>>from around the globe. It's the Cube with digital coverage of data automated and event. Siri's brought to you by Iot. Tahoe. Okay, we're back with Adam Worthington. Who's the CTO and co founder of Ethos Adam. Good to see you. How are things across the pond? >>Thank you. I'm sure that a little bit on your side. >>Okay, so let's let's set it up. Tell us about yourself. What your role is a CTO and give us the low down on those. >>Sure, So we get automatic. As you said CTO and co founder of A were pretty young company ourselves that we're in our sixth year and we specialize in emerging disruptive technologies within the infrastructure Data center kind of cloud space. And my role is the technical lead. So 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, is one of the reasons we deliberately focusing on on also kind of a validation and evaluation of new technologies. Yeah, >>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. That's a lot of what I like to speak to customers about on. Sometimes I get that gets lost, particularly with within highly technical field. I like the virtualization guy or a network like very quickly start talking about the nuts and bolts of technology on I'm a techie. I'm absolutely a nerd, like the best tech guitar but fundamentally reporting in technologies to meet. This is outcomes to solve business problems on on to enable a better way. >>Love it. We love tech, too, but really, it's all about the customer. So let's talk about smart data. You know, when you when you throw in terms like this is it kind of Canfield Buzz Wordy. But let's let's get into the meat on it. What does that mean to you? One of the critical aspects of so called smart data >>cool probably hoped to step back a little bit and set the scene a little bit more in in terms of kind of where I came from, the types of problems that I'm really an infrastructure solution architect trace on what I kind of benefits. We organically But over time my personal framework, I focused on three core design principles whatever it was I was designing. And obviously they need different things. Depending on what technology area is that we're working with. That's pretty good on. And what I realized that we realized we started with those principles could be it could be used more broadly in the the absolute best of breed of technologies. And those really disrupt, uh, significantly improve upon the status quo in one or more of those three areas. Ideally or more simple, more on if we look at the data of the challenges that organizations, enterprises organizations have criticized around data and smart fail over the best way. Maybe it's good to reflect on what the opposite end of the story is kind of why data is often quite dumb. The traditional approaches. We have limited visibility into the data that we're up to the story using within our infrastructure as what we kind of ended up with over time, through no fault of the organizations that have happened silos, everyone silos of expertise. So whether that be, that's going out. Specialized teams, socialization, networking. They have been, for example, silos of infrastructure, which trade state of fragmentation copies of data in different areas of the infrastructure on copies of replication in that data set or reputation in terms of application environments. I think that that's kind of what we tend to focus on, what it's becoming, um, resonating with more organizations. There's a survey that one of the vendors that we work with actually are launched vendor 5.5 years ago, a medical be gone. They work with any company called Phantom Born a first of a kind of global market, 900 respondents, all different vectors, a little different countries, the U. S. And Germany. And what they found was shocking. It was a recent survey so focused on secondary data, but the lessons learned the information taken out a survey applies right across the gamut of infrastructure data organizations. Just some stats just pull out the five minutes 85% off the organization surveyed store between two and five stores data in 3 to 5 clouds. 63% of organizations have between four and 16 coffees of exactly the same data. Nearly nine out of 10 respondents believe that organizations, secondly, data's fragmented across silos are touched on is would become nearly impossible to manage over the long term on. And 91% of the vast majority of organizations leadership were concerned about the level of visibility their teams. So they're the kind of areas that a smart approach to data will directly address. So reducing silos 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. I mean, Harry, so smarter we get with data is in my eyes. Anyway, the further we moved away from this, >>there was a lot in that answer, but I want to kind of summarize it if I can talk. You started with simplicity, flexibility, efficiency. Of course, that's what customers want. And then I was gonna ask you about you know, what challenges customers are facing, and I think you laid it out here. But I want to I want to pick on a couple of some of the data that you talked about the public cloud treat that adds complexity and diversity in skill requirements. The copies of data is so true, like data is just like like if rebels, If you Star Trek franchise, they just expand and replicate. So that's an expense, and it adds complexity. Silo data means you spend a lot of time trying to figure out who's got the right data. What's the real truth with a lot of manual processes involved in the visibility is obviously critical. So those are the problems on. But course you talked about how you address those, But But how does it work? I mean, how do you know what's what's involved in injecting smarts into your data? Lifecycle >>that plane, Think about it. So insurance of the infrastructure and say they were very good reasons why customers are in situations they have been in this situation because of the limits are traditional prices. So you look at something is fundamental. So a great example, um on applications that utilize the biggest fundamentally back ups are now often what that typically required is completely separate infrastructure to everything else. But when we're talking about the data set, so what would be a perfect is if we could back up data on use it for other things, and that's where a, uh, a technology provider like So So although it better technology is incredibly simple, it's also incredibly powerful and allows identification, consolidation. And then, if you look at just getting insight out of that fundamentally tradition approaches to infrastructure, they're put in a point of putting a requirement. And therefore it wasn't really incumbent exposed any information out of the data that's stored within the division, which makes it really tricky to do anything else outside of the application. That that's where something like Iot how come in in terms of abstracting away the complexity more directly, I So these are the kind of the area. So I think one of my I did not ready, but generally one of my favorite quotes from the French philosopher and a mathematician, Blaise Pascal, he says, 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, that computation of what we're talking about, it is actually really complicated to develop a technology capability 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 sense for the business users. Music. It's My belief is that the technology shouldn't mean that the users of the technology has to be a technology expert what we really want them to be. And they should be a business experts in any technology that you should enable on demand for the types of technologies to get me excited. They're not necessarily from a ftt complicated technology perspective, but those are really focused on impressive the capability. >>Yeah. Okay, so you talked about back up, We're gonna hear from Kohi City a little bit later and beyond backup data protection, Data Management, That insight piece you talked earlier about visibility, and that's what the Iot Tahoe's bringing table with its software. So that's another component of the tech stack, if you will, Um, and then you talk about simplicity. We're gonna hear from pure storage. They're all about simple storage. They call it the modern data experience. I think so. So those are some of the aspects and 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, it's that they said, in understanding both sides so that it keeps us on our ability to be able to deliver on exactly what you just said. It's being experts in the capabilities and new and better ways to do things but also having the kind of business under. I found it to be able to ask the right questions, identify how new a better price is positions and you touched on. Yet three vendors that we work with that you have on the panel are very genuinely of. I think of the most exciting around storage and pure is a great one. So yes, a lot of the way that they've made their way. The market is through impressive C and through producing data redundancy. But another area that I really like is with that platform, you can do more with less. And that's not just about using data redundancy. That's about creating application environment, that conservative, then the infrastructure to service different requirements are able to do that the random Io thing without getting too kind of low level as well as a sequential. So what that means is that you don't necessarily have to move data from application environment a do one thing. They disseminate it and then move it to the application environment. Be that based environment three in terms of an analytics on the left to right work. So keep the data where it is, use it for 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. Well at that again resonates that I want to pick up a soundbite that resonates with all of the vendors we have on the panel later. This is the way that they're able todo a better a better TCO better our alliance significantly reduce the value of data. But to answer your question, yeah, you're exactly right. So it's key to us to kind of position, understand? Customer climbs, position the right technology. >>Adam. I wonder if you could give us your insights based on your experience with customers in terms of what success looks like. I'm interested in what they're measuring. I'm big on and end cycle times and taking a systems view, but of course you know customers. They want to measure everything, whether it's the productivity of developers or, you know, time to insights, etcetera. What >>are >>they? One of the KP eyes that are driving success and outcomes? >>Those capabilities on historically in our space have always been a bit really. When you talk about total cost of ownership, talk about return on investment, you talk about time to value on. I've worked in many different companies, many different infrastructure, often quite complicated environments and infrastructure. I'm being able to put together anything Security realistic gets proven out. One solution gets turned around our alliance TCO is challenging. But now with these new, a better approach is that more efficient, enables you to really build a true story and on replicate whatever you want. Obviously ran kind of our life, and the key thing is to say from data, But now it's time to value. So what we what? We help in terms of the scoping on in terms of the understanding what the requirements are, we specifically called out business outcomes what organizations are looking to achieve and then back on those metrics, uh, to those outcomes. What that does is a few different things, but it provides a certain success criteria. Whether that's success criteria within a proof of concept of the mobile solutions on being able to speak that language on before, more directly meet the needs of the business kind of crystallized defined way is we're only really be able to do that. Now we work with >>Yeah, So when you think about the business case, they are a why benefit over cost benefit obviously lower tco you lower the denominator, you're going to increase the output in the value. And then I would I would really stress that I think the numerator, ultimately especially in a world of data, is the most important. And I think the TCO is fundamental. It's really becoming table stakes. You gotta have simple. You've gotta have efficient. You've got to be agile. But it enables that that numerator, whether that's new customer revenue, maybe, you know, maybe cost savings across the business. And again that comes from taking that systems view. Do you >>have >>examples that you can share with us even if they're anonymous, eyes the customers that you work with that or maybe a little further down on the journey, or maybe not things that you can share with us that are proof points here. >>Sure, it's quite easy and very gratifying when you've spoken to a customer. We know you've been doing this for 20 years, and this is the way that your infrastructure if you think about it like this, if we implemented that technology or this new approach, then we will enable you to get simple, often ready, populous. Reduce your back. I worked on a project where a customer accused that back book from I think it was. It was nine. Just under 10. It was nine fully loaded. Wraps back. We should just for the it you're providing the fundamental underlying storage architectures. And they were able to consolidate that that down on, provide additional capacity. Great performance. The less than half Uh huh. Looking at the you mentioned data protection earlier. So another organization. This is a project which is just kind of nearing completion of the moment. Huge organization. They're literally petabytes of data that was servicing their back up in archive. And what they have is not just the reams of data, they have the combined thing. I different backup. Yeah, that they have dependent on the what area of infrastructure they were backing up. So whether it was virtualization that was different, they were backing up. Pretty soon they're backing up another database environment 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 system what they were able to achieve. And this is again one of the clients have they've gone above the threshold of being able to back up. When they tried to do a CR, you been everything back up into in a second. They want people to achieve it. Within the timescales is a disaster recovery, business continuity. So with this, we're able to prove them with a proof up. Just before they went into production and the our test using the new approach. And they were able to recover everything the entire interest in minutes instead of a production production, workloads that this was in comparison to hours and that was those hours is just a handful of workloads. They were able to get up and running with the entire estate, and I think it was something like an hour on the core production systems. They were up and running practically instantaneously. So if you look at really stepping back what the customers are looking to the chief, they want to be able to if there is any issues recover from those issues, understand what they're dealing with. Yeah, On another, we have customers that we work with recently what they had huge challenges around and they were understandably very scared about GDP are. But this is a little while ago, actually, a bit still no up. A conversation has gone away. Just everybody are still speaks to issues and concerns around GDP are applying understanding whether they so put in them in us in a position to be able to effectively react. Subject That was something that was a key metric. A target for on infrastructure solution that we work with and we were able to provide them with the insight into their data on day enables them to react to compliance. And they're here to get a subject access request way created in significantly. I'm >>awesome. Thank you for that. I want to pick up on a little bit. So the first example you get your infrastructure in order to bust down those silos and what I've when I talk to customers. And I've talked to a number of banks, insurance companies, other financial services of manufacturers when they're able to sort of streamline that data lifecycle and bring in automation and intelligence, if you will. What they tell me is now they're able to obviously compress the time to value, but also they're loading up on way more initiatives and projects that they can deliver for the business. And you talk for about about the line of business having self served. The businesses feel like they actually are really invested in the data, that it's their data that it's not, you know, confusing and a lot of finger pointing. So so that's that's huge on. And I think that your other example is right on as well of really clear business value that organizations are seeing. So thanks for those you know. Now is the time really, t get these houses in order, if you will, because it really drives competitive advantage, especially take your second example in this isolation economy, you know, being able to respond things like privacy are just increasingly critical. Adam, give us the final thoughts. Bring us home in this segment, >>not the farm of built, something we didn't particularly touch on that I think it's It's fairly fairly hidden. It isn't spoken about as much as I think it is that digital approaches to infrastructure we've already touched on there could be complicated on lack of efficiency, impact, a user's ability to be agile, what you find with traditional approaches. And you already touched on some of the kind of benefits and new approaches that they're often very prescriptive, designed for a particular as the infrastructure environment, the way that it served up to the users in a kind of A packaged either way means that they need to use it in that whatever way, in places. So that kind of self service aspect that comes in from a flexibility standpoint that for me in this platform approach, which is the right way to address technology in my eyes enables it's the infrastructure to be used effectively so that the business uses of the data users what we find in this capability into their hand and start innovating in the way that they use that on the way that they bring benefits a platform to prescriptive, and they are able to do that. So what you're doing with these new approaches is all of the metrics that we touched on fantastic from a cost standpoint, from a visibility standpoint. But what it means is that the innovators in the business want to really, really understand what they're looking to achieve and now tools to innovate with us. Now, I think I've started to see that with projects that were completed, you could do it in the right way. You articulate the capability and empower the business users in the right way. Then very significantly better position. Take advantage of this on really match and significantly bigger than their competition. >>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. And thank you very much today. >>Alright, Stay safe and thank you. Everybody, this is Dave Volante for the Cube. Yeah, yeah, yeah, yeah

>> 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) >> 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)

>> Narrator: From around the globe, its theCUBE! With digital coverage of enterprise data automation. An event series brought to you by Io-Tahoe. >> Okay, we're back, welcome back to Data Automated, Ajay Vohora is CEO of Io-Tahoe. Ajay, good to see you, how are things in London? >> Things are doing well, things are doing well, we're making progress. Good to see you, hope you're doing well, and pleasure being back here on theCUBE. >> Yeah, it's always great to talk to you, we're talking enterprise data automation, as you know, within our community we've been pounding the whole DataOps conversation. A little different, though, we're going to dig into that a little bit, but let's start with, Ajay, how are you seeing the response to COVID, and I'm especially interested in the role that data has played in this pandemic. >> Yeah, absolutely, I think everyone's adapting, both socially and in business, the customers that I speak to, day in, day out, that we partner with, they're busy adapting their businesses to serve their customers, it's very much a game of ensuring that we can serve our customers to help their customers, and the adaptation that's happening here is trying to be more agile, trying to be more flexible, and there's a lot of pressure on data, lot of demand on data to deliver more value to the business, to serve that customer. >> Yeah, I mean data, machine intelligence and cloud are really three huge factors that have helped organizations in this pandemic, and the machine intelligence or AI piece, that's what automation is all about, how do you see automation helping organizations evolve, maybe faster than they thought they might have to? >> For sure, I think the necessity of these times, there's, as they say, there's a lot of demand on doing something with data, data, a lot of businesses talk about being data-driven. It's interesting, I sort of look behind that when we work with our customers, and it's all about the customer. My peers, CEOs, investors, shareholders, the common theme here is the customer, and that customer experience starts and ends with data. Being able to move from a point that is reacting to what the customer is expecting, and taking it to that step forward where you can be proactive to serve what that customer's expectation to, and that's definitely come alive now with the current time. >> Yeah, so as I said, we were talking about DataOps a lot, the idea being DevOps applied to the data pipeline, but talk about enterprise data automation, what is it to you and how is it different from DataOps? >> Yeah, great question, thank you. I think we're all familiar with, got more and more awareness around DevOps as it's applied to processes, methodologies that have become more mature over the past five years around DevOps, but managing change, managing application life cycles, managing software development, DevOps has been great, but breaking down those silos between different roles, functions, and bringing people together to collaborate. And we definitely see that those tools, those methodologies, those processes, that kind of thinking, lending itself to data with DataOps is exciting, we're excited about that, and shifting the focus from being IT versus business users to, who are the data producers and who are the data consumers, and in a lot of cases it can sit in many different lines of business. So with DataOps, those methods, those tools, those processes, what we look to do is build on top of that with data automation, it's the nuts and bolts of the algorithms, the models behind machine learning, the functions, that's where we invest our R&D. And bringing that in to build on top of the methods, the ways of thinking that break down those silos, and injecting that automation into the business processes that are going to drive a business to serve its customer. It's a layer beyond DevOps, DataOps, taking it to that point where, way I like to think about it is, is the automation behind the automation. We can take, I'll give you an example of a bank where we've done a lot of work to move them into accelerating their digital transformation, and what we're finding is that as we're able to automate the jobs related to data, and managing that data, and serving that data, that's going into them as a business automating their processes for their customer. So it's definitely having a compound effect. >> Yeah, I mean I think that DataOps for a lot of people is somewhat new, the whole DevOps, the DataOps thing is good and it's a nice framework, good methodology, there is obviously a level of automation in there, and collaboration across different roles, but it sounds like you're talking about sort of supercharging it if you will, the automation behind the automation. You know, organizations talk about being data-driven, you hear that thrown around a lot. A lot of times people will sit back and say "We don't make decisions without data." Okay, but really, being data-driven is, there's a lot of aspects there, there's cultural, but there's also putting data at the core of your organization, understanding how it affects monetization, and as you know well, silos have been built up, whether it's through M&A, data sprawl, outside data sources, so I'm interested in your thoughts on what data-driven means and specifically how Io-Tahoe plays there. >> Yeah, sure, I'd be happy to put that through, David. We've come a long way in the last three or four years, we started out with automating some of those simple, to codify, but have a high impact on an organization across a data lake, across a data warehouse. Those data-related tasks that help classify data. And a lot of our original patents and IP portfolio that were built up is very much around there. Automating, classifying data across different sources, and then being able to serve that for some purpose. So originally, some of those simpler challenges that we help our customers solve, were around data privacy. I've got a huge data lake here, I'm a telecoms business, so I've got millions of subscribers, and quite often a chief data office challenge is, how do I cover the operational risk here, where I've got so much data, I need to simplify my approach to automating, classifying that data. Reason is, can't do that manually, we can't throw people at it, and the scale of that is prohibitive. Quite often, if you were to do it manually, by the time you've got a good picture of it, it's already out of date. So in starting with those simple challenges that we've been able to address, we've then gone on and built on that to see, what else do we serve? What else do we serve for the chief data officer, chief marketing officer, and the CFO, and in these times, where those decision-makers are looking for, have a lot of choices in the platform options that they take, the tooling, they're very much looking for that Swiss army knife, being able to do one thing really well is great, but more and more, where that cost pressure challenge is coming in, is about how do we offer more across the organization, bring in those business, lines of business activities that depend on data, to not just with IT. >> So we like, in theCUBE sometimes we like to talk about okay, what is it, and then how does it work, and what's the business impact? We kind of covered what it is, I'd 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 could bring that up, I wonder, Ajay, if you could tell us, what is the secret sauce behind Io-Tahoe, and if you could take us through this slide. >> Ajay: Sure, I mean right there in the middle, the heart of what we do, it is the intellectual property that were built up over time, that takes from heterogeneous data sources, your Oracle relational database, your mainframe, your data lake, and increasingly APIs and devices that produce data. And now creates the ability to automatically discover that data, classify that data, after it's classified then have the ability to form relationship across those different source systems, silos, different lines of business, and once we've automated that, then we can start to do some cool things, such as put some context and meaning around that data. So it's moving it now from being data-driven, and increasingly where we have really smart, bright people in our customer organizations who want to do some of those advanced knowledge tasks, data scientists, and quants in some of the banks that we work with. The onus is on them, putting everything we've done there with automation, classifying it, relationship, understanding data quality, the policies that you can apply to that data, and putting it in context. Once you've got the ability to power a professional who's using data, to be able to put that data in context and search across the entire enterprise estate, then they can start to do some exciting things, and piece together the tapestry, the fabric, across their different system. Could be CRM, ELP systems, such as SAP, and some of the newer cloud databases that we work with, Snowflake is a great one. >> Yeah, so this is, you're describing sort of one of the reasons why there's so many stovepipes in organizations, 'cause data is kind of locked into these silos and applications, and I also want to point out that previously, to do discovery, to do that classification that you talked about, form those relationships, to glean context from data, a lot of that, if not most of that, in some cases all of that would've been manual. And of course it's out of date so quickly, nobody wants to do it because it's so hard, so this again is where automation comes into the idea of really becoming data-driven. >> Sure, I mean the efforts, if I look back maybe five years ago, we had a prevalence of data lake technologies at the cutting edge, and those have started to converge and move to some of the cloud platforms that we work with, such as Google and AWS. And I think very much as you've said it, those manual attempts to try and grasp what is such a complex challenge at scale, quickly runs out of steam, because once you've got your fingers on the details of what's in your data estate, it's changed. You've onboarded a new customer, you've signed up a new partner, a customer has adopted a new product that you've just launched, and that slew of data keeps coming, so it's keeping pace with that, the only answer really here is some form of automation. And what we've found is if we can tie automation with what I said before, the expertise, the subject matter experience that sometimes goes back many years within an organization's people, that augmentation between machine learning, AI, and that knowledge that sits inside the organization really tends to allot a lot of value in data. >> Yeah, so you know well, Ajay, you can't be as a smaller company all things to all people, so the ecosystem is critical. You're working with AWS, you're working with Google, you got Red Hat, IBM as partners. What is attracting those folks to your ecosystem, and give us your thoughts on the importance of ecosystem. >> Yeah, that's fundamental, I mean when I came into Io-Tahoe here as CEO, one of the trends that I wanted us to be part of was being open, having an open architecture that allowed one thing that was close to my heart, which was as a CEO, a CIO, well you've got a budget vision, and you've already made investments into your organization, and some of those are pretty long term bets, they could be going out five, 10 years sometimes, with a 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 plug in, using APIs that were available, to a lot of that sunk investment, and the cost that has already gone into managing an organization's IT, for business users to perform. So, part of the reason why we've been able to be successful with some of our partners like Google, AWS, and increasingly a number of technology players such as Red Hat, MongoDB is another one that we're doing a lot of good work with, and Snowflake, there 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 then leveraging the value that they've already committed to. >> Okay, so we've talked about what it is and how it works, now I want to get into the business impact, I would say what I would be looking for, from this, would be can you help me lower my operational risk, I've got tasks that I do, many are sequential, some are in parallel, but can you reduce my time to task, and can you help me reduce the labor intensity, and ultimately my labor cost, so I can put those resources elsewhere, and ultimately I want to reduce the end to end cycle time, because that is going to drive telephone number ROI, so am I missing anything, can you do those things, maybe you can give us some examples of the ROI and the business impact. >> Yeah, I mean the ROI, David, is built upon three things that I've mentioned, it's a combination of leveraging the existing investment with the existing estate, whether that's on Microsoft Azure, or AWS, or Google, IBM, and putting that to work, because the customers that we work with have made those choices. On top of that, it's ensuring that we have got the automation that is working right down to the level of data, at a column level or the file level. So we don't deal with metadata, it's being very specific, to be at the most granular level. So as we run our processes and the automation, classification, tagging, applying policies from across different compliance and regulatory needs an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome. It could be a customer who wants that experience on a mobile device, a tablet, or face to face, within a store. And being able to provision the right data, and enable our customers to do that for their customers, with the right data that they can trust, at the right time, just in that real time moment where a decision or an action is being expected, that's driving the ROI to be in some cases 20x plus, and that's really satisfying to see, that kind of impact, it's taking years down to month, and in many cases months of work down to days, and some cases hours, the time to value. I'm impressed with how quickly out of the box, with very little training a customer can pick up our tool, and use features such as search, data discovery, knowledge graph, and identifying duplicates, and redundant data. Straight off the bat, within hours. >> Well it's why investors are interested in this space, I mean they're looking for a big, total available market, they're looking for a significant return, 10x is, you got to have 10x, 20x is better. So that's exciting, and obviously strong management, and a strong team. I want to ask you about people, and culture. So you got people process technology, we've seen with this pandemic that the processes are really unpredictable, and the technology has to be able to adapt to any process, not the reverse, you can't force your process into some static software, so that's very very important, but at the end of the day, you got to get people on board. So I wonder if you could talk about this notion of culture, and a data-driven culture. >> Yeah, that's so important, I mean, current times is forcing the necessity of the moment to adapt, but as we start to work our way through these changes and adapt and work with our customers to adapt to these changing economic times, what we're seeing here is the ability to have the technology complement, in a really smart way, what those business users and IT knowledge workers are looking to achieve together. So, I'll give you an example. We have quite often with the data operations teams, in the companies that we are partnering with, have a lot of inbound inquiries on a day to day level, "I really need this set of data because I think it can help "my data scientists run a particular model," or "What would happen if we combine these two different "silos of data and get some enrichment going?" Now those requests can sometimes take weeks to realize, what we've been able to do with the power of (audio glitches) technology, is to get those answers being addressed by the business users themselves, and now, with our customers, they're coming to the data and IT folks saying "Hey, I've now built something in a development environment, "why don't we see how that can scale up "with these sets of data?" I don't need terabytes of it, I know exactly the columns and the feats in the data that I'm going to use, and that cuts out a lot of wastage, and time, and cost, to innovate. >> Well that's huge, I mean the whole notion of self-service in the lines of business actually feeling like they have ownership of the data, as opposed to IT or some technology group owning the data because then you've got data quality issues, or if it doesn't line up with their agenda, you're going to get a lot of finger pointing, so that is a really important piece of it. I'll give you a last word, Ajay, your final thoughts if you would. >> Yeah, we're excited to be on this path, and I think we've got some great customer examples here, where we're having a real impact in a really fast pace, whether it's helping them migrate to the cloud, helping them clean up their legacy data lake, and quite often now, the conversation is around data quality. As more of the applications that we enable to work more proficiently could be data, RPA, could be robotic process automation, a lot of the APIs that are now available in the cloud platforms, a lot of those are dependent on data quality and being able to automate for business users, to take accountability of being able to look at the trend of their data quality over time and get those signaled, is really driving trust, and that trust in data is helping in turn, the IT teams, the data operations teams they partner with, do more, and more quickly. So it comes back to culture, being able to apply the technology in such a way that it's visual, it's intuitive, and helping just like DevOps has with IT, DataOps, putting the intelligence in at the data level, to drive that collaboration. We're excited. >> You know, you remind me of something, I lied, I don't want to go yet, if it's okay. I know we're tight on time, but you mentioned a migration to the cloud, and I'm thinking about the conversation with Paula from Webster Bank. Migrations are, they're a nasty word for organizations, and we saw this with Webster, how are you able to help minimize the migration pain and why is that something that you guys are good at? >> Yeah, I mean there are many large, successful companies that we've worked with, Webster's a great example. Where I'd like to give you the analogy where, you've got a lot of bright people in your teams, if you're running a business as a CEO, and it's a bit like a living brain. But imagine if those different parts of your brain were not connected, that would certainly diminish how you're able to perform. So, what we're seeing, particularly with migration, is where banks, retailers, manufacturers have grown over the last 10 years, through acquisition, and through different initiatives to drive customer value. That sprawl in their data estate hasn't been fully dealt with. It's sometimes been a good thing to leave whatever you've acquired or created in situ, side by side with that legacy mainframe, and your Oracle ERP. And what we're able to do very quickly with that migration challenge is shine a light on all the different parts of data application at the column level, or at the file level if it's a data lake, and show an enterprise architect, a CDO, how everything's connected, where there may not be any documentation. The bright people that created some of those systems have long since moved on, or retired, or been promoted into other roles, and within days, being able to automatically generate and keep refreshed the states of that data, across that landscape, and put it into context, then allows you to look at a migration from a confidence that you're dealing with the facts, rather than what we've often seen in the past, is teams of consultants and business analysts and data analysts, spend months getting an approximation, and a good idea of what it could be in the current state, and try their very best to map that to the future target state. Now with Io-Tahoe being able to run those processes within hours of getting started, and build that picture, visualize that picture, and bring it to life. The ROI starts off the bat with finding data that should've been deleted, data that there's copies of, and being able to allow the architect, whether it's we have working on GCP, or in migration to any of the clouds such as AWS, or a multicloud landscape, quite often now. We're seeing, yeah. >> Yeah, that visi-- That visibility is key to sort of reducing operational risk, giving people confidence that they can move forward, and being able to do that and update that on an ongoing basis means you can scale. Ajay Vohora, thanks so much for coming to theCUBE and sharing your insights and your experiences, great to have you. >> Thank you David, look forward to talking again. >> All right, and keep it right there everybody, we're here with Data Automated on theCUBE, this is Dave Vellante, and we'll be right back right after this short break. (calm music)