>>Hello. I'm Professor Toru Suzuki Cherif cardiovascular medicine on associate dean of the College of Life Sciences at the University of Leicester in the United Kingdom, where I'm also director of the Lester Life Sciences accelerator. I'm also honorary consultant cardiologist within our university hospitals. It's part of the national health system NHS Trust. Today, I'd like to talk to you about our Lester Clinical Data Science Initiative. Now brief background on Lester. It's university in hospitals. Lester is in the center of England. The national health system is divided depending on the countries. The United Kingdom, which is comprised of, uh, England, Scotland to the north, whales to the west and Northern Ireland is another part in a different island. But national health system of England is what will be predominantly be discussed. Today has a history of about 70 years now, owing to the fact that we're basically in the center of England. Although this is only about one hour north of London, we have a catchment of about 100 miles, which takes us from the eastern coast of England, bordering with Birmingham to the west north just south of Liverpool, Manchester and just south to the tip of London. We have one of the busiest national health system trust in the United Kingdom, with a catchment about 100 miles and one million patients a year. Our main hospital, the General Hospital, which is actually called the Royal Infirmary, which can has an accident and emergency, which means Emergency Department is that has one of the busiest emergency departments in the nation. I work at Glen Field Hospital, which is one of the main cardiovascular hospitals of the United Kingdom and Europe. Academically, the Medical School of the University of Leicester is ranked 20th in the world on Lee, behind Cambridge, Oxford Imperial College and University College London. For the UK, this is very research. Waited, uh, ranking is Therefore we are very research focused universities as well for the cardiovascular research groups, with it mainly within Glenn Field Hospital, we are ranked as the 29th Independent research institution in the world which places us. A Suffield waited within our group. As you can see those their top ranked this is regardless of cardiology, include institutes like the Broad Institute and Whitehead Institute. Mitt Welcome Trust Sanger, Howard Hughes Medical Institute, Kemble, Cold Spring Harbor and as a hospital we rank within ah in this field in a relatively competitive manner as well. Therefore, we're very research focused. Hospital is well now to give you the unique selling points of Leicester. We're we're the largest and busiest national health system trust in the United Kingdom, but we also have a very large and stable as well as ethnically diverse population. The population ranges often into three generations, which allows us to do a lot of cohort based studies which allows us for the primary and secondary care cohorts, lot of which are well characterized and focused on genomics. In the past. We also have a biomedical research center focusing on chronic diseases, which is funded by the National Institutes of Health Research, which funds clinical research the hospitals of United Kingdom on we also have a very rich regional life science cluster, including med techs and small and medium sized enterprises. Now for this, the bottom line is that I am the director of the letter site left Sciences accelerator, >>which is tasked with industrial engagement in the local national sectors but not excluding the international sectors as well. Broadly, we have academics and clinicians with interest in health care, which includes science and engineering as well as non clinical researchers. And prior to the cove it outbreak, the government announced the £450 million investment into our university hospitals, which I hope will be going forward now to give you a brief background on where the scientific strategy the United Kingdom lies. Three industrial strategy was brought out a za part of the process which involved exiting the European Union, and part of that was the life science sector deal. And among this, as you will see, there were four grand challenges that were put in place a I and data economy, future of mobility, clean growth and aging society and as a medical research institute. A lot of the focus that we have been transitioning with within my group are projects are focused on using data and analytics using artificial intelligence, but also understanding how chronic diseases evolved as part of the aging society, and therefore we will be able to address these grand challenges for the country. Additionally, the national health system also has its long term plans, which we align to. One of those is digitally enabled care and that this hope you're going mainstream over the next 10 years. And to do this, what is envision will be The clinicians will be able to access and interact with patient records and care plants wherever they are with ready access to decision support and artificial intelligence, and that this will enable predictive techniques, which include linking with clinical genomic as well as other data supports, such as image ing a new medical breakthroughs. There has been what's called the Topol Review that discusses the future of health care in the United Kingdom and preparing the health care workforce for the delivery of the digital future, which clearly discusses in the end that we would be using automated image interpretation. Is using artificial intelligence predictive analytics using artificial intelligence as mentioned in the long term plans. That is part of that. We will also be engaging natural language processing speech recognition. I'm reading the genome amusing. Genomic announced this as well. We are in what is called the Midland's. As I mentioned previously, the Midland's comprised the East Midlands, where we are as Lester, other places such as Nottingham. We're here. The West Midland involves Birmingham, and here is ah collective. We are the Midlands. Here we comprise what is called the Midlands engine on the Midland's engine focuses on transport, accelerating innovation, trading with the world as well as the ultra connected region. And therefore our work will also involve connectivity moving forward. And it's part of that. It's part of our health care plans. We hope to also enable total digital connectivity moving forward and that will allow us to embrace digital data as well as collectivity. These three key words will ah Linkous our health care systems for the future. Now, to give you a vision for the future of medicine vision that there will be a very complex data set that we will need to work on, which will involve genomics Phanom ICS image ing which will called, uh oh mix analysis. But this is just meaning that is, uh complex data sets that we need to work on. This will integrate with our clinical data Platforms are bioinformatics, and we'll also get real time information of physiology through interfaces and wearables. Important for this is that we have computing, uh, processes that will now allow this kind of complex data analysis in real time using artificial intelligence and machine learning based applications to allow visualization Analytics, which could be out, put it through various user interfaces to the clinician and others. One of the characteristics of the United Kingdom is that the NHS is that we embrace data and captured data from when most citizens have been born from the cradle toe when they die to the grave. And it's important that we were able to link this data up to understand the journey of that patient. Over time. When they come to hospital, which is secondary care data, we will get disease data when they go to their primary care general practitioner, we will be able to get early check up data is Paula's follow monitoring monitoring, but also social care data. If this could be linked, allow us to understand how aging and deterioration as well as frailty, uh, encompasses thes patients. And to do this, we have many, many numerous data sets available, including clinical letters, blood tests, more advanced tests, which is genetics and imaging, which we can possibly, um, integrate into a patient journey which will allow us to understand the digital journey of that patient. I have called this the digital twin patient cohort to do a digital simulation of patient health journeys using data integration and analytics. This is a technique that has often been used in industrial manufacturing to understand the maintenance and service points for hardware and instruments. But we would be using this to stratify predict diseases. This'll would also be monitored and refined, using wearables and other types of complex data analysis to allow for, in the end, preemptive intervention to allow paradigm shifting. How we undertake medicine at this time, which is more reactive rather than proactive as infrastructure we are presently working on putting together what's it called the Data Safe haven or trusted research environment? One which with in the clinical environment, the university hospitals and curated and data manner, which allows us to enable data mining off the databases or, I should say, the trusted research environment within the clinical environment. Hopefully, we will then be able to anonymous that to allow ah used by academics and possibly also, uh, partnering industry to do further data mining and tool development, which we could then further field test again using our real world data base of patients that will be continually, uh, updating in our system. In the cardiovascular group, we have what's called the bricks cohort, which means biomedical research. Informatics Center for Cardiovascular Science, which was done, started long time even before I joined, uh, in 2010 which has today almost captured about 10,000 patients arm or who come through to Glenn Field Hospital for various treatments or and even those who have not on. We asked for their consent to their blood for genetics, but also for blood tests, uh, genomics testing, but also image ing as well as other consent. Hable medical information s so far there about 10,000 patients and we've been trying to extract and curate their data accordingly. Again, a za reminder of what the strengths of Leicester are. We have one of the largest and busiest trust with the very large, uh, patient cohort Ah, focused dr at the university, which allows for chronic diseases such as heart disease. I just mentioned our efforts on heart disease, uh which are about 10,000 patients ongoing right now. But we would wish thio include further chronic diseases such as diabetes, respiratory diseases, renal disease and further to understand the multi modality between these diseases so that we can understand how they >>interact as well. Finally, I like to talk about the lesser life science accelerator as well. This is a new project that was funded by >>the U started this January for three years. I'm the director for this and all the groups within the College of Life Sciences that are involved with healthcare but also clinical work are involved. And through this we hope to support innovative industrial partnerships and collaborations in the region, a swells nationally and further on into internationally as well. I realized that today is a talked to um, or business and commercial oriented audience. And we would welcome interest from your companies and partners to come to Leicester toe work with us on, uh, clinical health care data and to drive our agenda forward for this so that we can enable innovative research but also product development in partnership with you moving forward. Thank you for your time.

>>live >>from Denver, Colorado. It's the Q covering com vault Go 2019. Brought to you by combo. >>Hey, welcome to the Cube. Lisa Martin in Colorado for CONMEBOL Go 19. Statement. A man is with me this week, and we are pleased to welcome one of combos, longtime customers from the University of Leicester. We have Mark Penny, the systems specialist in infrastructure. Mark. Welcome to the Cube. >>Hi. It's good to be here. >>So you have been a convo customer at the UNI for nearly 10 years now, just giving folks an idea of about the union got 51 different academic departments about five research institutes. Cool research going on, by the way and between staff and students. About 20,000 folks, I'm sure all bringing multiple devices onto the campus. So talk to us about you came on board in 20 ton. It's hard to believe that was almost 10 years ago and said, All right, guys, we really got to get a strategy around back up, talk to us about way back then what? You guys were doing what you saw as an opportunity. What you're doing with combo today, a >>time and the There's a wide range of backup for us. There was no really assurance that we were getting back up. So we had a bit of convert seven that was backing up the Windows infrastructure. There was tyranny storage manager backing up a lot of Linux. And there was Amanda and open source thing. And then there was a LL sorts of scripts and things. So, for instance, of'em where backups were done by creating an array snapshot with the script, then mounting that script into that snapshot into another server backing up the server with calm bolt on the restore process is an absolute takes here. It was very, very difficult, long winded, required a lot of time on the checks. For this, it really was quite quite difficult to run it. Use a lot of stuff. Time we were, as far as the corporate side was concerned it exclusively on tape resource manager, we're using disc. Amanda was again for tape in a different, completely isolated system. Coupled with this, there had been a lack of investment in the data centers themselves, so the network hadn't really got a lot of throughput. This men that way were using data private backup networks in order to keep back up data off the production networks because there was really challenges over bandwidth contention backups on. So consider it over around and so on. If you got a back up coming into the working day defect student So Way started with a blank sheet of paper in many respects on went out to see what was available on Dhe. There was the usual ones it with the net back up, typically obviously again on convert Arc Serve has. But what was really interesting was deed Implication was starting to come in, But at the time, convo tonight just be released, and it had an absolutely killer feature for us, which was client side duplication. This men that we could now get rid of most of this private backup network that was making a lot of complex ISI. So it also did backup disk on back up to tape. So at that point, way went in with six Media agents. Way had a few 100 terabytes of disk storage. The strategy was to keep 28 days on disk and then the long term retention on tape into a tape library. WeII kept back through it about 2013 then took the decision. Disc was working, so let's just do disco only on save a whole load of effort. In even with a take life, you've got to refresh the tapes and things. So give it all on disk with D Duplication way, basically getting a 1 to 1. So if we had take my current figures about 1.5 petabytes of front side protected data, we've got about 1.5 petabytes in the back up system, which, because of all the synthetic fools and everything, we've got 12 months retention. We've got 28 days retention. It works really, really well in that and that that relationship, almost 1 to 1 with what's in the back up with all the attention with plants like data, has been fairly consistent since we went all disc >>mark. I wonder if you'd actually step back a second and talks about the role in importance of data in your organization because way went through a lot of the bits and bytes in that is there. But as a research organization, you know, I expect that data is, you know, quite a strategic component of the data >>forms your intellectual property. It's what is caught your research. It's the output of your investigations. So where were doing Earth Operational science. So we get data from satellites and that is then brought down roars time, little files. They then get a data set, which will consist of multiple packages of these, these vials and maybe even different measurements from different satellites that then combined and could be used to model scenarios climate change, temperature or pollution. All these types of things coming in. It's how you then take that raw data work with it. In our case, we use a lot of HPC haIf of computing to manipulate that data. And a lot of it is how smart researchers are in getting their code getting the maximum out of that data on. Then the output of that becomes a paper project on dhe finalized final set of of date, which is the results, which all goes with paper. We've also done the a lot of genetics and things like that because the DNA fingerprinting with Alec Jeffrey on what was very interesting with that one is how it was those techniques which then identified the bones that were dug up under the car park in Leicester, which is Richard >>Wright documentary. >>Yeah, on that really was quite exciting. The way that well do you really was quite. It's quite fitting, really, techniques that the university has discovered, which were then instrumental in identifying that. >>What? One of the interesting things I found in this part of the market is used to talk about just protecting my data. Yeah, a lot of times now it's about howto. Why leverage my data even Maur. How do I share my data? How do I extract more value out of the data in the 10 years you've been working with calm Boulder? Are you seeing that journey? Is that yes, the organization's going down. >>There's almost there's actually two conflicting things here because researchers love to share their data. But some of the data sets is so big that can be quite challenging. Some of the data sets. We take other people's Day to bring it in, combining with our own to do our own modeling. Then that goes out to provide some more for somebody else on. There's also issues about where data could exist, so there's a lot of very strict controls about the N. H s data. So health data, which so n hs England that can't then go out to Scotland on Booth. Sometimes the regulatory compliance almost gets sidelines with the excitement about research on way have quite a dichotomy of making sure that where we know about the data, that the appropriate controls are there and we understand it on Hopefully, people just don't go on, put it somewhere. It's not because some of the data sets for medical research, given the data which has got personal, identifiable information in it, that then has to be stripped out. So you've got an anonymous data set which they can then work with it Z assuring that the right data used the right information to remove so that you don't inadvertently go and then expose stuff s. So it's not just pure research on it going in this silo and in this silo it's actually ensuring that you've got the right bits in the right place, and it's being handled correctly >>to talk to us about has you know, as you pointed out, this massive growth and data volumes from a university perspective, health data perspective research perspective, the files are getting bigger and bigger In the time that you've started this foundation with combo in the last 9 10 years. Tremendous changes not just and data, but talking about complaints you've now got GDP are to deal with. Give us a perspective and snapshot of your of your con vault implementation and how you've evolved that as all the data changes, compliance changes and converts, technology has evolved. So if you take >>where we started off, we had a few 100 petabytes of disk. It's just before we migrated. Thio on Premise three Cloud Libraries That point. I think I got 2.1 petabytes of backup. Storage on the volume of data is exponentially growing covers the resolution of the instruments increases, so you can certainly have a four fold growth data that some of those are quite interesting things. They when I first joined the great excitement with a project which has just noticed Betty Colombo, which is the Mercury a year for in space agency to Demeter Mercury and they wanted 50 terabytes and way at that time, that was actually quite a big number way. We're thinking, well, we make the split. What? We need to be careful. Yes. Okay. 50 terrorizes that over the life of project. And now that's probably just to get us going. Not much actually happened with it. And then storage system changed and they still had their 50 terabytes with almost nothing in it way then understood that the spacecraft being launched and that once it had been launched, which was earlier this year, it was going to take a couple of years before the first data came back. Because it has to go to Venus. It has to go around Venus in the wrong direction, against gravity to slow it down. Then it goes to Mercury and the rial bolt data then starts coming back in. You'd have thought going to Mercury was dead easy. You just go boom straight in. But actually, if you did that because of gravity of the sun, it would just go in. You'd never stop. Just go straight into the sun. You lose your spacecraft. >>Nobody wants >>another. Eggs are really interesting. Is artfully Have you heard of the guy? A satellite? >>Yes. >>This is the one which is mapping a 1,000,000,000 stars in the Milky Way. It's now gone past its primary mission, and it's got most of that data. Huge data sets on DDE That data, there's, ah, it's already being worked on, but they are the university Thio task, packaging it and cleansing it. We're going to get a set of that data we're going to host. We're currently hosting a national HPC facility, which is for space research that's being replaced with an even bigger, more powerful one. Little probably fill one of our data centers completely. It's about 40 racks worth, and that's just to process that data because there's so much information that's come from it. And it's It's the resolution. It's the speed with which it can be computed on holding so much in memory. I mean, if you take across our current HPC systems, we've got 100 terabytes of memory across two systems, and those numbers were just unthinkable even 10 years ago, a terrible of memory. >>So Mark Lease and I would like to keep you here all way to talk about space, Mark todo of our favorite topics. But before we get towards the end, but a lot of changes, that combo, it's the whole new executive team they bought Hedvig. They land lost this metallic dot io. They've got new things. It's a longtime customer. What your viewpoint on com bold today and what what you've been seeing quite interesting to >>see how convoy has evolved on dhe. These change, which should have happened between 10 and 11 when they took the decision on the next generation platform that it would be this by industry. Sand is quite an aggressive pace of service packs, which are then come out onto this schedule. And to be fair, that schedule is being stuck to waken plan ahead. We know what's happening on Dhe. It's interesting that they're both patches and the new features and stuff, and it's really great to have that line to work, too. Now, Andi way with platform now supports natively stone Much stuff. And this was actually one of the decisions which took us around using our own on Prem Estimate Cloud Library. We were using as you to put a tear on data off site on with All is working Great. That can we do s3 on friend on. It's supported by convoy is just a cloud library. Now, When we first started that didn't exist. Way took the decision. It will proof of concept and so on, and it all worked, and we then got high for scale as well. It's interesting to see how convoy has gone down into the appliance 11 to, because people want to have to just have a box unpack it. Implicated. If you haven't got a technical team or strong yo skills in those area, why worry about putting your own system together? Haifa scale give you back up in a vault on the partnerships with were in HP customer So way we're using Apollo's RS in storage. Andi Yeah, the Apollo is actually the platform. If we bought Heifer Scale, it would have gone on an HP Apollo as well, because of the way with agreements, we've got invited. Actually, it's quite interesting how they've gone from software. Hardware is now come in, and it's evolving into this platform with Hedvig. I mean, there was a convoy object store buried in it, but it was very discreet. No one really knew about it. You occasionally could see a term on it would appear, but it it wasn't something which they published their butt object store with the increasing data volumes. Object Store is the only way to store. There's these volumes of data in a resilient and durable way. Eso Hedvig buying that and integrating in providing a really interesting way forward. And yet, for my perspective, I'm using three. So if we had gone down the Hedvig route from my perspective, what I would like to see is I have a story policy. I click on going to point it to s three, and it goes out it provision. The bucket does the whole lot in one a couple of clicks and that's it. Job done. I don't need to go out, create the use of create the bucket, and then get one out of every little written piece in there. And it's that tight integration, which is where I see benefits coming in you. It's giving value to the platform and giving the customer the assurance that you've configured correctly because the process is an automated in convoy has ensured that every step of the way the right decisions being made on that. Yet with metallic, that's everything is about it's actually tried and tested products with a very, very smart work for a process put round to ensure that the decisions you make. You don't need to be a convoy expert to get the outcome and get the backups. >>Excellent. Well, Mark, thank you for joining Student on the Cape Talking about tthe e evolution that the University of Leicester has gone through and your thoughts on com bolts evolution in parallel. We appreciate your time first to Minutemen. I'm Lisa Martin. You're watching the cue from combo go 19.

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

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