>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Welcome back to Mosconi North here in San Francisco, California. I'm student of my co host, A Volante. We're in day three of four days live. Walter. Wall coverage here at IBM think happened. Welcome back to the program. Talk about one of our favorite topics. Cube alarm. Steve Hill, who's the global head of innovation. That topic I mentioned from KPMG, Steve, welcome back to the program. >> Seems to have made good to see you. >> All right. So, you know, we know that the the only constant in our industry is change. And, you know, it's one of those things. You know, I look at my career, it's like innovation. Is it a buzz word? You know? Has innovation stalled out of the industry? But you know, you're living it. You you're you're swimming in it. Talkinto a lot of people on it. KPMG has lots of tools, so give us the update from from last year. >> Well, I think you know, we talked about several things last year, but innovation was a key theme. And and when I would share with you, is that I think across all industries, innovation as a capability has become more mature and more accepted, still not widely adopted across all industries and all competitors and all kinds of companies. But the reality is, innovation used to be kind of one person's job off in the closet today. I think a lot of organizations or realizing you have to have corporate muscle that is as engaged as in changing the status quo as the production muscle is in maintaining the status quo has >> become a cultural. >> It's become part of culture, and so I think innovation really is part of the evolution of corporate governance as far as I'm >> concerned. What one thing I worry about a little bit is, you know, I see a company like IBM. They have a long history of research that throws off innovation over the years. You know, I grew up, you know, in the backyard of Bell Labs and think about the innovation a drove today, the culture you know, faster, faster, faster and sometimes innovation. He does sit back. I need to be able to think longer, You know? How does how does an innovation culture fit into the ever changing, fast paced you? No need to deliver ninety day shot clock of reality of today. >> Well, I think innovation has to be smart, meaning you have to be able to feed the engines of growth. So your horizon one, if you will, of investments and your attention and efforts have to pay off the short term. But you also can't be strategically stupid and build yourself into an alleyway or to our corner, because you're just too short term thought through. Right? So you need to have a portfolio of what we call Horizon three blended with Horizon one and Horizon two types investment. So your short term, your middle term and your longer term needs are being met. Of course, if you think about it like a portfolio of investments, you're going tohave. Probably a smaller number of investments that air further out, more experimental and a larger proportion of them going to be helping you grow. You could say, almost tactically or sort of adjacent to where you are today, incrementally. But some of those disruptive things that you work on an H three could actually change your industry. Maybe you think about today where we are. Azan Economy intangibles are starting to creep into this notion of value ways we've never seen before. Today, the top five companies in terms of net worth all fundamentally rely on intangibles for their worth. Five years ago, it was one or two, and I would argue that the notion of intangibles, particularly data we'll drive a lot of very transformative types of investments for organizations going forward. So you've got to be careful not to starve a lot of those longer term investments, >> right? And it's almost become bromide. Large companies can innovate, but those five companies just mentioned well alluded to Amazon. Google, etcetera Facebook of Apple, Microsoft there, innovators, right? So absolutely and large companies innovate. >> Yes, clearly, yeah, but you have to have muscle, but it doesn't happen by accident, and you do put discipline and process and rigor and tools and leadership around innovation. But it's a different kind of discipline than you need in the operation, so I'll make him a ratio that makes sense. Maybe ninety five percent production, five percent innovation in an organization. That innovation engine is always challenging that ninety five percent Are you good enough? Are you relevant enough? Are you fast enough? Are you agile enough? You need that in every corporate organization in terms of governance to stay healthy and relevant overtime. >> So it's interesting. You know, I was in a session that Jack Welch talk wants, and he's like, I hear big companies can innovate is like big companies made up of people. People are the things that can innovate absolute. But, you know, I've worked in large organizations. We understand that the fossilization process and the goto market that you have, you know, will often kill, you know, those new flowers that are blooming, what separates the people that can drive innovation on DH? You know, put those positive place and kind of the also rans that, you know get left behind window disruption. >> Well, there's several. There's a couple things that I would highlight of a longer list, one of them we culture. I mean, I think innovation has been part of a culture. People in the institution have value innovation and want to be part of it. And there is, you know, a role that everyone can play. Just because you're in operations, if you will, doesn't mean you ignore change or you ignore the opportunity to improve the status quo. But you still have you get paid to operate what I find that is related to culture that gets a lot of people, you know, slow down or or roadblock is the disconnect between the operating part of the business and the innovative part of the business. If you try, if you build them to separately, what happens is you have a disconnection. And if you innovate the best idea in the world over here. But you can't scale it with production, you lose. So you have to make sure that, as as a leader overall, the entire enterprise you build those connections, rotations, leadership, You know, How do you engage the production, you know, engine into the innovation engine? It's to be very collaborative. It should be seamless. You know, everyone likes to say that, but that word, but relative seamlessness is, is heavy architecture. You've gotto build that, you know, collaboration into your model of of how you innovate >> and >> don't innovate in the vacuum. >> And it comes back to the cultural aspects we're talking about. Do you mentioned the ninety day shot? Clocks were here in the Bay Area. Silicon Valley. The most innovative place in the world. They've lived along the ninety day shot clock forever, and it seems to have not heard that so called short term thinking. Why is that? >> Well, there's so much start up here. I mean, at the end of the day, there is so much churn of new thinking and start up in V C. And there's so much activity that it's almost a microcosm, right? Not every place in the world smells, feels, looks like Silicon Valley, right? And the reason for it is in part because there's just so much innovation in what happens here. And these things change me. If you think about, uh, these unicorns that we have today. Today there's about three hundred ninety one unicorns. Just five years ago, there were one hundred sixty globally on before that. Hardly people didn't know they were hardly recognized. But that's all coming from pockets of innovation like Silicon Valley. So I'd argue that what you have here is an interesting amalgamation of culture being part of a macro environment region that that really rewards innovation and demonstrates that in in market valuations in capital raises, I mean, today one hundred million dollars capital raise is pretty common, especially for unicorns. Five, ten years ago. You never see me. It was very difficult to get a hundred million dollars capital, right? >> You mean you're seeing billion dollar companies do half a billion dollars raises today? I mean, it's >> all day, right? And some of them don't make a profit. Which is I mean, and that's kind of the irony, Which is, Are those companies? What did they get that the rest of us, you know, there was that live on Wall Street right out of in New York. What do we not see? Is that some secret that downstream there will be some massive inflow? Hard to say. I mean, look at Amazon is an example. They've used an intangible to take industries out that they were never in before they started selling books, and they leverage customer behavior data to move into other spaces. And this is kind of the intangible dynamic. And the infection >> data was the fuel for the digital disruption to travel around the world. You see that folks outside of Silicon Valley are really sort of maybe creating new innovation recipes? >> Yes. I think that what you see here is starting to go viral right on DH way that KPMG likes to share a holistic way to look at this for our clients. What is what we call the twenty first century enterprise. So the things that we used to do in the twentieth century to be successful, hire people, build more machines, right? You know, buy more assets, hard, durable assets. Those things don't necessarily give you the recipe for success in the twenty first century. And if you look at that and you think about the intangibles work that's been well written about there's there's all kinds of press on this today. You'll start to realize that the recipe for success in this new century is different, and you can't look at it in a silo to say, Okay, so I've gotta change my department or I've got a I've got to go change, You know, my widgets. What you've got to think is that your entire enterprise and so are construct called the twenty first Century prize. Looks at four things. Actually, it's five, and the fifth one is the technologies to enable change in the other four. And those technologies we talk about here and I have made him think which are, you know, cloud data, smart computers or a blockchain, etcetera. But those four pillars our first customer. How do you think about your customer experience today? How do you rethink your customer experience tomorrow? I think the customer dynamic, whether it's generational or it's technologically driven, change is happening more rapidly today than ever. And looking at that front office and the customer dementia, it is really important. The second is looking at your acid base. The value of your assets are changing, and intangibles are big category of that change. But do your do your hard assets make the difference today and forward. Or all these intangibles. Companies that don't have a date a strategy today are at peril of falling victim to competitors who will use data to come through a flank. And Amazons done that with groceries, right? The third category is as a service capabilities. So if you're growing contracting going into new markets are opening new channels. How do you build that capability to serve that? Well, there's a phenomenon today that we know is, you know, I think, very practised, but usually in functions called as a service by capability on the drink instead of going out and doing big BPO deals. Think about a pea eye's. Think about other kinds of ways of get access to build and scale very fucks Pierre your capabilities and in the last category, which actually is extremely important for any change you make elsewhere is your workforce. Um, culture is part of that, right? And a lot of organizations air bringing on chief culture officers. We and KPMG did the same thing, but that workforce is changing. It's not just people you hire into your four walls today. You've got contingent workforce. You have gig economy, workforce a lot of organizations. They're leveraging platform business models to bring on employees to either help customers with help. Dex needs or build code for problems that they like to solve for free. So when you talk about productivity, which we talked about last year and you start thinking about what's separating the leaders from a practical standpoint from the laggers from practically standpoint, a lot of those attributes of changing customer value of assets as a service growth and workforce are driving growth and productivity for that subset of our community and many injured. >> So when you look at the firm level you're seeing some real productivity gains versus just paying attention to the macro >> Correct, any macro way think proactive is relatively flat, and that's not untrue. It's because the bottom portion the laggards aren't growing. In fact, productivity is in many ways falling off, but the ones that are the frontier of those top ten percent fifteen hundred global clients we've looked at, uh, you know, you see that CD study show that they're actually driving growth and productivity substantially, and the chasm is getting larger. >> So, Steve, Steve, it's curious what this means for competition. I think about if I'm using external workforces in open source communities, you know, Cloud and I, you know, changes in the environment. A supposed toe I used to kind of have my internal innovation. Now I'm out in these communities s O You know, we're here than IBM show. You know, I think back the word Coop petition. I first heard in context of talking about how IBM works with their ecosystem. So how did those dynamics change of competition and innovation in this? You know, the gig. Economy with open source and cloud. May I? Everywhere. >> Big implications. I mean, I I think you know, and this is the funny point you made is nontraditional competitors, because I think most of our clients and ourselves recognized that we haven't incredible amount of nontraditional competitors entering our space in professional services. We have companies that are not overtly going after our space, but are creating capabilities for our clients to do for themselves what we used to do for them. Data collection, for example, is one of those areas where clients used to spend money for consultants coming in to gather data into aggregate data with tools today that's ah, a very short process, and they do it themselves. So that's a disintermediation or on bundling of our business. But every business has these types of competitive non Trish competitive threats, and what we're seeing is that those same principles that we talked about earlier of the twenty first century surprise applies, right? How are they leveraging there the base and how they leveraging their workforce? Are they? Do they have a data strategy to think through? Okay, what happens if somebody else knows more about my customers than I do? Right? What does that do to make those kinds of questions need to be asked an innovation as a capability I think is a good partner and driving that nothing I would say, is that eco systems and you made you mention that word, and I want to pick up on that. I mean, I think eco systems air becoming a force in competitive protection and competitive potential going forward. If you think about a lot of you know, household names relative Teo data, you know Amazon's one of them. They are involved in the back office in the middle ofthis have so many organizations they're in integrated in those supply chains. Value change, I think services firms, and particularly to be thinking about how do they integrate into the supply chains of their customers so that they transcend the boars of, you know, their four walls, those eco systems and IBM was We consider KPMG considers IBM to be part of our ecosystem, right? Um, as well as other technology. >> So they're one of one of the things we're hearing from IBM. Jenny talked about it yesterday, and her keynote was doubling down on trust. Essentially one. Could you be implying that trust is a barrier to ay? Ay adoption is that. Is that true? Is that what your data show? >> We we we see that very much in spades. In fact, um, you know, I I if you think about it quite frankly, our oppa has driven a lot of people to class to class three. Amalgamation czar opportunities. But what's happening is we're seeing a slowdown because the price of some of these initials were big. But trust, culture and trust are big issues. In fact, we just released recently. Aye, Aye. And control framework, which includes methods and tools assessments to help our clients that were working with the city of Amsterdam today on a system for their citizens that helped them have accountability. Make sure there's no bias in their systems. As a I systems learn and importantly, explain ability. Imagine, you know. Ah, newlywed couple going into a bank to get a house note and having the banker sit back and have his Aye, aye, driven. You know, assessment for mortgage applicability. Come up moored. Recommend air saying no. You Ugh. I can't offer you a mortgage because my data shows you guys going to be divorced, right? We don't want to tell it to a newlywed couple, right? So explain ability about why it's doing what it's doing and put it in terms that relate to customer service. I mean, that's a pretty it's a silly example, but it's a true example of the day. There's a lot of there's a lack of explain ability in terms of how a eyes coming up with some of its conclusions. Lockbox, right? So a trusted A I is a big issue. >> All right, Steve, Framework that you just talked about the twenty first century enterprise. Is there a book or their papers? So I just go to the website, Or do I need to be a client? Read more about, >> you know, absolutely. You can go to our website, kpmg dot com and you can get all the della you want on the twenty first century enterprise. It talks to how we connect our customers front to middle toe back offices. How they think about those those pillars, the technologies we can help them with. Make change happen there, etcetera. So I appreciate it that >> we'll check it out that way. Don't be left in the twentieth century. Come on. >> No, you can't use twentieth century answers to solve twenty first century challenges, right? >> Well, Steve, he'll really appreciate giving us the twenty first century update for day. Volante on student will be back with our next guest here. IBM think twenty nineteen. Thanks for watching you.

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Welcome back to the live coverage here in Mosconi North in San Francisco for IBM. Think this. The cubes coverage. I'm Jeffrey David. Launching a too great guest here. Carlos. Gavel, gavel. A chief date. Officer Clara, Columbia and Carlos. See? Good. Engage your manager. IBM data Science elite team a customer of IBM country around data science. Welcome to the Cube. Thanks for joining us. Thanks for having us. So we'll hear the street, the street to shut down a i N E. Where's the big theme? Multi cloud. But it's all about the data everywhere. People trying to put end to end solutions together to solve real business problems. Date is at the heart of all this moving date around from cloud to cloud using. Aye, aye. And technology get insights out of that. So take a minute to explain your situation, but you got to try to do. >> Okay. Okay, Perfect. Right now, we're working out a lot about the business thing because we need to use the machine learning models or all the artificial intelligence toe. Take best decisions for the company. Way. We're working with Carlo in a charming mother in order to know how how come with a boy the customers left the company Because for us it's very important to maintain our our customer toe. Now, how they're how are the cables is from them. There are two facility intelligences is next selling way to do it that way. Have a lot of challenge about that because, you know, we have a lot of data, different systems, that they're running the data way need to put all the information together to run them to run the mother's. The team that Carlo is leaving right now is helping to us a lot because we WeII know how to handle that. We know howto clean the data when you have to do the right governess for the data on the IBM iniquity is very compromised with us in there in order to do that safely. That is one of the union that is very close to us right now. She was working a lot with my team in order to run the models. You saying she was doing a lot of four. I mean, over fight on right now we are trained to do it in over the system, running this park on DH that is they? They Good way that we are. We are thinking that is going to get the gold for us way Need to maintain our customers. >> So years the largest telecommunications piece Claro in Mexico for boys and home services. Is that segments you guys are targeting? Yeah, Yeah. Scope. Size of how big is that? >> Clarisa? Largest company in Colombia For telecommunication. We have maybe fifty million customers in Colombia. More than fifty percent of the market marketer also way have many maybe two point five millions off forms in Colombia. That is more than fifty percent of the customers for from services on. Do you know that it's a big challenge for us because the competitors are all the time. Tryinto take our customers on DH the charm or they'll have toe. How's the boy that and how to I hope to do their artificial intelligence to do it much learning. It's a very good way to do that. >> So classic problem and telecommunications is Charon, right? So it's a date. A problem? Yeah, but So how did it all come about? So these guys came to you? >> Yeah. They help The game does. We got together. We talked about the problem and in turn was at the top right. These guys have a ton of data, so what we did is the team got together. We have really the way to data sensibly team works is we really helped clients in three areas. It's all about the right skills, the right people, the right tools and then the right process. So we put together a team. We put together some agile approaches on what we're going to do on DH. Then we'd get started by spinning up in environment. We took some data and we took there. And there's a lot of data is terabytes of data. We took their user data way, took their use users usage data, which is like how many text, cellphone and then bill on day that we pulled all that together and environment. Then the data scientists alongside what Carlos is team really worked on the problem, and they addressed it with, you know, machine learning, obviously target. In turn, they tried a variety of models, But actually, boost ended up being one of the better approaches on DH. They came up with a pretty good accuracy about nineties ninety two. Percent precision on the model. Predicting unpredictable turn. Yeah. >> So what did you do with that? That >> that that is a very good question because the company is preparing to handle that. I have a funny history. I said today to the business people. Okay, these customers are going to leave the company. Andi, I forget about that on DH. Two months later, I was asking Okay, what happened? They say, Okay, your model is very good. All the customers goes, >> Oh, my God, What >> this company with that they weren't working with a with information. That is the reason that we're thinking that the good ways to fame for on the right toe the left because twist them which is therefore, pulls the purposes toe Montana where our customers And in that case, we lose fifty thousand customers because we didn't do nothing Where we are close in the circle, we are taking care about that prescriptive boys could have tto do it on. OK, maybe that is her name. Voice problem. We need to correct them to fix the problem in orderto avoid that. But the fetus first parties toe predict toe. Get any score for the charm on Tau handled that with people obviously working. Also at the root cause analysis because way need to charm, way, need to fix from their road, >> Carla. So walk us through the scope of, like, just the project, because this is a concern we see in the industry a lot of data. How do I attack it? What's the scoop? You just come in and just into a data lake. How do you get to the value? These insights quickly because, honestly, they're starving for insights would take us through that quick process. >> Well, you know, every every problems with different. We helped hundreds of clients in different ways. But this pig a problem. It was a big data problem because we knew we had a lot of data. They had a new environment, but some of the data wasn't there. So what we did was way spun up a separate environment. We pulled some of the big data in there. We also pulled some of the other data together on DH. We started to do analysis on that kind of separately in the cloud, which is a little different, but we're working now to push that down into their Duke Data Lake, because not all the data is there, but some of the data is there, and we want to use some of that >> computer that almost to audit. Almost figure out what you want, what you want to pull in first, absolutely tie into the business on the business side. What would you guys like waiting for the answers? Or was that some of the on your side of process? How did it go down? >> I'm thinking about our business way. We're talking a little bit about about that about their detective tow hundred that I see before data within. That is a very good solution for that because we need infested toe, have us in orderto get the answers because finally we have a question we have question quite by. The customers are leaving us. Andi. What is data on the data handed in the good in a good way with governor? Dance with data cleaning with the rhyme orders toe. Do that on DH Right now, our concern is Business Section a business offer Because because the solution for the companies that way always, the new problems are coming from the data >> started ten years ago, you probably didn't have a new cluster to solve this problem. Data was maybe maybe isn't a data warehouse that maybe it wasn't And you probably weren't chief data officer back then. You know that roll kind of didn't exist, so a lot has changed in the last ten years. My question is, do you first of all be adjusting your comment on that? But do you see a point in which you could now take remedial action or maybe even automate some of that remedial action using machine intelligence and that data cloud or however else you do it to actually take action on behalf of the brand before humans who are without even human involvement foresee a day? >> Yeah. So just a comment on your thought about the times I've been doing technology for twenty something years, and data science is something has been around, but it's kind of evolved in software development. My thought is, uh, you know, we have these rolls of data scientists, but a lot of the feature engineering Data prep does require traditional people that were devious. And now Dave engineers and variety of skills come together, and that's what we try to do in every project. Just add that comment. A ce faras predicted ahead of time. Like, I think you're trying to say what data? Help me understand >> you. You know, you've got a ninety three percent accuracy. Okay, So I presume you take that, You give it to the business businesses, Okay? Let's maybe, you know, reach out to them, maybe do a little incentive or you know what kind of action in the machines take action on behalf of your brand? Do you foresee a day >> so that my thought is for Clara, Columbia and Carlos? But but obviously this is to me. Remain is the predictive models we build will obviously be deployed. And then it would interact with their digital mobile applications. So in real time, it'll react for the customers. And then obviously, you know, you want to make sure that claro and company trust that and it's making accurate predictions. And that's where a lot more, you know, we have to do some model validation and evaluation of that so they can begin to trust those predictions. I think is where >> I want to get your thoughts on this because you're doing a lot of learnings here. So can you guys each taking minutes playing the key Learnings from this As you go through the process? Certainly in the business side, there's a big imperative to do this. You want to have a business outcome that keeps the users there. But what did you learn? What was some of the learnings? You guys gone from the project? >> They the most important learning front from the company that wass teen in the data that that sound funny, but waiting in an alley, garbage in garbage, out on DH that wass very, very important for other was one of the things that we learn that we need to put cleaning date over the system. Also, the government's many people forget about the governments of the governments of the data on DH. Right now, we're working again with IBM in our government's >> so data quality problem? Yeah, they fight it and you report in to your CEO or the CEO. Seo, your spear of the CIA is OK. That >> is it. That's on another funny history, because because the company the company is right now, I am working for planning. This is saying they were working for planning for the company. >> Business planning? >> Yeah, for business planning. I was coming for an engineer engineering on DH. Right now, I'm working for a planning on trying to make money for the company, and you know that it's an engineer thinking how to get more money for the company I was talking about. So on some kind of analysis ticks, that is us Partial Analytics on I want you seeing that in engineer to know how the network handling how the quality of the network on right now using the same software this acknowledge, to know which is the better point to do sales is is a good combination finally and working. Ralph of planning on my boss, the planning the planet is working for the CEO and I heard about different organizations. Somebody's in Financial City owes in financial or the video for it is different. That depends from the company. Right now, I'm working for planning how to handle things, to make more money for the company, how to tow hundred children. And it is interesting because all the knowledge that I have engineering is perfect to do it >> Well, I would argue that's the job of a CDO is to figure out how to make money with data. Are saying money. Yeah. Absolute number one. Anyway, start there. >> Yeah, The thing we always talked about is really proving value. It starts with that use case. Identify where the real value is and then waken. You know, technology could come in the in the development work after that. So I agree with hundred percent. >> Carlos. Thanks for coming in. Largest telecommunication in Colombia. Great. Great customer reference. Carlo thinking men to explain real quick in a plug in for your data science elite team. What do you guys do? How do you engage? What? Some of the projects you work on Grey >> out. So we were a team of about one hundred data scientists worldwide. We work side by side with clients. In our job is to really understand the problem from end and help in all areas from skills, tools and technique. And we won't prototype in a three agile sprints. We use an agile methodology about six to eight weeks and we tied. It developed a really We call it a proof of value. It's it's not a M v P just yet or or poc But at the end of the day we prove out that we could get a model. We can do some prediction. We get a certain accuracy and it's gonna add value to the >> guys. Just >> It's not a freebie. It actually sorry. I'm sorry. It's not for paint service. It's a freebie is no cough you've got. But I don't like to use >> free way. Don't charge, but >> But it's something that clients could take advantage of if they're interesting problem and maybe eventually going to do some business. >> If you the largest telecommunication provider in the country, to get a freebie and then three keys, You guys dig in because its practitioners, real practitioners with the right skills, working on problems that way. Claro, >> Colombia's team. They were amazing. In Colombia. We had a really good time. Six to eight weeks working on it. You know, a problem on those guys. All loved it, too. They were. They were. Before they knew it. They were coding and python. And are they ready? Knew a lot of this stuff, but they're digging in with the team and became well together. >> This is the secret to modernization of digital transformation, Having sales process is getting co creating together. Absolutely. Guys do a great job, and I think this is a trend will see more of. Of course, the cubes bring you live coverage here in San Francisco at Mosconi. Nor That's where I said it is. They're shutting down the streets for IBM. Think twenty here in San Francisco, more cube coverage after the short break right back.