from around the globe it's thecube with digital coverage of exascale day made possible by hewlett packard enterprise welcome everyone to the cube celebration of exascale day ben bennett is here he's an hpc strategist and evangelist at hewlett-packard enterprise ben welcome good to see you good to see you too son hey well let's evangelize exascale a little bit you know what's exciting you uh in regards to the coming of exoskilled computing um well there's a couple of things really uh for me historically i've worked in super computing for many years and i have seen the coming of several milestones from you know actually i'm old enough to remember gigaflops uh coming through and teraflops and petaflops exascale is has been harder than many of us anticipated many years ago the sheer amount of technology that has been required to deliver machines of this performance has been has been us utterly staggering but the exascale era brings with it real solutions it gives us opportunities to do things that we've not been able to do before if you look at some of the the most powerful computers around today they've they've really helped with um the pandemic kovid but we're still you know orders of magnitude away from being able to design drugs in situ test them in memory and release them to the public you know we still have lots and lots of lab work to do and exascale machines are going to help with that we are going to be able to to do more um which ultimately will will aid humanity and they used to be called the grand challenges and i still think of them as that i still think of these challenges for scientists that exascale class machines will be able to help but also i'm a realist is that in 10 20 30 years time you know i should be able to look back at this hopefully touch wood look back at it and look at much faster machines and say do you remember the days when we thought exascale was faster yeah well you mentioned the pandemic and you know the present united states was tweeting this morning that he was upset that you know the the fda in the u.s is not allowing the the vaccine to proceed as fast as you'd like it in fact it the fda is loosening some of its uh restrictions and i wonder if you know high performance computing in part is helping with the simulations and maybe predicting because a lot of this is about probabilities um and concerns is is is that work that is going on today or are you saying that that exascale actually you know would be what we need to accelerate that what's the role of hpc that you see today in regards to sort of solving for that vaccine and any other sort of pandemic related drugs so so first a disclaimer i am not a geneticist i am not a biochemist um my son is he tries to explain it to me and it tends to go in one ear and out the other um um i just merely build the machines he uses so we're sort of even on that front um if you read if you had read the press there was a lot of people offering up systems and computational resources for scientists a lot of the work that has been done understanding the mechanisms of covid19 um have been you know uncovered by the use of very very powerful computers would exascale have helped well clearly the faster the computers the more simulations we can do i think if you look back historically no vaccine has come to fruition as fast ever under modern rules okay admittedly the first vaccine was you know edward jenner sat quietly um you know smearing a few people and hoping it worked um i think we're slightly beyond that the fda has rules and regulations for a reason and we you don't have to go back far in our history to understand the nature of uh drugs that work for 99 of the population you know and i think exascale widely available exoscale and much faster computers are going to assist with that imagine having a genetic map of very large numbers of people on the earth and being able to test your drug against that breadth of person and you know that 99 of the time it works fine under fda rules you could never sell it you could never do that but if you're confident in your testing if you can demonstrate that you can keep the one percent away for whom that drug doesn't work bingo you now have a drug for the majority of the people and so many drugs that have so many benefits are not released and drugs are expensive because they fail at the last few moments you know the more testing you can do the more testing in memory the better it's going to be for everybody uh personally are we at a point where we still need human trials yes do we still need due diligence yes um we're not there yet exascale is you know it's coming it's not there yet yeah well to your point the faster the computer the more simulations and the higher the the chance that we're actually going to going to going to get it right and maybe compress that time to market but talk about some of the problems that you're working on uh and and the challenges for you know for example with the uk government and maybe maybe others that you can you can share with us help us understand kind of what you're hoping to accomplish so um within the united kingdom there was a report published um for the um for the uk research institute i think it's the uk research institute it might be epsrc however it's the body of people responsible for funding um science and there was a case a science case done for exascale i'm not a scientist um a lot of the work that was in this documentation said that a number of things that can be done today aren't good enough that we need to look further out we need to look at machines that will do much more there's been a program funded called asimov and this is a sort of a commercial problem that the uk government is working with rolls royce and they're trying to research how you build a full engine model and by full engine model i mean one that takes into account both the flow of gases through it and how those flow of gases and temperatures change the physical dynamics of the engine and of course as you change the physical dynamics of the engine you change the flow so you need a closely coupled model as air travel becomes more and more under the microscope we need to make sure that the air travel we do is as efficient as possible and currently there aren't supercomputers that have the performance one of the things i'm going to be doing as part of this sequence of conversations is i'm going to be having an in detailed uh sorry an in-depth but it will be very detailed an in-depth conversation with professor mark parsons from the edinburgh parallel computing center he's the director there and the dean of research at edinburgh university and i'm going to be talking to him about the azimoth program and and mark's experience as the person responsible for looking at exascale within the uk to try and determine what are the sort of science problems that we can solve as we move into the exoscale era and what that means for humanity what are the benefits for humans yeah and that's what i wanted to ask you about the the rolls-royce example that you gave it wasn't i if i understood it wasn't so much safety as it was you said efficiency and so that's that's what fuel consumption um it's it's partly fuel consumption it is of course safety there is a um there is a very specific test called an extreme event or the fan blade off what happens is they build an engine and they put it in a cowling and then they run the engine at full speed and then they literally explode uh they fire off a little explosive and they fire a fan belt uh a fan blade off to make sure that it doesn't go through the cowling and the reason they do that is there has been in the past uh a uh a failure of a fan blade and it came through the cowling and came into the aircraft depressurized the aircraft i think somebody was killed as a result of that and the aircraft went down i don't think it was a total loss one death being one too many but as a result you now have to build a jet engine instrument it balance the blades put an explosive in it and then blow the fan blade off now you only really want to do that once it's like car crash testing you want to build a model of the car you want to demonstrate with the dummy that it is safe you don't want to have to build lots of cars and keep going back to the drawing board so you do it in computers memory right we're okay with cars we have computational power to resolve to the level to determine whether or not the accident would hurt a human being still a long way to go to make them more efficient uh new materials how you can get away with lighter structures but we haven't got there with aircraft yet i mean we can build a simulation and we can do that and we can be pretty sure we're right um we still need to build an engine which costs in excess of 10 million dollars and blow the fan blade off it so okay so you're talking about some pretty complex simulations obviously what are some of the the barriers and and the breakthroughs that are kind of required you know to to do some of these things that you're talking about that exascale is going to enable i mean presumably there are obviously technical barriers but maybe you can shed some light on that well some of them are very prosaic so for example power exoscale machines consume a lot of power um so you have to be able to design systems that consume less power and that goes into making sure they're cooled efficiently if you use water can you reuse the water i mean the if you take a laptop and sit it on your lap and you type away for four hours you'll notice it gets quite warm um an exascale computer is going to generate a lot more heat several megawatts actually um and it sounds prosaic but it's actually very important to people you've got to make sure that the systems can be cooled and that we can power them yeah so there's that another issue is the software the software models how do you take a software model and distribute the data over many tens of thousands of nodes how do you do that efficiently if you look at you know gigaflop machines they had hundreds of nodes and each node had effectively a processor a core a thread of application we're looking at many many tens of thousands of nodes cores parallel threads running how do you make that efficient so is the software ready i think the majority of people will tell you that it's the software that's the problem not the hardware of course my friends in hardware would tell you ah software is easy it's the hardware that's the problem i think for the universities and the users the challenge is going to be the software i think um it's going to have to evolve you you're just you want to look at your machine and you just want to be able to dump work onto it easily we're not there yet not by a long stretch of the imagination yeah consequently you know we one of the things that we're doing is that we have a lot of centers of excellence is we will provide well i hate say the word provide we we sell super computers and once the machine has gone in we work very closely with the establishments create centers of excellence to get the best out of the machines to improve the software um and if a machine's expensive you want to get the most out of it that you can you don't just want to run a synthetic benchmark and say look i'm the fastest supercomputer on the planet you know your users who want access to it are the people that really decide how useful it is and the work they get out of it yeah the economics is definitely a factor in fact the fastest supercomputer in the planet but you can't if you can't afford to use it what good is it uh you mentioned power uh and then the flip side of that coin is of course cooling you can reduce the power consumption but but how challenging is it to cool these systems um it's an engineering problem yeah we we have you know uh data centers in iceland where it gets um you know it doesn't get too warm we have a big air cooled data center in in the united kingdom where it never gets above 30 degrees centigrade so if you put in water at 40 degrees centigrade and it comes out at 50 degrees centigrade you can cool it by just pumping it round the air you know just putting it outside the building because the building will you know never gets above 30 so it'll easily drop it back to 40 to enable you to put it back into the machine um right other ways to do it um you know is to take the heat and use it commercially there's a there's a lovely story of they take the hot water out of the supercomputer in the nordics um and then they pump it into a brewery to keep the mash tuns warm you know that's that's the sort of engineering i can get behind yeah indeed that's a great application talk a little bit more about your conversation with professor parsons maybe we could double click into that what are some of the things that you're going to you're going to probe there what are you hoping to learn so i think some of the things that that are going to be interesting to uncover is just the breadth of science that can be uh that could take advantage of exascale you know there are there are many things going on that uh that people hear about you know we people are interested in um you know the nobel prize they might have no idea what it means but the nobel prize for physics was awarded um to do with research into black holes you know fascinating and truly insightful physics um could it benefit from exascale i have no idea uh i i really don't um you know one of the most profound pieces of knowledge in in the last few hundred years has been the theory of relativity you know an austrian patent clerk wrote e equals m c squared on the back of an envelope and and voila i i don't believe any form of exascale computing would have helped him get there any faster right that's maybe flippant but i think the point is is that there are areas in terms of weather prediction climate prediction drug discovery um material knowledge engineering uh problems that are going to be unlocked with the use of exascale class systems we are going to be able to provide more tools more insight [Music] and that's the purpose of computing you know it's not that it's not the data that that comes out and it's the insight we get from it yeah i often say data is plentiful insights are not um ben you're a bit of an industry historian so i've got to ask you you mentioned you mentioned mentioned gigaflop gigaflops before which i think goes back to the early 1970s uh but the history actually the 80s is it the 80s okay well the history of computing goes back even before that you know yes i thought i thought seymour cray was you know kind of father of super computing but perhaps you have another point of view as to the origination of high performance computing [Music] oh yes this is um this is this is one for all my colleagues globally um you know arguably he says getting ready to be attacked from all sides arguably you know um computing uh the parallel work and the research done during the war by alan turing is the father of high performance computing i think one of the problems we have is that so much of that work was classified so much of that work was kept away from commercial people that commercial computing evolved without that knowledge i uh i have done in in in a previous life i have done some work for the british science museum and i have had the great pleasure in walking through the the british science museum archives um to look at how computing has evolved from things like the the pascaline from blaise pascal you know napier's bones the babbage's machines uh to to look all the way through the analog machines you know what conrad zeus was doing on a desktop um i think i think what's important is it doesn't matter where you are is that it is the problem that drives the technology and it's having the problems that requires the you know the human race to look at solutions and be these kicks started by you know the terrible problem that the us has with its nuclear stockpile stewardship now you've invented them how do you keep them safe originally done through the ascii program that's driven a lot of computational advances ultimately it's our quest for knowledge that drives these machines and i think as long as we are interested as long as we want to find things out there will always be advances in computing to meet that need yeah and you know it was a great conversation uh you're a brilliant guest i i love this this this talk and uh and of course as the saying goes success has many fathers so there's probably a few polish mathematicians that would stake a claim in the uh the original enigma project as well i think i think they drove the algorithm i think the problem is is that the work of tommy flowers is the person who took the algorithms and the work that um that was being done and actually had to build the poor machine he's the guy that actually had to sit there and go how do i turn this into a machine that does that and and so you know people always remember touring very few people remember tommy flowers who actually had to turn the great work um into a working machine yeah super computer team sport well ben it's great to have you on thanks so much for your perspectives best of luck with your conversation with professor parsons we'll be looking forward to that and uh and thanks so much for coming on thecube a complete pleasure thank you and thank you everybody for watching this is dave vellante we're celebrating exascale day you're watching the cube [Music]

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

>>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

(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)