>> From Boston, Massachusetts, it's theCUBE. Covering LiveWorx 18, brought to you by PTC. >> Welcome back to Boston, everybody. This is theCUBE, the leader in live tech coverage, and we are here, day one of the PTC LiveWorx conference, IOT, blockchain, AI, all coming together in a confluence of innovation. I'm Dave Vellante with my co-host, Stu Miniman. Wolfgang Ulaga is here. He's the AT&T Professor of Services Leadership and Co-Executive Director, the Center for Services Leadership at Arizona State University. Wolfgang, welcome to theCUBE, thank you so much for coming on. >> Thank you. >> So services leadership, what should we know? Where do we start this conversation around services leadership? >> The Center of Services Leadership is a center that has been created 30 years ago around a simple idea, and that is putting services front and center of everything a company does. So this is all about service science, service business, service operations, people and culture. When you touch service, you immediately see that you have to be 360 in your approach. You have to look at all the aspects. You have to look at structures and people. You have to look at operations with a service-centric mindset. >> I mean, it sounds so obvious. Anytime we experience, as consumers, great service, we maybe fall in love with a company, we're loyal, we tell everybody. But so often, services fall down. I mean, it seems obvious. Why is it just not implemented in so many organizations? >> One of the problems is that companies tend to look at services as an afterthought. Think about the word after-sales service, which in my mind is already very telling about how it's from a cultural perspective perceived. It's something that you do after the sale has been done. That's why oftentimes, there is the risk that it falls back, it slips from the priority list. You do it once, you have done all the other things. But in reality, businesses are there to serve customers. Service should be the center of what the company does, not at the periphery. >> Or even an embedded component of what the company, I mean, is Amazon a good example of a company that has embraced that? Or is Netflix maybe even a better example? I don't even know what the service department looks like at Netflix, it's just there. Is that how we should envision modern-day service? >> It excites me at the conference at LiveWorx. We see so many companies talking about technology and changes. And you really can sense and see how all of them are thinking about how can they actually grow the business from historic activities into new data-enabled activities. But the interesting challenge for many firms is that this is going to be also journey of learning how to serve its customers through data analytics. So data-enabled services is going to be a huge issue in the next coming years. >> Wolfgang, you're speaking here at the conference. I believe you also wrote a book about advanced services. For those that aren't familiar with the term, maybe walk us through a little bit about what that is. >> Earlier this morning, I presented the book "Service Strategy in Action", which is a very managerial book that we wrote over 10 years of experience of doing studies, working with companies on this journey from a product-centric company that wants to go into a service and solution-centric world and business. Today we see many of the companies picking up the pace, going into that direction, and I would say that with data analytics, this is going to be an even more important phenomenon for the next years to come. >> A lot of companies struggle with service as well because they don't see it as a scale component of their business. It's harder to scale services than it is to scale software, for example. In thinking about embedding services into your core business, how do you deal as an organization with the scale problem? Is it a false problem? How are organizations dealing with that? >> No, you're absolutely right. Many companies know and learn when they are small and they control operations. It's easy to actually have your eyes on service excellence. Once you scale up, you run into this issue of how do you maintain service quality. How do you make sure that each and every time to replicate into different regions, into different territories, into different operations, that you keep that quality up and running. One way to do it is to create a service culture among the people because one way to control that quality level is to push responsibility as low as possible down so that each and every frontline employee knows what he or she has to do, can take action if something goes wrong, and can maintain that service quality at the level we want. That's where sometimes you see challenges and issues popping up. >> What role do you see machines playing? You're seeing a lot of things like Chatbox or voice response. What role will machines play in the services of the future? >> I think it's a fascinating movement that is now put in place where, machine, artificial intelligence, is there to actually enhance value being created for customers. Sometimes you hear this as a threat or as a danger, but I would rather see it as an opportunity to raise levels of service qualities, have this symbiosis between human and machine to actually provide better, outstanding service for customers. >> Could you share some examples of successes there or things that you've studied or researched? >> Yeah so for example, if I take a consumer marketing example. In Europe I worked with a company, which is Nespresso. They do this coffee machines and capsules. In their boutique, they don't call it a store, by the way, they call it a boutique, they have injected a lot of new technology into helping customers to have different touchpoints, get served the way they want to, at the time they want to, how they want to. So this multi-channel, multi-experience for customers, is actually a growing activity. When you look at it from a consumer perspective, I get more opportunities, I get more choices. I can pick and choose when, where, and how I want to be served. A similar example is Procter & Gamble here in the United States. P&G has recently rolled out a new service business, taking a brand, Tide, and creating Tide Dry Cleaners here in America. It's a fascinating example. They use technology like apps on a smartphone to give the customer a much better experience. I think there's many of these example we'll see in the future. >> When we talk about IOT, one of the things that caught our ear in the keynote this morning is, it's going to take 20 to 25 partners putting together this solution. Not only is there integration of software, but one of the big challenges there, I think, is how do you set up services and transform services to be able to live in this multi-vendor environment. I wonder if you could comment on that? >> I agree, I agree. What I see, which makes me as a business professor very excited and that is, of course there's technology, of course there's hardware and software. But one of the biggest challenges will be the business challenges. How do you implement all of these offers? How do you roll it out? One of my talk topics today were how do you commercialize it? How do you actually make money with it? How do you get paid for it? One of my research areas is what they call free to fee. How do you get the r out of the free, and make customers pay for value you create? What I find, especially in the digital services space, there's so much value being created, but not every company is able to capture the value. Getting adequately paid for the value, this is a huge challenge. In sum, I would say it's really an issue about business challenges as much as it's a technological issue or technical challenges. >> I think about IOT, so many of the different transfer protocols, it's open source, that free to fee. Any advice you can give to people out there as to how they capture that value and capture revenue? >> I think you have to be super careful where the commoditization will kick in. If over time, something that was a differentiator yesterday, with the open sources and everything, will become not so much differentiator tomorrow. So where is your competitive edge? How do you stand out from competition? I know these are very classic questions, but you know what? In the IOT and digital space, they resurface, they come back, and having the right answers on these questions will make the difference between you and competition. >> Last question, we got to go. The trend toward self-service, is that a good thing, a bad thing, a depends thing? >> I think everything that allows customers to have choices. Customers today want to be in charge. They want to be in control. They, in fact, want all of it. They want to have service when they want it, but they want to have a non-self-service option if they feel like. So I think the trick is to know, how can I be nimble and give customers all of these choices so that they are in charge and pick and choose. >> Wolfgang, thanks so much for coming to theCUBE. >> Appreciate it, >> It's a pleasure having you, >> thank you very much, >> good to see you. All right, keep it right there, everybody. Stu and I will be back with our next guest right after this short break. We're here at the PTC LiveWorx show, you're watching theCUBE. (electronic music)

>> Narrator: Live from Las Vegas, it's theCUBE covering ServiceNow Knowledge 2018, brought to you by ServiceNow. >> Welcome back everyone to theCUBE's live coverage of ServiceNow Knowledge 18. We are theCUBE, we are the leader in live tech coverage. I'm your host Rebecca Knight along with my cohost Dave Vellante. We're joined by Jason Wojahn. He is the managing director global ServiceNow practice lead at Accenture. Thanks so much for your, your returning guest. You're a CUBE veteran. >> Yeah, many times. >> Many time CUBE alum. >> Yes, >> Thanks for noticing. >> Back in the early days. >> But for those who have not had the pleasure of watching your CUBE clips, can you explain what your role is and what you do at Accenture? >> Sure, I'm the global ServiceNow practice lead at Accenture, I'm responsible for our global capabilities in ServiceNow for the company of Accenture. So you know, everything to do with ServiceNow from our consulting capability to our training capability. At Accenture we also have, kind of, what we call three estates of ServiceNow. We have the CIO estate, I know you had Andrew Wilson on theCUBE yesterday, and of course we are a fully deployed ServiceNow customer in our CIO's office. One of the top 10 customers of ServiceNow. We also utilize ServiceNow in our AO, IO, and PBO lines of business. Now in that case that's a go to market relationship where we're selling things like HR outsourcing that is platformed and delivered on ServiceNow and of course last but not least our consulting capabilities. Just over 3000 skilled ServiceNow resources across the world What makes us the largest practice for ServiceNow in the world as well. And those are our three estates of ServiceNow in Accenture. >> So don't hate me for saying this but when we first started following ServiceNow I remember Frank Slootman said to me Dave, this thing is a rocketship. We're going to blow through a billion dollars. We're going to be the next great software company. And one of the things Jeff and I said was well, the ecosystem has to grow. There were companies like Cloud Sherpas which nobody ever heard of which were specialists in the space. Now you fast forward five, six, seven years, Accenture gets into the game, other big SI's have gotten into the game and it is the real deal. It feels like the next ERP of the modern era. >> In my view there are three main big surges going on in the ServiceNow ecosystem and you can kind of tie them back to the CEO's. So you had the early day with Fred Luddy of course, kind of the zero to 150 million stage of ServiceNow. of course when Frank Slootman came in in the 2011 time frame you know you have the next big surge, see them getting IPO ready, you see them really ruggedizing their commercial selling capabilities, their delivery methodology capabilities, etc., and then we move all the way to today and with John Donahoe you see the third surge. And here you see every GSI on the planet wanting to do something with ServiceNow for a lot of the reasons that I just discussed. I mean ServiceNow has been a terribly strategic tool in Accenture across multiple aspects. Of course our go to market aspects, our consulting aspects and of course our internal use of the platform as well. >> It's not easy for software companies to reach escape velocity, certainly many of them can become unicorns and have a billion dollar valuation. It's really hard for them to get to a billion dollars of revenue. ServiceNow has blown through that. They'll probably do three billion or close to it this year. So they really are, in many ways, the next great software company, but you know, VMWare got there, Red Hat obviously doing really well. What are your perspectives on the software ecosystem? I mean, personally I think it's great that we see more competition but there seems to be always this pressure to consolidate. What's your sense of what's happening now? >> Well you see a lot of consolidation that ServiceNow is doing to round out their capabilities as a platform and I think that's terribly important. That's how people want to consume technology right now so we spend a ton of time at this event and you've heard ServiceNow as well, talking about experience management, service management, you know trying to get things away from, you know how do I do this and going to why would I do this versus how. And of course you utilize platforms to really set that tenancy. When you got platform like ServiceNow that has the ability to turn on intelligent automation machine learning capabilities across your platform, the ability to turn on chatbox across your platform, analytics across your platform, knowledge across your platform and of course manage your workflow the way they do with portals, etc. I mean there's no reason to go somewhere else but more importantly, the strategy underneath it you know ServiceNow is an outcome of something that's very important. You can't use AI, you can't use Chatbox, you can't automate if you don't have what we call a lake of data, a data lake. You've got to have that kind of single source of information so that you can do those compounded workflows and get that automation benefit and then when you start laying things like AI, machine learning, intelligent automation, chatbox in there, actually you have to have the data in there to make the suggestions, right, to do the modeling and the analyses to find those opportunities. So I think what you're going to see and what you're actually seeing right now is consolidations on platforms. And those platforms are kind of being used as a ubiquitous glue code for everything else behind the infrastructure and really looking at you know, this is the employee first experience. This is where the last yard of the field is being delivered to the individual. >> The red zone. >> Yeah. >> So the timing of the Accenture acquistion was actually fortuitous because it coincided with ServiceNow's push into the rest of the enterprise. Accenture obviously deep into lines of business, board levels, C-Suite, etc. Talk about how that's changed the whole relationship motion with your customers, how you've gone deeper and describe, sort of, that dynamic. >> Yeah, so, obviously within Accenture our diamond clients are paramount to the way we run our business and who we are as a business and what's great is we're seeing more and more of those clients where they have comprehensive relationships with Accenture, bringing ServiceNow to bear in that conversation and actually, again, using it as an overarching capability to help get things done better. You know it can be very austere to sit at a Cebol console or an Oracle console or those types of things. We're actually using ServiceNow to kind of keep that from having to happen but you're doing the same transaction on the back end. And again, like I said, you know, once you get some of those data points in there it tends to kind of start to gain some momentum because you get a little bit of automation here or a little bit of automation there and then suddenly that connects you to other aspects of the enterprise and other consolidation points. >> What makes Accenture different, you got all the SI's are now in, elbowing their way in. We want a piece of the action. Why Accenture? >> Well the ego in me says it's because we're number one. We have the largest single certified pool of resources across the globe. There's nobody bigger than us. There's nobody that does more influence revenue than ServiceNow, than us and there's no one with higher customer satisfaction than us We actually got that award two days ago from ServiceNow. So if you value those things, that's why you should work with Accenture. But more importantly than that we've really spent a lot of time making sure that we're doubling down on our methodologies, we're doubling down on our thought leadership, we're leveraging our capabilities that we're you know, trialing and piloting in our CIO's office across the 450,000 person company called Accenture. We're obviously leveraging the things we learn in our AO, IO, BPO practices where we have embedded ServiceNow into those go to market services. But we're bringing that all back to our consulting practice and it's a creed of to not only the way we handle CIO, AO, IO, BPO, but a way we handle our customers from a consulting perspective as well. >> It's the customercentric approach. >> Jason: It is, it is. >> Well Jason thanks so much for coming on the program. It's always fun to have you on theCUBE. >> Thanks a lot. >> Dave: Great to see you. >> Thanks. >> I'm Rebecca Knight for Dave Vellante. We will have more from theCUBE's live coverage of ServiceNow Knowledge 18 in just a little bit.

>> Hi, I'm Peter Burris. Welcome to Wikibon's Action Item. (light instrumental music) No one can argue that big data and related technologies have had significant impact on how businesses run, especially digital businesses. The evidence is everywhere. Just watch Amazon as it works its way through any number of different markets. It's highly dependent upon what you can get out of big data technologies to do a better job of anticipating customer needs, predict best actions, make recommendations, et cetera. On the other hand, nobody can argue, however, that the overall concept of big data has had significant issues from a standpoint of everybody being able to get similar types of value. It just hasn't happened. There have been a lot of failures. So today, from our Palo Alto studios, I've asked David Floyer, who's with me here, Jim Kobielus and Ralph Finos and George Gilbert are on the line, and what we're going to talk about is effectively where are we with big data pipelines and from a maturity standpoint to better increase the likelihood that all businesses are capable of getting value out of this. Jim, why don't you take us through it. What's the core issue as we think about the maturing of machine analytics, big data pipelines? >> Yeah, the core issue is the maturation of the machine learning pipeline, how mature is it? And the way Wikibon looks at the maturation of the machine learning pipeline independent of the platforms that are used to implement that pipeline are three issues. To what extent has it been standardized? Is there a standard conception, various tasks, phases, functions, and their sequence. Number two, to what extent has this pipeline at various points or end to end been automated to enable through point consistency. And number three, to what extent has this pipeline been accelerated not through just automation but through a very (static) and collaboration and handling things like governance in a repeatable way? Those are core issues in terms of the ML pipeline. But in the broader sense, the ML pipeline is only one work stream in the broader application development pipeline that includes code, development, and testing the pipeline. So really dev ops is really the broader phenomenon here. ML pipeline is one segment of the dev ops pipeline. >> So we need to start thinking about how we can envision the ML pipeline creating assets that businesses can use in a lot of different ways. For those assets specifically or models, machine learning models that can be used in more high value analytic systems. This pressure has been in place for quite a while. But David Floyer, there's a reason why right now this has become important. Why don't you give us a quick overview of kind of like where does this go? Why now? >> Why now? Why now is because automation is in full swing, and you've just seen the Amazon having the ability now to automate warehouses, and they've just announced the ability to automate stores, brick and mortar stores. You go in. You pick something up. You walk out. And that's all you have to do. No lines at checkout. No people in the checkout, a completely automated store. So that business model of automation of business processes is, to me, what all this has to lead up to. We have to take the existing automation that we have, which is the systems of record and other automation that we've had for many other years, and then we have to take the new capabilities of AI and other areas of automation, and apply those to those existing automation and start on this journey. It's a 10 year journey or more to automating as many of those business processes as possible. Something like 80% or 90% are there and can be automated. It's an exciting future, but what we have to focus on is being able to do it now and start doing it now. >> So that requires that we really do take an asset-oriented approach to all of this. At the end of the day, it's impossible to imagine business taking on increasing complexity within the technology infrastructure if it hasn't taken care of business in very core ways, not the least of which is do we have, as a business, have a consistent approach to thinking about how we build these models? So Jim, you've noted that there's kind of three overarching considerations. Help us go into it a little bit. Where are the problems that businesses are facing? Where are they seeing the lack of standardization creating the greatest issues? >> Yeah, well, first of all, the whole notion of a machine learning pipeline has a long vintage. It actually descends from the notion of a data mining pipeline, but the data mining industry, years ago, consolidated or had a consensus around some model called Crisp. I won't bore you with the details there. Taking it forward to an analytical pipeline or a machine learning pipeline, the critical issues we see now is the type of asset that's being built and productionized is a machine learning model, which is a statistical model that is increasingly built on artificial neural networks, you know, to drive things like data learning. Some of the critical things up front, the preparation of all the data in terms of ingest and transformation and cleansing, that's an old set of problems well established, and there's a lot of tools on the market that do that really well. That's all critical for data preparation prior to the modeling process really truly beginning. >> So is that breaking down, Jim? Is that the part that's breaking down? Is that the upfront understanding of the processes, or is it somewhere else in the pipeline process that is-- >> Yeah, it's in the middle, Peter. The modeling itself for machine learning is where, you know, there's a number of things that have to happen for these models to be highly predictive. A, you have to do something called feature engineering, and that's really fundamentally looking for the predictors in large data sets that you can build into models. And you can use various forms. So feature engineering is a highly manual process that to some increasingly is being automated. But a lot of it is really leading edge technology is in the research institutes of the world. That's a huge issue of how to automate more of the upfront feature engineering. That feeds into the second core issue is that there's 10 zillion ways to skin the statistical model cat, the algorithms. You know, from the older models, the port vic-machine, to the newer artificial neural networks convolution. Blah, blah, blah. So a core issue, okay, you have a feature set through feature engineering, which of the 10 zillion algorithms should you use to actually build the model based on that feature set. So there are tools on the market that can accelerate some of these selection and testing of those alternate ways of building out those models. But once again, that highly manual process, traditionally manual process and selecting the items, building the models, still needs a lot of manual care and feeding to really be done right. It's human judgment. You really need high power data scientists. And then three, once you have the models built, training them. Training is critical with actual data to determine whether the models actually are predictive or do face recognition or whatever it is with a high degree of accuracy. Training itself is a very complicated pipeline in its own right. It takes a lot of time. It takes a lot of resources, a lot of storage. You got to, you know, your data link and so forth. The whole issue of standardizing on training of machine learning models is a black art on its own. And I'm just scratching the surface of these issues that are outstanding in terms of actually getting greater automation into a highly manual, highly expert-driven process. Go ahead, David. >> Jim, can I just break in? You've mentioned three things. They're very much in the AI portion of this discussion. The endpoint has to be something which allows automation of the business process, and fundamentally, it's real time automation. I think you would agree with that. So the outcome of that model then has to be a piece of code that is going to be as part of the overall automation system in the enterprise and has to fit in, and if it's going to be real time, it's got to be really fast as well. >> In other words, if the asset that's created by this pipeline is going to be used in some other set of activities? >> Correct, so it needs to be tested in that set of activities and part of a normal curve. So what is the automation? What is that process to get that code into a place where it can actually be useful to the enterprise and save money? >> Yeah, David, it's called dev ops, and really dev ops means a number of different things including especially a source code, code control repository. You know, in the broader scheme of things that repository for your code for dev ops for continuous release and cycles needs to be expanded, and it's scoped to include machine learning models, deep learning, whatever it is you're building based on the data. What I'm getting at is a deepening repository of what I call logic that is driving your applications. It's code. It's Java, C++, or Sharp or whatever. It's statistical and predictive model. It's orchestration models you're using for BPM and so forth. It's maybe graph models. It's a deep and thickening layer of logic that needs to be pushed into your downstream applications to drive these levels of automation. >> Peter: So Jim? >> It has to be governed and consolidated. >> So Jim? The bottom line is we need maturity in the pipeline associated with machine learning and big data so that we can increase maturity in how we apply those assets elsewhere in the organization? Have I got that right? >> Right. >> George, what is that going to look like? >> Well, I want to build on what Jim was talking about earlier and my way of looking at this, at the pipeline, is actually to break it out into four different ones. And actually, Jim, as he's pointed out, there's more than potentially four. But the first is the design time for the applications, these new modern, operational, analytic applications, and I'll tie that back to the systems of record and effect. The second is the run time pipeline for these new operational, analytic applications, and those applications really have a separate pipeline for design time and run time of the machine learning models. And the reason I keep them separate is they are on a separate development and deployment and administration scaffolding from the operational applications. And the way it works with the systems of record, which of course, we're not going to be tearing out for decades, they might call out to one of these new applications, feed in some predictors, or have some calculated, and then they get a prediction or a prescription back for the system of record. I think the parts-- >> So George, what has to happen is we have to be able to ensure that the development activities that actually build the applications the business finds valuable and the processes by which we report into the business some of the outcomes of these things and the pipelines associated with building these models, which are the artifacts and the assets created by the pipelines, all have to come together. Are we talking about a single machine or big data pipeline? George, you mentioned four. Are we going to see pipelines for machine learning and pipelines for deep learning and pipelines for other types of AI? Are we going to see a portfolio of pipelines? What do you guys think? >> I think so, but here's the thing. I think there's going to be a consolidated data lake from which all of these pipelines draw the data that are used for modeling and downstream deployment. But if you look at training of models, you know, deep learning models, which are like their name indicates, they're deep, hierarchical. They're used for things like image recognition and so forth. The data there is video and speech and so forth. And there's different kinds of algorithms that they use to build, and there's different types of training that needs to happen for deep learning versus like other machine learning models versus whatever else-- >> So Jim, let me stop you because-- >> There are different processes. >> Jim, let me stop you. So I want to get to the meat of this guys. Tell me what a user needs to do from a design standpoint to inform their choice of pipeline building, and then secondarily, what kind of tools they're going to need. Does it start with the idea that there's different algorithms? There's different assets that are being created at the model level? Is it really going to feed that? And that's going to lead to a choice of tools? Is it the application requirements? How mature, how standardized, can we really put in place conventions for doing this now so it becomes a strategic business capability? >> I think there has to be a recognition. There's different use cases downstream. 'Cause these are different types of applications entirely built from AI in the broadest sense. And they require different data, different algorithm. But you look at the use cases. So in other words, the use cases, like Chatbox. That's a use case now for AI. That's a very different use case from say self-driving vehicle. So those need entirely different pipelines in every capacity to be able to build out and deploy and manage those disparate applications. >> Let me make sure I got this, Jim. What you're saying is that the process of creating a machine learning asset, a model, is going to be different at the pipeline level. It's not going to be different at the data level. It's going to be different at the pipeline level. George, does that make sense? Is that right? Do you see it that way, too, as we talk to folks? >> I do see what Jim is saying in the sense that if you're using sort of operational tooling or guardrails to maintain the fidelity of your model that's being called by an existing system of record, that's a very different tooling from what's going to be managing your IOT models, which have to get distributed and which may have sort of a central canonical version and then an edge specific instance. In other words, I do think we're going to see different tooling because we're going to see different types of applications being fed and maintained by these models. >> Organizationally, we might have a common framework or approach, but the different use cases will drive different technology selections, and those pipelines themselves will be regarded as assets that generate machine learning and other types of assets that then get applied inside these automation applications. Have I got that right, guys? >> Yes. >> Yes. A quick example to illustrate exactly what we're referring to here. So IOT, George brought up IOT analytics with AI built in its edge applications. We're going to see a bifurcation between IOT analytic applications where the training of the models is done in a centralized way because you've got huge amounts of data that needs to be training these very complex models that are running in the cloud but driving all these edge nodes and gateways and so forth, but then you're going to have another pipeline for edge-based training of models for things like autonomous operation where more of the actual training will happen at the edges, at the perimeter. It'll be different types of training using different types of data with different types of time lags and so forth built in. But there will be distinct pipelines that need to be managed in a broader architecture. >> So issues like the ownership of the data, the intellectual property control, the data, the location of the data, the degree to which regulatory compliance is associated with it, how it gets tested, all those types of issues are going to have an impact on the nature of the pipelines that we build here. >> Yes. >> So look, one of the biggest challenges that every IT organization has, in fact every business has, is the challenge that if you have this much going on, the slowest part of it slows everything else down. So there's always an impedance mismatch organizationally. Are we going to see a forcing of data science, application development, routines, practices, and conventions start to come together because the app development world, which is being asked to go faster and faster and faster is at some point in time say, I can't wait for these guys to do their sandbox stuff? What do you think, guys? Are we going to see that? David, I'll look at you first, and Jim, I'll go to you. >> Sure, I think that the central point of control for this is going to have to be the business case for developing this automation, and therefore from that, what's required in that system of record. >> Peter: Where the money is. >> Where the money is. What is required to make that automation happen, and therefore from that, what are you going to pick as your ways of doing that? And I think that at the moment, it seems to me as an outsider, it's much more driven by the data scientists rather than the people, the business line, and eventually the application developers themselves. I think that shift has to happen. >> Well, yeah, well, one of our predictions has been that the tools are improving and that that's going to allow for a separation, increased specialization of the data science world, and we'll see the difference between people who are really doing data science and people who are doing support work. And I think what we're saying here is those people who do support work are going to end up moving closer to the application development world. Jim, I think that's basically some research that you've done as well. Have I got that right? Okay, so let me wrap up our Action Item here. David Floyer, do you have a quick observation, a quick Action Item for this segment? >> For this segment? The Action Item to me is putting together a business case for automation, the fundamental reduction of costs and improvement of business model, and that to me, is what starts this off. How are you going to save money? Where is it most important? Where in your business model is it most important? And what we've done is some very recent research is put out a starting point for this discussion, a business model of a 10 billion dollar company, and we're predicting that it saves 14 billion dollars. >> Let's come to that. The Action Item is basically, start getting serious about this stuff because based on business cases, yeah. All right, so let me summarize very quickly. For Jim Kobielus and George Gilbert and Ralph Finos, who seem to have disappeared off our screens and David Floyer, our Action Item is this. That the leaders in the industry, in the digital world, are starting to apply things like machine learning, deep learning, and other AI forms very aggressively to compete, and that's going to force everybody to get better at this. The challenge, of course, is if you're forcing, or if you're spending most of your time on the underlying technology, you're not spending most of your time figuring out how to actually deliver the business results. Our expectation is that over the course of the next year, one of the things that are going to happen significantly within organizations will be a drive to improve the degree to which machine learning pipelines become more standardized reflecting of good data science practices within the business which itself will change based on the nature of the business, regulatory businesses versus non-regulatory businesses, for example. Having those activities be reflected in the tooling choices, have those tooling choices then be reflected in the types of models you want to build, and those models, those machine learning models ultimately reflecting the needs of the business case. This is going to be a domain that requires a lot of thought in a lot of IT organizations, a lot of inventions yet to be done here. But it's going to, we believe, drive a degree of specialization within the data science world as the tools improve and a realignment of crucial value-creating activities within the business so that what is data science becomes data science. What's more support, what's more related to building these pipelines, and operating these pipelines becomes more associated with dev ops and application development overall. All right, so for the Wikibon team, Jim Kobielus, Ralph Finos, George Gilbert, and here in the studio with me, David Floyer, this has been Wikibon's Action Item. We look forward to seeing you again. (light instrumental music)

(upbeat music) >> Narrator: Live, from Las Vegas, it's the CUBE. Covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. >> Welcome back everyone, we are here live in Las Vegas for the CUBE's special coverage of Oracle's ModernCX, Modern Customer Experience, this is the Cube, I'm John Furrier, my cohost Peter Burris. Our next guest is Tony Nadalin. Tony Nadalin is the global vice president of the Global Consulting at Oracle for the marketing cloud. Welcome to the CUBE. >> Well, thank you. Thank you for having me. >> So you've got to implement this stuff, and we've heard a lot of AI magic and there's a lot of meat on the bone there. People are talking about there's a lot of real things happening. Certainly, Oracle's acquired some great technologies over the years, integrated it all together. The proof is in the pudding. When you roll it out, the results have to speak for themselves. >> Tony: Yes, absolutely. >> So share with us some of those activities. What's the score board look like? What's the results? >> I think what's really important, and Lewis spoke about this yesterday, it's people and product. The customers are buying visions. They're looking at creating and changing the customer experience. They're not just buying a piece of technology. They're buying a transformation. I think what's really important and what we do a lot in services, in all services, not just Oracle Marketing Cloud Services, but just healthy services, is when customers are implementing, they're not just implementing technology, they're not just plumbing the pipes. They are putting in changes. They're looking at the people, the process, the technology. We have a really good relationship with our customers and our partners and we're constantly looking at the complete set of services, the complete suite. From what I call transformational services, where we come in and try to understand what are you trying to change? How are you trying to change your customer experience? As a marketer, owning not only what you do, and how all the different channels are working together across all the different products that they are. They purchase Eloqua, Responsys, BlueKai, Maxymiser, et cetera. >> So you're laying it all out, it's like you're sitting in a room, now I'm oversimplifying it, but it's not just rolling out stuff. You've got planning. >> Tony: You've got to plan it. >> Put the pieces together. >> You do, and it's a readiness. It's a readiness of the organization, you think about it, you've got within a marketing organization, you've got many teams coming together that have to be united around the brand, the consistency, how they're engaging with customers. But also, not only across like an acquisition team, or loyalty or an upsell and cross sell team, how does that, as we were looking at the products key notes, how does that then extend into the services engagement? How does it extend into the sales engagement? How are we making sure that everyone is using the same messaging, the same branding, leveraging each other? It's a real transformation at a people, process and technology level. So that when you're then implementing, you're implementing changes. And so we've got some great services and great partners that make sure that when the customers are going through that transformation, they're sort of going it fully readied. And our role, from a services perspective, is to ensure then, sort of define the transformation, define the strategy, like plan the plan, and then go execute the plan. And then putting in the plumbing, getting everyone readied. The analogy I used, I'm sure you've got kids, right? When we have toddlers, and you build the kid's first bikes. Your goal is to build that bike, put the training wheels on the bike, and ultimately sort of stand behind your child to a point that when you let them go, they're not going to graze their knees. Then from an ongoing basis, continue to stand behind them, then get ready to take the training wheels off. Then training wheels come off. Maybe at one point they may become BMX champions, right? But you're sort of behind them through the whole-- >> John: There's progression. >> Progression, exactly. >> With my kids, it's simply man to man, then zone defense. (laughter) >> But it's progression, right? A lot of customers, we have not only the onboarding and implementation services, but these ongoing services that are so key. Because obviously it's important to ensure that your customers are realizing. When I think of our services and the journey, there's the discovery, the transformation, and the strategy. That's like the discovery. But you've then got the realization. And then the optimization and the realization to me is that you're realizing that initial step. You're realizing the technology and you're realizing people and process. You're getting people stood up. Skills, people, organizations, technology, data. You're realizing it all so they can then take the next step. >> Alright, so what's the playbook? A lot of times, in my mind's eye, I can envision in a white board room, board room, laying it all out, putting the puzzle pieces together, and then rolling out implementation plan. But the world is going agile, not waterfall anymore, so it's a combination of battle mode, but also architectural thinking. So not just fashion, real architectural, foundational. >> Peter: Design thinking. >> Tony: Exactly, architectural. >> John: Design thinking. What's the playbook? What's the current state of the art in the current-- >> Well we have obviously product consultants, architects, solution consultants, content creators. It's the whole spectrum of where the customer needs to focus on. And I think-- >> John: So you assemble them based upon the engagement. >> Based upon the engagement and understanding, like what are the customer's strengths? Where are they now? Where are they trying to get to? There's some customers, you know, we have a whole range of services, and we have a whole range of customers. So there are some customers who are like, "We have our own teams today, "we want to augment our teams with your teams, "we want to have hybrid models." Or, "We have our own teams today, but not only have you got great people, but you've got great processes." So like, look at Maxymiser as an example. A lot of our Maxymiser customers, not only use our platform, but they use our people. They're not just buying our people, they're buying a sort of agile, Kanban, JavaScript development practices that are a different level of software development. It's not just the people that can code, it's the development practices. So it's that whole operational services where we bring to the table just a different degree of operational excellence. But we're also to go in to our customers that have their own teams and provide them also consulting perspective around how they can also sharpen their edge. If they want to sort of keep, you know. So whole spectrum of services. >> So let me see if I can throw something out there, in kind of like the center, the central thesis of what you do and how it's changed from what we used to do. Especially a company like Oracle, which has been a technology company at the vanguard of a lot of things. It used to be that customers had an idea of what they wanted to implement. They wanted to implement an accounting system. The processes are relatively known. What was unknown was the technology. How do, what do I buy? How do I configure? How do I set it up? How do I train? How do I make the software run? How do I fix? So it was known process, unknown technology. As a consequence, technology companies could largely say, yeah, that value is intrinsic to the product. So you buy the product, you've got it now. But as we move more towards a service world, as we move more toward engaging the customer world where the process is unknown, and the technology, like the cloud, becomes increasingly known. Now we're focused on more of an unknown process, known technology, and the value is in, does the customer actually use it. >> I think the value is actually in does the customer get value. I think there's a, I've managed customer success organizations and customer service organizations, and the one thing I see in SAS, is usage doesn't always equate to value. So I think as a services organization, it's important to understand the roadmap to value. Because a lot of times, I would say in commodity software, sort of the use of it by default in itself was enough. That you were moving to a software platform. I think SAS customers, especially marketers, are looking for transformation. They're looking for a transformation and a change in value. A change in value in the conversation they're having with the customer. A change in acquisition, loyalty, retention, a change in being relevant. As Joseph was saying this morning, being relevant with the customer, and that value is more than just implementing some technology. >> So it's focusing on ensuring that the customer is getting value utility out of whatever they purchase. >> Tony: Correct. >> Not just that they got what they purchased. So as we move into a world where we're embedding technology more and more complex, it's two things happen. One is, you have to become more familiar of the actual utilization. And what does it mean, and I think marketing cog helps that. What is marketing, how does it work? And second one, the historical norm has been, yeah, we're going to spend months and years building something, deploying something, but now we're trying to do it faster, and we can. So how is your organization starting to evolve its metrics? Is it focused on speed? Is it focused on, obviously value delivered, utilization. What are some of the things that you are guiding your people to focus on? >> Well I think, I very much take a outside-in view. So to me, if I look at why a customer is buying, and what do they want. Obviously most customers want fast time to value, as reduced effort, obviously, and little surprises. I think having a plan and being able to execute your plan. And this whole, as we were talking like one-to-many versus one-to-one. >> And timing too, no surprises and they want to execute. >> And time to value, right? And speed. And I think as we were talking, similar to as a marketer is trying to engage any customer and sort of going from that one-to-many to that one-to-you, what's important now for any organization, a services organization, any company, is to understand what does your business look like? Because why you bought from Oracle, whether you be in a certain vertical or a certain space, or a certain maturity as a customer, it's important that we have the play books, and we do, that say that if you're a customer of this size, of these products in this vertical, then we have the blueprints for success. They may not be absolutely perfect, but they're directional, that we can sort of put you on the fast path. That we've seen the potholes before, we've seen the bumps, we understand the nuances of your data, your systems, your people, your regulations. So that we can actually, we have a plan. And it's a plan that's relevant to you. It's not a generic plan. And I think that's the biggest thing where good companies show up then deliver solutions that they're not learning 100%. There's always going to be nuances and areas of gray that you work through, where the customer's just as much as vendors as they transform. We're not just swapping like for like, but when you transform, there's changes that occur on the customer side. There's new awarenesses of I didn't realize we did that. I didn't realize I want to change doing that. And I've actually changed maybe my whole thought. >> What's the change coming from this event? If you look at the show here, ModernCX, some really good directional positioning. The trajectory of where this is going, I believe is on a great path. Certainly directionally relevant, 100%. Some stuff will maybe shift in the marketplace. But for the most part, I'm really happy to see Oracle go down this road. But there's an impact factor to the customers, and the communities, and that's going to come to you, right? So what are you taking away from the show that's important for customers to understand as Oracle brings in adaptive intelligence? As more tightly coupled, highly cohesive elements come together? >> I think to me, it's transformation. Customers really do understand what are they trying to achieve as they transform? Not just by a piece of technology, but come into it understanding, okay, what are we trying to transform? And have we got like all change management? All transformational management? Have I got the right buy-in across the organization? As a marketer, if I'm trying to transform the organization, have I got the right stakeholders in the room with me? Am I trying to influence the right conversations? You look at the conversation yesterday with Netflix. The discussion, or Time-Warner, sorry. Around their transformation around data. That wasn't a single entity determining that. That was a company driven strategy. A company driven transformation. And I think to really change the customer experience, and control the brand of that across all touchpoints of the company, it requires transformation and it requires being realistic around also how long that journey takes. Depending on the complexity and size of the company. It requires investment of people, of energy, or resources and really understanding where is your customer today? Where is your competition? And to Mark's point, it's like the market is being won here, you're having to compete against your competition, you're having to be better than them, you're having to understand your competition just as much as you understand yourself, so you're leapfrogging. Because just as much as you're going after your competitors customers, your customers are coming up for your customers, right, your competitors are coming up for your customers. I think transformation and understanding how to engage the right services leaders, be it Oracle or any of our partners, to really transform your business is to me the biggest take away. The technology then, be it Chatbox or AI, I mean they augment, they help, they're going to be channels, but I think transformation is key. >> It's really not the technology, it's really what you're doing it with, at the end of the day. Tony, thanks for coming on the CUBE. We really appreciate it, and again, when the rubber hits the road, as Peter was saying earlier, it's going to be what happens with the product technologies for the outcomes. >> Tony: Absolutely. >> Thanks for sharing your insights here on the CUBE. Sharing the data, bringing it to you. I'm John Furrier with the CUBE with Peter Burris, more live coverage for the Mandalay Bay in Las Vegas from Oracle's ModernCX after this short break. (upbeat music) >> Narrator: Robert Herjavec >> Interviewer: People obviously know you from Shark Tank. But the Herjavec Group has been really laser focused on cyber security.