>> Live from San Jose, in the heart of Silicon Valley, it's theCube. Covering Datawork Summit 2018. Brought to you by Hortonworks. >> Welcome back to theCube's live coverage of Dataworks here in San Jose, California. I'm your host, Rebecca Knight along with my cohost James Kobielus. We have with us Vimal Endiran. He is the Global Business Data Group Ecosystem Lead, at Accenture. He's coming to us straight from the Motor City. So, welcome Vimal. >> Thank you, thank you Rebecca. Thank you Jim. Looking forward to talk to you for the next ten minutes. >> So, before the cameras were rolling we were talking about how data veracity and how managers can actually know that the data that they're getting, that they're seeing, is trustworthy. What's your take on that right now? >> So, in the today's age the data is coming at you in a velocity that you never thought about, right. So today, the organizations are gathering data probably in the magnitude of petabytes. This is a new normal. We used to talk about gigs and now it's in petabytes. And the data coming in the form of images, video files, from the edge, you know edge devices, sensors, social media and everything. So, the amount of data, this is becoming the fuel for the new economy, right. So that companies, who can find a way to take advantage and figure out a way to use this data going to have a competitive advantage over their competitors. So, for that purpose, even though it's coming at that volume and velocity doesn't mean it's useful. So the thing is if they can find a way to make the data can be trustworthy, by the organization, and at the same time it's governed and secured. That's what's going to happen. It used to be it's called data quality, we call it when the structure it's okay, everything is maintained in SAP or some system. It's good it's coming to you. But now, you need to take advantage of the tools like machine learning, artificial intelligence, combining these algorithms and tool sets and abilities of people's mind, putting that in there and making it somewhat... Things can happen to itself at the same time it's trustworthy, we have offerings around that Accenture is developing place... It differs from industry to industry. Given the fact if the data coming in is something it's only worth for 15 seconds. After that it has no use other than understanding how to prevent something, from a sense of data. So, we have our offerings putting into place to make the data in a trustworthy, governed, secured, for an organization to use it and help the organization to get there. That's what we are doing. >> The standard user of your tools is it a data steward in the traditional sense or is it a data scientist or data engineer who's trying to, for example, compile a body of training data for use in building and training machine learning models? Do you see those kinds of customers for your data veracity offerings, that customer segment growing? >> Yes. We see both sides pretty much all walk of customers in our life. So, you hit the nail on the head, yes. We do see that type of aspects and also becoming, the data scientists you're also getting another set of people, the citizen data scientist. The people--- >> What is that? That's a controversial term. I've used that term on a number of occasions and a lot of my colleagues and peers in terms of other analysts bat me down and say, "No, that demeans the profession of data science by calling it..." But you tell me what how Accenture's defining that. >> The thing is, it's not demeaning. The fact is to become a citizen data scientist you need the help of data scientists. Basically, every time you need to build a model. And then you feed some data to learn. And then have an outcome to put that out. So you have a data scientist creating algorithms. What a citizen data scientist means, say if I'm not a data scientist, I should be able to take advantage of a model built for my business scenario, feed something data in, whatever I need to feed in, get an output and that program, that tool's going to tell me, go do this or don't do this, kind of things. So I become a data scientist by using a predefined model that's developed by an expert. Minds of many experts together. But rather than me going and hiring hundred experts, I go and buy a model and able to have one person maintain or tweak this model continuously. So, how can I enable that large volume of people by using more models. That's what-- >> If a predictive analytics tool that you would license from whatever vendor. If that includes prebuilt machine learning models for a particular tasks in it does that... Do you as a user of that tool, do you become automatically a citizen data scientist or do you need to do some actual active work with that model or data to live up to the notion of being a citizen data scientist? >> It's a good question. In my mind, I don't want to do it, my job is something else. To make something for the company. So, my job is not creating a model and doing that. My job is, I know my sets of data, I want to feed it in. I want to get the outcome that I can go and say increase my profit, increase my sales. That's what I want to do. So I may become a citizen data scientist without me knowing. I won't even be told that I'm using a model. I will take this set of data, feed it in here, it's going to tell you something. So, our data veracity point of view, we have these models built into some of platforms. That can be a tool from foreign works, taking advantage of the data storage tool or any other... In our own algorithms put in that helps you to create and maintain the data veracity to a scale of, if you say one to five, one is being low, five is being bad, to maintain at the five level. So that's the objective of that. >> So you're democratizing the tools of data science for the rest of us to solve real business problems. >> Right. >> So the data veracity aside, you're saying the user of these tools is doing something to manage, to correct or enhance or augment the data that's used to feed into these prebuilt models to achieve these outcomes? >> Yes. The augmented data, the feed data and the training data it comes out with an outcome to say, go do something. It tells you to perform something or do not perform. It's still an action. Comes out with an action to achieve a target. That's what it's going to be. >> You mention Hortonworks and since we are here at Dataworks and the Hortonworks show, tell us a little bit about your relationship with that company. >> Definitely. So Hortonworks is one of our premiere strategic partners. We've been the number one implementers, the partners for last two years in a row, implementing their technology across many of our clients. From partnership point of view, we have jointly developed offerings. What Accenture is best at, we're very good at industry knowledge. So with our industry knowledge and with their technology together what we're doing is we're creating some offerings that you can take to market. For example, we used to have data warehouses like using Teradata and older technology data warehouses. They're still good but at the same time, people also want to take the structured, unstructured data, images files and able to incorporate into the existing data warehouses. And how I can get the value out of the whole thing together. That's where Hortonworks' type of tools comes to play. So we have developed offerings called Modern Data Warehouse, taking advantage of your legacy systems you have plus this new data coming together and immediately you can create an analytics case, used case to do something. So, we have prebuilt programs and different scripts that take in different types of data. Moving into a data lake, Hortonworks data lake and then use your existing legacy data and all those together help you to create analytics use cases. So we have that called data modernization offering, we have one of that. Then we have-- >> So that's a prebuilt model for a specific vertical industry requirements or a specific business function, predictive analytics, anomaly detection and natural language processing, am I understanding correctly? >> Yes. We have industry based solutions as well but also to begin with, the data supply chain itself. To bring the data into the lake to use it. That's one of the offerings we play-- >> ...Pipeline and prepackaged models and rules and so forth. >> Right, prepackaged data ingestion, transformation, that prepackaged to take advantage with the new data sets along with your legacy data. That's one offering called data modernization offering. That to cloud. So, we can take to cloud. Hortonworks in a cloud it can be a joure, WS, HP, any cloud plus moving data. So that's one type of offering. Today actually we announced another offering jointly with Hortonworks, Atlas and Grainger Tool to help GDPR compliance. >> Will you explain what that tool does specifically to help customers with GDPR points. Does it work out of the box with Hortonworks data stewards studio? >> Well, to me I can get your answers from my colleagues who are much more technical on that but the fact is I can tell you functionally what the tool does is. >> Okay, please. >> So you, today the GDPR is basically, there's account regulations about you need to know about your personal data and you have your own destiny about your personal data. You can call the company and say, "Forget about me." If you are an EU resident. Or say, "Modify my data." They have to do it within certain time frame. If not they get fined. The fine can be up to 4% of the company's... So it's going to be a very large fine. >> Total revenue, yeah. >> So what we do is, basically take this tool. Put it in, working with Hortonworks this Atlas and Granger tool, we can go in and scan your data leak and we can scan at the metadata level and come into showcase. Then you know where is your personal data information about a consumer lies and now I know everything. Because what used to be in a legacy situation, the data originated someplace, somebody takes it and puts a system then somebody else downloads to an X file, somebody will put in an access data base and this kind of things. So now your data's pulling it across, you don't know where that lies. In this case, in the lake we can scan it, put this information, the meta data and the lineage information. Now, you immediately know where the data lies when somebody calls. Rebecca calls and says, "No longer use my information." I exactly know it's stored in this place in this table, in this column, let me go and take it out from here so that Rebecca doesn't exist anymore. Or whoever doesn't exist anymore. So that's the idea behind it. Also, we can catalog the entire data lake and we know not just personal information, other information, everything about other dimensions as well. And we can use it for our business advantage. So that's what we announced today. >> We're almost out of time but I want to finally ask you about talent because this is a pressing issue in Silicon Valley and beyond in really the tech industry, finding the right people, putting them in the right jobs and then keeping them happy there. So recruiting, retaining, what's Accenture's approach? >> This area, talent is the hardest one. >> Yes! >> Thanks to Hortonworks and Hortonworks point of view >> Send them to Detroit where the housing is far less expensive. >> Not a bad idea. >> Exactly! But the fact is-- >> We're both for Detroiters. >> What we did was, Hortonworks, Accenture has access to Hortonworks University, all their educational aspects. So we decided we're going to take that advantage and we going to enhance our talent by bringing the people from our... Retraining the people, taking the people to the new. People who know the legacy data aspects. So take them to see how we take the new world. So then we have a plan to use Hortonworks together the University, the materials and the people help, together we going to train about 500 people in different geos, 500 per piece and also our the development centers in India, Philippines, these places, so we have a larger plan to retrain the legacy into new. So, let's go and get people from out of the college and stuff, start building them from there, from an analyst to a consultant to a technical level and so that's the best way we are doing and actually the group I work with. Our group technology officer Sanjiv Vohra, he's basically in charge of training about 90,000 people on different technologies in and around that space. So the magnet is high but that's our approach to go and try and people and take it to that. >> Are you training them to be well rounded professionals in all things data or are you training them for specific specialties? >> Very, very good question. We do have this call master data architect program, so basically in the different levels after these trainings people go through specially you have to do so many projects, come back have an interview with a panel of people and you get certified, within the company, at certain level. At the master architect level you go and help a customer transform their data transformation, architecture vision where do you want to go to, that level. So we have the program with a university and that's the way we've taken it step by step to people to that level. >> Great. Vimal, thank you so much for coming on theCube. >> Thank you. >> It was really fun talking to you. >> Thank you so much, thank you for having me. Thank you. >> I'm Rebecca Knight for James Kobielus we will have more, well we actually will not be having any more coming up from Dataworks. This has been the Dataworks show. Thank you for tuning in. >> Signing off for now. >> And we'll see you next time.

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum, World 2019 brought to you by IBM Wear and its ecosystem partners. >> And welcome back here, San Francisco Moscow Centre, North John Walls along with John Troyer. We're live here on the Cuban Veum World 2019 and right now we're joined by Christmas. Reynolds, who's a product in court product management and Clyde on data service, is for Centurylink. It's good to see you, sir. Good to be here. Thank you. And And he's gonna tell us today why Milliseconds matter, right? You are. >> That is the goal. Your >> your subject of ah, coming presentation. Just about 45 minutes or so. But we'll get to that a little bit. First off, let's just paint the picture of centurylink your presence here quite obvious. But you know what your portfolio includes? There what you're up to, and maybe starting to hint a little bit about why milliseconds matter to you. >> Makes it so. Where a technology company, global in nature. A lot of our roots started with fiber connectivity. Basic networking service is I. P Service is. But over the years we've become far more of a nightie service company. So there was an acquisition of Savvas a long time ago that brought a lot of those capabilities to our company. And we've made more fold in acquisitions that have also bolster those capabilities. We have invested heavily in Security Service's recently and about two weeks ago we had an announcement that said, We're investing heavily an edge compute getting workloads closer to end users. And that's really where milliseconds matters. You want the performance of those applications to consumers or machinery or whatever it may be toe work effectively and work well. And sometimes that requires that those workloads air in close proximity to the end users. >> Would you bring up ej compute? We were just having this discussion before we started, John asked of you. Okay, What? How do you define the because of there A lot of different slices of that, right? Different interpretations, different definitions. So with that being said, how do you define and or at least in your mind, how do you separate edge or what's true edge? Yeah, >> good questions. I think he was John question, not mine. I chuckled time, so because there is no perfect answer. Uh, the broadest definition I've seen is that you have core, and you can think eight of us Azure. You can think where the big core cloud nodes are that are pretty central, maybe 50 milliseconds away from the end users. There's two intermediate edges, if you will, and this is where there are varying opinions. To me, there's really only one if you're within five milliseconds of where your end users are, I consider that to be a market edge. Some people say there's a closer edge that's in within a millisecond of the end users, but I just I personally have not seen the use cases come out yet that require that low of a late unsee that don't actually reside where the end users are so >> going. Well, that's, um, so that's, um, modules at a at a warehouse or ah, manufacturing facility. Is that what? Is that what you consider like an edge? Uh, media marketed? >> Yeah, in >> theirs. It's interesting if you have 10 manufacturing plants in a geographic area, or maybe a better example is if you're a logistics company and you have sorting and distribution centers, you have multiple of those in an area that can all use the same compute as long as it's within five milliseconds, you can do the sorting lines and keep the machinery working. You can get routed into the rate vehicles for distribution. That's a good market edge. When you get all the way to that, the deep edge or on premise they think of an autonomous vehicle is a good example. There are certain things you're not gonna want to transmit and make driving decisions that don't reside on that vehicle. You don't want to crash into anyone. You need almost instantaneous decisions. And that would be the edge that intermediate one millisecond that sits between the two of those. I think it pushes one direction or the other. >> So Chris, here in the emerald 2019 obviously a lot of talking about cloud, but very specifics. This year. We have a lot of specifics around what Veum, where is doing Hybrid Cloud Israel and of course, hybrid cloud implies the network. And so one of the latest announcement from Centurylink is that you're providing via more cloud on AWS you're managing. You are able to help manage provide that as a managed service. I know you already do. Manage service is where you managing stuff in your data centers. But you could, I guess you can also manage workloads on prim and talk a little bit about that portfolio and how adding Veum VMC on AWS few more cloud nebulas adds to that. And then maybe we'll slide into the networking peace and how important that is. >> So we have AH, tool called Cloud Application Manager that has been built over the past handful of years that allows customers to deploy workloads to AWS toe azure and now to be emcee on AWS as well as private cloud environment. So maybe customers want to host those workloads on premise. Maybe it's regulatory compliance or whatever the reason may be. So we have a lot of experience of helping customers deploy those workloads, and then a lot of customers come to us and want to manage. I want us to manage the life cycle of those workloads, those air, the core capabilities. I think the reason that VMC on AWS is so compelling to customers is a lot of customers may not want to deal with the hardware refresh cycles that they do when it's their own private cloud environment or their own hardware stack. This gives them the opportunity to migrate those workloads and a relatively seamless fashion into an environment that is sitting in Maur of, ah, public cloud type model where it's it's Op X versus the Catholics in the headache. >> Go ahead. John was good, just in terms of so and so. Part of why you would work with Centurylink is you are experienced manage service provider. But also you have ah lot of the networking set up to do that efficiently, right? So maybe you talk about some of the workload is that you see going up there and some of the tools and, uh, performance folks can expect, >> Yeah, that's near the core part of my products that so near and dear to me for sure. We've developed a lot of capabilities over the last year and 1/2 around dynamic networking. So if you have your existing VM wear environment in your own data center, or maybe it's a private cloud that's managed by century link, we now have the ability for customers to go in and create net new connections, private network connections that have better Leighton see have better through putting performance between those environments and AWS or, in this case, VMC on AWS. And it allows customers to do a couple of things if they have their own environment and they're happy with it today. But it's not scaling, and they need to add more capacity. They could do that in the hybrid fashion in VMC on eight of us. If they're done with their existing environment hardware stack and they just want a forklift and move that into VMC on eight of us, they can create a big, large connection, push a ton of data over a few weeks, shut it down, and our building models and hourly billing models such that we're only charging them for as long as it's necessary. This gives them flexibility to manage where their workloads air sitting between those two locations as they see fit over time. >> So you're talking about all these new flexibilities new capabilities, much more agile systems being, I guess, interconnected with each other, right? But whether it's hybrid or whether it's multi cloud, whatever the case is, >> how you how to get >> everybody or everything that talk to each other in a way that works and provides, You know, the addresses, the Leighton see challenge, because to me, I'm again outside looking in. That's Ah, that's a big hurdle. As new capabilities get developed, new possibilities exists, but we gotta make it fast way, and we have to make sure they're they're speaking the same language. >> Yeah, it's a great question, and it is very challenging, and it is not all automated today as much as we would like. We have great integration to deploy workloads between environments. We've spent a ton of time from a networking standpoint of integrating with different cloud providers, and they each have their loan little nuances and to make it common between all of them takes a lot of time and effort. Where a lot of our focus is going in the next 12 months is how do you take those application, migration and management capabilities we have in one tool set? How do you marry that? With all of the dynamic networking capabilities and standardization across the cloud providers, we've done so the now it's not only are you moving network workloads, you're also creating the right underlying network to support those workloads in that multi cloud fashion well to capabilities we have. We just need to marry him up a little more clearly. >> I mean, what are you saying out there in the market with your customers? Multi Cloud Bright is perhaps another overused word like EJ. Are you seeing multi cloud portfolios? Are you seeing applications? Talk, actually use have data in one place, and and the and the computer and another. And obviously network becomes increasingly important if that's a reality today. But is that is that real, or is that still science fiction? >> It's becoming more riel so that there are a lot of customers. My pain, A lot of enterprises really bet big on one cloud provider because you have to build up the competency of capabilities inside your own shop and you become really good with working in Azure. Eight of us or Google or of'em were on the hunt. BP BMC Oh, the companies that are doing true multi cloud and using multiple cloud providers. Well, our companies that probably reside around here, so I won't say any of these specifically or doing this mutt. Companies like uber companies like Spotify companies that are born in the cloud that started with those core competencies will take the best of multiple cloud providers. So maybe the Big Data Analytics sitting in Google is most intriguing to them. But they love the tale of the storage cost. Price points on eight of us, and they love this. Ask spit in azure. They'll piece together components since they built it in a containerized fashion. And they take the best of what each cloud has to offer and into your point. The cloud providers air coming to centurylink and saying We need a better way to stitch together all of these different cloud environments because people, the cutting edge developers are pushing us in that direction. Now >> what about the the application network relationship? Um, changing is, you know, you see a shift there of some kind of as, uh, we're talking about, obviously a lot of new opportunities, a lot of developments, and so does that alter the dynamics of that relationship in any way >> It does, and it's the same conversations I just mentioned. Actually, that's driving it. I think today it is network engineers and network infrastructure. People reacting to applications not performing well are reacting to a software developers requested toe add this Google region or that VM wear on on AWS region over time. What's gonna happen, I believe, is their service mesh orchestration capabilities like SDO is a good example is the one Google is pushing hard and it would it allows people to do is from a rules driven perspective. I want my application to have these Leighton see requirements and you can't find me a network solution that is any worse than that. Or if you're seeing packet loss greater than 80% I want you to add more capacity to the network. It won't be humans the network engineers doing that. It's going to be application saying here are my criteria for me to work well, networks Let me see all the options I have out there now. I'm gonna go pick the best one and change it if I need you to make make myself work the way I need to. As an application. >> I love that that I've never connected Is Theo down as as an at, sir, as an APP service layer down to the network. Thank you. I just have a new I got a new thought. Eureka another reason >> why milliseconds matter. That's right. Hey, Chris. Thanks for the time. We appreciate that. I know this is a very busy time for you on. You do have a speaking engagements. We're gonna cut you loose for that. But thanks for spending time with us. And good luck. It centurylink appreciate it. Enjoyed it. Looking forward, Thio. More success. Back with more for Vimal. World 2019 after this short break right here on the Q.