(upbeat music) >> Welcome to theCube's coverage of AWS Storage Day. We're here with a couple of AWS product experts. Covering AWS's migration and transfer services, Randy Boutin is the general manager of AWS DataSync, and Mat Matthews, GM of AWS Transfer Family. Guys, good to see you again. Thanks for coming on. >> Dave, thanks. >> So look, we saw during the pandemic, the acceleration to cloud migration. We've tracked that, we've quantified that. What's driving that today? >> Yeah, so Dave, great to be back here. Saw you last year at Storage Day. >> Nice to be in studio too, isn't it? Thanks, guys, for coming in. >> We've conquered COVID. >> So yeah, I mean, this is a great question. I think digital transformation is really what's driving a lot of the focus right now from companies, and it's really not about just driving down costs. It's also about what are the opportunities available once you get into the cloud in terms of, what does that unlock in terms of innovation? So companies are focused on the usual things, optimizing costs, but ensuring they have the right security and agility. You know, a lot has happened over the last year, and companies need to be able to react, right? They need to be able to react quickly, so cloud gives them a lot of these capabilities, but the real benefit that we see is that once your data's in the cloud, it opens up the power of the cloud for analytics, for new application development, and things of that sort, so what we're seeing is that companies are really just focused on understanding cloud migration strategy, and how they can get their data there, and then use that to unlock that data for the value. >> I mean, if I've said it once, I've said it 100 times, if you weren't a digital business during the pandemic, you were out of business. You know, migration historically is a bad word in IT. Your CIOs see it and go, "Ugh." So what's the playbook for taking years of data on-prem, and moving it into the cloud? What are you seeing as best practice there? >> Yeah, so as you said, the migration historically has been painful, right? And it's a daunting task for any business or any IT executive, but fortunately, AWS has a broad suite of capabilities to help enable these migrations. And by that, I mean, we have tools to help you understand your existing on-prem workloads, understand what services in the AWS offering align to those needs, but also help you estimate the cost, right? Cost is a big part of this move. We can help you estimate that cost, and predict that cost, and then use tools like DataSync to help you move that data when that time comes. >> So you're saying you help predict the cost of the migration, or the cost of running in the cloud? >> Running in the cloud, right. Yeah, we can help estimate the run time. Based on the performance that we assess on-prem, we can then project that into a cloud service, and estimate that cost. >> So can you guys explain DataSync? Sometimes I get confused, DataSync, what's the difference between DataSync and Storage Gateway? And I want to get into when we should use each, but let's start there if we could. >> Yeah, sure, I'll take that. So Storage Gateway is primarily a means for a customer to access their data in the cloud from on-prem. All right, so if you have an application that you want to keep on-prem, you're not ready yet to migrate that application to the cloud, Gateway is a strong solution, because you can move a lot of that data, a lot of your cold or long tail data into something like S3 or EFS, but still access it from your on-prem location. DataSync's all about data movement, so if you need to move your data from A to B, DataSync is your optimized solution to do that. >> Are you finding that people, that's ideally a one time move, or is it actually, sometimes you're seeing customers do it more? Again, moving data, if I don't- Move as much data as you need to, but no more, to paraphrase Einstein. >> What we're seeing in DataSync is that customers do use DataSync for their initial migration. They'll also, as Matt was mentioning earlier, once you get your data into the cloud, that flywheel of potential starts to take hold, and customers want to ultimately move that data within the cloud to optimize its value. So you might move from service to service. You might move from EFS to S3, et cetera, to enable the cloud flywheel to benefit you. DataSync does that as well, so customers use us to initially migrate, they use us to move within the cloud, and also we just recently announced service for other clouds, so you can actually bring data in now from Google and Azure as well. >> Oh, how convenient. So okay, so that's cool. So you helped us understand the use cases, but can we dig one more layer, like what protocols are supported? I'm trying to understand really the right fit for the right job. >> Yeah, so that's really important. So for transfer specifically, one of the things that we see with customers is you've got obviously a lot of internal data within your company, but today it's a very highly interconnected world, so companies deal with lots of business partners, and historically they've used, there's a big prevalence of using file transfer to exchange data with business partners, and as you can imagine, there's a lot of value in that data, right? Sometimes it's purchase orders, inventory data from suppliers, or things like that. So historically customers have had protocols like SFTP or FTP to help them interface with or exchange data or files with external partners. So for transfer, that's what we focus on is helping customers exchange data over those existing protocols that they've used for many years. And the real focus is it's one thing to migrate your own data into the cloud, but you can't force thousands or tens of thousands sometimes of partners to also work in a different way to get you their data, so we want to make that very seamless for customers using the same exact protocols like SFTP that they've used for years. We just announced AS2 protocol, which is very heavily used in supply chains to exchange inventory and information across multi-tiers of partners, and things of that nature. So we're really focused on letting customers not have to impact their partners, and how they work and how they exchange, but also take advantage of the data, so get that data into the cloud so they can immediately unlock the value with analytics. >> So AS2 is specifically in the context of supply chain, and I'm presuming it's secure, and kind of governed, and safe. Can you explain that a little bit? >> Yeah, so AS2 has a lot of really interesting features for transactional type of exchanges, so it has signing and encryption built in, and also has notification so you can basically say, "Hey, I sent you this purchase order," and to prove that you received it, it has capability called non-repudiation, which means it's actually a legal transaction. So those things are very important in transactional type of exchanges, and allows customers in supply chains, whether it's vendors dealing with their suppliers, or transportation partners, or things like that to leverage file transfer for those types of exchanges. >> So encryption, providence of transactions, am I correct, without having to use the blockchain, and all the overhead associated with that? >> It's got some built in capabilities. >> I mean, I love blockchain, but there's drawbacks. >> Exactly, and that's why it's been popular. >> That's really interesting, 'cause Andy Jassy one day, I was on a phone call with him and John Furrier, and we were talking up crypto and blockchain. He said, "Well, why do, explain to me." You know Jassy, right? He always wants to go deeper. "Explain why I can't do this with some other approach." And so I think he was recognizing some of the drawbacks. So that's kind of a cool thing, and it leads me- We're running this obviously today, August 10th. Yesterday we had our Supercloud event in Palo Alto on August 9th, and it's all about the ecosystem. One of the observations we made about the 2020s is the cloud is totally different now. People are building value on top of the infrastructure that you guys have built out over the last 15 years. And so once an organization's data gets into the cloud, how does it affect, and it relates to AS2 somewhat, how does it affect the workflows in terms of interacting with external partners, and other ecosystem players that are also in the cloud? >> Yeah, great, yeah, again, we want to try and not have to affect those workflows, take them as they are as much as possible, get the data exchange working. One of the things that we focus on a lot is, how do you process this data once it comes in? Every company has governance requirements, security requirements, and things like that, so they usually have a set of things that they need to automate and orchestrate for the data as it's coming in, and a lot of these companies use something called Managed File Transfer Solutions that allow them to automate and orchestrate those things. We also see that many times this is very customer specific, so a bank might have a certain set of processes they have to follow, and it needs to be customized. As you know, AWS is a great solution for building custom solutions, and actually today, we're just announcing a new set of of partners in a program called the Service Delivery Program with AWS Transfer Family that allows customers to work with partners that are very well versed in transfer family and related services to help build a very specific solution that allows them to build that automation orchestration, and keep their partners kind of unaware that they're interfacing in a different way. >> And once this data is in the cloud, or actually, maybe stays on-prem in some cases, but it basically plugs in to the AWS services portfolio, the whole security model, the governance model, shared responsibility comes in, is that right? It's all, sort of all in there? >> Yeah, that's right, that's exactly right, and we're working with it's all about the customer's needs, and making sure that their investment in AWS doesn't disrupt their existing workflows and their relationships with their customers and their partners, and that's exactly what Matt's been describing is we're taking a close look at how we can extend the value of AWS, integrate into our customer's workflows, and bring that value to them with minimal investment or disruption. >> So follow up on that. So I love that, because less disruption means it's easier, less friction, and I think of like, trying to think of examples. Think about data de-duplication like purpose-built backup appliances, right? Data domain won that battle, because they could just plug right in. Avamar, they were trying to get you to redo everything, okay, and so we saw that movie play out. At the same time, I've talked to CIOs that say, "I love that, but the cloud opens up all these cool new opportunities for me to change my operating model." So are you seeing that as well? Where okay, we make it easy to get in. We're not disrupting workflows, and then once they get in, they say, "Well if we did it this way, we'd take out a bunch of costs. We'd accelerate our business." What's that dynamic like? >> Exactly that, right. So that moved to the Cloud Continuum. We don't think it's going to be binary. There's always going to be something on-prem. We accept that, but there's a continuum there, so day one, they'll migrate a portion of that workload into the cloud, start to extract and see value there, but then they'll continue, as you said, they'll continue to see opportunities. With all of the various capabilities that AWS has to offer, all the value that represents, they'll start to see that opportunity, and then start to engage and consume more of those features over time. >> Great, all right, give us the bumper sticker. What's next in transfer services from your perspectives? >> Yeah, so we're obviously always going to listen to our customers, that's our focus. >> You guys say that a lot. (all laughing) We say it a lot. But yeah, so we're focused on helping customers again increase that level of automation orchestration, again that suite of capability, generally, in our industry, known as managed file transfer, when a file comes in, it needs to get maybe encrypted, or decrypted, or compressed, or decompressed, scanned for viruses, those kind of capabilities, make that easier for customers. If you remember last year at Storage Day, we announced a low code workflow framework that allows customers to kind of build those steps. We're continuing to add built-in capabilities to that so customers can easily just say, "Okay, I want these set of activities to happen when files come in and out." So that's really what's next for us. >> All right, Randy, we'll give you the last word. Bring us home. >> I'm going to surprise you with the customer theme. >> Oh, great, love it. >> Yeah, so we're listening to customers, and what they're asking for our support for more sources, so we'll be adding support for more cloud sources, more on-prem sources, and giving the customers more options, also performance and usability, right? So we want to make it easier, as the enterprise continues to consume the cloud, we want to make DataSync and the movement of their data as easy as possible. >> I've always said it starts with the data. S3, that was the first service, and the other thing I've said a lot is the cloud is expanding. We're seeing connections to on-prem. We're seeing connections out to the edge. It's just becoming this massive global system, as Werner Vogels talks about all the time. Thanks, guys, really appreciate it. >> Dave, thank you very much. >> Thanks, Dave. >> All right, keep it right there for more coverage of AWS Storage Day 2022. You're watching theCube. (upbeat music)

(upbeat music) >> Good afternoon, everyone. Welcome back to theCUBE's live coverage of Snowflake Summit 22, the fourth annual Snowflake Summit. Lisa Martin here with Dave Vellante, We're live in Vegas, as I mentioned. We've got a couple of guests here with us. We're going to be unpacking some more great information that has come out of the show news today. Please welcome Chris Child back to theCUBE, Senior Director of Product Management at Snowflake, and Lisa Cramer is here, Head of Embedded Products at LiveRamp, guys welcome. >> Thank you. >> Hi. >> Tell us a little bit about LiveRamp, what you guys do, what your differentiators are and a little bit about the Snowflake partnership? >> Sure, well, LiveRamp makes it safe and easy to connect data. And we're powered by core identity resolution capabilities, which enable our clients to resolve their data, and connect it with other data sets. And so we've brought these identity infrastructure capabilities to Snowflake, and built into the Native Application Framework. We focused on two initial products around device resolution, which enables our clients to connect customer data from the digital ecosystem. This powers things like, measurement use cases, and understanding campaign effectiveness and ROI. And the second capability we built into the Native Application Framework is called transcoding. And this enables a translation layer between identifiers, so that parties can safely and effectively share data at a person-based view. >> Chris, talk to us about, Snowflake just announced a lot of news this morning, just announced, the new Snowflake Native Application Framework. You alluded to this, Lisa, talk to us about that. What does it mean for customers, what does it do? Give us all the backstory. >> Yeah, so we had seen a bunch of cases for our customers where they wanted to be able to take application logic, and have other people use it. So LiveRamp, as an example of that, they've built a bunch of complicated logic to help you figure out who is the same person in different systems. But the problem was always that, that application had to run outside of the Data Cloud. And that required you to take your data outside of Snowflake, entrust your data to a third party. And so every time that companies have to go, become a vendor, they have to go through a security review, and go through a long onerous process, to be able to be allowed to process the really sensitive data that these customers have. So with the Native Applications Framework, you can take your application code, all of the logic, and the data that's needed to build it together, and actually push that through secure data sharing into a customer's account, where it runs, and is able to access their data, join it with data from the provider, all without actually having to give that provider access to your core data assets themselves. >> Is it proper to think of the Native Application Framework as a PaaS layer within the Data Cloud? >> That's a great way to think about it. And so, this is where we've integrated with the marketplace as well. So providers like LiveRamp will be able to publish these applications. They'll run entirely on effectively a PaaS layer that's powered by Snowflake, and be able to deliver those to any region, any cloud, any place that Snowflake runs. >> So, we get a lot of grief for this term, but we've coined a term called "supercloud". Okay, and the supercloud is an abstraction layer that hovers above the hyperscale infrastructure. Companies like yours, build on top of that. So you don't have to worry about the underlying complexities. And we've said that, in order to make that a reality, you have to have a super PaaS. So is that essentially what you're doing? You're building your product on top of that? You're not worrying about, okay, now I'm going to go to Azure, I'm going to go to AWS, or I'm going to go to, wherever, is that a right way to think about it? >> That's exactly right. And I think, Snowflake has really helped us, kind of shift the paradigm in how we work with our customers, and enabled us to bring our capabilities to where their data lives, right? And enabled them to, kind of run the analytics, and run the identity resolution where their data sits. And so that's really exciting. And I think, specifically with the Native Application Framework, Snowflake delivered on the promise of minimizing data movement, right? The application is installed. You don't have to move your data at all. And so for us, that was a really compelling reason to build into it. And we love when our customers can maintain control of their data. >> So the difference between what you are doing as partners, and a SaaS, is that, you're not worrying about all the capabilities, there in the data, all the governance, and the security components. You're relying on the Data Cloud for that, is that right? Or is it a SaaS? >> Yeah, I think there's components, like certainly parts of our business still run in the SaaS model. But I think the ability to rely on some of the infrastructure that Snowflake provides, and honestly kind of the connectivity, and the verticalized solutions that Snowflake brings to bear with data providers, and technology providers, that matter most to that vertical, really enable us to kind of rely on some of that to ensure that we can serve our customers as they want us to. >> So you're extending your SaaS platform and bringing new capabilities, as opposed to building, or are you building new apps in the Data Cloud? This is, I'm sorry to be so pedantic, but I'm trying to understand from your perspective. >> Oh yeah, so we built new capabilities within the Data Cloud. It's based on our core identity infrastructure capabilities, but we wanted to build into the Native Application Framework, so that data doesn't have to move and we can serve our customers, and they can maintain control over their data in their environment. So we built new capabilities, but it's all based on our core identity infrastructure. >> So safe sharing reminds me of like when procurement says, do we have an MSA? Yes, okay, go. You know, it's just frictionless. Versus no, okay, send some paper, go back and forth and it just takes forever. >> That's one of the big goals that we see. And to your point on, is it a PaaS, is it a SaaS? We honestly think of it as something a little bit different, in a similar way to where, at Snowflake we saw a whole generation of SaaS business models, and as a utility, and a consumption-based model, we think of ourselves as different from a SaaS business model. We're now trying to enable application providers, like LiveRamp, to take the core technology in IP that they've built over many, many years, but deliver it in a completely new different way that wasn't possible. And so part of this is extending what they're doing, and making it a little easier to deploy, and not having to go through the MSA process in the same way. But also we do think that this will allow entirely new capabilities to be brought that wouldn't be possible, unless they could be deployed and run inside the Data Cloud. >> Is LiveRamp a consumption pricing model, or is it a subscription, or a combo? >> We are actually a subscription, but with some usage capabilities. >> It's an hybrid. >> Chris, talk a little bit about the framework that you guys have both discussed. How is it part of the overall Snowflake vision of delivering secure and governed, powerful analytics, and data sharing to customers, and ecosystem partners? >> So this, for us we view this as kind of the next evolution of Snowflake. So Snowflake was all built on helping people consolidate their data, bring all your data into one place and then run all of your different workloads on it. And what we've seen over the years is, there are still a lot of different use cases, where you need to take your data out of the Data Cloud, in order to do certain different things. So we made a bunch of announcements today around machine learning, so that you don't have to take your data out to train models. And native applications is built on the idea of don't bring your data to the applications you need. Whether they're machine learning models, whether they're identity resolution, whether they're really even just analytics. Instead, take the application logic and bring that into the Data Cloud, and run it right on your data where it is. And so the big benefit of that is, I don't need copies of my data that are getting out of sync, and getting out of date. I don't need to give a copy of my data to anyone else. I get to keep it, I get to govern it. I get to secure it. I know exactly what's going on. But now, we can open this up to workloads, not just ones that Snowflake's building, but workloads that partners like LiveRamp, or anyone else is building. All those workloads can then run in a single copy of your data, in a single secure environment. >> And when you say in one place, Chris, people can get confused by that, 'cause it's really not in one place. it's the global thing that Benoit stressed this morning >> And that right, and so these, once you write a native app once, so the native app that they've written is one piece of code, one application, that now can be deployed by customers in any region, or on any cloud that they're running on without any changes at all. So to your point on the PaaS, that's where it gets very PaaS-like, because they write once to the Snowflake APIs, and now it can run literally anywhere the Snowflake runs. >> But the premise that we've put forth in supercloud is that, this is a new era. It's not multicloud. And it's consistent with a digital business, right? You're building, you've got a digital business, and this is a new value layer of a digital business. If I've got capabilities, I want to bring them to the cloud. I want to bring them to, every company's a software company, software's eating the world, data's eating software. I mean, I could go on and on and on, but it's not like 10 years ago. This is a whole new life cycle that we're just starting. Is that valid? I mean do you feel that way about LiveRamp? >> Definitely, I mean, I think it's really exciting to see all of the data connectivity that is happening. At the same time, I think the challenges still remain, right? So there are still challenges around being able to resolve your data, and being able to connect your data to a person-based view in a privacy safe way, to be able to partner with others in a data collaboration model, right? And to be able to do all of that without sharing anything from a sensitive identifier standpoint, or not having a resolved data set. And so I think you're absolutely right. There's a lot of really cool, awesome innovation happening, but the customer challenges, kind of still exist. And so that's why it's exciting to build these applications that can now solve those problems, where that data is. >> It's the cloud benefit, the heavy lifting thing, for data? 'Cause you don't have to worry about all that. You can focus on campaign ROI, or whatever new innovation that you want to bring out. >> And think about it from the end customer's perspective. They now, can come into their single environment where they have all their data, they can say, I need to match the identity, and they can pull in LiveRamp with a few clicks, and then they can say, I'm ready to take some actions on this. And they can pull in action tools with just a few more clicks. And they haven't made current marketing stack that you see. There's 20 different tools and you're schlepping data back and forth between each of them, and LiveRamp's just one stop on your journey to get this data out to where I'm actually sending emails or targeting ads. Our vision is that, all that happens on one copy of the data, each of these different tools are grabbing the parts they need, again in a secure well-governed, well-controlled way, enriching in ways that they need, taking actions that they need, pulling in other data sets that they need. But the end consumer maintains control over the data, and over the process, the entire way through. >> So one copy data. So you sometimes might make a copy, right? But you'd make as many copies as you need to, but no more, kind of thing, to paraphrase Einstein, or is that right? >> There's literally one copy of the data. So one of the nice things with Snowflake, with data sharing, and with native applications, the data is stored once in one file on disc and S3, which eventually is a disc somewhere. >> Yeah, yeah, right. >> But what can happen is, I'm really just granting permission to these different applications, to read and write from that single copy of the data. So as soon as a new customer touches my website, that immediately shows up in my data. LiveRamp gets access to that instantly. They enrich it. Before I've even noticed that that new customer signed up, the data's already been enriched, the identity's been matched, and they're already put into a bucket about what campaign I should run against them. >> So the data stays where it is. You bring the ISO compute, but the application. And then you take the results, right? And then I can read them back? >> You bring the next application, right to that same copy of the data. So what'll happen is you'll have a view that LiveRamp is accessing and reading and making changes on, LiveRamp is exposing its own view, I have another application reading from the LiveRamp view, exposing its own view. And ultimately someone's taking an action based on that. But there's one copy of the data all the way through. That's the really powerful thing. >> Okay, so yeah, so you're not moving the data. So you're not dealing with latency problems, but I can, if I'm in Australia and I'm running on US West, it's not a problem? >> Yes, so there, if you do want to run across different clouds, we will copy the data in that case, we've found it's much faster. >> Okay, great, I thought I was losing my mind. >> No, but as long as you're staying within a single region, there will be no copies of the data. >> Yeah, okay, totally makes sense, great. >> One of the efficiency there in speed to be able to get the insights. That's what it's all about, being able to turn the volume up on the data from a value perspective. Thanks so much guys for joining us on the program today talking about what LiveRamp and Snowflake are doing together and breaking down the Snowflake Native Application Framework. We appreciate your insights and your time, And thanks for joining us. >> Thank you both. >> Thank you guys. >> Thank you. >> For our guests, and Dave Vellante, I'm Lisa Martin. You're watching theCUBE Live from Snowflake Summit 22 from Las Vegas. We'll be right back with our next guest. (upbeat music)

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante despite all the chatter about cloud repatriation and the exorbitant cost of cloud computing customer spending momentum continues to accelerate in the post-isolation economy if the pandemic was good for the cloud it seems that the benefits of cloud migration remain lasting in the late stages of covid and beyond and we believe this stickiness is going to continue for quite some time we expect i asked revenue for the big four hyperscalers to surpass 115 billion dollars in 2021 moreover the strength of aws specifically as well as microsoft azure remain notable such large organizations showing elevated spending momentum as shown in the etr survey results is perhaps unprecedented in the technology sector hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll share some some fresh july survey data that indicates accelerating momentum for the largest cloud computing firms importantly not only is the momentum broad-based but it's also notable in key strategic sectors namely ai and database there seems to be no stopping the cloud momentum there's certainly plenty of buzz about the cloud tax so-called cloud tax but other than wildly assumptive valuation models and some pockets of anecdotal evidence you don't really see the supposed backlash impacting cloud momentum our forecast calls for the big four hyperscalers aws azure alibaba and gcp to surpass 115 billion as we said in is revenue this year the latest etr survey results show that aws lambda has retaken the lead among all major cloud services tracked in the data set as measured in spending momentum this is the service with the most elevated scores azure overall azure functions vmware cloud on aws and aws overall also demonstrate very highly elevated performance all above that of gcp now impressively aws momentum in the all-important fortune 500 where it has always showed strength is also accelerating one concern in the most recent survey data is that the on-prem clouds and so-called hybrid platforms which we had previously reported as showing an upward spending trajectory seem to have cooled off a bit but the data is mixed and it's a little bit too early to draw firm conclusions nonetheless while hyperscalers are holding steady the spending data appears to be somewhat tepid for the on-prem players you know particularly for their cloud we'll study that further after etr drops its full results on july 23rd now turning our attention back to aws the aws cloud is showing strength across its entire portfolio and we're going to show you that shortly in particular we see notable strength relative to others in analytics ai and the all-important database category aurora and redshift are particularly strong but several other aws database services are showing elevated spending velocity which we'll quantify in a moment all that said snowflake continues to lead all database suppliers in spending momentum by a wide margin which again will quantify in this episode but before we dig into the survey let's take a look at our latest projections for the big four hyperscalers in is as you know we track quarterly revenues for the hyperscalers remember aws and alibaba ias data is pretty clean and reported in their respective earnings reports azure and gcp we have to extrapolate and strip out all a lot of the the apps and other certain revenue to make an apples-to-apples comparison with aws and alibaba and as you can see we have the 2021 market exceeding 115 billion dollars worldwide that's a torrid 35 growth rate on top of 41 in 2020 relative to 2019. aggressive yes but the data continues to point us in this direction until we see some clearer headwinds for the cloud players this is the call we're making aws is perhaps losing a sharepoint or so but it's also is so large that its annual incremental revenue is comparable to alibaba's and google's respective cloud business in total is business in total the big three u.s cloud companies all report at the end of july while alibaba is mid mid-august so we'll update these figures at that time okay let's move on and dig into the survey data we don't have the data yet on alibaba and we're limited as to what we can share until etr drops its research update on on the 23rd but here's a look at the net score timeline in the fortune 500 specifically so we filter the fortune 500 for cloud computing you got azure and the yellow aws and the black and gcp in blue so two points here stand out first is that aws and microsoft are converging and remember the customers who respond to the survey they probably include a fair amount of application software spending in their cloud answers so it favors microsoft in that respect and gcp second point is showing notable deceleration relative to the two leaders and the green call out is because this cut is from an aws point of view so in other words gcp declines are a positive for aws so that's how it should be interpreted now let's take a moment to better understand the idea of net score this is one of the fundamental metrics of the etr methodology here's the data for aws so we use that as a as a reference point net score is calculated by asking customers if they're adding a platform new that's the lime green bar that you see here in the current survey they're asking are you spending six percent or more in the second half relative to the first half of the year that's the forest green they're also asking is spending flat that's the gray or are you spending less that's the pink or are you replacing the platform i.e repatriating so not much spending going on in replacements now in fairness one percent of aws is half a billion dollars so i can see where some folks would get excited about that but in the grand scheme of things it's a sliver so again we don't see repatriation in the numbers okay back to net score subtract the reds from the greens and you get net score which in the case of aws is 61 now just for reference my personal subjective elevated net score level is 40 so anything above that is really impressive based on my experience and to have a company of this size be so elevated is meaningful same for microsoft by the way which is consistently well above the 50 mark in net score in the etr surveys so that's you can think about it that's even more impressive perhaps than aws because it's triple the revenue okay let's stay with aws and take a look at the portfolio and the strength across the board this chart shows net score for the past three surveys serverless is on fire by the way not just aws but azure and gcp functions as well but look at the aws portfolio every category is well above the 40 percent elevated red line the only exception is chime and even chime is showing an uptick and chime is meh if you've ever used chime every other category is well above 50 percent next net score very very strong for aws now as we've frequently reported ai is one of the four biggest focus areas from a spending standpoint along with cloud containers and rpa so it stands to reason that the company with the best ai and ml and the greatest momentum in that space has an advantage because ai is being embedded into apps data processes machines everywhere this chart compares the ai players on two dimensions net score on the vertical axis and market share or presence in the data set on the horizontal axis for companies with more than 15 citations in the survey aws has the highest net score and what's notable is the presence on the horizontal axis databricks is a company where high on also shows elevated scores above both google and microsoft who are showing strength in their own right and then you can see data iq data robot anaconda and salesforce with einstein all above that 40 percent mark and then below you can see the position of sap with leonardo ibm watson and oracle which is well below the 40 line all right let's look at at the all-important database category for a moment and we'll first take a look at the aws database portfolio this chart shows the database services in aws's arsenal and breaks down the net score components with the total net score superimposed on top of the bars point one is aurora is highly elevated with a net score above 70 percent that's due to heavy new adoptions redshift is also very strong as are virtually all aws database offerings with the exception of neptune which is the graph database rds dynamodb elastic document db time stream and quantum ledger database all show momentum above that all important 40 line so while a lot of people criticize the fragmentation of the aws data portfolio and their right tool for the right job approach the spending spending metrics tell a story and that that the strategy is working now let's take a look at the microsoft database portfolio there's a story here similar similar to that of aws azure sql and cosmos db microsoft's nosql distributed database are both very highly elevated as are azure database for mysql and mariadb azure cash for redis and azure for cassandra also microsoft is giving look at microsoft's giving customers a lot of options which is kind of interesting you know we've often said that oracle's strategy because we think about oracle they're building the oracle database cloud we've said oracle strategy should be to not just be the cloud for oracle databases but be the cloud for all databases i mean oracle's got a lot of specialty capability there but it looks like microsoft is beating oracle to that punch not that oracle is necessarily going there but we think it should to expand the appeal of its cloud okay last data chart that we'll show and then and then this one looks at database disruption the chart shows how the cloud database companies are doing in ibm oracle teradata in cloudera accounts the bars show the net score granularity as we described earlier and the etr callouts are interesting so first remember this is an aws this is in an aws context so with 47 responses etr rightly indicates that aws is very well positioned in these accounts with the 68 net score but look at snowflake it has an 81 percent net score which is just incredible and you can see google database is also very strong and the high 50 percent range while microsoft even though it's above the 40 percent mark is noticeably lower than the others as is mongodb with presumably atlas which is surprisingly low frankly but back to snowflake so the etr callout stresses that snowflake doesn't have a strong as strong a presence in the legacy database vendor accounts yet now i'm not sure i would put cloudair in the legacy database category but okay whatever cloudera they're positioning cdp is a hybrid platform as are all the on-prem players with their respective products and platforms but it's going to be interesting to see because snowflake has flat out said it's not straddling the cloud and on-prem rather it's all in on cloud but there is a big opportunity to connect on-prem to the cloud and across clouds which snowflake is pursuing that that ladder the cross-cloud the multi-cloud and snowflake is betting on incremental use cases that involve data sharing and federated governance while traditional players they're protecting their turf at the same time trying to compete in cloud native and of course across cloud i think there's room for both but clearly as we've shown cloud has the spending velocity and a tailwind at its back and aws along with microsoft seem to be getting stronger especially in the all-important categories related to machine intelligence ai and database now to be an essential infrastructure technology player in the data era it would seem obvious that you have to have database and or data management intellectual property in your portfolio or you're going to be less valuable to customers and investors okay we're going to leave it there for today remember these episodes they're all available as podcasts wherever you listen all you do is search breaking analysis podcast and please subscribe to the series check out etr's website at etr dot plus plus etr plus we also publish a full report every week on wikibon.com and siliconangle.com you can get in touch with me david.velante at siliconangle.com you can dm me at d vallante or you can hit hit me up on our linkedin post this is dave vellante for the cube insights powered by etr have a great week stay safe be well and we'll see you next time you

>>from the cube studios in Palo alto >>in boston >>connecting with thought leaders all around the >>world. This >>is a cute conversation. Hello and welcome to today's session of the AWS Global Public sector Partner Awards. I'm your host Natalie ehrlich. Today we're going to focus on the following award for best partner transformation. I'm pleased to introduce our guests, josh door smith, vice president of public sector at Effectual and jeremy Yates, deputy technology architect at jenny May. Welcome gentlemen so glad to have you on our show. >>Hi there. Very nice to be here. Thank you so much for having me >>terrific. Well josh, I'd like to start with you. How can companies leverage cloud native solutions to deliver higher quality services? >>So Natalie, that's a great question. And in the public sector and our our government customers, we run into this all the time. It's kind of our bread and butter. What what they can do is the first thing they need to be aware of is you don't have to be afraid of the cloud as some very obscure technology that is just emerging. It's been out for 10, 11 years now, customers across government space are using it lock stock and barrel to do everything from just managing simple applications, simple websites all the way through hosting their entire infrastructure, both in production and for disaster recovery purposes as well. So the first thing to note is just don't be afraid of the cloud. Um secondly, it's, it's imperative that they select the right partner who is able to kind of be there Sherpa to go into however far they want to dip their toe into the, into the proverbial cloud waters. Um to select somebody who knows whatever it is that they need to go do. So if they want to go Aws as we are talking about today, pick a partner who has the right experience, past performance designations and competencies with the cloud that they're interested in. >>Terrific. Well, you know, Jeremy, I'd love to move to you. What does modern modernization mean to jenny May? >>Sure, Thanks Natalie, great to be here. Thanks josh as well, you know. So for jenny May, modernization is really, it's not just technology is holistic across the organization. So that includes things like the business, um not just you know, the the I. T. Division. So we're looking at the various things to modernize like our culture and structural changes within the organization. Um moving to implement some, some proven practices like def sec ops and continuous integration and continuous delivery or deployment. Uh and then, you know, our overall overarching goal is to give the best and most secure technology to the business that we can to meet the Jeannie Mai mission and the needs of our customers >>terrific. Well josh, how is Effectual planning to support jenny Maes modernization plans? >>So we have been supporting jenny May for about 14 months now. Uh and back in september of last year, we rewarded a co prime 10 year contract for Jeannie Mai to do exactly that. It's to provide all things cloud to Jeannie Mai for 10 years on AWS and that's including reselling AWS. That's including providing all sorts of professional services to them. And it's, it's providing some third party software applications to help them support their applications themselves. So what Effectual is doing is kind of a threefold. We are supporting the modernization of their process, which jeremy mentioned a moment ago and that includes in stan shih ating a cloud center of Excellence for jenny May, which enables them to modernize the way they do cloud governance while they're modernizing their technology stack. We're also providing a very expert team of cloud architects and Dempsey cops engineers to be able to, to design the Jeannie Mai environment, collaborating with our co prime uh to ensure that it meets the security requirements, the compliance requirements that jerry mentions. Uh, Jeannie Mai is a federal entity, but it also has to adhere to all the finance industry uh compliance requirements as well. So very strenuous from that perspective. And then the third thing that we're doing to help them kind of along their modernization journey is in stan shih aging infrastructure as code. So in the cloud, rather than building everything in the AWS management console, we script everything to build it automatically, so it improves consistency, it improves the customer experience regardless of which resource is working on it. And it improves disaster recovery capability as well. And also, just quite frankly, the speed by which they can actually deploy things. >>And jeremy, how is this transition helping your security really enhancing it now? >>Uh From a security perspective we're implementing a number of various tools um both, you know, a W. S based as well as other software that josh mentioned. Um So we're able to utilize those in a more scalable manner than we could previously in the traditional data center. Um we've got a number of things such as we're looking at multiple vulnerability management products like 10 of Ohio and Wallace. Um we're using uh tools such as Centra fi for our our pam or privileged access management capabilities. Um Splunk a pretty industry standard. Um software for log and data correlation and analysis um will also be using that for some system and application monitoring. Um as well as uh the Mcafee envision product for endpoint and other cloud service security. So being able to pull all those in in a more scalable and more cost efficient way as well from cloud based services. Uh, it's really helped us be able to get those services and integrate them together in a way that, you know, we may not previously been able to. >>Yeah, terrific. Well, josh, let's move back to you and talk further about compliance. You know, any insight here, how Effectual is building a modern cloud infrastructure to integrate AWS services with third party tools to really achieve compliance with the government requirements. Just any further insight on that >>front? That's a great question. Natalie and I'm gonna tag team with Jeremy on this one if you don't mind, but I'll start off so jenny may obviously I mentioned earlier has federal requirements and financial requirements so focused right now on on those federal aspects. Um, so the tools that Jeremy mentioned a moment ago, we are integrating all of them with a W. S native meaning all of the way we do log aggregation in the various tools within AWS cloudwatch cloud trail. All of those things were implementing an AWS native, integrating them with Splunk to aggregate all of that information. But then one of the key requirements that's coming up with the federal government in the very near future is tick three dot or trusted internet connection. Basically in the first iteration a decade or so ago, the government wanted to limit the amount of points of presence that they have with the public facing internet fast forward several versions to today and they're pushing that that onus back on the various entities like jenny May and like hud, which Jeannie Mai is a part of but they still want to have that kind of central log repository to where all of the, all of the security logs and vulnerability logs and things like that. Get shipped to a central repository and that will be part of DHS. So what effectual has done in partnership with jenny May is create a, a W. S native solution leveraging some of those third party tools that we mentioned earlier to get all of those logs aggregated in a central repository for Ginny MaE to inspect ingest and take action from. But then also provide the mechanism to send that to DHS to do that and correlate that information with everything coming in from feeds across the government. Now that's not required just yet. But we're future proofing jenny Maes infrastructure in order to be able to facilitate adherence to those requirements when it becomes uh required. Um, and so jeremy, I'll pass it over to you to talk a little bit further about that because I know that's one of the things that's near and dear to your sister's heart as well as jenny may overall. >>Yeah, absolutely. Thanks josh. Um, so yeah, we, as you mentioned, we have implemented um, uh, sort of a hybrid tech model right now, um, to to handle compliance on that front. Um, so we're still using a, you know, some services from the legacy or our existing T two dot x models. That that josh was mentioning things such as m tips, um, uh, the Einstein sensors, etcetera. But we're also implementing that take 30 architecture on our own. As josh mentioned that that will allow us to sort of future proof and and seamlessly really transitioned to once we make that decision or guidance comes out or, you know, mandates or such. Um, so that effort is good to future proof house from a compliance perspective. Um, also, you know, the tools that I mentioned, uh, josh reiterated, those are extremely important to our our security and compliance right. Being able to ensure, you know, the integrity and the confidentiality of of our systems and our data is extremely important. Not both, not just both on the r not only on the government side, but as josh mentioned, the finance side as well. >>Terrific. Well, I'd love to get your insight to on AWS workspaces. Um, if either one of you would like to jump in on this question, how did they empower the jenny May team to work remotely through this pandemic? >>That's a great question. I guess I'll start and then we'll throw it to jeremy. Um, so obviously uh effectual started working with jenny May about three weeks after the pandemic formally started. So perfect timing for any new technology initiative. But anyway, we, we started talking with Jeremy and with his leadership team about what is required to actually facilitate and enable our team as well as the government resources and the other contractors working for jenny May to be able to leverage the new cloud environment that we were building and the very obvious solution was to implement a virtual desktop infrastructure uh type solution. And obviously Jeannie Mai had gone all in on amazon web services, so it became the national natural fit to look first at AWS workspaces. Um, so we have implemented that solution. There are now hundreds of jenny May and jenny make contractor resources that have a WS workspaces functioning in the GovCloud regions today and that's a very novel approach to how to facilitate and enable not only our team who is actually configuring the infrastructure, but all the application developers, the security folks and the leadership on the jenny may side to be able to access, review, inspect, check log etcetera, through this remote capability. It's interesting to note that Jeannie Mai has been entirely remote since the pandemic initiated. Jeremy's coming to us from, from west Virginia today, I'm coming to us from national harbor Maryland And we are operating totally remotely with a team of 60 folks about supporting this specific initiative for the cloud, not to mention the hundreds that are supporting the applications that Jamie runs to do its day to day business. So jeremy, if you wouldn't mind talking about that day to day business that jenny may has and, and kind of what the, the mission statement of Jeannie Mai is and how us enabling these workspaces uh facilitates that mission >>or you know, so the part of the overall mission of jenny Maes to, to ensure affordable housing is, is made available to uh, the american public. Um that's hud and, and jenny may as part of that and we provide um mortgage backed securities to help enable that. Um, so we back a lot of V A. Loans, um, F H A, those sort of loans, um, workspaces has been great in that manner from a technology perspective, I think because as you mentioned, josh, it's really eliminated the need for on premise infrastructure, right? We can be geographically dispersed, We can be mobile, um, whether we're from the east coast or west coast, we can access our environment securely. Uh, and then we can, you know, administer and operate and maintain the technology that the business needs to, to fulfill the mission. Um, and because we're able to do that quickly and securely and effectively, that's really helpful for the business >>Terrific. And um, you know, I'd like to shift gears a bit and uh you know, discuss what you're looking ahead toward. What is your vision for 2021? How do you see this partnership evolving? >>Yeah, you >>Take that 1/1. >>Sure. Yeah. Um you know, definitely some of the things we look forward to in 2021 as we evolve here is we're going to continue our cloud journey um you know, through practices like Deb said cops, you realize that uh that journey has never done. It's always a continual improvement process. It's a loop to continually work towards um a few specific things or at least one specific thing that we're looking forward to in the future, as josh mentioned earlier was our arctic three Oh Initiative. Um, so with that we think will be future proofed. Um as there's been a lot of um a lot of recent cyber security activity and things like that, that's going to create um opportunities I think for the government and Jeannie Mai is really looking forward to to leading in that area. >>Mhm and josh, can you weigh in quickly on that? >>Absolutely. Uh First and foremost we're very much looking forward to receiving authority to operate with our production environment. We have been preparing for that for this last year plus. Uh but later on this summer we will achieve that 80 oh status. And we look forward to starting to migrate the applications into production for jenny May. And then for future proof, it's as jerry jerry mentioned, it's a journey and we're looking forward to cloud optimizing all of their applications to ensure that they're spending the right money in the right places uh and and ensuring that they're not spending over on any of the one given area. So we're very excited to optimize and then see what the technology that we're being able to provide to them will bring to them from an idea and a conceptual future for jenny may. >>Well thank you both so very much for your insights. It's been a really fantastic interview. Our guests josh duggar smith as well as jeremy Gates. Really appreciate it. >>Thank you very much. >>Thank you so much. >>Terrific. Well, I'm your host for the cube Natalie or like to stay tuned for more coverage. Thanks so much for watching.

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante cloud repatriation is a term often used by technology companies the ones that don't operate a public cloud the marketing narrative most typically implies that customers have moved work to the public cloud and for a variety of reasons expense performance security etc are disillusioned with the cloud and as a result are repatriating workloads back to their safe comfy and cost-effective on-premises data center while we have no doubt this does sometimes happen the data suggests that this is a single digit de minimis phenomenon hello and welcome to this week's wikibon cube insights powered by etr some have written about the repatriation myth but in this breaking analysis we'll share hard data that we feel debunks the narrative and is currently being promoted by some we'll also take this opportunity to do our quarterly cloud revenue update and share with you our latest figures for the big four cloud vendors let's start by acknowledging that the definition of cloud is absolutely evolving and in this sense much of the vendor marketing is valid no longer is cloud just a distant set of remote services that lives up there in the cloud the cloud is increasingly becoming a ubiquitous sensing thinking acting set of resources that touches nearly every aspect of our lives the cloud is coming on prem and work is being done to connect clouds to each other and the cloud is extending to the near and far edge there's little question about that today's cloud is not just compute storage connectivity and spare capacity but increasingly it's a variety of services to analyze data and predict slash anticipate changes monitor and interpret streams of information apply machine intelligence to data to optimize business outcomes it's tooling to share data protect data visualize data and bring data to life supporting a whole new set of innovative applications notice there's a theme there data increasingly the cloud is where the high value data lives from a variety of sources and it's where organizations go to mine it because the cloud vendors have the best platforms for data and this is part of why the repatriation narrative is somewhat dubious actually a lot dubious because the volume of data in the cloud is growing at rates much faster than data on prem at least by a couple thousand basis points by our estimates annually so cloud data is where the action is and we'll talk about the edge in a moment but a new era of application development is emerging with containers at the center the concept of write wants run anywhere allows developers to take advantage of systems that run on-prem say a transaction system and tap data from multiple sources in various locations there might be multiple clouds or at the edge or wherever and combine that with immense cheap processing power that we've discussed extensively in previous breaking analysis episodes and you see this new breed of apps emerging that's powered by ai those are hitting the market so this is not a zero-sum game the cloud vendors have given the world an infrastructure gift by spending like crazy on capex more than a hundred billion last year on capex for example for the big four and in our view the players that don't own a cloud should stop being so defensive about it they should thank the hyperscalers and lay out a vision as to how they'll create a new abstraction layer on top of the public cloud and you know that's what they're doing and they'll certainly claim to be actively working on this vision but consider the pace of play between the hyperscalers and their traditional on-prem providers we believe the innovation gap is actually widening meaning the public cloud players are accelerating their innovation lead and will 100 compete for hybrid applications they have the resources the developer affinity they're doing custom silicon and have the expertise there and the tam expansion goals that loom large so while it's not a zero-sum game and hybrid is definitely real we think the cloud vendors continue to gain share most rapidly unless the hybrid crowd can move faster now of course there's the edge and that is a wild card but it seems that again the cloud players are very well positioned to innovate with custom silicon programmable infrastructure capex build-outs at the edge and new thinking around system architectures but let's get back to the core story here and take a look at cloud adoptions you hear many marketing messages that call into question the public cloud at its recent think conference ibm ceo arvind krishna said that only about 25 of workloads had moved into the public cloud and he made the statement that you know this might surprise you implying you might think it should be much higher than that well we're not surprised by that figure especially especially if you narrow it to mission critical work which ibm does in its annual report actually we think that's probably high for mission critical work moving to the cloud we think it's a lot lower than that but regardless we think there are other ways to measure cloud adoption and this chart here from david michelle's book c seeing digital shows the adoption rates for major technological innovations over the past century and the number of years how many years it took to get to 50 percent household adoption electricity took a long time as did telephones had that infrastructure that last mile build out radios and tvs were much faster given the lower infrastructure requirements pcs actually took a long time and the web around nine years from when the mosaic browser was introduced we took a stab at estimating the pace of adoption of public cloud and and within a decade it reached 50 percent adoption in top enterprises and today that figures easily north of 90 so as we said at the top cloud adoption is actually quite strong and that adoption is driving massive growth for the public cloud now we've updated our quarterly cloud figures and want to share them with you here are our latest estimates for the big four cloud players with only alibaba left to report now remember only aws and alibaba report clean or relatively clean i ass figures so we use survey data and financial analysis to estimate the actual numbers for microsoft in google it's a subset of what they report in q121 we estimate that the big 4is and pas revenue approached 27 billion that's q121 that figure represents about 40 growth relative to q1 2020. so our trailing 12-month calculation puts us at 94 billion so we're now on roughly 108 billion dollar run rate as you may recall we've predicted that figure will surpass 115 billion by year end when it's all said and done aws it remains the leader amongst the big four with just over half of the market that's down from around 63 percent for the full year of 2018. unquestionably as we've reported microsoft they're everywhere they're ubiquitous in the market and they continue to perform very well but anecdotally customers and partners in our community continue to report to us that the quality of the aws cloud is noticeably better in terms of reliability and overall security etc but it doesn't seem to change the trajectory of the share movements as microsoft's software dominance makes doing business with azure really easy now as of this recording alibaba has yet to report but we'll update these figures once their earnings are released let's dig into the growth rates associated with these revenue figures and make some specific comments there this chart here shows the growth trajectory for each of the big four google trails the pack in revenue but it's growing faster than the others from of course a smaller base google is being very aggressive on pricing and customer acquisition to that we say good google needs to grow faster in our view and they most certainly can afford to be aggressive as we said combined the big four are growing revenue at 40 on a trailing 12-month basis and that compares with low single-digit growth for on-prem infrastructure and we just don't see this picture changing in the near to midterm like storage growth revenue from the big public cloud players is expected to outpace spending on traditional on on-prem platforms by at least 2 000 basis points for the foreseeable future now interestingly while aws is growing more slowly than the others from a much larger 54 billion run rate we actually saw sequential quarterly growth from aws and q1 which breaks a two-year trend from where aws's q1 growth rate dropped sequentially from q4 interesting now of course at aws we're watching the changing of the guards andy jassy becoming ceo of amazon adam silipsky boomeranging back to aws from a very successful stint at tableau and max peterson taking over for for aws public sector replacing teresa carlson who is now president and heading up go to market at splunk so lots of changes and we think this is actually a real positive for aws as it promotes from within we like that it taps previous amazon dna from tableau salesforce and it promotes the head of aws to run all of amazon a signal to us that amazon will dig its heels in and further resist calls to split aws from the mothership so let's dig in a little bit more to this repatriation mythbuster theme the revenue numbers don't tell the entire story so it's worth drilling down a bit more let's look at the demand side of the equation and pull in some etr survey data now to set this up we want to explain the fundamental method used by etr around its net score metric net score measures spending momentum and measures five factors as shown in this wheel chart that shows the breakdown of spending for the aws cloud it shows the percentage of customers within the platform that are either one adopting the platform new that's the lime green in this wheel chart two increasing spending by more than five percent that's the forest green three flat spending between plus or minus five percent that's the gray and four decreasing spend by six percent or more that's the pink and finally five replacing the platform that's the bright red now dare i say that the bright red is a proxy for or at least an indicator of repatriation sure why not let's say that now net score is derived by subtracting the reds from the greens anything above 40 percent we consider to be elevated aws is at 57 so very high not much sign of leaving the cloud nest there but we know it's nuanced and you can make an argument for corner cases of repatriation but come on the numbers just don't bear out that narrative let's compare aws with some of the other vendors to test this theory theory a bit more this chart lines up net score granularity for aws microsoft and google it compares that to ibm and oracle now other than aws and google these figures include the entire portfolio for each company but humor me and let's make an assumption that cloud defections are lower than the overall portfolio average because cloud has more momentum it's getting more spend spending so just stare at the red bars for a moment the three cloud players show one two and three percent replacement rates respectively but ibm and oracle while still in the single digits which is good show noticeably higher replacement rates and meaningfully lower new adoptions in the lime green as well the spend more category in the forest green is much higher within the cloud companies and the spend less in the pink is notably lower and you can see the sample sizes on the right-hand side of the chart we're talking about many hundreds over 1300 in the case of microsoft and if we look if we put hpe or dell in the charts it would say several hundred responses many hundreds it would look similar to ibm and oracle where you have higher reds a bigger fat middle of gray and lower greens it's just the way it is it shouldn't surprise anyone and it's you know these are respectable but it's just what happens with mature companies so if customers are repatriating there's little evidence here we believe what's really happening is that vendor marketing people are talking to customers who are purposefully spinning up test and dev work in the cloud with the intent of running a workload or portions of that workload on prem and when they move into production they're counting that as repatriation and they're taking liberties with the data to flood the market okay well that's fair game and all's fair in tech marketing but that's not repatriation that's experimentation or sandboxing or testing and deving it's not i'm leaving the cloud because it's too expensive or less secure or doesn't perform for me we're not saying that those things don't happen but it's certainly not visible in the numbers as a meaningful trend that should factor into buying decisions now we perfectly recognize that organizations can't just refactor their entire applications application portfolios into the cloud and migrate and we also recognize that lift and shift without a change in operating model is not the best strategy in real migrations they take a long time six months to two years i used to have these conversations all the time with my colleague stu miniman and i spoke to him recently about these trends and i wanted to see if six months at red hat and ibm had changed his thinking on all this and the answer was a clear no but he did throw a little red hat kool-aid at me saying saying that the way they think about the cloud blueprint is from a developer perspective start by containerizing apps and then the devs don't need to think about where the apps live whether they're in the cloud whether they're on prem where they're at the edge and red hat the story is brings a consistency of operations for developers and operators and admins and the security team etc or any plat on any platform but i don't have to lock in to a platform and bring that everywhere with me i can work with anyone's platform so that's a very strong story there and it's how arvin krishna plans to win what he calls the architectural battle for hybrid cloud okay so let's take a take a look at how the big cloud vendors stack up with the not so big cloud platforms and all those in between this chart shows one of our favorite views plotting net score or spending velocity on the vertical axis and market share or pervasiveness in the data set on the horizontal axis the red shaded area is what we call the hybrid zone and the dotted red lines that's where the elite live anything above 40 percent net score on the on on the vertical axis we consider elevated anything to the right of 20 on the horizontal axis implies a strong market presence and by those kpis it's really a two horse race between aws and microsoft now as we suggested google still has a lot of work to do and if they're out buying market share that's a start now you see alibaba shown in the upper left hand corner high spending momentum but from a small sample size as etr's china respondent level is obviously much lower than it is in the u.s and europe and the rest of apac now that shaded res red zone is interesting and gives credence to the other big non-cloud owning vendor narrative that is out there that is the world is hybrid and it's true over the past several quarters we've seen this hybrid zone performing well prominent examples include vmware cloud on aws vmware cloud which would include vcf vmware cloud foundation dell's cloud which is heavily based on vmware and red hat open shift which perhaps is the most interesting given its ubiquity as we were talking about before and you can see it's very highly elevated on the net score axis right there with all the public cloud guys red hat is essentially the switzerland of cloud which in our view puts it in a very strong position and then there's a pack of companies hovering around the 20 vertical axis level that are hybrid that by the way you see openstack there that's from a large telco presence in the data set but any rate you see hpe oracle and ibm ibm's position in the cloud just tells you how important red hat is to ibm and without that acquisition you know ibm would be far less interesting in this picture oracle is oracle and actually has one of the strongest hybrid stories in the industry within its own little or not so little world of the red stack hpe is also interesting and we'll see how the big green lake ii as a service pricing push will impact its momentum in the cloud category remember the definition of cloud here is whatever the customer says it is so if a cio says we're buying cloud from hpe or ibm or cisco or dell or whomever we take her or his word for it and that's how it works cloud is in the eye of the buyer so you have the cloud expanding into the domain of on-premises and the on-prem guys finally getting their proverbial acts together with hybrid that they've been talking about since 2009 but it looks like it's finally becoming real and look it's true you're not going to migrate everything into the cloud but the cloud folks are in a very strong position they are on the growth flywheel as we've shown they each have adjacent businesses that are data based disruptive and dominant whether it's in retail or search or a huge software estate they are winning the data wars as well that seems to be pretty clear to us and they have a leg up in ai and i want to look at that can we all agree that ai is important i think we can machine intelligence is being infused into every application and today much of the ai work is being done in the cloud as modeling but in the future we see ai moving to the edge in real time and real-time inferencing is a dominant workload but today again 90 of it is building models and analyzing data a lot of that work happens in the cloud so who has the momentum in ai let's take a look here's that same xy graph with the net score against market share and look who has the dominant mind share and position and spending momentum microsoft aws and google you can see in the table insert in the lower right hand side they're the only three in the data set of 1 500 responses that have more than 100 n aws and microsoft have around 200 or even more in the case of microsoft and their net scores are all elevated above the 60 percent level remember that 40 percent that red line indicates the elevation mark the high elevation mark so the hyperscalers have both the market presence and the spend momentum so we think the rich get richer now they're not alone there are several companies above the 40 line databricks is bringing ai and data science to the world of data lakes with its managed services and it's executing very well salesforce is infusing infusing ai into its platform via einstein you got sap on there anaconda is kind of the gold standard that platform for data science and you can see c3 dot ai is tom siebel's company going after enterprise ai and data robot which like c3 ai is a small sample in the data set but they're highly elevated and they're simplifying machine learning now there's ibm watson it's actually doing okay i mean sure we'd like to see it higher given that ginny rometty essentially bet ibm's future on watson but it has a decent presence in the market and a respectable net score and ibm owns a cloud so okay at least it's a player not the dominance that many had hoped for when watson beat ken jennings in jeopardy back 10 years ago but it's okay and then is oracle they're now getting into the act like it always does they want they watched they waited they invested they spent money on r d and then boom they dove into the market and made a lot of noise and acted like they invented the concept oracle is infusing ai into its database with autonomous database and autonomous data warehouse and look that's what oracle does it takes best of breed industry concepts and technologies to make its products better you got to give oracle credit it invests in real tech and it runs the most mission critical apps in the world you can hate them if you want but they smoke everybody in that game all right let's take a look at another view of the cloud players and see how they stack up and where the big spenders live in the all-important fortune 500 this chart shows net score over time within the fortune 500 aws is particularly interesting because its net score overall is in the high 50s but in this large big spender category aws net score jumps noticeably to nearly 70 percent so there's a strong indication that aws the largest player also has momentum not just with small companies and startups but where it really counts from a revenue perspective in the largest companies so we think that's a very positive sign for aws all right let's wrap the realities of cloud repatriation are clear corner cases exist but it's not a trend to take to the bank although many public cloud users may think about repatriation most will not act on it those that do are the exception not the rule and the etr data shows that test and dev in the clouds is part of the cloud operating model even if the app will ultimately live on prem that's not repatriation that's just smart development practice and not every workload is will or should live in the cloud hybrid is real we agree and the big cloud players know it and they're positioning to bring their stacks on prem and to the edge and despite the risk of a lock-in and higher potential monthly bills and concerns over control the hyperscalers are well com positioned to compete in hybrid to win hybrid the legacy vendors must embrace the cloud and build on top of those giants and add value where the clouds aren't going to or can't or won't they got to find places where they can move faster than the hyperscalers and so far they haven't shown a clear propensity to do that hey that's how we see it what do you think okay well remember these episodes are all available as podcasts wherever you listen you do a search breaking analysis podcast and please subscribe to the series check out etr's website at dot plus we also publish a full report every week on wikibon.com and siliconangle.com a lot of ways to get in touch you can email me at david.velante at siliconangle.com or dm me at dvalante on twitter comment on our linkedin post i always appreciate that this is dave vellante for the cube insights powered by etr have a great week everybody stay safe be well and we'll see you next time you

>> Well, thank you, Gary. Well, you know, reasonable people could debate when the so-called big data era started. But for me it was in the fall of 2010 when I was sleepwalking through this conference in Dallas. And the conference was focused on data being a liability. And the whole conversation was about, how do you mitigate the risks of things like work in process and smoking-gun emails. I got a call from my business partner, John Fard, he said to me, "get to New York and come and see the future of data. We're doing theCUBE at Hadoop World tomorrow." I subsequently I canceled about a dozen meetings that I had scheduled for the week. And with only one exception, every one of the folks I was scheduled to meet said, "what's a Hadoop?" Well, I flew through an ice storm across country. I got to the New York Hilton around 3:00 AM, and I met John in the Dark Bar. If any of you remember that little facility. And I caught a little shut eye. And then the next day I met some of the most interesting people in tech during that time. They were thinking a lot differently than we were used to. They looked at data through a prism of value. And they were finding new ways to do things like deal with fraud, they were building out social networks, they were finding novel marketing vectors and identifying new investment strategies. The other thing they were doing is, they were taking these little tiny bits of code and bring it to really large sets of data. And they were doing things that I hadn't really heard of like no schema-on-write. And they were transforming their organizations by looking at data not as a liability, but as a monetization opportunity. And that opened my eyes and theCUBE, like a lot of others bet its business on data. Now over the past decade, customers have built up infrastructure and have been accommodating a lot of different use cases. Things like offloading ETL, data protection, mining data, analyzing data, visualizing. And as you know, you no doubt realize this was at a time when the cloud was, you know, really kind of nascent. And it was really about startups and experimentation. But today, we've evolved from the wild west of 2010, and many of these customers they're leveraging the cloud for of course, ease of use and flexibility it brings, but also they're finding out it brings complexity and risk. I want to tell you a quick story. Recently it was interviewing a CIO in theCUBE and he said to me, "if you just shove all your workloads into the cloud, you might get some benefit, but you're also going to miss the forest to the trees. You have to change your operating model and expand your mind as to what is cloud and create a cloud light experience that spans your on premises, workloads, multiple public clouds, and even the edge. And you have to re-imagine your business and the possibilities that this new architecture this new platform can bring." So we're going to talk about some of this today in a little bit more detail and specifically how we can better navigate the data storm. And what's the role of hybrid cloud. I'm really excited to have two great guests. Manish Dasaur is the managing director in the North America lead for analytics and artificial intelligence at Accenture. Anupam Singh is the chief customer officer for Cloudera. Gentlemen, welcome to theCUBE, great to see you. >> Hi Dave good to see you again. >> All right, guys, Anupam and Manish, you heard my little monologue upfront Anupam we'll start with you. What would you? Anything you'd add, amend, emphasize? You know, share a quick story. >> Yeah, Dave thank you for that introduction. It takes me back to the days when I was an article employee and went to this 14 people meet up. Just a couple of pizza talking about this thing called Hadoop. And I'm just amazed to see that today we are now at 2000 customers, who are using petabytes of data to make extremely critical decisions. Reminds me of the fact that this week, a lot of our customers are busy thinking about elections and what effect it would have on their data pipeline. Will it be more data? Will it be more stressful? So, totally agree with you. And also agree that cloud, is almost still in early days in times of the culture of IT on how to use the cloud. And I'm sure we'll talk about that today in greater detail. >> Yeah most definitely Manish I wonder if we could get your perspective on this. I mean, back when Anupam was at Oracle you'd shove a bunch of, you know, data, maybe you could attach a big honking disc drive, you'd buy some Oracle licenses, you know, it was a Unix box. Everything went into this, you know, this God box and then things changed quite dramatically, which was awesome, but also complex. And you guys have been there from the beginning. What's your perspective on all this? >> Yeah, it's been fascinating just to watch the market and the technology evolve. And I think the urgency to innovate is really just getting started. We're seeing companies drive growth from 20% in cloud today, to 80% cloud in the next few years. And I think the emergence of capabilities like hybrid cloud, we really get upfront a lot of flexibility for companies who need the ability to keep some data in a private setting, but be able to share the rest of the data in a public setting. I think we're just starting to scratch the surface of it. >> So let's talk a little bit about what is a hybrid cloud Anupam I wonder if you could take this one let's start with you and then Manish we come back to you and to get the customer perspective as well. I mean, it is a lot of things to a lot of people, but what is it? Why do we need it? And you know, what's the value? >> Yeah, I could speak poetic about Kubernetes and containers et cetera. But given that, you know, we talk to customers a lot, all three of us from the customer's perspective, hybrid cloud is a lot about collaboration and ease of procurement. A lot of our customers, whether they're in healthcare, banking or telco, are being asked to make the data available to regulatory authority, to subsidiaries outside of their geography. When you need that data to be available in other settings, taking a from on-prem and making it available in public cloud, enables extreme collaboration, extreme shared data experience if you will, inside the company. So we think about hybrid like that. >> Manish anything you'd add? How are your customers thinking about it? >> I mean, in a very simple way, it's a structure that where we are allowing mixed computing storage and service environments that's made of on-prem structures, private cloud structures, and public cloud structures. We're often calling it multicloud or mixcloud. And I think the really big advantage is, this model of cloud computing is enabling our clients to gain the benefits of public cloud setting, while maintaining your own private cloud for sensitive and mission critical and highly regulated computing services. That's also allowing our clients and organizations to leverage the pay-as-you-go model, which is really quite impressive and attractive to them because then they can scale their investments accordingly. Clients can combine one or more public cloud providers together in a private cloud, multicloud platform. The cloud can operate independently of each other, communicate over an encrypted connection. This dynamic solution offers a lot of flexibility and scalability which I think is really important to our clients. >> So Manish I wonder if we would stay there. How do they, how do your customers decide? How do you help them decide, you know, what the right mix is? What the equilibrium is? How much should it be in on-prem? How much should be in public or across clouds? Or, you know, eventually, well the edge will I guess decide for us. But, how do you go through, what are the decision points there? >> Yeah, I think that's a great question Dave. I would say there's several factors to consider when developing a cloud strategy that's the right strategy for you. Some of the factors that come to my mind when contemplating it, one would be security. Are there data sets that are highly sensitive that you don't want leaving the premise, versus data sets that need to be in a more shareable solution. Another factor I'd consider is speed and flexibility. Is there a need to stand up and stand down capabilities based on the seasonality of the business or some short-term demands? Is there a need to add and remove scale from the infrastructure and that quick pivot and that quick reaction is another factor they should consider. The third one I'd probably put out there is cost. Large data sets and large computing capacities often much more scalable and cost effective than a cloud infrastructure so there's lots of advantages to think through there. And maybe lastly I'd share is the native services. A lot of the cloud providers enable a set of native services for ingestion, for processing, of modeling, for machine learning, that organizations can really take advantage of. I would say if you're contemplating your strategy right now, my coaching would be, get help. It's a team sport. So definitely leverage your partners and think through the pros and cons of the strategy. Establish a primary hyperscaler, I think that's going to be super key and maximize your value through optimizing the workload, the data placement and really scaling the running operations. And lastly, maybe Dave move quickly right? Each day that you wait, you're incurring technical debt in your legacy environment, that's going to increase the cost and barrier to entry when moving to the new cloud hybrid driver. >> Thank you for that. Anupam I wonder if we could talk a little bit about the business impact. I mean, in the early days of big data, yes, it was a heavy lift, but it was really transformative. When you go to hybrid cloud, is it really about governance and compliance and security and getting the right mix in terms of latency? Are there other, you know, business impacts that are potentially as transformative as we saw in the early days? What are your thoughts on that? >> Absolutely. It's the other business impacts that are interesting. And you know, Dave, let's say you're in the line of business and I come to you and say, oh, there's cost, there's all these other security governance benefits. It doesn't ring the bell for you. But if I say, Dave used to wait 32 weeks, 32 weeks to procure hardware and install software, but I can give you the same thing in 30 minutes. It's literally that transformative, right? Even on-prem, if I use cloud native technology, I can give something today within days versus weeks. So we have banks, we have a bank in Ohio that would take 32 weeks to rack up a 42 node server. Yes, it's very powerful, you have 42 nodes on it, 42 things stacked on it, but still it's taking too much time. So when you get cloud native technologies in your data center, you start behaving like the cloud and you're responsive to the business. The responsiveness is very important. >> That's a great point. I mean, in fact, you know, there's always this debate about is the cloud public cloud probably cost more expensive? Is it more expensive to rent than it is to own? And you get debates back and forth based on your perspective. But I think at the end of the day, what, Anupam you just talked about, it may oftentimes could dwarf, you know, any cost factors, if you can actually, you know, move that fast. Now cost is always a consideration. But I want to talk about the migration path if we can Manish. Where do, how should customers think about migrating to the cloud migration's a, an evil word. How should they think about migrating to the cloud? What's the strategy there? Where should they start? >> No I think you should start with kind of a use case in mind. I think you should start with a particular data set in mind as well. I think starting with what you're really seeking to achieve from a business value perspective is always the right lens in my mind. This is the decade of time technology and cloud to the fitness value, right? So if you start with, I'm seeking to make a dramatic upsell or dramatic change to my top line or bottom line, start with the use case in mind and migrate the data sets and elements that are relevant to that use case, relevant to that value, relevant to that unlock that you're trying to create, that I think is the way to prioritize it. Most of our clients are going to have tons and tons of data in their legacy environment. I don't think the right way to start is to start with a strategy that's going to be focused on migrating all of that. I think the strategy is start with the prioritized items that are tied to the specific value or the use case you're seeking to drive and focus your transformation and your migration on that. >> So guys I've been around a long time in this business and been an observer for awhile. And back in the mainframe days, we used to have a joke called CTAM. When we talk about moving data, it was called the Chevy truck access method. So I want to ask you Anupam, how do you move the data? Do you, it's like an Einstein saying, right? Move as much data as you need to, but no more. So what's going on in that front? what's happening with data movement, and, you know, do you have to make changes to the applications when you move data to the cloud? >> So there's two design patterns, but I love your service story because you know, when you have a 30 petabyte system and you tell the customer, hey, just make a copy of the data and everything will be fine. That will take you one and a half years to make the copies aligned with each other. Instead, what we are seeing is the biggest magic is workload analysis. You analyze the workload, you analyze the behavior of the users, and say so let's say Dave runs dashboards that are very complicated and Manish waits for compute when Dave is running his dashboard. If you're able to gather that information, you can actually take some of the noise out of the system. So you take selected sets of hot data, and you move it to public cloud, process it in public cloud maybe even bring it back. Sounds like science fiction, but the good news is you don't need a Chevy to take all that data into public cloud. It's a small amount of data. That's one reason the other pattern that we have seen is, let's say Manish needs something as a data scientist. And he needs some really specific type of GPUs that are only available in the cloud. So you pull the data sets out that Manish needs so that he can get the new silicone, the new library in the cloud. Those are the two patterns that if you have a new type of compute requirement, you go to public cloud, or if you have a really noisy tenant, you take the hot data out into public cloud and process it there. Does that make sense? >> Yeah it does and it sort of sets up this notion I was sort of describing upfront that the cloud is not just, you know, the public cloud, it's the spans on-prem and multicloud and even the edge. And it seems to me that you've got a metadata opportunity I'll call it and a challenge as well. I mean, there's got to be a lot of R and D going on right now. You hear people talking about cloud native and I wonder on Anupam if you could stay on that, I mean, what's your sense as to how, what the journey is going to look like? I mean, we're not going to get there overnight. People have laid out a vision of this sort of expanding cloud and I'm saying it's a metadata opportunity, but I, you know, how do you, the system has to know what workload to put where based on a lot of those factors that you guys were talking about. The governance, the laws of the land, the latency issues, the cost issues is, you know, how is the industry Anupam sort of approaching this problem and solving this problem? >> I think the biggest thing is to reflect all your security governance across every cloud, as well as on-prem. So let's say, you know, a particular user named Manish cannot access financial data, revenue data. It's important that that data as it goes around the cloud, if it gets copied from on-prem to the cloud, it should carry that quality with it. A big danger is you copy it into some optic storage, and you're absolutely right Dave metadata is the goal there. If you copy the data into an object storage and you lose all metadata, you lose all security, you lose all authorization. So we have invested heavily in something called shared data experience. Which reflects policies from on-prem all the way to the cloud and back. We've seen customers needing to invest in that, but some customers went all hog on the cloud and they realize that putting data just in these buckets of optic storage, you lose all the metadata, and then you're exposing yourself to some breach and security issues. >> Manish I wonder if we could talk about, thank you for that Anupam. Manish I wonder if we could talk about, you know, I've imagined a project, okay? Wherever I am in my journey, maybe you can pick your sort of sweet spot in the market today. You know, what's the fat middle if you will. What does a project look like when I'm migrating to the cloud? I mean, what are some of the, who are the stakeholders? What are some of the outer scope maybe expectations that I better be thinking about? What kind of timeframe? How should I tackle this and so it's not like a, you know, a big, giant expensive? Can I take it in pieces? What's the state-of-the-art of a project look like today? >> Yeah, lots of thoughts come to mind, Dave, when you ask that question. So there's lots to pack. As far as who the buyer is or what the project is for, this is out of migration is directly relevant to every officer in the C-suite in my mind. It's very relevant for the CIO and CTO obviously it's going to be their infrastructure of the future, and certainly something that they're going to need to migrate to. It's very important for the CFO as well. These things require a significant migration and a significant investment from enterprises, different kind of position there. And it's very relevant all the way up to the CEO. Because if you get it right, the truly the power it unlocks is illuminates parts of your business that allow you to capture more value, capture a higher share of wallet, allows you to pivot. A lot of our clients right now are making a pivot from going from a products organization to an as a service organization and really using the capabilities of the cloud to make that pivot happen. So it's really relevant kind of across the C-suite. As far as what a typical program looks like, I always coach my clients just like I said, to start with the value case in mind. So typically, what I'll ask them to do is kind of prioritize their top three or five use cases that they really want to drive, and then we'll land a project team that will help them make that migration and really scale out data and analytics on the cloud that are focused on those use cases. >> Great, thank you for that. I'm glad you mentioned the shift in the mindset from product to as a service. We're seeing that across the board now, even infrastructure players are jumping on the bandwagon and borrowing some sort of best practices from the SaaS vendors. And I wanted to ask you guys about, I mean, as you move to the cloud, one of the other things that strikes me is that you actually get greater scale, but there's a broader ecosystem as well. So we're kind of moving from a product centric world and with SaaS we've got this sort of platform centric, and now it seems like ecosystems are really where the innovation is coming from. I wonder if you guys could comment on that, maybe Anupam you could start. >> Yeah, many of our customers as I said right? Are all about sharing data with more and more lines of businesses. So whenever we talk to our CXO partners, our CRO partners, they are being asked to open up the big data system to more tenants. The fear is, of course, if you add more tenants to a system, it could get, you know, the operational actually might get violated. So I think that's a very important part as more and more collaboration across the company, more and more collaboration across industries. So we have customers who create sandboxes. These are healthcare customers who create sandbox environments for other pharma companies to come in and look at clinical trial data. In that case, you need to be able to create these fenced environments that can be run in public cloud, but with the same security that you expect up. >> Yeah thank you. So Manish this is your wheelhouse as Accenture. You guys are one of the top, you know, two or three or four organizations in the world in terms of dealing with complexity, you've got deep industry expertise, and it seems like some of these ecosystems as Anupam was just sort of describing it in a form are around industries, whether it's healthcare, government, financial services and the like. Maybe your thoughts on the power of ecosystems versus the, you know, the power of many versus the resources of one. >> Yeah, listen, I always talk about this is a team sport right? And it's not about doing it alone. It's about developing as ecosystem partners and really leveraging the power of that collective group. And I've been for as my clients to start thinking about, you know, the key thing you want to think about is how you migrate to becoming a data driven enterprise. And in order for you to get there, you're going to need ecosystem partners to go along the journey with you, to help you drive that innovation. You're going to need to adopt a pervasive mindset to data and democratization of that data everywhere in your enterprise. And you're going to need to refocus your decision-making based on that data, right? So I think partner ecosystem partnerships are here to stay. I think what we're going to see Dave is, you know, at the beginning of the maturity cycle, you're going to see the ecosystem expand with lots of different players and technologies kind of focused on industry. And then I think you'll get to a point where it starts to mature and starts to consolidate as ecosystem partners start to join together through acquisitions and mergers and things like that. So I think ecosystem is just starting to change. I think the key message that I would give to our clients is take advantage of that. There's too much complexity for any one person to kind of navigate through on your own. It's a team sport, so take advantage of all the partnerships you can create. >> Well, Manish one of the things you just said that it kind of reminds me, you said data data-driven, you know, organizations and, you know, if you look at the pre-COVID narrative around digital transformation, certainly there was a lot of digital transformation going on, but there was a lot of complacency too. I talked to a lot of folks, companies that say, "you know, we're doing pretty well, our banks kicking butt right now, we're making a ton of money." Or you know, all that stuff that's kind of not on my watch. I'll be retired before then. And then it was the old, "if it ain't broke, don't fix it." And then COVID breaks everything. And now if you're not digital, you're out of business. And so Anupam I'll start with you. I mean, to build a data-driven culture, what does that mean? That means putting data at the center of your organization, as opposed to around in stove pipes. And this, again, we talked about this, it sort of started in there before even the early parts of last decade. And so it seems that there's cultural aspects there's obviously technology, but there's skillsets, there's processes, you've got a data lifecycle and a data, what I sometimes call a data pipeline, meaning an end to end cycle. And organizations are having to rethink really putting data at the core. What are you seeing? And specifically as it relates to this notion of data-driven organization and data culture, what's working? >> Yeah three favorite stories, and you're a 100% right. Digital transformation has been hyperaccelerated with COVID right? So our telco customers for example, you know, Manish had some technical problems with bandwidth just 10 minutes ago. Most likely is going to call his ISP. The ISP will most likely load up a dashboard in his zip code and the reason it gives me stress, this entire story is because most likely it's starting on a big data system that has to collect data every 15 minutes, and make it available. Because you'll have a very angry Manish on the other end, if you can't explain when is the internet coming back, right? So, as you said this is accelerated. Our telco providers, our telco customers ability to ingest data, because they have to get it in 15 minute increments, not in 24 hour increments. So that's one. On the banking sector what we have seen is uncertainty has created more needs for data. So next week is going to be very uncertain all of us know elections are upcoming. We have customers who are preparing for that additional variable capacity, elastic capacity, so that if investment bankers start running hundreds and thousands of reports, they better be ready. So it's changing the culture at a very fundamental level, right? And my last story is healthcare. You're running clinical trials, but everybody wants access to the data. Your partners, the government wants access to the data, manufacturers wants access to the data. So again, you have to actualize digital transformation on how do you share very sensitive, private healthcare data without violating any policy. But you have to do it quick. That's what COVID has started. >> Thank you for that. So I want to come back to hybrid cloud. I know a lot of people in the audience are, want to learn more about that. And they have a mandate really to go to cloud generally but hybrid specifically. So Manish I wonder if you could share with us, maybe there's some challenges, I mean what's the dark side of hybrid. What should people be thinking about that they, you know, they don't want to venture into, you know, this way, they want to go that way. What are some of the challenges that you're seeing with customers? And how are they mitigating them? >> Yeah, Dave it's a great question. I think there's a few items that I would coach my clients to prioritize and really get right when thinking about making the migration happen. First of all, I would say integration. Between your private and public components that can be complex, it can be challenging. It can be complicated based on the data itself, the organizational structure of the company, the number of touches and authors we have on that data and several other factors. So I think it's really important to get this integration right, with some clear accountabilities build automation where you can and really establish some consistent governance that allows you to maintain these assets. The second one I would say is security. When it comes to hybrid cloud management, any transfers of data you need to handle the strict policies and procedures, especially in industries where that's really relevant like healthcare and financial services. So using these policies in a way that's consistent across your environment and really well understood with anyone who's touching your environment is really important. And the third I would say is cost management. All the executives that I talk about have to have a cost management angle to it. Cloud migration provides ample opportunities for cost reduction. However many migration projects can go over budget when all the costs aren't factored in, right? So your cloud vendors. You've got to be mindful of kind of the charges on accessing on premise applications and scaling costs that maybe need to be budgeted for and where if possible anticipated and really plan for. >> Excellent. So Anupam I wonder if we could go a little deeper on, we talked a little bit about this, but kind of what you put where, which workloads. What are you seeing? I mean, how are people making the choice? Are they saying, okay, this cloud is good for analytics. This cloud is good. Well, I'm a customer of their software so I'm going to use this cloud or this one is the best infrastructure and they got, you know, the most features. How are people deciding really what to put where? Or is it, "hey, I don't want to be locked in to one cloud. I want to spread my risk around. What are you seeing specifically? >> I think the biggest thing is just to echo what Manish said. Is business comes in and as a complaint. So most projects that we see on digital transformation and on public cloud adoption is because businesses complaining about something. It's not architectural goodness, it is not for just innovation for innovation's sake. So, the biggest thing that we see is what we call noisy neighbors. A lot of dashboards, you know, because business has become so intense, click, click, click, click, you're actually putting a lot of load on the system. So isolating noisy neighbors into a cloud is one of the biggest patterns that you've seen. It takes the noisiest tenant on your cluster, noisiest workload and you take them to public cloud. The other one is data scientists. They want new libraries, they want to work with GPU's. And to your point Dave, that's where you select a particular cloud. Let's say there's a particular type silicone that is available only in that cloud. That GPU is available only in that cloud or that particular artificial intelligence library is available only in a particular cloud. That's when customers say, Hey miss, they decided, why don't you go to this cloud while the main workload might still be running on them, right? That's the two patterns that we are seeing. >> Right thank you. And I wonder if we can end on a little bit of looking to the future. Maybe how this is all going to evolve over the next several years. I mean, I like to look at it at a spectrum at a journey. It's not going to all come at once. I do think the edge is part of that. But it feels like today we've got, you know, multi clouds are loosely coupled and hybrid is also loosely coupled, but we're moving very quickly to a much more integrated, I think we Manish you talked about integration. Where you've got state, you've got the control plane, you've got the data plane. And all this stuff is really becoming native to the respective clouds and even bring that on-prem and you've got now hybrid applications and much much tighter integration and build this, build out of this massively distributed, maybe going from it's a hyper-converged to hyper-distributed again including the edge. So I wonder Manish we could start with you. How are your customers thinking about the future? How are they thinking about, you know, making sure that they're not going down a path where that's going to, they're going to incur a lot of technical debt? I know there's sort of infrastructure is code and containers and that seems it seems necessary, but insufficient there's a lot of talk about, well maybe we start with a functions based or a serverless architecture. There's some bets that have to be made to make sure that you can future proof yourself. What are you recommending there Manish? >> Yeah, I, listen I think we're just getting started in this journey. And like I said, it's really exciting time and I think there's a lot of evolution in front of us that we're going to see. I, you know, I think for example, I think we're going to see hybrid technologies evolve from public and private thinking to dedicated and shared thinking instead. And I think we're going to see advances in capabilities around automation and computer federation and evolution of consumption models of that data. But I think we've got a lot of kind of technology modifications and enhancements ahead of us. As far as companies and how they future proof themselves. I would offer the following. First of all, I think it's a time for action, right? So I would encourage all my class to take action now. Every day spent in legacy adds to the technical debt that you're going to incur, and it increases your barrier to entry. The second one would be move with agility and flexibility. That's the underlying value of hybrid cloud structures. So organizations really need to learn how to operate in that way and take advantage of that agility and that flexibility. We've talked about creating partnerships in ecosystems I think that's going to be really important. Gathering partners and thought leaders to help you navigate through that complexity. And lastly I would say monetizing your data. Making a value led approach to how you viewed your data assets and force a function where each decision in your enterprise is tied to the value that it creates and is backed by the data that supports it. And I think if you get those things right, the technology and the infrastructure will serve. >> Excellent and Anupam why don't you bring us home, I mean you've got a unique combination of technical acumen and business knowledge. How do you see this evolving over the next three to five years? >> Oh, thank you Dave. So technically speaking, adoption of containers is going to steadily make sure that you're not aware even of what cloud you're running on that day. So the multicloud will not be a requirement even, it will just be obviated when you have that abstraction there. Contrarily, it's going to be a bigger challenge. I would echo what Manish said start today, especially on the cultural side. It is great that you don't have to procure hardware anymore, but that also means that many of us don't know what our cloud bill is going to be next month. It is a very scary feeling for your CIO and your CFO that you don't know how much you're going to to spend next month forget next year, right? So you have to be agile in your financial planning as much you have to be agile in your technical planning. And finally I think you hit on it. Ecosystems are what makes data great. And so you have to start from day one that if I am going on this cloud solution, is the data shareable? Am I able to create an ecosystem around that data? Because without that, it's just somebody running a report may or may not have value to the business. >> That's awesome, guys. Thanks so much for a great conversation. We're at a time and I want to wish everybody a terrific event. Let me now hand it back to Vanita. She's going to take you through the rest of the day. This is Dave Vellante for theCUBE, thanks. (smooth calm music)

>> Narrator: From theCUBE's studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, this is Dave Vellante. Welcome to this CUBE conversation. We got a really cool company that we're going to introduce you to, and Anthony Brooks Williams is here. He's the CEO of that company, HVR. Anthony, good to see you. Thanks for coming on. >> Hey Dave, good to see you again, appreciate it. >> Yeah cheers, so tell us a little bit about HVR. Give us the background of the company, we'll get into a little bit of the history. >> Yeah sure, so at HVR we are changing the way companies routes and access their data. And as we know, data really is the lifeblood of organizations today, and if that stops moving, or stop circulating, well, there's a problem. And people want to make decisions on the freshest data. And so what we do is we move critical business data around these organizations, the most predominant place today is to the cloud, into platforms such as Snowflake, where we've seen massive traction. >> Yeah boy, have we ever. I mean, of course, last week, we saw the Snowflake IPO. The industry is abuzz with that, but so tell us a little bit more about the history of the company. What's the background of you guys? Where did you all come from? >> Sure, the company originated out of the Netherlands, at Amsterdam, founded in 2012, helping solve the issue that customer's was having moving data efficiently at scale across all across a wide area network. And obviously, the cloud is one of those endpoint. And therefore a company, such as the Dutch Postal Service personnel, where today we now move the data to Azure and AWS. But it was really around how you can efficiently move data at scale across these networks. And I have a bit of a background in this, dating back from early 2000s, when I founded a company that did auditing recovery, or SQL Server databases. And we did that through reading the logs. And so then sold that company to Golden Gate, and had that sort of foundation there, in those early days. So, I mean again, Azure haven't been moving data efficiently as we can across these organizations with it, with the key aim of allowing customers to make decisions on the freshest data. Which today's really, table stakes. >> Yeah, so, okay, so we should think about you, as I want to invoke Einstein here, move as much data as you need to, but no more, right? 'Cause it's hard to move data. So your high speeds kind of data mover, efficiency at scale. Is that how we should think about you? >> Absolutely, I mean, at our core, we are CDC trades that capture moving incremental workloads of data, moving the updates across the network, you mean, combined with the distributed architecture that's highly flexible and extensible. And these days, just that one point, customers want to make decisions on us as much as they can get. We have companies that we're doing this for, a large apparel company that's taking some of their not only their core sales data, but some of that IoT data that they get, and sort of blending that together. And given the ability to have a full view of the organization, so they can make better decisions. So it's moving as much data as they can, but also, you need to do that in a very efficient way. >> Yeah, I mean, you mentioned Snowflake, so what I'd like to do is take my old data warehouse, and whatever, let it do what it does, reporting and compliance, stuff like that, but then bring as much data as I need into my Snowflake, or whatever modern cloud database I'm using, and then apply whatever machine intelligence, and really analyze it. So really that is kind of the problem that you're solving, is getting all that data to a place where it actually can be acted on, and turned into insights, is that right? >> Absolutely, I mean, part of what we need to do is there's a whole story around multi-cloud, and that's obviously where Snowflake fit in as well. But from our point of views of supporting over 30 different platforms. I mean data is generated, data is created in a number of different source systems. And so our ability to support each of those in this very efficient way, using these techniques such as CDCs, is going to capture the data at source, and then weaving it together into some consolidated platform where they can do the type of analysis they need to do on that. And obviously, the cloud is the predominant target system of choice with something like a Snowflake there in either these clouds. I mean, we support a number of different technologies in there. But yeah, it's about getting all that data together so they can make decisions on all areas of the business. So I'd love to get into the secret sauce a little bit. I mean we've heard luminaries like Andy Jassie stand up at last year at Reinvent, he talked about Nitro, and the big pipes, and how hard it is to move data at scale. So what's the secret sauce that you guys have that allow you to be so effective at this? >> Absolutely, I mean, it starts with  how you going to acquire data? And you want to do that in the least obtrusive way to the database. So we'll actually go in, and we read the transaction logs of each of these databases. They all generate logs. And we go read the logs systems, all these different source systems, and then put it through our webs and secret sauce, and how we how we move the data, and how we compress that data as well. So, I mean, if you want to move data across a wide area network, I mean, the technique that a few companies use, such as ourselves, is change data capture. And you're moving incremental updates, incremental workloads, the change data across a network. But then combine that with the ability that we have around some of the compression techniques that we use, and, and then just into very distributed architecture, that was one of the things that made me join HVR after my previous experiences, and seeing that how that really fits in today's world of real time and cloud. I mean, those are table stakes things. >> Okay, so it's that change data capture? >> Yeah. >> Now, of course, you've got to initially seed the target. And so you do that, if I understand you use data reduction techniques, so that you're minimizing the amount of data. And then what? Do you use asynchronous methodologies, dial it down, dial it up, off hours, how does that work? >> Absolutely, exactly what you've said they mean. So we're going to we're, initially, there's an initial association, or an initial concept, where you take a copy of all of that data that sits in that source system, and replicating that over to the target system, you turn on that CDC mechanism, which is then weaving that change data. At the same time, you're compressing it, you're encrypting it, you're making sure it's highly secure, and loading that in the most efficient way into their target systems. And so we either do a lot of that, or we also work with, if there's a ETL vendor involved, that's doing some level of transformations, and they take over the transformation capabilities, or loading. We obviously do a fair amount of that ourselves as well. But it depends on what is the architecture that's in there for the customer as well. The key thing is that what we also have is, we have this compare and repair ability that's built into the product. So we will move data across, and we make sure that data that gets moved from A to B is absolutely accurate. I mean people want to know that their data can move faster, they want it to be efficient, but they also want it to be secure. They want to know that they have a peace of mind to make decisions on accurate data. And that's some stuff that we have built into the products as well, supported across all the different platforms as well. So something else that just sets us apart in that as well. >> So I want to understand the business case, if you will. I mean, is it as simple as, "Hey, we can move way more data faster. "We can do it at a lower cost." What's the business case for you guys, and the business impact? >> Absolutely, so I mean, the key thing is the business case is moving that data as efficiently as we can across this, so they can make these decisions. So our biggest online retailer in the US uses us, on the biggest busiest system. They have some standard vendors in there, but they use us, because of the scalability that we can achieve there, of making decisions on their financial data, and all the transactions that happen between the main E-commerce site, and all the third party vendors. That's us moving that data across there as efficiently as they can. And first we look at it as pretty much it's subscription based, and it's all connection based type pricing as well. >> Okay, I want to ask you about pricing. >> Yeah. >> Pricing transparency is a big topic in the industry today, but how do you how do you price? Let's start there. >> Yeah, we charge a simple per connection price. So what are the number of source systems, a connection is a source system or a target system. And we try to very simply, we try and keep it as simple as possible, and charge them on the connections. So they will buy a packet of five connections, they have source systems, two target systems. And it's pretty much as simple as that. >> You mentioned security before. So you're encrypting the data. So your data in motion's encrypted. What else do we need to know about security? >> Yeah, you mean, that we have this concept and how we handle, and we have this wallet concept, and how we integrate with the standard security systems that those customers have already, in the in this architecture. So it's something that we're constantly doing. I mean, there's there's a data encryption at rest. And initially, the whole aim is to make sure that the customer feels safe, that the data that is moving is highly secure. >> Let's talk a little bit about cloud, and maybe the architecture. Are you running in the cloud, are you running on prem, both, across clouds. How does that work? >> Yeah, all of the above. So I mean, what we see today is majority of the data is still generated on prem. And then the majority of the talks we see are in the cloud, and this is not a one time thing, this is continuous. I mean, they've moved their analytical workload into the cloud. You mean they have these large events a few times a year, and they want the ability to scale up and scale down. So we typically see you mean, right now, you need analytics, data warehouses, that type of workload is sitting in the cloud, because of the elasticity, and the scalability, and the reasons the cloud was brought on. So absolutely, we can support the cloud to cloud, we can support on prem to cloud, I think you mean, a lot of companies adopting this hybrid strategy that we've seen certainly for the foreseeable next five years. But yeah, absolutely. The source of target systems considered on prem or in the cloud. >> And where's the point of control? Is it wherever I want it to be? >> Absolutely. >> Is it in one of the clouds on prem? >> Yeah absolutely, you can put that point of control where you want it to be. We have a concept of agents, these agents search on the source and target systems. And then we have the, it's at the edge of your brain, the hub that is controlling what is happening. This data movement that can be sitting with a source system, separately, or on target system. So it's highly extensible and flexible architecture there as well. >> So if something goes wrong, it's the HVR brain that helps me recover, right? And make sure that I don't have all kinds of data corruption. Maybe you could explain that a little bit, what happens when something goes wrong? >> Yeah absolutely, I mean, we have things that are built into the product that help us highlight what has gone wrong, and how we can correct those. And then there's alerts that get sent back to us to the to the end customer. And there's been a whole bunch of training, and stuff that's taken place for then what actions they can take, but there's a lot of it is controlled through HVR core system that handles that. So we are working next step. So as we move as a service into more of an autonomous data integration model ourselves, whichever, a bunch of exciting things coming up, that just takes that off to the next levels. >> Right, well Golden Gate Heritage just sold that to Oracle, they're pretty hardcore about things like recovery. Anthony, how do you think about the market? The total available market? Can you take us through your opportunity broadly? >> Yeah absolutely, you mean, there's the core opportunity in the space that we play, as where customers want to move data, they don't want to do data integration, they want to move data from A to B. There's those that are then branching out more to moving a lot of their business workloads to the cloud on a continuous basis. And then where we're seeing a lot of traction around this particular data that resides in these critical business systems such as SAP, that is something you're asking earlier about, what are some core things on our product. We have the ability to unpack, to unlock that data that sits in some of these SAP environments. So we can go, and then decode this data that sits between these cluster pool tables, combine that with our CDC techniques, and move their data across a network. And so particularly, sort of bringing it back a little bit, what we're seeing today, people are adopting the cloud, the massive adoption of Snowflake. I mean, as we see their growth, a lot of that is driven through consumption, why? It's these big, large enterprises that are now ready to consume more. We've seen that tail wind from our perspective, as well as taking these workloads such as SAP, and moving that into something like these cloud platforms, such as a Snowflake. And so that's where we see the immediate opportunity for us. And then and then branching out from there further, but I mean, that is the core immediate area of focus right now. >> Okay, so we've talked about Snowflake a couple of times, and other platforms, they're not the only one, but they're the hot one right now. When you think about what organizations are doing, they're trying to really streamline their data pipeline to get to turn raw data into insights. So you're seeing that emerging organizations, that data pipeline, we've been talking about it for quite some time. I mean, Snowflake, obviously, is one piece of that. Where's your value in that pipeline? Is it all about getting the data into that stream? >> Yeah, you just mentioned something there that we have an issue internally that's called raw data to ready data. And that's about capturing this data, moving that across. And that's where we building value on that data as well, particularly around some of our SAP type initiatives, and solutions related to that, that we're bringing out as well. So one it's absolutely going in acquiring that data. It's then moving it as efficiently as we can at scale, which a lot of people talk about, we truly operate at scale, the biggest companies in the world use us to do that, across there and giving them that ability to make decisions on the freshest data. Therein lies the value of them being able to make decisions on data that is a few seconds, few minutes old, versus some other technology they may be using that takes hours days. You mean that is it, keeping large companies that we work with today. I mean keeping toner paper on shelves, I mean one thing that happened after COVID. I mean one of our big customers was making them out their former process, and making the shelves are full. Another healthcare provider being able to do analysis on what was happening on supplies from the hospital, and the other providers during this COVID crisis. So that's where it's a lot of that value, helping them reinvent their businesses, drive down that digital transformation strategy, is the key areas there. No data, they can't make those type of decisions. >> Yeah, so I mean, your vision really, I mean, you're betting on data. I always say don't bet against the data. But really, that's kind of the premise here. Is the data is going to continue to grow. And data, I often say data is plentiful insights aren't. And we use the Broma you said before. So really, maybe, good to summarize the vision for us, where you want to take this thing? Yeah, absolutely so we're going to continue building on what we have, making it easier to use. Certainly, as we move, as more customers move into the cloud. And then from there, I mean, we have some strategic initiatives of looking at some acquisitions as well, just to build on around offering, and some of the other core areas. But ultimately, it's getting closer to the business user. In today's world, there is many IT tech-savvy people sitting in the business side of organization, as they are in IT, if not more. And so as we go down that flow with our product, it's getting closer to those end users, because they're at the forefront of wanting this data. As we said that the data is the lifeblood of an organization. And so given an ability to drive the actual power that they need to run the data, is a core part of that vision. So we have some some strategic initiatives around some acquisitions, as well, but also continue to build on the product. I mean, there's, as I say, I mean sources and targets come and go, there's new ones that are created each week, and new adoptions, and so we've got to support those. That's our table stakes, and then continue to make it easier to use, scale even quicker, more autonomous, those type of things. >> And you're working with a lot of big companies, the company's well funded if Crunchbase is up to date, over $50 million in funding. Give us up right there. >> Yeah absolutely, I mean a company is well funded, we're on a good footing. Obviously, it's a very hot space to be in. With COVID this year, like everybody, we sat down and looked in sort of everyone said, "Okay well, let's have a look how "this whole thing's going to shake out, "and get good plan A, B and C in action." And we've sort of ended up with Plan A plus, we've done an annual budget for the year. We had our best quarter ever, and Q2, 193% year over year growth. And it's just, the momentum is just there, I think at large. I mean obviously, it sounds cliche, a lot of people say it around digital transformation and COVID. Absolutely, we've been building this engine for a few years now. And it's really clicked into gear. And I think projects due to COVID and things that would have taken nine, 12 months to happen, they're sort of taking a month or two now. It's been getting driven down from the top. So all of that's come together for us very fortunately, the timing has been ideal. And then tie in something like a Snowflake traction, as you said, we support many other platforms. But all of that together, it just set up really nicely for us, fortunately. >> That's amazing, I mean, with all the turmoil that's going on in the world right now. And all the pain in many businesses. I tell you, I interview people all day every day, and the technology business is really humming. So that's awesome to hear that you guys. I mean, especially if you're in the right place, and data is the place to be. Anthony, thanks so much for coming on theCUBE and summarizing your thoughts, and give us the update on HVR, really interesting. >> Absolutely, I appreciate the time and opportunity. >> Alright, and thank you for watching everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (upbeat music)

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

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

[Music] from the cube studios in Palo Alto in Boston it's the cube covering the IBM thing brought to you by IBM hello everybody this is state velocity of the cube and you're watching our wall-to-wall coverage of the IBM think digital experience at Justin Youngblood is here he's the vice president of IBM security Justin good to see you again thanks for coming on hey Dave good to be here thank you so look let's get right into it I mean we're here remote I wish we were you know for face-to-face and in Moscow II but things have changed dramatically there's a massive shift to work from home that's you know obviously kovat 19 has tightened the need for security but let's start with some of the things that you're seeing how you're responding the to secure those remote workers and let's get into some of the trends that you're seeing in the security space yeah absolutely some major trends and there is a big response around Cove at night 19 right now and and first of all you know what we tell all of our employees our clients our partners the entire ecosystem is number one priority stay safe and healthy of course even at IBM right now we have over 95% of IBM erse who are working from home we've seen that trend across our clients and partners as well and basically three themes keep popping up as it relates to security in Kovan 19 the first is clients are asking us to help them secure their remote workforce we have a number of tools technologies and services to help them do that the second is detecting and responding to accelerating threats amidst Cova 19 the threat actors are more active than ever they're driving some targeted attacks and phishing campaigns and our clients are asking us for help on that front and then the third is virtually extending security teams and operations and we've got a set of services managed services and and remote employees who can actually work with our clients and help them with their security operation centers and anything they need from a security program yeah I mean when you talk to CISOs they'll tell you look we you know our biggest problem is a lack of talent and we have all these fragmented tools and then now you throw kovat 19 at them and it's okay now overnight blank and secure the remote workforce so talk a little bit about this notion of platforms I've said often the security marketplace is very fragmented that accentuates the skills issue is you got to learn all these different tools and this is integration issues talk about platforms and how that might help solve this problem absolutely security platforms are on the rise do you see a lot of security platforms being announced by vendors today the problem statements are very clear oh as enterprises have moved along on their journey to cloud and digital transformation they now have workloads applications data users spread across multiple cloud environments every enterprise is using multiple clouds today so the problem statements become very clear for security security leaders have too many security tools they have too much data and they don't have enough people right so too many security tools that lack interoperability the average Enterprise has anywhere from 50 to 80 different security point products that don't talk to each other but trying to solve a security problem to pinpoint an issue actually takes looking at multiple screens too much data that comes without insights trying to stitch together all of this disparate data across a fragmented security landscape is very complex and it allows threats to be missed and then not enough people the shortage in cybersecurity is well documented over 2 million unfilled jobs today and that number continues to grow so enter security platforms that are that are on the value proposition of cleaning up this mess in November last year we announced the cloud pack for security that's IBM security platform and it has some some attributes that are powerful compelling we're seeing a lot of traction with client well you mentioned two things that really caught my attention the detection and the response because you know you're gonna get infiltrated everybody gets infiltrated and you know you've seen the stats it takes you know whatever 250 300 days before you can even detect it and then and then responses is critical so so talk about the cloud pack for security you know there are other platforms out there what makes yours different yeah are basically traditional security is broken we have a vision of modern security at centers on the cloud pack for security we set out two years ago with the concept of a next-generation platform it's a security control plane that works across hybrid multi cloud environments it connects all your security data and tools with a common platform that includes IBM and security tools and cloud platforms so whether you're using a sim like Q radar or Splunk endpoint detection systems like carbon black or CrowdStrike and any of the IBM any of the cloud platforms including IBM AWS or Azure it connects all of those and brings the insights together we work with over 50 enterprises and service providers help us co-create this solution and the attributes are its multi cloud capable but for security is multi cloud capable it can bring all the insights together from across these hybrid multi cloud environment it's open it's built and based on open standards and open technologies it's simple and it's composable in the sense that it has the ability to integrate with IBM and third-party technologies and add more capabilities over time what we see from other security platforms in the industry is they they basically approached the problem saying mr. customer bring all your data to our cloud will run the analytics on it and then provide you the insights what's different with cloud pack for security is we take the analytics to the data customers don't need to move their data from all the disparate sources where it exists we take the analytics to the data and bring those insights back to a common console or the or the security leaders and security analysts to take action on why you preaching to the choir now because well first of all you've got the the integration matrix and you've got the resources obviously I mean you mentioned a couple of really prominent and you know some hot products right now and this is the challenge right best to breathe versus fully integrated suite and what you're saying if I understand it correctly is we're not asking you to make that trade-off if you want to use you know of some tool go for it we're gonna integrate with that and give you the control and then the second piece is bringing that analytics capability to the data cuz that's the other thing you really don't want to move your data you the Einstein written move as much data as you have to but no more right absolutely this is a this is a team sport security is a team sport and that's where open technologies are so important the ability with an open API to integrate with any IBM or third-party technology this is not a rip and replace strategy clients can't afford to do that they want to work within their existing security tools but they need a common platform for bring it all together so we talked about the ability to gain complete insights across your hybrid multi cloud environment the ability to act faster with a set of playbooks and automation that basically runs security run books once a once an incident is detected to automatically go about about the fix and then third is the ability to run anywhere cloud pack for security like all of the IBM cloud packs is built on kubernetes and Red Hat openshift so it can be deployed on-premise or on the public cloud of the customers choosing complete choice and flexibility in that deployment I mean another key point you just made is automation and you talked earlier about that skills gap and the unfilled jobs automation is really the way certainly a way and probably a the most important way to close that gap I want to ask you about open could you think about you know security and networks and you know opens almost antithetical to secure I want close but you mean open in a different context and what if we could talk about that and maybe break down the key aspects of open as you defined it we've seen open technologies open standards open source be adopted across technology domains think of operating systems and Linux think of application development think of the management domain and kubernetes which now has a community of over 4,000 developers behind it it's more than any single vendor could put behind it so it's so open technologies really provide a force multiplier for any any industry security has been a laggard in adopting open standards and open source code so last year 2019 October time frame IBM partnered with McAfee and dozens of other vendors and launching the open Cyber Security Alliance focused on open standards that promote interoperability across security tools focused on open source code which we've adopted into an underpin the cloud pack I beams cloth pack for security focused on threat intelligence and analytics and ultimately sharing best practices and let me talk about run books this really comes down to the automated play books that customers need to run in response to a security threat or incident that's become really important automating actions to help security operations teams be more productive so all of those capabilities in total sum up what we're talking about with open technology for security and it underpins our IBM cloud pack for security solution well I've always felt that Open was part of the answer and like you said the industry was slowly to adopt adversary is highly capable he-she they're very well-funded do you think our industry is ready for this open approach we're absolutely ready for the open approach we see customers responding extremely positively to the cloud pack for security and the fact that it is built on open technologies many enterprises come to us and say they want that future proofing of their investments they want to know that what they purchased will interoperate with their existing environments without a rip rip and replace and the only way to get there is through open standards and open technology so it's it's already being well received and we're gonna see it grow just like it has any other technology domains operating systems application development management etc now is the time for security while Justin you're operating in one of the most important aspects of the IT value chain thank you for keeping us safe stay safe down there in Austin and thanks for coming on the queue thank you Dave good to be here take care and thank you for watching everybody watching the cubes coverage of IBM sync 2020 ibm's digital production keep it right there we're right back right after this short break [Music] you

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel. Along with it's ecosystem partners. >> Okay, welcome back everyone. It's theCUBE's live coverage here in Las Vegas for Amazon Web Series today, re:Invent 2019. It's theCUBE's seventh year covering re:Invent. Eight years they've been running this event. It gets bigger every year. It's been a great wave to ride on. I'm John Furrier, my cohost, Dave Vellante. We've been riding this wave, Dave, for years. It's so exciting, it gets bigger and more exciting. >> Lucky seven. >> This year more than ever. So much stuff is happening. It's been really exciting. I think there's a sea change happening, in terms of another wave coming. Quantum computing, big news here amongst other great tech. Our next guest is Bill Vass, VP of Technology, Storage Automation Management, part of the quantum announcement that went out. Bill, good to see you. >> Yeah, well, good to see you. Great to see you again. Thanks for having me on board. >> So, we love quantum, we talk about it all the time. My son loves it, everyone loves it. It's futuristic. It's going to crack everything. It's going to be the fastest thing in the world. Quantum supremacy. Andy referenced it in my one-on-one with him around quantum being important for Amazon. >> Yes, it is, it is. >> You guys launched it. Take us through the timing. Why, why now? >> Okay, so the Braket service, which is based on quantum notation made by Dirac, right? So we thought that was a good name for it. It provides for you the ability to do development in quantum algorithms using gate-based programming that's available, and then do simulation on classical computers, which is what we call our digital computers today now. (men chuckling) >> Yeah, it's a classic. >> These are classic computers all of a sudden right? And then, actually do execution of your algorithms on, today, three different quantum computers, one that's annealing and two-bit gate-based machines. And that gives you the ability to test them in parallel and separate from each other. In fact, last week, I was working with the team and we had two machines, an ion trap machine and an electromagnetic tunneling machine, solving the same problem and passing variables back and forth from each other, you could see the cloud watch metrics coming out, and the data was going to an S3 bucket on the output. And we do it all in a Jupiter notebook. So it was pretty amazing to see all that running together. I think it's probably the first time two different machines with two different technologies had worked together on a cloud computer, fully integrated with everything else, so it was pretty exciting. >> So, quantum supremacy has been a word kicked around. A lot of hand waving, IBM, Google. Depending on who you talk to, there's different versions. But at the end of the day, quantum is a leap in computing. >> Bill: Yes, it can be. >> It can be. It's still early days, it would be day zero. >> Yeah, well I think if you think of, we're about where computers were with tubes if you remember, if you go back that far, right, right? That's about where we are right now, where you got to kind of jiggle the tubes sometimes to get them running. >> A bug gets in there. Yeah, yeah, that bug can get in there, and all of those kind of things. >> Dave: You flip 'em off with a punch card. Yeah, yeah, so for example, a number of the machines, they run for four hours and then they come down for a half hour for calibration. And then they run for another four hours. So we're still sort of at that early stage, but you can do useful work on them. And more mature systems, like for example D-Wave, which is annealer, a little different than gate-based machines, is really quite mature, right? And so, I think as you go back and forth between these machines, the gate-based machines and annealers, you can really get a sense for what's capable today with Braket and that's what we want to do is get people to actually be able to try them out. Now, quantum supremacy is a fancy word for we did something you can't do on a classical computer, right? That's on a quantum computer for the first time. And quantum computers have the potential to exceed the processing power, especially on things like factoring and other things like that, or on Hamiltonian simulations for molecules, and those kids of things, because a quantum computer operates the way a molecule operates, right, in a lot of ways using quantum mechanics and things like that. And so, it's a fancy term for that. We don't really focus on that at Amazon. We focus on solving customer's problems. And the problem we're solving with Braket is to get them to learn it as it's evolving, and be ready for it, and continue to develop the environment. And then also offer a lot of choice. Amazon's always been big on choice. And if you look at our processing portfolio, we have AMD, Intel x86, great partners, great products from them. We have Nvidia, great partner, great products from them. But we also have our Graviton 1 and Graviton 2, and our new GPU-type chip. And those are great products, too, I've been doing a lot on those, as well. And the customer should have that choice, and with quantum computers, we're trying to do the same thing. We will have annealers, we will have ion trap machines, we will have electromagnetic machines, and others available on Braket. >> Can I ask a question on quantum if we can go back a bit? So you mentioned vacuum tubes, which was kind of funny. But the challenge there was with that, it was cooling and reliability, system downtime. What are the technical challenges with regard to quantum in terms of making it stable? >> Yeah, so some of it is on classical computers, as we call them, they have error-correction code built in. So you have, whether you know it or not, there's alpha particles that are flipping bits on your memory at all times, right? And if you don't have ECC, you'd get crashes constantly on your machine. And so, we've built in ECC, so we're trying to build the quantum computers with the proper error correction, right, to handle these things, 'cause nothing runs perfectly, you just think it's perfect because we're doing all the error correction under the covers, right? And so that needs to evolve on quantum computing. The ability to reproduce them in volume from an engineering perspective. Again, standard lithography has a yield rate, right? I mean, sometimes the yield is 40%, sometimes it's 20%, sometimes it's a really good fab and it's 80%, right? And so, you have a yield rate, as well. So, being able to do that. These machines also generally operate in a cryogenic world, that's a little bit more complicated, right? And they're also heavily affected by electromagnetic radiation, other things like that, so you have to sort of faraday cage them in some cases, and other things like that. So there's a lot that goes on there. So it's managing a physical environment like cryogenics is challenging to do well, having the fabrication to reproduce it in a new way is hard. The physics is actually, I shudder to say well understood. I would say the way the physics works is well understood, how it works is not, right? No one really knows how entanglement works, they just knows what it does, and that's understood really well, right? And so, so a lot of it is now, why we're excited about it, it's an engineering problem to solve, and we're pretty good at engineering. >> Talk about the practicality. Andy Jassy was on the record with me, quoted, said, "Quantum is very important to Amazon." >> Yes it is. >> You agree with that. He also said, "It's years out." You said that. He said, "But we want to make it practical "for customers." >> We do, we do. >> John: What is the practical thing? Is it just kicking the tires? Is it some of the things you mentioned? What's the core goal? >> So, in my opinion, we're at a point in the evolution of these quantum machines, and certainly with the work we're doing with Cal Tech and others, that the number of available cubits are starting to increase at an astronomic rate, a Moore's Law kind of of rate, right? Whether it's, no matter which machine you're looking at out there, and there's about 200 different companies building quantum computers now, and so, and they're all good technology. They've all got challenges, as well, as reproducibility, and those kind of things. And so now's a good time to start learning how to do this gate-based programming knowing that it's coming, because quantum computers, they won't replace a classical computer, so don't think that. Because there is no quantum ram, you can't run 200 petabytes of data through a quantum computer today, and those kind of things. What it can do is factoring very well, or it can do probability equations very well. It'll have affects on Monte Carlo simulations. It'll have affects specifically in material sciences where you can simulate molecules for the first time that you just can't do on classical computers. And when I say you can't do on classical computers, my quantum team always corrects me. They're like, "Well, no one has proven "that there's an algorithm you can run "on a classical computer that will do that yet," right? (men chuckle) So there may be times when you say, "Okay, I did this on a quantum computer," and you can only do it on a quantum computer. But then someone's very smart mathematician says, "Oh, I figured out how to do it on a regular computer. "You don't need a quantum computer for that." And that's constantly evolving, as well, in parallel, right? And so, and that's what's that argument between IBM and Google on quantum supremacy is that. And that's an unfortunate distraction in my opinion. What Google did was quite impressive, and if you're in the quantum world, you should be very happy with what they did. They had a very low error rate with a large number of cubits, and that's a big deal. >> Well, I just want to ask you, this industry is an arms race. But, with something like quantum where you've got 200 companies actually investing in it so early days, is collaboration maybe a model here? I mean, what do think? You mentioned Cal Tech. >> It certainly is for us because, like I said, we're going to have multiple quantum computers available, just like we collaborate with Intel, and AMD, and the other partners in that space, as well. That's sort of the nice thing about being a cloud service provider is we can give customers choice, and we can have our own innovation, plus their innovations available to customers, right? Innovation doesn't just happen in one place, right? We got a lot of smart people at Amazon, we don't invent everything, right? (Dave chuckles) >> So I got to ask you, obviously, we can take cube quantum and call it cubits, not to be confused with theCUBE video highlights. Joking aside, classical computers, will there be a classical cloud? Because this is kind of a futuristic-- >> Or you mean a quantum cloud? >> Quantum cloud, well then you get the classic cloud, you got the quantum cloud. >> Well no, they'll be together. So I think a quantum computer will be used like we used to use a math coprocessor if you like, or FPGAs are used today, right? So, you'll go along and you'll have your problem. And I'll give you a real, practical example. So let's say you had a machine with 125 cubits, okay? You could just start doing some really nice optimization algorithms on that. So imagine there's this company that ships stuff around a lot, I wonder who that could be? And they need to optimize continuously their delivery for a truck, right? And that changes all the time. Well that algorithm, if you're doing hundreds of deliveries in a truck, it's very complicated. That traveling salesman algorithm is a NP-hard problem when you do it, right? And so, what would be the fastest best path? But you got to take into account weather and traffic, so that's changing. So you might have a classical computer do those algorithms overnight for all the delivery trucks and then send them out to the trucks. The next morning they're driving around. But it takes a lot of computing power to do that, right? Well, a quantum computer can do that kind of problemistic or deterministic equation like that, not deterministic, a best-fit algorithm like that, much faster. And so, you could have it every second providing that. So your classical computer is sending out the manifests, interacting with the person, it's got the website on it. And then, it gets to the part where here's the problem to calculate, we call it a shot when you're on a quantum computer, it runs it in a few seconds that would take an hour or more. >> It's a fast job, yeah. >> And it comes right back with the result. And then it continues with it's thing, passes it to the driver. Another update occurs, (buzzing) and it's just going on all the time. So those kind of things are very practical and coming. >> I've got to ask for the younger generations, my sons super interested as I mentioned before you came on, quantum attracts the younger, smart kids coming into the workforce, engineering talent. What's the best path for someone who has an either advanced degree, or no degree, to get involved in quantum? Is there a certain advice you'd give someone? >> So the reality is, I mean, obviously having taken quantum mechanics in school and understanding the physics behind it to an extent, as much as you can understand the physics behind it, right? I think the other areas, there are programs at universities focused on quantum computing, there's a bunch of them. So, they can go into that direction. But even just regular computer science, or regular mechanical and electrical engineering are all neat. Mechanical around the cooling, and all that other stuff. Electrical, these are electrically-based machines, just like a classical computer is. And being able to code at low level is another area that's tremendously valuable right now. >> Got it. >> You mentioned best fit is coming, that use case. I mean, can you give us a sense of a timeframe? And people will say, "Oh, 10, 15, 20 years." But you're talking much sooner. >> Oh, I don't, I think it's sooner than that, I do. And it's hard for me to predict exactly when we'll have it. You can already do, with some of the annealing machines, like D- Wave, some of the best fit today, right? So it's a matter of people want to use a quantum computer because they need to do something fast, they don't care how much it costs, they need to do something fast. Or it's too expensive to do it on a classical computer, or you just can't do it at all on a classical computer. Today, there isn't much of that last one, you can't do it at all, but that's coming. As you get to around 52, 50, 52 cubits, it's very hard to simulate that on a classical computer. You're starting to reach the edge of what you can practically do on a classical computer. At about 125 cubits, you probably are at a point where you can't just simulate it anymore. >> But you're talking years, not decades, for this use case? >> Yeah, I think you're definitely talking years. I think, and you know, it's interesting, if you'd asked me two years ago how long it would take, I would've said decades. So that's how fast things are advancing right now, and I think that-- >> Yeah, and the computers just getting faster and faster. >> Yeah, but the ability to fabricate, the understanding, there's a number of architectures that are very well proven, it's just a matter of getting the error rates down, stability in place, the repeatable manufacturing in place, there's a lot of engineering problems. And engineering problems are good, we know how to do engineering problems, right? And we actually understand the physics, or at least we understand how the physics works. I won't claim that, what is it, "Spooky action at a distance," is what Einstein said for entanglement, right? And that's a core piece of this, right? And so, those are challenges, right? And that's part of the mystery of the quantum computer, I guess. >> So you're having fun? >> I am having fun, yeah. >> I mean, this is pretty intoxicating, technical problems, it's fun. >> It is. It is a lot of fun. Of course, the whole portfolio that I run over at AWS is just really a fun portfolio, between robotics, and autonomous systems, and IOT, and the advanced storage stuff that we do, and all the edge computing, and all the monitor and management systems, and all the real-time streaming. So like Kinesis Video, that's the back end for the Amazon ghost stores, and working with all that. It's a lot of fun, it really is, it's good. >> Well, Bill, we need an hour to get into that, so we may have to come up and see you, do a special story. >> Oh, definitely! >> We'd love to come up and dig in, and get a special feature program with you at some point. >> Yeah, happy to do that, happy to do that. >> Talk some robotics, some IOT, autonomous systems. >> Yeah, you can see all of it around here, we got it up and running around here, Dave. >> What a portfolio. >> Congratulations. >> Alright, thank you so much. >> Great news on the quantum. Quantum is here, quantum cloud is happening. Of course, theCUBE is going quantum. We've got a lot of cubits here. Lot of CUBE highlights, go to SiliconAngle.com. We got all the data here, we're sharing it with you. I'm John Furrier with Dave Vellante talking quantum. Want to give a shout out to Amazon Web Services and Intel for setting up this stage for us. Thanks to our sponsors, we wouldn't be able to make this happen if it wasn't for them. Thank you very much, and thanks for watching. We'll be back with more coverage after this short break. (upbeat music)

>> From the Silicon Angle Media Office in Boston, Massachusetts, it's The Cube. Now, here's your host Dave Vellante. >> Hi everybody. Welcome to this Cube Insights powered by ETR. In this breaking analysis we're going to look at recent spending data from the ETR Spending Intentions Survey. We believe tech spending is slowing down. Now, it's not falling off a cliff but it is reverting to pre-2018 spending levels. There's some concern in the bellwethers of specifically financial services and insurance accounts and large telcos. We're also seeing less redundancy. What we mean by that is in 2017 and 2018 you had a lot of experimentation going on. You had a lot of digital initiatives that were going into, not really production, but sort of proof of concept. And as a result you were seeing spending on both legacy infrastructure and emerging technologies. What we're seeing now is more replacements. In other words people saying, "Okay, we're now going into production. We've tried that. We're not going to go with A, we're going to double down on B." And we're seeing less experimentation with the emerging technology. So in other words people are pulling out, actually some of the legacy technologies. And they're not just spraying and praying across the entire emerging technology sector. So, as a result, spending is more focused. As they say, it's not a disaster, but it's definitely some cause for concern. So, what I'd like to do, Alex if you bring up the first slide. I want to give you some takeaways from the ETR, the Enterprise Technology Research Q4 Pulse Check Survey. ETR has a data platform of 4,500 practitioners that it surveys regularly. And the most recent spending intention survey will actually be made public on October 16th at the ETR Webcast. ETR is in its quiet period right now, but they've given me a little glimpse and allowed me to share with you, our Cube audience, some of the findings. So as I say, you know, overall tech spending is clearly slowing, but it's still healthy. There's a uniform slowdown, really, across the board. In virtually all sectors with very few exceptions, and I'll highlight some of the companies that are actually quite strong. Telco, large financial services, insurance. That's rippling through to AMIA, which is, as I've said, is over-weighted in banking. The Global 2000 is looking softer. And also the global public and private companies. GPP is what ETR calls it. They say this is one of the best indicators of spending intentions and is a harbinger for future growth or deceleration. So it's the largest public companies and the largest private companies. Think Mars, Deloitte, Cargo, Coke Industries. Big giant, private companies. We're also seeing a number of changes in responses from we're going to increase to more flat-ish. So, again, it's not a disaster. It's not falling off the cliff. And there are some clear winners and losers. So adoptions are really reverting back to 2018 levels. As I said, replacements are arising. You know, digital transformation is moving from test everything to okay, let's go, let's focus now and double-down on those technologies that we really think are winners. So this is hitting both legacy companies and the disrupters. One of the other key takeaways out of the ETR Survey is that Microsoft is getting very, very aggressive. It's extending and expanding its TAM further into cloud, into collaboration, into application performance management, into security. We saw the Surface announcement this past week. Microsoft is embracing Android. Windows is not the future of Microsoft. It's all these other markets that they're going after. They're essentially building out an API platform and focusing in on the user experience. And that's paying off because CIOs are clearly more comfortable with Microsoft. Okay, so now I'm going to take you through some themes. I'm going to make some specific vendor comments, particularly in Cloud, software, and infrastructure. And then we'll wrap. So here's some major themes that really we see going on. Investors still want growth. They're punishing misses on earnings and they're rewarding growth companies. And so you can see on this slide that it's really about growth metrics. What you're seeing is companies are focused on total revenue, total revenue growth, annual recurring revenue growth, billings growth. Companies that maybe aren't growing so fast, like Dell, are focused on share gains. Lately we've seen pullbacks in the software companies and their stock prices really due to higher valuations. So, there's some caution there. There's actually a somewhat surprising focus given the caution and all the discussion about, you know, slowing economy. There's some surprising lack of focus on key performance indicators like cash flow. A few years ago, Splunk actually stopped giving, for example, cash flow targets. You don't see as much focus on market capitalization or shareholders returns. You do see that from Oracle. You see that last week from the Dell Financial Analyst Meeting. I talked about that. But it's selective. You know these are the type of metrics that Oracle, Dell, VMware, IBM, HPE, you know generally HP Inc. as well will focus on. Another thing we see is the Global M&A across all industries is back to 2016 levels. It basically was down 16% in Q3. However, well and that's by the way due to trade wars and other uncertainties and other economic slowdowns and Brexit. But tech M&A has actually been pretty robust this year. I mean, you know take a look at some examples. I'll just name a few. Google with Looker, big acquisitions. Sales Force, huge acquisition. A $15 billion acquisition of Tableau. It also spent over a billion dollars on Click software. Facebook with CTRL-labs. NVIDIA, $7 billion acquisition of Mellanox. VMware just plunked down billion dollars for Carbon Black and its own, you know, sort of pivotal within the family. Splunk with a billion dollar plus acquisition of SignalFx. HP over a billion dollars with Cray. Amazon's been active. Uber's been active. Even nontraditional enterprise tech companies like McDonald's trying to automate some of the drive-through technology. Mastercard with Nets. And of course the stalwart M&A companies Apple, Intel, Microsoft have been pretty active as well as many others. You know but generally I think what's happening is valuations are high and companies are looking for exits. They've got some cool tech so they're putting it out there. That you know, hey now's the time to buy. They want to get out. That maybe IPO is not the best option. Maybe they don't feel like they've got, you know, a long-term, you know, plan that is going to really maximize shareholder value so they're, you know, putting forth themselves for M&A today. And so that's been pretty robust. And I would expect that's going to continue for a little bit here as there are, again, some good technology companies out there. Okay, now let's get into, Alex if you pull up the next slide of the Company Outlook. I want to start with Cloud. Cloud, as they say here, continues it's steady march. I'm going to focus on the Big 3. Microsoft, AWS, and Google. In the ETR Spending Surveys they're all very clearly strong. Microsoft is very strong. As I said it's expanding it's total available market. It's into collaboration now so it's going after Slack, Box, Dropbox, Atlassian. It's announced application performance management capabilities, so it's kind of going after new relic there. New SIM and security products. So IBM, Splunk, Elastic are some targets there. Microsoft is one of the companies that's gaining share overall. Let me talk about AWS. Microsoft is growing faster in Cloud than AWS, but AWS is much, much larger. And AWS's growth continues. So it's not as strong as 2018 but it's stronger, in fact, much stronger than its peers overall in the marketplace. AWS appears to be very well positioned according to the ETR Surveys in database and AI it continues to gain momentum there. The only sort of weak spot is the ECS, the container orchestration area. And that looks a little soft likely due to Kubernetes. Drop down to Google. Now Google, you know, there's some strength in Google's business but it's way behind in terms of market share, as you all know, Microsoft and AWS. You know, its AI and machine learning gains have stalled relative to Microsoft and AWS which continue to grow. Google's strength and strong suit has always been analytics. The ETR data shows that its holdings serve there. But there's deceleration in data warehousing, and even surprisingly in containers given, you know, its strength in contributing to the Kubernetes project. But the ETR 3 Year Outlook, when they do longer term outlook surveys, shows GCP, Google's Cloud platform, gaining. But there's really not a lot of evidence in the existing data, in the near-term data to show that. But the big three, you know, Cloud players, you know, continue to solidify their position. Particularly AWS and Microsoft. Now let's turn our attention to enterprise software. Just going to name a few. ETR will have an extensive at their webcast. We'll have an extensive review of these vendors, and I'll pick up on that. But I just want to pick out a few here. Some of the enterprise software winners. Workday continues to be very, very strong. Especially in healthcare and pharmaceutical. Salesforce, we're seeing a slight deceleration but it's pretty steady. Very strong in Fortune 100. And Einstein, its AI offering appears to be gaining as well. Some of the acquisitions Mulesoft and Tableu are also quite strong. Demandware is another acquisition that's also strong. The other one that's not so strong, ExactTarget is somewhat weakening. So Salesforce is a little bit mixed, but, you know, continues to be pretty steady. Splunk looks strong. Despite some anecdotal comments that point to pricing issues, and I know Splunk's been working on, you know, tweaking its pricing model. And maybe even some competition. There's no indication in the ETR data yet that Splunk's, you know, momentum is attenuating. Security as category generally is very, very strong. And it's lifting all ships. Splunk's analytics business is showing strength is particularly in healthcare and pharmaceuticals, as well as financial services. I like the healthcare and pharmaceuticals exposure because, you know, in a recession healthcare will, you know, continue to do pretty well. Financial services in general is down, so there's maybe some exposure there. UiPath, I did a segment on RPA a couple weeks ago. UiPath continues its rapid share expansion. The latest ETR Survey data shows that that momentum is continuing. And UiPath is distancing itself in the spending surveys from its broader competition as well. Another company we've been following and I did a segment on the analytics and enterprise data warehousing sector a couple weeks ago is Snowflake. Snowflake continues to expand its share. Its slightly slower than its previous highs, which were off the chart. We shared with you its Net Score. Snowflake and UiPath have some of the highest Net Scores in the ETR Survey data of 80+%. Net Score remembers. You take the we're adding the platform, we're spending more and you subtract we're leaving the platform or spending less and that gives you the Net Score. Snowflake and UiPath are two of the highest. So slightly slower than previous ties, but still very very strong. Especially in larger companies. So that's just some highlights in the software sector. The last sector I want to focus on is enterprise infrastructure. So Alex if you'd bring that up. I did a segment at the end of Q2, post Q2 looking at earning statements and also some ETR data on the storage spending segment. So I'll start with Pure Storage. They continue to have elevative spending intentions. Especially in that giant public and private, that leading indicator. There are some storage market headwinds. The storage market generally is still absorbing that all flash injection. I've talked about this before. There's still some competition from Cloud. When Pure came out with its earnings last quarter, the stock dropped. But then when everybody else announced, you know, negative growth or, in Dell's case, Dell's the leader, they were flat. Pure Storage bounced back because on a relative basis they're doing very well. The other indication is Pure storage is very strong in net app accounts. Net apps mix, they don't call them out here but we'll do some further analysis down the road of net apps. So I would expect Pure to continue to gain share and relative to the others in that space. But there are some headwinds overall in the market. VMware, let's talk about VMware. VMware's spending profile, according to ETR, looks like 2018. It's still very strong in Fortune 1000, or 100 rather, but weaker in Fortune 500 and the GPP, the global public and private companies. That's a bit of a concern because GPP is one of the leading indicators. VMware on Cloud on AWS looks very strong, so that continues. That's a strategic area for them. Pivotal looks weak. Carbon Black is not pacing with CrowdStrike. So clearly VMware has some work to do with some of its recent acquisitions. It hasn't completed them yet. But just like the AirWatch acquisition, where AirWatch wasn't the leader in that space, really Citrix was the leader. VMware brought that in, cleaned it up, really got focused. So that's what they're going to have to do with Carbon Black and Security, which is going to be a tougher road to hoe I would say than end user computing and Pivotal. So we'll see how that goes. Let's talk about Dell, Dell EMC, Dell Technologies. The client side of the business is holding strong. As I've said many times server and storage are decelerating. We're seeing market headwinds. People are spending less on server and storage relative to some of the overall initiatives. And so, that's got to bounce back at some point. People are going to still need compute, they're still going to need storage, as I say. Both are suffering from, you know, the Cloud overhang. As well, storage there was such a huge injection of flash it gave so much headroom in the marketplace that it somewhat tempered storage demand overall. Customers said, "Hey, I'm good for a while. Cause now I have performance headroom." Whereas before people would buy spinning discs, they buy the overprovision just to get more capacity. So, you know, that was kind of a funky value proposition. The other thing is VxRail is not as robust as previous years and that's something that Dell EMC talks about as, you know, one of the market share leaders. But it's showing a little bit of softness. So we'll keep an eye on that. Let's talk about Cisco. Networking spend is below a year ago. The overall networking market has been, you know, somewhat decelerating. Security is a bright spot for Cisco. Their security business has grown in double digits for the last couple of quarters. They've got work to do in multi-Cloud. Some bright spots Meraki and Duo are both showing strength. HP, talk about HPE it's mixed. Server and storage markets are soft, as I've said. But HPE remains strong in Fortune 500 and that critical GPP leading indicator. You know Nimble is growing, but maybe not as fast as it used to be and Simplivity is really not as strong as last year. So we'd like to see a little bit of an improvement there. On the bright side, Aruba is showing momentum. Particularly in Fortune 500. I'll make some comments about IBM, even though it's really, you know, this IBM enterprise infrastructure. It's really services, software, and yes some infrastructure. The Red Hat acquisition puts it firmly in infrastructure. But IBM is also mixed. It's bouncing back. IBM Classic, the core IBM is bouncing back in Fortune 100 and Fortune 500 and in that critical GPP indicator. It's showing strength, IBM, in Cloud and it's also showing strength in services. Which is over half of its business. So that's real positive. Its analytics and EDW software business are a little bit soft right now. So that's a bit of a concern that we're watching. The other concern we have is Red Hat has been significantly since the announcement of the merger and acquisition. Now what we don't know, is IBM able to inject Red Hat into its large service and outsourcing business? That might be hidden in some of the spending intention surveys. So we're going to have to look at income statement. And the public statements post earnings season to really dig into that. But we'll keep an eye on that. The last comment is Cloudera. Cloudera once was the high-flying darling. They are hitting all-time lows. They made the acquisition of Hortonworks, which created some consolidation. Our hope was that would allow them to focus and pick up. CEO left. Cloudera, again, hitting all-time lows. In particular, AWS and Snowflake are hurting Cloudera's business. They're particularly strong in Cloudera's shops. Okay, so let me wrap. Let's give some final thoughts. So buyers are planning for a slowdown in tech spending. That is clear, but the sky is not falling. Look we're in the tenth year of a major tech investment cycle, so slowdown, in my opinion, is healthy. Digital initiatives are really moving into higher gear. And that's causing some replacement on legacy technologies and some focus on bets. So we're not just going to bet on every new, emerging technology, were going to focus on those that we believe are going to drive business value. So we're moving from a try-everything mode to a more focused management style. At least for a period of time. We're going to absorb the spend, in my view, of the last two years and then double-down on the winners. So not withstanding the external factors, the trade wars, Brexit, other geopolitical concerns, I would expect that we're going to have a period of absorption. Obviously it's October, so the Stock Market is always nervous in October. You know, we'll see if we get Santa Claus rally going into the end of the year. But we'll keep an eye on that. This is Dave Vellante for Cube Insights powered by ETR. Thank you for watching this breaking analysis. We'll see you next time. (upbeat tech music)

>> From Cambridge, Massachusetts. It's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts, everybody. We're here at MIT, sweltering Cambridge, Massachusetts. You're watching theCUBE, the leader in live tech coverage, my name is Dave Vellante. I'm here with my co-host, Paul Gillin. Special coverage of the MITCDOIQ. The Chief Data Officer event, this is the 13th year of the event, we started seven years ago covering it, Mark Ramsey is here. He's the Chief Data and Analytics Officer Advisor at Ramsey International, LLC and former Chief Data Officer of GlaxoSmithKline. Big pharma, Mark, thanks for coming onto theCUBE. >> Thanks for having me. >> You're very welcome, fresh off the keynote. Fascinating keynote this evening, or this morning. Lot of interest here, tons of questions. And we have some as well, but let's start with your history in data. I sat down after 10 years, but I could have I could have stretched it to 20. I'll sit down with the young guns. But there was some folks in there with 30 plus year careers. How about you, what does your data journey look like? >> Well, my data journey, of course I was able to stand up for the whole time because I was in the front, but I actually started about 32, a little over 32 years ago and I was involved with building. What I always tell folks is that Data and Analytics has been a long journey, and the name has changed over the years, but we've been really trying to tackle the same problems of using data as a strategic asset. So when I started I was with an insurance and financial services company, building one of the first data warehouse environments in the insurance industry, and that was in the 87, 88 range, and then once I was able to deliver that, I ended up transitioning into being in consulting for IBM and basically spent 18 years with IBM in consulting and services. When I joined, the name had evolved from Data Warehousing to Business Intelligence and then over the years it was Master Data Management, Customer 360. Analytics and Optimization, Big Data. And then in 2013, I joined Samsung Mobile as their first Chief Data Officer. So, moving out of consulting, I really wanted to own the end-to-end delivery of advanced solutions in the Data Analytics space and so that made the transition to Samsung quite interesting, very much into consumer electronics, mobile phones, tablets and things of that nature, and then in 2015 I joined GSK as their first Chief Data Officer to deliver a Data Analytics solution. >> So you have long data history and Paul, Mark took us through. And you're right, Mark-o, it's a lot of the same narrative, same wine, new bottle but the technology's obviously changed. The opportunities are greater today. But you took us through Enterprise Data Warehouse which was ETL and then MAP and then Master Data Management which is kind of this mapping and abstraction layer, then an Enterprise Data Model, top-down. And then that all failed, so we turned to Governance which has been very very difficult and then you came up with another solution that we're going to dig into, but is it the same wine, new bottle from the industry? >> I think it has been over the last 20, 30 years, which is why I kind of did the experiment at the beginning of how long folks have been in the industry. I think that certainly, the technology has advanced, moving to reduction in the amount of schema that's required to move data so you can kind of move away from the map and move type of an approach of a data warehouse but it is tackling the same type of problems and like I said in the session it's a little bit like Einstein's phrase of doing the same thing over and over again and expecting a different answer is certainly the definition of insanity and what I really proposed at the session was let's come at this from a very different perspective. Let's actually use Data Analytics on the data to make it available for these purposes, and I do think I think it's a different wine now and so I think it's just now a matter of if folks can really take off and head that direction. >> What struck me about, you were ticking off some of the issues that have failed like Data Warehouses, I was surprised to hear you say Data Governance really hasn't worked because there's a lot of talk around that right now, but all of those are top-down initiatives, and what you did at GSK was really invert that model and go from the bottom up. What were some of the barriers that you had to face organizationally to get the cooperation of all these people in this different approach? >> Yeah, I think it's still key. It's not a complete bottoms up because then you do end up really just doing data for the sake of data, which is also something that's been tried and does not work. I think it has to be a balance and that's really striking that right balance of really tackling the data at full perspective but also making sure that you have very definitive use cases to deliver value for the organization and then striking the balance of how you do that and I think of the things that becomes a struggle is you're talking about very large breadth and any time you're covering multiple functions within a business it's getting the support of those different business functions and I think part of that is really around executive support and what that means, I did mention it in the session, that executive support to me is really stepping up and saying that the data across the organization is the organization's data. It isn't owned by a particular person or a particular scientist, and I think in a lot of organization, that gatekeeper mentality really does put barriers up to really tackling the full breadth of the data. >> So I had a question around digital initiatives. Everywhere you go, every C-level Executive is trying to get digital right, and a lot of this is top-down, a lot of it is big ideas and it's kind of the North Star. Do you think that that's the wrong approach? That maybe there should be a more tactical line of business alignment with that threaded leader as opposed to this big picture. We're going to change and transform our company, what are your thoughts? >> I think one of the struggles is just I'm not sure that organizations really have a good appreciation of what they mean when they talk about digital transformation. I think there's in most of the industries it is an initiative that's getting a lot of press within the organizations and folks want to go through digital transformation but in some cases that means having a more interactive experience with consumers and it's maybe through sensors or different ways to capture data but if they haven't solved the data problem it just becomes another source of data that we're going to mismanage and so I do think there's a risk that we're going to see the same outcome from digital that we have when folks have tried other approaches to integrate information, and if you don't solve the basic blocking and tackling having data that has higher velocity and more granularity, if you're not able to solve that because you haven't tackled the bigger problem, I'm not sure it's going to have the impact that folks really expect. >> You mentioned that at GSK you collected 15 petabytes of data of which only one petabyte was structured. So you had to make sense of all that unstructured data. What did you learn about that process? About how to unlock value from unstructured data as a result of that? >> Yeah, and I think this is something. I think it's extremely important in the unstructured data to apply advanced analytics against the data to go through a process of making sense of that information and a lot of folks talk about or have talked about historically around text mining of trying to extract an entity out of unstructured data and using that for the value. There's a few steps before you even get to that point, and first of all it's classifying the information to understand which documents do you care about and which documents do you not care about and I always use the story that in this vast amount of documents there's going to be, somebody has probably uploaded the cafeteria menu from 10 years ago. That has no scientific value, whereas a protocol document for a clinical trial has significant value, you don't want to look through manually a billion documents to separate those, so you have to apply the technology even in that first step of classification, and then there's a number of steps that ultimately lead you to understanding the relationship of the knowledge that's in the documents. >> Side question on that, so you had discussed okay, if it's a menu, get rid of it but there's certain restrictions where you got to keep data for decades. It struck me, what about work in process? Especially in the pharmaceutical industry. I mean, post Federal Rules of Civil Procedure was everybody looking for a smoking gun. So, how are organizations dealing with what to keep and what to get rid of? >> Yeah, and I think certainly the thinking has been to remove the excess and it's to your point, how do you draw the line as to what is excess, right, so you don't want to just keep every document because then if an organization is involved in any type of litigation and there's disclosure requirements, you don't want to have to have thousands of documents. At the same time, there are requirements and so it's like a lot of things. It's figuring out how do you abide by the requirements, but that is not an easy thing to do, and it really is another driver, certainly document retention has been a big thing over a number of years but I think people have not applied advanced analytics to the level that they can to really help support that. >> Another Einstein bro-mahd, you know. Keep everything you must but no more. So, you put forth a proposal where you basically had this sort of three approaches, well, combined three approaches. The crawlers to go, the spiders to go out and do the discovery and I presume that's where the classification is done? >> That's really the identification of all of the source information >> Okay, so find out what you got, okay. >> so that's kind of the start. Find out what you have. >> Step two is the data repository. Putting that in, I thought it was when I heard you I said okay it must be a logical data repository, but you said you basically told the CIO we're copying all the data and putting it into essentially one place. >> A physical location, yes. >> Okay, and then so I got another question about that and then use bots in the pipeline to move the data and then you sort of drew the diagram of the back end to all the databases. Unstructured, structured, and then all the fun stuff up front, visualization. >> Which people love to focus on the fun stuff, right? Especially, you can't tell how many articles are on you got to apply deep learning and machine learning and that's where the answers are, we have to have the data and that's the piece that people are missing. >> So, my question there is you had this tactical mindset, it seems like you picked a good workload, the clinical trials and you had at least conceptually a good chance of success. Is that a fair statement? >> Well, the clinical trials was one aspect. Again, we tackled the entire data landscape. So it was all of the data across all of R&D. It wasn't limited to just, that's that top down and bottom up, so the bottom up is tackle everything in the landscape. The top down is what's important to the organization for decision making. >> So, that's actually the entire R&D application portfolio. >> Both internal and external. >> So my follow up question there is so that largely was kind of an inside the four walls of GSK, workload or not necessarily. My question was what about, you hear about these emerging Edge applications, and that's got to be a nightmare for what you described. In other words, putting all the data into one physical place, so it must be like a snake swallowing a basketball. Thoughts on that? >> I think some of it really does depend on you're always going to have these, IOT is another example where it's a large amount of streaming information, and so I'm not proposing that all data in every format in every location needs to be centralized and homogenized, I think you have to add some intelligence on top of that but certainly from an edge perspective or an IOT perspective or sensors. The data that you want to then make decisions around, so you're probably going to have a filter level that will impact those things coming in, then you filter it down to where you're going to really want to make decisions on that and then that comes together with the other-- >> So it's a prioritization exercise, and that presumably can be automated. >> Right, but I think we always have these cases where we can say well what about this case, and you know I guess what I'm saying is I've not seen organizations tackle their own data landscape challenges and really do it in an aggressive way to get value out of the data that's within their four walls. It's always like I mentioned in the keynote. It's always let's do a very small proof of concept, let's take a very narrow chunk. And what ultimately ends up happening is that becomes the only solution they build and then they go to another area and they build another solution and that's why we end up with 15 or 25-- (all talk over each other) >> The conventional wisdom is you start small. >> And fail. >> And you go on from there, you fail and that's now how you get big things done. >> Well that's not how you support analytic algorithms like machine learning and deep learning. You can't feed those just fragmented data of one aspect of your business and expect it to learn intelligent things to then make recommendations, you've got to have a much broader perspective. >> I want to ask you about one statistic you shared. You found 26 thousand relational database schemas for capturing experimental data and you standardized those into one. How? >> Yeah, I mean we took advantage of the Tamr technology that Michael Stonebraker created here at MIT a number of years ago which is really, again, it's applying advanced analytics to the data and using the content of the data and the characteristics of the data to go from dispersed schemas into a unified schema. So if you look across 26 thousand schemas using machine learning, you then can understand what's the consolidated view that gives you one perspective across all of those different schemas, 'cause ultimately when you give people flexibility they love to take advantage of it but it doesn't mean that they're actually doing things in an extremely different way, 'cause ultimately they're capturing the same kind of data. They're just calling things different names and they might be using different formats but in that particular case we use Tamr very heavily, and that again is back to my example of using advanced analytics on the data to make it available to do the fun stuff. The visualization and the advanced analytics. >> So Mark, the last question is you well know that the CDO role emerged in these highly regulated industries and I guess in the case of pharma quasi-regulated industries but now it seems to be permeating all industries. We have Goka-lan from McDonald's and virtually every industry is at least thinking about this role or has some kind of de facto CDO, so if you were slotted in to a CDO role, let's make it generic. I know it depends on the industry but where do you start as a CDO for an organization large company that doesn't have a CDO. Even a mid-sized organization, where do you start? >> Yeah, I mean my approach is that a true CDO is maximizing the strategic value of data within the organization. It isn't a regulatory requirement. I know a lot of the banks started there 'cause they needed someone to be responsible for data quality and data privacy but for me the most critical thing is understanding the strategic objectives of the organization and how will data be used differently in the future to drive decisions and actions and the effectiveness of the business. In some cases, there was a lot of discussion around monetizing the value of data. People immediately took that to can we sell our data and make money as a different revenue stream, I'm not a proponent of that. It's internally monetizing your data. How do you triple the size of the business by using data as a strategic advantage and how do you change the executives so what is good enough today is not good enough tomorrow because they are really focused on using data as their decision making tool, and that to me is the difference that a CDO needs to make is really using data to drive those strategic decision points. >> And that nuance you mentioned I think is really important. Inderpal Bhandari, who is the Chief Data Officer of IBM often says how can you monetize the data and you're right, I don't think he means selling data, it's how does data contribute, if I could rephrase what you said, contribute to the value of the organization, that can be cutting costs, that can be driving new revenue streams, that could be saving lives if you're a hospital, improving productivity. >> Yeah, and I think what I've shared typically shared with executives when I've been in the CDO role is that they need to change their behavior, right? If a CDO comes in to an organization and a year later, the executives are still making decisions on the same data PowerPoints with spinning logos and they said ooh, we've got to have 'em. If they're still making decisions that way then the CDO has not been successful. The executives have to change what their level of expectation is in order to make a decision. >> Change agents, top down, bottom up, last question. >> Going back to GSK, now that they've completed this massive data consolidation project how are things different for that business? >> Yeah, I mean you look how Barron joined as the President of R&D about a year and a half ago and his primary focus is using data and analytics and machine learning to drive the decision making in the discovery of a new medicine and the environment that has been created is a key component to that strategic initiative and so they are actually completely changing the way they're selecting new targets for new medicines based on data and analytics. >> Mark, thanks so much for coming on theCUBE. >> Thanks for having me. >> Great keynote this morning, you're welcome. All right, keep it right there everybody. We'll be back with our next guest. This is theCUBE, Dave Vellante with Paul Gillin. Be right back from MIT. 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>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officers Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. I'm Dave Vellante and you're watching theCUBE, the leader in live tech coverage. This is the end of the day panel at the IBM Chief Data Officer Summit. This is the 10th CDO event that IBM has held and we love to to gather these panels. This is a data all-star panel and I've recruited Seth Dobrin who is the CDO of the analytics group at IBM. Seth, thank you for agreeing to chip in and be my co-host in this segment. >> Yeah, thanks Dave. Like I said before we started, I don't know if this is a promotion or a demotion. (Dave laughing) >> We'll let you know after the segment. So, the data all-star panel and the data all-star awards that you guys are giving out a little later in the event here, what's that all about? >> Yeah so this is our 10th CDU Summit. So two a year, so we've been doing this for 5 years. The data all-stars are those people that have been to four at least of the ten. And so these are five of the 16 people that got the award. And so thank you all for participating and I attended these like I said earlier, before I joined IBM they were immensely valuable to me and I was glad to see 16 other people that think it's valuable too. >> That is awesome. Thank you guys for coming on. So, here's the format. I'm going to introduce each of you individually and then ask you to talk about your role in your organization. What role you play, how you're using data, however you want to frame that. And the first question I want to ask is, what's a good day in the life of a data person? Or if you want to answer what's a bad day, that's fine too, you choose. So let's start with Lucia Mendoza-Ronquillo. Welcome, she's the Senior Vice President and the Head of BI and Data Governance at Wells Fargo. You told us that you work within the line of business group, right? So introduce your role and what's a good day for a data person? >> Okay, so my role basically is again business intelligence so I support what's called cards and retail services within Wells Fargo. And I also am responsible for data governance within the business. We roll up into what's called a data governance enterprise. So we comply with all the enterprise policies and my role is to make sure our line of business complies with data governance policies for enterprise. >> Okay, good day? What's a good day for you? >> A good day for me is really when I don't get a call that the regulators are knocking on our doors. (group laughs) Asking for additional reports or have questions on the data and so that would be a good day. >> Yeah, especially in your business. Okay, great. Parag Shrivastava is the Director of Data Architecture at McKesson, welcome. Thanks so much for coming on. So we got a healthcare, couple of healthcare examples here. But, Parag, introduce yourself, your role, and then what's a good day or if you want to choose a bad day, be fun the mix that up. >> Yeah, sounds good. Yeah, so mainly I'm responsible for the leader strategy and architecture at McKesson. What that means is McKesson has a lot of data around the pharmaceutical supply chain, around one-third of the world's pharmaceutical supply chain, clinical data, also around pharmacy automation data, and we want to leverage it for the better engagement of the patients and better engagement of our customers. And my team, which includes the data product owners, and data architects, we are all responsible for looking at the data holistically and creating the data foundation layer. So I lead the team across North America. So that's my current role. And going back to the question around what's a good day, I think I would say the good day, I'll start at the good day. Is really looking at when the data improves the business. And the first thing that comes to my mind is sort of like an example, of McKesson did an acquisition of an eight billion dollar pharmaceutical company in Europe and we were creating the synergy solution which was based around the analytics and data. And actually IBM was one of the partners in implementing that solution. When the solution got really implemented, I mean that was a big deal for me to see that all the effort that we did in plumbing the data, making sure doing some analytics, is really helping improve the business. I think that is really a good day I would say. I mean I wouldn't say a bad day is such, there are challenges, constant challenges, but I think one of the top priorities that we are having right now is to deal with the demand. As we look at the demand around the data, the role of data has got multiple facets to it now. For example, some of the very foundational, evidentiary, and compliance type of needs as you just talked about and then also profitability and the cost avoidance and those kind of aspects. So how to balance between that demand is the other aspect. >> All right good. And we'll get into a lot of that. So Carl Gold is the Chief Data Scientist at Zuora. Carl, tell us a little bit about Zuora. People might not be as familiar with how you guys do software for billing et cetera. Tell us about your role and what's a good day for a data scientist? >> Okay, sure, I'll start by a little bit about Zuora. Zuora is a subscription management platform. So any company who wants to offer a product or service as subscription and you don't want to build your billing and subscription management, revenue recognition, from scratch, you can use a product like ours. I say it lets anyone build a telco with a complicated plan, with tiers and stuff like that. I don't know if that's a good thing or not. You guys'll have to make up your own mind. My role is an interesting one. It's split, so I said I'm a chief data scientist and we work about 50% on product features based on data science. Things like churn prediction, or predictive payment retries are product areas where we offer AI-based solutions. And then but because Zuora is a subscription platform, we have an amazing set of data on the actual performance of companies using our product. So a really interesting part of my role has been leading what we call the subscription economy index and subscription economy benchmarks which are reports around best practices for subscription companies. And it's all based off this amazing dataset created from an anonymized data of our customers. So that's a really exciting part of my role. And for me, maybe this speaks to our level of data governance, I might be able to get some tips from some of my co-panelists, but for me a good day is when all the data for me and everyone on my team is where we left it the night before. And no schema changes, no data, you know records that you were depending on finding removed >> Pipeline failures. >> Yeah pipeline failures. And on a bad day is a schema change, some crucial data just went missing and someone on my team is like, "The code's broken." >> And everybody's stressed >> Yeah, so those are bad days. But, data governance issues maybe. >> Great, okay thank you. Jung Park is the COO of Latitude Food Allergy Care. Jung welcome. >> Yeah hi, thanks for having me and the rest of us here. So, I guess my role I like to put it as I'm really the support team. I'm part of the support team really for the medical practice so, Latitude Food Allergy Care is a specialty practice that treats patients with food allergies. So, I don't know if any of you guys have food allergies or maybe have friends, kids, who have food allergies, but, food allergies unfortunately have become a lot more prevalent. And what we've been able to do is take research and data really from clinical trials and other research institutions and really use that from the clinical trial setting, back to the clinical care model so that we can now treat patients who have food allergies by using a process called oral immunotherapy. It's fascinating and this is really personal to me because my son as food allergies and he's been to the ER four times. >> Wow. >> And one of the scariest events was when he went to an ER out of the country and as a parent, you know you prepare your child right? With the food, he takes the food. He was 13 years old and you had the chaperones, everyone all set up, but you get this call because accidentally he ate some peanut, right. And so I saw this unfold and it scared me so much that this is something I believe we just have to get people treated. So this process allows people to really eat a little bit of the food at a time and then you eat the food at the clinic and then you go home and eat it. Then you come back two weeks later and then you eat a little bit more until your body desensitizes. >> So you build up that immunity >> Exactly. >> and then you watch the data obviously. >> Yeah. So what's a good day for me? When our patients are done for the day and they have a smile on their face because they were able to progress to that next level. >> Now do you have a chief data officer or are you the de facto CFO? >> I'm the de facto. So, my career has been pretty varied. So I've been essentially chief data officer, CIO, at companies small and big. And what's unique about I guess in this role is that I'm able to really think about the data holistically through every component of the practice. So I like to think of it as a patient journey and I'm sure you guys all think of it similarly when you talk about your customers, but from a patient's perspective, before they even come in, you have to make sure the data behind the science of whatever you're treating is proper, right? Once that's there, then you have to have the acquisition part. How do you actually work with the community to make sure people are aware of really the services that you're providing? And when they're with you, how do you engage them? How do you make sure that they are compliant with the process? So in healthcare especially, oftentimes patients don't actually succeed all the way through because they don't continue all the way through. So it's that compliance. And then finally, it's really long-term care. And when you get the long-term care, you know that the patient that you've treated is able to really continue on six months, a year from now, and be able to eat the food. >> Great, thank you for that description. Awesome mission. Rolland Ho is the Vice President of Data and Analytics at Clover Health. Tell us a little bit about Clover Health and then your role. >> Yeah, sure. So Clover is a startup Medicare Advantage plan. So we provide Medicare, private Medicare to seniors. And what we do is we're because of the way we run our health plan, we're able to really lower a lot of the copay costs and protect seniors against out of pocket. If you're on regular Medicare, you get cancer, you have some horrible accident, your out of pocket is infinite potentially. Whereas with Medicare Advantage Plan it's limited to like five, $6,000 and you're always protected. One of the things I'm excited about being at Clover is our ability to really look at how can we bring the value of data analytics to healthcare? Something I've been in this industry for close to 20 years at this point and there's a lot of waste in healthcare. And there's also a lot of very poor application of preventive measures to the right populations. So one of the things that I'm excited about is that with today's models, if you're able to better identify with precision, the right patients to intervene with, then you fundamentally transform the economics of what can be done. Like if you had to pa $1,000 to intervene, but you were only 20% of the chance right, that's very expensive for each success. But, now if your model is 60, 70% right, then now it opens up a whole new world of what you can do. And that's what excites me. In terms of my best day? I'll give you two different angles. One as an MBA, one of my best days was, client calls me up, says, "Hey Rolland, you know, "your analytics brought us over $100 million "in new revenue last year." and I was like, cha-ching! Excellent! >> Which is my half? >> Yeah right. And then on the data geek side the best day was really, run a model, you train a model, you get ridiculous AUC score, so area under the curve, and then you expect that to just disintegrate as you go into validation testing and actual live production. But the 98 AUC score held up through production. And it's like holy cow, the model actually works! And literally we could cut out half of the workload because of how good that model was. >> Great, excellent, thank you. Seth, anything you'd add to the good day, bad day, as a CDO? >> So for me, well as a CDO or as CDO at IBM? 'Cause at IBM I spend most of my time traveling. So a good day is a day I'm home. >> Yeah, when you're not in an (group laughing) aluminum tube. >> Yeah. Hurdling through space (laughs). No, but a good day is when a GDPR compliance just happened, a good day for me was May 20th of last year when IBM was done and we were, or as done as we needed to be for GDPR so that was a good day for me last year. This year is really a good day is when we start implementing some new models to help IBM become a more effective company and increase our bottom line or increase our margins. >> Great, all right so I got a lot of questions as you know and so I want to give you a chance to jump in. >> All right. >> But, I can get it started or have you got something? >> I'll go ahead and get started. So this is a the 10th CDO Summit. So five years. I know personally I've had three jobs at two different companies. So over the course of the last five years, how many jobs, how many companies? Lucia? >> One job with one company. >> Oh my gosh you're boring. (group laughing) >> No, but actually, because I support basically the head of the business, we go into various areas. So, we're not just from an analytics perspective and business intelligence perspective and of course data governance, right? It's been a real journey. I mean there's a lot of work to be done. A lot of work has been accomplished and constantly improving the business, which is the first goal, right? Increasing market share through insights and business intelligence, tracking product performance to really helping us respond to regulators (laughs). So it's a variety of areas I've had to be involved in. >> So one company, 50 jobs. >> Exactly. So right now I wear different hats depending on the day. So that's really what's happening. >> So it's a good question, have you guys been jumping around? Sure, I mean I think of same company, one company, but two jobs. And I think those two jobs have two different layers. When I started at McKesson I was a solution leader or solution director for business intelligence and I think that's how I started. And over the five years I've seen the complete shift towards machine learning and my new role is actually focused around machine learning and AI. That's why we created this layer, so our own data product owners who understand the data science side of things and the ongoing and business architecture. So, same company but has seen a very different shift of data over the last five years. >> Anybody else? >> Sure, I'll say two companies. I'm going on four years at Zuora. I was at a different company for a year before that, although it was kind of the same job, first at the first company, and then at Zuora I was really focused on subscriber analytics and churn for my first couple a years. And then actually I kind of got a new job at Zuora by becoming the subscription economy expert. I become like an economist, even though I don't honestly have a background. My PhD's in biology, but now I'm a subscription economy guru. And a book author, I'm writing a book about my experiences in the area. >> Awesome. That's great. >> All right, I'll give a bit of a riddle. Four, how do you have four jobs, five companies? >> In five years. >> In five years. (group laughing) >> Through a series of acquisition, acquisition, acquisition, acquisition. Exactly, so yeah, I have to really, really count on that one (laughs). >> I've been with three companies over the past five years and I would say I've had seven jobs. But what's interesting is I think it kind of mirrors and kind of mimics what's been going on in the data world. So I started my career in data analytics and business intelligence. But then along with that I had the fortune to work with the IT team. So the IT came under me. And then after that, the opportunity came about in which I was presented to work with compliance. So I became a compliance officer. So in healthcare, it's very interesting because these things are tied together. When you look about the data, and then the IT, and then the regulations as it relates to healthcare, you have to have the proper compliance, both internal compliance, as well as external regulatory compliance. And then from there I became CIO and then ultimately the chief operating officer. But what's interesting is as I go through this it's all still the same common themes. It's how do you use the data? And if anything it just gets to a level in which you become closer with the business and that is the most important part. If you stand alone as a data scientist, or a data analyst, or the data officer, and you don't incorporate the business, you alienate the folks. There's a math I like to do. It's different from your basic math, right? I believe one plus one is equal to three because when you get the data and the business together, you create that synergy and then that's where the value is created. >> Yeah, I mean if you think about it, data's the only commodity that increases value when you use it correctly. >> Yeah. >> Yeah so then that kind of leads to a question that I had. There's this mantra, the more data the better. Or is it more of an Einstein derivative? Collect as much data as possible but not too much. What are your thoughts? Is more data better? >> I'll take it. So, I would say the curve has shifted over the years. Before it used to be data was the bottleneck. But now especially over the last five to 10 years, I feel like data is no longer oftentimes the bottleneck as much as the use case. The definition of what exactly we're going to apply to, how we're going to apply it to. Oftentimes once you have that clear, you can go get the data. And then in the case where there is not data, like in Mechanical Turk, you can all set up experiments, gather data, the cost of that is now so cheap to experiment that I think the bottleneck's really around the business understanding the use case. >> Mm-hmm. >> Mm-hmm. >> And I think the wave that we are seeing, I'm seeing this as there are, in some cases, more data is good, in some cases more data is not good. And I think I'll start it where it is not good. I think where quality is more required is the area where more data is not good. For example like regulation and compliance. So for example in McKesson's case, we have to report on opioid compliance for different states. How much opioid drugs we are giving to states and making sure we have very, very tight reporting and compliance regulations. There, highest quality of data is important. In our data organization, we have very, very dedicated focus around maintaining that quality. So, quality is most important, quantity is not if you will, in that case. Having the right data. Now on the other side of things, where we are doing some kind of exploratory analysis. Like what could be a right category management for our stores? Or where the product pricing could be the right ones. Product has around 140 attributes. We would like to look at all of them and see what patterns are we finding in our models. So there you could say more data is good. >> Well you could definitely see a lot of cases. But certainly in financial services and a lot of healthcare, particularly in pharmaceutical where you don't want work in process hanging around. >> Yeah. >> Some lawyer could find a smoking gun and say, "Ooh see." And then if that data doesn't get deleted. So, let's see, I would imagine it's a challenge in your business, I've heard people say, "Oh keep all the, now we can keep all the data, "it's so inexpensive to store." But that's not necessarily such a good thing is it? >> Well, we're required to store data. >> For N number of years, right? >> Yeah, N number of years. But, sometimes they go beyond those number of years when there's a legal requirements to comply or to answer questions. So we do keep more than, >> Like a legal hold for example. >> Yeah. So we keep more than seven years for example and seven years is the regulatory requirement. But in the case of more data, I'm a data junkie, so I like more data (laughs). Whenever I'm asked, "Is the data available?" I always say, "Give me time I'll find it for you." so that's really how we operate because again, we're the go-to team, we need to be able to respond to regulators to the business and make sure we understand the data. So that's the other key. I mean more data, but make sure you understand what that means. >> But has that perspective changed? Maybe go back 10 years, maybe 15 years ago, when you didn't have the tooling to be able to say, "Give me more data." "I'll get you the answer." Maybe, "Give me more data." "I'll get you the answer in three years." Whereas today, you're able to, >> I'm going to go get it off the backup tapes (laughs). >> (laughs) Yeah, right, exactly. (group laughing) >> That's fortunately for us, Wells Fargo has implemented data warehouse for so many number of years, I think more than 10 years. So we do have that capability. There's certainly a lot of platforms you have to navigate through, but if you are able to navigate, you can get to the data >> Yeah. >> within the required timeline. So I have, astonished you have the technology, team behind you. Jung, you want to add something? >> Yeah, so that's an interesting question. So, clearly in healthcare, there is a lot of data and as I've kind of come closer to the business, I also realize that there's a fine line between collecting the data and actually asking our folks, our clinicians, to generate the data. Because if you are focused only on generating data, the electronic medical records systems for example. There's burnout, you don't want the clinicians to be working to make sure you capture every element because if you do so, yes on the back end you have all kinds of great data, but on the other side, on the business side, it may not be necessarily a productive thing. And so we have to make a fine line judgment as to the data that's generated and who's generating that data and then ultimately how you end up using it. >> And I think there's a bit of a paradox here too, right? The geneticist in me says, "Don't ever throw anything away." >> Right. >> Right? I want to keep everything. But, the most interesting insights often come from small data which are a subset of that larger, keep everything inclination that we as data geeks have. I think also, as we're moving in to kind of the next phase of AI when you can start doing really, really doing things like transfer learning. That small data becomes even more valuable because you can take a model trained on one thing or a different domain and move it over to yours to have a starting point where you don't need as much data to get the insight. So, I think in my perspective, the answer is yes. >> Yeah (laughs). >> Okay, go. >> I'll go with that just to run with that question. I think it's a little bit of both 'cause people touched on different definitions of more data. In general, more observations can never hurt you. But, more features, or more types of things associated with those observations actually can if you bring in irrelevant stuff. So going back to Rolland's answer, the first thing that's good is like a good mental model. My PhD is actually in physical science, so I think about physical science, where you actually have a theory of how the thing works and you collect data around that theory. I think the approach of just, oh let's put in 2,000 features and see what sticks, you know you're leaving yourself open to all kinds of problems. >> That's why data science is not democratized, >> Yeah (laughing). >> because (laughing). >> Right, but first Carl, in your world, you don't have to guess anymore right, 'cause you have real data. >> Well yeah, of course, we have real data, but the collection, I mean for example, I've worked on a lot of customer churn problems. It's very easy to predict customer churn if you capture data that pertains to the value customers are receiving. If you don't capture that data, then you'll never predict churn by counting how many times they login or more crude measures of engagement. >> Right. >> All right guys, we got to go. The keynotes are spilling out. Seth thank you so much. >> That's it? >> Folks, thank you. I know, I'd love to carry on, right? >> Yeah. >> It goes fast. >> Great. >> Yeah. >> Guys, great, great content. >> Yeah, thanks. And congratulations on participating and being data all-stars. >> We'd love to do this again sometime. All right and thank you for watching everybody, it's a wrap from IBM CDOs, Dave Vellante from theCUBE. We'll see you next time. (light music)