>> Narrator: From around the globe its "theCUBE" presenting Adaptive Data Governance, brought to you by Io-Tahoe. >> And we're back with the Data Automation series. In this episode we're going to learn more about what Io-Tahoe is doing in the field of adaptive data governance, how can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin and I'm joined by Ajay Vohora the CEO of Io-Tahoe, and Lester Waters the CTO of Io-Tahoe. Gentlemen it's great to have you on the program. >> Thank you Lisa is good to be back. >> Great to see you Lisa. >> Likewise, very seriously this isn't cautious as we are. Lester were going to start with you, what's going on at Io-Tahoe, what's new? >> Well, I've been with Io-Tahoe for a little over the year, and one thing I've learned is every customer needs are just a bit different. So we've been working on our next major release of the Io-Tahoe product and to really try to address these customer concerns because we want to be flexible enough in order to come in and not just profile the data and not just understand data quality and lineage, but also to address the unique needs of each and every customer that we have. And so that required a platform rewrite of our product so that we could extend the product without building a new version of the product, we wanted to be able to have pluggable modules. We are also focused a lot on performance, that's very important with the bulk of data that we deal with and we're able to pass through that data in a single pass and do the analytics that are needed whether it's a lineage data quality or just identifying the underlying data. And we're incorporating all that we've learned, we're tuning up our machine learning, we're analyzing on more dimensions than we've ever done before, we're able to do data quality without doing an initial reggie expert for example, just out of the box. So I think it's all of these things are coming together to form our next version of our product and We're really excited about. >> Sounds exciting, Ajay from the CEOs level what's going on? >> Wow, I think just building on that, what Lester just mentioned now it's we're growing pretty quickly with our partners, and today here with Oracle we're excited to explain how that's shaping up lots of collaboration already with Oracle, and government in insurance and in banking. And we're excited because we get to have an impact, it's really satisfying to see how we're able to help businesses transform and redefine what's possible with their data. And having Oracle there as a partner to lean in with is definitely helping. >> Excellent, we're going to dig into that a little bit later. Lester let's go back over to you, explain adaptive data governance, help us understand that. >> Really adaptive data governance is about achieving business outcomes through automation. It's really also about establishing a data-driven culture and pushing what's traditionally managed in IT out to the business. And to do that, you've got to enable an environment where people can actually access and look at the information about the data, not necessarily access the underlying data because we've got privacy concern system, but they need to understand what kind of data they have, what shape it's in, what's dependent on it upstream and downstream, and so that they can make their educated decisions on what they need to do to achieve those business outcomes. A lot of frameworks these days are hardwired, so you can set up a set of business rules, and that set of business rules works for a very specific database and a specific schema. But imagine a world where you could just say, you know, (tapping) the start date of a loan must always be before the end date of a loan, and having that generic rule regardless of the underlying database, and applying it even when a new database comes online and having those rules applied, that's what adaptive data governance about. I like to think of it as the intersection of three circles, really it's the technical metadata coming together with policies and rules, and coming together with the business ontologies that are unique to that particular business. And bringing this all together allows you to enable rapid change in your environment, so, it's a mouthful adaptive data governance, but that's what it kind of comes down to. >> So Ajay help me understand this, is this what enterprise companies are doing now or are they not quite there yet? >> Well, you know Lisa I think every organization is going at his pace, but markets are changing economy and the speed at which some of the changes in the economy happening is compelling more businesses to look at being more digital in how they serve their own customers. So what we're saying is a number of trends here from heads of data, chief data officers, CIO stepping back from a one size fits all approach because they've tried that before and it just hasn't worked. They've spent millions of dollars on IT programs trying to drive value from that data, and they've ended up with large teams of manual processing around data to try and hard-wire these policies to fit with the context and each line of business, and that hasn't worked. So, the trends that we're seeing emerge really relate to how do I as a chief data officer, as a CIO, inject more automation and to allow these common tasks. And we've been able to see that impact, I think the news here is if you're trying to create a knowledge graph, a data catalog, or a business glossary, and you're trying to do that manually, well stop, you don't have to do that manual anymore. I think best example I can give is Lester and I we like Chinese food and Japanese food, and if you were sitting there with your chopsticks you wouldn't eat a bowl of rice with the chopsticks one grain at a time, what you'd want to do is to find a more productive way to enjoy that meal before it gets cold. And that's similar to how we're able to help organizations to digest their data is to get through it faster, enjoy the benefits of putting that data to work. >> And if it was me eating that food with you guys I would be not using chopsticks I would be using a fork and probably a spoon. So Lester how then does Io-Tahoe go about doing this and enabling customers to achieve this? >> Let me show you a little story here. So if you take a look at the challenges that most customers have they're very similar, but every customer is on a different data journey, so, but it all starts with what data do I have, what shape is that data in, how is it structured, what's dependent on it upstream and downstream, what insights can I derive from that data, and how can I answer all of those questions automatically? So if you look at the challenges for these data professionals, you know, they're either on a journey to the cloud, maybe they're doing a migration to Oracle, maybe they're doing some data governance changes, and it's about enabling this. So if you look at these challenges, I'm going to take you through a story here, and I want to introduce Amanda. Amanda is not Latin like anyone in any large organizations, she is looking around and she just sees stacks of data, I mean, different databases the one she knows about, the ones she doesn't know about but should know about, various different kinds of databases, and Amanda is this tasking with understanding all of this so that they can embark on her data journey program. So Amanda goes through and she's great, (snaps finger) "I've got some handy tools, I can start looking at these databases and getting an idea of what we've got." But when she digs into the databases she starts to see that not everything is as clear as she might've hoped it would be. Property names or column names have ambiguous names like Attribute one and Attribute two, or maybe Date one and Date two, so Amanda is starting to struggle even though she's got tools to visualize and look at these databases, she's still knows she's got a long road ahead, and with 2000 databases in her large enterprise, yes it's going to be a long journey. But Amanda is smart, so she pulls out her trusty spreadsheet to track all of her findings, and what she doesn't know about she raises a ticket or maybe tries to track down in order to find what that data means, and she's tracking all this information, but clearly this doesn't scale that well for Amanda. So maybe the organization will get 10 Amanda's to sort of divide and conquer that work. But even that doesn't work that well 'cause there's still ambiguities in the data. With Io-Tahoe what we do is we actually profile the underlying data. By looking at the underlying data, we can quickly see that Attribute one looks very much like a US social security number, and Attribute two looks like a ICD 10 medical code. And we do this by using ontologies, and dictionaries, and algorithms to help identify the underlying data and then tag it. Key to doing this automation is really being able to normalize things across different databases so that where there's differences in column names, I know that in fact they contain the same data. And by going through this exercise with Io-Tahoe, not only can we identify the data, but we also can gain insights about the data. So for example, we can see that 97% of that time, that column named Attribute one that's got US social security numbers, has something that looks like a social security number. But 3% of the time it doesn't quite look right, maybe there's a dash missing, maybe there's a digit dropped, or maybe there's even characters embedded in it, that may be indicative of a data quality issues, so we try to find those kinds of things. Going a step further, we also try to identify data quality relationships. So for example we have two columns, one date one date two, through observation we can see the date one 99% of the time is less than date two, 1% of the time it's not, probably indicative of the data quality issue, but going a step further we can also build a business rule that says date one is actually than date two, and so then when it pops up again we can quickly identify and remediate that problem. So these are the kinds of things that we can do with Io-Tahoe. Going even a step further, we can take your favorite data science solution, productionize it, and incorporate it into our next version as what we call a worker process to do your own bespoke analytics. >> Bespoke analytics, excellent, Lester thank you. So Ajay, talk us through some examples of where you're putting this to use, and also what is some of the feedback from some customers. >> Yeah, what I'm thinking how do you bring into life a little bit Lisa lets just talk through a case study. We put something together, I know it's available for download, but in a well-known telecommunications media company, they have a lot of the issues that lasted just spoke about lots of teams of Amanda's, super bright data practitioners, and are maybe looking to get more productivity out of their day, and deliver a good result for their own customers, for cell phone subscribers and broadband users. So, there are so many examples that we can see here is how we went about auto generating a lot of that old understanding of that data within hours. So, Amanda had her data catalog populated automatically, a business glossary built up, and maybe I would start to say, "Okay, where do I want to apply some policies to the data to set in place some controls, whether I want to adapt how different lines of business maybe tasks versus customer operations have different access or permissions to that data." And what we've been able to do that is to build up that picture to see how does data move across the entire organization, across the state, and monitor that over time for improvement. So we've taken it from being like reactive, let's do something to fix something to now more proactive. We can see what's happening with our data, who's using it, who's accessing it, how it's being used, how it's being combined, and from there taking a proactive approach is a real smart use of the tanons in that telco organization and the folks that work there with data. >> Okay Ajay, so digging into that a little bit deeper, and one of the things I was thinking when you were talking through some of those outcomes that you're helping customers achieve is ROI. How do customers measure ROI, What are they seeing with Io-Tahoe solution? >> Yeah, right now the big ticket item is time to value. And I think in data a lot of the upfront investment costs are quite expensive, they happen today with a lot of the larger vendors and technologies. Well, a CIO, an economic buyer really needs to be certain about this, how quickly can I get that ROI? And I think we've got something that we can show just pull up a before and after, and it really comes down to hours, days, and weeks where we've been able to have that impact. And in this playbook that we put together the before and after picture really shows those savings that committed a bit through providing data into some actionable form within hours and days to drive agility. But at the same time being able to enforce the controls to protect the use of that data and who has access to it, so atleast the number one thing I'd have to say is time, and we can see that on the graphic that we've just pulled up here. >> Excellent, so ostensible measurable outcomes that time to value. We talk about achieving adaptive data governance. Lester, you guys talk about automation, you talk about machine learning, how are you seeing those technologies being a facilitator of organizations adopting adaptive data governance? >> Well, as we see the manual date, the days of manual effort are out, so I think this is a multi-step process, but the very first step is understanding what you have in normalizing that across your data estate. So, you couple this with the ontologies that are unique to your business and algorithms, and you basically go across it and you identify and tag that data, that allows for the next steps to happen. So now I can write business rules not in terms of named columns, but I can write them in terms of the tags. Using that automated pattern recognition where we observed the loan starts should be before the loan (indistinct), being able to automate that is a huge time saver, and the fact that we can suggest that as a rule rather than waiting for a person to come along and say, "Oh wow, okay, I need this rule, I need this rule." These are steps that increase, or I should say decrease that time to value that Ajay talked about. And then lastly, a couple of machine learning, because even with great automation and being able to profile all your data and getting a good understanding, that brings you to a certain point, but there's still ambiguity in the data. So for example I might have two columns date one and date two, I may have even observed that date one should be less than date two, but I don't really know what date one and date two are other than a date. So, this is where it comes in and I'm like, "As the user said, can you help me identify what date one and day two are in this table?" It turns out they're a start date and an end date for a loan, that gets remembered, cycled into machine learning step by step to see this pattern of date one date two. Elsewhere I'm going to say, "Is it start date and end date?" Bringing all these things together with all this automation is really what's key to enable this data database, your data governance program. >> Great, thanks Lester. And Ajay I do want to wrap things up with something that you mentioned in the beginning about what you guys are doing with Oracle, take us out by telling us what you're doing there, how are you guys working together? >> Yeah, I think those of us who worked in IT for many years we've learned to trust Oracle's technology that they're shifting now to a hybrid on-prem cloud generation 2 platform which is exciting, and their existing customers and new customers moving to Oracle are on a journey. So Oracle came to us and said, "Now, we can see how quickly you're able to help us change mindsets," and as mindsets are locked in a way of thinking around operating models of IT that are maybe not agile or more siloed, and they're wanting to break free of that and adopt a more agile API driven approach with their data. So, a lot of the work that we're doing with Oracle is around accelerating what customers can do with understanding their data and to build digital apps by identifying the underlying data that has value. And the time we're able to do that in hours, days, and weeks, rather than many months is opening up the eyes to chief data officers, CIO is to say, "Well, maybe we can do this whole digital transformation this year, maybe we can bring that forward and transform who we are as a company." And that's driving innovation which we're excited about, and I know Oracle keen to drive through. >> And helping businesses transform digitally is so incredibly important in this time as we look to things changing in 2021. Ajay and Lester thank you so much for joining me on this segment, explaining adaptive data governance, how organizations can use it, benefit from it, and achieve ROI, thanks so much guys. >> Thanks you. >> Thanks again Lisa. (bright music)

>> Live from Las Vegas, it's theCUBE covering VMworld 2018. Brought to you by VMware and its ecosystem partners. >> Welcome back to theCUBE's coverage of VMworld 2018. Two sets, wall-to-wall coverage. We had Michael Dell on this morning. We had Pat Gelsinger on this afternoon. And happy to welcome to the program, first time guest, Keith Moran, who's the vice president with Nutanix. Keith, I've talked to you lots about theCUBE, you've watched theCUBE, first time on theCUBE. Thanks so much for joining us. Yeah, thanks for having me. It's a great show. >> Alright, so let's set the stage here. We're here in Vegas. It's my ninth year doing VMworld. How many of these have you done? >> So this is my fourth. >> Yeah? How's the energy of the show? The expo hall's hopping. You guys have a nice booth. What are you hearing from the customers here? >> I think that we're seeing just a lot of discussion around where the market's going with hybrid cloud. I think that it's a massive opportunity. I think people are trying to connect the dots on where it's going in the next five years. The vibe's extremely strong right now. >> I've met you at some of the Nutanix shows in the past and seen you at some of these, but tell us a little bit about your role, how long you've been there, where you came from before. >> I run the Central US for Nutanix, and I spent a long time in the converged, whether it was that app at EMC, through a few start-ups, and then I've been at Nutanix for four years. It's been a great ride, seeing how the market's adopting to hyperconverged. The core problem and vision that Dheeraj saw nine years ago is playing out. He's five chess moves ahead of everyone. I think there's, again, a massive opportunity as we move forward. >> Keith, I love your to share. I love people in the field. You're talking to customers every day. You hear their mindset. I think back over the last 15 years in my career, and when Blade Server first came out, or when we started building converged solutions. It was like, "Oh, wait." Getting the organization together, sorting out the budgets. There were so many hurdles because this was the way we did things, and this is the way we're organized, and this is the way the budgets go. I think we've worked through a number of those, but I'd love to hear from you where we are with most customers, how many of them are on board, and doing more things, modernizing, and making changes, and being more flexible. >> Yeah, so I think you're spot on in the sense that the silos was the enemy in the sense that people were doing business as usual and that there was process, and they didn't want to take risks. But I think that the wave of disruption has been so strong and that we're in this period of mass extinction where customers, They don't have a choice anymore. That they have to protect against the competitive threat or exploit opportunity, and I think that the speed and the agility with hyperconverged is, And what the market disruption is forcing them to make those changes and forcing them to innovate. At the end of the day, that's their core revenue stream is how they experiment, how they innovate. Again, you're seeing the disruptions coming so fast that people are changing to survive. >> Yeah, we have some interesting paradoxes in the industry. We're talking about things like hyperconverged, yet really what we're trying to do is build distributed architectures. >> Correct. >> We're talking about, "Oh, well I want simplicity, and I want to get rid of the silos, but now I've got multicloud environment where I've got lots of different SaaS pieces, I've got multiple public clouds, I often have multiple vendors in my public cloud, and I've like recreated silos and certifications and expertise." How do customers deal with that? How do you help, and your team help to educate and get them up so that hopefully the new modern era is a little bit better than what they were dealing with? >> Yeah, and I think that's part of where the opportunity is. I think that the private cloud people don't do public well, and I don't think that the public cloud vendors do private well. So that's why the opportunity's so big. And I think for us, we're going to continue to harden the IaaS stack of what we built, and then our vision is how do we build a control plane for the next generation. If you look at our acquisition strategy, and where we're putting in it, how do you have a single operating system that spans the user experience from the public to private, making an exact replica. Again, I think customers are struggling with this problem and that as apps scale up, and scale down, and the demand for them, that they want this ability to course correct and be able to move VMs and containers in a very seamless fashion from one app to the next and adjust for the business market conditions. >> Yeah, I had a comment actually by one of my guests this week. We now have pervasive multicloud. We spent a few years sorting out who are the public clouds going to be. And there's still moves and changes, but we know there's a handful of the real big guys, then there's the next tier of all of the server providers, and the software players, like Nutanix. Look, you're not trying to become a competitor at Amazon or Google. You're partners. I see Nutanix at those shows. So maybe explain what's the long-term strategy. How does Nutanix, as you've been talking about enterprise cloud for a number of years, but what's that long-term vision as to how Nutanix plays in this ecosystem? >> Yeah. So for us I think part of it is our own cloud, which is Xi, and it's living in this multicloud world where our customer can do DRs of service with that single operating system, moving it from a Nutanix on-prem solution, moving it to a Nutanix cloud, moving it to Azure, moving it up to TCP, or moving it to AWS. And they have to do with it with thought because clearly there are so many interdependencies with these apps. There's governance, there's laws of the land, there's physics. There's so many things that are going to make this a complex equation for customers. But again, they're demanding, and that's forcing the issue where customers have to make these decisions. >> Keith, I want to hear, when you talk to your customers, where are they with their cloud strategy? I heard a one conference, 85% of customers have a cloud strategy, and I kind of put tongue in cheek. I said, "Well 15% of the people got to figure something out, and the other 85, when you talk to them next quarter, the strategy probably has changed quite a bit." Because things are changing fast, and you need to be agile and be able to change and adjust with what's going on. So where do your customers, I'm sure it's a big spectrum but? >> It is. The interesting thing for me for cloud is on average, we're seeing that the utilization rate, specifically in AWS, is somewhere in the 25% rate for reserved entrance, which was very surprising to me because the whole point of cloud is to test it, to deploy it, and to scale up, and if you're running in an environment where the utilization rate that the economics aren't working. So I think that people are starting to look at, alright, what are the economics behind the app? Does it make sense in the cloud? Does it make sense on-prem? Again, what are the interdependencies of it? The classic problems they're having are still around. They're spending 80% of their time just managing firmware and drivers and spending thousands of hours per quarter just troubleshooting and not impacting the business. So I think, fundamentally, that's what the customers are trying to solve is how do we get out of this business of spending all our time keeping the lights on and how do we drive innovation. And that ratio has been historically for 20 years. And I think, again, Nutanix helps drive that in the sense that we're helping customers shift that ratio and that pain. I always say, "Put your smartest people on your hardest problems," and when you've got these high-end SAN administrators spending a lot of time, they should be working on automation, orchestration, repeatable process that gives scale and again, impacts the business. >> Yeah. A line that I used at your most recent Nutanix show is talking to customers. Step one was modernize the platform, and step two, they could modernize the application. >> Absolutely. >> Speak a little bit to that because in this environment, we know the journey we went through to virtualize a lot of applications. I talked to a Nutanix customer this morning and talked about deploying Oracle, and I said, "Tell me how that was," because how many years did we spend fighting as customers? "You want to virtualize Oracle?" And Oracle would be like, "No, no, no. You have to use OVM. You have to use Oracle this. You have to use Oracle that." We've gone through that. And is it certified on Nutanix? It's good to go. It's ready to go. He's like, "It was pretty easy." And I'm like, it's so refreshing to see that. But when you talk about new modern applications and customers have this whole journey to embrace things like Agile, LMC ICD, and the like. Where does Nutanix play in this, and how are you helping? >> Yeah, so I think on the first. When you look at the classic database, so things like Sequel were automating so that you can extract it in a very simple manner. You look at the mode 2 apps like Kubernetes, we're taking a 37 page deployment guide and automating it down into three clicks because customers want the speed, they want the deployment cycles, they want the automation associated with that. And it's having a big impact in the sense that these customers are trying to figure out, "Where am I going here in the next three years?" For us, we're seeing massive workloads, whether it's Oracle, Sequel, people deploying on it. And again, there's so much pressure for people to change and constantly disrupt themselves, and that's what we're seeing. And layer that all on top of a lot of legacy apps. So we've got oil and gas customers, and big retailers, and when they show us the dependency maps of their applications, it's incredible. How complex these are, and they want simplicity and speed, and how do they get out of that business of the tangled mess. >> Yeah. Keith, I wonder if you have an example, and you might not be able to use an exact customer, but you mentioned some industries, so here's something I hear at a show like this. Alright, I understand my virtualized environment. I've deployed HCI. I really need to start extending and using public cloud. What are some first steps that you've seen customers as to how they're making that successful? What are some of those important patterns, what works, and where's good places for them to start? >> I look at it almost, when I see some of the automation deployment cycles they have of how they get a VM through the full lifecycle, and behind the scenes they have such massive complexities that it's hindering their ability to create automations. So the first layer is how do you simplify the infrastructure underneath, and it goes back to that dependency map. So again, oil and gas, that's big retailers. When they show us what their infrastructure is, they want to simplify that layer first, and then from there they can build incredible automation that gives them a multiple in the return that is much greater than what they're seeing in today's infrastructure. >> Keith, what's exciting you in the marketplace today? You get to meet with a lot of customers. Just kind of an open-ended. >> So for me, it's I've worked in a lot of big legacy companies, and I've never seen customers that have the passion towards Nutanix. And I think that it's the problems that we're solving for them, the impacts we're having on the business is driving that loyal following. But again, how fast people are either trying to exploit a competitive advantage or protect against a threat, that it's interesting to be right in this, in the epicenter of this big shift that's happening, right? Tectonic plates are shifting in that you've got a massive cloud provider like AWS. You've got a big player like VMware. What's the next generation going to look like? For me it's fascinating to see how these businesses are competing. I look at a customer. I've got a Fortune 500, The CTO's comment to me was, "I'm one app away from disruption." So they're a massive commercial real estate organization, and he's terrified of what could happen next, and he's got to stay way ahead of the curve, and I think that the innovation rate that we're bringing, the support, the infrastructure. I think it's a great place because of how we're serving what we call the underserved customer and having a big impact. >> Yeah. It's interesting. We always poke at the how much are customers just dreading that potential disruption and how much are they excited about what they can do different. You talk about working with traditional vendors in IT for the last decade or so, it's like IT and the business were kind of fighting over it. There's a line one of our hosts here, Alan Cohen, used to use. Actually, the first time I heard it was at the Nutanix show in Miami when we had it on. And he said there's this triangle, and where you want to get people is away from the no and the slow, and get them to go. Do you feel more people are fearful, or more people are excited. Is it a mix of-- >> It is. >> Those for your customers? >> And again, I think that the marketforce is really helping because people there they have to shift to stay competitive, and they're pushing every day to the level of change and how people are embracing change is much faster than it was. Because again, these disruption cycles are much faster and they're coming at customers in a totally different way that they weren't prepared for. >> Alright, Keith, final word from you is how many of theCUBE interviews have you watched in the last bunch of years? >> The content, I mean, it's off the charts. Hundreds and hundreds of hours, I would say. >> Well, hey. Really appreciate you joining us. Keith Moran, not only a long-time watcher, but now a CUBE alumni with the thousands that we've done. So pleasure to talk with ya on-camera, as well as always off-camera. >> Yeah, great stuff, Stu. >> We'll be back with lots more coverage here from VMworld 2018. I'm Stu Miniman, and thanks for watching theCUBE. (upbeat music)