>> 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)

>> Announcer: From around the globe, it's theCUBE, with digital coverage of data automated and event series brought to you by IO Tahoe. >> Welcome back everybody to the power panel, driving business performance with smart data life cycles. Lester Waters is here. He's the chief technology officer from IO Tahoe, he's joined by Patrick Smith, who is field CTO from Pure Storage and Ezat Dayeh, who's a system engineering manager at Cohesity. Gentlemen, good to see you. Thanks so much for coming on this panel. >> Thank you, Dave. >> Let's start with Lester. I wonder if each of you could just give us a quick overview of your role and what's the number one problem that you're focused on solving for your customers? Let's start with Lester please. >> Yes, I'm Lester waters, chief technology officer for IO Tahoe, and really the number one problem that we are trying to solve for our customers is to help them understand what they have. 'Cause if they don't understand what they have in terms of their data, they can't manage it, they can't control it, they can't monitor it. They can't ensure compliance. So really that's finding all you can about your data that you have and building a catalog that can be readily consumed by the entire business is what we do. >> Great. All right, Patrick, field CTO in your title. That says to me you're talking to customers all the time. So you've got a good perspective on it. Give us you know, your take on things here. >> Yeah, absolutely. So my patch is EMEA and talk to customers and prospects in lots of different verticals across the region. And as they look at their environments and their data landscape, they're faced with massive growth in the data that they're trying to analyze and demands to be able to get in site faster and to deliver business value faster than they've ever had to do in the past. So big challenges that we're seeing across the region. >> Got it. And is that, Cohesity? You're like the new kid on the block, you guys are really growing rapidly, created this whole notion of data management backup and beyond, but from a system engineering manager, what are you seeing from customers, your role and the number one problem that you're solving? >> Yeah, sure. So the number one problem, I see time and again, speaking with customers, fall around data fragmentation. So due to things like organic growth, you know, even maybe budgetary limitations, infrastructure has grown over time, very piecemeal and it's highly distributed internally. And just to be clear, you know, when I say internally, you know, that could be that it's on multiple platforms or silos within an on-prem infrastructure, but that it also does extend to the cloud as well. So we've seen, you know, over the past few years, a big drive towards cloud consumption, almost at any cost in some examples. You know, there could be business reasons like moving from things like CapEx to a more of an OPEX model. And what this has done is it's gone to, to create further silos, you know, both on-prem and also in the cloud. And while short term needs may be met by doing that, what it's doing is it's causing longer term problems and it's reducing the agility for these customers to be able to change and transform. >> Right, hey cloud is cool. Everybody wants to be in the cloud, right? So you're right. It creates maybe unintended consequences. So let's start with the business outcome and kind of try to work backwards. I mean, people, you know, they want to get more insights from data. They want to have a more efficient data life cycle, but so Lester, let me start with you, thinking about like the North star to creating data-driven cultures, you know, what is the North star for customers here? >> I think the North star in a nutshell is driving value from your data without question. I mean, we differentiate ourselves these days by even in nuances in our data. Now, underpinning that there's a lot of things that have to happen to make that work out well, you know, for example, making sure you adequately protect your data, you know, do you have a good, do you have a good storage subsystem? Do you have a good backup and recovery point objectives, recovery time objectives? Do you, are you fully compliant? Are you ensuring that you're ticking all the boxes? There's a lot of regulations these days in term, with respect to compliance, data retention, data privacy, and so forth. Are you ticking those boxes? Are you being efficient with your data? You know, in other words, I think there's a statistic that someone mentioned to me the other day, that 53% of all businesses have between three and 15 copies of the same data. So, you know, finding and eliminating those is part of the, part of the problem is you need to chase. >> Yeah, so Patrick and Ezat, I mean, you know, Lester touched on a lot of the areas that you guys are involved in. I like to think of, you know, you're right. Lester, no doubt, business value, and a lot of that comes from reducing the end to end cycle times, but anything that you guys would, would add to that, Patrick, maybe start with Patrick. >> Yeah, I think, I think getting value from data really hits on, it hits on what everyone wants to achieve, but I think there are a couple of key steps in doing that. First of all, is getting access to the data and that really hits three big problems. Firstly, working out what you've got. Secondly, after working out what you've got, how to get access to it, because it's all very well knowing you've got some data, but if you can't get access to it, either because of privacy reasons, security reasons, then that's a big challenge. And then finally, once you've got access to the data, making sure that you can process that data in a timely manner and at the scale that you need to, to deliver your business objectives. So I think those are really three key steps in successfully getting value from the data within our organization. >> Ezat, I'll ask you, anything else you'd fill in? >> Yeah, so the guys have touched on a lot of things already. For me, you know, it would be that an organization has got a really good global view of all of its data. It understands the data flow and dependencies within their infrastructure, understands the precise legal and compliance requirements and have the ability to action changes or initiatives within their environment, forgive the pun, but with a cloud-like agility. You know, and that's no easy feat, right? That is hard work. Another thing as well is that it's for companies to be mature enough, to truly like delete and get rid of unneeded data from their system. You know, I've seen so many times in the past, organizations paying more than they need to because they've acquired a lot of data baggage. Like it just gets carried over from refresh to refresh. And, you know, if you can afford it great, but chances are, you want to be as competitive as possible. And what happens is that this results in, you know, spend that is unnecessary, not just in terms of acquisition, but also in terms of maintaining the infrastructure, but then the other knock on effect as well is, you know, from a compliance and a security point of view, you're exposing yourself. So, you know, if you don't need it, delete it or at least archive it. >> Okay, So we've talked about the challenges in some of the objectives, but there's a lot of blockers out there, and I want to understand how you guys are helping remove them. So Lester, what are some of those blockers? I mean, I can mention a couple, there's their skillsets. There's obviously you talked about the problem of siloed data, but there's also data ownership. That's my data. There's budget issues. What do you see as some of the big blockers in terms of people really leaning in to this smart data life cycle? >> Yeah, silos is probably one of the biggest one I see in businesses. Yes, it's my data, not your data. Lots of compartmentalization and breaking that down is one of the, one of the challenges and having the right tools to help you do that is only part of the solution. There's obviously a lot of cultural things that need to take place to break down those silos and work together. If you can identify where you have redundant data across your enterprise, you might be able to consolidate those, you know, bring together applications. A lot of companies, you know, it's not uncommon for a large enterprise to have, you know, several thousand applications, many of which have their own instance of the very same data. So if there's a customer list, for example, it might be in five or six different sources of truth. And there's no reason to have that, and bringing that together by bringing those things together, you will start to tear down the business boundary silos that automatically exist. I think, I think one of the other challenges too, is self service. As Patrick mentioned, gaining access to your data and being able to work with it in a safe and secure fashion, is key here. You know, right now you typically raise a ticket, wait for access to the data, and then maybe, you know, maybe a week later out pops the bit you need and really, you know, with data being such a commodity and having timeliness to it, being able to have quick access to that data is key. >> Yeah, so I want to go to Patrick. So, you know, one of the blockers that I see is legacy infrastructure, technical debt, sucking all the budget. You've got, you know, too many people having to look after, you know, storage. It's just, it's just too complicated. And I wonder if you have, obviously that's my perspective, what's your perspective on that? >> Yeah, absolutely. We'd agree with that. As you look at the infrastructure that supports people's data landscapes today, for primarily legacy reasons, the infrastructure itself is siloed. So you have different technologies with different underlying hardware, different management methodologies that are there for good reason, because historically you had to have specific fitness for purpose, for different data requirements. That's one of the challenges that we tackled head on at Pure with the flash blade technology and the concept of the data hub, a platform that can deliver in different characteristics for the different workloads, but from a consistent data platform. And it means that we get rid of those silos. It means that from an operational perspective, it's far more efficient. And once your data set is consolidated into the data hub, you don't have to move that data around. You can bring your applications and your workloads to the data rather than the other way around. >> Now, Ezat, I want to go to you because you know, in the world, in your world, which to me goes beyond backup. I mean, one of the challenges is, you know, they say backup is one thing. Recovery is everything, But as well, the CFO doesn't want to pay for just protection. And one of the things that I like about what you guys have done is you've broadened the perspective to get more value out of your, what was once seen as an insurance policy. I wonder if you could talk about that as a blocker and how you're having success removing it. >> Yeah, absolutely. So, you know, as well as what the guys have already said, you know, I do see one of the biggest blockers as the fact that the task at hand can, you know, can be overwhelming for customers and it can overwhelm them very, very quickly. And that's because, you know, this stuff is complicated. It's got risk, you know, people are used to the status quo, but the key here is to remember that it's not an overnight change. It's not, you know, a flick of a switch. It's something that can be tackled in a very piecemeal manner, and absolutely like you you said, you know, reduction in TCO and being able to leverage the data for other purposes is a key driver for this. So like you said, you know, for us specifically, one of the areas that we help customers around with first of all, it's usually data protection. It can also be things like consolidation of unstructured file data. And, you know, the reason why customers are doing this is because legacy data protection is very costly. You know, you'd be surprised how costly it is. A lot of people don't actually know how expensive it can be. And it's very complicated involving multiple vendors. And it's there really to achieve one goal. And the thing is, it's very inflexible and it doesn't help towards being an agile data driven company. So, you know, this can be, this can be resolved. It can be very, you know, pretty straightforward. It can be quite painless as well. Same goes for unstructured data, which is very complex to manage. And, you know, we've all heard the stats from the analysts, you know, data obviously is growing at an extremely rapid rate. But actually when you look at that, you know, how is it actually growing? 80% of that growth is actually in unstructured data. And only 20% of that growth is in structured data. So, you know, these are quick win areas that the customers can realize. Immediate TCO improvement and increased agility as well, when it comes to managing and automating their infrastructure. So, yeah, it's all about making, you know, doing more with, with what you have. >> So let's paint a picture of this guys, if you could bring up the life cycle, I want to explore that a little bit and ask each of you to provide a perspective on this. And so, you know, what you can see here is you've got this, this cycle, the data life cycle, and what we're wanting to do is really inject intelligence or smarts into this life cycle, you can see, you start with ingestion or creation of data. You're storing it. You got to put it somewhere, right? You got to classify it, you got to protect it. And then of course you want to, you know, reduce the copies, make it efficient, and then you want to prepare it, so the businesses can actually consume it. And then you've got clients and governance and privacy issues. And at some point when it's legal to do so, you want to get rid of it. We never get rid of stuff in technology. We keep it forever. But I wonder if we could start with you Lester. This is, you know, the picture of the life cycle. What role does automation play in terms of injecting smarts into the life cycle? >> Automation is key here. You know, especially from the discover catalog and classified perspective. I've seen companies where we, where they go and will take and dump their, all of their database schemes into a spreadsheet so that they can sit down and manually figure out what attribute 37 needs for a column name. And that's only the tip of the iceberg. So being able to automatically detect what you have, automatically deduce what's consuming the data, you know, upstream and downstream, being able to understand all of the things related to the life cycle of your data, backup archive, deletion. It is key. So having good tools is very important. >> So Patrick, obviously you participated in the store piece of this picture. So I wonder if you could just talk more specifically about that, but I'm also interested in how you affect the whole system view, the end to end cycle time. >> Yeah, I think Lester kind of hit the nail on the head in terms of the importance of automation, because data volumes are just so massive now that you, you can't, you can't effectively manage or understand or catalog your data without automation. But once you, once you understand the data and the value of the data, then that's where you can work out where the data needs to be at any point in time. And that's where we come into play. You know, if data needs to be online, if it's hot data, if it's data that needs to be analyzed, and, you know, we're moving to a world of analytics where some of our customers say, there's no such thing as cold data anymore, then it needs to be on a performance platform, but you need to understand exactly what the data is that you have to work out where to place it and where it fits into that data life cycle. And then there's that whole challenge of protecting it through the life cycle, whether that's protecting the hot data or as the data moves off into, you know, into an archive or into a cold store, still making sure you know where it is, and easily retrievable, should you need to move it back into the working set. So I think automation is key, but also making sure that it ties into understanding where you place your data at any point in time. >> Right, so Pure and Cohesity, obviously, partner to do that. And of course, Ezat, you guys are part of the protect, you're certainly part of the retain, but also you provide data management capabilities and analytics. I wonder if you could add some color there. >> Yeah, absolutely. So like you said, you know, we focus pretty heavily on data protection as just one of our areas and that infrastructure, it is just sitting there really you know, the legacy infrastructure, it's just sitting there, you know, consuming power, space cooling and pretty inefficient. And, you know, one of our main purposes is like we said, to make that data useful and automating that process is a key part of that, right? So, you know, not only are we doing things like obviously making it easier to manage, improving RPOs and RTOs with policy-based SLAs, but we're making it useful and having a system that can be automated through APIs and being an API first based system. It's almost mandatory now when you're going through a digital, you know, digital transformation. And one of the things that we can do is as part of that automation, is that we can make copies of data without consuming additional capacity available, pretty much instantaneously. You might want to do that for many different purposes. So examples of that could be, you know, for example, reproducing copies of production data for development purposes, or for testing new applications for example. And you know, how would you, how would you go about doing that in a legacy environment? The simple answer is it's painfully, right? So you just can't do those kinds of things. You know, I need more infrastructure to store the data. I need more compute to actually perform the things that I want to do on it, such as analytics, and to actually get a copy of that data, you know, I have to either manually copy it myself or I restore from a backup. And obviously all of that takes time, additional energy. And you end up with a big sprawling infrastructure, which isn't a manageable, like Patrick said, it's just the sheer amount of data, you know, it doesn't, it doesn't warrant doing that anymore. So, you know, if I have a modern day platform such as, you know, the Cohesity data platform, I can actually do a lot of analytics on that through applications. So we have a marketplace for apps. And the other great thing is that it's an open system, right? So anybody can develop an app. It's not just apps that are developed by us. It can be third parties, it could be customers. And with the data being consolidated in one place, you can then start to start to realize some of these benefits of deriving insights out of your data. >> Yeah, I'm glad you brought that up earlier in your little example there, because you're right. You know, how do you deal with that? You throw people at the problem and it becomes nights and weekends, and that sort of just fails. It doesn't scale. I wonder if we could talk about metadata. It's increasingly important. Metadata is data about the data, but Lester, maybe explain why it's so important and what role it plays in terms of creating smart data lifecycle. >> Well, yes, metadata, it does describe the data, but it's, a lot of people think it's just about the data itself, but there's a lot of extended characteristics about your data. So, imagine if for my data life cycle, I can communicate with the backup system from Cohesity and find out when the last time that data was backed up, or where it's backed up to. I can communicate exchange data with Pure Storage and find out what tier it's on. Is the data at the right tier commensurate with its use level that Patrick pointed out? And being able to share that metadata across systems. I think that's the direction that we're going in. Right now we're at the stage, we're just identifying the metadata and trying to bring it together and catalog it. The next stage will be, okay using the APIs that we have between our systems. Can we communicate and share that data and build good solutions for our customers to use? >> I think it's a huge point that you just made. I mean, you know, 10 years ago, automating classification was the big problem and it was machine intelligence. You know, we're obviously attacking that, but your point about as machines start communicating to each other and you start, you know, it's cloud to cloud, there's all kinds of metadata, kind of new metadata that's being created. I often joke that someday there's going to be more metadata than the data. So that brings us to cloud. And Ezat, I'd like to start with you, because you were talking about some cloud creep before. So what's your take on cloud? I mean, you've got private clouds, you got hybrid clouds, public clouds, inter clouds, IOT, and the edge is sort of another form of cloud. So how does cloud fit into the data life cycle? How does it affect the data life cycle? >> Yeah, sure. So, you know, I do think, you know, having the cloud is a great thing and it has got its role to play and you can have many different permutations and iterations of how you use it. And, you know, as I, as I may have sort of mentioned previously, you know, I've seen customers go into the cloud very, very quickly. And actually recently they're starting to remove web codes from the cloud. And the reason why this happens is that, you know, cloud has got its role to play, but it's not right for absolutely everything, especially in their current form as well. So, you know, a good analogy I like to use, and this may sound a little bit cliche, but you know, when you compare clouds versus on premises data centers, you can use the analogy of houses and hotels. So to give you an idea, so, you know, when we look at hotels, that's like the equivalent of a cloud, right? I can get everything I need from there. I can get my food, my water, my outdoor facilities. If I need to accommodate more people, I can rent some more rooms. I don't have to maintain the hotel. It's all done for me. When you look at houses, the equivalent to, you know, on premises infrastructure, I pretty much have to do everything myself, right? So I have to purchase the house. I have to maintain it. I have to buy my own food and water, eat it. I have to make improvements myself, but then why do we all live in houses, not in hotels? And the simple answer that I can, I can only think of is, is that it's cheaper, right? It's cheaper to do it myself, but that's not to say that hotels haven't got their role to play. You know, so for example, if I've got loads of visitors coming over for the weekend, I'm not going to go and build an extension to my house, just for them. I will burst into my hotel, into the cloud, and use it for, you know, for things like that. And you know, if I want to go somewhere on holiday, for example, then I'm not going to go buy a house there. I'm going to go in, I'm going to stay in a hotel, same thing. I need some temporary usage. You know, I'll use the cloud for that as well. Now, look, this is a loose analogy, right? But it kind of works. And it resonates with me at least anyway. So what I'm really saying is the cloud is great for many things, but it can work out costlier for certain applications while others are a perfect fit. So when customers do want to look at using the cloud, it really does need to be planned in an organized way, you know, so that you can avoid some of the pitfalls that we're talking about around, for example, creating additional silos, which are just going to make your life more complicated in the long run. So, you know, things like security planning, you know, adequate training for staff is absolutely a must. We've all seen the, you know, the horror stories in the press where certain data maybe has been left exposed in the cloud. Obviously nobody wants to see that. So as long as it's a well planned and considered approach, the cloud is great and it really does help customers out. >> Yeah, it's an interesting analogy. I hadn't thought of that before, but you're right. 'Cause I was going to say, well, part of it is you want the cloud experience everywhere, but you don't always want the cloud experience, especially, you know, when you're with your family, you want certain privacy. I've not heard that before Ezat, so that's a new perspective, so thank you. But so, but Patrick, I do want to come back to that cloud experience because in fact, that's what's happening in a lot of cases. Organizations are extending the cloud properties of automation on-prem and in hybrid. And certainly you guys have done that. You've created, you know, cloud-based capabilities. They can run in AWS or wherever, but what's your take on cloud? What's Pure's perspective? >> Yeah, I thought Ezat brought up a really interesting point and a great analogy for the use of the public cloud, and it really reinforces the importance of the hybrid and multicloud environment, because it gives you that flexibility to choose where is the optimal environment to run your business workloads. And that's what it's all about. And the flexibility to change which environment you're running in, either from one month to the next or from one year to the next, because workloads change and the characteristics that are available in the cloud change on a pretty frequent basis. It's a fast moving world. So one of the areas of focus for us with our cloud block store technology is to provide effectively a bridge between the on-prem cloud and the public cloud, to provide that consistent data management layer that allows customers to move their data where they need it when they need it. And the hybrid cloud is something that we've lived with ourselves at Pure. So our Pure1 management technology actually sits in a hybrid cloud environment. We started off entirely cloud native, but now we use the public cloud for compute and we use our own technology, the end of a high performance network link to support our data platform. So we get the best of both worlds. And I think that's where a lot of our customers are trying to get to is cloud flexibility, but also efficiency and optimization. >> All right, I want to come back in a moment there, but before we do, Lester, I wonder if we could talk a little bit about compliance governance and privacy. You know, that, a lot of that comes down to data, the EU right now, I think the Brits on this panel are still in the EU for now, but the EU are looking at new rules, new regulations going beyond GDPR, tightening things up in a, specifically kind of pointing at the cloud. Where does sort of privacy, governance, compliance fit in to the, to the data life cycle, then Ezat, I want your thoughts on this as well. >> Yeah, this is a very important point because the landscape for compliance around data privacy and data retention is changing very rapidly and being able to keep up with those changing regulations in an automated fashion is the only way you're going to be able to do it. Even, I think there's a, some sort of a, maybe a ruling coming out today or tomorrow with the change to GDPR. So this is, these are all very key points, and being able to codify those rules into some software, whether you know, IO Tahoe or your storage system or Cohesity that'll help you be compliant is crucial. >> Yeah, Esat, anything you can add there? I mean, this really is your wheelhouse. >> Yeah, absolutely. So, you know, I think anybody who's watching this probably has gotten the message that, you know, less silos is better. And then absolutely it also applies to data in the cloud as well. So, you know, by aiming to consolidate into fewer platforms, customers can realize a lot better control over their data. And then natural effect of this is that it makes meeting compliance and governance a lot easier. So when it's consolidated, you can start to confidently understand who is accessing your data, how frequently are they accessing the data? You can also do things like detecting anomalous file access activities, and quickly identify potential threats. You know, and this can be delivered by apps which are running on one platform that has consolidated the data as well. And you can also start getting into lots of things like, you know, rapidly searching for PII. So personally identifiable information across different file types. And you can report on all of this activity back to the business, by identifying, you know, where are you storing your copies of data? How many copies have you got and who has access to them? These are all becoming table stakes as far as I'm concerned. >> Right, right. >> The organizations continue that move into digital transformation and more regulation comes into law. So it's something that has to be taken very, very seriously. The easier you make your infrastructure, the easier it will be for you to comply with it. >> Okay, Patrick, we were talking, you talked earlier about storage optimization. We talked to Adam Worthington about the business case. You get the sort of numerator, which is the business value and then the denominator, which is the cost. And so storage efficiency is obviously a key part of it. It's part of your value proposition to pick up on your sort of earlier comments, and what's unique about Pure in this regard? >> Yeah, and I think there are, there are multiple dimensions to that. Firstly, if you look at the difference between legacy storage platforms, they used to take up racks or isles of space in a data center with flash technology that underpins flash blade, we effectively switch out racks for rack units. And it has a big play in terms of data center footprint, and the environmentals associated with the data center, but it doesn't stop at that. You know, we make sure that we efficiently store data on our platforms. We use advanced compression techniques to make sure that we make flash storage as cost competitive as we possibly can. And then if you look at extending out storage efficiencies and the benefits it brings, just the performance has a direct effect on staff, whether that's, you know, the staff and the simplicity of the platform, so that it's easy and efficient to manage, or whether it's the efficiency you get from your data scientists who are using the outcomes from the platform and making them more efficient. If you look at some of our customers in the financial space, their time to results are improved by 10 or 20 X by switching to our technology from legacy technologies for their analytics platforms. >> So guys we've been running, you know, CUBE interviews in our studios remotely for the last 120 days, it's probably the first interview I've done where I haven't started off talking about COVID, but digital transformation, you know, BC, before COVID. Yeah, it was real, but it was all of a buzzy wordy too. And now it's like a mandate. So Lester, I wonder if you could talk about smart data life cycle and how it fits into this isolation economy and hopefully what will soon be a post isolation economy? >> Yeah, COVID has dramatically accelerated the data economy. I think, you know, first and foremost, we've all learned to work at home. I, you know, we've all had that experience where, you know, there were people who would um and ah about being able to work at home just a couple of days a week. And here we are working five days a week. That's had a knock on impact to infrastructure to be able to support that. But going further than that, you know, the data economy is all about how a business can leverage their data to compete in this new world order that we are now in. So, you know, they've got to be able to drive that value from their data and if they're not prepared for it, they're going to falter. We've unfortunately seen a few companies that have faltered because they weren't prepared for this data economy. This is where all your value is driven from. So COVID has really been a forcing function to, you know, it's probably one of the few good things that have come out of COVID, is that we have been forced to adapt. And it's been an interesting journey and it continues to be so. >> Well, is that too, you know, everybody talks about business resiliency, ransomware comes into effect here, and Patrick, you, you may have some thoughts on this too, but Ezat, your thoughts on the whole work from home pivot and how it's impacting the data life cycle. >> Absolutely, like, like Lester said, you know, we've, we're seeing a huge impact here. You know, working from home has, has pretty much become the norm now. Companies have been forced into basically making it work. If you look at online retail, that's accelerated dramatically as well. Unified communications and video conferencing. So really, you know, the point here is that yes, absolutely. You know, we've compressed you know, in the past maybe four months, what probably would have taken maybe even five years, maybe 10 years or so. And so with all this digital capability, you know, when you talk about things like RPOs and RTOs, these things are, you know, very much, you know, front of mind basically and they're being taken very seriously. You know, with legacy infrastructure, you're pretty much limited with what you can do around that. But with next generation, it puts it front and center. And when it comes to, you know, to ransomware, of course, it's not a case of if it's going to happen, it's a case of when it's going to happen. Again, we've all seen lots of stuff in the press, different companies being impacted by this, you know, both private and public organizations. So it's a case of, you know, you have to think long and hard about how you're going to combat this, because actually malware also, it's becoming, it's becoming a lot more sophisticated. You know, what we're seeing now is that actually, when, when customers get impacted, the malware will sit in their environment and it will have a look around it, it won't actually do anything. And what it's actually trying to do is, it's trying to identify things like your backups, where are your backups? Because you know, what do, what do we all do? If we get hit by a situation like this, we go to our backups. But you know, the bad actors out there, they, you know, they're getting pretty smart as well. And if your legacy solution is sitting on a system that can be compromised quite easily, that's a really bad situation, you know, waiting to happen. And, you know, if you can't recover from your backups, essentially, unfortunately, you know, people are going to be making trips to the bank because you're going to have to pay to get your data back. And of course, nobody wants to see that happening. So one of the ways, for example, that we look to help customers defend against this is actually we have, we have a three pronged approach. So protect, detect, and respond. So what we mean by protect, and let me say, you know, first of all, this isn't a silver bullet, right? Security is an industry all of itself. It's very complicated. And the approach here is that you have to layer it. What Cohesity, for example, helps customers with, is around protecting that insurance policy, right? The backups. So by ensuring that that data is immutable, cannot be edited in any way, which is inherent to our file system. We make sure that nothing can affect that, but it's not just external actors you have to think about, it's also potentially internal bad actors as well. So things like being able to data lock your information so that even administrators can't change, edit or delete data, is just another way in which we help customers to protect. And then also you have things like multifactor authentication as well, but once we've okay, so we've protected the data. Now, when it comes, now it comes to detection. So again, being, you know, ingrained into data protection, we have a good view of what's happening with all of this data that's flowing around the organization. And if we start to see, for example, that backup times, or, you know, backup quantities, data quantities are suddenly spiking all of a sudden, we use things like, you know, AI machine learning to highlight these, and once we detect an anomaly such as this, we can then alert our users to this fact. And not only do we alert them and just say, look, we think something might be going on with your systems, but we'll also point them to a known good recovery point as well, so that they don't have to sit searching, well, when did this thing hit and you know, which recovery point do I have to use? And so, you know, and we use metadata to do all of these kinds of things with our global management platform called Helios. And that actually runs in the cloud as well. And so when we find this kind of stuff, we can basically recover it very, very quickly. And this comes back now to the RPOs and the RTOs. So your recovery point objective, we can shrink that, right? And essentially what that means is that you will lose less data. But more importantly, the RTO, your recovery time objective, it means that actually, should something happen and we need to recover that data, we can also shrink that dramatically. So again, when you think about other, you know, legacy technology out there, when something like this happens, you might be waiting hours, most likely days, possibly even weeks and months, depending on the severity. Whereas we're talking about being able to bring data back, you know, we're talking maybe, you know, a few hundred virtual machines in seconds and minutes. And so, you know, when you think about the value that that can give an organization, it becomes, it becomes a no brainer really, as far as, as far as I'm concerned. So, you know, that really covers how we respond to these situations. So protect, detect, and respond. >> Great, great summary. I mean, my summary is adverse, right? The adversaries are very, very capable. You got to put security practices in place. The backup Corpus becomes increasingly important. You got to have analytics to detect anomalous behavior and you got to have, you know, fast recovery. And thank you for that. We got to wrap, but so Lester, let me, let me ask you to sort of paint picture of the sort of journey or the maturity model that people have to take. You know, if they want to get into it, where do they start and where are they going? Give us that view. >> I think first it's knowing what you have. If you don't know what you have, you can't manage it, you can't control it, you can't secure it, you can't ensure it's compliant. So that's first and foremost. The second is really, you know, ensuring that you're compliant. Once you know what you have, are you securing it? Are you following the regulatory, the applicable regulations? Are you able to evidence that? How are you storing your data? Are you archiving it? Are you storing it effectively and efficiently? You know, have you, Nirvana from my perspective is really getting to a point where you've consolidated your data, you've broken down the silos and you have a virtually self service environment by which the business can consume and build upon their data. And really at the end of the day, as we said at the beginning, it's all about driving value out of your data. And the automation is key to this journey. >> That's awesome. And you just described sort of a winning data culture. Lester, Patrick, Ezat, thanks so much for participating in this power panel. >> Thank you, David. >> Thank you. >> Thank you for watching everybody. This is Dave Vellante for theCUBE. (bright music)

(upbeat music) >> Reporter: From around the globe, it's The Cube with digital coverage of enterprise data automation and event series brought to you by Io-Tahoe. >> Okay, we're back. Focusing on enterprise data automation, we're going to talk about the journey to the cloud. Remember, the hashtag is data automated. We're here with Lester Waters who's the CTO of Io-Tahoe, Lester, good to see you from across the pond on video, wish we were face to face, but it's great to have you on The Cube. >> Also I do, thank you for having me. >> Oh, you're very welcome. Hey, give us a little background on CTO, you got a deep expertise in a lot of different areas, but what do we need to know? >> Well, David, I started my career basically at Microsoft, where I started the Information Security Cryptography Group. They're the very first one that the company had and that led to a career in information security and of course, as you go along with the information security, data is the key element to be protected. So I always had my hands in data and that naturally progressed into a role with Io-Tahoe as their CTO. >> Guys, I have to invite you back, we'll talk crypto all day we'd love to do that but we're here talking about yeah, awesome, right? But we're here talking about the cloud and here we'll talk about the journey to the cloud and accelerate. Everybody's really interested obviously in cloud, even more interested now with the pandemic, but what's that all about? >> Well, moving to the cloud is quite an undertaking for most organizations. First of all, we've got as probably if you're a large enterprise, you probably have thousands of applications, you have hundreds and hundreds of database instances, and trying to shed some light on that, just to plan your move to the cloud is a real challenge. And some organizations try to tackle that manually. Really what Io-Tahoe is bringing is trying to tackle that in an automated version to help you with your journey to the cloud. >> Well, look at migrations are sometimes just an evil word to a lot of organizations, but at the same time, building up technical debt veneer after veneer and year, and year, and year is something that many companies are saying, "Okay, it's got to stop." So what's the prescription for that automation journey and simplifying that migration to the cloud? >> Well, I think the very first thing that's all about is data hygiene. You don't want to pick up your bad habits and take them to the cloud. You've got an opportunity here, so I see the journey to the cloud is an opportunity to really clean house, reorganize things, like moving out. You might move all your boxes, but you're kind of probably cherry pick what you're going to take with you and then you're going to organize it as you end up at your new destination. So from that, I get there's seven key principles that I like to operate by when I advise on the cloud migration. >> Okay. So, where do you start? >> Well, I think the first thing is understanding what you got, so discover and cataloging your data and your applications. If I don't know what I have, I can't move it, I can't improve it, I can't build up on it. And I have to understand there is dependency, so building that data catalog is the very first step. What do I got? >> Now, is that a metadata exercise? Sometimes there's more metadata than there is data. Is metadata part of that first step or? >> In deed, metadata is the first step so the metadata really describes the data you have. So, the metadata is going to tell me I have 2000 tables and maybe of those tables, there's an average of 25 columns each, and so that gives me a sketch if you will, of what I need to move. How big are the boxes I need to pack for my move to the cloud? >> Okay, and you're saying you can automate that data classification, categorization, discovery, correct using math machine intelligence, is that correct? >> Yeah, that's correct. So basically we go, and we will discover all of the schema, if you will, that's the metadata description of your tables and columns in your database in the data types. So we take, we will ingest that in, and we will build some insights around that. And we do that across a variety of platforms because everybody's organization has you've got a one yeah, an Oracle Database here, and you've got a Microsoft SQL Database here, you might have something else there that you need to bring site onto. And part of this journey is going to be about breaking down your data silos and understanding what you've got. >> Okay. So, we've done the audit, we know what we've got, what's next? Where do we go next? >> So the next thing is remediating that data. Where do I have duplicate data? Often times in an organization, data will get duplicated. So, somebody will take a snapshot of a data, and then ended up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer. And you can see where that will go when trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to understand where all your redundant data is. So when you go to the cloud, maybe you have an opportunity here to consolidate that data. >> Yeah, because you like to borrow in an Einstein or apply an Einstein Bromide right. Keep as much data as you can, but no more. >> Correct. >> Okay. So you get to the point to the second step you're kind of a one to reduce costs, then what? You figure out what to get rid of, or actually get rid of it, what's next? >> Yes, that would be the next step. So figuring out what you need and what you don't need often times I've found that there's obsolete columns of data in your databases that you just don't need, or maybe it's been superseded by another, you've got tables that have been superseded by other tables in your database. So you got to understand what's being used and what's not and then from that, you can decide, "I'm going to leave this stuff behind, "or I'm going to archive this stuff "cause I might need it for data retention "or I'm just going to delete it, "I don't need it at all." >> Well, Lester, most organizations, if they've been around a while, and the so-called incumbents, they've got data all over the place, their data marts, data warehouses, there are all kinds of different systems and the data lives in silos. So, how do you kind of deal with that problem? Is that part of the journey? >> That's a great point Dave, because you're right that the data silos happen because this business unit is chartered with this task another business unit has this task and that's how you get those instantiations of the same data occurring in multiple places. So as part of your cloud migration journey, you really want to plan where there's an opportunity to consolidate your data, because that means there'll be less to manage, there'll be less data to secure, and it'll have a smaller footprint, which means reduced costs. >> So, people always talk about a single version of the truth, data quality is a huge issue. I've talked to data practitioners and they've indicated that the quality metrics are in the single digits and they're trying to get to 90% plus, but maybe you could address data quality. Where does that fit in on the journey? >> That's, a very important point. First of all, you don't want to bring your legacy issues with you. As the point I made earlier, if you've got data quality issues, this is a good time to find those and identify and remediate them. But that can be a laborious task. We've had customers that have tried to do this by hand and it's very, very time consuming, cause you imagine if you've got 200 tables, 50,000 columns, imagine, the manual labor involved in doing that. And you could probably accomplish it, but it'll take a lot of work. So the opportunity to use tools here and automate that process is really will help you find those outliers there's that bad data and correct it before you move to the cloud. >> And you're just talking about that automation it's the same thing with data catalog and that one of the earlier steps. Organizations would do this manually or they try to do it manually and that's a lot of reason for the failure. They just, it's like cleaning out your data like you just don't want to do it (laughs). Okay, so then what's next? I think we're plowing through your steps here. What what's next on the journey? >> The next one is, in a nutshell, preserve your data format. Don't boil the ocean here to use a cliche. You want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables on which they sit, the columns and the way they're named. So, some degree you are going to be doing a lift and shift, but it's an intelligent lift and shift using all the insights you've gathered by cataloging the data, looking for data quality issues, looking for duplicate columns, doing planning consolidation. You don't want to also rewrite your application. So, in that aspect, I think it's important to do a bit of lift and shift and preserve those data formats as they sit. >> Okay, so let me follow up on that. That sounds really important to me, because if you're doing a conversion and you're rewriting applications, that means that you're going to have to freeze the existing application, and then you going to be refueling the plane as you're in midair and a lot of times, especially with mission critical systems, you're never going to bring those together and that's a recipe for disaster, isn't it? >> Great analogy unless you're with the air force, you'll (mumbles) (laughs). Now, that's correct. It's you want to have bite-sized steps and that's why it's important to plan your journey, take these steps. You're using automation where you can to make that journey to the cloud much easier and more straightforward. >> All right, I like that. So we're taking a kind of a systems view and end to end view of the data pipeline, if you will. What's next? I think we're through. I think I've counted six. What's the lucky seven? >> Lucky seven, involve your business users. Really, when you think about it, your data is in silos. Part of this migration to the cloud is an opportunity to break down these silos, these silos that naturally occur as part of the business unit. You've got to break these cultural barriers that sometimes exist between business and say, so for example, I always advise, there's an opportunity here to consolidate your sensitive data, your PII, your personally identifiable information, and if three different business units have the same source of truth for that, there's was an opportunity to consolidate that into one as you migrate. That might be a little bit of tweaking to some of the apps that you have that are dependent on it, but in the long run, that's what you really want to do. You want to have a single source of truth, you want to ring fence that sensitive data, and you want all your business users talking together so that you're not reinventing the wheel. >> Well, the reason I think too that's so important is that you're now I would say you're creating a data driven culture. I know that's sort of a buzz word, but what it's true and what that means to me is that your users, your lines of business feel like they actually own the data rather than pointing fingers at the data group, the IT group, the data quality people, data engineers, saying, "Oh, I don't believe it." If the lines of business own the data, they're going to lean in, they're going to maybe bring their own data science resources to the table, and it's going to be a much more collaborative effort as opposed to a non-productive argument. >> Yeah. And that's where we want to get to. DataOps is key, and maybe that's a term that's still evolving. But really, you want the data to drive the business because that's where your insights are, that's where your value is. You want to break down the silos between not only the business units, as I mentioned, but also as you pointed out, the roles of the people that are working with it. A self service data culture is the right way to go with the right security controls, putting on my security hat of course in place so that if I'm a developer and I'm building a new application, I'd love to be able to go to the data catalog, "Oh, there's already a database that has the customer "what the customers have clicked on when shopping." I could use that. I don't have to rebuild that, I'll just use that as for my application. That's the kind of problems you want to be able to solve and that's where your cost reductions come in across the board. >> Yeah. I want to talk a little bit about the business context here. We always talk about data, it's the new source of competitive advantage, I think there's not a lot of debate about that, but it's hard. A lot of companies are struggling to get value out of their data because it's so difficult. All the things we've talked about, the silos, the data quality, et cetera. So, you mentioned the term data apps, data apps is all about streamlining, that data, pipelining, infusing automation and machine intelligence into that pipeline and then ultimately taking a systems view and compressing that time to insights so that you can drive monetization, whether it's cut costs, maybe it's new revenue, drive productivity, but it's that end to end cycle time reduction that successful practitioners talk about as having the biggest business impact. Are you seeing that? >> Absolutely, but it is a journey and it's a huge cultural change for some companies that are. I've worked in many companies that are ticket based IT-driven and just do even the marginalist of change or get insight, raise a ticket, wait a week and then out the other end will pop maybe a change that I needed and it'll take a while for us to get to a culture that truly has a self service data-driven nature where I'm the business owner, and I want to bring in a data scientist because we're losing. For example, a business might be losing to a competitor and they want to find what insights, why is the customer churn, for example, happening every Tuesday? What is it about Tuesday? This is where your data scientist comes in. The last thing you want is to raise a ticket, wait for the snapshot of the data, you want to enable that data scientist to come in, securely connect into the data, and do his analysis, and come back and give you those insights, which will give you that competitive advantage. >> Well, I love your point about churn, maybe it talks about the Andreessen quote that "Software's eating the world," and all companies are our software companies, and SaaS companies, and churn is the killer of SaaS companies. So very, very important point you're making. My last question for you before we summarize is the tech behind all of these. What makes Io-Tahoe unique in its ability to help automate that data pipeline? >> Well, we've done a lot of research, we have I think now maybe 11 pending patent applications, I think one has been approved to be issued (mumbles), but really, it's really about sitting down and doing the right kind of analysis and figuring out how we can optimize this journey. Some of these stuff isn't rocket science. You can read a schema and into an open source solution, but you can't necessarily find the hidden insights. So if I want to find my foreign key dependencies, which aren't always declared in the database, or I want to identify columns by their content, which because the columns might be labeled attribute one, attribute two, attribute three, or I want to find out how my data flows between the various tables in my database. That's the point at which you need to bring in automation, you need to bring in data science solutions, and there's even a degree of machine learning because for example, we might deduce that data is flowing from this table to this table and upon when you present that to the user with a 87% confidence, for example, and the user can go, or the administrator can go. Now, it really goes the other way, it was an invalid collusion and that's the machine learning cycle. So the next time we see that pattern again, in that environment we will be able to make a better recommendation because some things aren't black and white, they need that human intervention loop. >> All right, I just want to summarize with Lester Waters' playbook to moving to the cloud and I'll go through them. Hopefully, I took some notes, hopefully, I got them right. So step one, you want to do that data discovery audit, you want to be fact-based. Two is you want to remediate that data redundancy, and then three identify what you can get rid of. Oftentimes you don't get rid of stuff in IT, or maybe archive it to cheaper media. Four is consolidate those data silos, which is critical, breaking down those data barriers. And then, five is attack the quality issues before you do the migration. Six, which I thought was really intriguing was preserve that data format, you don't want to do the rewrite applications and do that conversion. It's okay to do a little bit of lifting and shifting >> This comes in after the task. >> Yeah, and then finally, and probably the most important is you got to have that relationship with the lines of business, your users, get them involved, begin that cultural shift. So I think great recipe Lester for safe cloud migration. I really appreciate your time. I'll give you the final word if you will bring us home. >> All right. Well, I think the journey to the cloud it's a tough one. You will save money, I have heard people say, you got to the cloud, it's too expensive, it's too this, too that, but really, there is an opportunity for savings. I'll tell you when I run data services as a PaaS service in the cloud, it's wonderful because I can scale up and scale down almost by virtually turning a knob. And so I'll have complete control and visibility of my costs. And so for me, that's very important. Io also, it gives me the opportunity to really ring fence my sensitive data, because let's face it, most organizations like being in a cheese grater when you talk about security, because there's so many ways in and out. So I find that by consolidating and bringing together the crown jewels, if you will. As a security practitioner, it's much more easy to control. But it's very important. You can't get there without some automation and automating this discovery and analysis process. >> Well, great advice. Lester, thanks so much. It's clear that the capex investments on data centers are generally not a good investment for most companies. Lester, really appreciate, Lester waters CTO of Io-Tahoe. Let's watch this short video and we'll come right back. You're watching The Cube, thank you. (upbeat music)

(upbeat music) >> Reporter: From around the globe, it's The Cube with digital coverage of enterprise data automation and event series brought to you by Io-Tahoe. >> Okay, we're back. Focusing on enterprise data automation, we're going to talk about the journey to the cloud. Remember, the hashtag is data automated. We're here with Lester Waters who's the CTO of Io-Tahoe, Lester, good to see you from across the pond on video, wish we were face to face, but it's great to have you on The Cube. >> Also I do, thank you for having me. >> Oh, you're very welcome. Hey, give us a little background on CTO, you got a deep expertise in a lot of different areas, but what do we need to know? >> Well, David, I started my career basically at Microsoft, where I started the Information Security Cryptography Group. They're the very first one that the company had and that led to a career in information security and of course, as you go along with the information security, data is the key element to be protected. So I always had my hands in data and that naturally progressed into a role with Io-Tahoe as their CTO. >> Guys, I have to invite you back, we'll talk crypto all day we'd love to do that but we're here talking about yeah, awesome, right? But we're here talking about the cloud and here we'll talk about the journey to the cloud and accelerate. Everybody's really interested obviously in cloud, even more interested now with the pandemic, but what's that all about? >> Well, moving to the cloud is quite an undertaking for most organizations. First of all, we've got as probably if you're a large enterprise, you probably have thousands of applications, you have hundreds and hundreds of database instances, and trying to shed some light on that, just to plan your move to the cloud is a real challenge. And some organizations try to tackle that manually. Really what Io-Tahoe is bringing is trying to tackle that in an automated version to help you with your journey to the cloud. >> Well, look at migrations are sometimes just an evil word to a lot of organizations, but at the same time, building up technical debt veneer after veneer and year, and year, and year is something that many companies are saying, "Okay, it's got to stop." So what's the prescription for that automation journey and simplifying that migration to the cloud? >> Well, I think the very first thing that's all about is data hygiene. You don't want to pick up your bad habits and take them to the cloud. You've got an opportunity here, so I see the journey to the cloud is an opportunity to really clean house, reorganize things, like moving out. You might move all your boxes, but you're kind of probably cherry pick what you're going to take with you and then you're going to organize it as you end up at your new destination. So from that, I get there's seven key principles that I like to operate by when I advise on the cloud migration. >> Okay. So, where do you start? >> Well, I think the first thing is understanding what you got, so discover and cataloging your data and your applications. If I don't know what I have, I can't move it, I can't improve it, I can't build up on it. And I have to understand there is dependency, so building that data catalog is the very first step. What do I got? >> Now, is that a metadata exercise? Sometimes there's more metadata than there is data. Is metadata part of that first step or? >> In deed, metadata is the first step so the metadata really describes the data you have. So, the metadata is going to tell me I have 2000 tables and maybe of those tables, there's an average of 25 columns each, and so that gives me a sketch if you will, of what I need to move. How big are the boxes I need to pack for my move to the cloud? >> Okay, and you're saying you can automate that data classification, categorization, discovery, correct using math machine intelligence, is that correct? >> Yeah, that's correct. So basically we go, and we will discover all of the schema, if you will, that's the metadata description of your tables and columns in your database in the data types. So we take, we will ingest that in, and we will build some insights around that. And we do that across a variety of platforms because everybody's organization has you've got a one yeah, an Oracle Database here, and you've got a Microsoft SQL Database here, you might have something else there that you need to bring site onto. And part of this journey is going to be about breaking down your data silos and understanding what you've got. >> Okay. So, we've done the audit, we know what we've got, what's next? Where do we go next? >> So the next thing is remediating that data. Where do I have duplicate data? Often times in an organization, data will get duplicated. So, somebody will take a snapshot of a data, and then ended up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer. And you can see where that will go when trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to understand where all your redundant data is. So when you go to the cloud, maybe you have an opportunity here to consolidate that data. >> Yeah, because you like to borrow in an Einstein or apply an Einstein Bromide right. Keep as much data as you can, but no more. >> Correct. >> Okay. So you get to the point to the second step you're kind of a one to reduce costs, then what? You figure out what to get rid of, or actually get rid of it, what's next? >> Yes, that would be the next step. So figuring out what you need and what you don't need often times I've found that there's obsolete columns of data in your databases that you just don't need, or maybe it's been superseded by another, you've got tables that have been superseded by other tables in your database. So you got to understand what's being used and what's not and then from that, you can decide, "I'm going to leave this stuff behind, "or I'm going to archive this stuff "cause I might need it for data retention "or I'm just going to delete it, "I don't need it at all." >> Well, Lester, most organizations, if they've been around a while, and the so-called incumbents, they've got data all over the place, their data marts, data warehouses, there are all kinds of different systems and the data lives in silos. So, how do you kind of deal with that problem? Is that part of the journey? >> That's a great point Dave, because you're right that the data silos happen because this business unit is chartered with this task another business unit has this task and that's how you get those instantiations of the same data occurring in multiple places. So as part of your cloud migration journey, you really want to plan where there's an opportunity to consolidate your data, because that means there'll be less to manage, there'll be less data to secure, and it'll have a smaller footprint, which means reduced costs. >> So, people always talk about a single version of the truth, data quality is a huge issue. I've talked to data practitioners and they've indicated that the quality metrics are in the single digits and they're trying to get to 90% plus, but maybe you could address data quality. Where does that fit in on the journey? >> That's, a very important point. First of all, you don't want to bring your legacy issues with you. As the point I made earlier, if you've got data quality issues, this is a good time to find those and identify and remediate them. But that can be a laborious task. We've had customers that have tried to do this by hand and it's very, very time consuming, cause you imagine if you've got 200 tables, 50,000 columns, imagine, the manual labor involved in doing that. And you could probably accomplish it, but it'll take a lot of work. So the opportunity to use tools here and automate that process is really will help you find those outliers there's that bad data and correct it before you move to the cloud. >> And you're just talking about that automation it's the same thing with data catalog and that one of the earlier steps. Organizations would do this manually or they try to do it manually and that's a lot of reason for the failure. They just, it's like cleaning out your data like you just don't want to do it (laughs). Okay, so then what's next? I think we're plowing through your steps here. What what's next on the journey? >> The next one is, in a nutshell, preserve your data format. Don't boil the ocean here to use a cliche. You want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables on which they sit, the columns and the way they're named. So, some degree you are going to be doing a lift and shift, but it's an intelligent lift and shift using all the insights you've gathered by cataloging the data, looking for data quality issues, looking for duplicate columns, doing planning consolidation. You don't want to also rewrite your application. So, in that aspect, I think it's important to do a bit of lift and shift and preserve those data formats as they sit. >> Okay, so let me follow up on that. That sounds really important to me, because if you're doing a conversion and you're rewriting applications, that means that you're going to have to freeze the existing application, and then you going to be refueling the plane as you're in midair and a lot of times, especially with mission critical systems, you're never going to bring those together and that's a recipe for disaster, isn't it? >> Great analogy unless you're with the air force, you'll (mumbles) (laughs). Now, that's correct. It's you want to have bite-sized steps and that's why it's important to plan your journey, take these steps. You're using automation where you can to make that journey to the cloud much easier and more straightforward. >> All right, I like that. So we're taking a kind of a systems view and end to end view of the data pipeline, if you will. What's next? I think we're through. I think I've counted six. What's the lucky seven? >> Lucky seven, involve your business users. Really, when you think about it, your data is in silos. Part of this migration to the cloud is an opportunity to break down these silos, these silos that naturally occur as part of the business unit. You've got to break these cultural barriers that sometimes exist between business and say, so for example, I always advise, there's an opportunity here to consolidate your sensitive data, your PII, your personally identifiable information, and if three different business units have the same source of truth for that, there's was an opportunity to consolidate that into one as you migrate. That might be a little bit of tweaking to some of the apps that you have that are dependent on it, but in the long run, that's what you really want to do. You want to have a single source of truth, you want to ring fence that sensitive data, and you want all your business users talking together so that you're not reinventing the wheel. >> Well, the reason I think too that's so important is that you're now I would say you're creating a data driven culture. I know that's sort of a buzz word, but what it's true and what that means to me is that your users, your lines of business feel like they actually own the data rather than pointing fingers at the data group, the IT group, the data quality people, data engineers, saying, "Oh, I don't believe it." If the lines of business own the data, they're going to lean in, they're going to maybe bring their own data science resources to the table, and it's going to be a much more collaborative effort as opposed to a non-productive argument. >> Yeah. And that's where we want to get to. Data apps is key, and maybe that's a term that's still evolving. But really, you want the data to drive the business because that's where your insights are, that's where your value is. You want to break down the silos between not only the business units, as I mentioned, but also as you pointed out, the roles of the people that are working with it. A self service data culture is the right way to go with the right security controls, putting on my security hat of course in place so that if I'm a developer and I'm building a new application, I'd love to be able to go to the data catalog, "Oh, there's already a database that has the customer "what the customers have clicked on when shopping." I could use that. I don't have to rebuild that, I'll just use that as for my application. That's the kind of problems you want to be able to solve and that's where your cost reductions come in across the board. >> Yeah. I want to talk a little bit about the business context here. We always talk about data, it's the new source of competitive advantage, I think there's not a lot of debate about that, but it's hard. A lot of companies are struggling to get value out of their data because it's so difficult. All the things we've talked about, the silos, the data quality, et cetera. So, you mentioned the term data apps, data apps is all about streamlining, that data, pipelining, infusing automation and machine intelligence into that pipeline and then ultimately taking a systems view and compressing that time to insights so that you can drive monetization, whether it's cut costs, maybe it's new revenue, drive productivity, but it's that end to end cycle time reduction that successful practitioners talk about as having the biggest business impact. Are you seeing that? >> Absolutely, but it is a journey and it's a huge cultural change for some companies that are. I've worked in many companies that are ticket based IT-driven and just do even the marginalist of change or get insight, raise a ticket, wait a week and then out the other end will pop maybe a change that I needed and it'll take a while for us to get to a culture that truly has a self service data-driven nature where I'm the business owner, and I want to bring in a data scientist because we're losing. For example, a business might be losing to a competitor and they want to find what insights, why is the customer churn, for example, happening every Tuesday? What is it about Tuesday? This is where your data scientist comes in. The last thing you want is to raise a ticket, wait for the snapshot of the data, you want to enable that data scientist to come in, securely connect into the data, and do his analysis, and come back and give you those insights, which will give you that competitive advantage. >> Well, I love your point about churn, maybe it talks about the Andreessen quote that "Software's eating the world," and all companies are our software companies, and SaaS companies, and churn is the killer of SaaS companies. So very, very important point you're making. My last question for you before we summarize is the tech behind all of these. What makes Io-Tahoe unique in its ability to help automate that data pipeline? >> Well, we've done a lot of research, we have I think now maybe 11 pending patent applications, I think one has been approved to be issued (mumbles), but really, it's really about sitting down and doing the right kind of analysis and figuring out how we can optimize this journey. Some of these stuff isn't rocket science. You can read a schema and into an open source solution, but you can't necessarily find the hidden insights. So if I want to find my foreign key dependencies, which aren't always declared in the database, or I want to identify columns by their content, which because the columns might be labeled attribute one, attribute two, attribute three, or I want to find out how my data flows between the various tables in my database. That's the point at which you need to bring in automation, you need to bring in data science solutions, and there's even a degree of machine learning because for example, we might deduce that data is flowing from this table to this table and upon when you present that to the user with a 87% confidence, for example, and the user can go, or the administrator can go. Now, it really goes the other way, it was an invalid collusion and that's the machine learning cycle. So the next time we see that pattern again, in that environment we will be able to make a better recommendation because some things aren't black and white, they need that human intervention loop. >> All right, I just want to summarize with Lester Waters' playbook to moving to the cloud and I'll go through them. Hopefully, I took some notes, hopefully, I got them right. So step one, you want to do that data discovery audit, you want to be fact-based. Two is you want to remediate that data redundancy, and then three identify what you can get rid of. Oftentimes you don't get rid of stuff in IT, or maybe archive it to cheaper media. Four is consolidate those data silos, which is critical, breaking down those data barriers. And then, five is attack the quality issues before you do the migration. Six, which I thought was really intriguing was preserve that data format, you don't want to do the rewrite applications and do that conversion. It's okay to do a little bit of lifting and shifting >> This comes in after the task. >> Yeah, and then finally, and probably the most important is you got to have that relationship with the lines of business, your users, get them involved, begin that cultural shift. So I think great recipe Lester for safe cloud migration. I really appreciate your time. I'll give you the final word if you will bring us home. >> All right. Well, I think the journey to the cloud it's a tough one. You will save money, I have heard people say, you got to the cloud, it's too expensive, it's too this, too that, but really, there is an opportunity for savings. I'll tell you when I run data services as a PaaS service in the cloud, it's wonderful because I can scale up and scale down almost by virtually turning a knob. And so I'll have complete control and visibility of my costs. And so for me, that's very important. Io also, it gives me the opportunity to really ring fence my sensitive data, because let's face it, most organizations like being in a cheese grater when you talk about security, because there's so many ways in and out. So I find that by consolidating and bringing together the crown jewels, if you will. As a security practitioner, it's much more easy to control. But it's very important. You can't get there without some automation and automating this discovery and analysis process. >> Well, great advice. Lester, thanks so much. It's clear that the capex investments on data centers are generally not a good investment for most companies. Lester, really appreciate, Lester waters CTO of Io-Tahoe. Let's watch this short video and we'll come right back. You're watching The Cube, thank you. (upbeat music)

>>LA Las Vegas. It's the cube covering AWS reinvent 2019, brought to you by Amazon web services and they don't care along with its ecosystem partners. >>Fine. Oh, welcome back here to Las Vegas. We are alive at AWS. Reinvent a lot with Justin Warren. I'm John Walls day one of a jam pack show. We had great keynotes this morning from Andy Jassy, uh, also representatives from Goldman Sachs and number of other enterprises on this stage right now we're gonna talk about data. It's all about data with IO Tahoe, a couple of the companies, representatives, CEO H J for horror. Jorge J. Thanks for being with us. Thank you Joan. And uh, Lester waters is the CSO at IO Tahoe. Leicester. Good afternoon to you. Thanks for being with us. Thank you for having us. CJ, you brought a football with you there. I see. So you've come prepared for a sport sport. I love it. All right. But if this is that your booth and your, you're showing here I assume and exhibiting and I know you've got a big offering we're going to talk about a little bit later on. First tell us about IO Tahoe a little bit to inform our viewers right now who might not be too familiar with the company. >>Sure. Well, our background was dealing with enterprise scale data issues that were really about the complexity, the amount of data and different types of data. So 2014 around when we're in stealth, kind of working on our technology, uh, the, a lot of the common technologies around them were Apache base. So Hadoop, um, large enterprises that were working with like a GE, Comcast had a cow help us come out of stealth in 2017. Uh, and grave, it's gave us a great story of solving petabyte scale data challenges, uh, using machine learning. So, uh, that manual overhead, that more and more as we look at, uh, AWS services, how do we drive the automation and get the value from data, uh, automation. >>It's gotta be the way forwards. All right, so let's, let's jump onto that then. Uh, on, on that notion, you've got this exponential growth in data, obviously working off the edge internet of things. Um, all these inputs, right? And we have so much more information at our disposal. Some of it's great, some of it's not. How do we know the difference, especially in this world where this exponential increase has happened. Lester, I mean, just tackle that for, from a, uh, from a company perspective and identifying, you know, first off, how do we ever figure out what do we have that's that valuable? Where do we get the value out of that, right? And then, um, how do we make sense of it? How do we put it into practice? >>Yeah. So I think not most enterprises have a problem with data sprawl. There's project startup, we get a block of data and then all of a sudden the new, a new project comes along, they take a copy of that data. There's another instance of it. Then there's another instance for another project. >>And suddenly these different data sources become authoritative and become production. So now I have three, four, or five different instances. Oh, and then there's the three or four that got canceled and they're still sitting around. And as an information security professional, my challenge is to know where all of those pieces of data are so that, so that I can govern it and make sure that the stuff I don't need is gotten rid of it deleted. Uh, so you know, using the IO Tahoe software, I'm able to catalog all of that. I'm able to garner insights into that data using the, the nine patent pending algorithms that we have, uh, to, to find that, uh, to do intelligent tagging, if you will. So, uh, from my perspective, I'm very interested in making sure that I'm adhering to compliance rules. So the really cool thing about the stuff is that we go and tag data, we look at it and we actually tie it to lines of regulations. So you could go CC CCPA. This bit of text here applies to this. And that's really helpful for me as an information security professional because I'm not necessarily versed on every line of regulation, but when I can go and look at it handily like that, it makes it easier for me to go, Oh, okay, that's great. I know how to treat that in terms of control. So that for, that's the important bit for me. So if you don't know where your data is, you can't control it. You can't monitor it. >>Governance. Yeah. The, the knowing where stuff is, I'm familiar with a framework that was developed at Telstra back in Australia called the five no's, which is about exactly that. Knowing where your data is, what is it, who has access to it? Cause I actually being able to cattle on the data then like knowing what it is that you have. This is a mammoth task. I mean that's, that's hard enough 12 years ago. But like today with the amount of data that's actually actively being created every single day, so how, how does your system help CSOs tackle this, this kind of issue and maybe less listed. You can, you can start off and then, then you can tell us a bit more of yourself. >>Yeah, I mean I'll start off on that. It's a, a place to kind of see the feedback from our enterprise customers is as that veracity and volume of data increases. The, the challenge is definitely there to keep on top of governing that. So continually discovering that new data created, how is it different? How's it adding to the existing data? Uh, using machine learning and the models that we create, whether it's anomaly detection or classifying the data based on certain features in the data that allows us to tag it, load that in our catalog. So I've discovered it now we've made it accessible. Now any BI developer data engineer can search for that data in a catalog and make something from it. So if there were 10 steps in that data mile, we definitely sold the first four or five to of bring that momentum to getting value from that data. So discovering it, catalog it, tagging the data to make it searchable, and then it's free to pick up for whatever use case is out there, whether it's migration, security, compliance, um, security is a big one for you. >>And I would also add too, for the data scientists, you know, knowing all the assets they have available to them in order to, to drive those business value insights that they're so important these days. For companies because you know, a lot of companies compete on very thin margins and, and, and having insights into their data and to the way customers can use their data really can make, make or break a company these days. So that's, that's critical. And as Aja pointed out, being able to automate that through, through data ops if you will, uh, and drive those insights automatically is great. Like for example, from an information security standpoint, I want to fingerprint my data and I want to feed it into a DLP system. And so that, you know, I can really sort of keep an eye out if this data is actually going out. And it really is my data versus a standard reject kind of matching, which isn't the best, uh, techniques. So >>yeah. So walk us through that in a bit more detail. So you mentioned tagging is essentially that a couple of times. So let's go into the details a little bit about what that, what that actually means for customers. My understanding is that you're looking for things like a social security number that could be sitting somewhere in this data. So finding out where are all these social security numbers that I may not be aware of and it could be being shared with someone who shouldn't have access to that, but it is there, is that what it is or are they, are there other kinds of data that you're able to tag that traditional purchase? >>Yeah. Was wait straight out of the box. You've got your um, PII or personally, um, identifiable information, that kind of day that is covered under the CCPA GDPR. So there are those standards, regulatory driven definitions that is social security number name, address would fall under. Um, beyond that. Then in a large enterprise, you've got a clever data scientists, data engineers you through the nature of their work can combine sets of data that could include work patterns, IDs, um, lots of activity. You bring that together and that suddenly becomes, uh, under that umbrella of sensitive. Um, so being able to tag and classify data under those regulatory policies, but then is what and what could be an operational risk to an organization, whether it's a bank, insurance, utility, health care in particular, if you work in all those verticals or yeah, across the way, agnostic to any vertical. >>Okay. All right. And the nature of being able to do that is having that machine learning set up a baseline, um, around what is sensitive and then honing that to what is particular to that organization. So, you know, lots of people will use ever sort of seen here at AWS S three, uh, Aurora, Postgres or, or my sequel Redshift. Um, and also different ways the underlying sources of that data, whether it's a CRM system, a IOT, all of those sources have got nuances that makes every enterprise data landscape just slightly different. So China make a rules based, one size fits all approach is, is going to be limiting, um, that the increase your manual overhead. So customers like GE, Comcast, um, that move way beyond throwing people at the problem, that's no longer possible. Uh, so being smart about how to approach this, classifying the data, using features in the data crane, that metadata as an asset just as an eight data warehouse would be, allows you to, to enable the rest of the organization. >>So, I mean, you've talked about, um, you know, deriving value and identifying value. Um, how does ultimately, once you catalog your tag, what does this mean to the bottom line of terms of ROI? How does AWS play into that? Um, you know, why am I as, as a, as a company, you know, what value am I getting out of, of your abilities with AWS and then having that kind of capability. >>Yeah. We, we did a great study with Forester. Um, they calculated the ROI and it's a mixture of things. It's that manual personnel overhead who are locked into that. Um, pretty unpleasant low productivity role of wrangling with data for want of a better words to make something of it. They'd much rather be creating the dashboards that the BI or the insights. Um, so moving, you know, dozens of people from the back office manual wrangling into what's going to make difference to the chief marketing officer and your CFO bring down the cost of served your customer by getting those operational insights is how they want to get to working with that data. So that automation to take out the manual overhead of the upfront task is an allowing that, that resource to be better deployed onto the more interesting productive work. So that's one part of the ROI. >>The other is with AWS. What we've found here engaging with the AWS ecosystem is just that speed of migration to AWS. We can take months out of that by cataloging what's on premise and saying, huh, I date aside. So our data engineering team want to create products on for their own customers using Sage maker using Redshift, Athena. Um, but what is the exact data that we need to push into the cloud to use those services? Is it the 20 petabytes that we've accumulated over the 20 last 20 years? That's probably not going to be the case. So tiering the on prem and cloud, um, base of that data is, is really helpful to a data officer and an information architect to set themselves up to accelerate that migration to AWS. So for people who've used this kind of system and they've run through the tagging and seen the power of the platform that you've got there. So what are some of the things that they're now able to do once they've got these highly qual, high quality tagged data set? >>So it's not just tagging too. We also do, uh, we do, we do, we do fuzzy, fuzzy magic so we can find relationships in the data or even relationships within the data in terms of duplicate. So, so for example, somebody, somebody got married and they're really the same, you know, so now there's their surname has changed. We can help companies find that, those bits of a matching. And I think we had one customer where we saved about, saved him about a hundred thousand a year in mailing costs because they were sending, you know, to, you know, misses, you know, right there anymore. Her name was. And having the, you know, being able to deduplicate that kind of data really helps with that helps people save money. >>Yep. And that's kind of the next phase in our journey is moving beyond the tag in the classification is uh, our roadmap working with AWS is very much machine learning driven. So our engineering team, uh, what they're excited about is what's the next model, what's the next problem we can solve with AI machine learning to throw at the large scale data problem. So we'll continually be curating and creating that metadata catalog asset. So allow that to be used as a resource to enable the rest of the, the data landscape. >>And I think what's interesting about our product is we really have multiple audiences for it. We've got the chief data officer who wants to make sure that we're completely compliant because it doesn't want that 4% potential fine. You know, so being able to evidence that they're having due diligence and their data management will go a long way towards if there is a breach because zero days do happen. But if you can evidence that you've really been, been, had a good discipline, then you won't get that fine or hopefully you won't get a big fine. And that the second audience is going to be information security professionals who want to secure that perimeter. The third is going to be the data architects who are trying to, to uh, to, you know, manage and, and create new solutions with that data. And the fourth of course is the data scientists trying to drive >>new business value. >>Alright, well before we, we, we, we um, let y'all take off, I want to know about, uh, an offering that you've launched this week, uh, apparently to great success and you're pretty excited about just your space alone here, your presence here. But tell us a little bit about that before you take off. >>Yeah. So we're here also sponsoring the jam lounge and everybody's welcome to sign up. It's, um, a number of our friends there to competitively take some challenges, come into the jam lounge, use our products, and kind of understand what it means to accelerate that journey onto AWS. What can I do if I show what what? Yeah, give me, give me an idea about the blog. You can take some chances to discover data and understand what data is there. Isn't there fighting relationships and intuitively through our UI, start exploring that and, and joining the dots. Um, uh, what, what is my day that knowing your data and then creating policies to drive that data into use. Cool. Good. And maybe pick up a football along the way so I know. Yeah. Thanks for being with us. Thank you for half the time. And, uh, again, the jam lounge, right? Right, right here at the SAS Bora AWS reinvent. We are alive. And you're watching this right here on the queue.

>> Live from Las Vegas. It's theCUBE. Covering VMworld 2018. Brought to you by VMware and its ecosystem partners. >> Welcome to theCUBE. We are live at VMworld 2018. Day one, VMware's 20th anniversary. I am Lisa Martin, very excited to be joined by Dave Vellante. Hey, Dave! >> Hey, Lisa, good to see you again. >> Good to see you, too. We are welcoming back to theCUBE, an alumni, Geoff Waters, the VP of Global Cloud Sales for Vmware, hi, Geoff. >> Hi, great to be here, guys. Last year, we talked about the buzz, VMware getting the buzz back. Boy, this is a sonic boom this year. >> Yeah, it's a lot of buzz. >> Superpower infused. And we've also got David Wigglesworth, the Chief Revenue Officer for OVH. David, welcome to theCUBE. >> Thank you very much, both of you, Dave and Lisa. >> So, I have to ask first, do you have the VMware tattoo that Pat Gelsinger sported this morning? >> I don't have VMware, but I do have OVHcloud. Okay, so, speaking of OVH, David give our viewers an overview of what you guys are doing and what momentum you have created with VMware. >> Yeah, you know, it's an exciting time for us, especially to be here, as a Global Diamond sponsor, right? This is our second year, as OVHcloud, to be here. Last year, when we came, it was right after the vCloud Air acquisition of the asset from Vmware. Which is where our partnership just continued to grow more and more. And, so, for the last year, what we've been doing is we've really been focusing on deploying our data centers here, as well as getting our products ready to go to market. I always joke that OVHcloud is, probably, the best-kept secret in the US because that, when we acquired vCloud Air's assets, is when we kind of launched in the US. But, as Geoff can tell you in a few minutes, we've been a partner with VMware for years, right? And it's been really exciting. >> Yeah, I wonder if you could talk about that, Geoff, a little bit, I mean, the signal on vCloud Air early on, you guys kept having to tune the radio station, so to speak. >> Yep. >> Yep. >> And then, boom, finally it hit the OVH acquisition and then AWS deal, of course, IBM and other cloud service providers. Talk about how that all came about, and the track that you're on now. >> Yes, so, I mean, we've been partnering with OVH for actually nine years, I went back and I researched it. >> Did you? >> Yeah, back in Europe. So, they've actually been a seven-time Service Provider of the Year award winner. So, our relationship with OVH is nothing new. And we've been working with them for years. The other thing is the breadth of the portfolio adoption, the full SDDC stack, so not just vSphere, NSX, vSAN, the entire stack. So, you know, OVH is right in the forefront of our overall cloud strategy, and it has been for years. >> Yeah, and as a global infrastructure provider, we have almost a million 500 thousand customers, in 138 different countries. We have 28 data centers, three here in North America. We've got the breadth to go to the market in a big way. So, it's exciting to be here. >> So, lay out the options that you have for OVH customers. What services can they get from you? What are the platforms? >> No, it's a great question. So, obviously, have a very purpose-felt solution built on VMware, right, with our Hybrid Private Cloud. It's all built on the SDDC stack. So vSphere, vSAN, NSX, everything that Geoff mentioned. We also offer a bare metal solution. And then we also have a public cloud offering that's built on our relationship that we have with OpenStack. So, we give our customers three different choices on what they want to go to the market with. >> So, what do you make of, what's the AWS-VMware partnership mean for OVH? How do you guys take advantage of that? >> Well, I mean, you know, look. I think Pat, in his keynote this morning, talked about that eight out of every 10 customers is using cloud today, multi-cloud strategy. The average large customer is using, what did he say, eight clouds? >> Yep. >> He said that they're forecasting that there would be 10 clouds by the end of 2019. I'd like to take one of those two spots, if you don't mind. So, no, we think there's huge opportunity. I mean, Amazon's built a business on, and has created kind of the standard. We think there's plenty of room to play in a very large market. >> Well, the services market has always been highly fragmented. >> Yep. >> And it's always been local in nature. Maybe not as to the degree and scale, but, so, you've got, what did you say, a million and a half customers? >> Globally. >> So what are they telling you about their cloud strategy? >> Well, what our customers are asking for is they're asking for agility. They're looking for low cost. You know, we announced a partner program earlier this morning, where we're launching that. And our partners are coming to us saying, David, give us choice, give us flexibility, and help us save a little bit of money. I mean, all of our partners are dealing with margin erosion, as well as everybody else in the industry. So, if we can come to market and actually help them go acquire a customer, and help them do that in a way that's cost-effective, they're very excited about that. >> So, what's the conversation that you're having with customers? You know, we were, a lot of press, a lot of news came out this morning. A lot of great announcements made by Pat and team on stage. Customers talking about migrating from on-prem to the cloud, from public back to on-premises, for security compliance reasons. What are some of the things that you guys are hearing from customers, when you're having those business-level discussions about being able to execute a successful cloud strategy? >> You want to hit that first, and I'll come over. >> Go ahead. Well, I can. So, what our customers are talking about is simplicity. One of the things that we're excited to work about, to work with VMware on, is that our customers, when they move their solution on-prem to our hybrid cloud, they use the exact same resources that they use on-prem today. They don't have to go hire new people. It's all of the exact same economics that they've built to an on-prem solution, is in their off-prem solution with OVHcloud. That's what makes this so unique, right? I mean, look, part of the vCloud Air acquisition, what are we doing? We're migrating VMware customers, right, that are using VMware technology, that we're setting on vCloud Air into OVH data centers, using VMware technology to do it. And, so, it's. >> Just to add to that, the beauty is reducing day two complexity onto the operations, day two operations. So, instead of customers having to build out all themselves and integrating it, OVH is doing that already. Right out of the gate, in a hosted managed environment. >> That's because it is a like to like homogeneous, and you guys have laid that vision out years ago. >> Yep, yep. >> We sure did. >> When Maritz was running the company. But how does that actually manifest itself? So, a customer says, look, I'm sick of the heavy lifting, I want to get to the cloud. Alright, so they come to you guys, what are the steps that they take to get there? >> Well, there's, you know, the first thing you'll do is you'll sit down with the client. And some clients know exactly what they want to do and how they want to do it. And some customers say, hey, I think I need to be in the cloud, please help me. So we'll have that conversation, right, first of all. Yeah, exactly, it's from A to Z, soup to nuts, whatever you want to say. So, you know, a lot times we'll sit down and we'll walk them through that journey to the cloud. And then, once we determine what applications or workloads we want to move, then we'll back into, okay, well here's the best way to move that, right, and whatever technologies we then decide to do. And if it's vSphere based, it makes it real simple, right? >> And you hit the nail on the head. It starts with the application. It's always about the application. What is the end goal? Right, once you identify that, you start looking at the use cases, a lot of it's app migration, a lot of data center evacuation. A lot of these data centers, as the different leases are coming up, they want to get out of there. Right, and that's the opportunity to then have the discussion. There's also tools that we got. HDX, which allows for bulk migration of workloads and it reduces, you know, the complexity of going to another cloud and another hypervisor from, like, years down to months and weeks. We've had some customers that have done that, migrated hundreds of VMs over a weekend. >> Oh sure. And we're in the process of that right now. >> So, go ahead, please. >> Oh, thank you, I was going to say, could you give us an example of a customer, whether they're in Europe, where you guys have really had a lot success, or here in the Americas, that have really demonstrated substantial business outcomes, revenue, et cetera, leveraging the joint service? >> Well, sure, I mean, you know, we've got customers both in the U.S. and in EMEA, but, you know, I'm thinking about a customer in particular that's based in the U.K.. That, they're a MNA company, right? And, at one time, they had 97 data centers that they were trying to manage. The complexity of that. And, so, they originally went to vCloud Air because they were like, help us with this complexity, we're built on VMware, but we've got to close these data centers, right, we need to go to more of an asset-like model, and we need to be able to manage it effectively with the staff that I have that's already overworked. So that's how we won them as a client with vCloud Air. What's exciting is, is when we come in and we start talking about what we're doing with OVH, and some of the new technology that we're building, on the VMware stack, right, plus the fact that we own our own network. I don't charge ingress and egress charges, right. A lot of the things that we do, We've got 33 points of presence, you know, globally. Then we start having a conversation and they're like, listen I already had a great solution in vCloud Air on VMware, now I've got that on steroids. I've got the benefit of both companies coming together for a solution for my client. >> So how do you get the data from point A to point B? Do you back up the Chevy truck and load it on? >> You can do it that way. >> You talked about your network. What's the kind of best practice? >> Yeah, so the best practice is to come in and understand the actual environment we're working with. What is the tolerance to take that workload up or down? But, if we use technology like HDX, I don't have to take that workload down at all. I'm able to basically, essentially, and don't let me get over my skis, VMware guy, but I am going to essentially do a Vmotion over my network, right, no cost to the customer, into my data center, and the customer can continue to use the app while that's happening. >> And the time that takes is a function of, obviously, the volume of the data, >> Sure, of course. The bandwidth. >> The number of VMs, the complexity of that. >> So you'll schedule that out over a period of, what, days, weeks, months? >> Exactly Years, even, I mean, maybe not years but, maybe I have a multi-year strategy, right? So that's how you're seeing people do it? It's sort of a planned approach. >> Weeks and months is sort of. >> I would say, typically. >> It's project based, yeah. >> So, within months, I can get an entire data center from my on-premises into your platform. Is that a fair statement? >> And if you ever wanted to bring it back, we can do that real easy too. >> You see that happening? >> We see customers moving workloads back and forth, it depends on seasonality. I mean, you take the retail industry, right? There's a lot of times where, during the retail industry, they'll send things to us, they'll flip it around, and, after the holidays are over, they'll bring there on-prem or what have you. >> And, more importantly, I think having network access back into the on-prem data center, with HDX, allows you to have a network connection. So it does need a talk back. The whole workload may not move back, but you need to have communications back into the network. And that's what HDX, their technology, allows. >> Right. >> So it allows me to leave whatever component of my workload I want to keep there. >> Yep, that's right. >> When I'm talking to each other. >> That's right. >> Okay, so for years at VMware, we heard this theme, any app, any workload, really anywhere in the world. >> Exactly. >> Now, you guys, right, you guys have an open source based public cloud. Vmware, obviously, like, hey, some of these cloud native apps, we'd like a piece of that action. You hear Pat talking about Kubernetes and containers. So what's that conversation like, between you guys, I mean you want some of that, right? Are you talking about Edge? Is that more integration? You guys got some work to do there to really compete in the that space? >> Well, I mean, it's your solution. But I'll start off of on the Edge. So, the announcement on Edge today, I don't know if you guys have heard it yet, but really exciting. We've actually announced a lot of different solutions around automation of the data center. I mean, this whole cloud operations is becoming sort of a major problem, as we have eight to 10 global service providers in most enterprises. So, reducing the complexity of that down is incredibly important. All the pieces that we're announcing, a VMware as a service, we're going to roll to our service providers in a managed service environment. So all these new technologies that we just announced, right, David and OVH are going to get access to that and have the same capability. >> That's right. >> I'll let you guys speak, specifically on your OpenStack. >> Well, I mean, listen, the beautiful thing about OpenStack is it's open, right, so, I mean, it doesn't really matter what cloud's out there, we can interface with it, right? So, that's the beauty of it, right? And it doesn't change at all the way that we go to market. It's just, really, we're giving the customers choice. What do you want? And it depends on the app, right? That's what's beautiful about it, is when we've sit down and meet with customers or partners, it's, like, what do you want to do, what workload would you want to move? And we've got choice for you. >> Yeah, I remember when we talked to Pat about this, years ago, when OpenStack was kind of the hot new toy, and he said, OpenStack, we like OpenStack, that's cool, we'll embrace it, no problem, and we're like, really? Yeah, I mean, that's kind of exactly what's happened. I mean, you're seeing the same thing with Kubernetes, and containers, and the like. But, again, you guys still got some work to do to really earn their business for those types of workloads, and I presume you're hard at work. >> We are. I don't know if you wanted to hit on some of the announcements that you. >> Yeah, I'd love to. >> Yeah, let's do that. >> So, the real thing I'm excited about is this morning we announced the announcement of our partner program at OVHcloud. It's an exciting day for us on that because, if you'll remember a few minutes ago, I was talking about all of the things we've been doing for the last year, right, getting our data centers ready, and, also, building out our product stack to be able to go to market, and migrating our customers. Well, the fourth thing we were doing, for the last nine to 12 months, is we've been meeting with partners. And I'm fortunate, from my years at EMC-Vmware, and my team, we have a lot of relationships out there. And so we were able to go meet with these partners and say, listen, here's what we're thinking, what do you guys think, what are you looking for, right? We've got all these big players out there, obviously we know all the names, but what differentiation could we bring to your business to help you go grow revenue? And, you know, they came back to us and they said, Wiggs, what we really want to be able to do is we want to be able to come in slowly, expand that as much as we can, make big commitments, make small commitments, we want the ability to be agile, we want to be able to, help us figure out a way that we can save money and worry about that. Help us resolve that issue of that margin erosion. That's a big thing that a lot of the channel's dealing with today. And, so, that's what we did. We came up with a program of four different levels, right? You can dip your toe in, and with a very minimum commitment, the higher commitment you make, not only do you get a better price, but you also get a ton of support on the backend. So, I actually come in and work with you on your messaging. I have sales teams that can actually go out and help them sell the solution, with us as the infrastructure layer in the underpinning, right, and, so far, it's been really good. >> So these are, don't hate me for saying this, these are sort of traditional box sellers, now trying to transform their business, right, and add more value, or their value added supply. Maybe they're SAP. >> Well, you've got manage service providers. You've got manage service providers. >> Okay, so hosting. >> You've got the SI's and the OS's, right? So, you know, some of these guys they either want a private label, right? Or white label your solution? Some guys just want to go to mark up their solution and they just need an asset like model, right? They're just exhausted with, you know, investing in infrastructure, right? So, they're like, "Listen >> And bodies. >> And body, you take that over and let us worry about that. >> You see, from VMware's perspective, that's exactly what we're seeing. We've got an ecosystem of 42 hundred global service providers. They build their own data centers, have a VMwares based hosted solution of some type. A lot of different flavors. They want to get out of the hardware space and out of the data center management space. This is why it's a great solution for OVH, they want to focus on, and, again, we call this asset light, they want to focus on high margin trusted value. Things that they're good at, where they can make a lot of money. >> Which is what? Like, I always see there's a consulting piece up front, security. >> It could be security specialist. >> Yep, security security services. >> Patching monitor, you know, automation, migration services, I mean, the exact discussion we just talked about, right? Customers need that journey. So OVH abstracts a way, the need to do hardware, and that allows them to go focus on the rich or higher margin services that they offer. >> And how are they making it sticky? Because, obviously, they want that, right? So what do you see there and how are you helping them? >> I think anytime you're adding a value added service, if you add that value it is sticky, right? >> Yeah. >> I mean, for an example, to help our relationship with Vmware, and just how strong it is, you know, FusionStormers was one of the partners that we had announced today, right? And they had a quote in there. And I was just sitting in Pat's keynote, next to our customer. You know, and I'm like, so, you know, I get this, it makes sense, you're looking for this, you know, infrastructure as a service play. He's like, David, what we're trying to do is help our customers that love the VMware stack, we're trying to help them to get to the Cloud, right? They don't care about the infrastructure, all they want is great service, right, and great support. And he said, that's my secret sauce, that I am able to offer that. And he goes, you guys handle the infrastructure. He said, it's perfect. >> Last question, David, for you. What are people going to be able to see and feel and touch at the OVH booth here at VMWorld? >> Oh, that's a great question. So, you're going to be able to go over, and you're going to be able to learn about some of our other announcements, with VMwares. Specifically, around what we're doing on the whole SCDC as a stack, right? In the VMware Cloud foundation, and the announcement we had on that this morning. Or, actually, I think that was Friday. You're actually going to be able to go over and they'll pull up and they'll show you some demos, and be able to see the technology live. I think they have a show every hour, and you go over there. And if you go over, you might win a Yeti mug. I think they're giving a Yeti mug to whoever pays the most attention. (Lisa and Dave ooh) So, go over there and learn about that. >> Can always use another Yeti, yeah, I love the Yeti. >> Yeah >> You can't have too many Yeti's. >> Does it come with caffeine? Because that, I'm all over it. >> No, well, we'll leave it clean, yes, maybe caffeine. >> Okay, awesome. David, Geoff, thanks so much for joining Dave and me this morning. >> Thank you so much, we really enjoyed it. >> You're watching theCUBE, live from VMWorld 2018. Day one, Lisa Martin for Dave Vellante, stick around, we'll be right back. (electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering The VMworld 2017, brought to you by VMware and its ecosystem partner. (techno music) >> Welcome back to theCUBE, we are live in Las Vegas, day three of VMworld 2017. We also have our voices, which is pretty good. I'm Lisa Martin, my co-host for this segment is Peter Burris. Peter and I are joined by a couple of guests. We have Geoff Waters, Vice President of Global Cloud Sales from VMware, first time on theCUBE? >> Yes, yes. >> Good to have you here. >> You know, I'm a big fan, first time here, I'm excited to be here, guys. Thanks for having me. >> Awesome. >> And we have Cube alumni, Roger Frey, VP of Alliances and Business Development from Skytap, welcome back. >> Thank you very much, great to be here. >> So day three, both of you still smiling, that's good, you know, we're kind of close to that happy hour time, almost. So, Geoff, you've been with VMworld 11 years. What is your takeaway from the announcements that VMworld made at the show this year, and what you're hearing from your partners and your customers. >> So, I mean clearly, there's a buzz, there's a buzz again, right? I saw some articles saying there was a lacking of a buzz, but it is here, it's strong. Clearly, Pat knocked it out of the park on the opening keynote, great to see all the logos, customers, and I think our overall cloud strategy. I think it's really come on, and I think it's resonating with customers and partners. >> So, tell us how VMware works with Skytap, Roger, I guess I'll throw that to you. What are you guys doing together, and what's the story there? >> So, Skytap, we're a public cloud provider, and we're focused on enterprise applications, and basically, what we do, is we enable customers to take their on-prem legacy applications, move them to the cloud, modernize them, do parallel processing, add value-added service to them in the cloud, and for us to do that, we rely on VMware technologies that underpin our solutions. >> And what are the key things, just really quickly, that you're hearing from your customers who are using VMware in terms of the value that they're getting from this collaboration? >> I think the biggest thing that we hear from customers is that they need to be more agile, they need to be faster, they need to get to market more quickly. With the framework of VMware and using VMware underneath us, people are comfortable with our solution, they understand how we're going to interact with their application stacks, and it provides for a better solution for our customers. >> Roger, the statement that we are a public cloud provider for traditional applications-- >> Roger: Yes. >> Is a huge statement, there's a lot of implications. Take us through a little bit, how does a customer think through this process, working with you, and then we'll get to the technology choices that make it easier or more difficult? >> Sure. >> So, how does this process work? >> That's a great question, so, when we talk to customers, we're really leading with a business discussion, talking about how are we going to make them more effective? How are we going to make them more agile? How are we going to help them drive revenue or reduce cost? And typically, what we'll see with a customer, is we'll do an inventory of their application environment, so with Skytap, basically, we'll look at your customer's entire application environment from the applications all the way down to the networking, and they'll say, you know what? "Based on our understanding, these are the applications we think that we can migrate to the Cloud, these other applications we think we have to keep on-prem. And we actually come in and say, you know what? These legacy applications that you have that may have been written five, 10, 15 years ago based on networking requirements or hardware requirements. We can actually take that, we can lift it, put it into the Skytap cloud, so we can bring a more complete vision to our customers on their cloud journey, so things that they thought were going to have to stay on-prem, they can actually now take to the cloud and enjoy those efficiencies. >> So identify, do some pattern recognition for us, so identify what are those attributes when you look at a couple of applications, or a set of workloads. What are some of the characteristics that determine whether it's ready for public cloud, or whether it should stay where it is? >> That's a great question, so most public clouds today, they're really geared for net new development, born in the cloud applications, mobile, things like that that we're all very familiar with. Again, if you're a bank, or an insurance company, or a hospital, you've written applications that maybe at one time were specifically dependent on physical MAC addresses, maybe you're putting multiple IP Addresses on physical NICs, maybe you're doing some interesting VPN or tunneling things that you had to develop five, 10, 15 years ago because that's what you had to do, then. A lot of our customers have applications that they don't want to touch, they're running, they're mission critical, and they're absolutely scared to break it, so with Skytap, basically, we can draw a circle around their complete application stack, down to the level two networking layer, take that, put it into a public cloud, and enable those developers to self-service, to make clones, to self-provision, to do whatever work that they need, and then, if they want, integrate that back into their on-prem production environment, or take it to a cloud-based production environment, as well. >> So it sounds as though, and correct me if I'm wrong, but it sounds as though, in many respects, the first thing you're looking at is, okay, you've got these workloads working really well, but you've done things at various hardware levels that could benefit from virtualization. >> Absolutely. >> So in many respects, the first thing you're doing is identifying what about these workloads can be virtualized, and that's part of the lift, which is where VMware comes in, have I got that right? >> Exactly, and that's why VMware is such a great partner of ours, because again, most enterprises today, virtually all enterprises today use VMware, so they're very comfortable with the solution, they understand how we're leveraging the technology, and we can focus on the business discussion, versus spending a lot of time in the technical discussions, trying to see if this is going to work or not. And that's really where we want to focus our energies. >> From a VMware perspective, there's hundreds of thousands of customers out there that have invested in, and everything from vSphere, NSX, vSAN, giving them the opportunity for another incredible cloud partner, you know, it was fantastic for us. We're seeing things like burstability, I mean, our hearts and thoughts are in Houston, with the big storm, but things like that will have a big impact on companies, on insurance companies, for instance, there's going to be a huge burst. So things like that, data center extensibility, consolidation, DR, these are the sort of things that they want to be able to tap into their VMware invest. >> Or emergency services is getting a whole bunch of work right now. One of the nice things is it sounds as though a lot of that infrastructure hasn't gone down despite the flooding and I've got to believe there's a whole bunch of IT guys that are doing a lot of God's work right now, to try to make sure that people stay alive. >> Yep. >> Yeah, absolutely. >> So talk to us about the innovation in terms of how VMware and Skytap are sort of working together. Did you see customers bringing you guys together, wanting more flexibility, wanting more advice and guidance on what should we move, what should we virtualize, what should we keep, or what should we move. How have your customers facilitated the innovations that you're achieving together? >> Yeah, maybe, I'll take a first shot at it, and then give it to Roger. So first of all, Skytap's a premier partner of ours in the VMware Cloud Provider Program. So, we're really excited about that, we just announced that today. >> Congratulations. >> Thank you. >> And on-- >> Like getting a scholarship on a football team. (laughing) >> Oh, I thought we said we weren't going to talk sports. >> Peter: Oh, that's right. >> No sports, right. >> No sports. >> Go Patriots. Anyway, you know what this does it allows us from a field level, it allows us to collaborate deeper from a partnership with our core sales team working with Skytap's. The second thing is around joint go-to-market, and messaging, it allows us to do a lot more in the market together. And then thirdly, around innovation, it's not just about the VMware install base, but it's also working with them on different cloud tools, leveraging that, integrated it in all the different technologies across the board. So, that's sort of a three-prong approach when you are one of our top premier partners. >> That's exactly right, it's a technology, it's a marketing, and it's a go-to-market and sales partnership that we have, so we're very happy with it. We're excited about being a premier partner. We've really boned up on our own technical capabilities within Skytap, to be more expert in VMware technologies, and now we want to be able to roll that out into the field with our joint customers. And getting back to your question, from a joint sales perspective, a joint go-to-market perspective, VMware is doing a great job of motivating its own sales force to become more cloud-ready and cloud-friendly, and it's a great fit for what we do. Their sales reps get compensated on Skytap, so it makes for a very good and smooth motion out in the field, which is where we're really all, it's where it matters. >> So Roger, I'm going to admit that I'm a little bit on edge about the word innovation. I've always believed that there's a difference between inventing something, which is an engineering act, and innovating, which is a social act, getting people to do things differently. And partnerships have always been a crucial feature of the computing industry in that innovation front, how you go to market, and especially, how you get businesses to adopt new things faster, more completely, so that they can be more successful. And as we go through this significant transformation, partners have to play another role, and that is they have to feed back to some core technology companies what they're hearing, what is working, what isn't working. How is that part of the relationship working? You as an advocate for customers as VMware evolves its platforms? >> That's a great question. Again, our customers, when we talk to them, they're really looking to get more agile, to be more innovative, to get to revenue sooner. And the things they they're asking from us as a public Cloud provider is self-service, is the ability to set up resources of their own without having to wait for central IT. They're looking to enable their team members across the globe. With Skytap and using VMware technology, we can clone images of our customers' environments, we can ship them globally. So, you may have a team in San Francisco, you may have a team in Seattle, you may have a team in Tokyo. With Skytap, you can send these images or these clones all over. They can be shared, they can be put back together, and a lot of that capability was feedback directly, that we see from our own customers. So, that's how we keep that feedback loop going, and that's the feedback that we give back to VMware. >> Yeah. If I could add to that, VMware is an incredible enterprise software company, we all know that. The last few years, we've been pivoting to develop a products and services for service providers. Part of being in our premier program as a VMware Cloud provider is you're getting access into some of those feedbacks and loops, and giving direct feedback and things, whether it's DR, us productizing products just for these guys for DR, or replication, like VCD. There's other multi-tenant, self-service portals that we're working in collaboration with our top partners. So, those are some of the other sorts of innovation that we're trying to also, beyond just the enterprise, in a service way where they can service the enterprise in the commercial space. >> Excellent, well guys, thank you so much for coming on theCUBE today, and talking with Peter and me about what's going on with VMware and Skytap together. We wish you continued success in your partnership. >> Great, thanks for being here, Lisa. >> Thank you so much. >> Appreciate it, thank you. >> Alright, for Geoff, and Roger, and my co-host Peter Burris, I am Lisa Martin, you've been watching theCUBE, we are again live at Vmworld 2017, continuing coverage day three. Stick around, we'll be right back. (techno music)

>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE Conversation. >> Hello. Welcome to today's session of the AWS Startup Showcase, The Next Big Thing in AI, Security & Life Sciences. Today featuring TetraScience for the life sciences track. I'm your host Natalie Erlich, and now we are joined by our special guests, Michelle Bradbury, VP of Product at TetraScience, as well as Mike Tarselli, the Chief Scientific Officer at TetraScience. We're going to talk about the R&D Data Cloud movement in life sciences, unlocking experimental data to accelerate discovery. Thank you both very much for joining us today. >> Thank you for having us. >> Yeah, thank you. Great to be here. >> Well, while traditionally slower to adopt cloud technology in R&D, global pharmas are now launching digital lab initiatives to improve time to market for therapeutics. Now, can you discuss some of the key challenges still facing big pharma in terms of digital transformation? >> Sure. I guess I'll start in. The big pharma sort of organization that we have today happens to work very well in its particular way, i.e., they have some architecture they've installed, usually on-premises. They are sort of tentatively sticking their foot into the cloud. They're learning how to move forward into that, and in order to process and automate their data streams. However, we would argue they haven't done enough fast enough and that they need to get there faster in order to deliver patient value and efficiencies to their businesses. >> Well, how specifically, now for Michelle, can R&D Data Cloud help big pharma in this digital transformation? >> So the big thing that large pharmas face is a couple different things. So the ecosystem within large pharma is a lot of diverse data types, a lot of diverse file types. So that's one thing that the data cloud handles very well to be able to parse through, harmonize, and bring together your data so that it can be leveraged for things like AI and machine learning at large-scale, which is sort of the other part where I think one of the large sort of challenges that pharma faces is sort of a proliferation of data. And what cloud offers, specifically, is a better way to store, more scalable storage, better ability to even tier your storage while still making it searchable, maintainable, and offer a lot of flexibility to the actual pharma companies. >> And what about security and compliance, or even governance? What are those implications? >> Sure. I'll jump into that one. So security and compliance, every large pharma is a regulated industry. Everyone watching this probably is aware of that. And so we therefore have to abide by the same tenets that they would. So 21 CFR Part 11 compliance, getting ready for GXP ready systems, And in fact, doing extra certifications around a SOC 2 Type 2, ISO 9001, really every single regulation that would allow our cloud solution to be quality, ready, inspectable, and really performant for what needs to be done for an eventual FDA submission. >> And can you also speak about some of the advances that we're seeing in machine learning and artificial intelligence, and how that will impact pharma, and what your role is in that at TetraScience? >> Sure. I'll pass this one to Michelle first. >> I was going to say I can take that one. So one of the things that we're seeing in terms of where AI and ML will go with large pharma is their ability to not only search and build models against the data that they have access to right now, which is very limited in the way they search, but the ability to go through the historical amount of data, the ability to leverage mass parallel compute on top of these giant data clusters, and what that means in terms of not only faster time to market for drugs, but also, I think, more accurate and precise testing coming in the future. So I think there's so much opportunity for this really data-rich vertical and industry to leverage in a lot of the modern tooling that it hasn't been able to leverage so far. >> And Mike, what would you say are the benefits that a fully automated lab could bring with increased fairness and data liquidity? >> Yeah, sure. Let's go five years into the future. I am a bench chemist, and I'm trying to get some results in, and it's amazing because I can look up everything the rest of my colleagues have ever done on this particular project with a single click of a button in a simple term set in natural language. I can then find and retrieve those results, easily visualize them in our platform or in any other platform I choose to use. And then I can inspect those, interrogate those, and say, "Actually, I'm going to be able to set up this automation cascade." I'll probably have it ready by the afternoon. All the data that's returned to me through this is going to be easily integratable, harmonized, and you're going to be able to find it, obviously. You're going to interoperate it with any system, so if I suddenly decide that I need to send a report over to another division in their preferred vis tool or data system of choice, great! I click three buttons, configure it. Boom. There goes that report to them. This should be a simple vision to achieve even faster than five years. And that data liquidity that enables you to sort of pass results around outside of your division, and outside of even your sort of company or division, to other who are able to see it should be fairly easy to achieve if all that data is ingested the right way. >> Well, I'd love to ask this next question to both of you. What is your defining contribution to the future of cloud scale? >> Mike, you want to go first? >> (chuckles) I would love to. So right now the pharmaceutical and life sciences companies, they aren't seeing data increase linearly. They're seeing it increase exponentially, right? We are living in the exabyte era, and really have on the internet since about 2016. It's only going to get bigger, and it's going to get bigger in a power law, right? So you're going to see, as sequencing comes on, as larger form microscopy comes on, and as more and more companies are taking on more and more data about each individual sample, retaining that data for longer, doing more analytics of that data, and also doing personalized medicine, right, more data about a specific patient, or animal, or cell line. You're just going to see this absolute data explosion. And because of that, the only thing you can really do to keep up with that is be in the cloud. On-prem, you will be buying disk drives and out of physical materials before you're going to outstrip the data. Michelle? >> Yeah. And, I think, to go along with not just the data storage scale, I think the compute scale. Mike is absolutely right. We're seeing personalized drugs. We're seeing customers that want to, within a matter of three, four hours, get to a personalized drug for patients. And that kind of scale on a compute basis not just requires a ton of data, but requires mass compute ability to be able to get it right, right? And so it really becomes this marriage of getting a huge amount of data, and getting the mass compute to be able to really leverage that per patient. And then the one thing that... Sort of enabling that ecosystem to come centrally together across such a diverse dataset is sort of that driving force. If you can get the data together but you can't compute it, if you can compute it but you can't get it together, it all needs to come together. Otherwise it just doesn't work. >> Yeah. Well, on your website you have all these great case studies, and I'd love it if you could outline some of your success stories for us, some specific, concrete examples. >> Sure. I'll take one first, and then they'll pass to Michelle. One really great concrete example is we were able to take data format processing for a biotech that had basically previously had instruments sitting off in a corner that they could not connect, were integratable for a high throughput screening cascade. We were able to bring them online. We were able to get the datasets interpretable, and get literally their processing time for these screens from the order of weeks to the order of minutes. So they could basically be doing probably a couple hundred more screens per year than they could have otherwise. Michelle? >> We have one customer that is in the process of automating their entire lab, even using robotics arms. So it's a huge mix of being able to ingest IoT data, send experiment data to them, understand sampling, getting the results back, and really automating that whole process, which when they even walked me through it, I was like, "Wow," and I'm like, "so cool." (chuckles) And there's a lot of... I think a lot of pharma companies want, and life science companies, want to move forward in innovation and do really creative and cool things for patients. But at the end of it, you sort of have to also realize it's like their core competency is focusing on drugs, and getting that to market, and making patients better. And we're just one part of that, really helping to enable that process and that ecosystem come to life, so it's really cool to watch. >> Right, right. And I mean, in this last year we've seen how critical the healthcare sector is to people all over the world. Now, looking forward, what do you anticipate some of the big innovations in the sector will be in the next five years, and where do you see TetraScience's role in that? >> So I think some of the larger innovations are... Mike mentioned one of them already. It's going to be sort of the personalized drugs the personalized health care. I think it is absolutely going to go to full lab automation to some degree, because who knows when the next pandemic will hit, right? And we're all going to have to go home, right? I think the days of trying to move around data manually and trying to work through that is just... If we don't plan for that to be a thing of the past, I think we're all going to do ourselves a disservice. So I think you'll see more automation. I think you'll see more personalization, and you'll see more things that leverage larger amounts of data. I think where we hope to sit is really at the ecosystem enablement part of that. We want to remain open. That's one of the cornerstones. We're not a single partner platform. We're not tied to any vendors. We really want to become that central aid and the ecosystem enabler for the labs. >> Yeah, to that point- >> And I'd also love to get your insight. >> Oh! Sorry. (chuckles) Thank you. To that point, we're really trying to unlock discovery, right? Many other horizontal cloud players will do something like you can upload files, or you can do some massive compute, but they won't have the vertical expertise that we do, right? They won't have the actual deep life sciences dedication. We have several PhDs, postdocs, et cetera, on staff who have done this for a living and can do this going forward. So you're going to see the realization of something that was really exciting in sort of 2005, 2006, that is fully automated experimentation. So get a robot to about an experiment, design it, have a human operator assist with putting together all the automation, and then run that over and over again cyclically until you get the result you want. I don't think that the compute was ready for that at the time. I don't think that the resources were up to snuff, but now you can do it, and you can do it with any tool, instrument, technique you want, because to Michelle's point, we're a vendor-agnostic partner networked platform. So you can actually assemble this learning automation cascade and have it run in the background while you go home and sleep. >> Yeah, and we often hear about automation, but tell us a little bit more specifically what is the harmonizing effect of TetraScience? I mean, that's not something that we usually hear, so what's unique about that? >> You want to take that, or you want me to go? >> You go, please. (chuckles) >> All right. So, really, it's about... It's about normalizing and harmonizing the data. And what does that... What that means is that whether you're a chromatography machine from, let's say Waters, or another vendor, ideally you'd like to be able to leverage all of your chromatography data and do research across all of it. Most of our customers have machinery that is of same sort from different customers, or sorry, from different vendors. And so it's really the ability to bring that data together, and sometimes it's even diverse instrumentation. So if I track a molecule, or a project, or a sample through one piece, one set of instrumentation, and I want to see how it got impacted in another set of instrumentation, or what the results were, I'm able to quickly and easily be able to sort of leverage that harmonized data and come to those results quickly. Mike, I'm sure you have a- >> May I offer a metaphor from something outside of science? Hopefully that's not off par for this, but let's say you had a parking lot, right, filled with different kinds of cars. And let's say you said at the beginning of that parking lot, "No, I'm sorry. We only have space right here for a Ford Fusion 2019 black with leather interior and this kind of tires." That would be crazy. You would never put that kind of limitation on who could park in a parking lot. So why do specific proprietary data systems put that kind of limitation on how data can be processed? We want to make it so that any car, any kind of data, can be processed and considered together in that same parking lot. >> Fascinating. Well, thank you both so much for your insights. Really appreciate it. Wonderful to hear about R&D Data Cloud movement in big pharma, and that of course is Michelle Bradbury, VP of Product at TetraScience, as well as Mike Tarselli, the Chief Scientific Officer at TetraScience. Thanks again very much for your insights. I'm your host for theCUBE, Natalie Erlich. Catch us again for the next session of the AWS Startup Session. Thank you. (smooth music)

>>from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>And we're back with the data automation. Siri's. In this episode, we're gonna learn more about what I owe Tahoe is doing in the field of adaptive data governance how it can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin, and I'm joined by a J. Bihar on the CEO of Iot Tahoe and Lester Waters, the CEO of Bio Tahoe. Gentlemen, it's great to have you on the program. >>Thank you. Lisa is good to be back. >>Great. Staley's >>likewise very socially distant. Of course as we are. Listen, we're gonna start with you. What's going on? And I am Tahoe. What's name? Well, >>I've been with Iot Tahoe for a little over the year, and one thing I've learned is every customer needs air just a bit different. So we've been working on our next major release of the I O. Tahoe product. But to really try to address these customer concerns because, you know, we wanna we wanna be flexible enough in order to come in and not just profile the date 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, uh, extend the product without building a new version of the product. We wanted to be able to have plausible modules. We also focused a lot on performance. That's very important with the bulk of data that we deal with that we're able to pass through that data in a single pass and do the analytics that are needed, whether it's, uh, 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 MAWR dimensions than we've ever done before. We're able to do data quality without doing a Nen initial rejects for, for example, just out of the box. So I think it's all of these things were coming together to form our next version of our product. We're really excited by it, >>So it's exciting a J from the CEO's level. What's going on? >>Wow, I think just building on that. But let's still just mentioned there. It's were growing pretty quickly with our partners. And today, here with Oracle are excited. Thio explain how that shaping up lots of collaboration already with Oracle in government, in insurance, on in banking and we're excited because we get to have an impact. It's real satisfying to see how we're able. Thio. Help businesses transform, Redefine what's possible with their data on bond. Having I recall there is a partner, uh, to lean in with is definitely helping. >>Excellent. We're gonna dig into that a little bit later. Let's 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 I t out to the business. And to do that, you've got to you've got Thio. 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 concerns itself. 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 could make their educated decisions on on what they need to do to achieve those business outcomes. >>Ah, >>lot of 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, the start date of alone must always be before the end date of alone 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 is the intersection of three circles, Really. It's the technical metadata coming together with policies and rules and coming together with the business ontology ease that are that are unique to that particular business. And this all of this. 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, Angie, help me understand this. Is this book enterprise companies are doing now? Are they not quite there yet. >>Well, you know, Lisa, I think every organization is is going at its pace. But, you know, markets are changing the economy and the speed at which, um, some of the changes in the economy happening is is compelling more businesses to look at being more digital in how they serve their own customers. Eh? So what we're seeing is a number of trends here from heads of data Chief Data Officers, CEO, stepping back from, ah, one size fits all approach because they've tried that before, and it it just hasn't worked. They've spent millions of dollars on I T programs China Dr Value from that data on Bennett. And they've ended up with large teams of manual processing around data to try and hardwire these policies to fit with the context and each line of business and on that hasn't worked. So the trends that we're seeing emerge really relate. Thio, How do I There's a chief data officer as a CEO. Inject more automation into a lot of these common tax. Andi, you know, we've been able toc that impact. I think the news here is you know, if you're trying to create a knowledge graph a data catalog or Ah, business glossary. And you're trying to do that manually will stop you. You don't have to do that manually anymore. I think best example I can give is Lester and I We we like Chinese food and Japanese food on. If you were sitting there with your chopsticks, you wouldn't eat the 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 to enjoy that meal before it gets cold. Andi, that's similar to how we're able to help the 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 eso Lester, how then does iota who go about doing this and enabling customers to achieve this? >>Let me, uh, let me show you a little story have here. So if you take a look at the challenges the most customers have, they're very similar, but every customers on a different data journey, so but it all starts with what data do I have? What questions or what shape is that data in? Uh, how is it structured? What's dependent on it? Upstream and downstream. Um, 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 oracle. Maybe they're doing some data governance changes on bits about enabling this. So if you look at these challenges and I'm gonna take you through a >>story here, E, >>I want to introduce Amanda. Man does not live like, uh, anyone in any large organization. She's looking around and she just sees stacks of data. I mean, different databases, the one she knows about, the one she doesn't know about what should know about various different kinds of databases. And a man is just tasking with understanding all of this so that they can embark on her data journey program. So So a man who goes through and she's great. I've got some handy tools. I can start looking at these databases and getting an idea of what we've got. Well, as she digs into the databases, she starts to see that not everything is as clear as she might have hoped it would be. You know, property names or column names, or have ambiguous names like Attribute one and attribute to or maybe date one and date to s Oh, man is starting to struggle, even though she's get tools to visualize. And look what look at these databases. She still No, she's got a long road ahead. And with 2000 databases in her large enterprise, yes, it's gonna be a long turkey but Amanda Smart. So she pulls out her trusty spreadsheet to track all of her findings on what she doesn't know about. She raises a ticket or maybe tries to track down the owner to find what the data means. And she's tracking all this information. Clearly, this doesn't scale that well for Amanda, you know? So maybe organization will get 10 Amanda's to sort of divide and conquer that work. But even that doesn't work that well because they're still ambiguities in the data with Iota ho. 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 U. S. Social Security number and attribute to looks like a I c D 10 medical code. And we do this by using anthologies and dictionaries and algorithms to help identify the underlying data and then tag it. Key Thio Doing, uh, 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 contain the same data. And by going through this exercise with a Tahoe, not only can we identify the data, but we also could 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. So there may be that may be indicative of a data quality issues, so we try to find those kind 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 to through Ah, observation. We can see that date 1 99% of the time is less than date, too. 1% of the time. It's not probably indicative of a data quality issue, but going a step further, we can also build a business rule that says Day one is less than date to. And so then when it pops up again, we can quickly identify and re mediate that problem. So these are the kinds of things that we could do with with iota going even a step further. You could take your your favorite data science solution production ISAT and incorporated into our next version a zey what we call a worker process to do your own bespoke analytics. >>We spoke analytics. Excellent, Lester. Thank you. So a J talk us through some examples of where you're putting this to use. And also what is some of the feedback from >>some customers? But I think it helped do this Bring it to life a little bit. Lisa is just to talk through a case study way. Pull something together. I know it's available for download, but in ah, well known telecommunications media company, they had a lot of the issues that lasted. You spoke about lots of teams of Amanda's, um, super bright data practitioners, um, on baby looking to to get more productivity out of their day on, deliver a good result for their own customers for cell phone subscribers, Um, on broadband users. So you know that some of the examples that we can see here is how we went about auto generating a lot of that understanding off that data within hours. So Amanda had her data catalog populated automatically. A business class three built up on it. Really? Then start to see. Okay, where do I want Thio? Apply some policies to the data to to set in place some controls where they want to adapt, how different lines of business, maybe tax versus customer operations have different access or permissions to that data on What we've been able to do there is, is to build up that picture to see how does data move across the entire organization across the state. Andi on monitor that overtime for improvement, so have taken it from being a reactive. Let's do something Thio. Fix something. Thio, 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. Um, on from there. Taking a proactive approach is a real smart use of of the talents in in that telco organization Onda folks that worked there with data. >>Okay, Jason, dig 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 our ally. How do customers measure are? Why? What are they seeing with iota host >>solution? Yeah, right now that the big ticket item is time to value on. And I think in data, a lot of the upfront investment cause quite expensive. They have been today with a lot of the larger vendors and technologies. So what a CEO and economic bio really needs to be certain of is how quickly can I get that are away. I think we've got something we can show. Just pull up a before and after, and it really comes down to hours, days and weeks. Um, where we've been able Thio have that impact on in this playbook that we pulled together before and after picture really shows. You know, those savings that committed a bit through providing data into some actionable form within hours and days to to drive agility, but at the same time being out and forced the controls to protect the use of that data who has access to it. So these are the number one thing I'd have to say. It's time on. We can see that on the the graphic that we've just pulled up here. >>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, >>Azaz, we see Mitt Emmanuel day. The days of manual effort are so I think you know 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 ontology, that air unique to your business. There is no algorithms, and you basically go across and you identify and tag tag that data that allows for the next steps toe happen. So now I can write business rules not in terms of columns named columns, but I could write him in terms of the tags 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 will thes air steps that increased that are, I should say, decrease that time to value that A. J talked about and then, lastly, a couple of machine learning because even with even with great automation and being able to profile all of your data and getting a good understanding, that brings you to a certain point. But there's still ambiguities in the data. So, for example, I might have to columns date one and date to. I may have even observed the date. One should be less than day two, but I don't really know what date one and date to our other than a date. So this is where it comes in, and I might ask the user said, >>Can >>you help me identify what date? One and date You are in this in this table. Turns out they're a start date and an end date for alone That gets remembered, cycled into the machine learning. So if I start to see this pattern of date one day to elsewhere, I'm going to say, Is it start dating and date? And these Bringing all these things together with this all this automation is really what's key to enabling this This'll data governance. Yeah, >>great. Thanks. Lester and a j wanna wrap things up with something that you mentioned in the beginning about what you guys were 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 i t for many years we've We've learned Thio trust articles technology that they're shifting now to ah, hybrid on Prohm Cloud Generation to platform, which is exciting. Andi on their existing customers and new customers moving to article on a journey. So? So Oracle came to us and said, you know, we can see how quickly you're able to help us change mindsets Ondas mindsets are locked in a way of thinking around operating models of I t. That there may be no agile and what siloed on day wanting to break free of that and adopt a more agile A p I at driven approach. A lot of the work that we're doing with our recall no is around, uh, accelerating what customers conduce with understanding their data and to build digital APS by identifying the the underlying data that has value. Onda at the time were able to do that in in in hours, days and weeks. Rather many months. Is opening up the eyes to Chief Data Officers CEO to say, Well, maybe we can do this whole digital transformation this year. Maybe we can bring that forward and and transform who we are as a company on that's driving innovation, which we're excited about it. I know Oracle, a keen Thio to drive through and >>helping businesses transformed digitally is so incredibly important in this time as we look Thio things changing in 2021 a. J. 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 our Oi. Thanks so much, guys. >>Thank you. Thanks again, Lisa. >>In a moment, we'll look a adaptive data governance in banking. This is the Cube, your global leader in high tech coverage. >>Innovation, impact influence. Welcome to the Cube. Disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader in high tech digital coverage. >>Our next segment here is an interesting panel you're gonna hear from three gentlemen about adaptive data. Governments want to talk a lot about that. Please welcome Yusuf Khan, the global director of data services for Iot Tahoe. We also have Santiago Castor, the chief data officer at the First Bank of Nigeria, and good John Vander Wal, Oracle's senior manager of digital transformation and industries. Gentlemen, it's great to have you joining us in this in this panel. Great >>to be >>tried for me. >>Alright, Santiago, we're going to start with you. Can you talk to the audience a little bit about the first Bank of Nigeria and its scale? This is beyond Nigeria. Talk to us about that. >>Yes, eso First Bank of Nigeria was created 125 years ago. One of the oldest ignored the old in Africa because of the history he grew everywhere in the region on beyond the region. I am calling based in London, where it's kind of the headquarters and it really promotes trade, finance, institutional banking, corporate banking, private banking around the world in particular, in relationship to Africa. We are also in Asia in in the Middle East. >>So, Sanjay, go talk to me about what adaptive data governance means to you. And how does it help the first Bank of Nigeria to be able to innovate faster with the data that you have? >>Yes, I like that concept off adaptive data governor, because it's kind of Ah, I would say an approach that can really happen today with the new technologies before it was much more difficult to implement. So just to give you a little bit of context, I I used to work in consulting for 16, 17 years before joining the president of Nigeria, and I saw many organizations trying to apply different type of approaches in the governance on by the beginning early days was really kind of a year. A Chicago A. A top down approach where data governance was seeing as implement a set of rules, policies and procedures. But really, from the top down on is important. It's important to have the battle off your sea level of your of your director. Whatever I saw, just the way it fails, you really need to have a complimentary approach. You can say bottom are actually as a CEO are really trying to decentralize the governor's. Really, Instead of imposing a framework that some people in the business don't understand or don't care about it, it really needs to come from them. So what I'm trying to say is that data basically support business objectives on what you need to do is every business area needs information on the detector decisions toe actually be able to be more efficient or create value etcetera. Now, depending on the business questions they have to solve, they will need certain data set. So they need actually to be ableto have data quality for their own. For us now, when they understand that they become the stores naturally on their own data sets. And that is where my bottom line is meeting my top down. You can guide them from the top, but they need themselves to be also empower and be actually, in a way flexible to adapt the different questions that they have in orderto be able to respond to the business needs. Now I cannot impose at the finish for everyone. I need them to adapt and to bring their answers toe their own business questions. That is adaptive data governor and all That is possible because we have. And I was saying at the very beginning just to finalize the point, we have new technologies that allow you to do this method data classifications, uh, in a very sophisticated way that you can actually create analitico of your metadata. You can understand your different data sources in order to be able to create those classifications like nationalities, a way of classifying your customers, your products, etcetera. >>So one of the things that you just said Santa kind of struck me to enable the users to be adaptive. They probably don't want to be logging in support ticket. So how do you support that sort of self service to meet the demand of the users so that they can be adaptive. >>More and more business users wants autonomy, and they want to basically be ableto grab the data and answer their own question. Now when you have, that is great, because then you have demand of businesses asking for data. They're asking for the insight. Eso How do you actually support that? I would say there is a changing culture that is happening more and more. I would say even the current pandemic has helped a lot into that because you have had, in a way, off course, technology is one of the biggest winners without technology. We couldn't have been working remotely without these technologies where people can actually looking from their homes and still have a market data marketplaces where they self serve their their information. But even beyond that data is a big winner. Data because the pandemic has shown us that crisis happened, that we cannot predict everything and that we are actually facing a new kind of situation out of our comfort zone, where we need to explore that we need to adapt and we need to be flexible. How do we do that with data. Every single company either saw the revenue going down or the revenue going very up For those companies that are very digital already. Now it changed the reality, so they needed to adapt. But for that they needed information. In order to think on innovate, try toe, create responses So that type of, uh, self service off data Haider for data in order to be able to understand what's happening when the prospect is changing is something that is becoming more, uh, the topic today because off the condemning because of the new abilities, the technologies that allow that and then you then are allowed to basically help your data. Citizens that call them in the organization people that no other business and can actually start playing and an answer their own questions. Eso so these technologies that gives more accessibility to the data that is some cataloging so they can understand where to go or what to find lineage and relationships. All this is is basically the new type of platforms and tools that allow you to create what are called a data marketplace. I think these new tools are really strong because they are now allowing for people that are not technology or I t people to be able to play with data because it comes in the digital world There. Used to a given example without your who You have a very interesting search functionality. Where if you want to find your data you want to sell, Sir, you go there in that search and you actually go on book for your data. Everybody knows how to search in Google, everybody's searching Internet. So this is part of the data culture, the digital culture. They know how to use those schools. Now, similarly, that data marketplace is, uh, in you can, for example, see which data sources they're mostly used >>and enabling that speed that we're all demanding today during these unprecedented times. Goodwin, I wanted to go to you as we talk about in the spirit of evolution, technology is changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? >>Yeah, Thank you. Um, well, Oracle Digital is a business unit that Oracle EMEA on. We focus on emerging countries as well as low and enterprises in the mid market, in more developed countries and four years ago. This started with the idea to engage digital with our customers. Fear Central helps across EMEA. That means engaging with video, having conference calls, having a wall, a green wall where we stand in front and engage with our customers. No one at that time could have foreseen how this is the situation today, and this helps us to engage with our customers in the way we were already doing and then about my team. The focus of my team is to have early stage conversations with our with our customers on digital transformation and innovation. And we also have a team off industry experts who engaged with our customers and share expertise across EMEA, and we inspire our customers. The outcome of these conversations for Oracle is a deep understanding of our customer needs, which is very important so we can help the customer and for the customer means that we will help them with our technology and our resource is to achieve their goals. >>It's all about outcomes, right? Good Ron. So in terms of automation, what are some of the things Oracle's doing there to help your clients leverage automation to improve agility? So that they can innovate faster, which in these interesting times it's demanded. >>Yeah, thank you. Well, traditionally, Oracle is known for their databases, which have bean innovated year over year. So here's the first lunch on the latest innovation is the autonomous database and autonomous data warehouse. For our customers, this means a reduction in operational costs by 90% with a multi medal converts, database and machine learning based automation for full life cycle management. Our databases self driving. This means we automate database provisioning, tuning and scaling. The database is self securing. This means ultimate data protection and security, and it's self repairing the automates failure, detection fail over and repair. And then the question is for our customers, What does it mean? It means they can focus on their on their business instead off maintaining their infrastructure and their operations. >>That's absolutely critical use if I want to go over to you now. Some of the things that we've talked about, just the massive progression and technology, the evolution of that. But we know that whether we're talking about beta management or digital transformation, a one size fits all approach doesn't work to address the challenges that the business has, um that the i t folks have, as you're looking through the industry with what Santiago told us about first Bank of Nigeria. What are some of the changes that you're seeing that I owe Tahoe seeing throughout the industry? >>Uh, well, Lisa, I think the first way I'd characterize it is to say, the traditional kind of top down approach to data where you have almost a data Policeman who tells you what you can and can't do, just doesn't work anymore. It's too slow. It's too resource intensive. Uh, data management data, governments, digital transformation itself. It has to be collaborative on. There has to be in a personalization to data users. Um, in the environment we find ourselves in. Now, it has to be about enabling self service as well. Um, a one size fits all model when it comes to those things around. Data doesn't work. As Santiago was saying, it needs to be adapted toe how the data is used. Andi, who is using it on in order to do this cos enterprises organizations really need to know their data. They need to understand what data they hold, where it is on what the sensitivity of it is they can then any more agile way apply appropriate controls on access so that people themselves are and groups within businesses are our job and could innovate. Otherwise, everything grinds to a halt, and you risk falling behind your competitors. >>Yeah, that one size fits all term just doesn't apply when you're talking about adaptive and agility. So we heard from Santiago about some of the impact that they're making with First Bank of Nigeria. Used to talk to us about some of the business outcomes that you're seeing other customers make leveraging automation that they could not do >>before it's it's automatically being able to classify terabytes, terabytes of data or even petabytes of data across different sources to find duplicates, which you can then re mediate on. Deletes now, with the capabilities that iota offers on the Oracle offers, you can do things not just where the five times or 10 times improvement, but it actually enables you to do projects for Stop that otherwise would fail or you would just not be able to dio I mean, uh, classifying multi terrible and multi petabytes states across different sources, formats very large volumes of data in many scenarios. You just can't do that manually. I mean, we've worked with government departments on the issues there is expect are the result of fragmented data. There's a lot of different sources. There's lot of different formats and without these newer technologies to address it with automation on machine learning, the project isn't durable. But now it is on that that could lead to a revolution in some of these businesses organizations >>to enable that revolution that there's got to be the right cultural mindset. And one of the when Santiago was talking about folks really kind of adapted that. The thing I always call that getting comfortably uncomfortable. But that's hard for organizations to. The technology is here to enable that. But well, you're talking with customers use. How do you help them build the trust in the confidence that the new technologies and a new approaches can deliver what they need? How do you help drive the kind of a tech in the culture? >>It's really good question is because it can be quite scary. I think the first thing we'd start with is to say, Look, the technology is here with businesses like I Tahoe. Unlike Oracle, it's already arrived. What you need to be comfortable doing is experimenting being agile around it, Andi trying new ways of doing things. Uh, if you don't wanna get less behind that Santiago on the team that fbn are a great example off embracing it, testing it on a small scale on, then scaling up a Toyota, we offer what we call a data health check, which can actually be done very quickly in a matter of a few weeks. So we'll work with a customer. Picky use case, install the application, uh, analyzed data. Drive out Cem Cem quick winds. So we worked in the last few weeks of a large entity energy supplier, and in about 20 days, we were able to give them an accurate understanding of their critical data. Elements apply. Helping apply data protection policies. Minimize copies of the data on work out what data they needed to delete to reduce their infrastructure. Spend eso. It's about experimenting on that small scale, being agile on, then scaling up in a kind of very modern way. >>Great advice. Uh, Santiago, I'd like to go back to Is we kind of look at again that that topic of culture and the need to get that mindset there to facilitate these rapid changes, I want to understand kind of last question for you about how you're doing that from a digital transformation perspective. We know everything is accelerating in 2020. So how are you building resilience into your data architecture and also driving that cultural change that can help everyone in this shift to remote working and a lot of the the digital challenges and changes that we're all going through? >>The new technologies allowed us to discover the dating anyway. Toe flawed and see very quickly Information toe. Have new models off over in the data on giving autonomy to our different data units. Now, from that autonomy, they can then compose an innovator own ways. So for me now, we're talking about resilience because in a way, autonomy and flexibility in a organization in a data structure with platform gives you resilience. The organizations and the business units that I have experienced in the pandemic are working well. Are those that actually because they're not physically present during more in the office, you need to give them their autonomy and let them actually engaged on their own side that do their own job and trust them in a way on as you give them, that they start innovating and they start having a really interesting ideas. So autonomy and flexibility. I think this is a key component off the new infrastructure. But even the new reality that on then it show us that, yes, we used to be very kind off structure, policies, procedures as very important. But now we learn flexibility and adaptability of the same side. Now, when you have that a key, other components of resiliency speed, because people want, you know, to access the data and access it fast and on the site fast, especially changes are changing so quickly nowadays that you need to be ableto do you know, interact. Reiterate with your information to answer your questions. Pretty, um, so technology that allows you toe be flexible iterating on in a very fast job way continue will allow you toe actually be resilient in that way, because you are flexible, you adapt your job and you continue answering questions as they come without having everything, setting a structure that is too hard. We also are a partner off Oracle and Oracle. Embodies is great. They have embedded within the transactional system many algorithms that are allowing us to calculate as the transactions happened. What happened there is that when our customers engaged with algorithms and again without your powers, well, the machine learning that is there for for speeding the automation of how you find your data allows you to create a new alliance with the machine. The machine is their toe, actually, in a way to your best friend to actually have more volume of data calculated faster. In a way, it's cover more variety. I mean, we couldn't hope without being connected to this algorithm on >>that engagement is absolutely critical. Santiago. Thank you for sharing that. I do wanna rap really quickly. Good On one last question for you, Santiago talked about Oracle. You've talked about a little bit. As we look at digital resilience, talk to us a little bit in the last minute about the evolution of Oracle. What you guys were doing there to help your customers get the resilience that they have toe have to be not just survive but thrive. >>Yeah. Oracle has a cloud offering for infrastructure, database, platform service and a complete solutions offered a South on Daz. As Santiago also mentioned, We are using AI across our entire portfolio and by this will help our customers to focus on their business innovation and capitalize on data by enabling new business models. Um, and Oracle has a global conference with our cloud regions. It's massively investing and innovating and expanding their clouds. And by offering clouds as public cloud in our data centers and also as private cloud with clouded customer, we can meet every sovereignty and security requirements. And in this way we help people to see data in new ways. We discover insights and unlock endless possibilities. And and maybe 11 of my takeaways is if I If I speak with customers, I always tell them you better start collecting your data. Now we enable this partners like Iota help us as well. If you collect your data now, you are ready for tomorrow. You can never collect your data backwards, So that is my take away for today. >>You can't collect your data backwards. Excellently, John. Gentlemen, thank you for sharing all of your insights. Very informative conversation in a moment, we'll address the question. Do you know your data? >>Are you interested in test driving the iota Ho platform kick Start the benefits of data automation for your business through the Iota Ho Data Health check program. Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iota ho. Look time with a data engineer to learn more and see Io Tahoe in action from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>In this next segment, we're gonna be talking to you about getting to know your data. And specifically you're gonna hear from two folks at Io Tahoe. We've got enterprise account execs to be to Davis here, as well as Enterprise Data engineer Patrick Simon. They're gonna be sharing insights and tips and tricks for how you could get to know your data and quickly on. We also want to encourage you to engage with the media and Patrick, use the chat feature to the right, send comments, questions or feedback so you can participate. All right, Patrick Savita, take it away. Alright. >>Thankfully saw great to be here as Lisa mentioned guys, I'm the enterprise account executive here in Ohio. Tahoe you Pat? >>Yeah. Hey, everyone so great to be here. I said my name is Patrick Samit. I'm the enterprise data engineer here in Ohio Tahoe. And we're so excited to be here and talk about this topic as one thing we're really trying to perpetuate is that data is everyone's business. >>So, guys, what patent I got? I've actually had multiple discussions with clients from different organizations with different roles. So we spoke with both your technical and your non technical audience. So while they were interested in different aspects of our platform, we found that what they had in common was they wanted to make data easy to understand and usable. So that comes back. The pats point off to being everybody's business because no matter your role, we're all dependent on data. So what Pan I wanted to do today was wanted to walk you guys through some of those client questions, slash pain points that we're hearing from different industries and different rules and demo how our platform here, like Tahoe, is used for automating Dozier related tasks. So with that said are you ready for the first one, Pat? >>Yeah, Let's do it. >>Great. So I'm gonna put my technical hat on for this one. So I'm a data practitioner. I just started my job. ABC Bank. I have, like, over 100 different data sources. So I have data kept in Data Lakes, legacy data, sources, even the cloud. So my issue is I don't know what those data sources hold. I don't know what data sensitive, and I don't even understand how that data is connected. So how can I saw who help? >>Yeah, I think that's a very common experience many are facing and definitely something I've encountered in my past. Typically, the first step is to catalog the data and then start mapping the relationships between your various data stores. Now, more often than not, this has tackled through numerous meetings and a combination of excel and something similar to video which are too great tools in their own part. But they're very difficult to maintain. Just due to the rate that we are creating data in the modern world. It starts to beg for an idea that can scale with your business needs. And this is where a platform like Io Tahoe becomes so appealing, you can see here visualization of the data relationships created by the I. O. Tahoe service. Now, what is fantastic about this is it's not only laid out in a very human and digestible format in the same action of creating this view, the data catalog was constructed. >>Um so is the data catalog automatically populated? Correct. Okay, so So what I'm using Iota hope at what I'm getting is this complete, unified automated platform without the added cost? Of course. >>Exactly. And that's at the heart of Iota Ho. A great feature with that data catalog is that Iota Ho will also profile your data as it creates the catalog, assigning some meaning to those pesky column underscore ones and custom variable underscore tents. They're always such a joy to deal with. Now, by leveraging this interface, we can start to answer the first part of your question and understand where the core relationships within our data exists. Uh, personally, I'm a big fan of this view, as it really just helps the i b naturally John to these focal points that coincide with these key columns following that train of thought, Let's examine the customer I D column that seems to be at the center of a lot of these relationships. We can see that it's a fairly important column as it's maintaining the relationship between at least three other tables. >>Now you >>notice all the connectors are in this blue color. This means that their system defined relationships. But I hope Tahoe goes that extra mile and actually creates thes orange colored connectors as well. These air ones that are machine learning algorithms have predicted to be relationships on. You can leverage to try and make new and powerful relationships within your data. >>Eso So this is really cool, and I can see how this could be leverage quickly now. What if I added new data sources or your multiple data sources and need toe identify what data sensitive can iota who detect that? >>Yeah, definitely. Within the hotel platform. There, already over 300 pre defined policies such as hip for C, C, P. A and the like one can choose which of these policies to run against their data along for flexibility and efficiency and running the policies that affect organization. >>Okay, so so 300 is an exceptional number. I'll give you that. But what about internal policies that apply to my organization? Is there any ability for me to write custom policies? >>Yeah, that's no issue. And it's something that clients leverage fairly often to utilize this function when simply has to write a rejects that our team has helped many deploy. After that, the custom policy is stored for future use to profile sensitive data. One then selects the data sources they're interested in and select the policies that meet your particular needs. The interface will automatically take your data according to the policies of detects, after which you can review the discoveries confirming or rejecting the tagging. All of these insights are easily exported through the interface. Someone can work these into the action items within your project management systems, and I think this lends to the collaboration as a team can work through the discovery simultaneously, and as each item is confirmed or rejected, they can see it ni instantaneously. All this translates to a confidence that with iota hope, you can be sure you're in compliance. >>So I'm glad you mentioned compliance because that's extremely important to my organization. So what you're saying when I use the eye a Tahoe automated platform, we'd be 90% more compliant that before were other than if you were going to be using a human. >>Yeah, definitely the collaboration and documentation that the Iot Tahoe interface lends itself to really help you build that confidence that your compliance is sound. >>So we're planning a migration. Andi, I have a set of reports I need to migrate. But what I need to know is, uh well, what what data sources? Those report those reports are dependent on. And what's feeding those tables? >>Yeah, it's a fantastic questions to be toe identifying critical data elements, and the interdependencies within the various databases could be a time consuming but vital process and the migration initiative. Luckily, Iota Ho does have an answer, and again, it's presented in a very visual format. >>Eso So what I'm looking at here is my entire day landscape. >>Yes, exactly. >>Let's say I add another data source. I can still see that unified 3 60 view. >>Yeah, One future that is particularly helpful is the ability to add data sources after the data lineage. Discovery has finished alone for the flexibility and scope necessary for any data migration project. If you only need need to select a few databases or your entirety, this service will provide the answers. You're looking for things. Visual representation of the connectivity makes the identification of critical data elements a simple matter. The connections air driven by both system defined flows as well as those predicted by our algorithms, the confidence of which, uh, can actually be customized to make sure that they're meeting the needs of the initiative that you have in place. This also provides tabular output in case you needed for your own internal documentation or for your action items, which we can see right here. Uh, in this interface, you can actually also confirm or deny the pair rejection the pair directions, allowing to make sure that the data is as accurate as possible. Does that help with your data lineage needs? >>Definitely. So So, Pat, My next big question here is So now I know a little bit about my data. How do I know I can trust >>it? So >>what I'm interested in knowing, really is is it in a fit state for me to use it? Is it accurate? Does it conform to the right format? >>Yeah, that's a great question. And I think that is a pain point felt across the board, be it by data practitioners or data consumers alike. Another service that I owe Tahoe provides is the ability to write custom data quality rules and understand how well the data pertains to these rules. This dashboard gives a unified view of the strength of these rules, and your dad is overall quality. >>Okay, so Pat s o on on the accuracy scores there. So if my marketing team needs to run, a campaign can read dependent those accuracy scores to know what what tables have quality data to use for our marketing campaign. >>Yeah, this view would allow you to understand your overall accuracy as well as dive into the minutia to see which data elements are of the highest quality. So for that marketing campaign, if you need everything in a strong form, you'll be able to see very quickly with these high level numbers. But if you're only dependent on a few columns to get that information out the door, you can find that within this view, eso >>you >>no longer have to rely on reports about reports, but instead just come to this one platform to help drive conversations between stakeholders and data practitioners. >>So I get now the value of IATA who brings by automatically capturing all those technical metadata from sources. But how do we match that with the business glossary? >>Yeah, within the same data quality service that we just reviewed, one can actually add business rules detailing the definitions and the business domains that these fall into. What's more is that the data quality rules were just looking at can then be tied into these definitions. Allowing insight into the strength of these business rules is this service that empowers stakeholders across the business to be involved with the data life cycle and take ownership over the rules that fall within their domain. >>Okay, >>so those custom rules can I apply that across data sources? >>Yeah, you could bring in as many data sources as you need, so long as you could tie them to that unified definition. >>Okay, great. Thanks so much bad. And we just want to quickly say to everyone working in data, we understand your pain, so please feel free to reach out to us. we are Website the chapel. Oh, Arlington. And let's get a conversation started on how iota Who can help you guys automate all those manual task to help save you time and money. Thank you. Thank >>you. Your Honor, >>if I could ask you one quick question, how do you advise customers? You just walk in this great example this banking example that you instantly to talk through. How do you advise customers get started? >>Yeah, I think the number one thing that customers could do to get started with our platform is to just run the tag discovery and build up that data catalog. It lends itself very quickly to the other needs you might have, such as thes quality rules. A swell is identifying those kind of tricky columns that might exist in your data. Those custom variable underscore tens I mentioned before >>last questions to be to anything to add to what Pat just described as a starting place. >>I'm no, I think actually passed something that pretty well, I mean, just just by automating all those manual task. I mean, it definitely can save your company a lot of time and money, so we we encourage you just reach out to us. Let's get that conversation >>started. Excellent. So, Pete and Pat, thank you so much. We hope you have learned a lot from these folks about how to get to know your data. Make sure that it's quality, something you can maximize the value of it. Thanks >>for watching. Thanks again, Lisa, for that very insightful and useful deep dive into the world of adaptive data governance with Iota Ho Oracle First Bank of Nigeria This is Dave a lot You won't wanna mess Iota, whose fifth episode in the data automation Siri's in that we'll talk to experts from Red Hat and Happiest Minds about their best practices for managing data across hybrid cloud Inter Cloud multi Cloud I T environment So market calendar for Wednesday, January 27th That's Episode five. You're watching the Cube Global Leader digital event technique

>> Hello, my name is Daniel Wichs, I'm a senior scientist at NTT research and a professor at Northeastern University. Today I want to tell you about incompressible encodings. This is a recent work from Crypto 2020 and it's a joint work with Tal Moran. So let me start with a question. How much space would it take to store all of Wikipedia? So it turns out that you can download Wikipedia for offline use and some reasonable version of it is about 50 gigabytes in size. So as you'd expect, it's a lot of data, it's quite large. But there's another way to store Wikipedia which is just to store the link www.wikipedia.org that only takes 17 bytes. And for all intents and purposes as long as you have a connection to the internet storing this link is as good as storing the Wikipedia data. You can access a Wikipedia with this link whenever you want. And the point I want to make is that when it comes to public data like Wikipedia, even though the data is huge, it's trivial to compress it down because it is public just by storing a small link to it. And the question for this talk is, can we come up with an incompressible representation of public data like Wikipedia? In other words can we take Wikipedia and represent it in some way such that this representation requires the full 50 gigabytes of storage store, even for someone who has the link to the underlying Wikipedia data and can get the underlying data for free. So let me actually tell you what this means in more detail. So this is the notion of incompressible encodings that we'll focus on in this work. So incompressible encoding consists of an encoding algorithm and a decoding algorithm, these are public algorithms. There's no secret key. Anybody can run these algorithms. The encoding algorithm takes some data m, let's say the Wikipedia data and encodes it in some probabilistic randomized way to derive a codeword c. And the codeword c, you can think of it as just an alternate representation of the Wikipedia data. Anybody can come and decode the codeword to recover the underlying data m. And the correctness property we want here is that no matter what data you start with, if you encode the data m and then decode it, you get back the original data m. This should hold with probably one over the randomness of the encoding procedure. Now for security, we want to consider an adversary that knows the underlying data m, let's say has a link to Wikipedia and can access the Wikipedia data for free does not pay for storing it. The goal of the adversary is to compress this codeword that we created this new randomized representation of the Wikipedia data. So the adversary consists of two procedures a compression procedure and a decompression procedure. The compression procedure takes its input the codeword c and output some smaller compressed value w and the decompression procedure takes w and its goal is to recover the codeword c. And a security property says that no efficient adversary should be able to succeed in this game with better than negligible property. So there are two parameters of interest in this problem. One is the codeword size, which we'll denote by alpha, and ideally we want the codeword size alpha to be as close as possible to the original data size. In other words we don't want the encoding to add too much overhead to the data. The second parameter is the incompressibility parameter beta and that tells us how much space, how much storage and adversary needs to use in order to store the codeword. And ideally, we want this beta to be as close as possible to the codeword size alpha, which should also be as close as possible to the original data size. So I want to mention that there is a trivial construction of incompressible encodings that achieves very poor parameters. So the trivial construction is just take the data m and add some randomness, concatenate some randomness to it and store the original data m plus the concatenated randomness as the codeword. And now even an adversary that knows the underlying data m cannot compress the randomness. So the incompressibility, so we ensure that this construction is incompressible with incompressibility parameter beta that just corresponds to the size of this randomness we added. So essentially the adversary cannot compress the red part of the codeword. So this gets us a scheme where alpha the size of the codeword, is the original data size m plus the incompressible parameter beta. And it turns out that you cannot do better than this information theoretically. So this is not what we want for this we want to focus on what I will call good incompressible encodings. So here, the codeword size should be as close as possible to the data size, should be just one plus little o one of the data size. And the incompressibility should be as essential as large as the entire codeword the adversary cannot compress the codeword almost at all, the incompressible parameter beta is one minus little o one of the data size or the codeword size. And in essence, what this means is that we're somehow want to take the randomness of the encoding procedure and spread it around in some clever way throughout the codeword in such a way that's impossible for the adversary to separate out the randomness and the data, and only store the randomness and rely on the fact that it can get the data for free. We want to make sure it's impossible that adversary accesses essentially this entire code word which contains both the randomness and data and some carefully intertwined way and cannot compress it down using the fact that it knows the data parts. So this notion of incompressible encodings was defined actually in a prior work of Damgard-Ganesh and Orlandi from crypto 2019. They defined a variant of this notion, they had a different name for it. As a tool or a building block for a more complex cryptographic primitive that they called Proofs of Replicated Storage. And I'm not going to talk about what these are. But in this context of constructing these Proofs of Replicated Storage, they also constructed incompressible encodings albeit with some major caveats. So in particular, their construction relied on the random Oracle models, the heuristic construction and it was not known whether you could do this in the standard model, the encoding and decoding time of the construction was quadratic in the data size. And in particular, here we want to apply this, we want to use these types of incompressible encodings on fairly large data like Wikipedia data, 50 gigabytes in size. So quadratic runtime on such huge data is really impractical. And lastly the proof of security for their construction was flawed or someone incompleted, didn't consider general adversaries. And the slope was actually also noticed by concurrent work of Garg-Lu and Waters. And they managed to give a fixed proof for this construction but this required actually quite a lot of effort. It was a highly non-trivial and subtle proof to proof the original construction of Damgard-Ganesh and Orlandi secure. So in our work, we give a new construction of these types of incompressible encodings, our construction already achieved some form of security in the Common Reference String Model come Random String Model without the use of Random Oracles. We have a linear encoding time, linear in the data size. So we get rid of the quadratic and we have a fairly simple proof of security. In fact, I'm hoping to show you a slightly simplified form of it and the stock. We also give some lower bounds and negative results showing that our construction is optimal in some aspects and lastly we give a new application of this notion of incompressible encodings to something called big-key cryptography. And so I want to tell you about this application, hopefully it'll give you some intuition about why incompressible encodings are interesting and useful, and also some intuition about what their real goal is or what it is that they're trying to achieve. So, the application of big-key cryptography is concerned with the problem of system compromise. So, a computer system can become compromised either because the user downloads a malware or remote attacker manages to hack into it. And when this happens, the remote attacker gains control over the system and any cryptographic keys that are stored on the system can easily be exfiltrated or just downloaded out of the system by the attacker and therefore, any security that these cryptographic keys were meant to provide is going to be completely lost. And the idea of big-key cryptography is to mitigate against such attacks by making the secret keys intentionally huge on the order of many gigabytes to even terabytes. And the idea is that by having a very large secret key it would make it harder to exfiltrate such a secret key. Either because the adversary's bandwidth to the compromised system is just not large enough to exfiltrate such a large key or because it might not be cost-effective to have to download so much data of compromised system and store so much data to be able to use the key in the future, especially if the attacker wants to do this on some mass scale or because the system might have some other mechanisms let's say firewall that would detect such large amounts of leakage out of the compromised system and block it in some way. So there's been a lot of work on this idea building big-key crypto systems. So crypto systems where the secret key can be set arbitrarily huge and these crypto systems should testify two goals. So one is security, security should hold even if a large amount of data about the secret key is out, as long as it's not the entire secret key. So when you have an attacker download let's say 90% of the data of the secret key, the security of the system should be preserved. And the second property is that even though the secret key of the system can be huge, many gigabytes or terabytes, we still want the crypto system to remain efficient even though the secret is huge. And particularly this means that the crypto system can even read the entire secret key during each cryptographic operation because that would already be too inefficient. So it can only read some small number of bits of the secret key during each operation, then it performs. And so there's been a lot of work constructing these types of crypto systems but one common problem for all these works is that they require the user to waste a lot of their storage the storage on their computer in storing this huge secret key which is useless for any other purpose, other than providing security. And users might not want to do this. So that's the problem that we address here. And the new idea in our work is let's make the secret key useful instead of just having a secret key with some useless, random data that the cryptographic scheme picks, let's have a secret key that stores let's say the Wikipedia data at which a user might want to store in their system anyway or the user's movie collection or music collection et cetera and the data that the user would want to store on their system. Anyway, we want to convert it. We want to use that as the secret key. Now we think about this for a few seconds. Well, is it a good idea to use Wikipedia as a secret key? No, that sounds like a terrible idea. Wikipedia is not secret, it's public, it's online, Anyone can access it whenever they want. So it's not what we're suggesting. We're suggesting to use an incompressible encoding of Wikipedia as a secret key. Now, even though Wikipedia is public the incompressible encoding is randomized. And therefore the accuracy does not know the value of this incompressible encoding. Moreover, because it's incompressible in order for the adversary to steal, to exfiltrate the entire secret key, it would have to download a very large amount of data out of the compromised system. So there's some hope that this could provide security and we show how to build public encryption schemes and the setting that make use of a secret key which is an incompressible coding of some useful data like Wikipedia. So the secret key is an incompressible encoding of useful data and security ensures that the adversary will need to exfiltrate almost entire key to break the security of this critical system. So in the last few minutes, let me give you a very brief overview of our construction of incompressible encodings. And for this part, we're going to pretend we have something a real beautiful cryptographic object called Lossy Trapdoor Permutations. It turns out we don't quite have an object that's this beautiful and in the full construction, we relax this notion somewhat in order to be able to get our full construction. So Lossy Trapdoor Permutation is a function f we just key by some public key pk and it maps end bits to end bits. And we can sample the public key in one of two indistinguishable modes. In injective mode, this function of fPK is a permutation, and there's in fact, a trapdoor that allows us to invert it efficiently. And in the Lossy mode, if we sample the public in Lossy mode, then if we take some value, random value x and give you fpk of x, then this loses a lot of information about x. And in particular, the image size of the function is very small, much smaller than two to the n and so fpk of x does not contain all the information about x. Okay, so using this type of Lossy Trapdoor Permutation, here's the encoding of a message m using long random CRS come random string. So the encoding just consists of sampling the public key of this Lossy Trapdoor Permutation in injected mode, along with the trapdoor. And the encoding is just going to take the message m, x over it with a common reference string, come random string and invert the trapdoor permutation on this value. And then Coding will just be the public key and the inverse x. So this is something anybody can decode by just taking fpk of x, x over it with the CRS. And that will recover the original message. Now, to add the security, we're going to in the proof, we're going to switch to choosing the value x uniformly at random. So the x component of the codeword is going to be chosen uniformly random and we're going to set the CRS to be fpk of x, x over the message. And if you look at it for a second this distribution is exactly equivalent. It's just a different way of sampling the exact same distribution. And in particular, the relation between the CRS and X is preserved. Now in the second step, we're going to switch the public key to Lossy mode. And now when we do this, then the Codeword part, sorry then the CRS fpk of x, x over m only leaks some small amount of information about the random value x. In other words, even if that resists these, the CRS then the value x and the codeword has a lot of entropy. And because it has a lot of entropy it's incompressible. So what we did here is that we actually start to show that the code word and the CRS are indistinguishable from a different way of sampling them where we placed information about the message and the CRS and the codeword actually is truly random, has a lot of real entropy. And therefore even given the CRS the Codeword is incompressible that's the main idea behind the proof. I just want to make two remarks, our full constructions rely on a relaxed notion of Lossy Trapdoor Permutations which we're able to construct from either the decisional residuoisity or the learning with errors assumption. So in particular, we don't actually know how to construct trapdoor permutations from LWE from any postquantum assumption but the relaxed notion that we need for our actual construction, we can achieve from post quantum assumptions that get post quantum security. I want to mention two caveats of the construction. So one is that in order to make this work, the CRS needs to be long essentially as long as the message size. And also this construction achieves a weak form of selective security where the adversary decides to choose the message before seeing the CRS. And we show that both of these caveats are inherent. We show this by black-box separation and one can overcome them only in the random oracle model. Unless I want to just end with an interesting open question. I think one of the most interesting open questions in this area all of the constructions of incompressible encodings from our work and prior work required the use of some public key crypto assumptions some sort of trapdoor permutations or trapdoor functions. And one of the interesting open question is can you construct and incompressible encodings without relying on public key crypto, using one way functions or just the random oracle model. We conjecture this is not possible, but we don't know. So I want to end with that open questions and thank you very much for listening.

>>from around the globe. It's the Cube with digital coverage of data automated and event. Siri's Brought to You by Iot Tahoe Welcome, everyone to the second episode in our data automated Siri's made possible with support from Iot Tahoe. Today we're gonna drill into the data lifecycle, meaning the sequence of stages that data travels through from creation to consumption to archive. The problem, as we discussed in our last episode, is that data pipelines, they're complicated, They're cumbersome, that disjointed, and they involve highly manual processes. Ah, smart data lifecycle uses automation and metadata to approve agility, performance, data quality and governance and ultimately reduce costs and time to outcomes. Now, in today's session will define the data lifecycle in detail and provide perspectives on what makes a data lifecycle smart and importantly, how to build smarts into your processes. In a moment, we'll be back with Adam Worthington from ethos to kick things off, and then we'll go into an export power panel to dig into the tech behind smart data life cycles, and it will hop into the crowdchat and give you a chance to ask questions. So stay right there. You're watching the cube innovation impact influence. Welcome >>to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. >>High tech digital coverage. Okay, we're back with Adam Worthington. Adam, good to see you. How are things across the pond? >>Thank you, I'm sure. >>Okay, so let's let's set it up. Tell us about yourself. What? Your role is a CTO and >>automatically. As you said, we found a way to have a pretty in company ourselves that we're in our third year on. Do we specialize in emerging disruptive technologies within the infrastructure? That's the kind of cloud space on my phone is the technical lead. So I kind of my job to be an expert in all of the technologies that we work with, which can be a bit of a challenge if you have a huge for phone is one of the reasons, like deliberately focusing on on also kind of pieces a successful validation and evaluation of new technologies. >>So you guys really technology experts, data experts and probably also expert in process and delivering customer outcomes. Right? >>That's a great word there, Dave Outcomes. That's a lot of what I like to speak to customers about. >>Let's talk about smart data, you know, when you when you throw in terms like this is it kind of can feel buzz, wordy. But what are the critical aspects of so called smart data? >>Help to step back a little bit, seen a little bit more in terms of kind of where I can see the types of problems I saw. I'm really an infrastructure solution architect trace on and what I kind of benefit we organically. But over time my personal framework, I focused on three core design principal simplicity, flexibility, inefficient, whatever it was designing. And obviously they need different things, depending on what the technology area is working with. But that's a pretty good. So they're the kind of areas that a smart approach to data will directly address. Reducing silos that comes from simplifying, so moving away from conflict of infrastructure, reducing the amount of copies of data that we have across the infrastructure and reducing the amount of application environments that need different areas so smarter get with data in my eyes anyway, the further we moved away from this. >>But how does it work? I mean, how do you know what's what's involved in injecting smarts into your data lifecycle? >>I think one of my I actually did not ready, but generally one of my favorite quotes from the French lost a mathematician, Blaise Pascal. He said, If I get this right, I have written a short letter, but I didn't have time. But Israel, I love that quite for lots of reasons >>why >>direct application in terms of what we're talking about, it is actually really complicated. These developers technology capabilities to make things simple, more directly meet the needs of the business. So you provide self service capabilities that they just need to stop driving. I mean, making data on infrastructure makes the business users using >>your job. Correct me. If I'm wrong is to kind of put that all together in a solution and then help the customer realize that we talked about earlier that business out. >>Yeah, enough if they said in understanding both sides so that it keeps us on our ability to deliver on exactly what you just said is big experts in the capabilities and new a better way to do things but also having the kind of the business understanding to be able to ask the right questions. That's how new a better price is. Positions another area that I really like his stuff with their platforms. You can do more with less. And that's not just about using data redundancy. That's about creating application environments, that conservative and then the infrastructure to service different requirements that are able to use the random Io thing without getting too kind of low level as well as the sequential. So what that means is you don't necessarily have to move data from application environment a do one thing related, and then move it to the application environment. Be that environment free terms of an analytics on the left Right works. Both keep the data where it is, use it or different different requirements within the infrastructure and again do more with less. And what that does is not just about simplicity and efficiency. It significantly reduces the time to value of that as well. >>Do you have examples that you can share with us even if they're anonymous customers that you work with that are maybe a little further down on the journey. Or maybe not >>looking at the you mentioned data protection earlier. So another organization This is a project which is just kind of hearing confessions moment, huge organization. They're literally petabytes of data that was servicing their back up in archive. And what they have is not just this realization they have combined. I think I different that they have dependent on the what area of infrastructure they were backing up, whether it was virtualization, that was different because they were backing up PC's June 6th. They're backing up another database environment, using something else in the cloud knowledge bases approach that we recommended to work with them on. They were able to significantly reduce complexity and reduce the amount of time that it systems of what they were able to achieve and what this is again. One of the clients have They've gone above the threshold of being able to back up for that. >>Adam, give us the final thoughts, bring us home. In this segment, >>the family built something we didn't particularly such on, that I think it is really barely hidden. It is spoken about as much as I think it is, that agile approaches to infrastructure we're going to be touched on there could be complicated on the lack of it efficient, the impact, a user's ability to be agile. But what you find with traditional approaches and you already touched on some of the kind of benefits new approaches there. It's often very prescriptive, designed for a particular as the infrastructure environment, the way that it served up the users in kind of a packaged. Either way, it means that they need to use it in that whatever wave in data bases, that kind of service of as it comes in from a flexibility standpoint. But for this platform approach, which is the right way to address technology in my eyes enables, it's the infrastructure to be used. Flexible piece of it, the business users of the data users what we find this capability into their innovating in the way they use that on the White House. I bring benefits. This is a platform to prescriptive, and they are able to do that. What you're doing with these new approaches is all of the metrics that we touched on and pass it from a cost standpoint from a visibility standpoint, but what it means is that the innovators in the business want really, is to really understand what they're looking to achieve and now have to to innovate with us. Now, I think I've started to see that with projects season places. If you do it in the right way, you articulate the capability and empower the business users in the right ways. Very significantly. Better position. The advantages on really matching significantly bigger than their competition. Yeah, >>Super Adam in a really exciting space. And we spent the last 10 years gathering all this data, you know, trying to slog through it and figure it out. And now, with the tools that we have and the automation capabilities, it really is a new era of innovation and insights. So, Adam or they didn't thanks so much for coming on the Cube and participating in this program. >>Exciting times with that. Thank you very much Today. >>Now we're going to go into the power panel and go deeper into the technologies that enable smart data life cycles. Stay right there. You're watching the cube. Are >>you interested in test driving? The i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program. Ah, flexible, scalable sandbox environment on the cloud of your choice with set up a service and support provided by Iot. Top. Click on the Link and connect with the data engineer to learn more and see Iot Tahoe in action. >>Welcome back, everybody to the power panel driving business performance with smart data life cycles. Leicester Waters is here. He's the chief technology officer from Iot Tahoe. He's joined by Patrick Smith, who was field CTO from pure storage. And is that data? Who's a system engineering manager at KohI City? Gentlemen, good to see you. Thanks so much for coming on this panel. >>Thank you. >>Let's start with Lester. I wonder if each of you could just give us a quick overview of your role. And what's the number one problem that you're focused on solving for your customers? Let's start with Lester Fleet. >>Yes, I'm Lost Waters, chief technology officer for Iot Tahoe and really the number one problem that we're trying to solve for our customers is to understand, help them understand what they have, because if they don't understand what they have in terms of their data. They can't manage it. They can't control it. The cap monitor. They can't ensure compliance. So really, that's finding all you can about your data that you have. And building a catalog that could be readily consumed by the entire business is what we do. >>Patrick Field, CTO in your title That says to me, You're talking to customers all the time, so you got a good perspective on it. Give us your take on things here. >>Yeah, absolutely. So my patches in here on day talkto customers and prospects in lots of different verticals across the region. And as they look at their environments and their data landscape, they're faced with massive growth in the data that they're trying to analyze and demands to be able to get insight our stuff and to deliver better business value faster than they've ever had to do in the past. So >>got it. And is that of course, Kohi City. You're like the new kid on the block. You guys were really growing rapidly created this whole notion of data management, backup and and beyond. But I'm assistant system engineering manager. What are you seeing from from from customers your role and the number one problem that you're solving. >>Yeah, sure. So the number one problem I see time and again speaking with customers. It's around data fragmentation. So do two things like organic growth, even maybe budgetary limitations. Infrastructure has grown over time very piecemeal, and it's highly distributed internally. And just to be clear, you know, when I say internally, that >>could be >>that it's on multiple platforms or silos within an on Prem infrastructure that it also does extend to the cloud as well. >>Right Cloud is cool. Everybody wants to be in the cloud, right? So you're right, It creates, Ah, maybe unintended consequences. So let's start with the business outcome and kind of try to work backwards to people you know. They want to get more insights from data they want to have. Ah, Mawr efficient data lifecycle. But so let's let me start with you were thinking about like the North Star for creating data driven cultures. You know, what is the North Star or customers >>here? I think the North Star, in a nutshell, is driving value from your data. Without question, I mean way, differentiate ourselves these days by even nuances in our data now, underpinning that, there's a lot of things that have to happen to make that work out. Well, you know, for example, making sure you adequately protect your data, you know? Do you have a good You have a good storage sub system? Do you have a good backup and recovery point objectives? Recovery time objective. How do you Ah, are you fully compliant? Are you ensuring that you're taking all the boxes? There's a lot of regulations these days in terms with respect to compliance, data retention, data, privacy and so forth. Are you taking those boxes? Are you being efficient with your, uh, your your your data? You know, In other words, I think there's a statistic that someone mentioned me the other day that 53% of all businesses have between three and 15 copies of the same data. So you know, finding and eliminating does is it is part of the part of the problem is when you do a chase, >>um, I I like to think of you're right, no doubt, business value and and a lot of that comes from reducing the end in cycle times. But anything that you guys would would add to that. Patrick, Maybe start with Patrick. >>Yeah, I think I think in value from your data really hits on tips on what everyone wants to achieve. But I think there are a couple of key steps in doing that. First of all, is getting access to the data and asked that, Really, it's three big problems, firstly, working out what you've got. Secondly, looking at what? After working on what you've got, how to get access to it? Because it's all very well knowing that you've got some data. But if you can't get access to it either because of privacy reasons, security reasons, then that's a big challenge. And then finally, once you've got access to the data making sure that you can process that data in a timely manner >>for me, you know it would be that an organization has got a really good global view of all of its data. It understands the data flow and dependencies within their infrastructure, understands that precise legal and compliance requirements, and you had the ability to action changes or initiatives within their environment to give the fun. But with a cloud like agility. Um, you know, and that's no easy feat, right? That is hard work. >>Okay, so we've we've talked about. The challenge is in some of the objectives, but there's a lot of blockers out there, and I want to understand how you guys are helping remove them. So So, Lester. But what do you see as some of the big blockers in terms of people really leaning in? So this smart data lifecycle >>yeah, Silos is is probably one of the biggest one I see in business is yes, it's it's my data, not your data. Lots of lots of compartmentalization. Breaking that down is one of the one of the challenges. And having the right tools to help you do that is only part of the solution. There's obviously a lot of cultural things that need to take place Teoh to break down those silos and work together. If you can identify where you have redundant data across your enterprise, you might be able to consolidate those. >>So, Patrick, so one of the blockers that I see is legacy infrastructure, technical debt, sucking all the budget you got. You know, too many people have having to look after, >>as you look at the infrastructure that supports people's data landscapes today for primarily legacy reasons. The infrastructure itself is siloed. So you have different technologies with different underlying hardware and different management methodologies that they're there for good reason, because historically you have to have specific fitness, the purpose for different data requirements. And that's one of the challenges that we tackled head on a pure with with the flash blade technology and the concept of the data, a platform that can deliver in different characteristics for the different workloads. But from a consistent data platform >>now is that I want to go to you because, you know, in the world in your world, which to me goes beyond backup. And one of the challenges is, you know, they say backup is one thing. Recovery is everything, but as well. The the CFO doesn't want to pay for just protection, and one of things that I like about what you guys have done is you. You broadened the perspective to get more value out of your what was once seen as an insurance policy. >>I do see one of the one of the biggest blockers as the fact that the task at hand can, you know, can be overwhelming for customers. But the key here is to remember that it's not an overnight change. It's not, you know, a flick of a switch. It's something that can be tackled in a very piecemeal manner on. Absolutely. Like you said, You know, reduction in TCO and being able to leverage the data for other purposes is a key driver for this. So, you know, this can be this can be resolved. It would be very, you know, pretty straightforward. It can be quite painless as well. Same goes for unstructured data, which is very complex to manage. And, you know, we've all heard the stats from the the analysts. You know, data obviously is growing at an extremely rapid rate, but actually, when you look at that, you know how is actually growing. 80% of that growth is actually in unstructured data, and only 20% of that growth is in unstructured data. S o. You know, these are quick win areas that customers can realize immediate tco improvement and increased agility as well >>paint a picture of this guy that you could bring up the life cycle. You know what you can see here is you've got this this cycle, the data lifecycle and what we're wanting to do is inject intelligence or smarts into this, like like life cycles. You see, you start with ingestion or creation of data. You're you're storing it. You got to put it somewhere, right? You gotta classify it. You got to protect it. And then, of course, you want to reduce the copies, make it, you know, efficient on. And then you want to prepare it so that businesses can actually sumit. And then you've got clients and governance and privacy issues, and I wonder if we could start with you. Lester, this is, you know, the picture of the life cycle. What role does automation play in terms of injecting smarts into the lifecycle? >>Automation is key here, especially from the discover it catalog and classify perspective. I've seen companies where they geo and will take and dump their all of their database scheme is into a spreadsheet so that they can sit down and manually figure out what attributes 37 means for a column names, Uh, and that's that's only the tip of the iceberg. So being able to do automatically detect what you have automatically deduced where what's consuming the data, you know, upstream and downstream. Being able to understand all of the things related to the lifecycle of your data. Back up archive deletion. It is key. And so we're having having good tool. IShares is very >>important. So, Patrick, obviously you participate in the store piece of this picture s I wonder if you could talk more specifically about that. But I'm also interested in how you effect the whole system view the the end end cycle time. >>Yeah, I think Leicester kind of hit the nail on the head in terms of the importance of automation because the data volumes are just just so massive. Now that you can, you can you can effectively manage or understand or catalog your data without automation. Once you understand the data and the value of the data, then that's where you can work out where the data needs to be at any point in >>time, right? So pure and kohi city obviously partner to do that and of course, is that you guys were part of the protect you certainly part of the retain. But Also, you provide data management capabilities and analytics. I wonder if you could add some color there. >>Yeah, absolutely. So, like you said, you know, we focused pretty heavily on data protection. Is just one of our one of our areas on that infrastructure. It is just sitting there, really? Can, you know, with the legacy infrastructure, It's just sitting there, you know, consuming power, space cooling and pretty inefficient. And what, if anything, that protest is a key part of that. If I If I have a modern data platform such as, you know, the cohesive data platform, I can actually do a lot of analytics on that through application. So we have a marketplace for APS. >>I wonder if we could talk about metadata. It's It's increasingly important. Metadata is data about the data, but Leicester maybe explain why it's so important and what role it plays in terms of creating smart data lifecycle. A >>lot of people think it's just about the data itself, but there's a lot of extended characteristics about your data. So so imagine if or my data life cycle I can communicate with the backup system from Kohi City and find out when the last time that data was backed up or where is backed up to. I can communicate exchange data with pure storage and find out what two years? And is the data at the right tier commensurate with its use level pointed out and being able to share that metadata across systems? I think that's the direction that we're going in right now. We're at the stage where just identifying the metadata and trying to bring it together and catalog the next stage will be OK using the AP eyes it that that we have between our systems can't communicate and share that data and build good solutions for customers to use. >>It's a huge point that you just made. I mean, you know, 10 years ago, automating classification was the big problem, and it was machine intelligence, you know, obviously attacking that, But your point about as machines start communicating to each other and you start, it's cloud to cloud. There's all kinds of metadata, uh, kind of new meta data that's being created. I often joke that someday there's gonna be more metadata than data, so that brings us to cloud and that I'd like to start with you. >>You know, I do think, you know, having the cloud is a great thing. And it has got its role to play, and you can have many different permutations and iterations of how you use it on. Um, you know, I may have sort of mentioned previously. You know, I've seen customers go into the cloud very, very quickly, and actually recently, they're starting to remove workloads from the cloud. And the reason why this happens is that, you know, Cloud has got its role to play, but it's not right for absolutely everything, especially in their current form as well. A good analogy I like to use on this may sound a little bit cliche, but you know, when you compare clouds versus on premises data centers, you can use the analogy of houses and hotels. So to give you an idea so you know, when we look at hotels, that's like the equivalent of a cloud, right? I can get everything I need from there. I can get my food, my water, my outdoor facilities. If I need to accommodate more people, I can rent some more rooms. I don't have to maintain the hotel. It's all done for me. When you look at houses the equivalent to on premises infrastructure, I pretty much have to do everything myself, right. So I have to purchase the house. I have to maintain it. I have to buy my own food and water. Eat it. You have to make improvements myself. But then why do we all live in houses? No, in hotels. And the simple answer that I can I can only think of is, is that it's cheaper, right. It's cheaper to do it myself. But that's not to say that hotels haven't got their role to play. Um, you know? So, for example, if I've got loads of visitors coming over for the weekend, I'm not going to go build an extension to my house just for them. I will burst into my hotel into the cloud, um, and use it for, you know, for for things like that. So what I'm really saying is the cloud is great for many things, but it can work out costlier for certain applications, while others are a perfect >>It's an interesting analogy. I hadn't thought of that before, but you're right because I was going to say Well, part of it is you want the cloud experience everywhere, but you don't always want the cloud experience especially, you know, when you're with your family, you want certain privacy that I've not heard that before. He's out. So that's the new perspective s Oh, thank you, but but But Patrick, I do want to come back to that cloud experience because, in fact, that's what's happening. In a lot of cases, organizations are extending the cloud properties of automation on Prem. >>Yeah, I thought, as I thought, a really interesting point and a great analogy for the use of the public cloud. And it really reinforces the importance of the hybrid and multi cloud environment because it gives you the flexibility to choose where is the optimal environment to run your business workloads? And that's what it's all about and the flexibility to change which environment you're running in, either for more months to the next or from one year to the next. Because workloads change and the characteristics that are available in the cloud change, the hybrid cloud is something that we've we've lived with ourselves of pure, So our pure one management technology actually sits in hybrid cloud and what we we started off entirely cloud native. But now we use public cloud for compute. We use our own technology at the end of a high performance network link to support our data platform. So we get the best of both worlds and I think that's where a lot of our customers are trying to get to. >>Alright, I want to come back in a moment there. But before we do, let's see, I wonder if we could talk a little bit about compliance, governance and privacy. I think the Brits hung on. This panel is still in the EU for now, but the you are looking at new rules. New regulations going beyond GDP are where does sort of privacy governance, compliance fit in the data lifecycle, then, is that I want your thoughts on this as well. >>Yeah, this is this is a very important point because the landscape for for compliance, around data privacy and data retention is changing very rapidly. And being able to keep up with those changing regulations in an automated fashion is the only way you're gonna be able to do it. Even I think there's a some sort of Ah, maybe ruling coming out today or tomorrow with the changed in the r. So this is things are all very key points and being able to codify those rules into some software. Whether you know, Iot Tahoe or or your storage system or kohi city, it will help you be compliant is crucial. >>Yeah. Is that anything you can add there? I mean, it's really is your wheelhouse. >>Yeah, absolutely. So, you know, I think anybody who's watching this probably has gotten the message that, you know, less silos is better. And it absolutely it also applies to data in the cloud is where as well. So you know, my aiming Teoh consolidate into fewer platforms, customers can realize a lot better control over their data. And the natural effect of this is that it makes meeting compliance and governance a lot easier. So when it's consolidated, you can start to confidently understand who's accessing your data. How frequently are they accessing the data? You can also do things like, you know, detecting anomalous file access activities and quickly identify potential threats. >>Okay, Patrick, we were talking. You talked earlier about storage optimization. We talked to Adam Worthington about the business case, the numerator, which is the business value, and then the denominator, which is the cost and what's unique about pure in this regard. >>Yeah, and I think there are. There are multiple time dimensions to that. Firstly, if you look at the difference between legacy storage platforms that used to take up racks or aisles of space in the data center, the flash technology that underpins flash blade way effectively switch out racks rack units on. It has a big play in terms of data center footprint, and the environmental is associated with the data center. If you look at extending out storage efficiencies and the benefits it brings, just the performance has a direct effect on start we whether that's, you know, the start from the simplicity that platform so that it's easy and efficient to manage, whether it's the efficiency you get from your data. Scientists who are using the outcomes from the platform, making them more efficient to new. If you look at some of our customers in the financial space there, their time to results are improved by 10 or 20 x by switching to our technology from legacy technologies for their analytics, platforms. >>The guys we've been running, you know, Cube interviews in our studios remotely for the last 120 days is probably the first interview I've done where haven't started off talking about Cove it, Lester. I wonder if you could talk about smart data lifecycle and how it fits into this isolation economy. And hopefully, what will soon be a post isolation economy? >>Yeah, Come. It has dramatically accelerated the data economy. I think. You know, first and foremost, we've all learned to work at home. You know, we've all had that experience where, you know, people would have been all about being able to work at home just a couple days a week. And here we are working five days. That's how to knock on impact to infrastructure, to be able to support that. But going further than that, you know, the data economy is all about how a business can leverage their data to compete in this New World order that we are now in code has really been a forcing function to, you know, it's probably one of the few good things that have come out of government is that we've been forced to adapt and It's a zoo. Been an interesting journey and it continues to be so >>like Lester said, you know, we've We're seeing huge impact here. Working from home has pretty much become the norm. Now, you know, companies have been forced into basically making it work. If you look online retail, that's accelerated dramatically as well. Unified communications and videoconferencing. So really, you know the point here, is that Yes, absolutely. We're you know, we've compressed, you know, in the past, maybe four months. What already would have taken maybe even five years, maybe 10 years or so >>We got to wrap. But Celester Louis, let me ask you to sort of get paint. A picture of the sort of journey the maturity model that people have to take. You know, if they want to get into it, where did they start? And where are they going to give us that view, >>I think, versus knowing what you have. You don't know what you have. You can't manage it. You can't control that. You can't secure what you can't ensure. It's a compliant s so that that's first and foremost. Uh, the second is really, you know, ensuring that your compliance once, once you know what you have. Are you securing it? Are you following the regulatory? The applicable regulations? Are you able to evidence that, uh, how are you storing your data? Are you archiving it? Are you storing it effectively and efficiently? Um, you know, have you Nirvana from my perspective, is really getting to a point where you you've consolidated your data, you've broken down the silos and you have a virtually self service environment by which the business can consume and build upon their data. And really, at the end of the day, as we said at the beginning, it's all about driving value out of your data. And ah, the automation is is key to this, sir. This journey >>that's awesome and you just described is sort of a winning data culture. Lester, Patrick, thanks so much for participating in this power panel. >>Thank you, David. >>Alright, So great overview of the steps in the data lifecycle and how to inject smarts into the process is really to drive business outcomes. Now it's your turn. Hop into the crowd chat, please log in with Twitter or linked in or Facebook. Ask questions, answer questions and engage with the community. Let's crowdchat, right. Yeah, yeah, yeah.

(upbeat music) >> Voiceover: From around the globe, it's theCUBE with digital coverage of Data Automated. An event series brought to you by Io-Tahoe. >> Welcome everyone to the second episode in our Data Automated series made possible with support from Io-Tahoe. Today, we're going to drill into the data lifecycle. Meaning the sequence of stages that data travels through from creation to consumption to archive. The problem as we discussed in our last episode is that data pipelines are complicated, they're cumbersome, they're disjointed and they involve highly manual processes. A smart data lifecycle uses automation and metadata to improve agility, performance, data quality and governance. And ultimately, reduce costs and time to outcomes. Now, in today's session we'll define the data lifecycle in detail and provide perspectives on what makes a data lifecycle smart? And importantly, how to build smarts into your processes. In a moment we'll be back with Adam Worthington from Ethos to kick things off. And then, we'll go into an expert power panel to dig into the tech behind smart data lifecyles. And, then we'll hop into the crowd chat and give you a chance to ask questions. So, stay right there, you're watching theCUBE. (upbeat music) >> Voiceover: Innovation. Impact. Influence. Welcome to theCUBE. Disruptors. Developers. And, practitioners. Learn from the voices of leaders, who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on theCUBE. Your global leader in high tech digital coverage. >> Okay, we're back with Adam Worthington. Adam, good to see you, how are things across the pond? >> Good thank you, I'm sure our weather's a little bit worse than yours is over the other side, but good. >> Hey, so let's set it up, tell us about yourself, what your role is as CTO and--- >> Yeah, Adam Worthington as you said, CTO and co-founder of Ethos. But, we're a pretty young company ourselves, so we're in our sixth year. And, we specialize in emerging disruptive technology. So, within the infrastructure data center kind of cloud space. And, my role is a technical lead, so I, it's kind of my job to be an expert in all of the technologies that we work with. Which can be a bit of a challenge if you have a huge portfolio. One of the reasons we got to deliberately focus on. And also, kind of pieces of technical validation and evaluation of new technologies. >> So, you guys are really technology experts, data experts, and probably also expert in process and delivering customer outcomes, right? >> That's a great word there Dave, outcomes. I mean, that's a lot of what I like to speak to customers about. >> Let's talk about smart data you know, when you throw out terms like this it kind of can feel buzz wordy but what are the critical aspects of so-called smart data? >> Cool, well typically I had to step back a little bit and set the scene a little bit more in terms of kind of where I came from. So, and the types of problems I've sorted out. So, I'm really an infrastructure or solution architect by trade. And, what I kind of, relatively organically, but over time my personal framework and approach. I focused on three core design principles. So, simplicity, flexibility and efficiency. So, whatever it was I was designing and obviously they need different things depending on what the technology area is that we're working with. So, that's for me a pretty good step. So, they're the kind of areas that a smart approach in data will directly address both reducing silos. So, that comes from simplifying. So, moving away from complexity of infrastructure. Reducing the amount of copies of data that we have across the infrastructure. And, reducing the amount of application environment for the need for different areas. So, the smarter we get with data it's in my eyes anyway, the further we move away from those traditional legacy. >> But, how does it work? I mean, how, in other words, what's involved in injecting smarts into your data lifecycle? >> I think one of my, well actually I didn't have this quote ready, but genuinely one of my favorite quotes is from the French philosopher and mathematician, Blaise Pascal and he says, if I get this right, "I'd have written you a shorter letter, but I didn't have the time." So, there's real, I love that quote for lots of reasons. >> Dave: Alright. >> That's direct applications in terms of what we're talking about. In terms of, it's actually really complicated to develop a technology capability to make things simple. Be more directly meeting the needs of the business through tech. So, you provide self-service capability. And, I don't just mean self-driving, I mean making data and infrastructure make sense to the business users that are using it. >> Your job, correct me if I'm wrong, is to kind of put that all together in a solution. And then, help the customer you know, realize what we talked about earlier that business out. >> Yeah, and that's, it's sitting at both sides and understanding both sides. So, kind of key to us in our abilities to be able to deliver on exactly what you've just said, is being experts in the capabilities and new and better ways of doing things. But also, having the kind of, better business understanding to be able to ask the right questions to identify how can you better approach this 'cause it helps solve these issues. But, another area that I really like is the, with the platforms you can do more with less. And, that's not just about reducing data redundancy, that's about creating application environments that can service, an infrastructure to service different requirements that are able to do the random IO thing without getting too kind of low level tech. As well as the sequential. So, what that means is, that you don't necessarily have to move data from application environment A, do one thing with it, collate it and then move it to the application environment B, to application environment C, in terms of an analytics kind of left to right workload, you keep your data where it is, use it for different requirements within the infrastructure and again, do more with less. And, what that does, it's not just about simplicity and efficiency, it significantly reduces the times of value that that faces, as well. >> Do you have examples that you can share with us, even if they're anonymized of customers that you've worked with, that are maybe a little further down on the journey. Or, maybe not and--- >> Looking at the, you mentioned data protection earlier. So, another organization this is a project which is just coming nearing completion at the moment. Huge organization, that literally petabytes of data that was servicing their backup and archive. And, what they had is not just this reams of data. They had, I think I'm right in saying, five different backup applications that they had depending on the, what area of infrastructure they were backing up. So, whether it was virtualization, that was different to if they were backing up, different if they were backing up another data base environment they were using something else in the cloud. So, a consolidated approach that we recommended to work with them on. They were able to significantly reduce complexity and reduce the amount of time that it took them. So, what they were able to achieve and this was again, one of the key departments they had. They'd gone above the threshold of being able to backup all of them. >> Adam, give us the final thoughts, bring us home in this segment. >> Well, the final thoughts, so this is something, yeah we didn't particularly touch on. But, I think it's kind of slightly hidden, it isn't spoken about as much as I think it could be. Is the traditional approaches to infrastructure. We've already touched on that they can be complicated and there's a lack of efficiency. It impacts a user's ability to be agile. But, what you find with traditional approaches and we've already touched on some of the kind of benefits to new approaches there, is that they're often very prescriptive. They're designed for a particular firm. The infrastructure environment, the way that it's served up to the users in a kind of a packaged kind of way, means that they need to use it in that, whatever way it's been dictated. So, that kind of self-service aspect, as it comes in from a flexibility standpoint. But, these platforms and these platform approaches is the right way to address technology in my eyes. Enables the infrastructure to be used flexibly. So, the business users and the data users, what we find is that if we put in this capability into their hands. They start innovating the way that they use that data. And, the way that they bring benefits. And, if a platform is too prescriptive and they aren't able to do that, then what you're doing with these new approaches is get all of the metrics that we've touched on. It's fantastic from a cost standpoint, from an agility standpoint. But, what it means is that the innovators in the business, the ones that really understand what they're looking to achieve, they now have the tools to innovate with that. And, I think, and I've started to see that with projects that we've completed, if you do it in the right way, if you articulate the capability and you empower the business users in the right way. Then, they're in a significantly better position, these businesses to take advantages and really sort of match and significantly beat off their competition environment spaces. >> Super Adam, I mean a really exciting space. I mean we spent the last 10 years gathering all this data. You know, trying to slog through it and figure it out and now, with the tools that we have and the automation capabilities, it really is a new era of innovation and insight. So, Adam Worthington, thanks so much for coming in theCUBE and participating in this program. >> Yeah, exciting times and thank you very much Dave for inviting me, and yeah big pleasure. >> Now, we're going to go into the power panel and go deeper into the technologies that enable smart data lifecyles. And, stay right there, you're watching theCUBE. (light music) >> Voiceover: Are you interested in test-driving the Io-Tahoe platform? Kickstart the benefits of Data Automation for your business through the IoLabs program. A flexible, scalable, sandbox environment on the cloud of your choice. With setup, service and support provided by Io-Tahoe. Click on the link and connect with a data engineer to learn more and see Io-Tahoe in action. >> Welcome back everybody to the power panel, driving business performance with smart data lifecyles. Lester Waters is here, he's the Chief Technology Officer from Io-Tahoe. He's joined by Patrick Smith, who is field CTO from Pure Storage. And, Ezat Dayeh who is Assistant Engineering Manager at Cohesity. Gentlemen, good to see you, thanks so much for coming on this panel. >> Thank you, Dave. >> Yes. >> Thank you, Dave. >> Let's start with Lester, I wonder if each of you could just give us a quick overview of your role and what's the number one problem that you're focused on solving for your customers? Let's start with Lester, please. >> Ah yes, I'm Lester Waters, Chief Technology Officer for Io-Tahoe. And really, the number one problem that we are trying to solve for our customers is to help them understand what they have. 'Cause if they don't understand what they have in terms of their data, they can't manage it, they can't control it, they can't monitor it, they can't ensure compliance. So, really that's finding all that you can about your data that you have and building a catalog that can be readily consumed by the entire business is what we do. >> Patrick, field CTO in your title, that says to me you're talking to customers all the time so you've got a good perspective on it. Give us you know, your take on things here. >> Yeah absolutely, so my patch is in the air and talk to customers and prospects in lots of different verticals across the region. And, as they look at their environments and their data landscape, they're faced with massive growth in the data that they're trying to analyze. And, demands to be able to get inside are faster. And, to deliver business value faster than they've ever had to do in the past, so. >> Got it and then Ezat at Cohesity, you're like the new kid on the block. You guys are really growing rapidly. You created this whole notion of data management, backup and beyond, but from Assistant Engineering Manager what are you seeing from customers, your role and the number one problem that you're solving? >> Yeah sure, so the number one problem I see you know, time and again speaking with customers it's all around data fragmentation. So, due to things like organic growth you know, even maybe budgetary limitations, infrastructure has grown you know, over time, very piecemeal. And, it's highly distributed internally. And, just to be clear you know, when I say internally you know, that could be that it's on multiple platforms or silos within an on-prem infrastructure. But, that it also does extend to the cloud, as well. >> Right hey, cloud is cool, everybody wants to be in the cloud, right? So, you're right it creates maybe unattended consequences. So, let's start with the business outcome and kind of try to work backwards. I mean people you know, they want to get more insights from data, they want to have a more efficient data lifecyle. But, so Lester let me start with you, in thinking about like, the North Star, creating data driven cultures you know, what is the North Star for customers here? >> I think the North Star in a nutshell is driving value from your data. Without question, I mean we differentiate ourselves these days by even the nuances in our data. Now, underpinning that there's a lot of things that have to happen to make that work out well. You know for example, making sure you adequately protect your data. You know, do you have a good storage system? Do you have a good backup and recovery point objectives, recovering time objectives? Do you, are you fully compliant? Are you ensuring that you're ticking all the boxes? There's a lot of regulations these days in terms, with respect to compliance, data retention, data privacy and so fourth. Are you ticking those boxes? Are you being efficient with your data? You know, in other words I think there's a statistic that someone mentioned to me the other day that 53% of all businesses have between three and 15 copies of the same data. So you know, finding and eliminating those is part of the problems you need to chase. >> I like to think of you know, you're right. Lester, no doubt, business value and a lot of that comes from reducing the end to end cycle times. But, anything that you guys would add to that, Patrick and Ezat, maybe start with Patrick. >> Yeah, I think getting value from data really hits on, it hits on what everyone wants to achieve. But, I think there are a couple of key steps in doing that. First of all is getting access to the data. And that's, that really hits three big problems. Firstly, working out what you've got. Secondly, after working out what you've got, how to get access to it. Because, it's all very well knowing that you've got some data but if you can't get access to it. Either, because of privacy reasons, security reasons. Then, that's a big challenge. And then finally, once you've got access to the data, making sure that you can process that data in a timely manner. >> For me you know, it would be that an organization has got a really good global view of all of its data. It understands the data flow and dependencies within their infrastructure. Understands the precise legal and compliance requirements. And, has the ability to action changes or initiatives within their environment. Forgive the pun, but with a cloud like agility. You know, and that's no easy feat, right? That is hard work. >> Okay, so we've talked about the challenges and some of the objectives, but there's a lot of blockers out there and I want to understand how you guys are helping remove them? So, Lester what do you see as some of the big blockers in terms of people really leaning in to this smart data lifecycle. >> Yeah silos, is probably one of the biggest one I see in businesses. Yes, it's my data not your data. Lots of compartmentalization. And, breaking that down is one of the challenges. And, having the right tools to help you do that is only part of the solution. There's obviously a lot of cultural things that need to take place to break down those silos and work together. If you can identify where you have redundant data across your enterprise, you might be able to consolidate those. >> Yeah so, over to Patrick, so you know, one of the blockers that I see is legacy infrastructure, technical debt sucking all the budget. You got you know, too many people having to look after. >> As you look at the infrastructure that supports peoples data landscapes today. For primarily legacy reasons, the infrastructure itself is siloed. So, you have different technologies with different underlying hardware, different management methodologies that are there for good reason. Because, historically you had to have specific fitness for purpose for different data requirements. >> Dave: Ah-hm. >> And, that's one of the challenges that we tackled head on at Pure. With the flash plate technology and the concept of the data hub. A platform that can deliver in different characteristics for the different workloads. But, from a consistent data platform. >> Now, Ezat I want to go to you because you know, in the world, in your world which to me goes beyond backup and one of the challenges is you know, they say backup is one thing, recovery is everything. But as well, the CFO doesn't want to pay for just protection. Now, one of the things that I like about what you guys have done is you've broadened the perspective to get more value out of your what was once seen as an insurance policy. >> I do see one of the biggest blockers as the fact that the task at hand can you know, be overwhelming for customers. But, the key here is to remember that it's not an overnight change, it's not you know, the flick of the switch. It's something that can be tackled in a very piecemeal manner. And, absolutely like you've said you know, reduction in TCO and being able to leverage the data for other purposes is a key driver for this. So you know, this can be resolved. It can be very you know, pretty straightforward. It can be quite painless, as well. Same goes for unstructured data, which is very complex to manage. And you know, we've all heard the stats from the analysts, you know data obviously is growing at an extremely rapid rate. But, actually when you look at that you know, how is it actually growing? 80% of that growth is actually in unstructured data and only 20% of that growth is in structured data. So you know, these are quick win areas that the customers can realize immediate TCO improvement and increased agility, as well. >> Let's paint a picture of this guys, if I can bring up the lifecyle. You know what you can see here is you've got this cycle, the data lifecycle and what we're wanting to do is inject intelligence or smarts into this lifecyle. So, you can see you start with ingestion or creation of data. You're storing it, you've got to put it somewhere, right? You've got to classify it, you've got to protect it. And then, of course you want to you know, reduce the copies, make it you know, efficient. And then, you want to prepare it so that businesses can actually consume it and then you've got compliance and governance and privacy issues. And, I wonder if we could start with you Lester, this is you know, the picture of the lifecycle. What role does automation play in terms of injecting smarts into the lifecycle? >> Automation is key here, you know. Especially from the discover, catalog and classify perspective. I've seen companies where they go and we'll take and dump all of their data base schemes into a spreadsheet. So, that they can sit down and manually figure out what attribute 37 means for a column name. And, that's only the tip of the iceberg. So, being able to automatically detect what you have, automatically deduce where, what's consuming the data, you know upstream and downstream, being able to understand all of the things related to the lifecycle of your data backup, archive, deletion, it is key. And so, having good toolage areas is very important. >> So Patrick, obviously you participate in the store piece of this picture. So, I wondered if you could just talk more specifically about that, but I'm also interested in how you affect the whole system view, the end-to-end cycle time. >> Yeah, I think Lester kind of hit the nail on the head in terms of the importance of automation. Because, the data volumes are just so massive now that you can't effectively manage or understand or catalog your data without automation. Once you understand the data and the value of the data, then that's where you can work out where the data needs to be at any point in time. >> Right, so Pure and Cohesity obviously partnered to do that and of course, Ezat you guys are part of the protect, you're certainly part of the retain. But also, you provide data management capabilities and analytics, I wonder if you could add some color there? >> Yeah absolutely, so like you said you know, we focus pretty heavily on data protection as just one of our areas. And, that infrastructure it is just sitting there really can you know, the legacy infrastructure it's just sitting there you know, consuming power, space, cooling and pretty inefficient. And, automating that process is a key part of that. If I have a modern day platform such as you know, the Cohesity data platform I can actually do a lot of analytics on that through applications. So, we have a marketplace for apps. >> I wonder if we could talk about metadata. It's increasingly important you know, metadata is data about the data. But, Lester maybe explain why it's so important and what role it plays in terms of creating smart data lifecycle. >> A lot of people think it's just about the data itself. But, there's a lot of extended characteristics about your data. So, imagine if for my data lifecycle I can communicate with the backup system from Cohesity. And, find out when the last time that data was backed up or where it's backed up to. I can communicate, exchange data with Pure Storage and find out what tier it's on. Is the data at the right tier commencer with it's use level? If I could point it out. And, being able to share that metadata across systems. I think that's the direction that we're going in. Right now, we're at the stage we're just identifying the metadata and trying to bring it together and catalog it. The next stage will be okay, using the APIs and that we have between our systems. Can we communicate and share that data and build good solutions for customers to use? >> I think it's a huge point that you just made, I mean you know 10 years ago, automating classification was the big problem. And you know, with machine intelligence you know, we're obviously attacking that. But, your point about as machines start communicating to each other and you start you know, it's cloud to cloud. There's all kinds of metadata, kind of new metadata that's being created. I often joke that some day there's going to be more metadata than data. So, that brings us to cloud and Ezat, I'd like to start with you. >> You know, I do think that you know, having the cloud is a great thing. And, it has got its role to play and you can have many different you know, permutations and iterations of how you use it. And, you know, as I've may have sort of mentioned previously you know, I've seen customers go into the cloud very, very quickly and actually recently they're starting to remove workloads from the cloud. And, the reason why this happens is that you know, cloud has got its role to play but it's not right for absolutely everything. Especially in their current form, as well. A good analogy I like to use and this may sound a little bit clique but you know, when you compare clouds versus on premises data centers. You can use the analogies of houses and hotels. So, to give you an idea, so you know, when we look at hotels that's like the equivalent of a cloud, right? I can get everything I need from there. I can get my food, my water, my outdoor facilities, if I need to accommodate more people, I can rent some more rooms. I don't have to maintain the hotel, it's all done for me. When you look at houses the equivalent to you know, on premises infrastructure. I pretty much have to do everything myself, right? So, I have to purchase the house, I have to maintain it, I have buy my own food and water, eat it, I have to make improvements myself. But, then why do we all live in houses, not in hotels? And, the simple answer that I can only think of is, is that it's cheaper, right? It's cheaper to do it myself, but that's not to say that hotels haven't got their role to play. You know, so for example if I've got loads of visitors coming over for the weekend, I'm not going to go and build an extension to my house, just for them. I will burst into my hotel, into the cloud. And, you use it for you know, for things like that. So, what I'm really saying is the cloud is great for many things, but it can work out costlier for certain applications, while others are a perfect fit. >> That's an interesting analogy, I hadn't thought of that before. But, you're right, 'cause I was going to say well part of it is you want the cloud experience everywhere. But, you don't always want the cloud experience, especially you know, when you're with your family, you want certain privacy. I've not heard that before, Ezat. So, that's a new perspective, so thank you. But, Patrick I do want to come back to that cloud experience because in fact that's what's happening in a lot of cases. Organizations are extending the cloud properties of automation on-prem. >> Yeah, I thought Ezat brought up a really interesting point and a great analogy for the use of the public cloud. And, it really reinforces the importance of the Hybrid and the multicloud environment. Because, it gives you that flexibility to choose where is the optimal environment to run your business workloads. And, that's what it's all about. And, the flexibility to change which environment you're running in, either from one month to the next or from one year to the next. Because, workloads change and the characteristics that are available in the cloud change. The Hybrid cloud is something that we've lived with ourselves at Pure. So, our Pure management technology actually sits in a Hybrid cloud environment. We started off entirely cloud native but now, we use the public cloud for compute and we use our own technology at the end of a high performance network link to support our data platform. So, we're getting the best of both worlds. I think that's where a lot of our customers are trying to get to. >> All right, I want to come back in a moment there. But before we do, Lester I wonder if we could talk a little bit about compliance and governance and privacy. I think the Brits on this panel, we're still in the EU for now but the EU are looking at new rules, new regulations going beyond GDPR. Where does sort of privacy, governance, compliance fit in for the data lifecycle. And Ezat, I want your thought on this as well? >> Ah yeah, this is a very important point because the landscape for compliance around data privacy and data retention is changing very rapidly. And, being able to keep up with those changing regulations in an automated fashion is the only way you're going to be able to do it. Even, I think there's a some sort of a maybe ruling coming out today or tomorrow with a change to GDPR. So, this is, these are all very key points and being able to codify those rules into some software whether you know, Io-Tahoe or your storage system or Cohesity, it'll help you be compliant is crucial. >> Yeah, Ezat anything you can add there, I mean this really is your wheel house? >> Yeah, absolutely, so you know, I think anybody who's watching this probably has gotten the message that you know, less silos is better. And, it absolutely it also applies to data in the cloud, as well. So you know, by aiming to consolidate into you know, fewer platforms customers can realize a lot better control over their data. And, the natural affect of this is that it makes meeting compliance and governance a lot easier. So, when it's consolidated you can start to confidently understand who's accessing your data, how frequently are they accessing the data. You can also do things like you know, detecting an ominous file access activities and quickly identify potential threats. >> Okay Patrick, we were talking, you talked earlier about storage optimization. We talked to Adam Worthington about the business case, you've got the sort numerator which is the business value and then a denominator which is the cost. And, what's unique about Pure in this regard? >> Yeah, and I think there are multiple dimensions to that. Firstly, if you look at the difference between legacy storage platforms, they used to take up racks or aisles of space in a data center. With flash technology that underpins flash played we effectively switch out racks for rack units. And, it has a big play in terms of data center footprint and the environmentals associated with a data center. If you look at extending out storage efficiencies and the benefits it brings. Just the performance has a direct effect on staff. Whether that's you know, the staff and the simplicity of the platform so that it's easy and efficient to manage. Or, whether it's the efficiency you get from your data scientists who are using the outcomes from the platform and making them more efficient. If you look at some of our customers in the financial space their time to results are improved by 10 or 20 x by switching to our technology. From legacy technologies for their analytics platforms. >> So guys, we've been running you know, CUBE interviews in our studios remotely for the last 120 days. This is probably the first interview I've done where I haven't started off talking about COVID. Lester, I wondered if you could talk about smart data lifecycle and how it fits into this isolation economy and hopefully what will soon be a post-isolation economy? >> Yeah, COVID has dramatically accelerated the data economy. I think you know, first and foremost we've all learned to work at home. I you know, we've all had that experience where you know, people would hum and har about being able to work at home just a couple of days a week. And, here we are working five days a week. That's had a knock on impact to infrastructure to be able to support that. But, going further than that you know, the data economy is all about how a business can leverage their data to compete in this new world order that we are now in. COVID has really been a forcing function to you know, it's probably one of the few good things that have come out of COVID is that we've been forced to adapt. And, it's been an interesting journey and it continues to be so. >> Like Lester said you know, we're seeing huge impact here. You know, working from home has pretty much become the norm now. You know, companies have been forced into making it work. If you look at online retail, that's accelerated dramatically, as well. Unified communications and video conferencing. So, really you know, that the point here is that, yes absolutely we've compressed you know, in the past maybe four months what probably would have taken maybe even five years, maybe 10 years or so. >> We've got to wrap, but so Lester let me ask you, sort of paint a picture of the sort of journey the maturity model that people have to take. You know, if they want to get into it, where do they start and where are they going? Give us that view. >> Yeah, I think first is knowing what you have. If you don't know what you have you can't manage it, you can't control it, you can't secure it, you can't ensure it's compliant. So, that's first and foremost. The second is really you know, ensuring that you're compliant once you know what you have, are you securing it? Are you following the regulatory, the regulations? Are you able to evidence that? How are you storing your data? Are you archiving it? Are you storing it effectively and efficiently? You know, have you, nirvana from my perspective is really getting to a point where you've consolidated your data, you've broken down the silos and you have a virtually self-service environment by which the business can consume and build upon their data. And, really at the end of the day as we said at the beginning, it's all about driving value out of your data. And, automation is key to this journey. >> That's awesome and you've just described like sort of a winning data culture. Lester, Patrick, Ezat, thanks so much for participating in this power panel. >> Thank you, David. >> Thank you. >> All right, so great overview of the steps in the data lifecyle and how to inject smarts into the processes, really to drive business outcomes. Now, it's your turn, hop into the crowd chat. Please log in with Twitter or LinkedIn or Facebook, ask questions, answer questions and engage with the community. Let's crowd chat! (bright music)

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe Welcome everybody to Enterprise Data Automation. Ah co created digital program on the Cube with support from my hotel. So my name is Dave Volante. And today we're using the hashtag data automated. You know, organizations. They really struggle to get more value out of their data, time to data driven insights that drive cost savings or new revenue opportunities. They simply take too long. So today we're gonna talk about how organizations can streamline their data operations through automation, machine intelligence and really simplifying data migrations to the cloud. We'll be talking to technologists, visionaries, hands on practitioners and experts that are not just talking about streamlining their data pipelines. They're actually doing it. So keep it right there. We'll be back shortly with a J ahora who's the CEO of Iot Tahoe to kick off the program. You're watching the Cube, the leader in digital global coverage. We're right back right after this short break. Innovation impact influence. Welcome to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. High tech digital coverage from around the globe. It's the Cube with digital coverage of enterprise, data, automation and event. Siri's brought to you by Iot. Tahoe. Okay, we're back. Welcome back to Data Automated. A J ahora is CEO of I O ta ho, JJ. Good to see how things in London >>Thanks doing well. Things in, well, customers that I speak to on day in, day out that we partner with, um, they're busy adapting their businesses to serve their customers. It's very much a game of ensuring the week and serve our customers to help their customers. Um, you know, the adaptation that's happening here is, um, trying to be more agile. Got to be more flexible. Um, a lot of pressure on data, a lot of demand on data and to deliver more value to the business, too. So that customers, >>as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline, But talk about enterprise data automation. What is it to you. And how is it different from data off >>Dev Ops, you know, has been great for breaking down those silos between different roles functions and bring people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, lending itself to data with data is exciting. We look to do is build on top of that when data automation, it's the it's the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors, our r and d on bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is is the automation behind new dimension. We've come a long way in the last few years. Boy is, we started out with automating some of those simple, um, to codify, um, I have a high impact on organization across the data a cost effective way house. There's data related tasks that classify data on and a lot of our original pattern certain people value that were built up is is very much around that >>love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, >>sure. I mean right there in the middle that the heart of what we do it is, you know, the intellectual property now that we've built up over time that takes from Hacha genius data sources. Your Oracle Relational database. Short your mainframe. It's a lay and increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data. Classify that data after it's classified. Them have the ability to form relationships across those different source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts of contact and meaning around that data. So it's moving it now from bringing data driven on increasingly where we have really smile, right people in our customer organizations you want I do some of those advanced knowledge tasks data scientists and ah, yeah, quants in some of the banks that we work with, the the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality, the policies that you can apply to that data. I'm putting it in context once you've got the ability to power. Okay, a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the the tapestry that fabric across that different system could be crm air P system such as s AP and some of the newer brown databases that we work with. Snowflake is a great well, if I look back maybe five years ago, we had prevalence of daily technologies at the cutting edge. Those are converging to some of the cloud platforms that we work with Google and AWS and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenges scale quickly runs out of steam because once, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data state? It's changed, You know, you've onboard a new customer. You signed up a new partner. Um, customer has, you know, adopted a new product that you just Lawrence and there that that slew of data keeps coming. So it's keeping pace with that. The only answer really is is some form of automation >>you're working with AWS. You're working with Google, You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>That's fundamental. So, I mean, when I caimans where you tell here is the CEO of one of the, um, trends that I wanted us CIO to be part of was being open, having an open architecture allowed one thing that was close to my heart, which is as a CEO, um, a c i o where you go, a budget vision on and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with the CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using AP eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before. So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um and snowflake here is, um Is those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that. And they're leveraging the value that they've already committed to. >>Yeah, and maybe you could give us some examples of the r A y and the business impact. >>Yeah, I mean, the r a y David is is built upon on three things that I mentioned is a combination off. You're leveraging the existing investment with the existing estate, whether that's on Microsoft Azure or AWS or Google, IBM, and I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have got the automation that is working right down to the level off data, a column level or the file level we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome now without hoping out which run those processes within hours of getting started And, um, Bill that picture, visualize that picture and bring it to life. You know, the PR Oh, I that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on GCB or a migration to any other clouds such as AWS or a multi cloud landscape right off the map. >>A. J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have you. >>Thank you, David. Look who is smoking in >>now. We want to bring in the customer perspective. We have a great conversation with Paul Damico, senior vice president data architecture, Webster Bank. So keep it right there. >>Utah Data automated Improve efficiency, Drive down costs and make your enterprise data work for you. Yeah, we're on a mission to enable our customers to automate the management of data to realise maximum strategic and operational benefits. We envisage a world where data users consume accurate, up to date unified data distilled from many silos to deliver transformational outcomes, activate your data and avoid manual processing. Accelerate data projects by enabling non I t resources and data experts to consolidate categorize and master data. Automate your data operations Power digital transformations by automating a significant portion of data management through human guided machine learning. Yeah, get value from the start. Increase the velocity of business outcomes with complete accurate data curated automatically for data, visualization tours and analytic insights. Improve the security and quality of your data. Data automation improves security by reducing the number of individuals who have access to sensitive data, and it can improve quality. Many companies report double digit era reduction in data entry and other repetitive tasks. Trust the way data works for you. Data automation by our Tahoe learns as it works and can ornament business user behavior. It learns from exception handling and scales up or down is needed to prevent system or application overloads or crashes. It also allows for innate knowledge to be socialized rather than individualized. No longer will your companies struggle when the employee who knows how this report is done, retires or takes another job, the work continues on without the need for detailed information transfer. Continue supporting the digital shift. Perhaps most importantly, data automation allows companies to begin making moves towards a broader, more aspirational transformation, but on a small scale but is easy to implement and manage and delivers quick wins. Digital is the buzzword of the day, but many companies recognized that it is a complex strategy requires time and investment. Once you get started with data automation, the digital transformation initiated and leaders and employees alike become more eager to invest time and effort in a broader digital transformational agenda. Yeah, >>everybody, we're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise Data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Nice to see you too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the >>bank. Yeah, Webster Bank is regional, Boston. And that again in New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated bank regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community. And, um, are really moving forward. Technology lives. Currently, today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on off something to offer that >>at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change >>the ability to give the customer what they need at the time when they need it? And what I mean by that is that we have, um, customer interactions and multiple weights, right? And I want to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look and also to be able to offer them the next best offer for them. >>Part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity >>exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. >>Do you see the potential to increase the data sources and hence the quality of the data? Or is that sort of premature? >>Oh, no. Um, exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of runnin system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into, like, an s three bucket Where that data king, we can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake Good, um, utilize that data or we can give it out to our market. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on, and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. I have eight engineers, data architects, they database administrators, right, um, and then data traditional data forwarding people, Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of read regiment that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things. This is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data, and we read the data flows and data redundancy and things like that and help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, Yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. >>In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure, and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing ai or machine intelligence into the data pipeline is really how you're attacking automation, right? >>Exactly. So you're able to let's say that I have I have seven cause lines of business that are asking me questions. And one of the questions I'll ask me is, um, we want to know if this customer is okay to contact, right? And you know, there's different avenues so you can go online to go. Do not contact me. You can go to the bank And you could say, I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said Okay to contact the other one says, You know, just for one to pray all these, you know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another of analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say yes we already have that documentation. Here it is. And this is where you can find where the customer has said, You know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. I'm using Iot typos eight automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not, um a It's an on prem. It's an oracle database. Um, and it's 15 years old, so it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>What's your vision or your your data driven organization? >>Um, I want for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers. >>That's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that's a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Next, we'll talk with Lester Waters, who's the CTO of Iot Toe cluster takes us through the key considerations of moving to the cloud. >>Yeah, right. The entire platform Automated data Discovery data Discovery is the first step to knowing your data auto discover data across any application on any infrastructure and identify all unknown data relationships across the entire siloed data landscape. smart data catalog. Know how everything is connected? Understand everything in context, regained ownership and trust in your data and maintain a single source of truth across cloud platforms, SAS applications, reference data and legacy systems and power business users to quickly discover and understand the data that matters to them with a smart data catalog continuously updated ensuring business teams always have access to the most trusted data available. Automated data mapping and linking automate the identification of unknown relationships within and across data silos throughout the organization. Build your business glossary automatically using in house common business terms, vocabulary and definitions. Discovered relationships appears connections or dependencies between data entities such as customer account, address invoice and these data entities have many discovery properties. At a granular level, data signals dashboards. Get up to date feeds on the health of your data for faster improved data management. See trends, view for history. Compare versions and get accurate and timely visual insights from across the organization. Automated data flows automatically captured every data flow to locate all the dependencies across systems. Visualize how they work together collectively and know who within your organization has access to data. Understand the source and destination for all your business data with comprehensive data lineage constructed automatically during with data discovery phase and continuously load results into the smart Data catalog. Active, geeky automated data quality assessments Powered by active geek You ensure data is fit for consumption that meets the needs of enterprise data users. Keep information about the current data quality state readily available faster Improved decision making Data policy. Governor Automate data governance End to end over the entire data lifecycle with automation, instant transparency and control Automate data policy assessments with glossaries, metadata and policies for sensitive data discovery that automatically tag link and annotate with metadata to provide enterprise wide search for all lines of business self service knowledge graph Digitize and search your enterprise knowledge. Turn multiple siloed data sources into machine Understandable knowledge from a single data canvas searching Explore data content across systems including GRP CRM billing systems, social media to fuel data pipelines >>Yeah, yeah, focusing on enterprise data automation. We're gonna talk about the journey to the cloud Remember, the hashtag is data automate and we're here with Leicester Waters. Who's the CTO of Iot Tahoe? Give us a little background CTO, You've got a deep, deep expertise in a lot of different areas. But what do we need to know? >>Well, David, I started my career basically at Microsoft, uh, where I started the information Security Cryptography group. They're the very 1st 1 that the company had, and that led to a career in information, security. And and, of course, as easy as you go along with information security data is the key element to be protected. Eso I always had my hands and data not naturally progressed into a roll out Iot talk was their CTO. >>What's the prescription for that automation journey and simplifying that migration to the cloud? >>Well, I think the first thing is understanding what you've got. So discover and cataloging your data and your applications. You know, I don't know what I have. I can't move it. I can't. I can't improve it. I can't build upon it. And I have to understand there's dependence. And so building that data catalog is the very first step What I got. Okay, >>so So we've done the audit. We know we've got what's what's next? Where do we go >>next? So the next thing is remediating that data you know, where do I have duplicate data? I may have often times in an organization. Uh, data will get duplicated. So somebody will take a snapshot of the data, you know, and then end up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer, and you can see where that will go. And trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to sort of understand where all your redundant data is? So when you go to the cloud, maybe you have an opportunity here to do you consolidate that that data, >>then what? You figure out what to get rid of our actually get rid of it. What's what's next? >>Yes, yes, that would be the next step. So figure out what you need. What, you don't need you Often times I've found that there's obsolete columns of data in your databases that you just don't need. Or maybe it's been superseded by another. You've got tables have been superseded by other tables in your database, so you got to kind of understand what's being used and what's not. And then from that, you can decide. I'm gonna leave this stuff behind or I'm gonna I'm gonna archive this stuff because I might need it for data retention where I'm just gonna delete it. You don't need it. All were >>plowing through your steps here. What's next on the >>journey? The next one is is in a nutshell. Preserve your data format. Don't. Don't, Don't. Don't boil the ocean here at music Cliche. You know, you you want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables in which they sent the columns and the way they're named. So some degree, you are gonna be doing a lift and ship, but it's an intelligent lift and ship. The >>data lives in silos. So how do you kind of deal with that? Problem? Is that is that part of the journey? >>That's that's great pointed because you're right that the data silos happen because, you know, this business unit is start chartered with this task. Another business unit has this task and that's how you get those in stance creations of the same data occurring in multiple places. So you really want to is part of your cloud migration. You really want a plan where there's an opportunity to consolidate your data because that means it will be less to manage. Would be less data to secure, and it will be. It will have a smaller footprint, which means reduce costs. >>But maybe you could address data quality. Where does that fit in on the >>journey? That's that's a very important point, you know. First of all, you don't want to bring your legacy issues with U. S. As the point I made earlier. If you've got data quality issues, this is a good time to find those and and identify and remediate them. But that could be a laborious task, and you could probably accomplish. It will take a lot of work. So the opportunity used tools you and automate that process is really will help you find those outliers that >>what's next? I think we're through. I think I've counted six. What's the What's the lucky seven >>Lucky seven involved your business users. Really, When you think about it, you're your data is in silos, part of part of this migration to cloud as an opportunity to break down the silos. These silence that naturally occurs are the business. You, uh, you've got to break these cultural barriers that sometimes exists between business and say so. For example, I always advise there's an opportunity year to consolidate your sensitive data. Your P I. I personally identifiable information and and three different business units have the same source of truth From that, there's an opportunity to consolidate that into one. >>Well, great advice, Lester. Thanks so much. I mean, it's clear that the Cap Ex investments on data centers they're generally not a good investment for most companies. Lester really appreciate Lester Water CTO of Iot Tahoe. Let's watch this short video and we'll come right back. >>Use cases. Data migration. Accelerate digitization of business by providing automated data migration work flows that save time in achieving project milestones. Eradicate operational risk and minimize labor intensive manual processes that demand costly overhead data quality. You know the data swamp and re establish trust in the data to enable data signs and Data analytics data governance. Ensure that business and technology understand critical data elements and have control over the enterprise data landscape Data Analytics ENABLEMENT Data Discovery to enable data scientists and Data Analytics teams to identify the right data set through self service for business demands or analytical reporting that advanced too complex regulatory compliance. Government mandated data privacy requirements. GDP Our CCP, A, e, p, R HIPPA and Data Lake Management. Identify late contents cleanup manage ongoing activity. Data mapping and knowledge graph Creates BKG models on business enterprise data with automated mapping to a specific ontology enabling semantic search across all sources in the data estate data ops scale as a foundation to automate data management presences. >>Are you interested in test driving the i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program? Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iot. Top Click on the link and connect with the data engineer to learn more and see Iot Tahoe in action. Everybody, we're back. We're talking about enterprise data automation. The hashtag is data automated and we're going to really dig into data migrations, data migrations. They're risky, they're time consuming and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So let's set up the problem a little bit. And then I want to get into some of the data said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>I think I mean, all migrations have to start with knowing the facts about your data. Uh, and you can try and do this manually. But when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. >>So I want to dig into this migration and let's let's pull up graphic. It will talk about We'll talk about what a typical migration project looks like. So what you see, here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this, uh and then use if I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones That Blue Bar is the time to test so you can see the second step. Data analysis. It's 24 weeks so very time consuming, and then let's not get dig into the stuff in the middle of the fine print. But there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom, and you can see hi is that sort of brown and and you could see a number of data analysis data staging data prep, the trial, the implementation post implementation fixtures, the transition to be a Blu, which I think is business as usual. >>The key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers, and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the basis of the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and probably the area that risk you could be. Stack your project team of lots and lots of people to do the next phase, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis, um, manual processes, as we all know, on the layer of trying to relate data that's in different grocery stores relating individual tables and columns, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools. Aziz said earlier the people who understand some of those systems may have left a while ago. CEO even higher risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data met on the data flow has been generated automatically, much less time and effort and much less cars stopped. >>Yeah. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the sort of Cisco said accelerated by Iot, Tom. Okay, great. And we're gonna talk about this, but look, what happens to the operational risk. A dramatic reduction in that, That that graph and then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis, data staging data prep trialling post implementation fixtures in transition to be a you all those went from high labor intensity. So we've now attacked that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its price States catalog. If you like, imagine trying to do that manually, you need to go into every individual data store. You need a DB, a business analyst, reach data store. They need to do an extract of the data. But it on the table was individually they need to cross reference that with other data school, it stores and schemers and tables you probably with the mother of all Lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. I'm not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data in the first place, get rid of duplication that sounds migrate to cloud store where things are really accessible. It's easy to build connections and to use native machine learning tools. You well, on the way up to the maturity card, you can start to use some of the more advanced applications >>massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Much appreciated. Yeah, yeah, yeah, yeah

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M I. T. Everybody. This is the Cube. The leader in live coverage. My name is David wanted. I'm here with my co host, Paul Gill, in this day to coverage of the M I t cdo I Q conference. A lot of acronym stands for M I. T. Of course, the great institution. But Chief Data officer information quality event is his 13th annual event. Lars to Maria's here is the managing partner of Brass Rat Capital. Cool name Lars. Welcome to the Cube. Great. Very much. Glad I start with a name brass around Capitol was That's >> rat is reference to the M I t school. Okay, Beaver? Well, he is, but the students call it a brass rat, and I'm third generation M i t. So it's just seen absolutely appropriate. That is a brass rods and capital is not a reference to money, but is actually referenced to the intellectual capital. They if you have five or six brass rats in the same company, you know, we Sometimes engineers arrive and they could do some things. >> And it Boy, if you put in some data data capital in there, you really explosions. We cause a few problems. So we're gonna talk about some new regulations that are coming down. New legislation that's coming down that you exposed me to yesterday, which is gonna have downstream implications. You get ahead of this stuff and understand it. You can really first of all, prepare, make sure you're in compliance, but then potentially take advantage for your business. So explain to us this notion of open government act. >> Um, in the last five years, six years or so, there's been an effort going on to increase the transparency across all levels of government. Okay, State, local and federal government. The first of federal government laws was called the the Open Data Act of 2014 and that was an act. They was acted unanimously by Congress and signed by Obama. They was taking the departments of the various agencies of the United States government and trying to roll up all the expenses into one kind of expense. This is where we spent our money and who got the money and doing that. That's what they were trying to do. >> Big picture type of thing. >> Yeah, big picture type thing. But unfortunately, it didn't work, okay? Because they forgot to include this odd word called mentalities. So the same departments meant the same thing. Data problem. They have a really big data problem. They still have it. So they're to G et o reports out criticizing how was done, and the government's gonna try and correct it. Then in earlier this year, there was another open government date act which said in it was signed by Trump. Now, this time you had, like, maybe 25 negative votes, but essentially otherwise passed Congress completely. I was called the Open as all capital O >> P E >> n Government Data act. Okay, and that's not been implemented yet. But there's live talking around this conference today in various Chief date officers are talking about this requirement that every single non intelligence defense, you know, vital protection of the people type stuff all the like, um, interior, treasury, transportation, those type of systems. If you produce a report these days, which is machine, I mean human readable. You must now in two years or three years. I forget the exact invitation date. Have it also be machine readable. Now, some people think machine riddle mil means like pdf formats, but no, >> In fact, what the government did is it >> said it must be machine readable. So you must be able to get into the reports, and you have to be able to extract out the information and attach it to the tree of knowledge. Okay, so we're all of sudden having context like they're currently machine readable, Quote unquote, easy reports. But you can get into those SEC reports. You pull out the net net income information and says its net income, but you don't know what it attaches to on the tree of knowledge. So, um, we are helping the government in some sense able, machine readable type reporting that weaken, do machine to machine without people being involved. >> Would you say the tree of knowledge You're talking about the constant >> man tick semantic tree of knowledge so that, you know, we all come from one concept like the human is example of a living thing living beast, a living Beeston example Living thing. So it also goes back, and they're serving as you get farther and farther out the tree, there's more distance or semantic distance, but you can attach it back to concept so you can attach context to the various data. Is this essentially metadata? That's what people call it. But if I would go over see sale here at M I t, they would turn around. They call it the Tree of Knowledge or semantic data. Okay, it's referred to his semantic dated, So you are passing not only the data itself, but the context that >> goes along with the data. Okay, how does this relate to the financial transparency? >> Well, Financial Transparency Act was introduced by representative Issa, who's a Republican out of California. He's run the government Affairs Committee in the House. He retired from Congress this past November, but in 2017 he introduced what's got referred to his H R 15 30 Um, and the 15 30 is going to dramatically change the way, um, financial regulators work in the United States. Um, it is about it was about to be introduced two weeks ago when the labor of digital currency stuff came up. So it's been delayed a little bit because they're trying to add some of the digital currency legislation to that law. >> A front run that Well, >> I don't know exactly what the remember soul coming out of Maxine Waters Committee. So the staff is working on a bunch of different things at once. But, um, we own g was asked to consult with them on looking at the 15 30 act and saying, How would we improve quote unquote, given our technical, you know, not doing policy. We just don't have the technical aspects of the act. How would we want to see it improved? So one of the things we have advised is that for the first time in the United States codes history, they're gonna include interesting term called ontology. You know what intelligence? Well, everyone gets scared by the word. And when I read run into people, they say, Are you a doctor? I said, no, no, no. I'm just a date. A guy. Um, but an intolerant tea is like a taxonomy, but it had order has important, and an ontology allows you to do it is ah, kinda, you know, giving some context of linking something to something else. And so you're able Thio give Maur information with an intolerant that you're able to you with a tax on it. >> Okay, so it's a taxonomy on steroids? >> Yes, exactly what? More flexible, >> Yes, but it's critically important for artificial intelligence machine warning because if I can give them until ology of sort of how it goes up and down the semantics, I can turn around, do a I and machine learning problems on the >> order of 100 >> 1000 even 10,000 times faster. And it has context. It has contacts in just having a little bit of context speeds up these problems so dramatically so and it is that what enables the machine to machine? New notion? No, the machine to machine is coming in with son called SP R M just standard business report model. It's a OMG sophistication of way of allowing the computers or machines, as we call them these days to get into a standard business report. Okay, so let's say you're ah drug company. You have thio certify you >> drugged you manufactured in India, get United States safely. Okay, you have various >> reporting requirements on the way. You've got to give extra easy the FDA et cetera that will always be a standard format. The SEC has a different format. FERC has a different format. Okay, so what s p r m does it allows it to describe in an intolerant he what's in the report? And then it also allows one to attach an ontology to the cells in the report. So if you like at a sec 10 Q 10 k report, you can attach a US gap taxonomy or ontology to it and say, OK, net income annual. That's part of the income statement. You should never see that in a balance sheet type item. You know his example? Okay. Or you can for the first time by having that context you can say are solid problem, which suggested that you can file these machine readable reports that air wrong. So they believe or not, There were about 50 cases in the last 10 years where SEC reports have been filed where the assets don't equal total liabilities, plus cheryl equity, you know, just they didn't add >> up. So this to, >> you know, to entry accounting doesn't work. >> Okay, so so you could have the machines go and check scale. Hey, we got a problem We've >> got a problem here, and you don't have to get humans evolved. So we're gonna, um uh, Holland in Australia or two leaders ahead of the United States. In this area, they seem dramatic pickups. I mean, Holland's reporting something on the order of 90%. Pick up Australia's reporting 60% pickup. >> We say pick up. You're talking about pickup of errors. No efficiency, productivity, productivity. Okay, >> you're taking people out of the whole cycle. It's dramatic. >> Okay, now what's the OMG is rolling on the hoof. Explain the OMG >> Object Management Group. I'm not speaking on behalf of them. It's a membership run organization. You remember? I am a >> member of cold. >> I'm a khalid of it. But I don't represent omg. It's the membership has to collectively vote that this is what we think. Okay, so I can't speak on them, right? I have a pretty significant role with them. I run on behalf of OMG something called the Federated Enterprise Risk Management Group. That's the group which is focusing on risk management for large entities like the federal government's Veterans Affairs or Department offense upstairs. I think talking right now is the Chief date Officer for transportation. OK, that's a large organization, which they, they're instructed by own be at the, um, chief financial officer level. The one number one thing to do for the government is to get an effective enterprise worst management model going in the government agencies. And so they come to own G let just like NIST or just like DARPA does from the defense or intelligence side, saying we need to have standards in this area. So not only can we talk thio you effectively, but we can talk with our industry partners effectively on space. Programs are on retail, on medical programs, on finance programs, and so they're at OMG. There are two significant financial programs, or Sanders, that exist once called figgy financial instrument global identifier, which is a way of identifying a swap. Its way of identifying a security does not have to be used for a que ce it, but a worldwide. You can identify that you know, IBM stock did trade in Tokyo, so it's a different identifier has different, you know, the liberals against the one trading New York. Okay, so those air called figgy identifiers them. There are attributes associated with that security or that beast the being identified, which is generally comes out of 50 which is the financial industry business ontology. So you know, it says for a corporate bond, it has coupon maturity, semi annual payment, bullets. You know, it is an example. So that gives you all the information that you would need to go through to the calculation, assuming you could have a calculation routine to do it, then you need thio. Then turn around and set up your well. Call your environment. You know where Ford Yield Curves are with mortgage backed securities or any portable call. Will bond sort of probabilistic lee run their numbers many times and come up with effective duration? Um, And then you do your Vader's analytics. No aggregating the portfolio and looking at Shortfalls versus your funding. Or however you're doing risk management and then finally do reporting, which is where the standardized business reporting model comes in. So that kind of the five parts of doing a full enterprise risk model and Alex So what >> does >> this mean for first? Well, who does his impact on? What does it mean for organizations? >> Well, it's gonna change the world for basically everyone because it's like doing a clue ends of a software upgrade. Conversion one's version two point. Oh, and you know how software upgrades Everyone hates and it hurts because everyone's gonna have to now start using the same standard ontology. And, of course, that Sarah Ontology No one completely agrees with the regulators have agreed to it. The and the ultimate controlling authority in this thing is going to be F sock, which is the Dodd frank mandated response to not ever having another chart. So the secretary of Treasury heads it. It's Ah, I forget it's the, uh, federal systemic oversight committee or something like that. All eight regulators report into it. And, oh, if our stands is being the adviser Teff sock for all the analytics, what these laws were doing, you're getting over farm or more power to turn around and look at how we're going to find data across the three so we can come up consistent analytics and we can therefore hopefully take one day. Like Goldman, Sachs is pre payment model on mortgages. Apply it to Citibank Portfolio so we can look at consistency of analytics as well. It is only apply to regulated businesses. It's gonna apply to regulated financial businesses. Okay, so it's gonna capture all your mutual funds, is gonna capture all your investment adviser is gonna catch her. Most of your insurance companies through the medical air side, it's gonna capture all your commercial banks is gonna capture most of you community banks. Okay, Not all of them, because some of they're so small, they're not regularly on a federal basis. The one regulator which is being skipped at this point, is the National Association Insurance Commissioners. But they're apparently coming along as well. Independent federal legislation. Remember, they're regulated on the state level, not regularly on the federal level. But they've kind of realized where the ball's going and, >> well, let's make life better or simply more complex. >> It's going to make life horrible at first, but we're gonna take out incredible efficiency gains, probably after the first time you get it done. Okay, is gonna be the problem of getting it done to everyone agreeing. We use the same definitions >> of the same data. Who gets the efficiency gains? The regulators, The companies are both >> all everyone. Can you imagine that? You know Ah, Goldman Sachs earnings report comes out. You're an analyst. Looking at How do I know what Goldman? Good or bad? You have your own equity model. You just give the model to the semantic worksheet and all turn around. Say, Oh, those numbers are all good. This is what expected. Did it? Did it? Didn't you? Haven't. You could do that. There are examples of companies here in the United States where they used to have, um, competitive analysis. Okay. They would be taking somewhere on the order of 600 to 7. How 100 man hours to do the competitive analysis by having an available electronically, they cut those 600 hours down to five to do a competitive analysis. Okay, that's an example of the type of productivity you're gonna see both on the investment side when you're doing analysis, but also on the regulatory site. Can you now imagine you get a regulatory reports say, Oh, there's they're out of their way out of whack. I can tell you this fraud going on here because their numbers are too much in X y z. You know, you had to fudge numbers today, >> and so the securities analyst can spend Mme. Or his or her time looking forward, doing forecasts exactly analysis than having a look back and reconcile all this >> right? And you know, you hear it through this conference, for instance, something like 80 to 85% of the time of analysts to spend getting the data ready. >> You hear the same thing with data scientists, >> right? And so it's extent that we can helped define the data. We're going thio speed things up dramatically. But then what's really instinct to me, being an M I t engineer is that we have great possibilities. An A I I mean, really great possibilities. Right now, most of the A miles or pattern matching like you know, this idea using face shield technology that's just really doing patterns. You can do wonderful predictive analytics of a I and but we just need to give ah lot of the a m a. I am a I models the contact so they can run more quickly. OK, so we're going to see a world which is gonna found funny, But we're going to see a world. We talk about semantic analytics. Okay. Semantic analytics means I'm getting all the inputs for the analysis with context to each one of the variables. And when I and what comes out of it will be a variable results. But you also have semantics with it. So one in the future not too distant future. Where are we? We're in some of the national labs. Where are you doing it? You're doing pipelines of one model goes to next model goes the next mile. On it goes Next model. So you're gonna software pipelines, Believe or not, you get them running out of an Excel spreadsheet. You know, our modern Enhanced Excel spreadsheet, and that's where the future is gonna be. So you really? If you're gonna be really good in this business, you're gonna have to be able to use your brain. You have to understand what data means You're going to figure out what your modeling really means. What happens if we were, You know, normally for a lot of the stuff we do bell curves. Okay, well, that doesn't have to be the only distribution you could do fat tail. So if you did fat tail descriptions that a bell curve gets you much different results. Now, which one's better? I don't know, but, you know, and just using example >> to another cut in the data. So our view now talk about more about the tech behind this. He's mentioned a I What about math? Machine learning? Deep learning. Yeah, that's a color to that. >> Well, the tech behind it is, believe or not, some relatively old tech. There is a technology called rd F, which is kind of turned around for a long time. It's a science kind of, ah, machine learning, not machine wearing. I'm sorry. Machine code type. Fairly simplistic definitions. Lots of angle brackets and all this stuff there is a higher level. That was your distracted, I think put into standard in, like, 2000 for 2005. Called out. Well, two point. Oh, and it does a lot at a higher level. The same stuff that already f does. Okay, you could also create, um, believer, not your own special ways of a communicating and ontology just using XML. Okay, So, uh, x b r l is an enhanced version of XML, okay? And so some of these older technologies, quote unquote old 20 years old, are essentially gonna be driving a lot of this stuff. So you know you know Corbett, right? Corba? Is that what a maid omg you know, on the communication and press thing, do you realize that basically every single device in the world has a corpus standard at okay? Yeah, omg Standard isn't all your smartphones and all your computers. And and that's how they communicate. It turns out that a lot of this old stuff quote unquote, is so rigidly well defined. Well done that you can build modern stuff that takes us to the Mars based on these old standards. >> All right, we got to go. But I gotta give you the award for the most acronyms >> HR 15 30 fi G o m g s b r >> m fsoc tarp. Oh, fr already halfway. We knew that Owl XML ex brl corba, Which of course >> I do. But that's well done. Like thanks so much for coming. Everyone tried to have you. All right, keep it right there, everybody, We'll be back with our next guest from M i t cdo I Q right after this short, brief short message. Thank you

>> Live from New York, It's the CUBE! Covering theCUBE, New York City 2018. Brought to you by SiliconANGLE Media and its Ecosystem partners. >> Welcome back to the live CUBE coverage here in New York City for CUBE NYC, #CubeNYC. This coverage of all things data, all things cloud, all things machine learning here in the big data realm. I'm John Furrier and Dave Vellante. We've got two great guests from Cisco. We got DD who is the Vice President of Data Center Marketing at Cisco, and Han Yang who is the Senior Product Manager at Cisco. Guys, welcome to the Cube. Thanks for coming on again. >> Good to see ya. >> Thanks for having us. >> So obviously one of the things that has come up this year at the Big Data Show, used to be called Hadoop World, Strata Data, now it's called, the latest name. And obviously CUBE NYC, we changed from Big Data NYC to CUBE NYC, because there's a lot more going on. I heard hallway conversations around blockchain, cryptocurrency, Kubernetes has been said on theCUBE already at least a dozen times here today, multicloud. So you're seeing the analytical world try to be, in a way, brought into the dynamics around IT infrastructure operations, both cloud and on premises. So interesting dynamics this year, almost a dev ops kind of culture to analytics. This is a new kind of sign from this community. Your thoughts? >> Absolutely, I think data and analytics is one of those things that's pervasive. Every industry, it doesn't matter. Even at Cisco, I know we're going to talk a little more about the new AI and ML workload, but for the last few years, we've been using AI and ML techniques to improve networking, to improve security, to improve collaboration. So it's everywhere. >> You mean internally, in your own IT? >> Internally, yeah. Not just in IT, in the way we're designing our network equipment. We're storing data that's flowing through the data center, flowing in and out of clouds, and using that data to make better predictions for better networking application performance, security, what have you. >> The first topic I want to talk to you guys about is around the data center. Obviously, you do data center marketing, that's where all the action is. The cloud, obviously, has been all the buzz, people going to the cloud, but Andy Jassy's announcement at VMworld really is a validation that we're seeing, for the first time, hybrid multicloud validated. Amazon announced RDS on VMware on-premises. >> That's right. This is the first time Amazon's ever done anything of this magnitude on-premises. So this is a signal from the customers voting with their wallet that on-premises is a dynamic. The data center is where the data is, that's where the main footprint of IT is. This is important. What's the impact of that dynamic, of data center, where the data is with the option of a cloud. How does that impact data, machine learning, and the things that you guys see as relevant? >> I'll start and Han, feel free to chime in here. So I think those boundaries between this is a data center, and this a cloud, and this is campus, and this is the edge, I think those boundaries are going away. Like you said, data center is where the data is. And it's the ability of our customers to be able to capture that data, process it, curate it, and use it for insight to take decision locally. A drone is a data center that flies, and boat is a data center that floats, right? >> And a cloud is a data center that no one sees. >> That's right. So those boundaries are going away. We at Cisco see this as a continuum. It's the edge cloud continuum. The edge is exploding, right? There's just more and more devices, and those devices are cranking out more data than ever before. Like I said, it's the ability of our customers to harness the data to make more meaningful decisions. So Cisco's take on this is the new architectural approach. It starts with the network, because the network is the one piece that connects everything- every device, every edge, every individual, every cloud. There's a lot of data within the network which we're using to make better decisions. >> I've been pretty close with Cisco over the years, since '95 timeframe. I've had hundreds of meetings, some technical, some kind of business. But I've heard that term edge the network many times over the years. This is not a new concept at Cisco. Edge of the network actually means something in Cisco parlance. The edge of the network >> Yeah. >> that the packets are moving around. So again, this is not a new idea at Cisco. It's just materialized itself in a new way. >> It's not, but what's happening is the edge is just now generating so much data, and if you can use that data, convert it into insight and make decisions, that's the exciting thing. And that's why this whole thing about machine learning and artificial intelligence, it's the data that's being generated by these cameras, these sensors. So that's what is really, really interesting. >> Go ahead, please. >> One of our own studies pointed out that by 2021, there will be 847 zettabytes of information out there, but only 1.3 zettabytes will actually ever make it back to the data center. That just means an opportunity for analytics at the edge to make sense of that information before it ever makes it home. >> What were those numbers again? >> I think it was like 847 zettabytes of information. >> And how much makes it back? >> About 1.3. >> Yeah, there you go. So- >> So a huge compression- >> That confirms your research, Dave. >> We've been saying for a while now that most of the data is going to stay at the edge. There's no reason to move it back. The economics don't support it, the latency doesn't make sense. >> The network cost alone is going to kill you. >> That's right. >> I think you really want to collect it, you want to clean it, and you want to correlate it before ever sending it back. Otherwise, sending that information, of useless information, that status is wonderful. Well that's not very valuable. And 99.9 percent, "things are going well." >> Temperature hasn't changed. (laughs) >> If it really goes wrong, that's when you want to alert or send more information. How did it go bad? Why did it go bad? Those are the more insightful things that you want to send back. >> This is not just for IoT. I mean, cat pictures moving between campuses cost money too, so why not just keep them local, right? But the basic concepts of networking. This is what I want to get in my point, too. You guys have some new announcements around UCS and some of the hardware and the gear and the software. What are some of the new announcements that you're announcing here in New York, and what does it mean for customers? Because they want to know not only speeds and feeds. It's a software-driven world. How does the software relate? How does the gear work? What's the management look like? Where's the control plane? Where's the management plane? Give us all the data. >> I think the biggest issues starts from this. Data scientists, their task is to export different data sources, find out the value. But at the same time, IT is somewhat lagging behind. Because as the data scientists go from data source A to data source B, it could be 3 petabytes of difference. IT is like, 3 petabytes? That's only from Monday through Wednesday? That's a huge infrastructure requirement change. So Cisco's way to help the customer is to make sure that we're able to come out with blueprints. Blueprints enabling the IT team to scale, so that the data scientists can work beyond their own laptop. As they work through the petabytes of data that's come in from all these different sources, they're able to collaborate well together and make sense of that information. Only by scaling with IT helping the data scientists to work the scale, that's the only way they can succeed. So that's why we announced a new server. It's called a C480ML. Happens to have 8 GPUs from Nvidia inside helping customers that want to do that deep learning kind of capabilities. >> What are some of the use cases on these as products? It's got some new data capabilities. What are some of the impacts? >> Some of the things that Han just mentioned. For me, I think the biggest differentiation in our solution is things that we put around the box. So the management layer, right? I mean, this is not going to be one server and one data center. It's going to be multiple of them. You're never going to have one data center. You're going to have multiple data centers. And we've got a really cool management tool called Intersight, and this is supported in Intersight, day one. And Intersight also uses machine learning techniques to look at data from multiple data centers. And that's really where the innovation is. Honestly, I think every vendor is bend sheet metal around the latest chipset, and we've done the same. But the real differentiation is how we manage it, how we use the data for more meaningful insight. I think that's where some of our magic is. >> Can you add some code to that, in terms of infrastructure for AI and ML, how is it different than traditional infrastructures? So is the management different? The sheet metal is not different, you're saying. But what are some of those nuances that we should understand. >> I think especially for deep learning, multiple scientists around the world have pointed that if you're able to use GPUs, they're able to run the deep learning frameworks faster by roughly two waters magnitude. So that's part of the reason why, from an infrastructure perspective, we want to bring in that GPUs. But for the IT teams, we didn't want them to just add yet another infrastructure silo just to support AI or ML. Therefore, we wanted to make sure it fits in with a UCS-managed unified architecture, enabling the IT team to scale but without adding more infrastructures and silos just for that new workload. But having that unified architecture, it helps the IT to be more efficient and, at the same time, is better support of the data scientists. >> The other thing I would add is, again, the things around the box. Look, this industry is still pretty nascent. There is lots of start-ups, there is lots of different solutions, and when we build a server like this, we don't just build a server and toss it over the fence to the customer and say "figure it out." No, we've done validated design guides. With Google, with some of the leading vendors in the space to make sure that everything works as we say it would. And so it's all of those integrations, those partnerships, all the way through our systems integrators, to really understand a customer's AI and ML environment and can fine tune it for the environment. >> So is that really where a lot of the innovation comes from? Doing that hard work to say, "yes, it's going to be a solution that's going to work in this environment. Here's what you have to do to ensure best practice," etc.? Is that right? >> So I think some of our blueprints or validated designs is basically enabling the IT team to scale. Scale their stores, scale their CPU, scale their GPU, and scale their network. But do it in a way so that we work with partners like Hortonworks or Cloudera. So that they're able to take advantage of the data lake. And adding in the GPU so they're able to do the deep learning with Tensorflow, with Pytorch, or whatever curated deep learning framework the data scientists need to be able to get value out of those multiple data sources. These are the kind of solutions that we're putting together, making sure our customers are able to get to that business outcome sooner and faster, not just a-- >> Right, so there's innovation at all altitudes. There's the hardware, there's the integrations, there's the management. So it's innovation. >> So not to go too much into the weeds, but I'm curious. As you introduce these alternate processing units, what is the relationship between traditional CPUs and these GPUs? Are you managing them differently, kind of communicating somehow, or are they sort of fenced off architecturally. I wonder if you could describe that. >> We actually want it to be integrated, because by having it separated and fenced off, well that's an IT infrastructure silo. You're not going to have the same security policy or the storage mechanisms. We want it to be unified so it's easier on IT teams to support the data scientists. So therefore, the latest software is able to manage both CPUs and GPUs, as well as having a new file system. Those are the solutions that we're putting forth, so that ARC-IT folks can scale, our data scientists can succeed. >> So IT's managing a logical block. >> That's right. And even for things like inventory management, or going back and adding patches in the event of some security event, it's so much better to have one integrated system rather than silos of management, which we see in the industry. >> So the hard news is basically UCS for AI and ML workloads? >> That's right. This is our first server custom built ground up to support these deep learning, machine learning workloads. We partnered with Nvidia, with Google. We announced earlier this week, and the phone is ringing constantly. >> I don't want to say godbot. I just said it. (laughs) This is basically the power tool for deep learning. >> Absolutely. >> That's how you guys see it. Well, great. Thanks for coming out. Appreciate it, good to see you guys at Cisco. Again, deep learning dedicated technology around the box, not just the box itself. Ecosystem, Nvidia, good call. Those guys really get the hot GPUs out there. Saw those guys last night, great success they're having. They're a key partner with you guys. >> Absolutely. >> Who else is partnering, real quick before we end the segment? >> We've been partnering with software sci, we partner with folks like Anaconda, with their Anaconda Enterprise, which data scientists love to use as their Python data science framework. We're working with Google, with their Kubeflow, which is open source project integrating Tensorflow on top of Kubernetes. And of course we've been working with folks like Caldera as well as Hortonworks to access the data lake from a big data perspective. >> Yeah, I know you guys didn't get a lot of credit. Google Cloud, we were certainly amplifying it. You guys were co-developing the Google Cloud servers with Google. I know they were announcing it, and you guys had Chuck on stage there with Diane Greene, so it was pretty positive. Good integration with Google can make a >> Absolutely. >> Thanks for coming on theCUBE, thanks, we appreciate the commentary. Cisco here on theCUBE. We're in New York City for theCUBE NYC. This is where the world of data is converging in with IT infrastructure, developers, operators, all running analytics for future business. We'll be back with more coverage, after this short break. (upbeat digital music)