 Live from Las Vegas, it's theCUBE. Covering Informatica World 2018. Not to you by Informatica. Okay, welcome back everyone. We're here live at the Venetian, we're at the Sands Convention Center, Venetian de Palazzo for Informatica World 2018. I'm John Furrier with Peter Burris, my co-host for theCUBE. Our next guest is Arun Varajan, who's the VP of AI and analytics at Cognizant. Great to see you in a while. Thanks for coming on. Thank you, thank you John, it's wonderful meeting you again. So last time you were on was 2015 on theCUBE, we were at the San Francisco where the event was. You kind of nailed the real time piece, also the disruption of data. Looking forward right now, we're kind of right at the spot we were talking about there. What's different? What's new for you? Obviously data's at the center of the value proposition. People are now realizing I need to have a 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, competitive tactic to deal with the fact that digital natives 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 three layer standpoint. The first layer being your business model itself, because if you're a traditional taxi service and you're dealing with the Uber world, 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 have 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 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 make 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. Plenty of choices that 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 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 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 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 business and whether or not a business treats data as an asset. Now in every business and every business strategy, the most simple, straightforward, bottom line thing you can acknowledge is that businesses organize work around assets. So does it comport with your observation that in many respects what we're talking about here is how are we re-institutionalizing work around data and what impact does that have on our business model, our operating model and our technology selection. Is that lineup 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, right? The second block very clearly is fantastic. I've re-imagined customer experience, I've re-imagined how I'm going to engage with my customer. Is your product and service intelligent enough to deliver 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 four 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 layer, whether it's my supply chain, whether it's my order 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, right? So I want to take that and overlay kind of like what's going on in the landscape of the industry. You had infrastructure world, would you buy some rack and stack, the servers, clouds now on the scene, so there's overlapping there. You used to have a big data category, you know, Hadoop, that's now AI and machine learning, and data warehouse, it's kind of its own category called AI, and then you have kind of emerging tech, whether you call it blockchain, these kind of confluence of all these things. But there's a data component that sits at the center of all these things. Security, data, IoT, traverse, infrastructure, cloud, the classic data, industry, analytics, AI, and emerging. So you need data that traverses all these new environments. How does someone set up their architecture for that? Because now I say, okay, I got a big data analytics package over here, I'm doing some analytics, next-gen analytics, but now I got to move data around for whether it's cloud services or for an application. So you're seeing data as they're being architected to be addressable across multiple industries. Great point, John. In fact, that leads logically to the next big thing that me and my team are working on. So we're 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. You know, I think Charles Darwin 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 a survival of those who can adapt to change, right? So to me, your data foundation has to be super adaptive. So what we've done is, in fact, you know, my notion and I keep throwing this at you every time I meet you, in my opinion, big data is legacy. Yeah, I agree with that. And it's becoming, it's pretty much legacy in my mind. Today it's all about scale out responsive compute in the data world. Now, if you looked at most of the architectures of the past in 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. And therefore what we've 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've not seen it. They've not able to envision. This is new, this is net new. Exactly. They're not able to envision it. So what I tell my clients is that 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, high velocity resolutions? Whether it's identity resolution of a client who's calling in, whether it's a resolution of the right product and service to deliver to the client, whether it's your process orchestration layer able to quickly resolve that this distribution center is better capable of servicing that customer need, you better have that kind of an 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 can barely get to have a reasonable release on a monthly basis. Forget about 11.56 seconds. So if they have to move to that kind of velocity and that kind of responsiveness, they need to reimagine their data foundation. And what we've done is we've tried to break it down into three broad components. The first component that we're saying is you need a highly responsive architecture, the question that you raised. And in a highly responsive architecture, we have defined, we've got about seven to eight attributes that defines what a responsive architecture is. And in my mind, you know, a lot of, you'll hear, I've been hearing a lot of this refrain even in today's conference, right? People are saying, oh, it's going to be a hybrid world. There's going to be on-prem. There's going to be cloud. It's going to be multi-cloud. 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've got to move to a very simplified responsive architecture right up front. Well, you'd be prepared for any architecture. I mean, I've always said we've debated this many times. I think it's all, it's a cloud world, public cloud, everything, where the data center on premise just isn't a huge edge. 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 fundamental, I don't think it's inconsistent. Our fundamental position is that 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. And 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. So 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 decisioning and execution. Yeah, exactly. Right? Data's got to be close to the activity. It has to be very close to the activity. A locality still matters. Exactly, exactly. And my view is that 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 compute and analytical options. Well, also microservice, and also microservices. We had a customer on earlier who's moved to the cloud. And this is what we're saying about the edge being data center. Hybrid cloud just means you're running cloud operations, which means you have to have a data architecture that supports cloud operations, which means orchestration, not having siloed systems, but essentially having this kind of data traversal, but workload management. And I think that seems to be the consistency there. And this plays right into what you were saying. That adaptive platform has to enable that. Exactly. If it forecloses it, then you're missing an opportunity. Okay, tell me about a customer where you have the opportunity to do the adaptive platform. And then they say, no, no, I want a silo inside my network. I got the cloud for that, but I got a proprietary system here, which is essentially foreclosing their future. 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 perspective. And what I mean by that is, let's say my 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 deliver to that outcome? Because like you said and you said, you know, there's stuff on the data center that stuff all over the place is going to be difficult to take that all away. But can I create a purpose for change, right? 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 client, keep what you have, right? So you've got all the spaghetti mess, as somebody said, right? You've got all of the spaghetti 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 ecosystem where we can truly get to the 11.56 seconds analytical opportunity getting productionized, right? Move to an experiment as a service. That's another concept. So all of that, in my opinion, John, is if we can put a purpose around it, as opposed to saying let's rip and replace, let's do this large scale transformation program, those things cost a lot of money. Well, the good news is containers and Kubernetes is showing a way to get those projects moving cloud native as fast as possible. Love the architecture vision. Love to follow up with you on that great conversation. I think that's a path, in my opinion. Now short term, there's the houses on fire in many areas. I want to get your thoughts on this final question. GDPR, I don't mean the houses on fire. It's kind of critical. It's kind of tactical. People are freaking out saying, okay, what does this mean? Okay, it's a signal. Data is important, but it could say technical mess. I mean, where's the data? What schema? John Furrier, am I Jay Furrier or Furrier or John? There's data on me everywhere inside a company. It's hard. So how are you guys helping customers figure out and navigate the landscape of GDPR? Oh, GDPR is a whole, it's actually a much bigger problem than we all thought it was, right? It is securing things at the source system because there's vulnerabilities at the source system. Forget about even it entering into any sort of mastering environment or data warehouse. They're securing at source that is so critical, right? Then, as you said, the same John Furrier who is probably exposed to GDPR is defined in 10 different ways. How do I make sure that those 10 definitions are managed? It doesn't need an adaptive data platform to understand. So right now, most of our work is just doing that impact analysis, right? Whether it's at the source system level, it has data governance 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 18th, I mean, 2018 May was supposed for you to show evidence of a plan, right? So I think they're- The plan is we have no plan. The plan of the plan, I guess, is what they're going to show in 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. Arun, great to have you on again. Great to see you. Okay, live here at Informatica World Day 1 of two days of coverage of theCUBE here. In Las Vegas, I'm John Furrier, Peter Burris with more coverage after this short break.