 Live from San Francisco, it's theCUBE, covering Informatica World 2016. Brought to you by Informatica. Now, here are your hosts, John Furrier and Peter Burris. Okay, welcome back everyone. We are here live in San Francisco, California for Informatica 2016. Exclusive coverage from SiliconANGLE Media as theCUBE. It's our flagship program. We go out to the events and extract the signals and noise. I'm John Furrier, my co-host Peter Burris. Our next guest is Nick Milman, Managing Director for Big Data and Insight Analytics at Accenture. You cover Europe, Africa, Latin America for Accenture Digital. Welcome to theCUBE. Thank you. Welcome to being a CUBE alumni. So I got to ask you, obviously Accenture has relationship with Informatica among all of the partners. You guys serve as a steward to the customer, their customer and your client base. Big Data certainly has been well past the hype stage into the reality of integrating in cloud, apps. What are the big trends that you're seeing right now with your clients? You guys are on the front lines. Tier one integrator, you're on a global basis. What is the biggest thing that you're talking about with customers? There's a number of things, obviously, we're talking about with our customers. Some really significant trends, some of which I talked about in the keynote here at Informatica World this morning. One is the whole shift towards intelligent automation and we're certainly seeing a number of solutions with artificial intelligence at their core and those solutions require significant amount of data and then clearly provide their own kind of data and analytics opportunities to actually measure what the machines are doing and fine tune and make the algorithms more effective over time as well. So there's certainly a big trend around artificial intelligence and machine learning. How about Accenture's practice you guys have? You guys have very comprehensive, it's almost like your own cloud company. I know a few of your data science we've interviewed in the past. You're like a zillion data scientists now on staff. I'm the exact number, but a lot. Is that part of the go-to market for you guys to help customers by getting down the dirty in the trenches with data science and part of the delivery? Yes, our approach is to provide the end-to-end service that our clients might need in the data analytics space. So we help with everything from the upfront strategy what might be the use cases in this industry or this function that drive value from big data through to data management, data governance, the architectural thinking, and then the implementation and also the advanced analytics and providing the data science capability for our clients. If they don't have that capability themselves they can use Accenture's data science capability. So I've got to ask you a question. I've got some feedback from your keynote you mentioned it. You were explaining how data-driven digital health helps cardio patients. Really with this emphasis of platform economy shifts and shifting the business model. Notable tweets from your keynote. Can you explain and be some color around what you meant by that tweet? Yes, what we've been doing with the digital health platform is actually looking at the data that we can capture through the patient's experience through hospital, both the kind of pathway that they take through the hospital process from admission, how long they spend in each stage of the process and looking for whether there's any ways to make that an efficient and more effective experience for the patient. Also looking at the care management. So how has the diagnosis done? Who's done what in terms of the medical procedures with the individual patient? And then looking at the whole kind of patient engagement part of the equation as well. So how has the patient been communicated to when they were first diagnosed, when they were brought in? And then the ongoing communication as they're discharged, all with a view to improving the patient's experience and eliminate or reducing the number of readmissions. Yes, so hold on before it gets on to a question. But is that holistically? Is that more historically a real time? Or is it just the journey of the patient historically when they come in real time? This is using the historic data in order to predict and make the right decisions in near real time. So actually using it in a decision making sense. So it's all the data. So it's all the data you can get. Yes. That is an application that's predicated on the idea that people are going to change their behavior so that over an extended period of time they'll adopt new behaviors because the data provides visibility into the correlation between a behavior and the outcome. In your practice at Accenture, how are businesses transitioning from episodic uses of big data to operational or let's call it more holistic, comprehensive, persistent uses of big data? Are you starting to see your clients starting to move from periodic success to broad changes in how they do business? We are. I think it depends a lot on the specific use case that we're thinking about. Certainly as the use of data and analytics becomes more pervasive, then actually it's becoming more automated into business processes. So it's a regular part of doing the business process. So let's say, for example, you're looking for fraudulent transactions in an insurance context. As long as you have the algorithms and they're well tuned and they're finding the right cases to investigate, then that's kind of a regular repeatable success that you're going to get every time someone makes a fraudulent claim. In other cases, of course, I think probably what you might be referring to is sometimes the management decision-making in organizations, sometimes it's data-driven, sometimes it's more gut-field-driven, and that's part of our overall approach from an analytics perspective is looking not just at the kind of technology that you implement, but also how you change the culture and the operating model of the organization to make it more data-driven. You have visibility into how Accenture operates with clients in Europe, but also Africa and Latin America. How are different regions approaching common problems differently as a consequence of big data? Yeah, I get to see what clients are doing across different parts of Europe, Africa, and Latin America. There are probably more similarities than differences in terms of the kinds of industry issues that our clients are facing and in terms of the kinds of approaches that they might be using with cloud-based platforms, machine learning techniques, advanced analytics, and then wrapping that into kind of a comprehensive data management kind of solution. There's more similarities than differences, I would say. Okay, we have machine learning and AI. We love the buzz. We used to cover in Google I.O. as well as Sapphire Now, which is the SAP conference last week, up this week, you used to send your folks on there. You're seeing the digital transformation on the enterprise side as clear as day. That's pretty obvious. That has to get worked on. People are working on it. Then you spin over to Google I.O. at Shoreline. You have machine learning, augmented reality, certainly virtual reality, Facebook's talking about that, and AI, the center of the action. How do you guys see that rendering itself with the customer environment? You mentioned some of the healthcare stuff, but is this now on the forefront, on the radar of your clients, AI and bots and automation at the workload level? Or is that still kind of come later? I think there are some real examples out there of organizations who have really done the automated intelligence. So I know, for example, that Siemens is running a lights out factory in one of its locations in Germany, where much of the actual physical activity in the factory and the supply chain planning around that is being done by the machines. And in a consumer context, we've recently developed a prototype that I talked about in my keynote this morning, a robotic mortgage advisor, who can actually make sure that financial institutions, when they're, for example, recommending mortgage products to their consumer clients, are following the legislation and regulations about providing the right impartial advice to the consumer before they take out the mortgage. How are you doing this? Is this basically text-based analysis of the communications between the mortgage institution and the client? It works essentially as a robotic agent that uses natural language processing and machine learning to ask the appropriate questions and then interpret your responses and then ask the appropriate follow-up questions in line with the kind of legislation and regulations. Got it. What's the landscape of the tier one system integrators? And can you share what you guys do differently? Because it seems now that in the old days, go back in the early days, 25 years ago during when an SAP started at their 25th anniversary, you had delivery, long delivery cycles, the big six accounting firms, now Accenture wasn't even around then, now Accenture, but deliveries changed, mentioned data sciences. And each one now, each integrator, this is an integrator, whatever they call these days, PS, whatever, has interesting differentiation. You are now becoming software companies and technology companies, not just delivery. Talk about how you guys differentiate, not necessarily comparing which other guys, Accenture specifically, and how you engage with clients. Yes, I mean, I can talk to what we see in the market in terms of what our clients want. I think there's probably broadly two sorts of client request or client demand around analytics. The first is for clients who've got a specific job to be done, like help me identify the fraudulent transactions in insurance context or help me identify what my maintenance schedule should be for my offshore liquefied natural gas asset because I want to make sure that I'm following the kind of the best maintenance routine to prevent outages, et cetera. So they've got a specific function in mind that they want to apply data and analytics to the problem. And then what we're looking to offer is a very industry focused, if you like, almost product type of solution where we're coming with a proven approach and using that to solve the specific business problem. And in order to facilitate that, we've actually created something called the Accenture Insights platform, which is a pre-configured platform of some of the leading technologies which we can offer to our clients on a cloud basis, i.e. as a service and scale up, scale down. So you walk in, solve a problem, productize it and share it with your other customers. If it's generally available, I mean, I'd say generally available. Some customers might have different data, but you can look at the patterns and get these recipes or use cases. Is that what I, you know? Yes, the idea is, the idea of course is to use the Accenture Insights platform, make it very focused on specific industry issues that we can then provide a rapid solution using a cloud-based approach. The second sort of area of client demand that I think we get in the data analytics spaces is a kind of broader help us transform to be more data-driven and insight-powered where there isn't a kind of one-size-fits-all approach, but it's a matter of looking at the strategy and the vision for the organization, working out where data analytics fits in that, and then having a roadmap and an architectural approach and looking as much at the organization and operating model as it is about the technology and the processes. How about the silos? I mean, one of the things we hear is, you know, silos being busted down. I know Accenture has a lot of different practices. They have a cloud, an Azure practice or a practice over here. All kinds of different practices. You are involved in a practice. When a customer has a crossover between this, how does that work internally for you guys? Single-pointed contact stage with the customer? Do you manage that internally? How do you guys think about this in terms of, you know, being agile yourselves? You know how to tell what customers ought to be agile. Yeah, I think we generally do a good job of that. I mean, our dominant access around that is the client themselves. So we have teams that focus specifically on our main client accounts and then they help to orchestrate the rest of Accenture to bring the right solution to the client problem. And of course, big complex problems require different parts of Accenture, but we're quite used to kind of pulling those together and creating the right team for the client. What's the big barriers for customers, as they say? Okay, digital transformation. I got to get on this track. I've taken some baby steps. Now I'm on the beginning phases of that journey. What is the biggest barrier to that? Is it organizational? Is it actually data architecture? Is it lack of certain technologies? Is it the unknown processes? What's the core thing that you keep on bumping into if it's a repeatable pattern? Yes, every organization is obviously slightly different, but there's probably at least a couple of things which can come up as pretty common challenges. One is, as you mentioned, the organizational aspects. So working out in a kind of enterprise transformation, who's actually going to own it? Is it something which is going to be run out of the CIO function, something out of one of the business functions? Is there going to be a center of excellence to kind of look after the initial implementation but then become the kind of place to go for the data analytics after the transformation? I mean, these tricky organizational issues need working through and there needs to be a clear operating model. And the second common challenge is in the type of large scale clients, often with global presence that Accenture works with, then they typically have quite complex architecture in terms of the source systems that we're going to be relying on for the data analytics. So then looking at the data architecture and actually coming up with the right data supply chain to allow the data to flow in and out of the right parts of the enterprise and put the right kind of governance wrapper around that is also a kind of key challenge. That suggests that the services that you provide are also going to be provided through a lot of different parts of Accenture as well. Are you, do you anticipate that at some point in time that Accenture as a supplier of insight and know how to build big data-oriented systems will increasingly be expressed through other parts, the application parts of Accenture? Or is it something that customers are going to engage directly for an extended period of time? Sorry, I'm not sure I really followed the question. Well, as these technologies and these approaches get embedded into different classes of systems, presumably Accenture as it solves those customers or customers' problems around those classes of systems will embed big data techniques directly into their delivery structure. Are you going, do you anticipate that you will be a service provider inside Accenture? Just as you said, the organizational challenges within a business are myriad. How do you anticipate that Accenture will view big data as it was going to be a set of capabilities or going to be something that's embedded in other solutions? Yes, I understand, okay, fine. And I think that we have some good examples of I think what you're describing where we are, for example, doing BPO business process operations for some of our clients and we embed what we would recommend for client organizations in how we run the BPO for those organizations. So we're making it insight-driven operations. So yes, it makes sense to provide the kind of service to our clients using the kind of the techniques and the trends that we see in the rest of the market. So on the big picture, customers, what would you say is their priorities right now when they move to the cloud, with respect to data? Data first, if you will, and they have all this stuff going on, legacy. I mean enterprise, a true enterprise. What do you see in terms of their priorities as they look at the cloud? What's your takeaway? What's your thoughts on what customers should be thinking about? And what anecdotal things you've seen or you can share? Yes, there's no doubt quite a significant shift towards doing cloud-based data and analytics. I think when organizations first start to look at it, then the first question they typically ask themselves is the security question. And then we also then get into things in terms of the latency, how it's going to be populated, how it's going to integrate with existing systems in the organization. But in most cases, there are solutions to that. And we've architected, for example, the solution in a number of places where we're using data that's coming from the client's internal systems going into the cloud and then can also be backwards integrated into existing solutions they might have for business intelligence and analytics. What advice do you give clients when they say, I want to go to the cloud, I got to use Amazon, Amazon's, I see the results of $10 billion of which Wikibon predicted first. But that kind of came out of nowhere but they're getting into the enterprise space, right? And a lot of people look at the cloud like Amazon and certainly Informatica's got to play there because they're kind of agnostic. That might be a knee-jerk reaction for some customers. How do you guys advise them and say, whoa, not so fast or, I mean, what is the take? What's your take on that? Because now you're also international so you have a global perspective. So what is that conversation like? What happens on those whiteboards or in those conferences when you say, whoa, you're a global company, settle down. What does that happen? Well, you won't be surprised to know that Accenture takes a fairly agnostic view on this. Or Azure Cloud in general, non-prem, non-prem. We're favoring one cloud product over another. In terms of does it make sense to move to the cloud? Then I guess that just depends on the client's situation and we look at each case on its merits. So it's very hard to give a general answer in terms of what we would do. But then if we think moving to the cloud is the right answer, then of course we would look at what cloud variants are out there, what might best suit their circumstances and the kind of commercial basis on which they can do it. And it differs by country, I imagine, right? The different policies and governance. Does that take a big play into it? It's part of it, of course, and in different geographies, very complicated topic. We could spend the next 20 minutes on probably, but... Germany versus Ireland, right? Exactly. Different countries have different data sovereignty rules. But of course the mainstream cloud providers are kind of cognizant of that and coming up with ways of addressing it. So it's something that has to be born in mind as designing the solution. So Nick, I got to ask you the question here at Informatical World 2016. We're live here in San Francisco. What is the biggest thing that you see here that jumps out at you from a trend standpoint or notable news or technology or product? Yeah, what's really been noticeable from my perspective has just been the whole kind of vibe around the event. I think that in my view, when I've been working in data analytics for more than 20 years, there's never been a more exciting time to be working in data analytics with these new technology trends that we've talked a little bit about today with the fact that it's now pretty much on most C-level agendas to actually extract the value out of data analytics for an organization. This is a really exciting time to be working in the space and I think that's come across to me as I've been in the sessions and walking around the event, just the kind of the enthusiasm and the excitement is really, really strong. So we had one of the guests on early and I said, you know, I'd like to draw a line down the middle of the street and one side's the old way and then the new way. What pattern or winning formula do you see that's emerging as kind of a best use case example of what the winning formula is on this new way? Everyone's trying some stuff. You see Hadoop out there, now it's more like big data, data lake, you know, can make it more integrated. You see a lot of things happening on this new way. Old way is kind of stagnant, slow. We kind of know what that is but how do you tell the difference between who's the old way and who's the new way and what's the winning pattern that's emerging that you could point to that's emerging for the people that are doing it the new way? So I think it's probably a big question but the easy part to answer I think is how do you tell who's doing it right? I think you know that an organization has really cracked it around data analytics if it's almost become pervasive. It's almost part of the normal everyday process. So if for example, your Thames water and you're using information and data coming off the sensors around your water treatment plants and actually using that in near real time to decide how best to optimize your operation and be able to respond in near real time when critical things happen like a leak or a weather incident, et cetera. And it's almost embedded in the way you run the business and it's very easy to kind of pick those examples and say well actually if it's embedded in the way the business is running, then you've got it right. So the progress bar almost can be pegged by how well people are actually thinking about their business outcomes and applying some sort of analytics framework and the further they go along, the more in use it is. Or is that kind of how you're thinking about it? Yes, I agree with that. I mean, I think the more that there's to be human intervention or human interpretation the results of course that's required in some use cases but actually really get it embedded and pervasive in the business process and then you're going to maximize the value out of analytics. Nick, final question to end the segment. I'm going to put you in the spot. Share with the folks who are watching something about the big data analytics space that you've been involved in. You said it had 20-year history. That's happening now that they might not know about. That's really worth sharing. Like, hey, you know, could be a friend, could be a colleague, could be a practitioner. What thing could you highlight? What data point or example can you highlight or technology that they should know about that they might not know about? That's worth pointing out. I think the one area that I'm certainly quite excited about and it's very early stages but is the kind of convergence of some of the digital technologies that are out there and we're probably only starting to kind of scratch the surfaces a little bit but if you look at digital technologies like virtual reality and augmented reality, I'm really excited about how analytics can become part of that immersive environment and how in the future I think we might have new ways of looking at information and interpreting it. It won't be kind of getting your tablet out or your phone out and looking at the data. You'll actually be more immersed in the environment and we've started to look at some of those kind of ideas and that's certainly something that I'm really excited about. That's actually changing the user experience, not the consumption of the data. It's actually directly impacting the users. Yes, exactly. Nick, thanks so much for sharing the insight and the data on theCUBE, really appreciate it. Nick Milman with Accenture, sharing his insights here at Informatica World 2016. I'm John Furrier with Peter Burris. You're watching theCUBE. Hi, this is Christi.