dramatic music) >> Narrator: Live from Fisherman's Wharf in San Francisco. It's theCube. Covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back, everybody. Jeff Frank here with theCube. We are wrapping up day one at the IBM CEO Strategy Summit Spring 2017 here at the Fisherman's Wharf Hyatt. A new venue for us, never been here. It's kind of a cool venue. Joined by Peter Burris, Chief Research Officer from Wikibon, and we're excited to have practitioners. We love getting practitioners on. So we're joined by this segment by Bruce Tyler. He's a VP Data Analytics for IBM Global Business Services. Bruce, nice to see you. >> Thank you. >> And he's brought along Fawad Butt, the Chief Data Governance Officer for Kaiser Permanente. Welcome. >> Thank you, thank you. >> So Kaiser Permanente. Regulated industry, health care, a lot of complex medical issues, medical devices, electronic health records, insurance. You are in a data cornucopia, I guess. >> It's data heaven all the way. So as you mentioned, Kaiser is a vertically integrated organization, Kaiser Permanente is. And as such the opportunity for us is the fact that we have access to a tremendous amount of data. So we sell insurance, we run hospitals, medical practices, pharmacies, research labs, you name it. So it's an end to end healthcare system that generates a tremendous amount of dataset. And for us the real opportunity is to be able to figure out all the data we have and the best uses for it. >> I guess I never really thought of it from the vertical stack perspective. I used to think it was just the hospital, but the fact that you have all those layers of the cake, if you will, and can operate within them, trade data within them, and it gives you a lot of kind of classic vertical stack integration. That fits. >> Very much so. And I didn't give you the whole stack. I mean, we're actually building a medical school in Southern California. We have a residency program in addition to everything else we've talked about. But yeah, the vertical stack does provide us access to data and assets related to data that are quite unique. On the one side, it's a great opportunity. On the other side, it has to be all managed and protected and served in the best interest of our patrons and members. >> Jeff: Right, right. And just the whole electronic health records by themselves that people want access to that, they want to take them with. But then there's all kinds of scary regulations around access to that data. >> So the portability, I think what you're talking about is the medical record portability, which is becoming a really new construct in the industry because people want to be able to move from practitioner to practitioner and have that access to records. There are some regulation that provide cover at a national scale but a lot of this also is impacted by the states that you're operating in. So there's a lot of opportunities where I can tell some of the regulation in this space over time and I think that will, then we'll see a lot more adoption in terms of these portability standards which tend to be a little one off right now. >> Right, right. So I guess the obvious question is how the heck do you prioritize? (laughter) You got a lot of things going on. >> You know, I think it's really the standard blocking/tackling sort of situation, right? So one of the things that we've done is taken a look at our holistic dataset end to end and broken it down into pieces. How do you solve this big problem? You solve it by piecing it out a little bit. So what we've done is that we've put our critical dataset into a set of what we call data domains. Patient, member, providers, workers, HR, finance, you name it. And then that gives us the opportunity to not only just say how good is our data holistically but we can also go and say how good is our patient data versus member data versus provider data versus HR data. And then not only just know how good it is but it also gives us the opportunity to sort of say, "Hey, there's no conceivable way we can invest "in all 20 of these areas at any given point." So what's the priority that aligns with business objectives and goals? If you think about corporate strategy in general, it's based on customers and demand and availability and opportunities but now we're adding one more tool set and giving that to our executives. As they're making decisions on investments in longer term, and this isn't just KP, it's happening across industries, is that the data folks are bringing another lens to the table, which is to say what dataset do we want to invest in over the course of the next five years? If you had to choose between 20, what are the three that you prioritize first versus the other. So I think it's another lever, it's another mechanism to prioritize your strategy and your investments associated with that. >> But you're specifically focused on governance. >> Fawad: I am. >> In the health care industry, software for example is governed by a different set of rules as softwares in other areas. Data is governed by a different set of rules than data is governed in most other industries. >> Fawad: Correct. >> Finance has its own set of things and then some others. What does data governance mean at KP? Which is a great company by the way. A Bay Area company. >> Absolutely. >> What does it mean to KP? >> It's a great question, first of all. Every data governance program has to be independent and unique because it should be trying to solve for a set of things that are relevant in that context. For us at KP, there are a few drivers. So first is, as you mentioned, regulation. There's increased regulation. There's increased regulatory scrutiny in pressure. Some things that have happened in financial services over the last eight or ten years are starting to come and trickle in to the healthcare space. So there's that. There's also a changing environment in terms of how, at least from an insurance standpoint, how people acquire health insurance. It used to be that your employer provided a lot of that, those services and those insurances. Now you have private marketplaces where a lot of people are buying their own insurance. And you're going from a B2B construct to a B2C construct in certain ways. And these folks are walking around with their Android phones or their iPhones and they're used to accessing all sorts of information. So that's the customer experience that you to to deliver to them. So there's this digital transformation that's happening that's driving some of the need around governance. The other areas that I think are front and center for us are obviously privacy and security. So we're custodians of a lot of datasets that relate to patients' health information and their personal information. And that's a great responsibility and I think from a governance standpoint that's one of the key drivers that define our focus areas in the governance space. There are other things that are happening. There's obviously our mission within the organization which is to deliver the highest coverage and care at the lowest cost. So there's the ability for us to leverage our data and govern our data in a way which supports those two mission statements, but the bigger challenge in nuts and bolts terms for organizations like ours, which are vertically integrated, is around understanding and taking stock of the entire dataset first. Two, protecting it and making sure that all the defenses are in place. But then three, figuring out the right purposes to use this, to use the data. So data production is great but data consumption is where a lot of the value gets captured. So for us some of the things that data governance facilitates above all is what data gets shared for what purposes and how. Those are things that an organization of our size deliver a tremendous amount of value both on the offensive and the defensive side. >> So in our research we've discovered that there are a lot of big data functions or analytic functions that fail because they started with the idea of setting up the infrastructure, creating a place to put the data. Then they never actually got to the use case or when they did get to the use case they didn't know what to do next. And what a surprise. No returns, lot of costs, boom. >> Yep. >> The companies that tend to start with the use case independently individual technologies actually have a clear path and then the challenge is to accrete knowledge, >> Yes. >> accrete experience and turn it into knowledge. So from a governance standpoint, what role do you play at KP to make sure that people stay focused in use cases, that the lessons you learn about pursuing those use cases then turn to a general business capability in KP. >> I mean, again, I think you hit it right on the head. Data governance, data quality, data management, they're all great words, right? But what do they support in terms of the outcomes? So from our standpoint, we have a tremendous amount of use cases that if we weren't careful, we would sort of be scatterbrained around. You can't solve for everything all at once. So you have to find the first set of key use cases that you were trying to solve for. For us, privacy and security is a big part of that. To be able to, there's a regulatory pressure there so in some cases if you lose a patient record, it may end up costing you $250,000 for a record. So I think it's clear and critical for us to be able to continue to support that function in an outstanding way. The second thing is agility. So for us one of the things that we're trying to do with governance and data management in general, is to increase our agility. If you think about it, a lot of companies go on these transformation journeys. Whether it's transforming HR or trying to transform their finance functions or their business in general, and that requires transforming their systems. A lot of that work, people don't realize, is supported and around data. It's about integrating your old data with the new business processes that you're putting out. And if you don't have that governance or that data management function in place to be able to support that from the beginning or have some maturity in place, a lot of those activities end up costing you a lot more, taking a lot longer, having a lower success rate. So for us delivering value by creating additional agility for a set of activities that as an organization, we have committed to, is one for of core use cases. So we're doing a transformation. We're doing some transformation around HR. That's an area where we're making a lot of investments from a data governance standpoint to be able to support that as well as inpatient care and membership management. >> Great, great lessons. Really good feedback for fellow practitioners. Bruce, I want to get your perspective. You're kind of sitting on the other side of the table. As you look at the experience at Kaiser Permanente, how does this equate with what you're seeing with some of your other customers, is this leading edge or? >> Clearly on point. In fact, we were talking about this before we came up and I'm not saying that you guys led, we led the witness here but really how do you master around the foundational aspects around the data, because at the end of the day it's always about the data. But then how do you start to drive the value out of that and go down that cognitive journey that's going to either increase value onto your insights or improve your business optimization? We've done a healthy business within IBM helping customers go through those transformation processes. I would say five years ago or even three years ago we would start big. Let's solve the data aspect of it. Let's build the foundational management processes around there so that it ensures that level of integrity and trusted data source that you need across an organization like KP because they're massive because of all the different types of business entities that they have. So those transformation initiatives, they delivered but it was more from an IT perspective so the business partners that really need to adopt and are going to get the value out of that were kind of in a waiting game until that came about. So what we're seeing now is looking at things around from a use case-driven approach. Let's start small. So whether you're looking at trying to do something within your call center and looking at how to improve automation and insights in that spec, build a proof of value point around a subset of the data, prove that value, and those things can typically go from 10 to 12 weeks, and once you've demonstrated that, now how do can you scale? But you're doing it under your core foundational aspects around the architecture, how you're going to be able to sustain and maintain and govern the data that you have out there. >> It's a really important lesson all three of you have mentioned now. That old method of let's just get all the infrastructure in place is really not a path to success. You getting hung up, spend a lot of money, people get pissed off and oh by the way, today your competitors are transforming right around you while you're >> Unless they're also putting >> tying your shoes. >> infrastructure. >> Unless they're also >> That's right. (laughter) >> tying their shoes too. >> Build it and they will come sounds great, but in the data space, it's a change management function. One of my favorite lines that I use these days is data management is a team sport. So this isn't about IT, or this isn't just about business, and can you can't call business one monolith. So it's about the various stakeholders and their needs and your ability to satisfy them to the changes you're about to implement. And I think that gets lost a lot of times. It turns into a technical conversation around just capability development versus actually solving and solutioning for that business problem set that are at hand. >> Jeff: Yeah. >> Peter: But you got to do both, right? >> You have to. >> Bruce: Absolutely, yeah. >> Can I ask you, do we have time for another couple of questions? >> Absolutely. >> So really quickly, Fawad, do you have staff? >> Fawad: I do. >> Tell us about the people on your staff, where they came from, what you're looking for. >> So one of the core components of data governance program are stewards, data stewards. So to me, there are multiple dimensions to what stewards, what skills they should have. So for stewards, I'm looking for somebody that has some sort of data background. They would come from design, they would come from architecture, they would come from development. It doesn't really matter as long as they have some understanding. >> As long as you know what a data structure is and how you do data monitoring. >> Absolutely. The second aspect is that they have to have an understanding of what influence means. Be able to influence outcomes, to be able to influence conversations and discussions way above their pay grade, so to be able to punch above your weight so to speak in the influence game. And that's a science. That's a very, very definitive science. >> Yeah, we've heard many times today that politics is an absolute crucial game you have to play. >> It is part of the game and if you're not accounting for it, it's going to hit you in the face when you least expect it. >> Right. >> And the third thing is, I look for people that have some sort of an execution background. So ability to execute. It's great to be able to know data and understand data and go out and influence people and get them to agree with you, but then you have to deliver. So you have to be able to deliver against that. So those are the dimensions I look at typically when I'm looking at talent as it relates particularly to stewardship talent. In terms of where I find it, I try to find it within the organization because if I do find it within the organization, it gives me that organizational understanding and those relationship portfolios that people bring to the table which tend to be part of that influence-building process. I can teach people data, I can teach them some execution, I can't teach them how to do influence management. That just has to-- >> You can't teach them to social network. >> Fawad: (laughing) That's exactly right. >> Are they like are the frustrated individuals that have been seen the data that they're like (screams) this is-- >> They come from a lot of different backgrounds. So I have a steward that is an attorney, is a lawyer. She comes from that background. I have a steward that used to be a data modeler. I have a steward that used to run compliance function within HR. I have a steward that comes from a strong IT background. So it's not one formula. It's a combination of skills and everybody's going to have a different set of strengths and weaknesses and as long as you can balance those out. >> So people who had an operational role, but now are more in an execution setup role. >> Fawad: Yeah, very much so. >> They probably have a common theme, though, across them that they understand the data, they understand the value of it, and they're able to build consensus to make an action. >> Fawad: That's correct. >> That's great. That's perfect close. They understand it and they can influence, and they can get to action. Pretty much sums it up, I think so. All right. >> Bruce: All right thank you. >> Well, thanks a lot, Bruce and Fawad for stopping by. Great story. Love all the commercials on the Warriors, I'm a big fan and watch KNBR. (laughter) But really a cool story and thanks for sharing it and continued success. >> Thank you for the opportunity. >> Absolutely. All right, with Peter Burris, I'm Jeff Frank. You're watching theCube from the IBM Chief Data Officer Strategy Summit Spring 2017 from Fisherman's Wharf, San Francisco. We'll be right back after this short break. Thanks for watching. (electronic music)

>> Announcer: Live from Fisherman's Wharf in San Francisco. It's theCUBE. Covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey Welcome back everybody. Jeff Frick with theCUBE, along with Peter Burris from Wikibon. We are in Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. Coming to the end of a busy day, running out of steam. Blah, blah, blah. I need more water. But Joe's going to take us home. We're joined by Joe Selle. He is the global operations analytic solution lead for IBM. Joe, welcome. >> Thank you, thank you very much. It's great to be here. >> So you've been in sessions all day. I'm just curious to get kind of your general impressions of the event and any surprises or kind of validations that are coming out of these sessions. >> Well, general impression is that everybody is thrilled to be here and the participants, the speakers, the audience members all know that they're at the cusp of a moment in business history of great change. And that is as we graduate from regular analytics which are descriptive and dashboarding into the world of cognitive which is taking the capabilities to a whole other level. Many levels actually advanced from the basic things. >> And you're in a really interesting position because IBM has accepted the charter of basically consuming your own champagne, drinking your own champagne, whatever expression you want to use. >> I'm so glad you said that cause most people say eating your dog food. >> Well, if we were in Germany we'd talk about beer, but you know, we'll stick with the champagne analogy. But really, trying to build, not only to build and demonstrate the values that you're trying to sell to your customers within IBM but then actually documenting it and delivering it basically, it's called the blueprint, in October. We've already been told it's coming in October. So what a great opportunity. >> Part of that is the fact that Ginni Rometty, our CEO, had her start in IBM in the consulting part of IBM, GBS, Global Business Services. She was all about consulting to clients and creating big change in other organizations. Then she went through a series of job roles and now she's CEO and she's driving two things. One is the internal transformation of IBM, which is where I am, part of my role is, I should say. Reporting to the chief data officer and the chief analytics officer and their jobs are to accelerate the transformation of big blue into the cognitive era. And Ginni also talks about showcasing what we're doing internally for the rest of the world and the rest of the economy to see because parts of this other companies can do. They can emulate our road map, the blueprint rather, sorry, that Inderpal introduced, is going to be presented in the fall. That's our own blueprint for how we've been transforming ourselves so, some part of that blueprint is going to be valid and relevant for other companies. >> So you have a dual reporting relationship, you said. The chief data officer, which is this group, but also the chief analytics officer. What's the difference between the Chief data officer, the chief data analytics officer and how does that combination drive your mission? >> Well, the difference really is the chief data officer is in charge of making some very long-term investments, including short-term investments, but let me talk about the long-term investment. Anything around an enterprise data lake would be considered a long-term investment. This is where you're creating an environment where users can go in, these would be internal to IBM or whatever client company we're talking about, where they can use some themes around self-service, get out this information, create analysis, everything's available to them. They can grab external data. They can grab internal data. They can observe Twitter feeds. They can look at weather company information. In our case we get that because we're partnered with the weather company. That's the long-term vision of the chief data officer is to create a data lake environment that serves to democratize all of this for users within a company, within IBM. The chief analytics officer has the responsibility to deliver projects that are sort of the leading projects that prove out the value of analytics. So on that side of my dual relationship, we're forming projects that can deliver a result literally in a 10 or a 12 week time period. Or a half a year. Not a year and a half but short term and we're sprinting to the finish, we're delivering something. It's quite minimally scaled. The first project is always a minimally viable product or project. It's using as few data sources as we can and still getting a notable result. >> The chief analytics officer is at the vanguard of helping the business think about use cases, going after those use cases, asking problems the right way, finding data with effectiveness as well as efficiency and leading the charge. And then the Chief data officer is helping to accrete that experience and institutionalize it in the technology, the practices, the people, et cetera. So the business builds a capability over time. >> Yes, scalable. It's sort of an issue of it can scale. Once Inderpal and the Chief data officer come to the equation, we're going to scale this thing massively. So, high volume, high speed, that's all coming from a data lake and the early wins and the medium term wins maybe will be more in the realm of the chief analytics officer. So on your first summary a second ago, you're right in that the chief analytics officer is going around, and the team that I'm working with is doing this, to each functional group of IBM. HR, Legal, Supply Chain, Finance, you name it, and we're engaging in cognitive discovery sessions with them. You know, what is your roadmap? You're doing some dashboarding now, you're doing some first generation analytics or something but, what is your roadmap for getting cognitive? So we're helping to burst the boundaries of what their roadmap is, really build it out into something that was bigger then they had been conceiving of it. Adding the cognitive projects and then, program managing this giant portfolio so that we're making some progress and milestones that we can report to various stake holders like Ginni Rometty or Jim Kavanaugh who are driving this from a senior senior executive standpoint. We need to be able to tell them, in one case, every couple of weeks, what have you gotten done. Which is a terrible cadence, by the way, it's too fast. >> So in many Respects-- >> But we have to get there every couple of weeks we've got to deliver another few nuggets. >> So in many respects, analytics becomes the capability and data becomes the asset. >> Yes, that's true. Analytics has assets as well though. >> Paul: Sure, of course. >> Because we have models and we have techniques and we bake the models into a business process to make it real so people actually use it. It doesn't just sit over there as this really nifty science experiment. >> Right but kind of where are we on the journey? It's real still early days, right? Because, you know, we hear all the time about machine learning and deep learning and AI and VR and AI and all this stuff. >> We're patchy, every organization is patchy even IBM, but I'm learning from being here, so this is end of day one, I'm learning. I'm getting a little more perspective on the fact that we at IBM are actually, 'cause we've been investing in this heavily for a number of years. I came through the ranks and supply chain. We've been investing in these capabilities for six or seven years. We were some of the early adopters within IBM. But, I would say that maybe 10% of the people at this conference are sort of in the category of I'm running fast and I'm doing things. So that's 10%. Then there's maybe another 30% that are jogging or fast walking. And then there's the rest of them, so maybe 50%, if my math is right, it's been a long day. Are kind of looking and saying, yeah, I got to get that going at some point and I have two or three initiatives but I'm really looking forward to scaling it at some point. >> Right. >> I've just painted a picture to you of the fact that the industry in general is just starting this whole journey and the big potential is still in front of us. >> And then on the Champagne. So you've got the cognitive, you've got the brute and then you've got the Watson. And you know, there's a lot of, from the outside looking in at IBM, there's a lot of messaging about Watson and a lot of messaging about cognitive. How the two mesh and do they mesh within some of the projects that you're working on? Or how should people think of the two of them? >> Well, people should know that Watson is a brand and there are many specific technologies under the Watson brand. So, and then, think of it more as capabilities instead of technologies. Things like being able to absorb unstructured information. So you've heard, if you've been to any conferences, whether they're analytics or data, any company, any industry, 80% of your data is unstructured and invisible and you're probably working with 20% of your data on an active basis. So, do you want to go the 80%-- >> With 40% shrinking. >> As a percentage. >> That's true. >> As a percentage. >> Yeah because the volumes are growing. >> Tripling in size but shrinking as a percentage. >> Right, right. So, just, you know, think about that. >> Is Watson really then kind of the packaging of cognitive, more specific application? Because we're walking for health or. >> I'll tell you, Watson is a mechanism and a tool to achieve the outcome of cognitive business. That's a good way to think of it. And Watson capabilities that I was just about to get to are things like reading, if you will. In Watson Health, he reads oncology articles and they know, once one of them has been read, it's never forgotten. And by the way, you can read 200 a week and you can create the smartest doctor that there is on oncology. So, a Watson capability is absorbing information, reading. It's in an automated fashion, improving its abilities. So these are concepts around deep learning and machine learning. So the algorithms are either self correcting or people are providing feedback to correct them. So there's two forms of learning in there. >> Right, right. >> But these are kind of capabilities all around Watson. I mean, there are so many more. Optical, character recognition. >> Right. >> Retrieve and rank. >> Right. >> So giving me a strategy and telling me there's an 85% chance, Joe, that you're best move right now, given all these factors is to do x. And then I can say, well, x wouldn't work because of this other constraint which maybe the system didn't know about. >> Jeff: Right. >> Then the system will tell me, in that case, you should consider y and it's still an 81% chance of success verses the first which was at 85. >> Jeff: Right. >> So retrieving and ranking, these are capabilities that we call Watson. >> Jeff: Okay. >> And we try to work those in to all the job roles. >> Jeff: Okay. >> So again, whether you're in HR, legal, intellectual property management, environmental compliance. You know, regulations around the globe are changing all the time. Trade compliance. And if you violate some of these rules and regs, then you're prohibited from doing business in a certain geography. >> Jeff: Right. >> It's devastating. The stakes are really high. So these are the kind of tools we want. >> So I'm just curious, from your perspective, you've got a corporate edict behind you at the highest level, and your customers, your internal customers, have that same edict to go execute quickly. So given that you're not in that kind of slow moving or walking or observing half, what are the biggest challenges that you have to overcome even given the fact that you've got the highest level most senior edict both behind you as well as your internal customers. >> Yeah, well it, guess what, it comes down to data. Often, a lot of times, it comes to data. We can put together an example of a solution that is a minimally viable solution which might have only three or four or five different pieces of data and that's pretty neat and we can deliver a good result. But if we want to scale it and really move the needle so that it's something that Ginni Rometty sees and cares about, or a shareholder, then we have to scale. Then we need a lot of data, so then we come back to Inderpal, and the chief data officer role. So the constraint is on many of the programs and projects is if you want to get beyond the initial proof of concept, >> Jeff: Right. >> You need to access and be able to manipulate the big data and then you need to train these cognitive systems. This is the other area that's taking a lot of time. And I think we're going to have some technology and innovation here, but you have to train a cognitive system. You don't program it. You do some painstaking back and forth. You take a room full of your best experts in whatever the process is and they interact with the system. They provide input, yes, no. They rank the efficacy of the recommendations coming out of the system and the system improves. But it takes months. >> That's even the starting point. >> Joe: That's a problem. >> And then you trade it over often, an extended period of time. >> Joe: A lot of it gets better over time. >> Exactly. >> As long as you use this thing, like a corpus of information is built and then you can mine the corpus. >> But a lot of people seem to believe that you roll all this data, you run a bunch of algorithms and suddenly, boom, you've got this new way of doing things. And it is a very very deep set of relationships between people who are being given recommendations as you said, weighing them, voting them, voting on them, et cetera. This is a highly interactive process. >> Yeah, it is. If you're expecting lightning fast results, you're really talking about a more deterministic kind of solution. You know, if/then. If this is, then that's the answer. But we're talking about systems that understand and they reason and they tap you on the shoulder with a recommendation and tell you that there's an 85% chance that this is what you should do. And you can talk back to the system, like my story a minute ago, and you can say, well it makes sense, but, or great, thanks very much Watson, and then go ahead and do it. Those systems that are expert systems that have expertise just woven through them, you cannot just turn those on. But, as I was saying, one of the things we talked about on some of the panels today, was there's new techniques around training. There's new techniques around working with these corpuses of information. Actually, I'm not sure what the plural of corpus. Corpi? It's not Corpi. >> Jeff: I can look that up. >> Yeah, somebody look that up. >> It's not corpi. >> So anyway, I want to give you the last word, Jeff. So you've been doing this for a while, what advice would you give to someone kind of in your role at another company who's trying to be the catalyst to get these things moving. What kind of tips and tricks would you share, you know, having gone through it and working on this for a while? >> Sure. I would, the first thing I would do is, in your first move, keep the projects tightly defined and small with a minimum of input and keep, contain your risk and your risk of failure, and make sure that if you do three projects, at least one of them is going to be a hands down winner. And then once you have a winner, tout it through your organization. A lot of folks get so enamored with the technology that they start talking more about the technology than the business impact. And what you should be touting and bragging about is not the fact that I was able to simultaneously read 5,000 procurement contracts with this tool, you should be saying, it used to take us three weeks in a conference room with a team of one dozen lawyers and now we can do that whole thing in one week with six lawyers. That's what you should talk about, not the technology piece of it. >> Great, great. Well thank you very much for sharing and I'm glad to hear the conference is going so well. Thank you. >> And it's Corpa. >> Corpa? >> The answer to the question? Corpa. >> Peter: Not corpuses. >> With Joe, Peter, and Jeff, you're watching theCUBE. We'll be right back from the IBM chief data operator's strategy summit. Thanks for watching.

>> Announcer: Live from Fisherman's Wharf in San Francisco, it's theCUBE, covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. It's a mouthful, it's 170 people here, all high-level CXOs learning about data, and it's part of an ongoing series that IBM is doing around chief data officers and data, part of a big initiative with Cognitive and Watson, I'm sure you've heard all about it, Watson TV if nothing else, if not going to the shows, and we're really excited to have the drivers behind this activity with us today, also Peter Burris from Wikibon, chief strategy officer, but we've got Caitlin Lepech who's really driving this whole show. She is the Communications and Client Engagement Executive, IBM Global Chief Data Office. That's a mouthful, she's got a really big card. And Cortnie Abercrombie, who I'm thrilled to see you, seen her many, many times, I'm sure, at the MIT CDOIQ, so she's been playing in this space for a long time. She is a Cognitive and Analytics Offerings leader, IBM Global Business. So first off, welcome. >> Thank you, great to be here. >> Thanks, always a pleasure on theCUBE. It's so comfortable, I forget you guys aren't just buddies hanging out. >> Before we jump into it, let's talk about kind of what is this series? Because it's not World of Watson, it's not InterConnect, it's a much smaller, more intimate event, but you're having a series of them, and in the keynote is a lot of talk about what's coming next and what's coming in October, so I don't know. >> Let me let you start, because this was originally Cortnie's program. >> This was a long time ago. >> 2014. >> Yeah, 2014, the role was just starting, and I was tasked with can we identify and start to build relationships with this new line of business role that's cropping up everywhere. And at that time there were only 50 chief data officers worldwide. And so I-- >> Jeff: 50? In 2014. >> 50, and I can tell you that earnestly because I knew every single of them. >> More than that here today. >> I made it a point of my career over the last three years to get to know every single chief data officer as they took their jobs. I would literally, well, hopefully I'm not a chief data officer stalker, but I basically was calling them once I'd see them on LinkedIn, or if I saw a press announcement, I would call them up and say, "You've got a tough job. "Let me help connect you with each other "and share best practices." And before we knew, it became a whole summit. It became, there were so many always asking to be connected to each other, and how do we share best practices, and what do you guys know as IBM because you're always working with different clients on this stuff? >> And Cortnie and I first started working in 2014, we wrote IBM's first paper on chief data officers, and at the time, there was a lot of skepticism within our organization, why spend the time with data officers? There's other C-suite roles you may want to focus on instead. But we were saying just the rise of data, external data, unstructured data, lot of opportunity to rise in the role, and so, I think we're seeing it reflected in the numbers. Again, first summit three years ago, 30 participants. We have 170 data executives, clients joining us today and tomorrow. >> And six papers later, and we're goin' strong still. >> And six papers later. >> Exactly, exactly. >> Before we jump into the details, some of the really top-level stuff that, again, you talked about with John and David, MIT CDOIQ, in terms of reporting structure. Where do CDOs report? What exactly are they responsible for? You covered some of that earlier in the keynote, I wonder if you can review some of those findings. >> Yeah, that was amazing >> Sure, I can share that, and then, have Cortnie add. So, we find about a third report directly to the CEO, a third report through the CIO's office, sort of the traditional relationship with CIOs, and then, a third, and what we see growing quite a bit, are CXOs, so functional or business line function. Originally, traditionally it was really a spin-off of CIO, a lot of technical folks coming up, and we're seeing more and more the shift to business expertise, and the focus on making sure we're demonstrating the business impact these data programs are driving for our organization. >> Yeah, it kind of started more as a data governance type of role, and so, it was born out of IT to some degree because, but IT was having problems with getting the line of business leaders to come to the table, and we knew that there had to be a shift over to the business leaders to get them to come and share their domain expertise because as every chief data officer will tell you, you can't have lineage or know anything about all of this great data unless you have the experts who have been sitting there creating all of that data through their processes. And so, that's kind of how we came to have this line of business type of function. >> And Inderpal really talked about, in terms of the strategy, if you don't start from the business strategy-- >> Inderpal? >> Yeah, on the keynote. >> Peter: Yeah, yeah, yeah, yeah. >> You are really in big risk of the boiling the ocean problem. I mean, you can't just come at it from the data first. You really have to come at it from the business problem first. >> It was interesting, so Inderpal was one of our clients as a CEO three times prior to rejoining IBM a year ago, and so, Cortnie and I have known him-- >> Express Scripts, Cambia. >> Exactly, we've interviewed him, featured him in our research prior, too, so when he joined IBM in December a year ago, his first task was data strategy. And where we see a lot of our clients struggle is they make data strategy an 18-month, 24-month process, getting the strategy mapped out and implemented. And we say, "You don't have the time for it." You don't have 18 months to come to data, to come to a data strategy and get by and get it implemented. >> Nail something right away. >> Exactly. >> Get it in the door, start showing some results right away. You cannot wait, or your line of business people will just, you know. >> What is a data strategy? >> Sure, so I can say what we've done internally, and then, I know you've worked with a lot of clients on what they're building. For us internally, it started with the value proposition of the data office, and so, we got very clear on what that was, and it was the ability to take internal, external data, structured, unstructured, and pull that together. If I can summarize it, it's drive to cognitive business, and it's infusing cognition across all of our business processes internally. And then, we identified all of these use cases that'll help accelerate, and the catalyst that will get us there faster. And so, Client 360, product catalog, et cetera. We took data strategy, got buy-in at the highest levels at our organization, senior vice president level, and then, once we had that support and mandate from the top, went to the implementation piece. It was moving very quickly to specify, for us, it's about transforming to cognitive business. That then guides what's critical data and critical use cases for us. >> Before you answer, before you get into it, so is a data strategy a means to cognitive, or is it an end in itself? >> I would say it, to be most effective, it's a succinct, one-page description of how you're going to get to that end. And so, we always say-- >> Peter: Of cognitive? >> Exactly, for us, it's cognitive. So, we always ask very simple question, how is your company going to make money? Not today, what's its monetization strategy for the future? For us, it's coming to cognitive business. I have a lot of clients that say, "We're product-centric. "We want to become customer, client-centric. "That's our key piece there." So, it's that key at the highest level for us becoming a cognitive business. >> Well, and data strategies are as big or as small as you want them to be, quite frankly. They're better when they have a larger vision, but let's just face it, some companies have a crisis going on, and they need to know, what's my data strategy to get myself through this crisis and into the next step so that I don't become the person whose cheese moved overnight. Am I giving myself away? Do you all know the cheese, you know, Who Moved My Cheese? >> Every time the new iOS comes up, my wife's like-- >> I don't know if the younger people don't know that term, I don't think. >> Ah, but who cares about them? >> Who cares about the millenials? I do, I love the millenials. But yes, cheese, you don't want your cheese to move overnight. >> But the reason I ask the question, and the reason why I think it's important is because strategy is many things to many people, but anybody who has a view on strategy ultimately concludes that the strategic process is what's important. It's the process of creating consensus amongst planners, executives, financial people about what we're going to do. And so, the concept of a data strategy has to be, I presume, as crucial to getting the organization to build a consensus about the role the data's going to play in business. >> Absolutely. >> And that is the hardest. That is the hardest job. Everybody thinks of a data officer as being a technical, highly technical person, when in fact, the best thing you can be as a chief data officer is political, very, very adept at politics and understanding what drives the business forward and how to bring results that the CEO will get behind and that the C-suite table will get behind. >> And by politics here you mean influencing others to get on board and participate in this process? >> Even just understanding, sometimes leaders of business don't articulate very well in terms of data and analytics, what is it that they actually need to accomplish to get to their end goal, and you find them kind of stammering when it comes to, "Well, I don't really know "how you as Inderpal Bhandari can help me, "but here's what I've got to do." And it's a crisis usually. "I've got to get this done, "and I've got to make these numbers by this date. "How can you help me do that?" And that's when the chief data officer kicks into gear and is very creative and actually brings a whole new mindset to the person to understand their business and really dive in and understand, "Okay, this is how "we're going to help you meet that sales number," or, "This is how we're going to help you "get the new revenue growth." >> In certain respects, there's a business strategy, and then, you have to resource the business strategy. And the data strategy then is how are we going to use data as a resource to achieve our business strategy? >> Cortnie: Yes. >> So, let me test something. The way that we at SiliconANGLE, Wikibon have defined digital business is that a business, a digital business uses data as an asset to differentially create and keep customers. >> Caitlin: Right. >> Does that work for you guys? >> Cortnie: Yeah, sure. >> It's focused on, and therefore, you can look at a business and say is it more or less digital based on how, whether it's more or less focused on data as an asset and as a resource that's going to differentiate how it's business behaves and what it does for customers. >> Cortnie: And it goes from the front office all the way to the back. >> Yes, because it's not just, but that's what, create and keep, I'm borrowing from Peter Drucker, right. Peter Drucker said the goal of business is to create and keep customers. >> Yeah, that's right. Absolutely, at the end of the day-- >> He included front end and back end. >> You got to make money and you got to have customers. >> Exactly. >> You got to have customers to make the money. >> So data becomes a de-differentiating asset in the digital business, and increasingly, digital is becoming the differentiating approach in all business. >> I would argue it's not the data, because everybody's drowning in data, it's how you use the data and how creative you can be to come up with the methods that you're going to employ. And I'll give you an example. Here's just an example that I've been using with retailers lately. I can look at all kinds of digital exhaust, that's what we call it these days. Let's say you have a personal digital shopping experience that you're creating for these new millenials, we'll go with that example, because shoppers, 'cause retailers really do need to get more millenials in the door. They're used to their Amazon.coms and their online shopping, so they're trying to get more of them in the door. When you start to combine all of that data that's underlying all of these cool things that you're doing, so personal shopping, thumbs up, thumb down, you like this dress, you like that cut, you like these heels? Yeah, yes, yes or no, yes or no. I'm getting all this rich data that I'm building with my app, 'cause you got to be opted in, no violating privacy here, but you're opting in all the way along, and we're building and building, and so, we even have, for us, we have this Metro Pulse retail asset that we use that actually has hyperlocal information. So, you could, knowing that millenials like, for example, food trucks, we all like food trucks, let's just face it, but millenials really love food trucks. You could even, if you are a retailer, you could even provide a fashion truck directly to their location outside their office equipped with things that you know they like because you've mined that digital exhaust that's coming off the personal digital shopping experience, and you've understood how they like to pair up what they've got, so you're doing a next best action type of thing where you're cross-selling, up-selling. And now, you bring it into the actual real world for them, and you take it straight to them. That's a new experience, that's a new millennial experience for retail. But it's how creative you are with all that data, 'cause you could have just sat there before and done nothing about that. You could have just looked at it and said, "Well, let's run some reports, "let's look at a dashboard." But unless you actually have someone creative enough, and usually it's a pairing of data scientist, chief data officers, digital officers all working together who come up with these great ideas, and it's all based, if you go back to what my example was, that example is how do I create a new experience that will get millenials through my doors, or at least get them buying from me in a different way. If you think about that was the goal, but how I combined it was data, a digital process, and then, I put it together in a brand new way to take action on it. That's how you get somewhere. >> Let me see if I can summarize very quickly. And again, just as an also test, 'cause this is the way we're looking at it as well, that there's human beings operate and businesses operate in an analog world, so the first test is to take analog data and turn it into digital data. IOT does that. >> Cortnie: Otherwise, there's not digital exhaust. >> Otherwise, there's no digital anything. >> Cortnie: That's right. >> And we call it IOT and P, Internet of Things and People, because of the people element is so crucial in this process. Then we have analytics, big data, that's taking those data streams and turning them into models that have suggestions and predictions about what might be the right way to go about doing things, and then there's these systems of action, or what we've been calling systems of enactment, but we're going to lose that battle, it's probably going to be called systems of action that then take and transduce the output of the model back into the real world, and that's going to be a combination of digital and physical. >> And robotic process automation. We won't even introduce that yet. >> Which is all great. >> But that's fun. >> That's going to be in October. >> But I really like the example that you gave of the fashion truck because people don't look at a truck and say, "Oh, that's digital business." >> Cortnie: Right, but it manifested in that. >> But it absolutely is digital business because the data allows you to bring a more personal experience >> Understand it, that's right. >> right there at that moment, and it's virtually impossible to even conceive of how you can make money doing that unless you're able to intercept that person with that ensemble in a way that makes both parties happy. >> And wouldn't that be cheaper than having big, huge retail stores? Someone's going to take me up on that. Retailers are going to take me up on this, I'm telling you. >> But I think the other part is-- >> Right next to the taco truck. >> There could be other trucks in that, a much cleaner truck, and this and that. But one thing, Cortnie, you talk about and you got to still have a hypothesis, I think of the early false promises of big data and Hadoop, just that you throw all this stuff in, and the answer just comes out. That just isn't the way. You've got to be creative, and you have to have a hypothesis to test, and I'm just curious from your experience, how ready are people to take in the external data sources and the unstructured data sources and start to incorporate that in with the proprietary data, 'cause that's a really important piece of the puzzle? It's very different now. >> I think they're ready to do it, it depends on who in the business you are working with. Digital offices, marketing offices, merchandising offices, medical offices, they're very interested in how can we do this, but they don't know what they need. They need guidance from a data officer or a data science head, or something like this, because it's all about the creativity of what can I bring together to actually reach that patient diagnostic, that whatever the case may be, the right fashion truck mix, or whatever. Taco Tuesday. >> So, does somebody from the chief data office, if you will, you know, get assigned to, you're assigned to marketing and you're assigned to finance, and you're assigned to sales. >> I have somebody assigned to us. >> To put this in-- >> Caitlin: Exactly, exactly. >> To put this in kind of a common or more modern parlance, there's a design element. You have to have use case design, and what are we going, how are we going to get better at designing use cases so we can go off and explore the role that data is going to play, how we're going to combine it with other things, and to your point, and it's a great point, how that turns into a new business activity. >> And if I can connect two points there, the single biggest question I get from clients is how do you prioritize your use cases. >> Oh, gosh, yeah. >> How can you help me select where I'm going to have the biggest impact? And it goes, I think my thing's falling again. (laughing) >> Jeff: It's nice and quiet in here. >> Okay, good. It goes back to what you were saying about data strategy. We say what's your data strategy? What's your overarching mission of the organization? For us, it's becoming cognitive business, so for us, it's selecting projects where we can infuse cognition the quickest way, so Client 360, for example. We'll often say what's your strategy, and that guides your prioritization. That's the question we get the most, what use case do I select? Where am I going to have the most impact for the business, and that's where you have to work with close partnership with the business. >> But is it the most impact, which just sounds scary, and you could get in analysis paralysis, or where can I show some impact the easiest or the fastest? >> You're going to delineate both, right? >> Exactly. >> Inderpal's got his shortlist, and he's got his long list. Here's the long term that we need to be focused on to make sure that we are becoming holistically a cognitive company so that we can be flexible and agile in this marketplace and respond to all kinds of different situations, whether they're HR and we need more skills and talent, 'cause let's face it, we're a technology company who's rapidly evolving to fit with the marketplace, or whether it's just good old-fashioned we need more consultants. Whatever the case may be. >> Always, always. >> Yes! >> I worked my business in. >> More consultants! >> Alright, we could go, we could go and go and go, but we're running out of time, we had a full slate. >> Caitlin: We just started. >> I know. >> I agree, we're just starting this convers, I started a whole other conversation to him. We haven't even hit the robotics yet. >> We need to keep going, guys. >> Get control. >> Cortnie: Less coffee for us. >> What do people think about when they think about this series? What should they look forward to, what's the next one for the people that didn't make it here today, where should they go on the calendar and book in their calendars? >> So, I'll speak to the summits first. It's great, we do Spring in San Francisco. We'll come back, reconvene in Boston in fall, so that'll be September, October frame. I'm seeing two other trends, which I'm quite excited about, we're also looking at more industry-specific CDO summits. So, for those of our friends that are in government sectors, we'll be in June 6th and 7th at a government CDO summit in D.C., so we're starting to see more of the industry-specific, as well as global, so we just ran our first in Rio, Brazil for that area. We're working on a South Africa summit. >> Cortnie: I know, right. >> We actually have a CDO here with us that traveled from South Africa from a bank to see our summit here and hoping to take some of that back. >> We have several from Peru and Mexico and Chile, so yeah. >> We'll continue to do our two flagship North America-based summits, but I'm seeing a lot of growth out in our geographies, which is fantastic. >> And it was interesting, too, in your keynote talking about people's request for more networking time. You know, it is really a sharing of best practices amongst peers, and that cannot be overstated. >> Well, it's community. A community is building. >> It really is. >> It's a family, it really is. >> We joke, this is a reunion. >> We all come in and hug, I don't know if you noticed, but we're all hugging each other. >> Everybody likes to hug their own team. It's a CUBE thing, too. >> It's like therapy. It's like data therapy, that's what it is. >> Alright, well, Caitlin, Cortnie, again, thanks for having us, congratulations on a great event, and I'm sure it's going to be a super productive day. >> Thank you so much. Pleasure. >> Thanks. >> Jeff Frick with Peter Burris, you're watchin' theCUBE from the IBM Chief Data Officer Summit Spring 2017 San Francisco, thanks for watching. (electronic keyboard music)