>> 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)

>> Announcer: Live from midtown Manhattan, it's theCUBE, covering BigData New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Hello everyone, welcome back to theCUBE's special BigData NYC coverage of theCUBE here in Manhattan in New York City, we're in Hell's Kitchen. I'm John Furrier, with my cohost Jim Kobielus, whose Wikibon analyst for BigData. In conjunction with Strata Data going on right around the corner, this is our annual event where we break down the big data, the AI, the cloud, all the goodness of what's going on in big data. Our next guest is Tendu Yogurtcu who's the Chief Technology Officer at Syncsort. Great to see you again, CUBE alumni, been on multiple times. Always great to have you on, get the perspective, a CTO perspective and the Syncsort update, so good to see you. >> Good seeing you John and Jim. It's a pleasure being here too. Again the pulse of big data is in New York, and it's a great week with a lot of happening. >> I always borrow the quote from Pat Gelsinger, who's the CEO of VMware, he said on theCUBE in I think 2011, before he joined VMware as CEO he was at EMC. He said if you're not out in front of that next wave, you're driftwood. And the key to being successful is to ride the waves, and the big waves are coming in now with AI, certainly big data has been rising tide for its own bubble but now the aperture of the scale of data's larger, Syncsort has been riding the wave with us, we've been having you guys on multiple times. And it was important to the mainframe in the early days, but now Syncsort just keeps on adding more and more capabilities, and you're riding the wave, the big wave, the big data wave. What's the update now with you guys, where are you guys now in context of today's emerging data landscape? >> Absolutely. As organizations progress with their modern data architectures and building the next generation analytics platforms, leveraging machine learning, leveraging cloud elasticity, we have observed that data quality and data governance have become more critical than ever. Couple of years we have been seeing this trend, I would like to create a data lake, data as a service, and enable bigger insights from the data, and this year, really every enterprise is trying to have that trusted data set created, because data lakes are turning into data swamps, as Dave Vellante refers often (John laughs) and collection of this diverse data sets, whether it's mainframe, whether it's messaging queues, whether it's relational data warehouse environments is challenging the customers, and we can take one simple use case like Customer 360, which we have been talking for decades now, right? Yet still it's a complex problem. Everybody is trying to get that trusted single view of their customers so that they can serve the customer needs in a better way, offer better solutions and products to customers, get better insights about the customer behavior, whether leveraging deep learning, machine learning, et cetera. However, in order to do that, the data has to be in a clean, trusted, valid format, and every business is going global. You have data sets coming from Asia, from Europe, from Latin America, and many different places, in different formats and it's becoming challenge. We acquired Trillium Software in December 2016, and our vision was really to bring that world leader enterprise grade data quality into the big data environments. So last week we announced our Trillium Quality for Big Data product. This product brings unmatched capabilities of data validation, cleansing, enrichment, and matching, fuzzy matching to the data lake. We are also leveraging our Intelligent eXecution engine that we developed for data integration product, the MX8. So we are enabling the organizations to take this data quality offering, whether it's in Hadoop, MapReduce or Apache Spark, whichever computer framework it's going to be in the future. So we are very excited about that now. >> Congratulations, you mentioned the data lake being a swamp, that Dave Vellante referred to. It's interesting, because how does it become a swamp if it's a silo, right? We've seen data silos being antithesis to governance, it challenges, certainly IoT. Then you've got the complication of geopolitical borders, you mentioned that earlier. So you still got to integrate the data, you need data quality, which has been around for a while but now it's more complex. What specifically about the cleansing and the quality of the data that's more important now in the landscape now? Is it those factors, are that the drivers of the challenges today and what's the opportunity for customers, how do they figure this out? >> Complexity is because of many different factors. Some of it from being global. Every business is trying to have global presence, and the data is originating from web, from mobile, from many different data sets, and if we just take a simple address, these address formats are different in every single country. Trillium Quality for Big Data, we support over 150 postal data from different countries, and data enrichment with this data. So it becomes really complex, because you have to deal with different types of data from different countries, and the matching also becomes very difficult, whether it's John Furrier, J Furrier, John Currier, you have to be >> All my handles on Twitter, knowing that's about. (Tendu laughs) >> All of the handles you have. Every business is trying to have a better targeting in terms of offering product and understanding the single and one and only John Furrier as a customer. That creates a complexity, and any data management and data processing challenge, the variety of data and the speed that data is really being populated is higher than ever we have observed. >> Hold on Jim, I want to get Jim involved in this one conversation, 'cause I want to just make sure those guys can get settled in on, and adjust your microphone there. Jim, she's bringing up a good point, I want you to weigh in just to kind of add to the conversation and take it in the direction of where the automation's happening. If you look at what Tendu's saying as to complexity is going to have an opportunity in software. Machine learning, root-level cleanliness can be automated, because Facebook and others have shown that you can apply machine learning and techniques to the volume of data. No human can get at all the nuances. How is that impacting the data platforms and some of the tooling out there, in your opinion? >> Yeah well, much of the issue, one of the core issues is where do you place the data matching and data cleansing logic or execution in this distributed infrastructure. At the source, in the cloud, at the consumer level in terms of rolling up the disparate versions of data into a common view. So by acquiring a very strong, well-established reputable brand in data cleansing, Trillium, as Syncsort has done, a great service to your portfolio, to your customers. You know, Trillium is well known for offering lots of options in terms of where to configure the logic, where to deploy it within distributed hybrid architectures. Give us a sense for going forward the range of options you're going to be providing with for customers on where to place the cleansing and matching logic. How you're going to support, Syncsort, a flexible workflows in terms of curation of the data and so forth, because the curation cycle for data is critically important, the stewardship. So how do you plan to address all of that going forward in your product portfolio, Tendu? >> Thank you for asking the question, Jim, because that's exactly the challenge that we hear from our customers, especially from larger enterprise and financial services, banking and insurance. So our plan is our actually next upcoming release end of the year, is targeting very flexible deployment. Flexible deployment in the sense that you might be creating, when you understand the data and create the business rules and said what kind of matching and enrichment that you'll be performing on the data sets, you can actually have those business rules executed in the source of the data or in the data lake or switch between the source and the enterprise data lake that you are creating. That flexibility is what we are targeting, that's one area. On the data creation side, we see these percentages, 80% of data stewards' time is spent on data prep, data creation and data cleansing, and it is actually really a very high percentage. From our customers we see this still being a challenge. One area that we started investing is using the machine learning to understand the data, and using that discovery of the data capabilities we currently have to make recommendations what those business rules can be, or what kind of data validation and cleansing and matching might be required. So that's an area that we will be investing. >> Are you contemplating in terms of incorporating in your product portfolio, using machine learning to drive a sort of, the term I like to use is recommendation engine, that presents recommendations to the data stewards, human beings, about different data schemas or different ways of matching the data, different ways of, the optimal way of reconciling different versions of customer data. So is there going to be like a recommendation engine of that sort >> It's going to be >> In line with your >> That's what our plan currently recommendations so the users can opt to apply or not, or to modify them, because sometimes when you go too far with automation you still need some human intervention in making these decisions because you might be operating on a sample of data versus the full data set, and you may actually have to infuse some human understanding and insight as well. So our plan is to make as a recommendation in the first phase at least, that's what we are planning. And when we look at the portfolio of the products and our CEO Josh is actually today was also in theCUBE, part of Splunk .conf. We have acquisitions happening, we have organic innovation that's happening, and we really try to stay focused in terms of how do we create more value from your data, and how do we increase the business serviceability, whether it's with our Ironstream product, we made an announcement this week, Ironstream transaction tracing to create more visibility to application performance and more visibility to IT operations, for example when you make a payment with your mobile, you might be having problem and you want to be able to trace back to the back end, which is usually a legacy mainframe environment, or whether you are populating the data lake and you want to keep the data in sync and fresh with the data source, and apply the change as a CDC, or whether you are making that data from raw data set to more consumable data by creating the trusted, high quality data set. We are very much focused on creating more value and bigger insights out of the data sets. >> And Josh'll be on tomorrow, so folks watching, we're going to get the business perspective. I have some pointed questions I'm going to ask him, but I'll take one of the questions I was going to ask him but I want to get your response from a technical perspective as CTO. As Syncsort continues your journey, you keep on adding more and more things, it's been quite impressive, you guys done a great job, >> Tendu: Thank you. >> We enjoy covering the success there, watching you guys really evolve. What is the value proposition for Syncsort today, technically? If you go in, talk to a customer, and prospective new customer, why Syncsort, what's the enabling value that you're providing under the hood, technically for customers? >> We are enabling our customers to access and integrate data sets in a trusted manner. So we are ultimately liberating the data from all of the enterprise data stores, and making that data consumable in a trusted manner. And everything we provide in that data management stack, is about making data available, making data accessible and integrated the modern data architecture, bridging the gap between those legacy environments and the modern data architecture. And it becomes really a big challenge because this is a cross-platform play. It is not a single environment that enterprises are working with. Hadoop is real now, right? Hadoop is in the center of data warehouse architecture, and whether it's on-premise or in the cloud, there is also a big trend about the cloud. >> And certainly batch, they own the batch thing. >> Yeah, and as part of that, it becomes very important to be able to leverage the existing data assets in the enterprise, and that requires an understanding of the legacy data stores, and existing infrastructure, and existing data warehouse attributes. >> John: And you guys say you provide that. >> We provide that and that's our baby and provide that in enterprise grade manner. >> Hold on Jim, one second, just let her finish the thought. Okay, so given that, okay, cool you got that out there. What's the problem that you're solving for customers today? What's the big problem in the enterprise and in the data world today that you address? >> I want to have a single view of my data, and whether that data is originating on the mobile or that data is originating on the mainframe, or in the legacy data warehouse, and we provide that single view in a trusted manner. >> When you mentioned Ironstream, that reminded me that one of the core things that we're seeing in Wikibon in terms of, IT operations is increasingly being automated through AI, some call it AI ops and whatnot, we're going deeper on the research there. Ironstream, by bringing mainframe and transactional data, like the use case you brought in was IT operations data, into a data lake alongside machine data that you might source from the internet of things and so forth. Seem to me that that's a great enabler potentially for Syncsort if it wished to play your solutions or position them into IT operations as an enabler, leveraging your machine learning investments to build more automated anomaly detection and remediation into your capabilities. What are your thoughts? Is that where you're going or do you see it as an opportunity, AI for IT ops, for Syncsort going forward? >> Absolutely. We target use cases around IT operations and application performance. We integrate with Splunk ITSI, and we also provide this data available in the big data analytics platforms. So those are really application performance and IT operations are the main uses cases we target, and as part of the advanced analytics platform, for example, we can correlate that data set with other machine data that's originating in other platforms in the enterprise. Nobody's looking at what's happening on mainframe or what's happening in my Hadoop cluster or what's happening on my VMware environment, right. They want to correlate the data that's closed platform, and that's one of the biggest values we bring, whether it's on the machine data, or on the application data. >> Yeah, that's quite a differentiator for you. >> Tendu, thanks for coming on theCUBE, great to see you. Congratulations on your success. Thanks for sharing. >> Thank you. >> Okay, CUBE coverage here in BigData NYC, exclusive coverage of our event, BigData NYC, in conjunction with Strata Hadoop right around the corner. This is our annual event for SiliconANGLE, and theCUBE and Wikibon. I'm John Furrier, with Jim Kobielus, who's our analyst at Wikibon on big data. Peter Burris has been on theCUBE, he's here as well. Big three days of wall-to-wall coverage on what's happening in the data world. This is theCUBE, thanks for watching, be right back with more after this short break.