>> Voiceover: Live from Las Vegas, it's theCUBE. Covering Informatica World 2018, brought to you by Informatica. >> Hey, welcome back everyone, we're here live at the Venetian, we're at the Sands Convention Center, Venetian, the Palazzo, for Informatica World 2018. I'm John Furrier, with Peter Burris, my co-host with you. Our next guest, Arun Varadarajan, who's the VP of AI and Analytics at Cognizant. Great to see you. It's been awhile. Thanks for coming on. >> Thank you. Thank you John, it's wonderful meeting you again. >> So, last time you were on was 2015 in the queue. We were at the San Francisco, where the event was. You kind of nailed the real time piece; also, the disruption of data. Look ing forward, right now, we're kind of right at the spot you were talking about there. What's different? What's new for you? ASI data's at the center of the value preposition. >> Arun: Yep. People are now realizing, I need to have strategic data plan, not just store it, and go do analytics on it. GDPR is a signal; obviously we're seeing that. What's new? >> So, I think a couple of things, John. One is, I think the customers have realized that there is a need to have a very deliberate approach. Last time, when we spoke, we spoke about digital transformation; it was a cool thing. It had this nice feel to it. But I think what has happened in the last couple of years is that we've been able to help our clients understand what exactly is digital transformation, apart from it being a very simple comparative tactic to deal with the fact that digital natives are, you know, barking down your path. It also is an opportunity for you to really reimagine your business architecture. So, what we're telling our clients is that when you're thinking about digital transformation, think of it from a 3-layer standpoint, the first layer being your business model itself, right? Because, if you're a traditional taxi service, and you're dealing with the Uber war, you better reimagine your business model. It starts there. And then, if your business model has to change to compete in the digital world, your operating model has to be extremely aligned to that new business model paradigm that you've defined. And, to that, if you don't have a technology model that is adapting to that change, none of this is going to happen. So, we're telling our clients, when you think about digital transformation, think of it from these three dimensions. >> It's interesting, because back in the old days, your technology model dictated what you could do. It's almost flipped around, where the business model is dictating the direction. So, business model, operating model, technology model. Is that because technology is more versatile? Or, as Peter says, processes are known, and you can manage it? It used to be, hey, let's pick a technology decision. Which database, and we're off to the races. Now it seems to be flipped around. >> There are two reasons for that. One is, I think, technology itself has proliferated so much that there are so many choices to be made. And if you start looking at technology first, you get kind of burdened by the choices you need to make. Because, at the end of the day, the choice you make on technology has to have a very strong alignment and impact to business. So, what we're telling our clients is, choices are there; there are plenty of choices. There are compute strategies available that are out there. There's new analytical capabilities. There's a whole lot of that. But if you do not purpose and engineer your technology model to a specific business objective, it's lost. So, when we think about business architecture, and really competing in the digital space, it's really about you saying, how do I make sure that my business model is such that I can thwart the competition that is likely to come from digital natives? You saw Amazon the other day, right? They bought an insurance company. Who knows what they're going to buy next? My view is that Uber may buy one of the auto companies, and completely change the car industry. So, what does Ford do? What does General Motors do? And, if they're going to go about this in a very incremental fashion, my view is that they may not exist. >> So, we have been in our research arguing that digital transformation does mean something. We think that it's the difference between a business and a digital business is the role that data plays in a digital 6business, and whether or not a business treats data as an asset. Now, in every business, in every business strategy, the most simple, straightforward, bottom-line thing you can acknowledge is that businesses organize work around assets. >> John: Yep. >> So, does it comport with your observation that, to many respects, what we're talking about here is, how are we reinstitutionalizing work around data, and what impact does that have on our business model, our operating model, and our technology selection? Does that line up for you? >> Totally, totally. So, if you think about business model change, to me, it starts by re-imagining your engagement process with your customers. Re-imagining customer experience. Now, how are you going to be able to re-imagine customer experience and customer engagement if you don't know your customer? Right? So, the first building block in my mind is, do you have customer intelligence? So, when you're talking about data as an asset, to me, the asset is intelligence, right? So, customer intelligence, to me, is the first analytical building block for you to start re-imagining your business model. The second block, very clearly, is fantastic. I've re-imagined customer experience. I've re-imagined how I am going to engage with my customer. Is your product, and service, intelligent enough to develop that experience? Because, experience has to change with customers wanting new things. You know, today I was okay with buying that item online, and getting the shipment done to me in 4 days. But, that may change; I may need overnight shipping. How do you know that, right? Are you really aware of my preferences, and how quickly is your product and service aligning to that change? And, to your point, if I have customer intelligence, and product intelligence sorted out, I better make sure that my business processes are equally capable of institutionalizing intelligence. Right? So, my process orchestration, whether it's my supply chain, whether it's my auto management, whether it's my, you know, let's say fulfillment process; all of these must be equally intelligent. So, in my mind, these are three intelligent blocks: there's customer intelligence, product intelligence, and operations intelligence. If you have these three building blocks in place, then I think you can start thinking about what should your new data foundation look like. >> I want to take that and overlay kind of like, what's going on in the landscape of the industry. You have infrastructure world, which you buy some rack and stack the servers; clouds now on the scene, so there's overlapping there. We used to have a big data category. You know, ADO; but, that's now AI and machine learning, and data ware. It's kind of its own category, call it AI. And then, you have kind of emerging tech, whether you call, block chain, these kind of... confluence of all these things. But there's a data component that sits in the center of all these things. Security, data, IOT, traverse infrastructure, cloud, the classic data industry, analytics, AI, and emerging. You need data that traverses all these new environments. How does someone set up their architecture so that, because now I say, okay, I got a dat big data analytics package over here. I'm doing some analytics, next gen analytics. But, now I got to move data around for its cloud services, or for an application. So, you're seeing data as to being architected to be addressable across multiple industries. >> Great point John. In fact, that leads logically to the next thing that me and my team are working on. So we are calling it the Adaptive Data Foundation. Right? The reason why we chose the word adaptive is because in my mind it's all about adapting to change. I think Chal Salvan, or somebody said that the survival of the fittest is not, the survival is not of the survival of the fittest or the survival of the species that is intelligent, but it's the survival of those who can adapt to change, right? To me, your data foundation has to be super adaptive. So what we've done is, in fact, my notion, and I keep throwing this at you every time I meet you, in my opinion, big data is legacy. >> John: Yeah, I would agree with that. >> And its coming.. >> John: The debate. >> It's pretty much legacy in my mind. Today it's all about scale-out, responsive, compute. The data world. Now, if you looked at most of the architectures of the past of the data world, it was all about store and forward. Right? I would, it's a left to right architecture. To me it's become a multi-directional architecture. Therefore what we have done is, and this is where I think the industry is still struggling, and so are our customers. I understand I need to have a new modern data foundation, but what does that look like? What does it feel like? So with the Adaptive Data Foundation... >> They've never seen it before by the way. >> They have not seen it. >> This is new. >> They are not able to envision it. >> It is net new. >> Exactly. They're not able to envision it. So what I tell my clients is, if you really want to reimagine, just as you're reimagining your business model, your operating model, you better reimagine your data model. Is your data model capable of high velocity resolutions? Whether it's identity resolution of a client who's calling in. Whether it's the resolution of the right product and service to deliver to the client. Whether it's your process orchestration, they're able to quickly resolve that this data, this distribution center is better capable of servicing their customer need. You better have that kind of environment, right? So, somebody told me the other day that Amazon can identify an analytical opportunity and deliver a new experience and productionize it in 11.56 seconds. Today my customers, on average, the enterprise customers, barely get to have a reasonable release on a monthly basis. Forget about 11.56 seconds. So if they have to move at that kind of velocity, and that kind of responsiveness, they need to reimagine their data foundation. What we have done is, we have tried to break it down into three broad components. The first component that they're saying is that you need a highly responsive architecture. The question that you asked. And a highly responsive architecture, we've defined, we've got about seven to eight attributes that defines what a responsive architecture is. And in my mind, you'll hear a lot of, I've been hearing a lot of this that a friend, even in today's conference, people are saying, 'Oh, its going to be a hybrid world. There's going to be Onprim, there's going to be cloud, there's going to be multicloud. My view is, if you're going to have all of that mess, you're going to die, right? So I know I'm being a little harsh on this subject, but my view is you got to move to a very simplified responsive architecture right up front. >> Well you'd be prepared for any architecture. >> I've always said, we've debated this many times, I think it's a cloud world, public cloud, everything. Where the data center on premise is a huge edge. Right, so? If you think of the data center as an edge, you can say okay, it's a large edge. It's a big fat edge. >> Our fundamentalists, I don't think it exists. Our fundamental position is data increasingly, the physical realities of data, the legal realities of data, the intellectual property control realities of data, the cost realities of data are going to dictate where the processing actually takes place. There's going to be a tendency to try to move the activity as close to the data as possible so you don't have to move the data. It's not in opposition, but we think increasingly people are going to not move the data to the cloud, but move the cloud to the data. That's how we think. >> That's an interesting notion. My view is that the data has to be really close to the source of position and execution, right? >> Peter: Yeah. Data has got to be close to the activity. >> It has to be very close to the activity. >> The locality matters. >> Exactly, exactly, and my view is, if you can, I know it's tough, but a lot of our clients are struggling with that, I'm pushing them to move their data to the cloud, only for one purpose. It gives them that accessibility to a wide ranging of computer and analytical options. >> And also microservices. >> Oh yeah. >> We had a customer on earlier who's moved to the cloud. This is what we're saying about the edge being data centered. Hybrid cloud just means you're running cloud operations. Which just means you got to have a data architecture that supports cloud operations. Which means orchestration, not having siloed systems, but essentially having these kind of, data traversal, but workload management, and I think that seems to be the consistency there. This plays right into what you're saying. That adaptive platform has to enable that. >> Exactly. >> If it forecloses it, then you're missing an opportunity. I guess, how do you... Okay tell me about a customer where you had the opportunity to do the adaptive platform, and they say no, I want a silo inside my network. I got the cloud for that. I got the proprietary system here. Which is eventually foreclosing their future revenue. How do you handle that scenario? >> So the way we handle that scenario, is again, focusing on what the end objective, that the client has, from an analytical opportunity, respectfully. What I mean by that is that semi-customer says I need to be significantly more responsive in my service management, right? So if he says I want to get that achieved, then what we start thinking about is, what is that responsive data architecture that can tell us a better outcome because like you said, and you said, there's stuff on the data center, there's stuff all over the place, it's going to be difficult to take that all away. But can I create a purpose for change? Many times you need a purpose for change. So the purpose being if I can get to a much more intelligent service management framework, I will be able to either take cost out or I can increase my revenue through services. It has to be tied to an outcome. So then the conversation becomes very easy because you're building a business case for investing in change, resulting in a measurable, business outcome. So that engineer to purpose is the way I'm finding it easier to have that conversation. And I'm telling the plan, keep what you have so you've got all the speckety messes somebody said, right? You've got all of the speckety mess out there. Let us focus on, if there are 15 data sets, that we think are relevant for us to deliver service management intelligence, let's focus on those 15 data sets. Let's get that into a new scalable, hyper responsive modern architecture. Then it becomes easier. Then I can tell the customer, now we have created an equal system where we can truly get to the 11.56 seconds analytical opportunity getting productionized. Move to an experiment as a service. That's another concept. So all of that, in my opinion John, is if he can put a purpose around it, as opposed to saying let's rip and replay, let's do this large scale transformation program, those things cost a lot of money. >> Well the good news is containers and Cubernetties is stowing away to get those projects moving cloud natives as fast as possible. Love the architecture vision. Love to fault with you on that. Great conversation. I think that's a path, in my opinion. Now short-term, the house in on fire in many areas. I want to get your thoughts on this final question. GDPR, the house is on fire, it's kind of critical, it's kind of tactical. People don't like freaking out. Saying okay, saying what does this mean? Okay, it's a signal, it is important. I think it's a technical mess. I mean where's the data? What schema? John Furrier, am I J Furrier, or Furrier, John? There's data on me everywhere inside the company. It's hard. >> Arun: It is. >> So, how are you guys helping customers and navigate the landscape of GDPR? >> GDPR is a whole, it's actually a much bigger problem than we all thought it was. It is securing things at the source system because there's volatibilities of source system. Forget about it entering into any sort of mastering or data barrels. They're securing its source, that is so critical. Then, as you said, the same John Furrier, who was probably exposed to GDPR is defined in ten different ways. How do I make sure that those ten definitions are managed? >> Tells you, you need an adaptive data platform to understands. >> So right now most of our work, is just doing that impactive analysis, right? Whether it's at a source system level, it has data coverance issues, it has data security issues, it has mastering issues. So it's a fairly complex problem. I think customers are still grappling with it. They're barely, in my opinion, getting to the point of having that plan because May 18, 2018 May, was supposed to, for you to show evidence of a plan. So I think there... >> The plan is we have no plan. >> Right, the plan of the plan, I guess is what they're going to show. It may, as opposed to the plan. >> Well I'm sure it's keeping you guys super busy. I know it's on everyone's mind. We've been talking a lot about it. Great to have you on again. Great to see you. Live here at Informatica World. Day one of two days of coverage at theCUBE here. In Las Vegas, I'm John here with Peter Burris with more coverage after this short break. (techno music)