(upbeat techno music) >> From around the globe, it's theCUBE. With digital coverage of Postgres Vision 2021. Brought to you by EBD. >> Hello everyone, this is David Vellante, for the CUBE. And we're here covering Postgres Vision 2021. The virtual version, thCUBE virtual, if you will. And welcome to our power-panel. Now in this session, we'll dig into database modernization. We want to better understand how and why customers are tapping open source to drive innovation. But at the same time, they've got to deliver the resiliency and enterprise capabilities that they're used to that are now necessary to support today's digital business requirements. And with me are three experts on these matters. Abdul Sheik, is Global CTO and President of Cintra. Young Il Cho, aka Charlie, is High Availability Cluster Sales Manager, at Daone CNS. And Alan Villalobos, is the Director of Development Partnerships, at IBM. Gentlemen, welcome to theCUBE. >> Thank you, Dave, nice to be here. >> Thank you, Dave. >> All right, let's talk trends and frame the problem. Abdul, I want to start with you? Cintra you're all about this topic. Accelerating innovation using EDB Postgres helping customers move to modern platforms. And doing so, you got to do it cost-effectively but what's driving these moves? What are the problems that you're seeing at the organizations that you serve? >> Oh, so let me quickly introduce, Abdul Sheik, CTO. I'll quickly introduce Cintra. So we are a multicloud and database architecture MSP. And we've been around for 25 plus years. Headquartered in New York and the UK. But as a global organization, we're serving our SMB customers as well as large enterprise customers. And the trends we're seeing certainly in this day and age is transformation and modernization. And what that means is, customers looking to get out of the legacy platforms, get out of the legacy data centers and really move towards a modern strategy with a lower cost base, while still retaining resiliency and freedom. Ultimately, in terms of where they're going. The key words that really I see driving this, number one is choice. They've been historically locked into vendors. With limited choice with a high cost base. So choice, freedom to choose in terms of what database technologies they apply to which workloads and certainly EDB and the work that has been done to closely marry what enterprise RD platforms offer with EDBs in a work that they've done in terms of filling those gaps and addressing where the resiliency monitoring performance and security requirements are, are certainly are required from an enterprise customer perspective. Choice is driving the move that we see and choice towards a lower cost platform that can be deployed anywhere. Both on-prem modernization customers are looking to retain on premise platforms or moving into any multi clouds whether it's an infrastructure cloud play or a platform cloud play. And certainly with EDBs offering in terms of, you know the latest cloud native offerings also very interesting. And lastly, aside from just cost and the freedom to choose where they deploy those platforms the SLA, the service level model where is the resiliency requirement where the which system is going to bronze, silver, gold? Which ones are the tier one revenue platform revenue generating platforms which are the lower, lower utility platforms. So a combination of choice, a combination of freedom to deploy anywhere and while still maintaining the resiliency and the service levels that the customers need to deliver to their businesses >> Abdul that was a beautiful setup. And, and we've got so much to talk about here because customers want to move from point A to point B but getting there and they, they need help. It's sometimes not trivial. So Charlie Daone is a consultancy. You've got a strong technical capabilities. What are you seeing in this space? You know, what are the major trends? Why are organizations considering that move? And what are some of the considerations there? >> Well, like in other country in South Korea or so our a lot of customers, banking's a manufacturing distributor. They are 90, over 90%. They are all are using Oracle DB and a rack system. But as the previous presenters pointed out, a lot of customers that are sick of the Oracle and they have to undergo the huge cost of a maintenance costs. They want to move away from this cost stress. And secondly, they can think about they're providing service to customer on their cloud base which is a private or the public. So we cannot imagine running on database, Oracle database running on the cloud the system that's not matches on this cloud. And first and second, and finally the customer what they want is the cost and they want to move away from the Oracle locking. They cannot be just a slave at Oracle for a long time and the premium for the new cloud the service for the customer. >> Great. Thank you for that. Oh, go ahead. Yeah. Did you have something else to add Charlie go ahead and please. >> No that's all. >> Okay, great. Yeah, Allen, welcome to theCUBE. You know, it's very interesting to us. IBM, you, you, of course, you're a big player in database. You have a lot of expertise here. And you partner with EDB, you're offering Postgres to customers, you know, what are you seeing? Charlie was talking about Oracle and RAC. I mean, the, the, the thing there is obviously, we talked about the maintenance costs but there's also a lot of high availability capabilities. That's something that IBM really understands well. Do you see this as largely a cloud migration trend? Is it more modernization? Interested in what's IBM's perspective on this? >> I think modernization is the right word. The points that the previous panelists brought up or are on point, right? You know, lower TCO or lower costs in general but that of agility and then availability for developers and data scientists as well. And then of course, you know, hybrid cloud, right? You know, you want to be able to deploy on prem or in the cloud, or both in a mixture of all of that. And I think, I think what ties it together is the customers are looking for insights, right? And, you know, especially in larger organizations there's a myriad of data sources that they're already working with. And, you know, we, you know we want to be able to play in that space. We want to give an offering that is based on Postgres and open source and be able to further what they're strong at at and kind of, you know on top of that, you know, a layer of, of of need that we see is, is seamless data governance across all of those different stores. >> All right, I'm going to go right to the heart of the hard problem here. So if, I mean, I want to, it's just that I want to get from point A to point B, I want to save money. I want to modernize, but if I'm the canary in the coal mine at the customer, I'm saying guys, migration scares me. How do I do that? What are the considerations? And what do I need to know that I don't know. So Abdul, maybe you could walk us through what are some of the concerns that customers have? How do you help mitigate those? Whether it's other application dependencies, you know freezing code, you know, getting, again from that point A to point B without risking my existing business processes how do you handle that? >> Yeah, certainly I think a customer needs to understand what the journey looks like to begin with. So we've actually developed our own methodology that we call Rocket Cloud, which is also part of our cloud modernization strategy that builds in and database modernization strategy built into it starts with an assessment in terms of current state discovery. Not all customers totally understand where they are today. So understanding where the database state is, you know where the risks lie what are the criticality of the various databases? What technologies are used, where we have RAC or we don't have RAC but we have data God, where we have encryption. And so on. That gives the customer a very good insight in terms of the current state, both commercially and technically that's a key point to understand how they're licensed today and what costs could be freed up to free the journey to effectively fund the journey. It's a big, big topic, but once we do that, we get an idea and we've actually developed a tool called rapid discovery. That's able to discover a largest stake without knowing the database list. We just put the scripts at the database servers themselves and it tells us exactly which databases are suited to be you know, effectively migrated to Postgres with in terms of the feature function usage in terms of how heavy they are, would store procedures in the database amount of business logic use of technologies like RAC data guard and how they convert over to to Postgres specifically. That ultimately gives us the ability to give that customer an assessment and that assessment in a short sharp few weeks and get the customer view of all of my hundreds of databases. Here are the subset of candidates for Postgres and specifically than we do the schemer advisor tool the actual assessment tool from EDB, which gives us a sense of how well the schema gets converted and how best to then also look at the stored procedure conversion as well. That gives the customer a full view of their architecture mapping their specific candidate databases and then a cost analysis in terms of what that migration looks like and how we migrate. We also run and maintain those platforms once we're on EDB. >> Thank you for that again, very clear but so you're not replacing, doing an organ transplant. You may, you're you're, you know, this is not I don't mean this as a pejorative, but you're kind of cherry picking those workloads that are appropriate for EDB and then moving those and then maybe, maybe through attrition or, you know over time, sun-setting those other, those other core pieces. >> Exactly. >> Charlie, let me ask you, so we talked about RAC, real application clusters, data guard. These are, you know kind of high profile Oracle capabilities. Can you, can you really replicate the kind of resiliency at lower costs with open source, with EDB Postgres and how do you do that? >> It's my turn? >> yes, please. >> Quite technically, again, I go on in depths and technically the RAC, RAC system is so-called is the best you know, best the tool to protect data and especially in the Unix system, but apart from the RAC by the some nice data replication solution we just stream the application and log shipping and something and then monitor Pam and, and EFM solution which is enterprise failover manager. So even though it be compared to Apple the Apple RAC versus with EDB solution, we can definitely say that RAC is more stable one, but after migration, whatever, we can overcome the, you know, drawbacks of the HA cluster system by providing the EDB tools. So whatever the customer feel that after a successful migration, utilizing the EDB high availability failable solution they can make of themselves at home. So that's, that's how we approach it with the customers. >> So, Alan, again, to me, IBM is fascinating here with your level of involvement because you're the, you guys are sort of historically the master of proprietary the mainframes, VCM, CICF, EB2, all that stuff. And then, you know IBM was the first I remember Steve Mills actually announced we're going to invest a billion dollars in open source with Linux. And that was a major industry milestone. And of course, the, the acquisition of red hat. So you've got now this open source mindset this open source culture. So we, you know, as it's all about recovery in, in database and enterprise database and all the acid properties in two phase commits, and we're talking about, you know the things that Charlie just talked about. So what's your perspective here? IBM knows a lot about this. How do you help customers get there? >> Yeah, well, I mean the main, the main thrust right now IBM has a offering called IBM cloud Pak for data which from here, which runs EDB, right? EDB, Postgres runs on top of cloud Pak for Data But the, you know I think going back to Abdul's points about, you know migrating whatever's needed and whatever can be migrated to Postgres and maybe migrating other things other places, we have data virtualization and autoSQL, right? So once you have migrated those parts of your database or those schemes that can be, having, you know a single point where you can query across them and by the way, being able to query across them you know, before, during and after migration as well. Right? So we're kind of have that seamless experience of layer of SQL. And now with autoSQL of spark SQL as well, as you're, as you're migrating and after is, I'd say, you know, key to this. >> What, what's the typical migration look like? I know I'm sorry, but it's a consultant question but thinking about the, you know, the average, in terms of timeframe, what are the teams look like? You know who are the stakeholders that I need to get involved? If I'm a customer to really make this a success? maybe Abdul, you could talk about that and Charlie and Alan can chime in. >> Well, I think, well, number one you knew the exact sponsors bought into it in terms of the business case, supporting the business case an architect has got a big picture understanding not only database technology but also infrastructure that they're coming from as well as the target cloud platforms and how you ensure that the infrastructure can deliver the performance. So the architect role is important, of course the core DBA that lives within the scope of the database understands the schema of the data model the business logic itself, and the application on it. That's key specifically around the application certification testing connectivity and the migration of the code. And specifically in terms of timeline just to touch on that quickly. I mean, in our experience so far and we're seeing the momentum really really take off the last 18 months, a small project with limited business logic within the database itself can we migrate it in a couple of months but typically with all the testing and rigor around that you typically say three months timeline a medium-sized complexity projects, a six month timeline and a large complex project could be anything from nine months and beyond, but it really comes down to how heavy the database is with business logic and the database and how much effort it will take to re-engineer effectively migrate that PLC code, business logic into EDB given the compatibility level between Oracle and EDB it's relatively certainly an easier path than any other target platform in terms of options. Yeah. Not perspective. That's certainly looks like the composition of a team and timeline >> Charlie or Alan, anything you guys would add. >> Yeah. So, so I think all those personas make sense. I think you might, on the consumer side of the consumer the consumer of the data side the data scientists often we see, you know during migrations and then obviously the dev ops, I think or any operations, right, have to be heavily involved. And then lastly, you know, you see more and more data steward role or data steward type persona, CDO office type type person coming in there make sure that, you know, whatever data governance that is already in place or wants to be in place after the migration is also part of the conversation. >> Why EDB? You know, there's a lot of databases out there you know, it's funny, I always say like, you know, 10, 15 years ago databases were kind of sort of a boring market, right? It was like, okay, you're going to work or whatever. And now it's exploded. You got open source databases, you got, you know not only sequel databases, you got graph databases you know, you get cloud databases, it's going crazy. Why EDB? You wonder if you guys could address that? >> Allan why don't you go first this time? I'll compliment your answers. >> Yeah. I mean, again, I think it goes back to, to the, the I guess varying needs and, and enterprises. Right. And I think that's, what's driven this explosion in databases, whether it's a document store like you're saying, or, or new types of RDBMS, the needs that we talked about at the beginning, like lower TCO, and the push to open source. But you know, the fact of the matter is that that yes, there is a myriad, an ecosystem of databases, pretty much any organization. And so, yeah, we want to tap into that. And why EDB? EDB has done a great job of taking Postgres and making it enterprise ready, you know, that's what they're, they're good at and that, you know fits very nicely with the IBM story obviously. And, and so, you know, and they've they've worked with us as well. They have an operator on, on the runs on red hat OpenShift. So that makes it portable as well but also part of the IBM cloud Pak for data story. And, and yeah, you know, we want to break down those silos. We realized that that need is there for all of these, you know, there's this ecosystem of databases. And so, you know, we're, we see our role as being that platform, whether it's red hat OpenShift, or IBM cloud Pak for data that, that unifies, and kind of gives you that single pane of glass across all of those sources. >> And Charlie, you're obviously all in, you've got EDB in your background. Why EDB for you? >> Before talking about EDB you asked about the previous question about how the migration was different from Oracle to EDB. We had a couple of success story in Korea telecom and some banking area, and it was easier. So EDB provide MTK tool as a people know but it was an appropriate, like a 90%. So we are the channel partner of the EDB for four years. So what we have done was to hire the Oracle expert. So we train Oracle export as as EDB expert at the same time so that they can approach customer and make it easy. So you have no worry about that. Just migrating EDB, Oracle to EDB. There is a no issue. Those telltales include all the tasks, you know Stratus test and trainee, and a POC that we there. So by investing that Oracle expert that we could overcome and persuade the customer to adopt EDB. So, why EDB? Simply I can say there, is there any database they can finally replaced Oracle in the world? Why is the, it's the interoperability between Oracle to EDB as the many experts pointed out there is no other DBE. They can, you know, 90, 90% in compatibility and intercooperability with EDB. That's why, of course, there's the somewhat, you know budget issues or maintenance issue cost the issue escape from Oracle lock-in. But I think the the number one reason was the interoperability and the compatibility with database itself, Oracle database. That was a reason, I guess >> Great Abdul we've talked about, we all know the, as is, you've got a high maintenance costs. You got a lot of tuning, and it's just a lot of complexity. What about the 2B maybe you could share with us sort of the outcome some of the outcomes you've seen what the business impact has been of some of these migrations? >> Sure. I mean, I'll give you a very simple example then just the idea of running Oracle on prem a lot of customer systems teams, for example will drive a virtualization VMware strategy. We know some of the challenges of running Oracle MBM where from a license perspective. So giving the business the ability where I want to go customer in the financial services market in New York, heavy virtualization strategy the ability for them to move away from Oracle on, you know expensive hardware on to Postgres EDB on virtualization just leverage existing skillsets, leveraging existing investment in terms of infrastructure, and also give them portability in AWS. The other clouds, you know, in terms of a migration. More from a business perspective as well, I would say about some of the Allan's points in terms of just freeing up the ability for data scientists and data consumers, to, you know, to consume some of that data from an Postgres perspective more accessibility spinning up environments quicker less latency in terms of the agility is another key word in terms of the tangible differences, the business, lower cost agility, and the freedom to deploy anywhere at the end of the day. Choices, I think the key word that we could come back to and knowing that we can do that to Charlie's point specifically around maintaining service levels. And as architects, we support some of the big, big names out there in terms of airlines, online, cosmetic retailers, financial services, trading applications, hedge funds, and they all want one thing as architect: for us to deliver that resiliency and stand behind them. And as the MSP we're accountable to ensure those systems are up and running and performing. So knowing that the EDB is provided the compatibility but also plugged the specific requirements around performance management, security availability that's fundamentally been key. >> [Dave I mean, having done a lot of TCO studies in this area, it's, it's it Oracle's different. You know, normally the biggest component of TCO is labor with Oracle. The biggest component of TCO is licensed and maintenance costs. So if you can virtualize and reduce those costs and of course, of course the Oracle will fight you and say we won't support it in a VMware environment. Of course, you know, they will, but, but you got to really, you got to battle. But, so here's my last question. So if I'm a customer in that state that you described you know, a lot of sort of Oracle sprawl a lot of databases out there, high maintenance costs, the whole lock-in thing. I got to choices. I, you know, a lot of choices out there. One is EDB. You guys have convinced me that you've got the expertise If I can partner with firms like yours, it's safer route. Okay, cool. My other choice is Oracle is going to, The Oracle sales reps is going to get me in a headlock and talk about exit data and how their Oracle cloud, and how it's, they've invested a lot there. And they have, and, I can pay by the drink all this sort of modern sort of discussion, you know, Oracle act like they invented it late to the game. And then here we are. So, so help me. What's the pitch as to, well, that's kind of compelling. It's maybe the safe bet they're there. They're working with my CIO, whatever. Why should I go with the open source route versus that route? It sounds kind of attractive to me, help me understand that each of you maybe take me through that. Abdul, why don't you start. >> Yeah. I'd say, you know, Oracle's being the defacto for so many years that people have just assumed and defaulted saying, high availability, RAC, DR. Data guard, you know, and I'll apply to any database need that I have. And at the end of the day customers have a three tier database requirement: the lowest, less critical, bronze level databases that really don't need RAC or a high availability, silver tier that are departmental solutions. That means some level of resiliency. And then you've got your gold revenue producing brand impact databases that are they're down. And certainly they won. You see no reason why the bronze and silver databases can be targeted towards EDB. Admittedly, we have some of our largest customers are running platforms, are running $5 million an hour e-commerce platform or airlines running large e-commerce platforms. And exit data certainly has a place. RAC has a place in those, in those scenarios. Were not saying that the EDB is a solution for everything in all scenarios, but apply the technology where it's appropriate where it's required and, you know, generally wherever Oracle has being the defacto and it's being applied across the estate, that's fundamentally what's changed. It doesn't have to be the only answer you have multiple choices now. EDB provides us with the ability to probably address, you know more than 50% of the databases' state, and comfortably cope with that and just apply that more expensive kind of gold tier one cost-based but also capability, you know from the highest requirements of performance and availability where it's appropriate. >> Yeah. Very pragmatic approach. Abdul, thank you for that. And Charlie. Charlie, what's your perspective? Give us your closing thoughts. >> Well, it has been, Oracle has been dominating in Asia in South Korea has market or over many years. So customers got tired of this, continuous spending money for the maintenance costs and there is no discount. There is no negotiation. So they want to move away from expensive stuff. And they were looking for a flexible platform with the easygoing and the high speed and performance open source database like a possibly as career. And now the EDB cannot replace a hundred percent of existing legacy worker, but 10%, 20% 50% as time goes on the trend that will continue. And it will be reaching some high point or replacing the existing Oracle system. And it can, it can also leading to good business chance to a channel partner and EDB steps and other related business in open source. >> Great. Thank you, Charlie and Allen, bring us home here. Give us your follow up >> I think my, co- panelists hit the nail on the head, right? It's a menu, right? That's as things become more diverse and as people make more choices and as everybody wants more agility, you have to provide, I mean, and so that, that's where that's coming in and I liked the way that Andul I kind of split it into gold silver and bronze. Yeah. And I think that that's where, we're going, right? I mean you should ask your developers right? Are your developers like pining to start up a new instance of Oracle every time you're starting a new project? Probably not reach for their Postgres right? And so, because of that, that's where this is coming from and that's not going to change. And, and yeah, that that ecosystem is going to continue to, to thrive. And there'll be lots of different flavors in the growing open source ecosystem. >> Yeah. I mean, open source absolutely is the underpinning you know, the, the bedrock of innovation, these days. Gentlemen, great power panel. Thanks so much for bringing your perspectives and best of luck in the future. >> Thank you, next time we'll try and match our backgrounds >> Next time. Well, we'll up our game. Okay. And thank you for watching everybody. This is Dave Volante for theCUBE. Stay tuned for more great coverage. Postgres vision, 21. Be right back. (upbeat techno music)

(upbeat techno music) >> From around the globe, it's theCUBE. With digital coverage of Postgres Vision 2021. Brought to you by enterprise Enterprise DB. >> Hello everyone, this is David Vellante, for the CUBE. And we're here covering Postgres Vision 2021. The virtual version, thCUBE virtual, if you will. And welcome to our power-panel. Now in this session, we'll dig into database modernization. We want to better understand how and why customers are tapping open source to drive innovation. But at the same time, they've got to deliver the resiliency and enterprise capabilities that they're used to that are now necessary to support today's digital business requirements. And with me are three experts on these matters. Abdul Sheik, is Global CTO and President of Cintra. Young Il Cho, aka Charlie, is High Availability Cluster Sales Manager, at Daone CNS. And Alan Villalobos, is the Director of Development Partnerships, at IBM. Gentlemen, welcome to theCUBE. >> Thank you, Dave, nice to be here. >> Thank you, Dave. >> All right, let's talk trends and frame the problem. Abdul, I want to start with you? Cintra you're all about this topic. Accelerating innovation using EDB Postgres helping customers move to modern platforms. And doing so, you got to do it cost-effectively but what's driving these moves? What are the problems that you're seeing at the organizations that you serve? >> Oh, so let me quickly introduce, Abdul Sheik, CTO. I'll quickly introduce Cintra. So we are a multicloud and database architecture MSP. And we've been around for 25 plus years. Headquartered in New York and the UK. But as a global organization, we're serving our SMB customers as well as large enterprise customers. And the trends we're seeing certainly in this day and age is transformation and modernization. And what that means is, customers looking to get out of the legacy platforms, get out of the legacy data centers and really move towards a modern strategy with a lower cost base, while still retaining resiliency and freedom. Ultimately, in terms of where they're going. The key words that really I see driving this, number one is choice. They've been historically locked into vendors. With limited choice with a high cost base. So choice, freedom to choose in terms of what database technologies they apply to which workloads and certainly EDB and the work that has been done to closely marry what enterprise RD platforms offer with EDBs in a work that they've done in terms of filling those gaps and addressing where the resiliency monitoring performance and security requirements are, are certainly are required from an enterprise customer perspective. Choice is driving the move that we see and choice towards a lower cost platform that can be deployed anywhere. Both on-prem modernization customers are looking to retain on premise platforms or moving into any multi clouds whether it's an infrastructure cloud play or a platform cloud play. And certainly with EDBs offering in terms of, you know the latest cloud native offerings also very interesting. And lastly, aside from just cost and the freedom to choose where they deploy those platforms the SLA, the service level model where is the resiliency requirement where the which system is going to bronze, silver, gold? Which ones are the tier one revenue platform revenue generating platforms which are the lower, lower utility platforms. So a combination of choice, a combination of freedom to deploy anywhere and while still maintaining the resiliency and the service levels that the customers need to deliver to their businesses >> Abdul that was a beautiful setup. And, and we've got so much to talk about here because customers want to move from point A to point B but getting there and they, they need help. It's sometimes not trivial. So Charlie Daone is a consultancy. You've got a strong technical capabilities. What are you seeing in this space? You know, what are the major trends? Why are organizations considering that move? And what are some of the considerations there? >> Well, like in other country in South Korea or so our a lot of customers, banking's a manufacturing distributor. They are 90, over 90%. They are all are using Oracle DB and a rack system. But as the previous presenters pointed out, a lot of customers that are sick of the Oracle and they have to undergo the huge cost of a maintenance costs. They want to move away from this cost stress. And secondly, they can think about they're providing service to customer on their cloud base which is a private or the public. So we cannot imagine running on database, Oracle database running on the cloud the system that's not matches on this cloud. And first and second, and finally the customer what they want is the cost and they want to move away from the Oracle locking. They cannot be just a slave at Oracle for a long time and the premium for the new cloud the service for the customer. >> Great. Thank you for that. Oh, go ahead. Yeah. Did you have something else to add Charlie go ahead and please. >> No that's all. >> Okay, great. Yeah, Allen, welcome to theCUBE. You know, it's very interesting to us. IBM, you, you, of course, you're a big player in database. You have a lot of expertise here. And you partner with EDB, you're offering Postgres to customers, you know, what are you seeing? Charlie was talking about Oracle and RAC. I mean, the, the, the thing there is obviously, we talked about the maintenance costs but there's also a lot of high availability capabilities. That's something that IBM really understands well. Do you see this as largely a cloud migration trend? Is it more modernization? Interested in what's IBM's perspective on this? >> I think modernization is the right word. The points that the previous panelists brought up or are on point, right? You know, lower TCO or lower costs in general but that of agility and then availability for developers and data scientists as well. And then of course, you know, hybrid cloud, right? You know, you want to be able to deploy on prem or in the cloud, or both in a mixture of all of that. And I think, I think what ties it together is the customers are looking for insights, right? And, you know, especially in larger organizations there's a myriad of data sources that they're already working with. And, you know, we, you know we want to be able to play in that space. We want to give an offering that is based on Postgres and open source and be able to further what they're strong at at and kind of, you know on top of that, you know, a layer of, of of need that we see is, is seamless data governance across all of those different stores. >> All right, I'm going to go right to the heart of the hard problem here. So if, I mean, I want to, it's just that I want to get from point A to point B, I want to save money. I want to modernize, but if I'm the canary in the coal mine at the customer, I'm saying guys, migration scares me. How do I do that? What are the considerations? And what do I need to know that I don't know. So Abdul, maybe you could walk us through what are some of the concerns that customers have? How do you help mitigate those? Whether it's other application dependencies, you know freezing code, you know, getting, again from that point A to point B without risking my existing business processes how do you handle that? >> Yeah, certainly I think a customer needs to understand what the journey looks like to begin with. So we've actually developed our own methodology that we call Rocket Cloud, which is also part of our cloud modernization strategy that builds in and database modernization strategy built into it starts with an assessment in terms of current state discovery. Not all customers totally understand where they are today. So understanding where the database state is, you know where the risks lie what are the criticality of the various databases? What technologies are used, where we have RAC or we don't have RAC but we have data God, where we have encryption. And so on. That gives the customer a very good insight in terms of the current state, both commercially and technically that's a key point to understand how they're licensed today and what costs could be freed up to free the journey to effectively fund the journey. It's a big, big topic, but once we do that, we get an idea and we've actually developed a tool called rapid discovery. That's able to discover a largest stake without knowing the database list. We just put the scripts at the database servers themselves and it tells us exactly which databases are suited to be you know, effectively migrated to Postgres with in terms of the feature function usage in terms of how heavy they are, would store procedures in the database amount of business logic use of technologies like RAC data guard and how they convert over to to Postgres specifically. That ultimately gives us the ability to give that customer an assessment and that assessment in a short sharp few weeks and get the customer view of all of my hundreds of databases. Here are the subset of candidates for Postgres and specifically than we do the schemer advisor tool the actual assessment tool from EDB, which gives us a sense of how well the schema gets converted and how best to then also look at the stored procedure conversion as well. That gives the customer a full view of their architecture mapping their specific candidate databases and then a cost analysis in terms of what that migration looks like and how we migrate. We also run and maintain those platforms once we're on EDB. >> Thank you for that again, very clear but so you're not replacing, doing an organ transplant. You may, you're you're, you know, this is not I don't mean this as a pejorative, but you're kind of cherry picking those workloads that are appropriate for EDB and then moving those and then maybe, maybe through attrition or, you know over time, sun-setting those other, those other core pieces. >> Exactly. >> Charlie, let me ask you, so we talked about RAC, real application clusters, data guard. These are, you know kind of high profile Oracle capabilities. Can you, can you really replicate the kind of resiliency at lower costs with open source, with EDB Postgres and how do you do that? >> It's my turn? >> yes, please. >> Quite technically, again, I go on in depths and technically the RAC, RAC system is so-called is the best you know, best the tool to protect data and especially in the Unix system, but apart from the RAC by the some nice data replication solution we just stream the application and log shipping and something and then monitor Pam and, and EFM solution which is enterprise failover manager. So even though it be compared to Apple the Apple RAC versus with EDB solution, we can definitely say that RAC is more stable one, but after migration, whatever, we can overcome the, you know, drawbacks of the HA cluster system by providing the EDB tools. So whatever the customer feel that after a successful migration, utilizing the EDB high availability failable solution they can make of themselves at home. So that's, that's how we approach it with the customers. >> So, Alan, again, to me, IBM is fascinating here with your level of involvement because you're the, you guys are sort of historically the master of proprietary the mainframes, VCM, CICF, EB2, all that stuff. And then, you know IBM was the first I remember Steve Mills actually announced we're going to invest a billion dollars in open source with Linux. And that was a major industry milestone. And of course, the, the acquisition of red hat. So you've got now this open source mindset this open source culture. So we, you know, as it's all about recovery in, in database and enterprise database and all the acid properties in two phase commits, and we're talking about, you know the things that Charlie just talked about. So what's your perspective here? IBM knows a lot about this. How do you help customers get there? >> Yeah, well, I mean the main, the main thrust right now IBM has a offering called IBM cloud Pak for data which from here, which runs EDB, right? EDB, Postgres runs on top of cloud Pak for Data But the, you know I think going back to Abdul's points about, you know migrating whatever's needed and whatever can be migrated to Postgres and maybe migrating other things other places, we have data virtualization and auto-sequel, right? So once you have migrated those parts of your database or those schemes that can be, having, you know a single point where you can query across them and by the way, being able to query across them you know, before, during and after migration as well. Right? So we're kind of have that seamless experience of layer of sequel. And now with auto sequel of sparks sequel as well, as you're, as you're migrating and after is, I'd say, you know, key to this. >> What, what's the typical migration look like? I know I'm sorry, but it's a consultant question but thinking about the, you know, the average, in terms of timeframe, what are the teams look like? You know who are the stakeholders that I need to get involved? If I'm a customer to really make this a success? maybe Abdul, you could talk about that and Charlie and Alan can chime in. >> Well, I think, well, number one you knew the exact sponsors bought into it in terms of the business case, supporting the business case an architect has got a big picture understanding not only database technology but also infrastructure that they're coming from as well as the target cloud platforms and how you ensure that the infrastructure can deliver the performance. So the architect role is important, of course the core DBA that lives within the scope of the database understands the schema of the data model the business logic itself, and the application on it. That's key specifically around the application certification testing connectivity and the migration of the code. And specifically in terms of timeline just to touch on that quickly. I mean, in our experience so far and we're seeing the momentum really really take off the last 18 months, a small project with limited business logic within the database itself can we migrate it in a couple of months but typically with all the testing and rigor around that you typically say three months timeline a medium-sized complexity projects, a six month timeline and a large complex project could be anything from nine months and beyond, but it really comes down to how heavy the database is with business logic and the database and how much effort it will take to re-engineer effectively migrate that PLC code, business logic into EDB given the compatibility level between Oracle and EDB it's relatively certainly an easier path than any other target platform in terms of options. Yeah. Not perspective. That's certainly looks like the composition of a team and timeline >> Charlie or Alan, anything you guys would add. >> Yeah. So, so I think all those personas make sense. I think you might, on the consumer side of the consumer the consumer of the data side the data scientists often we see, you know during migrations and then obviously the dev ops, I think or any operations, right, have to be heavily involved. And then lastly, you know, you see more and more data steward role or data steward type persona, CDO office type type person coming in there make sure that, you know, whatever data governance that is already in place or wants to be in place after the migration is also part of the conversation. >> Why EDB? You know, there's a lot of databases out there you know, it's funny, I always say like, you know, 10, 15 years ago databases were kind of sort of a boring market, right? It was like, okay, you're going to work or whatever. And now it's exploded. You got open source databases, you got, you know not only sequel databases, you got graph databases you know, you get cloud databases, it's going crazy. Why EDB? You wonder if you guys could address that? >> Allan why don't you go first this time? I'll compliment your answers. >> Yeah. I mean, again, I think it goes back to, to the, the I guess varying needs and, and enterprises. Right. And I think that's, what's driven this explosion in databases, whether it's a document store like you're saying, or, or new types of RDBMS, the needs that we talked about at the beginning, like lower TCO, and the push to open source. But you know, the fact of the matter is that that yes, there is a myriad, an ecosystem of databases, pretty much any organization. And so, yeah, we want to tap into that. And why EDB? EDB has done a great job of taking Postgres and making it enterprise ready, you know, that's what they're, they're good at and that, you know fits very nicely with the IBM story obviously. And, and so, you know, and they've they've worked with us as well. They have an operator on, on the runs on red hat OpenShift. So that makes it portable as well but also part of the IBM cloud Pak for data story. And, and yeah, you know, we want to break down those silos. We realized that that need is there for all of these, you know, there's this ecosystem of databases. And so, you know, we're, we see our role as being that platform, whether it's red hat OpenShift, or IBM cloud Pak for data that, that unifies, and kind of gives you that single pane of glass across all of those sources. >> And Charlie, you're obviously all in, you've got EDB in your background. Why EDB for you? >> Before talking about EDB you asked about the previous question about how the migration was different from Oracle to EDB. We had a couple of success story in Korea telecom and some banking area, and it was easier. So EDB provide MTK tool as a people know but it was an appropriate, like a 90%. So we are the channel partner of the EDB for four years. So what we have done was to hire the Oracle expert. So we train Oracle export as as EDB expert at the same time so that they can approach customer and make it easy. So you have no worry about that. Just migrating EDB, Oracle to EDB. There is a no issue. Those telltales include all the tasks, you know Stratus test and trainee, and a POC that we there. So by investing that Oracle expert that we could overcome and persuade the customer to adopt EDB. So, why EDB? Simply I can say there, is there any database they can finally replaced Oracle in the world? Why is the, it's the interoperability between Oracle to EDB as the many experts pointed out there is no other DBE. They can, you know, 90, 90% in compatibility and intercooperability with EDB. That's why, of course, there's the somewhat, you know budget issues or maintenance issue cost the issue escape from Oracle lock-in. But I think the the number one reason was the interoperability and the compatibility with database itself, Oracle database. That was a reason, I guess >> Great Abdul we've talked about, we all know the, as is, you've got a high maintenance costs. You got a lot of tuning, and it's just a lot of complexity. What about the 2B maybe you could share with us sort of the outcome some of the outcomes you've seen what the business impact has been of some of these migrations? >> Sure. I mean, I'll give you a very simple example then just the idea of running Oracle on prem a lot of customer systems teams, for example will drive a virtualization VMware strategy. We know some of the challenges of running Oracle MBM where from a license perspective. So giving the business the ability where I want to go customer in the financial services market in New York, heavy virtualization strategy the ability for them to move away from Oracle on, you know expensive hardware on to Postgres EDB on virtualization just leverage existing skillsets, leveraging existing investment in terms of infrastructure, and also give them portability in AWS. The other clouds, you know, in terms of a migration. More from a business perspective as well, I would say about some of the Allan's points in terms of just freeing up the ability for data scientists and data consumers, to, you know, to consume some of that data from an Postgres perspective more accessibility spinning up environments quicker less latency in terms of the agility is another key word in terms of the tangible differences, the business, lower cost agility, and the freedom to deploy anywhere at the end of the day. Choices, I think the key word that we could come back to and knowing that we can do that to Charlie's point specifically around maintaining service levels. And as architects, we support some of the big, big names out there in terms of airlines, online, cosmetic retailers, financial services, trading applications, hedge funds, and they all want one thing as architect: for us to deliver that resiliency and stand behind them. And as the MSP we're accountable to ensure those systems are up and running and performing. So knowing that the EDB is provided the compatibility but also plugged the specific requirements around performance management, security availability that's fundamentally been key. >> [Dave I mean, having done a lot of TCO studies in this area, it's, it's it Oracle's different. You know, normally the biggest component of TCO is labor with Oracle. The biggest component of TCO is licensed and maintenance costs. So if you can virtualize and reduce those costs and of course, of course the Oracle will fight you and say we won't support it in a VMware environment. Of course, you know, they will, but, but you got to really, you got to battle. But, so here's my last question. So if I'm a customer in that state that you described you know, a lot of sort of Oracle sprawl a lot of databases out there, high maintenance costs, the whole lock-in thing. I got to choices. I, you know, a lot of choices out there. One is EDB. You guys have convinced me that you've got the expertise If I can partner with firms like yours, it's safer route. Okay, cool. My other choice is Oracle is going to, The Oracle sales reps is going to get me in a headlock and talk about exit data and how their Oracle cloud, and how it's, they've invested a lot there. And they have, and, I can pay by the drink all this sort of modern sort of discussion, you know, Oracle act like they invented it late to the game. And then here we are. So, so help me. What's the pitch as to, well, that's kind of compelling. It's maybe the safe bet they're there. They're working with my CIO, whatever. Why should I go with the open source route versus that route? It sounds kind of attractive to me, help me understand that each of you maybe take me through that. Abdul, why don't you start. >> Yeah. I'd say, you know, Oracle's being the defacto for so many years that people have just assumed and defaulted saying, high availability, RAC, DR. Data guard, you know, and I'll apply to any database need that I have. And at the end of the day customers have a three tier database requirement: the lowest, less critical, bronze level databases that really don't need RAC or a high availability, silver tier that are departmental solutions. That means some level of resiliency. And then you've got your gold revenue producing brand impact databases that are they're down. And certainly they won. You see no reason why the bronze and silver databases can be targeted towards EDB. Admittedly, we have some of our largest customers are running platforms, are running $5 million an hour e-commerce platform or airlines running large e-commerce platforms. And exit data certainly has a place. RAC has a place in those, in those scenarios. Were not saying that the EDB is a solution for everything in all scenarios, but apply the technology where it's appropriate where it's required and, you know, generally wherever Oracle has being the defacto and it's being applied across the estate, that's fundamentally what's changed. It doesn't have to be the only answer you have multiple choices now. EDB provides us with the ability to probably address, you know more than 50% of the databases' state, and comfortably cope with that and just apply that more expensive kind of gold tier one cost-based but also capability, you know from the highest requirements of performance and availability where it's appropriate. >> Yeah. Very pragmatic approach. Abdul, thank you for that. And Charlie. Charlie, what's your perspective? Give us your closing thoughts. >> Well, it has been, Oracle has been dominating in Asia in South Korea has market or over many years. So customers got tired of this, continuous spending money for the maintenance costs and there is no discount. There is no negotiation. So they want to move away from expensive stuff. And they were looking for a flexible platform with the easygoing and the high speed and performance open source database like a possibly as career. And now the EDB cannot replace a hundred percent of existing legacy worker, but 10%, 20% 50% as time goes on the trend that will continue. And it will be reaching some high point or replacing the existing Oracle system. And it can, it can also leading to good business chance to a channel partner and EDB steps and other related business in open source. >> Great. Thank you, Charlie and Allen, bring us home here. Give us your follow up >> I think my, co- panelists hit the nail on the head, right? It's a menu, right? That's as things become more diverse and as people make more choices and as everybody wants more agility, you have to provide, I mean, and so that, that's where that's coming in and I liked the way that Andul I kind of split it into gold silver and bronze. Yeah. And I think that that's where, we're going, right? I mean you should ask your developers right? Are your developers like pining to start up a new instance of Oracle every time you're starting a new project? Probably not reach for their Postgres right? And so, because of that, that's where this is coming from and that's not going to change. And, and yeah, that that ecosystem is going to continue to, to thrive. And there'll be lots of different flavors in the growing open source ecosystem. >> Yeah. I mean, open source absolutely is the underpinning you know, the, the bedrock of innovation, these days. Gentlemen, great power panel. Thanks so much for bringing your perspectives and best of luck in the future. >> Thank you, next time we'll try and match our backgrounds >> Next time. Well, we'll up our game. Okay. And thank you for watching everybody. This is Dave Volante for theCUBE. Stay tuned for more great coverage. Postgres vision, 21. Be right back. (upbeat techno music)

>> From around the globe, It's theCUBE. With digital coverage of IBM Think 2021, brought to you by IBM. >> Hi, welcome back to theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier host of the CUBE. We got a great guest here. Brian Bouchard is the co-founder president and CEO of Alacrinet. Brian great to see you remoting in all the way from Puerto Rico to Palo Alto. >> That's right. >> Great to see you. >> Thanks for First of all, thanks John, for having me. I really appreciate the opportunity. >> Yeah, great to see you. Thanks for coming on. First of all, before we get into what you guys do and and how this all ties in to Think. What do you guys do at Alacrinet? Why the name? A it's good you're at the top of the list and alphabetically, but tell us the, the, the the secret behind the name and what you guys do. >> So, first of all Alacrinet is based on the root word alacrity which means a prompt and willing, a prompt a joyous prompt to, excuse me, to achieve a common goal. So we ultimately are a network of individuals with the traits of alacrity. So Alacrinet. So that's our name. >> Great. So what's your relationship with IBM and how you guys have been able to leverage the partnership program in the marketplace? Take us through the relationship. >> So, well, first of all Alacrinet is a platinum IBM business partner and it was awarded recently the 2020 IBM North American partner of the year award. And we were selected amongst 1600 other business partners across North America. We've been actually a consulting, an IT consulting company for almost 20 years now. And we were founded in 2002 in Palo Alto and we have focused specifically on cyber security since 2013. And then as part, go ahead. >> What are some of the things that you guys are working on? Because obviously, you know, the business is hot right now. Everyone's kind of looking at COVID saying we're going to double down on the most critical projects and no time for leisurely activities when it comes to IT. And cloud scale projects, you know mission critical stuff's happening what are you guys working on? >> So we're, we're focused on cybersecurity, our security services really compliment IBM's suite of security solutions and cover the full spectrum from our research and penetration testing, which helps identify vulnerabilities before a breach occurs. And we also have managed security services which helps prevent, detect and remediate attacks in real time. And then finally, we also have a security staffing division and a software resell division, which kind of rounds out the full amount of offerings that we have to provide protection for our clients. >> What are some of the biggest challenges you guys have as a business, and how's IBM helping you address those? >> Well, as you know, John, we all know the importance of cybersecurity in today's world, right? So it's increasing in both demand and importance and it's not expected to wane anytime soon. Cyber attacks are on the rise and there's no there's no expected end in sight to this. And in fact, just this week on 60 minutes, Jay Powell, the chairman of the federal reserve board he noted that cyber attacks were the number one threat to the stability of the US economy. Also this week, a public school in Buffalo New York was hacked with ransomware and the school you know, this, the school district is just contemplating you know, paying the ransom to the hackers. So there's literally thousands of these attacks happening every day, whether it's in local school district or a state government, or an enterprise even if you don't hear about them, they're happening In adding to the complexity that the cyber attackers pose is the complexity of the actual cybersecurity tools themselves. There isn't a single solution provider or a single technology, that can ensure a company's security. Our customers need to work with many different companies and disconnected tools and processes to build an individual strategy that can adequately protect their organizations. >> You know, I love this conversation whenever I talk to practitioners on cybersecurity, you know that first of all, they're super smart, usually cyber punks and they also have some kinds of eclectic backgrounds, but more importantly is that there's different approaches in terms of what you hear. Do you, do you put more if you add more firefighters, so to speak to put out the fires and solve the problems? Or do you spend your time preventing the fires from happening in the first place? You know, and you know, the buildings are burning down don't make fire fire, don't make wood make fire resistance, you know, more of a priority. So there's less fires needing firefighters So it's that balance. You throw more firefighters at the problem or do you make the supply or the material the business fireproof, what's your take on that? >> Yeah, well, it kind of works both ways. I mean, we've seen customers want it. They really want choice. They want to, in some cases they want to be the firefighter. And in some cases they want the firefighter to come in and solve their problems. So, the common problem set that we're seeing with our that our customers encounter is that they struggle one, with too many disparate tools. And then they also have too much data being collected by all these disparate tools. And then they have a lack of talent in their environment to manage their environments. So what we've done at Alacrinet is we've taken our cybersecurity practice and we've really specifically tailored our offerings to address these core challenges. So first, to address the too many disparate tools problem, we've been recommending that our clients look at security platforms like the IBM Cloud Pak for security the IBM Cloud Pak for security is built on a security platform that allows interoperability across various security tools using open standards. So our customers have been responding extremely positively to this approach and look at it as a way to future-proof their investments and begin taking advantage of interoperability with, and, tools integration. >> How about where you see your business going with this because, you know, there's not a shortage of need or demand How are you guys flexing with the market? What's the strategy? Are you going to use technology enablement? You're going to more human driven. Brian, how do you see your business unfolding? >> Well, actually really good. We're doing very well. I mean, obviously we made the, the top the business partner for IBM in 2020. They have some significant growth and a lot of interest. I think we really attack the market in a, in a with a good strategy which was to help defragment the market if you will. There's a lot of point solutions and a lot of point vendors that various, you know, they they spent specialized in one piece of the whole problem. And what we've decided to do is find them the highest priority list, every CSO and CIO has a tick list. So that how that, you know, first thing we need we need a SIM, we need an EDR, we need a managed service. We need, what's the third solution that we're doing? So we, we need some new talent in-house. So we actually have added that as well. So we added a security staffing division to help that piece of it as well. So to give you an idea of the cybersecurity market size it was valued at 150 billion in 2019 and that is expected to grow to 300 billion by 2027. And Alacrinet is well-positioned to consolidate the many fragmented aspects of the security marketplace and offer our customers more integrated and easier to manage solutions. And we will continue to help our customers select the best suite of solutions to address all types of cybersecurity, cybersecurity threats. >> You know, it's it's such a really important point you're making because you know, the tools just have piled up in the tool shed. I call it like that. It's like, it's like you don't even know what's in there anymore. And then you've got to support them. Then the world's changed. You get cloud native, the service areas increasing and then the CSOs are also challenged. Do I, how many CLAWs do I build on? Do I optimize my development teams for AWS or Azure? I mean, now that's kind of a factor. So, you have all this tooling going on they're building their own stuff they're building their own core competency. And yet the CSO still needs to be like maintaining kind of like a relevance list. That's almost like a a stock market for the for the products. You're providing that it sounds like you're providing that kind of service as well, right? >> Yeah, well, we, we distill all of the products that are out there. There's thousands of cybersecurity products out there in the marketplace and we kind of do all that distillation for the customer. We find using, you know, using a combination of things. We use Forrester and Gartner and all the market analysts to shortlist our proposed solutions that we offer customers. But then we also use our experience. And so since 2013, we've been deploying these solutions across organizations and corporations across America and we've, we've gained a large body of experience and we can take that experience and knowledge to our customers and help them, you know, make make some good decisions. So they don't have to, you know, make them go through the pitfalls that many companies do when selecting these types of solutions. >> Well congratulations, you've got a great business and you know, that's just a basic search making things easier for the CSO, more so they can be safe and secure in their environment. It's funny, you know, cyber warfare, you know the private companies have to fight their own battles got to build their own armies. Certainly the government's not helping them. And then they're confused even with how to handle all this stuff. So they need, they need your service. I'm just curious as this continues to unfold and you start to see much more of a holistic view, what's the IBM angle in here? How, why are you such a big partner of theirs? Is it because their customers are working with you they're bringing you into business? Is it because you have an affinity towards some of their products? What's the connection with IBM? >> All of the above. (chuckles) So I think it probably started with our affinity to IBM QRadar product. And we have, we have a lot of expertise in that and that solution. So that's, that's where it started. And then I think IBM's leadership in this space has been remarkable, really. So like what's happening now with the IBM Cloud Pak for security you know, building up a security platform to allow all these point solutions to work together. That's the roadmap we want to put our customers on because we believe that's the that's the future for this, this, this marketplace. >> Yeah. And the vision of hybrid cloud having that underpinning be with Red Hat it's a Linux kernel, model of all things >> Yeah. Super NetEase. >> Locked in >> It's portable, multiple, you can run it on Azure. IBM Cloud, AWS. It's portable. I mean, yeah, all this openness, as you probably know cyber security is really a laggard in the security in the information technology space as far as adopting open standards. And IBM is I think leading that charge and you'll be able to have a force multiplier with the open standards in this space. >> Open innovation with open source is incredible. I mean, if you, if, if if open source can embrace a common platform and build that kind of control plane and openness to allow thriving companies to just build out then you have an entire hybrid distributed architecture. >> Yeah. Well, I think companies want to use the best in breed. So when we, when we show these solutions to customers they want the best in breed. They always say, I don't, when it comes to security they don't want second best. They want the best it's out there because they're securing their crown jewels. So that makes sense. So the problem with, you know having all these different disparate solutions that are all top in their category none of them talk to each other. So we need to address that problem because without that being solved, this is just going to be more it's going to compound the complexity of the problems we solve day to day. >> Awesome. Congratulations, Brian, great story. You know entrepreneur built a great business over the years. I think the product's amazing. I think that's exactly what the market needs and just shows you what the ecosystem is all about. This is the power of the ecosystem. You know, a thousand flowers are blooming. You got a great product. IBM is helping as well. Good partnership, network effects built in and and still a lot more to do. Congratulations. >> Absolutely. >> Okay. >> Thank you very much >> Brian Bouchard >> Made my impression. I appreciate that >> Thanks for coming on theCUBE Appreciate it. I'm John Furrier with IBM thinks 2021 virtual coverage. Thanks for watching. (outro music plays)

>> Announcer: From around the globe it's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back to theCUBE's coverage of IBM Think 2021 Virtual. I'm John Furrier, host of theCUBE. And it's theCUBE Virtual and Uli Homann who's here, Corporate Vice President of Cloud & AI at Microsoft. Thanks for comin' on. I love this session. Obviously, Microsoft one of the big clouds. Awesome. You guys partnering with IBM here, at IBM Think. I remember during the client-server days in the '80s, late '80s to early '90s the open systems interconnect was a big part of opening up the computer industry. That was networking, intra-networking and really created more LANs and more connections for PCs et cetera, and the world just went on from there. Similar now with hybrid cloud, you're seeing that same kind of vibe, right? You're seeing that same kind of alignment with distributed computing architectures for businesses. Where now you have, it's not just networking and plumbing, and connecting, you know, LANs and PCs, and printers, it's connecting everything. It's kind of almost a whole 'nother world, but similar movie, if you will. So this is really going to be good for people who understand that market. IBM does, you guys do. Talk about the alignment between IBM and Microsoft in this new hybrid cloud space. It's really kind of now standardized, but yet it's just now coming. >> Yeah, so again, fantastic question. So the way I think about this is first of all, Microsoft and IBM are philosophically very much aligned. We're both investing in key open source initiatives like the Cloud Native Compute Foundation, CNCF, something that we both believe in. We're both partnering with the Red Hat organization so Red Hat forms a common bond, if you so want to, between Microsoft and IBM. And again, part of this is how can we establish a system of capabilities that every client has access to, and then build on top of that stack. And again, IBM does this very well with their Cloud Paks which are coming out now with data and AI, and others. So open source, open standards are key elements and then you mentioned something critical which I believe is not under, misunderstood, but certainly not appreciated enough is this is about connectivity between businesses and so part of the power of the IBM perspective together with Microsoft is bringing together key business applications for health care, for retail, for manufacturing and really make them work together so that our clients that are-- critical scenarios get the support they need from both IBM as well as Microsoft on top of this common foundation of the CNCF and other open standards. >> You know, it's interesting, I love that point. I'm going to double-down and amplify that and continue to bring it up. Connecting between businesses is one thread but now, people, because you have an edge that's also industrial, business, but also people. People are also participating in open source, people have wearables, people are connected so they can, and also they're connecting with collaboration. So this kind of brings a whole 'nother architecture which I want to get into the solutions with you on on how you see that playing out. But first, I know, you know, you're a veteran with Microsoft for many, many years, for decades. Microsoft's core competency has been ecosystems, developer ecosystems, customer ecosystems. Today, that the services motion is build around ecosystems. You guys have that playbook, IBM's well versed in it, as well. How does that impact your partnerships, your solutions, and how you deal with down this open marketplace? >> Well, let's start with the obvious. Obviously, Microsoft and IBM will work together in common ecosystems. Again, I'm going to reference the CNCF again as the foundation for a lot of these initiatives. But then we are also working together in the Red Hat ecosystem because Red Hat has built an ecosystem that Microsoft and IBM are players in that ecosystem. However, we also are looking higher level 'cause a lot of times when people think ecosystems, it's fairly low-level technology. But Microsoft and IBM are talking about partnerships that are focused on industry scenarios. Again, retail for example, or health care and others where we're building on top of these lower-level ecosystem capabilities and then bringing together the solution scenarios where the strength of IBM capabilities is coupled with Microsoft capabilities to drive this very famous one plus one equals three. And then the other piece that I think we both agree on is the open source ecosystem for software development and software development collaboration. And GitHub is a common anchor that we both believe can feed the world's economy with respect to the software solutions that are needed to really, yeah, bring the capabilities forward, help improve the world's economy and so forth by effectively bringing together brilliant minds across the ecosystem and again, just Microsoft and IBM bringing some people, but the rest of the world obviously participating in that, as well. So thinking again, open source, open standards, and then industry-specific collaboration and capabilities being a key part. You mentioned people. We certainly believe that people play a key role, software developers and the GitHub notion being a key one. But there are others where again, Microsoft with Microsoft 365 has a lot of capabilities in connecting people within the organization and across organizations. And while we're using Zoom, here, a lot of people are utilizing Teams 'cause Teams is on the one side of collaboration platform, but on the other side is also an application host. And so bringing together people collaboration supported and powered by applications from IBM, from Microsoft and others, is going to be, I think, a huge differentiation in terms of how people interact with software in the future. >> Yeah, and I think that whole joint development is a big part of this new people equation where it's not just partnering in market, it's also at the tech, and you've got open source, and it's a just phenomenal innovation formula, there. So let's ask what solutions, here. I want to get into some of the top solutions you're doing that Microsoft that maybe with IBM. But your title as the Corporate Vice President Cloud & AI, c'mon, could you get a better department? I mean, more relevant than that? I mean, it's exciting. You know, cloud scale is driving tons of innovation, AI is eating software or changing the software paradigm. We're going to see that playing out. I've done dozens of interviews just in this past month on how AI's a more, certainly with machine learning, and having a control plane with data, changing the game. So tell us, what are the hot solutions for hybrid cloud and why is this a different ballgame than say, public cloud? >> Well, so first of all, let's talk a little bit about the AI capabilities and data because I think they're two categories. You are seeing an evolution of AI capabilities that are coming out. And again, I just read IBM's announcement about integrating the Cloud Pak with IBM Satellite. I think that's a key capability that IBM is putting out there and we are partnering with IBM in two directions, there. IBM has done a fantastic job to build AI capabilities that are relevant for industries, health care being a very good example, again, retail being another one. And I believe Microsoft and IBM will work on both partnership on the technology side as well as the AI usage in specific verticals. Microsoft is doing similar things. Within our Dynamics product line, we're using AI for business applications, for planning, scheduling, optimizations, risk assessments, those kind of scenarios. And of course, we're using those in the Microsoft 365 environment, as well. I always joke that despite my 30 years at Microsoft, I still don't know how to really use PowerPoint and I can't do a PowerPoint slide for the life of me, but with a new designer, I can actually get help from the system to make beautiful PowerPoint happen. So bringing AI into real life usage I think is the key part. The hybrid scenario is critical here, as well, especially when you start to think about real life scenarios like safety, worker safety in a critical environment, freshness of product. We're seeing retailers deploying cameras and AI inside the retail stores to effectively make sure that the shelves are stocked, that the quality of the vegetables, for example, continues to be high and monitored. And previously, people would do this on an occasional basis running around in the store. Now the store is monitored 24/7 and people get notified when things need fixing. Another really cool scenario set is quality. We are working with a Finnish steel producer that effectively is looking at the stainless steel as it's being produced and they have cameras on this steel that look at specific marks. And if these marks show up then they know that the stainless steel will be bad. And I don't know if you have looked at a manufacturing process, but the earlier they can get failures detected, the better it is because they can most likely, or more often than not, return the product back into the beginning of the funnel and start over. And that's what they're using. So you can see molten steel, logically speaking, with a camera and AI. And previously, humans did this which is obviously A, less reliable and B, dangerous because this is very, very hot, this is very glowing steel. And so increasing safety while at the same time improving the quality is something that we see in hybrid scenarios. Again, autonomous driving, another great scenario where perception AI is going to be utilized. So there's a bunch of capabilities out there that really are hybrid in nature and will help us move forward with key scenarios, safety, quality, and autonomous behaviors like driving and so forth. >> Uli, great, great insight. Great product vision. Great alignment with IBM's hybrid cloud space what all customers are lookin' for, now. And certainly multicloud around the horizon. So great to have you on. Great agility, and congratulations for your continued success. You've got a great area, cloud and AI, and we'll be keeping in touch. I'd love to do a deep dive, sometime. Thanks for coming on. >> John, thank you very much for the invitation and great questions, great interview. Love it, appreciate it. >> Thank you very much. Okay, theCUBE coverage here, at IBM Think 2021 Virtual. I'm John Furrier, your host. Thanks for watching. (soft electronic music) ♪ Dah-De-Da ♪ ♪ Dah-De ♪

>> Voice Over: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Okay. Welcome back everyone. To theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We've got a great segment here on the power of hybrid cloud and AI. And I'm excited to have Rob Thomas, Senior Vice President of IBM's cloud and Data platform, CUBE alumni. Been on going back years and years talking about data. Rob, great to see you, a leader at IBM. Thanks for joining. >> John. Great to see you hope everybody is safe and well and great to be with you again. >> Yeah, love the progress, love the Hybrid Cloud distributed computing, meets operating systems, meets modern applications at the center of it is the new cloud equation. And of course data continues to be the value proposition as the platform. And as you quoted many times and I love your favorite quote. There's no AI without IA. So you got to have the architecture. So that still rings true today and it's just so evergreen and so relevant and cooler than ever with machine learning and AI operations. So let's just jump in. IBM's announced, host a new products and updates at Think. Tell us what you're most excited about and what should people pay attention to. >> Maybe I'll connect two thoughts here. There is no AI without IA, still true today. Meaning, customers that want to do AI need an information architecture. There was an IDC report just last year that said, "Despite all the progress on data, still 90% of data in organizations is either unused or underutilized." So what's amazing is after all the time we've been talking John, we're still really just getting started. Then that kind of connects to another thought, which is I still believe that AI is not going to replace managers, but managers that use AI will replace the managers that do not. And I'd say that's the backdrop for all the announcements that we're doing this week. It's things like auto SQL. How do you actually automate the creation of SQL queries in a large distributed data warehouse? It's never been done before, now we're doing it. It's things like Watson Orchestrate which is super powers in the hands of any business user, just to ask for something to get done. Just ask for a task to get completed. Watson Orchestrator will do that for you. It's maximo mobile. So anybody working in the field now has access to an AI system on their device for how they're managing their assets. So this is all about empowering people and users that use these products are going to have an advantage over the users that are not, that's what I'm really excited about. >> So one of the things that's coming out as Cloud Pak for Data, AI powered automation these are kind of two that you kind of touched upon the SQL thing their. Cloud Pak is there, you got it for Data and this automation trend. What is that about? Why is it important? Can you share with us the relevance of those two things? >> Let's talk broadly about automation. There's two huge markets here. There's the market for RPA business process, $30 billion market. There's the market for AIOps, which is growing 22%, that's on its way to $40 billion. These are enormous markets. Probably the biggest bet IBM has made in the last year is in automation. Explicitly in Watson AIOps. Last June in Think we announced Watson AIOps, then we did the acquisition of Instana, then we announced our intent to acquire Turbonomic. At this point, we're the only company that has all the pieces for automating how you run your IT systems. That's what I mean when I say AIOps. So really pleased with the progress that we've made there. But again, we're just getting started. >> Yeah. Congratulations on the Turbonomic. I was just commenting on that when that announced. IBM buying into the Cloud and the Hybrid cloud is interesting because the shift has happened. It's Public Cloud, it's on premises as Edge. Those two things as a system, it's more important ever than the modernization of the apps that you guys are talking about and having the under the cover capabilities. So as Cloud and Data merge, this kind of control plane concept, this architecture, as you'd said IA. You can't have AI without IA. What is that architecture look like? Can you break down the elements of what's involved? I know there's predictive analytics, there's automation and security. What are the pillars of this architecture? What are the four concepts? If you can explain that. >> Yeah, let's start with the basics. So Hybrid Cloud is about you build your software runs once and you run it anywhere you want, any public cloud,any private cloud. That assumes containers are important to the future of software. We are a hundred percent convinced that is true. OpenShift is the platform that we build on and that many software companies in the world are now building on because it gives you portability for your applications. So then you start to think about if you have that common fabric for Hybrid Cloud, how do you deliver value to customers in addition to the platform? To me, that's four big things. It's automation, we talked about that. It's security, it's predictions. How do you actually make predictions on your data? And then it's modernization. Meaning, how do you actually help customers modernize their applications and get to the Cloud? So those are the things we always talk about, automate, secure, modernize, predict. I think those are the four most important things for every company that's thinking about Cloud and AI. >> Yeah, it's interesting. I love the security side is one of the big conversations in AIOps and day two operations or whatever it's called is shifting left, getting security into the Cloud native kind of development pipeline. But speaking of secure, you have a customer that was talking about this Dow Chemical. About IB empowering Dow zero trust architecture. Could you explain that deal and how that's working? Because that's again, huge enterprise customer, very big scale at scale, zero trust is big, part of it. What is this? >> Let's start with the basics. So what is zero trust mean? It means to have a secure business, you have to start with the assumption that nothing can be trusted. That means you have to think about all aspects of your security practice. How do you align on a security strategy? How do you protect your data assets? How do you manage security threats? So we always talk about a line, protect, manage back to modernize, which is how do you bring all your systems forward to do this? That's exactly what we're doing with the Dow as you heard in that session, which is they've kind of done that whole journey from how they built a security strategy that was designed with zero trust in mind, they're protecting data assets, they're managing cyber threats in real time with a relatively low number of false positives which are the issue that most companies have. They're a tremendous example of a company that jumped on this and has had a really big impact. And they've done it without interfering with their business operations, meaning anybody can lock everything down but then you can't really run your business if you're doing that. They've done it, I think in a really intelligent way. >> That's awesome. We always talk about the big waves. You always give great color commentary on the trends. Right now though, the tsunami seems to be a confluence of many things coming together. What are some of the big trends in waves you're seeing now specifically on the tech side, on the technology side, as well as the business side right now? 'Cause coming out of post COVID, it's pretty clear cloud-native is powering a new growth strategy for customers. Dow was one of them, you just commented on it but there's a bigger wave happening here, both on the tech theater and in the business theater. Can you share your views on and your opinions and envision on these trends? >> I think there's three profound trends that are actually pretty simple to understand. One is, technology is going to decentralize again. We've always gone from centralized architectures to decentralized. Mainframe was centralized, internet mobile decentralized. The first version of public cloud was centralized, meaning bringing everything to one place. Technology is decentralized and again, with Hybrid Cloud, with Edge, pretty straight forward I think that's a trend that we can ride and lead for the next decade. Next is around automation that we talked about. There was a McKinsey report that said, "120 billion hours a year are going to be automated with things like Watson Orchestrator, Watson AIOps." What we're doing around Cloud Pak for automation, we think that time is now. We think you can start to automate in your business today and you may have seen the--example where we're doing customer care and they're now automating 70% of their inbound customer inquiries. It's really amazing. And then the third is around data. The classical problem, I mentioned 90% is still unused or underutilized. This trend on data is not about to slow down because the data being collected is still multiplying 10 X every year and companies have to find a way to organize that data as they collected. So that's going to be a trend that continues. >> You know, I just kind of pinched myself sometimes and hearing you talk with some of our earlier conversations in theCUBE, people who have been on this data mindset have really been successful because it's evolving and growing and it's changing and it's adding more input into the system and the technology is getting better. There's more cloud scales. You mentioned automation and scale are huge. And I think this really kind of wakes everyone up. And certainly the pandemic has woken everyone up to the fact that this is driving new experiences for users and businesses, right? So this is, and then those experiences become expectations. This is the classic UX paradigm that grows from new things. So I got to ask you, with the pandemic what is the been the most compelling ways you seen people operate, create new expectations? Because new things are coming, new big things, and new incremental things are happening. So evolution and revolutionary capabilities. Can you share some examples and your thoughts? >> We've collected a decent bit of data on this. And what's interesting is how much AI has accelerated since the pandemic started. And it's really in five areas, it's customer care that we talked about, virtual agents, customer service, how you do that. It's employee experience. So somewhere to customer care but how do you take care of your employees using AI? Third is around AIOps, we talked about that. Fourth is around regulatory compliance and fifth is around financial planning and budgeting. These are the five major use cases of AI that are getting into production in companies over the last year that's going to continue to accelerate. So I think it's actually fairly clarifying now that we really understand these are the five big things. I encourage anybody watching, pick one of these, get started, then pick the second, then pick the third. If you are not doing all five of these, 12, 18, 24 months from now, you are going to be behind. >> So give us an example of some things that have surprised you in the pandemic and things that blew you away. Like, wow, I didn't see that coming. Can you share on things that you've seen evolve? Cause you're a year ahead of the business units of Cloud and Data, big part of IBM and you see customer examples. Just quickly share some notable use cases or just anecdotal examples of just things that jumped out at you that said, "Wow, that's going to be a double-down moment or that's not going to be anymore." Exposes, the pandemic exposes the good, bad and the ugly. I mean, people got caught off guard, some got a tailwind, some had a headwind, some are retooling. What's your thoughts on what you can you share any examples? >> Like everybody, many things have surprised me in the last year. I am encouraged at how fast many companies were able to adjust and adapt for this world. So that's a credit to all the resiliency that they built into their processes, their systems and their people over time. Related to that, the thing that really sticks out to me again, is this idea of using AI to serve your customers and to serve your employees. We had a hundred customers that went live with one of those two use cases in the first 35 days of the pandemic. Just think about that acceleration. I think without the pandemic, for those hundred it might've taken three years and it happened in 35 days. It's proof that the technology today is so powerful. Sometimes it just takes the initiative to get started and to do something. And all those companies have really benefited from this. So it's great to see. >> Great. Rob, great to have you on. Great to have your commentary on theCUBE. Could you just quickly share in 30 seconds, what is the most important thing people should pay attention to and Think this year from your perspective? What's the big aha moment that you think they could walk away with? >> We have intentionally made this a very technology centric event. Just go look at the demos, play with the technology. I think you will be impressed and start to see, let's say a bit of a new IBM in terms of how we're making technology accessible and easy for anybody to use. >> All right. Rob Thomas, Senior Vice President of IBM cloud and Data platform. Great to have you on and looking forward to seeing more of you this year and hopefully in person. Thanks for coming on theCUBE virtual. >> Thanks, John. >> Okay. I'm John Furrier with theCUBE. Keep coverage of IBM Think 2021. Thank you for watching. (soft music)

(soft music) >> Voice Over: From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Okay. Welcome back everyone. To theCUBE's coverage of IBM Think 2021 virtual. I'm John Furrier, host of theCUBE. We've got a great segment here on the power of hybrid cloud and AI. And I'm excited to have Rob Thomas, Senior Vice President of IBM's cloud and Data platform, CUBE alumni. Been on going back years and years talking about data. Rob, great to see you, a leader at IBM. Thanks for joining. >> John. Great to see you hope everybody is safe and well and great to be with you again. >> Yeah, love the progress, love the Hybrid Cloud distributed computing, meets operating systems, meets modern applications at the center of it is the new cloud equation. And of course data continues to be the value proposition as the platform. And as you quoted many times and I love your favorite quote. There's no AI without IA. So you got to have the architecture. So that still rings true today and it's just so evergreen and so relevant and cooler than ever with machine learning and AI operations. So let's just jump in. IBM's announced, host a new products and updates at Think. Tell us what you're most excited about and what should people pay attention to. >> Maybe I'll connect two thoughts here. There is no AI without IA, still true today. Meaning, customers that want to do AI need an information architecture. There was an IDC report just last year that said, "Despite all the progress on data, still 90% of data in organizations is either unused or underutilized." So what's amazing is after all the time we've been talking John, we're still really just getting started. Then that kind of connects to another thought, which is I still believe that AI is not going to replace managers, but managers that use AI will replace the managers that do not. And I'd say that's the backdrop for all the announcements that we're doing this week. It's things like auto SQL. How do you actually automate the creation of SQL queries in a large distributed data warehouse? It's never been done before, now we're doing it. It's things like Watson Orchestrate which is super powers in the hands of any business user, just to ask for something to get done. Just ask for a task to get completed. Watson Orchestrator will do that for you. It's Maximo Mbo. So anybody working in the field now has access to an AI system on their device for how they're managing their assets. So this is all about empowering people and users that use these products are going to have an advantage over the users that are not, that's what I'm really excited about. >> So one of the things that's coming out as Cloud Pak for Data, AI powered automation these are kind of two that you kind of touched upon the SQL thing their. Cloud Pak is there, you got it for Data and this automation trend. What is that about? Why is it important? Can you share with us the relevance of those two things? >> Let's talk broadly about automation. There's two huge markets here. There's the market for RPA business process, $30 billion market. There's the market for AIOps, which is growing 22%, that's on its way to $40 billion. These are enormous markets. Probably the biggest bet IBM has made in the last year is in automation. Explicitly in Watson AIOps. Last June in Think we announced Watson AIOps, then we did the acquisition of Instana, then we announced our intent to acquire Turbonomic. At this point, we're the only company that has all the pieces for automating how you run your IT systems. That's what I mean when I say AIOps. So really pleased with the progress that we've made there. But again, we're just getting started. >> Yeah. Congratulations on the Turbonomic. I was just commenting on that when that announced. IBM buying into the Cloud and the Hybrid cloud is interesting because the shift has happened. It's Public Cloud, it's on premises as Edge. Those two things as a system, it's more important ever than the modernization of the apps that you guys are talking about and having the under the cover capabilities. So as Cloud and Data merge, this kind of control plane concept, this architecture, as you'd said IA. You can't have AI without IA. What is that architecture look like? Can you break down the elements of what's involved? I know there's predictive analytics, there's automation and security. What are the pillars of this architecture? What are the four concepts? If you can explain that. >> Yeah, let's start with the basics. So Hybrid Cloud is about you build your software runs once and you run it anywhere you want, any public cloud,any private cloud. That assumes containers are important to the future of software. We are a hundred percent convinced that is true. OpenShift is the platform that we build on and that many software companies in the world are now building on because it gives you portability for your applications. So then you start to think about if you have that common fabric for Hybrid Cloud, how do you deliver value to customers in addition to the platform? To me, that's four big things. It's automation, we talked about that. It's security, it's predictions. How do you actually make predictions on your data? And then it's modernization. Meaning, how do you actually help customers modernize their applications and get to the Cloud? So those are the things we always talk about, automate, secure, modernize, predict. I think those are the four most important things for every company that's thinking about Cloud and AI. >> Yeah, it's interesting. I love the security side is one of the big conversations in AIOps and day two operations or whatever it's called is shifting left, getting security into the Cloud native kind of development pipeline. But speaking of secure, you have a customer that was talking about this Dow Chemical. About IB empowering Dow zero trust architecture. Could you explain that deal and how that's working? Because that's again, huge enterprise customer, very big scale at scale, zero trust is big, part of it. What is this? >> Let's start with the basics. So what is zero trust mean? It means to have a secure business, you have to start with the assumption that nothing can be trusted. That means you have to think about all aspects of your security practice. How do you align on a security strategy? How do you protect your data assets? How do you manage security threats? So we always talk about a line, protect, manage back to modernize, which is how do you bring all your systems forward to do this? That's exactly what we're doing with the Dow as you heard in that session, which is they've kind of done that whole journey from how they built a security strategy that was designed with zero trust in mind, they're protecting data assets, they're managing cyber threats in real time with a relatively low number of false positives which are the issue that most companies have. They're a tremendous example of a company that jumped on this and has had a really big impact. And they've done it without interfering with their business operations, meaning anybody can lock everything down but then you can't really run your business if you're doing that. They've done it, I think in a really intelligent way. >> That's awesome. We always talk about the big waves. You always give great color commentary on the trends. Right now though, the tsunami seems to be a confluence of many things coming together. What are some of the big trends in waves you're seeing now specifically on the tech side, on the technology side, as well as the business side right now? 'Cause coming out of post COVID, it's pretty clear cloud-native is powering a new growth strategy for customers. Dow was one of them, you just commented on it but there's a bigger wave happening here, both on the tech theater and in the business theater. Can you share your views on and your opinions and envision on these trends? >> I think there's three profound trends that are actually pretty simple to understand. One is, technology is going to decentralize again. We've always gone from centralized architectures to decentralized. Mainframe was centralized, internet mobile decentralized. The first version of public cloud was centralized, meaning bringing everything to one place. Technology is decentralized and again, with Hybrid Cloud, with Edge, pretty straight forward I think that's a trend that we can ride and lead for the next decade. Next is around automation that we talked about. There was a McKinsey report that said, "120 billion hours a year are going to be automated with things like Watson Orchestrator, Watson AIOps." What we're doing around Cloud Pak for automation, we think that time is now. We think you can start to automate in your business today and you may have seen the C QVS example where we're doing customer care and they're now automating 70% of their inbound customer inquiries. It's really amazing. And then the third is around data. The classical problem, I mentioned 90% is still unused or underutilized. This trend on data is not about the slow down because the data being collected is still multiplying 10 X every year and companies have to find a way to organize that data as they collected. So that's going to be a trend that continues. >> You know, I just kind of pinched myself sometimes and hearing you talk with some of our earlier conversations in theCUBE, people who have been on this data mindset have really been successful because it's evolving and growing and it's changing and it's adding more input into the system and the technology is getting better. There's more cloud scales. You mentioned automation and scale are huge. And I think this really kind of wakes everyone up. And certainly the pandemic has woken everyone up to the fact that this is driving new experiences for users and businesses, right? So this is, and then those experiences become expectations. This is the classic UX paradigm that grows from new things. So I got to ask you, with the pandemic what is the been the most compelling ways you seen people operate, create new expectations? Because new things are coming, new big things, and new incremental things are happening. So evolution and revolutionary capabilities. Can you share some examples and your thoughts? >> We've collected a decent bit of data on this. And what's interesting is how much AI has accelerated since the pandemic started. And it's really in five areas, it's customer care that we talked about, virtual agents, customer service, how you do that. It's employee experience. So somewhere to customer care but how do you take care of your employees using AI? Third is around AIOps, we talked about that. Fourth is around regulatory compliance and fifth is around financial planning and budgeting. These are the five major use cases of AI that are getting into production in companies over the last year that's going to continue to accelerate. So I think it's actually fairly clarifying now that we really understand these are the five big things. I encourage anybody watching, pick one of these, get started, then pick the second, then pick the third. If you are not doing all five of these, 12, 18, 24 months from now, you are going to be behind. >> So give us an example of some things that have surprised you in the pandemic and things that blew you away. Like, wow, I didn't see that coming. Can you share on things that you've seen evolve? Cause you're a year ahead of the business units of Cloud and Data, big part of IBM and you see customer examples. Just quickly share some notable use cases or just anecdotal examples of just things that jumped out at you that said, "Wow, that's going to be a double-down moment or that's not going to be anymore." Exposes, the pandemic exposes the good, bad and the ugly. I mean, people got caught off guard, some got a tailwind, some had a headwind, some are retooling. What's your thoughts on what you can you share any examples? >> Like everybody, many things have surprised me in the last year. I am encouraged at how fast many companies were able to adjust and adapt for this world. So that's a credit to all the resiliency that they built into their processes, their systems and their people over time. Related to that, the thing that really sticks out to me again, is this idea of using AI to serve your customers and to serve your employees. We had a hundred customers that went live with one of those two use cases in the first 35 days of the pandemic. Just think about that acceleration. I think without the pandemic, for those hundred it might've taken three years and it happened in 35 days. It's proof that the technology today is so powerful. Sometimes it just takes the initiative to get started and to do something. And all those companies have really benefited from this. So it's great to see. >> Great. Rob, great to have you on. Great to have your commentary on theCUBE. Could you just quickly share in 30 seconds, what is the most important thing people should pay attention to and Think this year from your perspective? What's the big aha moment that you think they could walk away with? >> We have intentionally made this a very technology centric event. Just go look at the demos, play with the technology. I think you will be impressed and start to see, let's say a bit of a new IBM in terms of how we're making technology accessible and easy for anybody to use. >> All right. Rob Thomas, Senior Vice President of IBM cloud and Data platform. Great to have you on and looking forward to seeing more of you this year and hopefully in person. Thanks for coming on theCUBE virtual. >> Thanks, John. >> Okay. I'm John Furrier with theCUBE. Keep coverage of IBM Think 2021. Thank you for watching. (soft music)

>> >> Announcer: From around the globe, it's theCUBE, with digital coverage of IBM Think2021, brought to you by IBM. >> Hey, welcome back to theCUBE's coverage of IBM Think2021 virtual. I'm John Furrier, host of theCUBE. We're here with Madhu Kochar who's the vice president, product management for IBM Data and AI, also CUBE alumni. Great to see you, Madhu, thanks for coming on theCUBE remotely soon to be in person, I hope soon. Great to see you. >> Thanks, John. Obviously very, very happy to be here and yeah, like you said, hopefully next time face-to-face. >> Yeah, I can't, we've had many conversations in the past on theCUBE about data, machine learning. Now more than ever it's prime time. And as companies put the AI to work, they're facing more and more challenges, especially with the growing data complexity and obviously quality, what are they doing to solve it, solve these problems today? I mean, as cloud scales here, it's transformation, innovation cloud scale, still complexity. What are they doing to solve this? >> Yeah, you're right, John, right? The data complexity is just becoming overwhelming and it's threatening what I would say progress. And you would agree to that, right? As organizations are struggling to turn these complex data landscapes and to get some sustained value out of it and it's becoming costly. And I believe the worst of it is because of the rapid pace of digitization is happening right now generate, the data is just getting generated at higher velocity, all different types of data and all different touch points. And traditionally, trying to move replicate data, integrate data and bring it all into the big data store. It's just not working out, right? It's becoming costly and the stats shows that 97% of enterprise data is still not trusted or analyzed. And with all the movements happening on to the migrating of data to multiclouds is just adding a lot of complexity. Our point of view, all of us has been, we've been talking a lot about hybrid. And to me, hybrid approach is the one that truly accepts the reality, that data will be everywhere and is going to be changing by the minute. And it turned and we have to figure out how do we turn that problem into an advantage. To me, automation is going to be inevitable. And what we truly believe in is like you got to leave the data where it is, bring AI to your data and that means what's AI for business here, right? So, that truly means that you have to be able to understand the language of the business, automates workflows and experiences and truly deliver the trust and predictability in these outcomes. That is where John I firmly believe where we need to be going. >> Madhu you know that's so right on. And I think the business case is well understood, what's interesting is, is that we were talking to some other IBMers about autonomous shipping, about ships that are being powered by automation and getting all that data and integrating in all kinds of diverse sources, is also a business challenge. So autonomous vehicles, autonomous everything these days requires massive amounts of data ingestion and processing and insights and operationalizing and decision-making, all kind of coming in. This is like the Holy Grail of automation. So I have to ask you what is the IBM's perspective on managing data that exists in different forms and on across different environments in an organization? Because this is where the diversity comes in and it's actually better for the data, because AI loves diverse data 'cause the better the data, the better the AI, right? So, but it's still complicated. How are you guys looking at this perspective of managing data that exists in different forms? >> Yeah, I know, that's a great question. And I think this is where we need to be thinking about, what we are talking about here is that you need what I call an intelligent data fabric, data fabric it's a term which is just picking up in the industry and the definition, which I would say an intelligent data fabric is what weaves together and automates data and AI lifecycle over anything. Over anything means any data, any cloud anywhere, right? And what do you get with this? What you get with this is that you're able to then unlock totally new insights from unified data. You're able to democratize your trusted data usage across more people. You unleash truly productivity, right? And you reduce cost and risk and you make AI for business easier. Like I was talking about you bring your AI to the data, it's all about AI for business. You make AI for business easier, faster and more trusted. And underneath the covers, automation is what's going to help us to scale all this. So by truly bringing the intelligent data fabric together which helps us automate and view all these things is going to be the answer. >> I love noises like democratizing, trusted data usage and unifying data and getting all those insights 'cause that tries the value. I'm sold on that and I love it. And I understand it, the question I have for you and I heard this term and I'd love to get you to help me define it for the audience, the notion of distributed data life cycles. Can you describe and define what does that mean for an organization? >> So truly that would just mean, right? Like I was talking about what is intelligent data fabric. This is all about data is everywhere, right? You're, it is in your operational data stores, your data warehouses and now with spans of multiple clouds, people are moving their workloads to various clouds, it's everywhere. When you have to make a decision, you have to be able to have access to all that data, right? You have to keep in mind what are your data privacy rules? What is your data residency rules around it, right? And how do I make, how do I analyze that data? How do I categorize that data to have some business outcomes, right? And anything what you're doing right now, it's going to require AI to it. How do I apply AI to where data lives? That is where the whole aspect of the intelligent data fabric needs to come into the picture. >> Yeah, and my next question is around some of the new announcements that you guys have here at Think and I want to get this new upgrade, and new data pack announcement, because I think, I mean, Cloud Pak for Data because having data become operationalized is much more sensitive than it was in the Wild West in the early days. Like, Hey, the data is everywhere. People are concerned and there's compliance, there's risk and getting sued and different sovereignty issues. So, you guys have had this IBM Cloud Pak for Data for a few years now, helping clients with the chance, some of the data challenges. I think we've talked about it. You guys are announcing more here, the next generation. Could you explain this announcement? >> Yes, yes and yeah, you're right. Cloud Pak for Data was announced about three years ago, we launched it then, many customers in production with that, so very proud. And it really, Cloud Pak for Data in simple terms is all about your data platform and analytics platform, right? So at this think, we are announcing what I call the next generation of Cloud Pak for Data. A lot of enhancements going in but I would like to focus on three top key capabilities which we are bringing in. And it's all about how we are weaving together as part of the intelligent data fabric I was talking about earlier. So the three capabilities which I want the audience to walk away is, number one, auto privacy. What does that mean? It means how do we automate how you enforce universal data and usage policies across hybrid data and cloud ecosystems of various sources and users and how to express that to users in business terms and why? Because this is going to further simplify risk mitigation across an organization of self-serve data consumers. So that's all about auto privacy. The second key capabilities will be around auto catalog. Very, very critical. I call it the brain of it, right? This is where, how we automate, how it is, the data is discovered, how is cataloged and enriched for users, relevance of maintenance of knowledge for business ready data, right? Which is spread again across hybrid sources and multiple cloud landscapes. The third thing, very critical is what we call AutoSQL. This is how you're going to automate how you access, update and unify data spread across distributed data and cloud landscapes without the need of actually doing any data movements or replication if it's needed. So, and part of that data access is also needs to be, is it optimized for performance, right? Can I get to the petabytes of scale of data and how does the visual query building experiences look on top of that? So we are just so super excited about obviously with Cloud Pak for Data with this new enhancements, how we are reading the story around with data fabric, intelligent data fabric, with three things, right? Auto privacy, auto catalog and AutoSQL. And John, this is also on top of what we've been also talking about AutoAI for a while and this is all about AI life cycle management. There's going to be tons of enhancements coming in as to how we are simplifying federated training, right? Federated training across complex and siloed data sets is going to be important fact sheets. How do we improve the model quality and explainability that is going to be very critical for us and the time series optimizations? So all these auto stuff weaved with our intelligent data fabric, is going to be our next generation of Cloud Pak for Data. And I must say, John, lot of our clients are early adopters, early customers, been giving us the fantastic feedback. And it's like, this is what's needed. And you mentioned that earlier, right? Complexity is known, everybody wants a solution. What a great way to get these quick outcomes of the data, right? We've got to monetize the data. And that's what it's going to lead to. >> Madhu, is very exciting, great insight and congratulations. I love the auto name, autopilot, AutoAI. Just, it sounds automated. And I guess my final question for you is one that's a little bit more current around hybrid and that is the big theme that we're seeing. And we've been reporting and kind of connecting the dots here in the CUBE, through all the different interviews with you guys and all your partners is this ecosystem dynamic now with hybrid cloud is more important than ever before because cloud and cloud operations is API based. So more and more people are connected. So is this where auto privacy, auto catalog and AutoSQL and AI connect in? Is that where it's relevant? Because hybrid cloud really speaks about ecosystems and partnering 'cause no one does it alone anymore. >> Absolutely, you hit it on the nail, right? Hybrid is all not just about on-prem and one cloud is all about intra clouds and inter clouds and everywhere. And that is where the data fabric helps us, right? And that auto privacy meaning your data is spread across, how do I understand what are the policies across? Or do I respect my data residency? So exactly to the point that is what this gives us the solution. >> That's awesome. I do remember the old school inter networking was a category in the industry, connecting networks together. Now we have inter clouding, inter DataOps. It's all good, exciting. Thanks for coming on, really appreciate your insights. You're a pro and love the work you're doing over there in the product management. Great job and looking forward to hearing more. Thanks for coming on. >> Thank you, thank you John. >> Okay, Madhu Kochar, here at VP of product management IBM Data and AI, the hottest area in hybrid cloud. Of course, it's the CUBE coverage for IBM Think2021, I'm John Furrier. Thanks for watching. (upbeat music)

(upbeat music) >> From around the globe. It's theCUBE with digital coverage of IBM Think 2021 brought to you by IBM. >> Welcome to theCUBE's coverage of IBM Think, The Digital Experience. I'm Lisa Martin. I've got two guests with me here today. Ryan Dennings joins us, Manager of ECM Solutions at Auto-Owners Insurance Company, Ryan, welcome to the program. >> Thank you. And Michelle Christensen is here as well, VP of Enterprise Report Management Practice at enChoice, Michelle, it's good to have you on the program. >> Thank you. Thank you. So let's, Ryan let's go ahead and start with you. You guys are a customer of enChoice and IBM, talk to us a little bit about Auto-Owners Company. I know this is a fortune 500. This was founded in 1916. You've got about nearly 3 million policy holders but give us an overview of Auto-Owners Insurance. >> Sure. So Auto-Owners Insurance is an insurance company that's headquartered in Lansing, Michigan. We write insurance in 26 States throughout the United States. Despite our name being Auto-Owners Insurance, which is how we started, we write all personal lines, commercial lines, and also have a life insurance company. >> So comprehensive and that across those nearly 3 million policy holders. Michelle, tell us a little bit about enChoice. I know this, you guys are an IBM Gold Business Partner but this is enChoice's first time on the Cube, so give us a background. >> Sure, sure, great. So enChoice are an IBM Gold Business Partner. We have had 28 years success with IBM as a business partner. Our headquarters are in areas of Austin, Texas, and Tempe, Arizona, as well as Shelton, Connecticut. We cover all of North America and we are a hundred percent focused on the IBM Digital Business Automation Space. We have about 500 customers now that we've helped through the years and we continue to be a leading support provider as well as an implementation partner with all the IBM Solutions. >> And talk to me a little bit Michelle about how it is that you work with with Auto-Owners. >> So we assisted Auto-Owners recently in their digital transformations journey and they were dealing with an antiquated product and wanted to get moving forward, you know provide a better customer satisfaction experience for their client's agents, and so we partnered with them and with IBM and bringing them a content manager on-demand solution as well as navigator and several other products within the IBM Digital Business Automation Portfolio. >> Excellent, Ryan Oh, sorry Michelle, go ahead. >> Nope. That's that's fine. All right, Ryan, tell us a little bit about Auto-Owners, your relationship with IBM and enChoice and how is it helping you to address some of the challenges in the market today? >> Sure. So Auto-Owners has a long-term relationship with IBM originally starting back years ago as a mainframe customer and then, you know more recently helping us with different modern technology initiatives. They were instrumental in the nineties when we redid our initial web offerings, and then more recently they've been helping us with our Digital Business Automation which has helped us to mature our content offering at Owners. >> So you have had a long standing relationship with IBM, Ryan, and then you mentioned the nineties at a time when we didn't have to wear masks on our faces. (laughing) So a couple of decades it goes back, yeah? >> Yes. For sure. Yes. Even further than that, that, you know back into the seventies from the mainframe side of things. >> The seventies, another good time. (laughing) All right. So Michelle, talk to me a little bit about what enChoice is doing with IBM Solutions to help Auto-Owners from a digital transformation perspective is as I said this is a company that was founded in 1916, and I always love to hear how history companies like that are actually working with technology companies to facilitate that transformation. It's a lot harder than it sounds. >> Well, that's correct. Yes. As I mentioned, we're focused on helping customers develop their strategy, their digital strategy and creating those transformative solutions. So we're helping organizations like Auto-Owners with their journey, by first realizing their existing digital state, what challenges they might have and what needs they might need, and then we break that down or we deconstruct those technical and processizations and finally we re-invent their strategic offering with modern capabilities. So we're focused on technologies like RPA, machine learning, artificial intelligence, they're more efficient, scalable, and secure, so any way we can bring those technologies into the equation we go for it. So this offers us, our clients smarter and more intuitive interfaces creating basically a better user experience, and a better user experience then becomes disruptive to their competition. So they gain a better place in the market space. >> Ryan talked to us about that process as much as you were involved in it. I liked that Michelle said, you know we kind of look at the environment, we deconstruct it and then we re-invent it. Talk to me about how IBM and enChoice has helped Auto-Owners to do that so that your digital infrastructure is much more modern, and I presume much more resilient when there are market dynamics like we're living in now. >> Yeah, for sure. So, you know, we've, we've gone through a couple of transformation journeys at Auto-Owners with IBM. When I started the team about seven years ago we originally started using file NATS and data cap, and case manager, and content aggregator as our first movement from a traditional platform that we had for content management into a more modern platform, and that helped us a lot to improve our business process, improve how we capture content and bring it into the system and make it actionable. More recently, we've been working with Michelle and the enChoice team on our migration to a content management on-demand platform, and that's really going to be transformative in terms of how we're able to present content and documents and bills to our agents and customers, to be able to transform that content and show it in ways that are important for our customers to be able to see it, to engage with Auto-Owners in a, in a digital era. >> So Ryan, just a couple of questions on that, is that is that a facilitation of like the digitization of processes that had some paper involved cause you guys have about 48,000 agents, so a lot of folks, a lot of content, tell me a little bit more about how that like content manager on-demand, for example and what you're doing with ECF, how has that really revolutionizing and driving part of that digital transformation? >> Sure. So, you know, there's two parts to that in terms of that content management on-demand journey. One is the technology portion of it, but IBM's provided, and that suite of software gives us some functionality that we haven't had in the past. Specifically, some functionality around searching and searchability of our content that will make it easier for people to find the content that they're looking for, ability to implement records management policies and other things that help us manage that content more effectively, as well as some different options to be able to present the content to our customers and agents in a in a better and more modern way and enChoice's role in that has really been to guide us on that journey to help us make the right choices along the way on the project and help us get to a successful implementation and production. >> Excellent. Michelle, talk to me about Hybrid Cloud AI Data a big theme of IBM Think this year. How is enChoice using Hybrid Cloud and AI? You mentioned some of the other ways but kind of break into that a little bit more about how you're helping customers like Auto-Owners and others really take advantage of those modern technologies. >> Well, sure, sure. So of course with the Cloud Pak offerings that IBM has come forward with and where we focus in the Cloud Pak for automation, several of those offerings are some of them are built specifically to survive or to to be hosted in a hybrid environment, and as we're working with Auto-Owners transforming their platforms going forward for example, they just invested in, in a, a I just lost the word here. They just invested in a, a new platform, mainframe platform where they're going to be leveraging the red hats, and from there they'll drive forward into containerization. So Ryan mentioned some of the ways that we'll be presenting the content for his agents and his customers in a particular that entire viewing platform itself can be moved to a containerization state. So, so it's going to be a lot easier for him to transition into that and to maintain it and to manage it. And of course, just that whole, the ease of function around it will be a lot easier. So we are in our area as an IBM business partner, we work with these solutions to try to stay ahead of the game, to try to be able to assist our customers to understand what makes sense, when is it time to move into those. It's great to take advantage of the new stuff but nobody wants to be, you know, the bleeding game. We want to be the leading game. And so that's some of the areas we focus with our clients to really stay tight with the labs, tight with IBM and understanding their strategies and convey those and educate our customers on those. >> Excellent leading edge. Ryan, talk to me a little bit. I love this a bank, sorry an insurance company from the early 1900's moving into the using container technology. I love stories like that. Talk to me a little bit about Hybrid Cloud AI and how those technologies are going to be facilitators of the continuation of the digital transformation, and probably enabling more opportunities for your agents to meet more needs from from your policy holders. >> Yeah, for sure. So first and foremost, we were a Red Hat OpenShift customer before IBM acquired them and we were doing microservices development and things like that on the platform, and then we were super excited about IBM's digital business automation strategy to move to a Cloud Pak and have that available for software products to run on OpenShift. At the end of last year, we updated our licensing so that we can move in that direction, and we're starting to deploy digital business automation products on our OpenShift platform which is super exciting for me. It's going to make for faster upgrades, more scalability, just a lot of ease of use things for my team to make their jobs easier but also easier for us to adapt new upgrades and software offerings from IBM. There's also a number of products that are in the containerized or OpenShift only offering as they're initially coming out, whether it's mobile capture or automated document processing to name a couple. And those are both things that we're looking at Auto-Owners to continue to mature in this space and be able to offer more functionality to our associates, our customers, and our agents to continue to grow the business. >> Very forward-thinking, awesome Ryan. Thanks for sharing with us what Auto-Owners Insurance is doing, how you're being successful and how you've done so much transformation already. I want to throw the last question to Michelle. Take us out Michelle with what's next from enChoice's perspective in terms of your digital transformation. >> Well, we have been a hundred percent focused on helping all of our customers develop their digital strategy and and creating their own transformative solutions. So as we continue to work with our clients, take them through the journey, as I mentioned before, we try to encourage them not to focus on the, the technology itself, but really to focus on creating their exceptional customer experience when driving their digital strategy. And we see ourselves as, you know helping transform our client's experience such that you know customer experience becomes what enChoice does best. So we see not only our own organization going through the transformation, but making sure that we're taking our clients with us and with 500 clients we're, we're really busy. So that's always good. >> That is good. It sounds like the last year has been very fruitful for you, and I love that you mentioned customer experience, Michelle. I think that is so important and as well as employee experience, but having a good customer experience, especially these days. Table-stakes. I thank you both so much for sharing what you guys are doing with IBM Solutions, the transformation that both of your companies are on and we look forward to hearing what's to come. Thank you both for your time. >> Thank you. >> Thank you for Ryan Dennings and Michelle Christiansen. I'm Lisa Martin. You're watching theCUBE's coverage of IBM Think The Digital Experience. (upbeat music)

(upbeat music) >> Hello everyone and welcome to theCUBE. In this special power panel we're going to dig into and take a peek at the future of cloud. You know a lot has transpired in the last decade. The cloud itself, we've seen a data explosion. The AI winter turned into machine intelligence going mainstream. We've seen the emergence of As-a-Service models. And as we look forward to the next 10 years we see the whole idea of cloud expanding, new definitions occurring. Yes, the world is hybrid but the situation is more nuanced than that. You've got remote locations, smaller data centers, clandestine facilities, oil rigs, autonomous vehicles, windmills, you name it. Technology is connecting our world, data is flowing through the pipes like water, and AI is helping us make sense of the noise. All of this, and more is driving a new digital economy. And with me to talk about these topics are three great guests from IBM. Maia Sisk is the Director of SaaS Offering Management, at IBM Data and AI. And she's within the IBM Cloud and Cognitive Software Group. Stephanie Walter is the Program Director for data and AI Offering Management, same group IBM Cloud and Cognitive Software. And Daniel Berg is a Distinguished Engineer. He's focused on IBM Cloud Kubernetes Service. He's in the Cloud Organization. And he's going to talk today a lot about IBM's cloud Satellite and of course Containers. Wow, two girls, two boys on a panel, we did it. Folks welcome to theCUBE. (chuckles) >> Thank you. >> Thank you. >> Glad to be here. >> So Maia, I want to start with you and have some other folks chime in here. And really want to dig into the problem statement and what you're seeing with customers and you know, what are some of the challenges that you're hearing from customers? >> Yeah, I think a big challenge that we face is, (indistinct) talked about it earlier just data is everywhere. And when we look at opportunities to apply the cloud and apply an As-a-Service model, one of the challenges that we typically face is that the data isn't all nice cleanly package where you can bring it all together, and you know, one AI models on it, run analytics on it, get it in an easy and clean way. It's messy. And what we're finding is that customers are challenged with the problem of having to bring all of the data together on a single cloud in order to leverage it. So we're now looking at IBM and how we flip that paradigm around. And instead of bringing the data to the cloud bring the cloud to the data , in order to help clients manage that challenge and really harness the value of the data, regardless of where you live. >> I love that because data is distributed by its very nature it's silo, Daniel, anything you'd add? >> Yeah, I mean, I would definitely echo that, what Maia was saying, because we're seeing this with a number of customers that they have certain amount of data that while they're strategically looking that moving to the cloud, there's data that for various reasons they can not move itself into the cloud. And in order to reduce latency and get the fastest amount of processing time, they going to move the processing closer to that data. And that's something that we're looking at providing for our customers as well. The other services within IBM Cloud, through our notion of IBM Cloud Satellite. How to help teams and organizations get processing power manage them to service, but closer to where their data may reside. >> And just to play off of that with one other comment. Then the other thing I think we see a lot today is heightened concerned about risks, about data security, about data privacy. And you're trying to figure out how to manage that challenge of especially when you start sending data over the wire, wanting to make sure that it is still safe, it is still secure and it is still resident in the appropriate places. And that kind of need to manage the governance of the data kind of adds an additional layer of complexity. >> Right, if it's not secure, it's a, non-starter, Stephanie let's bring you into the conversation and talk about, you know, some of the waves that you're seeing. Maybe some of the trends, we've certainly seen digital accelerate as a result of the pandemic. It's no longer I'll get to that someday. It's really, it become a mandate you're out of business, if you don't have a digital business. What are some of the markets shifts that you're seeing? >> Well, I mean, really at the end of the day our clients want to infuse AI into their organizations. And so, you know, really the goal is to achieve ambient AI, AI that's just running in the background unchoosibly helping our clients make these really important business decisions. They're also really focused on trust. That's a big issue here. They're really focused on, you know, being able to explain how their AI is making these decisions and also being able to feel confident that they're not introducing harmful biases into their decision-making. So I say that because when you think about, you know digital organization going digital, that's what our customers want to focus on. They don't want to focus on managing IT. They don't want to focus on managing software. They don't want to to have to focus on, you know, patching and upgrading. And so we're seeing more of a move to manage services As-a-Service technologies, where the clients can really focus on their business problems and using The technologies like AI, to help improve their businesses. And not have to worry so much about building them from the ground up. >> So let's stay on that for a minute. And maybe Maia, Daniel, you can comment. So you, Stephanie, you said that customers want to infuse AI and kind of gave some reasons why, but I want to stay on that for a minute. That, what is that really that main outcome that they're looking for? Maybe there are several, they're trying to get to insight. You mentioned that trynna be more efficient it sounds like they're trynna automate governance and compliance, Maia, Daniel can you sort of add anything to this conversation? >> Yeah, well, I would, I would definitely say that, you know at the end of the day, customers are looking to use the data that they have to make smarter decisions. And in order to make smarter decisions it's not enough to just have the insight. The insight has to, you know, meet the business person that needs it, you know in the context, you know, in the application, in the customer interaction. So I think that that's really important. And then everything else becomes like the the superstructure that helps power, that decision and the decision being embedded in the business process. So we at IBM talk a lot about a concept we call the Ladder to AI. And the the short tagline is there is no AI without IA. You know, there is no Artificial Intelligence without Information Architecture. It is so critical, you know, Maia's version this is the garbage in garbage out. You have to have high quality data. You have to have that data be well-organized and well-managed so that you're using it appropriately. And all of that is just, you know then becomes the fuel that powers your AI. But if you have the AI without having that super structure, you know, you're going to end up making, get bad decisions. And ultimately, you know our customers making their customers experience less than it could and should be. And in a digital world, that's, you know, at the end of the day, it's all about digitizing that interaction with whoever the end customer whoever the end consumer is and making that experience the best it can be, because that's what fuels innovation and growth. >> Okay. So we've heard Arvind Krishna talk about, he actually made this statement IBM has to win the architectural battle for cloud. And I'm wondering maybe Daniel you can comment, on what that architectural framework looks like. I mean Maia just talked about the Information Architecture. You can't have AI without that foundation but we know what does Arvind mean by that? How is IBM thinking about that? >> Yeah, I mean, this is where we're really striving to allow our customers really focusing on their business and focusing on the goals that they're trying to achieve without forcing them to worry as much about the IT and the infrastructure and the platform for which they're going to run. Typically, if you're anchored by your data and the data is not able to move into the cloud, generally we would say that you don't have access to cloud services. You must go and install and run and operate your own software to perform the duties or the processing that you require. And that's a huge burden to push onto a customer because they couldn't move their data to your cloud. Now you're pushing a lot of responsibilities back onto them. So what we're really striving for here is, how can we give them that cloud experience where they can process their data? They can run their run book. They can have all of that managed As-a-Service so that they could focus on their business but get that closer to where the data actually resides. And that's what we're really striving for as far as the architecture is concerned. So with IBM Cloud Satellite, we're pushing the core platform and the platform services that we support in IBM Cloud outside of our data centers and into locations where it's closer to your data. And all of that is underpinned by Containerizations, Containers, Kubernetes and OpenShift. Is fundamentally the platform for which we're building upon. >> Okay. So that, so really it's still it's always a data problem, right? Data is you don't want to move it if you don't have to. Right. So it's, so Stephanie, should we think about this as a new emergent data architecture I guess that's what IA is all about. How do you see that evolving? >> Well I mean, I see it evolving as, I mean, first of all our clients, you know, we know that data is the lifeblood of AI. We know the vast majority of our clients are using more than one cloud. And we know that the client's data may be located in different clouds, and that could be due to costs, that could be due to location. So we have to ask the question, how are our clients supposed to deal with this? This is incredibly complex environments they're are incredibly complex reasons sometimes for the data to be where it is. It can include anything from costs to laws, that our clients have to abide by. So what we need to do, is we need to adapt to these different environments and provide clients with the consistent experience and lower complexity to be able to handle data and be able to use AI in these complex environments. And so, you know, we know data, we also know data science talent is scarce. And if each one of these environments have their own tools that need to be used, depending on where the data is located, that's a huge time sink, for these data scientist and our clients don't want to waste their talents time on problems like this. So what we're seeing is, we're seeing more of a acceptance and realization that this is what our clients are dealing with. We have to make it easier. We have to do Innovative things like figure out how to bring the AI to the data, how to bring the AI to where the clients need it and make it much easier and accessible for them to take advantage of. >> And I think there's an additional point to make on this one, which is it's not just easy and accessible but it's also unified. I mean, one of the challenges that customers face in this multicloud environment and many customers are multicloud, you know, not necessarily by intent but just because of how, you know, businesses have adopted as a service. But to then have all of that experience be fragmented and have different tools not just of data, but different pools of, again catalog, different pools of data science it's extremely complex to manage. So I think one of the powerful things that we're doing here, is we're kind of bringing those multiple clouds together, into more of an integrated or a unified, you know window into the client's data in AI state. So not only does the end-user not have to worry about you know, the technologies of dealing with multiple individual clouds, but also, you know it all comes together in one place. So it can be give managed in a more unified way so that assets can be shared across, and it becomes more of a unified approach. The way I like to think of it is, you know, it's true hybrid multicloud, in that it is all connected as opposed to multi-cloud, but it's pools of multiple clouds, one cloud at a time. >> So it can we stay on that for a second because it's, you're saying it's unified but the data stays where it is. The data is distributed by nature. So it's unified logically, but it's decentralized. Is that, am I getting that right? Correct. Okay. Correct. All right. I'm really interested in how you do this. And maybe we can talk about maybe the approach that you take for some of your offerings and maybe get specific on that. So maybe Stephanie, why don't you start, you know, Yes so, what do you have in your basket? Like Cloud Pak So what we have in our basket I mean lets talk about that. >> We have, so Cloud Pak for Data as a Service. This is our premier data and AI platform. It's offered as a service, its fully managed, and there's roughly, there's 30 services integrated services in our services catalog and growing. So we have services to help you through the entire AI life cycle from preparing your data, which is Maia was saying it's very, very, very important. It's critical to any successful AI project. From building your models, from running the models and then monitoring them to make sure that as I was saying before, you can trust them. You don't have to make sure that, you need to make sure that there's not biased. You need to be able to manage these models and then the life cycle them retrain them if needed. So our platform handles all of that. It's hosted on IBM Cloud. And what we're doing now, which is really exciting, is we're going to use, and you mentioned before IBM Cloud Satellite, as a way for us to send our AI to data that perhaps is located on another cloud or another environment. So how this would work is that the services that are integrated with Cloud Pak for Data as a Service they'll be able to use satellite locations to send their AI workloads, to run next to the data. And this means that the data doesn't need to be moved. You don't have to worry about high egress charges. You can see, you can reduce latency and see much stronger performance by running these AI workloads where it counts. We're really excited to to add this capability to our platform. Because, you know, we spent a lot of time talking about earlier all of these challenges that our clients have and this is going to make a big difference in helping them overcome them. Okay. So Daniel, how to Containers fit in? I mean, obviously the Red Hat acquisition was so strategic. We're seeing the real, the ascendancy of OpenShift in particular. Talk about Containers and where it fits into the IBM Cloud Satellite strategy. >> Yeah. So a lot of this builds on top of how we run our cloud business today. Today the vast majority of the services that are available in IBM cloud catalog, actually runs as Containers, runs in a Kubernetes based environment and runs on top of the services that we provide to our customers. So the Container Platform that we provide to our customers is the same one that we're using to run our own cloud services. And those are underpinned with Containers, Kubernetes, and OpenShift. And IBM cloud satellite, based on the way that the designed our Container Platform using Kubernetes and Containers and OpenShift, allows us to take that same design and the same principles and extended outside of our data centers with user provided infrastructure. And this, this goes back to what Stephanie was saying is a satellite location. So using that technology, that same technology and the fact that we've already containerized many of our services and run them on our own platform, we are now distributing our platform outside of IBM Cloud Data Centers using satellite locations and making those available for our cloud service teams, to make their services available in those locations. >> I see and Maia, this, it is as a service. It's a OPEX. Is that right? Absolutely Okay. Absolutely >> Yeah, it's with the two different options on how we can run. One is we can leverage IBM Cloud Satellite and reach into a customer's operating environment. They provide the infrastructure, but we've provide the As-a-Service experience for the Container on up. The other option that we have is for some of our capabilities like our data science capability, where, you know customer might need something a little bit more turnkey because it's, you know, more of a business person or somebody in the CTO's office consuming the As-a-Service. We'll also offer select workloads in an IBM own satellite and environment. I, you know, so that it kind of soup to nuts managed by us. But that is the key is that other than, you know providing the operating environment and then connecting what we do to, you know, their data sources, really the rest is up to us. We're responsible for, you know everything that you would expect in an As-a-Service environment. That things are running, that they're updated, that they're secure, that they're compliant, that's all part of our responsibility. >> Yeah. So a lot of options for customers and it's kind of the way they want to consume. Let's talk about the business impact. You know, you guys, IBM, very consultative selling, you know, tight relationships with customers. What's the business case look like when you go into a client? What's the conversation like? What's possible? What can you share? Stephanie, can you maybe start things off there? Any examples, use-cases, business case, help us understand the metrics. >> Yeah. I mean, so let's talk about a couple of use cases here. So let's say I'm an investment firm, and I'm using data points from all kinds of data sources right? To use AI, to create models to inform my investment decisions. So I'm going to be using, I may be using data sources you know, like regulatory filings, newspaper articles that are pretty standard. I may also be using things like satellite data that monitors parking lots or maybe even weather data, weather forecast data. And all of this data is coming together and being, it needs to be used for models to predict, you know when to buy, sell, trade, however, due to costs, due to just availability of the data they may be located on completely different clouds. You know, and we know that especially capital markets things are fast, fast, fast. So I need to bring my AI to my data, and need to do it quickly so that I can build these models where the data resides, and then be able to make my investment decisions, very fast. And these models get updated often because conditions change, markets change. And this is one way to provide a unified set of AI tools that my data scientists can use. We don't have to be trained on I'm told depending on what cloud the data is stored on. And they can actually build these models much faster and even cheaper. If you would take into egress charges into consideration, you know, moving all the all this data around. Another use case that we're seeing is you know, something like let's say, a multinational telecommunications company that has locations in multiple countries and maybe they want to reduce their customer churn. So they have say customer data that it's stored in different countries and different countries may have different regulations, or the company may have policies that, that data can't be moved out to those country. So what can we do? Again, what we can do is we can send our AI to this data. We can make a customer churn prediction model, that when my customer service representative is on the phone with a customer, and put their information, and see how likely they are to stop using my service and tailor my phone interaction and the offers that I would offer them as this customer service representative to them. If there's a high likelihood that they're going to churn I will probably sweeten the deal. And I can do all that while I'm being fast, right. Because we know that these interactions need to happen quickly. But also while complying with whatever policies or even regulations that are in place for my multinational company. So you know, if you think back to the use cases that I was just talking about you know, latency, performance, reducing costs and also being able to comply with any policy or regulations that our customers might have are really, are really the key pieces of the use cases that we've been seeing. >> Yeah. So Maia there's a theme here. I bring five megabytes of code to a petabyte of data kind of thing. And so Stephanie was talking about speed. There's a an inherent compliance and governance piece. It's it sounds like it's not a bolt on, it's not an afterthought, it's fundamental. So maybe you could add to the conversation, just specifically interested in, you know, what should a client expect? I mean, you're putting data in the hands of you know domain experts in the line of business. There's a self-serve component here, presumably. So there's cross selling is what I heard in some of what Stephanie was just talking about. So it was revenue, there's cost cutting, there's risk reduction, that I'm seeing the business case form. What can you add? >> Yeah, absolutely. I think that the only other thing I would add, is going back to the conversation that we had about, Oh you know, a lot of this is being driven by, you know the digitization of business and you know even moreso this year. You know, at the end of the day there's a lot of costs benefits to leveraging and As-a-Service model, you know, to leveraging that experience in economies of scale from a service provider, as well as, you know leveraging satellite kind of takes that to the next level of, you know, reducing some other costs. But I always go back to, you know at the end of the day, this is about customer experience. It's about revenue creation, and it's about, you know, creating, you know enhanced customer satisfaction and loyalty. So there's a top-line benefits here, you know, of having the best possible AI, you know plugging that into the customer experience, the application where that application resides. So it's not just about where the data resides. You can also put it on the other side and say, you know, we're bringing the AI, we're bringing the machine learning model to the application so that the experiences at excellent the application is responsive there's less latency and that can help clients then leverage AI to create those revenue benefits, you know, of having the the satisfied customer and of having the, you know the right decision at the right time in order to, you know propel them to, to spend and spend more. >> So Daniel bring us home. I mean, there's a lot of engineering going on here. There's the technology, the people in the process if I'm a client, I'm going to say, okay, I'm going to rely on IBM R&D to cut my labor costs, to drive automation, to help me, you know, automate governance and reduce my risks, you know, take care of the technology. You know, I'll focus my efforts on my process, my people but it's a journey. So how do you see that shaping out in the next, you know several years or, or the coming decade, bring us home. >> Yeah. I mean what we're seeing here is that there's a realization that customers have highly skilled individuals. And we're not saying that these highly skilled individuals couldn't run and operate these platforms and the software themselves, they absolutely could. In some cases, maybe they can't but in many cases they could. But we're also talking about these are they're highly skilled individuals that are focusing on platform and platform services and not their business. And the realization here is that companies want their best and brightest focused on their business, not the platform. If they can get that platform from another vendor that they rely on and can provide the necessary compute services, in a timely and available fashion. The other aspect of this is, people have grown to appreciate those cloud services. They like that on demand experience. And they want that in almost every aspect of what they're working on. And the problem is, sometimes you have to have that experience in localities that are remote. They're very difficult. There's no cloud in some of these remote parts of the world. You might think that clouds everywhere, but it's not. It's actually in very specific locations across the world, but there are many remote locations that they want and need these services from the cloud that they can get. Something like IBM Cloud Satellite. That is what we're pursuing here, is being able to bring that cloud experience into these remote locations where you can't get it today. And that's where you can run your AI workloads. You don't have to run it yourself, we will run it and you can put it in those remote locations. And remote locations don't actually have to be like in the middle of a jungle, they could be in your, on your plant floor or within a port that you have across the world, right? It could be in a warehouse. I mean, there's lots of areas where there's data that needs to be processed quickly, and you want to have that cloud experience, that usage pay model for that processing. And that's exactly what we're trying to achieve with IBM Cloud Satellite and what we're trying to achieve with the IBM Cloud Pak for Data as a Service as well. Running on satellite is to give you those cloud experiences. Those services managed as a service in those remote locations that you absolutely need them and want them. >> Well, you guys are making a lot of progress in the next decade is not going to look like the last decade. I can pretty confident in that prediction. Guys thanks so much for coming on the cube and sharing your insights, really great conversation. >> Absolutely. Thank you, Dave. >> Thank you. >> You're welcome, and thank you for watching everybody. This is Dave Vellante from the cube. We'll see you next time. (upbeat music)

(soft electronic music) >> Welcome to this CUBE Virtual Conversation. I'm your host, Rebecca Knight. Today, I'm joined by Hermanth Manda. He is the Executive Director, IBM Data and AI, responsible for Cloud Pak for Data. Thanks so much for coming on the show, Hermanth. >> Thank you, Rebecca. >> So we're talking now about the release of Cloud Pak for Data version 3.5. I want to explore it for, from a lot of different angles, but do you want to just talk a little bit about why it is unique in the marketplace, in particular, accelerating innovation, reducing costs, and reducing complexity? >> Absolutely, Rebecca. I mean, this is something very unique from an IBM perspective. Frankly speaking, this is unique in the marketplace because what we are doing is we are bringing together all of our data and AI capabilities into a single offering, single platform. And we have continued, as I said, we made it run on any cloud. So we are giving customers the flexibility. So it's innovation across multiple fronts. It's still in consolidation. It's, in doing automation and infusing collaboration and also having customers to basically modernize to the cloud-native world and pick their own cloud which is what we are seeing in the market today. So I would say this is a unique across multiple fronts. >> When we talk about any new platform, one of the big concerns is always around internal skills and maintenance tasks. What changes are you introducing with version 3.5 that does, that help clients be more flexible and sort of streamline their tasks? >> Yeah, it's an interesting question. We are doing a lot of things with respect to 3.5, the latest release. Number one, we are simplifying the management of the platform, made it a lot simpler. We are infusing a lot of automation into it. We are embracing the concept of operators that are not open shelf has introduced into the market. So simple things such as provisioning, installation, upgrades, scaling it up and down, autopilot management. So all of that is taken care of as part of the latest release. Also, what we are doing is we are making the collaboration and user onboarding very easy to drive self service and use the productivity. So overall, this helps, basically, reduce the cost for our customers. >> One of the things that's so striking is the speed of the innovation. I mean, you've only been in the marketplace for two and a half years. This is already version 3.5. Can you talk a little bit about, about sort of the, the innovation that it takes to do this? >> Absolutely. You're right, we've been in the market for slightly over two and a half years, 3.5's our ninth release. So frankly speaking, for any company, or even for startups doing nine releases in 2.5 years is unheard of, and definitely unheard of at IBM. So we are acting and behaving like a startup while addressing the go to market, and the reach of IBM. So I would say that we are doing a lot here. And as I said before, we're trying to address the unique needs of the market, the need to modernize to the cloud-native architectures to move to the cloud also while addressing the needs of our existing customers, because there are two things we are trying to focus, here. First of all, make sure that we have a modern platform across the different capabilities in data and AI, that's number one. Number two is also how do we modernize our existing install base. We have six plus billion dollar business for data and AI across significant real estates. We're providing a platform through Cloud Pak for Data to those existing install base and existing customers to more nice, too. >> I want to talk about how you are addressing the needs of customers, but I want to delve into something you said earlier, and that is that you are behaving like a startup. How do you make sure that your employees have that kind of mindset that, that kind of experimental innovative, creative, resourceful mindset, particularly at a more mature company like IBM? What kinds of skills do you try to instill and cultivate in your, in your team? >> That's a very interesting question, Rebecca. I think there's no single answer, I would say. It starts with listening to the customers, trying to pay detailed attention to what's happening in the market. How competent is it reacting. Looking at the startups, themselves. What we did uniquely, that I didn't touch upon earlier is that we are also building an open ecosystem here, so we position ourselves as an open platform. Yes, there's a lot of IBM unique technology here, but we also are leveraging open source. We are, we have an ecosystem of 50 plus third party ISVs. So by doing that, we are able to drive a lot more innovation and a lot faster because when you are trying to do everything by yourself, it's a bit challenging. But when you're part of an open ecosystem, infusing open source and third party, it becomes a lot easier. In terms of culture, I just want to highlight one thing. I think we are making it a point to emphasize speed over being perfect, progress over perfection. And that, I think, that is something net new for IBM because at IBM, we pride ourselves in quality, scalability, trying to be perfect on day one. I think we didn't do that in this particular case. Initially, when we launched our offense two and a half years back, we tried to be quick to the market. Our time to market was prioritized over being perfect. But now that is not the case anymore, right? I think we will make sure we are exponentially better and those things are addressed for the past two and one-half years. >> Well, perfect is the enemy of the good, as we know. One of the things that your customers demand is flexibility when building with machine learning pipeline. What have you done to improve IBM machine learning tools on this platform? >> So there's a lot of things we've done. Number one, I want to emphasize our building AI, the initial problem that most of our customers concerned about, but in my opinion, that's 10% of the problem. Actually deploying those AI models or managing them and covering them at scales for the enterprise is a bigger challenge. So what we have is very unique. We have the end-to-end AI lifecycle, we have tools for all the way from building, deploying, managing, governing these models. Second is we are introducing net new capabilities as part of a latest release. We have this call or this new service called WMLA, Watson Machine Learning Accelerator that addresses the unique challenges of deep learning capabilities, managing GPUs, et cetera. We are also making the auto AI capabilities a lot more robust. And finally, we are introducing a net new concept called Federator Learning that allows you to build AI across distributed datasets, which is very unique. I'm not aware of any other vendor doing this, so you can actually have your data distributed across multiple clouds, and you can build an aggregated AI model without actually looking at the data that is spread across these clouds. And this concept, in my opinion, is going to get a lot more traction as we move forward. >> One of the things that IBM has always been proud of is the way it partners with ISVs and other vendors. Can you talk about how you work with your partners and foster this ecosystem of third-party capabilities that integrate into the platform? >> Yes, it's always a challenge. I mean, for this to be a platform, as I said before, you need to be open and you need to build an ecosystem. And so we made that a priority since day one and we have 53 third party ISVs, today. It's a chicken and egg problem, Rebecca, because you need to obviously showcase success and make it a priority for your partners to onboard and work with you closely. So, we obviously invest, we co-invest with our partners and we take them to market. We have different models. We have a tactical relationship with some of our third party ISVs. We also have a strategic relationship. So we partner with them depending on their ability to partner with us and we go invest and make sure that we are not only integrating them technically, but also we are integrating with them from a go-to-market perspective. >> I wonder if you can talk a little bit about the current environment that we're in. Of course, we're all living through a global health emergency in the form of the COVID-19 pandemic. So much of the knowledge work is being done from home. It is being done remotely. Teams are working asynchronously over different kinds of digital platforms. How have you seen these changes affect the team, your team at IBM, what kinds of new kinds of capabilities, collaborations, what kinds of skills have you seen your team have to gain and have to gain quite quickly in this environment? >> Absolutely. I think historically, IBM had quite a, quite a portion of our workforce working remotely so we are used to this, but not at the scale that the current situation has compelled us to. So we made a lot more investments earlier this year in digital technologies, whether it is Zoom and WebEx or trying to use tools, digital tools that helps us coordinate and collaborate effectively. So part of it is technical, right? Part of it is also a cultural shift. And that came all the way from our CEO in terms of making sure that we have the necessary processes in place to ensure that our employees are not in getting burnt out, that they're being productive and effective. And so a combination of what I would say, technical investments, plus process and leadership initiatives helped us essentially embrace the changes that we've seen, today. >> And I want you to close us out, here. Talk a little bit about the future, both for Cloud Pak for Data, but also for the companies and clients that you work for. What do you see in the next 12 to 24 months changing in the term, in terms of how we have re-imagined the future of work. I know you said this was already version nine. You've only been in the marketplace for, for not even three years. That's incredible innovation and speed. Talk a little bit about changes you see coming down the pike. >> So I think everything that we have done is going to get amplified and accelerated as we move forward, shift to cloud, embracing AI, adopting AI into business processes to automate and amplify new business models, collaboration, to a certain extent, consolidation of the different offerings into platforms. So all of this, we, I obviously see that being accelerated and that acceleration will continue as we move forward. And the real challenge I see with our customers and all the enterprises is, I see them in two buckets. There's one bucket which are resisting change, like to stick to the old concepts, and there's one bucket of enterprises who are embracing the change and moving forward, and actually get accelerating this transformation and change. I think it will be successful over the next one to five years. You know, it could be under the other bucket and if you're not, I think it's, you're going to get, you're going to miss out and that is getting amplified and accelerated, as we speak. >> So for those ones in the bucket that are resistant to the change, how do you get them onboard? I mean, this is classic change management that they teach at business schools around the world. But what are some advice that you would have to those who are resisting the change? >> So, and again, frankly speaking, we, at IBM, are going through that transition so I can speak from experience. >> Rebecca: You're drinking the Kool-Aid. >> Yeah, when, when I think, one way to address this is basically take one step at a time, like as opposed to completely revolutionizing the way you do your business. You can transform your business one step at a time while keeping the end objective as your goal, as your end goal. So, and it just want a little highlight that with full factor, that's exactly what we are enabling because what we do is we enable you to actually run anywhere you like. So if most of your systems, most of your data and your models, and analytics are on-premise, you can actually start your journey there while you plan for the future of a public cloud or a managed service. So my advice is pretty simple. You start the journey, but you can take, you can, you don't need to, you don't need to do it as a big bang. You, it could be a journey, it could be a gradual transformation, but you need to start the journey today. If you don't, you're going to miss out. >> Baby steps. Hey Hermanth Manda, thank you so much for joining us for this Virtual CUBE Conversation >> Thank you very much, Rebecca. >> I'm Rebecca Knight, stay tuned for more of theCUBE Virtual. (soft electronic music)

(upbeat music) >> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back everybody. Jeff Frick here with the CUBE, coming to you from our Palo Alto studios today for a CUBE conversation. we've got a couple of a CUBE alumni veterans who've been on a lot of times. They've got some exciting announcements to tell us today, so we're excited to jump into it, So let's go. First we're joined by Eric Herzog. He's the CMO and VP worldwide storage channels for IBM Storage, made his time on theCUBE Eric, great to see you. >> Great, thanks very much for having us today. >> Jeff: Absolutely. And joining him, I think all the way from North Carolina, Sam Werner, the VP of, and offering manager business line executive storage for IBM. Sam, great to see you as well. >> Great to be here, thank you. >> Absolutely. So let's jump into it. So Sam you're in North Carolina, I think that's where the Red Hat people are. You guys have Red Hat, a lot of conversations about containers, containers are going nuts. We know containers are going nuts and it was Docker and then Kubernetes. And really a lot of traction. Wonder if you can reflect on, on what you see from your point of view and how that impacts what you guys are working on. >> Yeah, you know, it's interesting. We talk, everybody hears about containers constantly. Obviously it's a hot part of digital transformation. What's interesting about it though is most of those initiatives are being driven out of business lines. I spend a lot of time with the people who do infrastructure management, particularly the storage teams, the teams that have to support all of that data in the data center. And they're struggling to be honest with you. These initiatives are coming at them, from application developers and they're being asked to figure out how to deliver the same level of SLAs the same level of performance, governance, security recovery times, availability. And it's a scramble for them to be quite honest they're trying to figure out how to automate their storage. They're trying to figure out how to leverage the investments they've made as they go through a digital transformation and keep in mind, a lot of these initiatives are accelerating right now because of this global pandemic we're living through. I don't know that the strategy's necessarily changed, but there's been an acceleration. So all of a sudden these storage people kind of trying to get up to speed or being thrown right into the mix. So we're working directly with them. You'll see, in some of our announcements, we're helping them, you know, get on that journey and provide the infrastructure their teams need. >> And a lot of this is driven by multicloud and hybrid cloud, which we're seeing, you know, a really aggressive move to before it was kind of this rush to public cloud. And that everybody figured out, "Well maybe public cloud isn't necessarily right for everything." And it's kind of this horses for courses, if you will, with multicloud and hybrid cloud, another kind of complexity thrown into the storage mix that you guys have to deal with. >> Yeah, and that's another big challenge. Now in the early days of cloud, people were lifting and shifting applications trying to get lower capex. And they were also starting to deploy DevOps, in the public cloud in order to improve agility. And what they found is there were a lot of challenges with that, where they thought lifting and shifting an application will lower their capital costs the TCO actually went up significantly. Where they started building new applications in the cloud. They found they were becoming trapped there and they couldn't get the connectivity they needed back into their core applications. So now we're at this point where they're trying to really, transform the rest of it and they're using containers, to modernize the rest of the infrastructure and complete the digital transformation. They want to get into a hybrid cloud environment. What we found is, enterprises get two and a half X more value out of the IT when they use a hybrid multicloud infrastructure model versus an all public cloud model. So what they're trying to figure out is how to piece those different components together. So you need a software-driven storage infrastructure that gives you the flexibility, to deploy in a common way and automate in a common way, both in a public cloud but on premises and give you that flexibility. And that's what we're working on at IBM and with our colleagues at Red Hat. >> So Eric, you've been in the business a long time and you know, it's amazing as it just continues to evolve, continues to evolve this kind of unsexy thing under the covers called storage, which is so foundational. And now as data has become, you know, maybe a liability 'cause I have to buy a bunch of storage. Now it is the core asset of the company. And in fact a lot of valuations on a lot of companies is based on its value, that's data and what they can do. So clearly you've got a couple of aces in the hole you always do. So tell us what you guys are up to at IBM to take advantage of the opportunity. >> Well, what we're doing is we are launching, a number of solutions for various workloads and applications built with a strong container element. For example, a number of solutions about modern data protection cyber resiliency. In fact, we announced last year almost a year ago actually it's only a year ago last week, Sam and I were on stage, and one of our developers did a demo of us protecting data in a container environment. So now we're extending that beyond what we showed a year ago. We have other solutions that involve what we do with AI big data and analytic applications, that are in a container environment. What if I told you, instead of having to replicate and duplicate and have another set of storage right with the OpenShift Container configuration, that you could connect to an existing external exabyte class data lake. So that not only could your container apps get to it, but the existing apps, whether they'll be bare-metal or virtualized, all of them could get to the same data lake. Wow, that's a concept saving time, saving money. One pool of storage that'll work for all those environments. And now that containers are being deployed in production, that's something we're announcing as well. So we've got a lot of announcements today across the board. Most of which are container and some of which are not, for example, LTO-9, the latest high performance and high capacity tape. We're announcing some solutions around there. But the bulk of what we're announcing today, is really on what IBM is doing to continue to be the leader in container storage support. >> And it's great, 'cause you talked about a couple of very specific applications that we hear about all the time. One obviously on the big data and analytics side, you know, as that continues to do, to kind of chase history of honor of ultimately getting the right information to the right people at the right time so they can make the right decision. And the other piece you talked about was business continuity and data replication, and to bring people back. And one of the hot topics we've talked to a lot of people about now is kind of this shift in a security threat around ransomware. And the fact that these guys are a little bit more sophisticated and will actually go after your backup before they let you know that they're into your primary storage. So these are two, really important market areas that we could see continue activity, as all the people that we talk to every day. You must be seeing the same thing. >> Absolutely we are indeed. You know, containers are the wave. I'm a native California and I'm coming to you from Silicon Valley and you don't fight the wave, you ride it. So at IBM we're doing that. We've been the leader in container storage. We, as you know, way back when we invented the hard drive, which is the foundation of almost this entire storage industry and we were responsible for that. So we're making sure that as container is the coming wave that we are riding that in and doing the right things for our customers, for our channel partners that support those customers, whether they be existing customers, and obviously, with this move to containers, is going to be some people searching for probably a new vendor. And that's something that's going to go right into our wheelhouse because of the things we're doing. And some of our capabilities, for example, with our FlashSystems, with our Spectrum Virtualize, we're actually going to be able to support CSI snapshots not only for IBM Storage, but our Spectrum Virtualize products supports over 500 different arrays, most of which aren't ours. So if you got that old EMC VNX2 or that HPE, 3PAR or aNimble or all kinds of other storage, if you need CSI snapshot support, you can get it from IBM, with our Spectrum Virtualize software that runs on our FlashSystems, which of course cuts capex and opex, in a heterogeneous environment, but gives them that advanced container support that they don't get, because they're on older product from, you know, another vendor. We're making sure that we can pull our storage and even our competitor storage into the world of containers and do it in the right way for the end user. >> That's great. Sam, I want to go back to you and talk about the relationship with the Red Hat. I think it was about a year ago, I don't have my notes in front of me, when IBM purchased Red Hat. Clearly you guys have been working very closely together. What does that mean for you? You've been in the business for a long time. You've been at IBM for a long time, to have a partner you know, kind of embed with you, with Red Hat and bringing some of their capabilities into your portfolio. >> It's been an incredible experience, and I always say my friends at Red Hat because we spend so much time together. We're looking at now, leveraging a community that's really on the front edge of this movement to containers. They bring that, along with their experience around storage and containers, along with the years and years of enterprise class storage delivery that we have in the IBM Storage portfolio. And we're bringing those pieces together. And this is a case of truly one plus one equals three. And you know, an example you'll see in this announcement is the integration of our data protection portfolio with their container native storage. We allow you to in any environment, take a snapshot of that data. You know, this move towards modern data protection is all about a movement to doing data protection in a different way which is about leveraging snapshots, taking instant copies of data that are application aware, allowing you to reuse and mount that data for different purposes, be able to protect yourself from ransomware. Our data protection portfolio has industry leading ransomware protection and detection in it. So we'll actually detect it before it becomes a problem. We're taking that, industry leading data protection software and we are integrating it into Red Hat, Container Native Storage, giving you the ability to solve one of the biggest challenges in this digital transformation which is backing up your data. Now that you're moving towards, stateful containers and persistent storage. So that's one area we're collaborating. We're working on ensuring that our storage arrays, that Eric was talking about, that they integrate tightly with OpenShift and that they also work again with, OpenShift Container Storage, the Cloud Native Storage portfolio from, Red Hat. So we're bringing these pieces together. And on top of that, we're doing some really, interesting things with licensing. We allow you to consume the Red Hat Storage portfolio along with the IBM software-defined Storage portfolio under a single license. And you can deploy the different pieces you need, under one single license. So you get this ultimate investment protection and ability to deploy anywhere. So we're, I think we're adding a lot of value for our customers and helping them on this journey. >> Yeah Eric, I wonder if you could share your perspective on multicloud management. I know that's a big piece of what you guys are behind and it's a big piece of kind of the real world as we've kind of gotten through the hype and now we're into production, and it is a multicloud world and it is, you got to manage this stuff it's all over the place. I wonder if you could speak to kind of how that challenge you know, factors into your design decisions and how you guys are about, you know, kind of the future. >> Well we've done this in a couple of ways in things that are coming out in this launch. First of all, IBM has produced with a container-centric model, what they call the Multicloud Manager. It's the IBM Cloud Pak for multicloud management. That product is designed to manage multiple clouds not just the IBM Cloud, but Amazon, Azure, et cetera. What we've done is taken our Spectrum Protect Plus and we've integrated it into the multicloud manager. So what that means, to save time, to save money and make it easier to use, when the customer is in the multicloud manager, they can actually select Spectrum Protect Plus, launch it and then start to protect data. So that's one thing we've done in this launch. The other thing we've done is integrate the capability of IBM Spectrum Virtualize, running in a FlashSystem to also take the capability of supporting OCP, the OpenShift Container Platform in a Clustered environment. So what we can do there, is on-premise, if there really was an earthquake in Silicon Valley right now, that OpenShift is sitting on a server. The servers just got crushed by the roof when it caved in. So you want to make sure you've got disaster recovery. So what we can do is take that OpenShift Container Platform Cluster, we can support it with our Spectrum Virtualize software running on our FlashSystem, just like we can do heterogeneous storage that's not ours, in this case, we're doing it with Red Hat. And then what we can do is to provide disaster recovery and business continuity to different cloud vendors not just to IBM Cloud, but to several cloud vendors. We can give them the capability of replicating and protecting that Cluster to a cloud configuration. So if there really was an earthquake, they could then go to the cloud, they could recover that Red Hat Cluster, to a different data center and run it on-prem. So we're not only doing the integration with a multicloud manager, which is multicloud-centric allowing ease of use with our Spectrum Protect Plus, but incase of a really tough situation of fire in a data center, earthquake, hurricane, whatever, the Red Hat OpenShift Cluster can be replicated out to a cloud, with our Spectrum Virtualize Software. So in most, in both cases, multicloud examples because in the first one of course the multicloud manager is designed and does support multiple clouds. In the second example, we support multiple clouds where our Spectrum Virtualize for public clouds software so you can take that OpenShift Cluster replicate it and not just deal with one cloud vendor but with several. So showing that multicloud management is important and then leverage that in this launch with a very strong element of container centricity. >> Right >> Yeah, I just want to add, you know, and I'm glad you brought that up Eric, this whole multicloud capability with, the Spectrum Virtualize. And I could see the same for our Spectrum Scale Family, which is our storage infrastructure for AI and big data. We actually, in this announcement have containerized the client making it very simple to deploy in Kubernetes Cluster. But one of the really special things about Spectrum Scale is it's active file management. This allows you to build out a file system not only on-premises for your, Kubernetes Cluster but you can actually extend that to a public cloud and it automatically will extend the file system. If you were to go into a public cloud marketplace which it's available in more than one, you can go in there click deploy, for example, in AWS Marketplace, click deploy it will deploy your Spectrum Scale Cluster. You've now extended your file system from on-prem into the cloud. If you need to access any of that data, you can access it and it will automatically cash you on locally and we'll manage all the file access for you. >> Yeah, it's an interesting kind of paradox between, you know, kind of the complexity of what's going on in the back end, but really trying to deliver simplicity on the front end. Again, this ultimate goal of getting the right data to the right person at the right time. You just had a blog post Eric recently, that you talked about every piece of data isn't equal. And I think it's really highlighted in this conversation we just had about recovery and how you prioritize and how you, you know, think about, your data because you know, the relative value of any particular piece might be highly variable, which should drive the way that you treated in your system. So I wonder if you can speak a little bit, you know, to helping people think about data in the right way. As you know, they both have all their operational data which they've always had, but now they've got all this unstructured data that's coming in like crazy and all data isn't created equal, as you said. And if there is an earthquake or there is a ransomware attack, you need to be smart about what you have available to bring back quickly. And maybe what's not quite so important. >> Well, I think the key thing, let me go to, you know a modern data protection term. These are two very technical terms was, one is the recovery time. How long does it take you to get that data back? And the second one is the recovery point, at what point in time, are you recovering the data from? And the reason those are critical, is when you look at your datasets, whether you replicate, you snap, you do a backup. The key thing you've got to figure out is what is my recovery time? How long is it going to take me? What's my recovery point. Obviously in certain industries you want to recover as rapidly as possible. And you also want to have the absolute most recent data. So then once you know what it takes you to do that, okay from an RPO and an RTO perspective, recovery point objective, recovery time objective. Once you know that, then you need to look at your datasets and look at what does it take to run the company if there really was a fire and your data center was destroyed. So you take a look at those datasets, you see what are the ones that I need to recover first, to keep the company up and rolling. So let's take an example, the sales database or the support database. I would say those are pretty critical to almost any company, whether you'd be a high-tech company, whether you'd be a furniture company, whether you'd be a delivery company. However, there also is probably a database of assets. For example, IBM is a big company. We have buildings all over, well, guess what? We don't lease a chair or a table or a whiteboard. We buy them. Those are physical assets that the company has to pay, you know, do write downs on and all this other stuff, they need to track it. If we close a building, we need to move the desk to another building. Like even if we leasing a building now, the furniture is ours, right? So does an asset database need to be recovered instantaneously? Probably not. So we should focus on another thing. So let's say on a bank. Banks are both online and brick and mortar. I happened to be a Wells Fargo person. So guess what? There's Wells Fargo banks, two of them in the city I'm in, okay? So, the assets of the money, in this case now, I don't think the brick and mortar of the building of Wells Fargo or their desks in there but now you're talking financial assets or their high velocity trading apps. Those things need to be recovered almost instantaneously. And that's what you need to do when you're looking at datasets, is figure out what's critical to the business to keep it up and rolling, what's the next most critical. And you do it in basically the way you would tear anything. What's the most important thing, what's the next most important thing. It doesn't matter how you approach your job, how you used to approach school, what are the classes I have to get an A and what classes can I not get an A and depending on what your major was, all that sort of stuff, you're setting priorities, right? And the dataset, since data is the most critical asset of any company, whether it's a Global Fortune 500 or whether it's Herzog Cigar Store, all of those assets, that data is the most valuable. So you've got to make sure, recover what you need as rapidly as you need it. But you can't recover all of it. You just, there's just no way to do that. So that's why you really ranked the importance of the data to use sameware, with malware and ransomware. If you have a malware or ransomware attack, certain data you need to recover as soon as you can. So if there, for example, as a, in fact there was one Jeff, here in Silicon Valley as well. You've probably read about the University of California San Francisco, ended up having to pay over a million dollars of ransom because some of the data related to COVID research University of California, San Francisco, it was the health care center for the University of California in Northern California. They are working on COVID and guess what? The stuff was held for ransom. They had no choice, but to pay them. And they really did pay, this is around end of June, of this year. So, okay, you don't really want to do that. >> Jeff: Right >> So you need to look at everything from malware and ransomware, the importance of the data. And that's how you figure this stuff out, whether be in a container environment, a traditional environment or virtualized environment. And that's why data protection is so important. And with this launch, not only are we doing the data protection we've been doing for years, but now taking it to the heart of the new wave, which is the wave of containers. >> Yeah, let me add just quickly on that Eric. So think about those different cases you talked about. You're probably going to want for your mission critically. You're going to want snapshots of that data that can be recovered near instantaneously. And then, for some of your data, you might decide you want to store it out in cloud. And with Spectrum Protect, we just announced our ability to now store data out in Google cloud. In addition to, we already supported AWS Azure IBM Cloud, in various on-prem object stores. So we already provided that capability. And then we're in this announcement talking about LTL-9. And you got to also be smart about which data do you need to keep, according to regulation for long periods of time, or is it just important to archive? You're not going to beat the economics nor the safety of storing data out on tape. But like Eric said, if all of your data is out on tape and you have an event, you're not going to be able to restore it quickly enough at least the mission critical things. And so those are the things that need to be in snapshot. And that's one of the main things we're announcing here for Kubernetes environments is the ability to quickly snapshot application aware backups, of your mission critical data in your Kubernetes environments. It can very quickly to be recovered. >> That's good. So I'll give you the last word then we're going to sign off, we are out of time, but I do want to get this in it's 2020, if I didn't ask the COVID question, I would be in big trouble. So, you know, you've all seen the memes and the jokes about really COVID being an accelerant to digital transformation, not necessarily change, but certainly a huge accelerant. I mean, you guys have a, I'm sure a product roadmap that's baked pretty far and advanced, but I wonder if you can speak to, you know, from your perspective, as COVID has accelerated digital transformation you guys are so foundational to executing that, you know, kind of what is it done in terms of what you're seeing with your customers, you know, kind of the demand and how you're seeing this kind of validation as to an accelerant to move to these better types of architectures? Let's start with you Sam. >> Yeah, you know I, and I think i said this, but I mean the strategy really hasn't changed for the enterprises, but of course it is accelerating it. And I see storage teams more quickly getting into trouble, trying to solve some of these challenges. So we're working closely with them. They're looking for more automation. They have less people in the data center on-premises. They're looking to do more automation simplify the management of the environment. We're doing a lot around Ansible to help them with that. We're accelerating our roadmaps around that sort of integration and automation. They're looking for better visibility into their environments. So we've made a lot of investments around our storage insights SaaS platform, that allows them to get complete visibility into their data center and not just in their data center. We also give them visibility to the stores they're deploying in the cloud. So we're making it easier for them to monitor and manage and automate their storage infrastructure. And then of course, if you look at everything we're doing in this announcement, it's about enabling our software and our storage infrastructure to integrate directly into these new Kubernetes, initiatives. That way as this digital transformation accelerates and application developers are demanding more and more Kubernetes capabilities. They're able to deliver the same SLAs and the same level of security and the same level of governance, that their customers expect from them, but in this new world. So that's what we're doing. If you look at our announcement, you'll see that across, across the sets of capabilities that we're delivering here. >> Eric, we'll give you the last word, and then we're going to go to Eric Cigar Shop, as soon as this is over. (laughs) >> So it's clearly all about storage made simple, in a Kubernetes environment, in a container environment, whether it's block storage, file storage, whether it be object storage and IBM's goal is to offer ever increasing sophisticated services for the enterprise at the same time, make it easier and easier to use and to consume. If you go back to the old days, the storage admins manage X amount of gigabytes, maybe terabytes. Now the same admin is managing 10 petabytes of data. So the data explosion is real across all environments, container environments, even old bare-metal. And of course the not quite so new anymore virtualized environments. The admins need to manage that more and more easily and automated point and click. Use AI based automated tiering. For example, we have with our Easy Tier technology, that automatically moves data when it's hot to the fastest tier. And when it's not as hot, it's cool, it pushes down to a slower tier, but it's all automated. You point and you click. Let's take our migration capabilities. We built it into our software. I buy a new array, I need to migrate the data. You point, you click, and we automatic transparent migration in the background on the fly without taking the servers or the storage down. And we always favor the application workload. So if the application workload is heavy at certain times a day, we slow the migration. At night for sake of argument, If it's a company that is not truly 24 by seven, you know, heavily 24 by seven, and at night, it slows down, we accelerate the migration. All about automation. We've done it with Ansible, here in this launch, we've done it with additional integration with other platforms. So our Spectrum Scale for example, can use the OpenShift management framework to configure and to grow our Spectrum Scale or elastic storage system clusters. We've done it, in this case with our Spectrum Protect Plus, as you saw integration into the multicloud manager. So for us, it's storage made simple, incredibly new features all the time, but at the same time we do that, make sure that it's easier and easier to use. And in some cases like with Ansible, not even the real storage people, but God forbid, that DevOps guy messes with a storage and loses that data, wow. So by, if you're using something like Ansible and that Ansible framework, we make sure that essentially the DevOps guy, the test guy, the analytics guy, basically doesn't lose the data and screw up the storage. And that's a big, big issue. So all about storage made simple, in the right way with incredible enterprise features that essentially we make easy and easy to use. We're trying to make everything essentially like your iPhone, that easy to use. That's the goal. And with a lot less storage admins in the world then there has been an incredible storage growth every single year. You'd better make it easy for the same person to manage all that storage. 'Cause it's not shrinking. It is, someone who's sitting at 50 petabytes today, is 150 petabytes the next year and five years from now, they'll be sitting on an exabyte of production data, and they're not going to hire tons of admins. It's going to be the same two or four people that were doing the work. Now they got to manage an exabyte, which is why this storage made simplest is such a strong effort for us with integration, with the Open, with the Kubernetes frameworks or done with OpenShift, heck, even what we used to do in the old days with vCenter Ops from VMware, VASA, VAAI, all those old VMware tools, we made sure tight integration, easy to use, easy to manage, but sophisticated features to go with that. Simplicity is really about how you manage storage. It's not about making your storage dumb. People want smarter and smarter storage. Do you make it smarter, but you make it just easy to use at the same time. >> Right. >> Well, great summary. And I don't think I could do a better job. So I think we'll just leave it right there. So congratulations to both of you and the teams for these announcement after a whole lot of hard work and sweat went in, over the last little while and continued success. And thanks for the, check in, always great to see you. >> Thank you. We love being on theCUBE as always. >> All right, thanks again. All right, he's Eric, he was Sam, I'm I'm Jeff, you're watching theCUBE. We'll see you next time, thanks for watching. (upbeat music)

>> Narrator: From around the globe. It's theCUBE, with digital coverage of Actifio Data Driven 2020, brought to you by Actifio. >> Welcome back. I'm Stuart Miniman and this is theCUBE's coverage of Actifio Data Driven 2020. We wish everybody could join us in Boston, but instead we're doing it online this year, of course, and really excited. We're going to be digging into the value of data, how DataOps, data scientists are leveraging data. And joining me on the program, Scott Buckles, he's the North American Business Executive for database data science and DataOps with IBM, Scott, welcome to theCUBE. >> Thanks Stuart, thanks for having me, great to see you. >> Start with the Actifio-IBM partnership. Anyone that knows that Actifio knows that the IBM partnership is really the oldest one that they've had, either it's hardware through software, those joint solutions go together. So tell us about the partnership here in 2020. >> Sure. So it's been a fabulous partnership. In the DataOps world where we are looking to help, all of our customers gain efficiency and effectiveness in their data pipeline and getting value out of their data, Actifio really compliments a lot of the solutions that we have very well. So the folks from everybody from the up top, all the way through the engineering team, is a great team to work with. We're very, very fortunate to have them. How many or any specific examples or anonymized examples that you can share about joint (indistinct). >> I'm going to stay safe and go on the anonymized side. But we've had a lot of great wins, several significantly large wins, where we've had clients that have been struggling with their different data pipelines. And I say data pipeline, I mean getting value from understanding their data, to developing models and and doing the testing on that, and we can get into this in a minute, but those folks have really needed a solution where Actifio has stepped in and provided that solution. To do that at several of the largest banks in the world, including one that was a very recent merger down in the Southeast, where we were able to bring in the Actifio solution and address our, the customer's needs around how they were testing and how they were trying to really move through that testing cycle, because it was a very iterative process, a very sequential process, and they just weren't doing it fast enough, and Actifio stepped in and helped us deliver that in a much more effective way, in a much more efficient way, especially when you into a bank or two banks rather that are merging and have a lot of work to convert systems into one another and converge data, not an easy task. And that was one of the best wins that we've had in the recent months. And again, going back to the partnership, it was an awesome, awesome opportunity to work with them. >> Well, Scott, as I teed up for the beginning of the conversation, you've got data science and DataOps, help us understand how this isn't just a storage solution, when you're talking about BDP. How does DevOps fit into this? Talk a little bit about some of the constituents inside your customers that are engaging with the solution. >> Yeah. So we call it DataOps, and DataOps is both a methodology, which is really trying to combine the best of the way that we've transformed how we develop applications with DevOps and Agile Development. So going back 20 years ago, everything was a waterfall approach, everything was very slow , and then you had to wait a long time to figure out whether you had success or failure in the application that you had developed and whether it was the right application. And with the advent of DevOps and continuous delivery, the advent of things like Agile Development methodologies, DataOps is really converging that and applying that to our data pipelines. So when we look at the opportunity ahead of us, with the world exploding with data, we see it all the time. And it's not just structured data anymore, it's unstructured data, it's how do we take advantage of all the data that we have so that we can make that impact to our business. But oftentimes we are seeing where it's still a very slow process. Data scientists are struggling or business analysts are struggling to get the data in the right form so that they can create a model, and then they're having to go through a long process of trying to figure out whether that model that they've created in Python or R is an effective model. So DataOps is all about driving more efficiency, more speed to that process, and doing it in a much more effective manner. And we've had a lot of good success, and so it's part methodology, which is really cool, and applying that to certain use cases within the, in the data science world, and then it's also a part of how do we build our solutions within IBM, so that we are aligning with that methodology and taking advantage of it. So that we have the AI machine learning capabilities built in to increase that speed which is required by our customers. Because data science is great, AI is great, but you still have to have good data underneath and you have to do it at speed. Well, yeah, Scott, definitely a theme that I heard loud and clear read. IBM think this year, we do a lot of interviews with theCUBE there, it was helping with the tools, helping with the processes, and as you said, helping customers move fast. A big piece of IBM strategy there are the Cloud Paks. My understanding you've got an update with regards to BDP and Cloud Pak. So to tell us what the new releases here for the show. >> Yeah. So in our (indistinct) release that's coming up, we will be to launch BDP directly from Cloud Pak, so that you can take advantage of the Activio capabilities, which we call virtual data pipeline, straight from within Cloud Pak. So it's a native integration, and that's the first of many things to come with how we are tying those two capabilities and those two solutions more closely together. So we're excited about it and we're looking forward to getting it in our customer's hands. >> All right. And that's the Cloud Pak for Data, if I have that correct, right? >> That's called Cloud Pak for data, correct, sorry, yes. Absolutely, I should have been more clear. >> No, it's all right. It's, it's definitely, we've been watching that, those different solutions that IBM is building out with the Cloud Paks, and of course data, as we said, it's so important. Bring us inside a little bit, if you could, the customers. What are the use cases, those problems that you're helping your customers solve with these solution? >> Sure. So there's three primary use cases. One is about accelerating the development process. Getting into how do you take data from its raw form, which may or may not be usable, in a lot of cases it's not, and getting it to a business ready state, so that your data scientists, your business, your data models can take advantage of it, about speed. The second is about reducing storage costs. As data has exponentially grown so has storage costs. We've been in the test data management world for a number of years now. And our ability to help customers reduce that storage footprint is also tied to actually the acceleration piece, but helping them reduce that cost is a big part of it. And then the third part is about mitigating risk. With the amount of data security challenges that we've seen, customers are continuously looking for ways to mitigate their exposure to somebody manipulating data, accessing production data and manipulating production data, especially sensitive data. And by virtualizing that data, we really almost fully mitigate that risk of them being able to do that. Somebody either unintentionally or intentionally altering that data and exposing a client. >> Scott, I know IBM is speaking at the Data Driven event. I read through some of the pieces that they're talking about. It looks like really what you talk about accelerating customer outcomes, helping them be more productive, if you could, what, what are some of key measurements, KPIs that your customers have when they successfully deploy the solution? >> So when it comes to speed, it's really about, we're looking at about how are we reducing the time of that project, right? Are we able to have a material impact on the amount of time that we see clients get through a testing cycle, right? Are we taking them from months to days, are we taking them from weeks to hours? Having that type of material impact. The other piece on storage costs is certainly looking at what is the future growth? You're not necessarily going to reduce storage costs, but are you reducing the growth or the speed at which your storage costs are growing. And then the third piece is really looking at how are we minimizing the vulnerabilities that we have. And when you go through an audit, internally or externally around your data, understanding that the number of exposures and helping find a material impact there, those vulnerabilities are reduced. >> Scott, last question I have for you. You talk about making data scientists more efficient and the like, what are you seeing organizationally, have teams come together or are they planning together, who has the enablement to be able to leverage some of the more modern technologies out there? >> Well, that's a great question. And it varies. I think the organizations that we see that have the most impact are the ones that are most open to bringing their data science as close to the business as possible. The ones that are integrating their data organizations, either the CDO organization or wherever that may set it. Even if you don't have a CDO, that data organization and who owned those data scientists, and folding them and integrating them into the business so that they're an integral part of it, rather than a standalone organization. I think the ones that sort of weave them into the fabric of the business are the ones that get the most benefit and we've seen have the most success thus far. >> Well, Scott, absolutely. We know how important data is and getting full value out of those data scientists, critical initiative for customers. Thanks so much for joining us. Great to get the updates. >> Oh, thank you for having me. Greatly appreciated. >> Stay tuned for more coverage from Activio Data Driven 2020. I'm Stuart Miniman, and thank you for watching theCUBE. (upbeat music)

>> Announcer: From around the globe, it's theCUBE with coverage of KubeCon + CloudNativeCon Europe 2020 Virtual brought to you by Red Hat, the Cloud Native Computing Foundation and Ecosystem partners. >> Welcome back I'm Stu Miniman, this is theCUBE's coverage of KubeCon + CloudNativeCon Europe 2020, the virtual event. Of course, when we talk about Cloud Native we talk about Kubernetes there's a lot that's happening to modernize the infrastructure but a very important thing that we're going to talk about today is also what's happening up the stack, what sits on top of it and some of the new use cases and applications that are enabled by all of this modern environment and for that we're going to talk about artificial intelligence and machine learning or AI and ML as we tend to talk in the industry, so happy to welcome to the program. We have two first time guests joining us from Red Hat. First of all, we have Abhinav Joshi and Tushar Katarki they are both senior managers, part of the OpenShift group. Abhinav is in the product marketing and Tushar is in product management. Abhinav and Tushar thank you so much for joining us. >> Thanks a lot, Stu, we're glad to be here. >> Thanks Stu and glad to be here at KubeCon. >> All right, so Abhinav I mentioned in the intro here, modernization of the infrastructure is awesome but really it's an enabler. We know... I'm an infrastructure person the whole reason we have infrastructure is to be able to drive those applications, interact with my data and the like and of course, AI and ML are exciting a lot going on there but can also be challenging. So, Abhinav if I could start with you bring us inside your customers that you're talking to, what are the challenges, the opportunities? What are they seeing in this space? Maybe what's been holding them back from really unlocking the value that is expected? >> Yup, that's a very good question to kick off the conversation. So what we are seeing as an organization they typically face a lot of challenges when they're trying to build an AI/ML environment, right? And the first one is like a talent shortage. There is a limited amount of the AI, ML expertise in the market and especially the data scientists that are responsible for building out the machine learning and the deep learning models. So yeah, it's hard to find them and to be able to retain them and also other talents like a data engineer or app DevOps folks as well and the lack of talent can actually stall the project. And the second key challenge that we see is the lack of the readily usable data. So the businesses collect a lot of data but they must find the right data and make it ready for the data scientists to be able to build out, to be able to test and train the machine learning models. If you don't have the right kind of data to the predictions that your model is going to do in the real world is only going to be so good. So that becomes a challenge as well, to be able to find and be able to wrangle the right kind of data. And the third key challenge that we see is the lack of the rapid availability of the compute infrastructure, the data and machine learning, and the app dev tools for the various personas like a data scientist or data engineer, the software developers and so on that can also slow down the project, right? Because if all your teams are waiting on the infrastructure and the tooling of their choice to be provisioned on a recurring basis and they don't get it in a timely manner, it can stall the projects. And then the next one is the lack of collaboration. So you have all these kinds of teams that are involved in the AI project, and they have to collaborate with each other because the work one of the team does has a dependency on a different team like say for example, the data scientists are responsible for building the machine learning models and then what they have to do is they have to work with the app dev teams to make sure the models get integrated as part of the app dev processes and ultimately rolled out into the production. So if all these teams are operating in say silos and there is lack of collaboration between the teams, so this can stall the projects as well. And finally, what we see is the data scientists they typically start the machine learning modeling on their individual PCs or laptops and they don't focus on the operational aspects of the solution. So what this means is when the IT teams have to roll all this out into a production kind of deployment, so they get challenged to take all the work that has been done by the individuals and then be able to make sense out of it, be able to make sure that it can be seamlessly brought up in a production environment in a consistent way, be it on-premises, be it in the cloud or be it say at the edge. So these are some of the key challenges that we see that the organizations are facing, as they say try to take the AI projects from pilot to production. >> Well, some of those things seem like repetition of what we've had in the past. Obviously silos have been the bane of IT moving forward and of course, for many years we've been talking about that gap between developers and what's happening in the operation side. So Tushar, help us connect the dots, containers, Kubernetes, the whole DevOps movement. How is this setting us up to actually be successful for solutions like AI and ML? >> Sure Stu I mean, in fact you said it right like in the world of software, in the world of microservices, in the world of app modernization, in the world of DevOps in the past 10, 15 years, but we have seen this evolution revolution happen with containers and Kubernetes driving more DevOps behavior, driving more agile behavior so this in fact is what we are trying to say here can ease up the cable to EIML also. So the various containers, Kubernetes, DevOps and OpenShift for software development is directly applicable for AI projects to make them move agile, to get them into production, to make them more valuable to organization so that they can realize the full potential of AI. We already touched upon a few personas so it's useful to think about who the users are, who the personas are. Abhinav I talked about data scientists these are the people who obviously do the machine learning itself, do the modeling. Then there are data engineers who do the plumbing who provide the essential data. Data is so essential to machine learning and deep learning and so there are data engineers that are app developers who in some ways will then use the output of what the data scientists have produced in terms of models and then incorporate them into services and of course, none of these things are purely cast in stone there's a lot of overlap you could find that data scientists are app developers as well, you'll see some of app developers being data scientist later data engineer. So it's a continuum rather than strict boundaries, but regardless what all of these personas groups of people need or experts need is self service to that preferred tools and compute and storage resources to be productive and then let's not forget the IT, engineering and operations teams that need to make all this happen in an easy, reliable, available manner and something that is really safe and secure. So containers help you, they help you quickly and easily deploy a broad set of machine learning tools, data tools across the cloud, the hybrid cloud from data center to public cloud to the edge in a very consistent way. Teams can therefore alternatively modify, change a shared container images, machine learning models with (indistinct) and track changes. And this could be applicable to both containers as well as to the data by the way and be transparent and transparency helps in collaboration but also it could help with the regulatory reasons later on in the process. And then with containers because of the inherent processes solution, resource control and protection from threat they can also be very secure. Now, Kubernetes takes it to the next level first of all, it forms a cluster of all your compute and data resources, and it helps you to run your containerized tools and whatever you develop on them in a consistent way with access to these shared compute and centralized compute and storage and networking resources from the data center, the edge or the public cloud. They provide things like resource management, workload scheduling, multi-tendency controls so that you can be a proper neighbors if you will, and quota enforcement right? Now that's Kubernetes now if you want to up level it further if you want to enhance what Kubernetes offers then you go into how do you write applications? How do you actually make those models into services? And that's where... and how do you lifecycle them? And that's sort of the power of Helm and for the more Kubernetes operators really comes into the picture and while Helm helps in installing some of this for a complete life cycle experience. A kubernetes operator is the way to go and they simplify the acceleration and deployment and life cycle management from end-to-end of your entire AI, ML tool chain. So all in all organizations therefore you'll see that they need to dial up and define models rapidly just like applications that's how they get ready out of it quickly. There is a lack of collaboration across teams as Abhinav pointed out earlier, as you noticed that has happened still in the world of software also. So we're talking about how do you bring those best practices here to AI, ML. DevOps approaches for machine learning operations or many analysts and others have started calling as MLOps. So how do you kind of bring DevOps to machine learning, and fosters better collaboration between teams, application developers and IT operations and create this feedback loop so that the time to production and the ability to take more machine learning into production and ML-powered applications into production increase is significant. So that's kind of the, where I wanted shine the light on what you were referring to earlier, Stu. >> All right, Abhinav of course one of the good things about OpenShift is you have quite a lot of customers that have deployed the solution over the years, bring us inside some of your customers what are they doing for AI, ML and help us understand really what differentiates OpenShift in the marketplace for this solution set. >> Yeah, absolutely that's a very good question as well and we're seeing a lot of traction in terms of all kinds of industries, right? Be it the financial services like healthcare, automotive, insurance, oil and gas, manufacturing and so on. For a wide variety of use cases and what we are seeing is at the end of the day like all these deployments are focused on helping improve the customer experience, be able to automate the business processes and then be able to help them increase the revenue, serve their customers better, and also be able to save costs. If you go to openshift.com/ai-ml it's got like a lot of customer stories in there but today I will not touch on three of the customers we have in terms of the different industries. The first one is like Royal Bank of Canada. So they are a top global financial institution based out of Canada and they have more than 17 million clients globally. So they recently announced that they build out an AI-powered private cloud platform that was based on OpenShift as well as the NVIDIA DGX AI compute system and this whole solution is actually helping them to transform the customer banking experience by being able to deliver an AI-powered intelligent apps and also at the same time being able to improve the operational efficiency of their organization. And now with this kind of a solution, what they're able to do is they're able to run thousands of simulations and be able to analyze millions of data points in a fraction of time as compared to the solution that they had before. Yeah, so like a lot of great work going on there but now the next one is the ETCA healthcare. So like ETCA is one of the leading healthcare providers in the country and they're based out of the Nashville, Tennessee. And they have more than 184 hospitals as well as more than 2,000 sites of care in the U.S. as well as in the UK. So what they did was they developed a very innovative machine learning power data platform on top of our OpenShift to help save lives. The first use case was to help with the early detection of sepsis like it's a life-threatening condition and then more recently they've been able to use OpenShift in the same kind of stack to be able to roll out the new applications that are powered by machine learning and deep learning let say to help them fight COVID-19. And recently they did a webinar as well that had all the details on the challenges they had like how did they go about it? Like the people, process and technology and then what the outcomes are. And we are proud to be a partner in the solution to help with such a noble cause. And the third example I want to share here is the BMW group and our partner DXC Technology what they've done is they've actually developed a very high performing data-driven data platform, a development platform based on OpenShift to be able to analyze the massive amount of data from the test fleet, the data and the speed of the say to help speed up the autonomous driving initiatives. And what they've also done is they've redesigned the connected drive capability that they have on top of OpenShift that's actually helping them provide various use cases to help improve the customer experience. With the customers and all of the customers are able to leverage a lot of different value-add services directly from within the car, their own cars. And then like last year at the Red Hat Summit they had a keynote as well and then this year at Summit, they were one of the Innovation Award winners. And we have a lot more stories but these are the three that I thought are actually compelling that I should talk about here on theCUBE. >> Yeah Abhinav just a quick follow up for you. One of the things of course we're looking at in 2020 is how has the COVID-19 pandemic, people working from home how has that impacted projects? I have to think that AI and ML are one of those projects that take a little bit longer to deploy, is it something that you see are they accelerating it? Are they putting on pause or are new project kicking off? Anything you can share from customers you're hearing right now as to the impact that they're seeing this year? >> Yeah what we are seeing is that the customers are now even more keen to be able to roll out the digital (indistinct) but we see a lot of customers are now on the accelerated timeline to be able to say complete the AI, ML project. So yeah, it's picking up a lot of momentum and we talk to a lot of analyst as well and they are reporting the same thing as well. But there is the interest that is actually like ramping up on the AI, ML projects like across their customer base. So yeah it's the right time to be looking at the innovation services that it can help improve the customer experience in the new virtual world that we live in now about COVID-19. >> All right, Tushar you mentioned that there's a few projects involved and of course we know at this conference there's a very large ecosystem. Red Hat is a strong contributor to many, many open source projects. Give us a little bit of a view as to in the AI, ML space who's involved, which pieces are important and how Red Hat looks at this entire ecosystem? >> Thank you, Stu so as you know technology partnerships and the power of open is really what is driving the technology world these days in any ways and particularly in the AI ecosystem. And that is mainly because one of the machine learning is in a bootstrap in the past 10 years or so and a lot of that emerging technology to take advantage of the emerging data as well as compute power has been built on the kind of the Linux ecosystem with openness and languages like popular languages like Python, et cetera. And so what you... and of course tons of technology based in Java but the point really here is that the ecosystem plays a big role and open plays a big role and that's kind of Red Hat's best cup of tea, if you will. And that really has plays a leadership role in the open ecosystem so if we take your question and kind of put it into two parts, what is the... what we are doing in the community and then what we are doing in terms of partnerships themselves, commercial partnerships, technology partnerships we'll take it one step at a time. In terms of the community itself, if you step back to the three years, we worked with other vendors and users, including Google and NVIDIA and H2O and other Seldon, et cetera, and both startups and big companies to develop this Kubeflow ecosystem. The Kubeflow is upstream community that is focused on developing MLOps as we talked about earlier end-to-end machine learning on top of Kubernetes. So Kubeflow right now is in 1.0 it happened a few months ago now it's actually at 1.1 you'll see that coupon here and then so that's the Kubeflow community in addition to that we are augmenting that with the Open Data Hub community which is something that extends the capabilities of the Kubeflow community to also add some of the data pipelining stuff and some of the data stuff that I talked about and forms a reference architecture on how to run some of this on top of OpenShift. So the Open Data Hub community also has a great way of including partners from a technology partnership perspective and then tie that with something that I mentioned earlier, which is the idea of Kubernetes operators. Now, if you take a step back as I mentioned earlier, Kubernetes operators help manage the life cycle of the entire application or containerized application including not only the configuration on day one but also day two activities like update and backups, restore et cetera whatever the application needs. Afford proper functioning that a "operator" needs for it to make sure so anyways, the Kubernetes operators ecosystem is also flourishing and we haven't faced that with the OperatorHub.io which is a community marketplace if you will, I don't call it marketplace a community hub because it's just comprised of community operators. So the Open Data Hub actually can take community operators and can show you how to run that on top of OpenShift and manage the life cycle. Now that's the reference architecture. Now, the other aspect of it really is as I mentioned earlier is the commercial aspect of it. It is from a customer point of view, how do I get certified, supported software? And to that extent, what we have is at the top of the... from a user experience point of view, we have certified operators and certified applications from the AI, ML, ISV community in the Red Hat marketplace. And from the Red Hat marketplace is where it becomes easy for end users to easily deploy these ISVs and manage the complete life cycle as I said. Some of the examples of these kinds of ISVs include startups like H2O although H2O is kind of well known in certain sectors PerceptiLabs, Cnvrg, Seldon, Starburst et cetera and then on the other side, we do have other big giants also in this which includes partnerships with NVIDIA, Cloudera et cetera that we have announced, including our also SaaS I got to mention. So anyways these provide... create that rich ecosystem for data scientists to take advantage of. A TEDx Summit back in April, we along with Cloudera, SaaS Anaconda showcased a live demo that shows all these things to working together on top of OpenShift with this operator kind of idea that I talked about. So I welcome people to go and take a look the openshift.com/ai-ml that Abhinav already referenced should have a link to that it take a simple Google search might download if you need some of that, but anyways and the other part of it is really our work with the hardware OEMs right? And so obviously NVIDIA GPUs is obviously hardware, and that accelerations is really important in this world but we are also working with other OEM partners like HP and Dell to produce this accelerated AI platform that turnkey solutions to run your data-- to create this open AI platform for "private cloud" or the data center. The other thing obviously is IBM, IBM Cloud Pak for Data is based on OpenShift that has been around for some time and is seeing very good traction, if you think about a very turnkey solution, IBM Cloud Pak is definitely kind of well ahead in that and then finally Red Hat is about driving innovation in the open-source community. So, as I said earlier, we are doing the Open Data Hub which that reference architecture that showcases a combination of upstream open source projects and all these ISV ecosystems coming together. So I welcome you to take a look at that at opendatahub.io So I think that would be kind of the some total of how we are not only doing open and community building but also doing certifications and providing to our customers that assurance that they can run these tools in production with the help of a rich certified ecosystem. >> And customer is always key to us so that's the other thing that the goal here is to provide our customers with a choice, right? They can go with open source or they can go with a commercial solution as well. So you want to make sure that they get the best in cloud experience on top of our OpenShift and our broader portfolio as well. >> All right great, great note to end on, Abhinav thank you so much and Tushar great to see the maturation in this space, such an important use case. Really appreciate you sharing this with theCUBE and Kubecon community. >> Thank you, Stu. >> Thank you, Stu. >> Okay thank you and thanks a lot and have a great rest of the show. Thanks everyone, stay safe. >> Thanks you and stay with us for a lot more coverage from KubeCon + CloudNativeCon Europe 2020, the virtual edition I'm Stu Miniman and thank you as always for watching theCUBE. (soft upbeat music plays)

>> Announcer: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering IBM Think brought to you by IBM. >> Hi, there, this is Dave Vellante. We're back at the IBM Think 2020 Digital Event Experience are socially responsible and distant. I'm here in the studios in Marlborough, our team in Palo Alto. We've been going wall to wall coverage of IBM Think, Kit Chee here is the Vice President, and general manager of Cloud and Enterprise sales at Intel. Kit, thanks for coming on. Good to see you. >> Thank you, Dave. Thank you for having me on. >> You're welcome, and Evaristus Mainsah, Mainsah is here. Mainsah, he is the general manager of the IBM Cloud Pack Ecosystem for the IBM Cloud. Evaristus, it's good to see you again. Thank you very much, I appreciate your time. >> Thank you, Dave. Thank you very much. Thanks for having me. >> You're welcome, so Kit, let me start with you. How are you guys doing? You know, there's this pandemic, never seen it before. How're things where you are? >> Yeah, so we were quite fortunate. Intel's had an epidemic leadership team. For about 15 years now, we have a team consisting of medical safety and operational professionals, and this same team has, who has navigated as across several other health issues like bad flu, Ebola, Zika and each one and one virus then navigating us at this point with this pandemic. Obviously, our top priority as it would be for IBM is protecting the health and well being of employees while keeping the business running for our customers. The company has taken the following measures to take care of it direct and indirect workforce, Dave and to ensure business continuity throughout the developing situation. They're from areas like work from home policies, keeping hourly workers home and reimbursing for daycare, elderly care, helping with WiFi policies. So that's been what we've been up to Intel's manufacturing and supply chain operations around the world world are working hard to meet demand and we are collaborating with supply pains of our customers and partners globally as well. And more recently, we have about $16 Million to support communities, from frontline health care workers and technology initiatives like online education, telemedicine and compute need to research. So that's what we've been up to date. Pretty much, you know, busy. >> You know, every society that come to you, I have to say my entire career have been in the technology business and you know, sometimes you hear negative toward the big tech but, but I got to say, just as Kit was saying, big tech has really stepped up in this crisis. IBM has been no different and, you know, tech for good and I was actually I'm really proud. How are you doing in New York City? >> Evaristus: No, thank you, Dave, for that, you know, we are, we're doing great and, and our focus has been absolutely the same, so obviously, because we provide services to clients. At a time like this, your clients need you even more, but we need to focus on our employees to make sure that their health and their safety and their well being is protected. And so we've taken this really seriously, and actually, we have two ways of doing this. One of them is just on to purpose as a, as a company, on our clients, but the other is trying to activate the ecosystem because problems of this magnitude require you to work across a broad ecosystem to, to bring forth in a solution that are long lasting, for example, we have a call for code, which where we go out and we ask developers to use their skills and open source technologies to help solve some technical problems. This year, the focus was per AVADA initiatives around computing resources, how you track the Coronavirus and other services that are provided free of charge to our clients. Let me give you a bit more color, so, so IBM recently formed the high performance computing consortium made up of the feYderal government industry and academic leaders focus on providing high performance computing to solve the COVID-19 problem. So we're currently we have 33 members, now we have 27 active products, deploying something like 400 teraflops as our petaflop 400 petaflops of compute to solve the problem. >> Well, it certainly is challenging times, but at the same time, you're both in the, in the sweet spot, which is Cloud. I've talked to a number of CIOs who have said, you know, this is really, we had a cloud strategy before but we're really accelerating our cloud strategy now and, and we see this as sort of a permanent effect. I mean, Kit, you guys, big, big on ecosystem, you, you want frankly, a level playing field, the more optionality that you can give to customers, you know, the better and Cloud is really been exploding and you guys are powering, you know, all the world's Clouds. >> We are, Dave and honestly, that's a huge responsibility that we undertake. Before the pandemic, we saw the market through the lens of four key mega trends and the experiences we are all having currently now deepens our belief in the importance of addressing these mega trends, but specifically, we see marketplace needs around key areas of cloudification of everything below point, the amount of online activities that have spiked just in the last 60 days. It's a testimony of that. Pervasive AI is the second big area that we have seen and we are now resolute on investments in that area, 5G network transformation and the edge build out. Applications run the business and we know enterprise IT faces challenges when deploying applications that require data movement between Clouds and Cloud native technologies like containers and Kubernetes will be key enablers in delivering end to end data analytics, AI, machine learning and other critical workloads and Cloud environments at the edge. Pairing Intel's data centric portfolio, including Intel's obtain SSPs with Red Hat, Openshift, and IBM Cloud Paks, enterprise can now break through storage bottlenecks and have unconstrained data availability in the hybrid and multicloud environments, so we're pretty happy with the progress we're making that together with IBM. >> Yeah, Evaristus, I mean, you guys are making some big bets. I've, you know, written and discussed in my breaking analysis, I think a lot of people misunderstand IBM Cloud, Ginni Rometty arm and a bow said, hey, you know, we're after only 20% of the workloads are in cloud, we're going after the really difficult to move workloads and the hybrid workloads, that's really the fourth foundation that Arvin you know, talks about, that you and IBM has built, you know, your mainframes, you have middleware services, and in hybrid Cloud is really that fourth sort of platform that you're building out, but you're making some bets in AI. You got other services in the Cloud like, like blockchain, you know, quantum, we've been having really interesting discussions around quantum, so I wonder if you can talk a little bit about sort of where you're allocating resources, some of the big bets that, that you're making for the next decade. >> Well, thank you very much, Dave, for that. I think what we're seeing with clients is that there's increasing focus on and, and really an acceptance, that the best way to take advantage of the Cloud is through a hybrid cloud strategy, infused with data, so it's not just the Cloud itself, but actually what you need to do to data in order to make sure that you can really, truly transform yourself digitally, to enable you to, to improve your operations, and in use your data to improve the way that you work and improve the way that you serve your clients. And what we see is and you see studies out there that say that if you adopt a hybrid cloud strategy, instead of 2.5 times more effective than a public cloud only strategy, and Why is that? Well, you get thi6ngs such as you know, the opportunity to move your application, the extent to which you move your applications to the Cloud. You get things such as you know, reduction in, in, in risk, you, you get a more flexible architecture, especially if you focus on open certification, reduction and certification reduction, some of the tools that you use, and so we see clients looking at that. The other thing that's really important, especially in this moment is business agility, and resilience. Our business agility says that if my customers used to come in, now, they can't come in anymore, because we need them to stay at home, we still need to figure out a way to serve them and we write our applications quickly enough in order to serve this new client, service client in a new way. And well, if your applications haven't been modernized, even if you've moved to the Cloud, you don't have the opportunity to do that and so many clients that have made that transformation, figure out they're much more agile, they can move more easily in this environment, and we're seeing the whole for clients saying yes, I do need to move to the Cloud, but I need somebody to help improve my business agility, so that I can transform, I can change with the needs of my clients, and with the demands of competition and this leads you then to, you know, what sort of platform do you need to enable you to do this, it's something that's open, so that you can write that application once you can run it anywhere, which is why I think the IBM position with our ecosystem and Red Hat with this open container Kubernetes environment that allows you to write application once and deploy it anywhere, is really important for clients in this environment, especially, and the Cloud Paks which is developed, which I, you know, General Manager of the Cloud Pak Ecosystem, the logic of the Cloud Paks is exactly that you'll want plans and want to modernize one, write the applications that are cloud native so that they can react more quickly to market conditions, they can react more quickly to what the clients need and they, but if they do so, they're not unlocked in a specific infrastructure that keeps them away from some of the technologies that may be available in other Clouds. So we have talked about it blockchain, we've got, you know, Watson AI, AI technologies, which is available on our Cloud. We've got the weather, company assets, those are key asset for, for many, many clients, because weather influences more than we realize, so, but if you are locked in a Cloud that didn't give you access to any of those, because you hadn't written on the same platform, you know, that's not something that you you want to support. So Red Hat's platform, which is our platform, which is open, allows you to write your application once and deploy it anyways, particularly our customers in this particular environment together with the data pieces that come on top of that, so that you can scale, scale, because, you know, you've got six people, but you need 600 of them. How do you scale them or they can use data and AI in it? >> Okay, this must be music to your ears, this whole notion of you know, multicloud because, you know, Intel's pervasive and so, because the more Clouds that are out there, the better for you, better for your customers, as I said before, the more optionality. Can you6 talk a little bit about the rela6tionship today between IBM and Intel because it's obviously evolved over the years, PC, servers, you know, other collaboration, nearly the Cloud is, you know, the latest 6and probably the most rel6evant, you know, part of your, your collaboration, but, but talk more about what that's like you guys are doing together that's, that'6s interesting and relevant. >> You know, IBM and Intel have had a very rich history of collaboration starting with the invention of the PC. So for those of us who may take a PC for granted, that was an invention over 40 years ago, between the two companies, all the way to optimizing leadership, IBM software like BB2 to run the best on Intel's data center products today, right? But what's more germane today is the Red Hat piece of the study and how that plays into a partnership with IBM going forward, Intel was one of Red Hat's earliest investors back in 1998, again, something that most people may not realize that we were in early investment with Red Hat. And we've been a longtime pioneer of open source. In fact, Levin Shenoy, Intel's Executive Vice President of Data Platforms Group was part of COBOL Commies pick up a Red Hat summit just last week, you should definitely go listen to that session, but in summary, together Intel and Red Hat have made commercial open source viable and enterprise and worldwide competing globally. Basically, now we've65 used by nearly every vertical and horizontal industr6y. We are bringing our customers choice, scalability and speed of innovation for key technologies today, such as security, Telco, NFV, and containers, or even at ease and most recently Red Hat Openshift. We're very excited to see IBM Cloud Packs, for example, standardized on top of Openshift as that builds the foundation for IBM chapter two, and allows for Intel's value to scale to the Cloud packs and ultimately IBM customers. Intel began partnering with IBM on what is now called Pax over two years ago and we 6are committed to that success and scaling that, try ecosystem, hardware partners, ISVs and our channel. >> Yeah, so theCUBE by the way, covered Red Hat summit last week, Steve Minima and I did a detailed analysis. It was awesome, like if we do say so ourselves, but awesome in the sense of, it allowed us to really sort of unpack what's going on at Red Hat and what's happening at IBM. Evaristus, so I want to come back to you on this Cloud Pack, you got, it's, it's the kind of brand that you guys have, you got Cloud Packs all over the place, you got Cloud Packs for applications, data, integration, automation, multicloud management, what do we need to know about Cloud pack? What are the relevant components there? >> Evaristus: I think the key components is so this is think of this as you know, software that is designed that is Cloud native is designed for specific core use cases and it's built on Red Hat Enterprise Linux with Red Hat Openshift container Kubernetes environment, and then on top of that, so you get a set of common services that look right across all of them and then on top of that, you've got specific both open source and IBM software that deals with specific plant situations. So if you're dealing with applications, for example, the open source and IBM software would be the run times that you need to write and, and to blow applications to have setups. If you're dealing with data, then you've got Cloud Pack to data. The foundation is still Red Hat Enterprise Linux sitting on top of with Red Hat Openshift container Kubernetes environment sitting on top of that providing you with a set of common services and then you'll get a combination of IBM zone open, so IBM software as well as open source will have third party software that sits on top of that, as well as all of our AI infrastructure that sits on top of that and machine learning, to enable you to do everything that you need to do, data to get insights updates, you've got automation to speed up and to enable us to do work more efficiently, more effectively, to make your smart workers better, to make management easier, to help management manage work and processes, and then you've got multicloud management that allows you to see from a single pane, all of your applications that you've deployed in the different Cloud, because the idea here, of course, is that not all sitting in the same Cloud. Some of it is on prem, some of it is in other Cloud, and you want to be able to see and deploy applications across all of those. And then you've got the Cloud Pack to security, which has a combination of third party offerings, as well as ISV offerings, as well as AI offerings. Again, the structure is the same, REL, Red Hat Openshift and then you've got the software that enables you to manage all aspects of security and to deal with incidents when, when they arise. So that gives you data applications and then there's integration, as every time you start writing an application, you need to integrate, you need to access data security from someplace, you need to bring two pipes together for them to communicate and we use a Cloud Pack for integration to allow us to do that. You can open up API's and expose those API so others writing application and gain access to those API's. And again, this idea of resilience, this idea of agility, so you can make changes and you can adapt data things about it. So that's what the Cloud Pack provides for you and Intel has been an absolutely fantastic partner for us. One of the things that we do with Intel, of course, is to, to work on the reference architectures to help our certification program for our hardware OEMs so that we can scale that process, get many more OEMs adopt and be ready for the Cloud Packs and then we work with them on some of the ISV partners and then right up front. >> Got it, let's talk about the edge. Kity, you mentioned 5G. I mean it's a really exciting time, (laughs) You got windmills, you got autonomous vehicles, you got factories, you got to ship, you know, shipping containers. I mean, everything's getting instrumented, data everywhere and so I'm interested in, let's start with Intel's point of view on the edge, how that's going to evolve, you know what it means to Cloud. >> You know, Dave, it's, its definitely the future and we're excited to partner with IBM here. In addition to enterprise edge, the communication service providers think of the Telcos and take advantage of running standardized open software at the Telco edge, enabling a range of new workloads via scalable services, something that, you know, didn't happen in the past, right? Earlier this year, Intel announced a new C on second generation, scalable, atom based processes targeting the 5G radio access network, so this is a new area for us, in terms of investments going to 5G ran by deploying these new technologies, with Cloud native platforms like Red Hat Openshift and IBM Cloud Packs, comm service providers can now make full use of their network investments and bring new services such as Artificial Intelligence, augmented reality, virtual reality and gaming to the market. We've only touched the surface as it comes to 5G and Telco but IBM Red Hat and Intel compute together that I would say, you know, this space is super, super interesting, as more developed with just getting started. >> Evaristus, what do you think this means for Cloud and how that will evolve? Is this sort of a new Cloud that will form at the edge? Obviously, a lot of data is going to stay at the edge, probably new architectures are going to emerge and again, to me, it's all about data, you can create more data, push more data back to the Cloud, so you can model it. Some of the data is going to have to be done in real time at the edge, but it just really extends the network to new horizons. >> Evaristus: It does exactly that, Dave and we think of it and which is why I thought it will impact the same, right? You wouldn't be surprised to see that the platform is based on open containers and that Kubernetes is container environment provided by Red Hat and so whether your data ends up living at the edge or your data lives in a private data center, or it lives in some public Cloud, and how it flows between all of them. We want to make it easy for our clients to be able to do that. So this is very exciting for us. We just announced IBM Edge Application Manager that allows you to basically deploy and manage applications at endpoints of all these devices. So we're not talking about 2030, we're talking about thousands or hundreds of thousands. And in fact, we're working with, we're getting divided Intel's device onboarding, which will enable us to use that because you can get that and you can onboard devices very, very easily at scale, which if you get that combined with IBM Edge Application Manager, then it helps you onboard the devices and it helps you divide both central devices. So we think this is really important. We see lots of work that moving on the edge devices, many of these devices and endpoints now have sufficient compute to be able to run them, but right now, if they are IoT devices, the data has been transferred to hundreds of miles away to some data center to be processed and enormous pass and then only 1% of that actually is useful, right? 99% of it gets thrown away. Some of that actually has data residency requirements, so you may not be able to move the data to process, so why wouldn't you just process the data where the data is created around your analytics where the data is spread, or you have situations that are disconnected as well. So you can't actually do that. You don't want to stop this still in the supermarket, because there's, you lost connectivity with your data center and so the importance of being able to work offline and IBM Edge Application Manager actually allows you so it's tournament so you can do all of this without using lots of people because it's a process that is all sort or automated, but you can work whether you're connected or you're disconnected, and then you get replication when you get really, really powerful for. >> All right, I think the developer model is going to be really interesting here. There's so many new use cases and applications. Of course, Intel's always had a very strong developer ecosystem. You know, IBM understands the importance of developers. Guys, we've got to wrap up, but I wonder if you could each, maybe start with Kit. Give us your sense as to where you want to see this, this partnership go, what can we expect over the next, you know, two to five years and beyond? >> I think it's just the area of, you know, 5G, and how that plays out in terms of edge build out that we just touched on. I think that's a really interesting space, what Evaristus has said is spot on, you know, the processing, and the analytics at the edge is still fairly nascent today and that's growing. So that's one area, building out the Cloud for the different enterprise applications is the other one and obviously, it's going to be a hybrid world. It's not just a public Cloud world on prem world. So the whole hybrid build out What I call hybrid to DoD zero, it's a policy and so the, the work that both of us need to do IBM and Intel will be critical to ensure that, you know, enterprise IT, it has solutions across the hybrid sector. >> Great. Evaristus, give us the last word, bring us home. >> Evaristus: And I would agree with that as well, Kit. I will say this work that you do around the Intel's market ready solutions, right, where we can bring our ecosystem together to do even more on Edge, some of these use cases, this work that we're doing around blockchain, which I think you know, again, another important piece of work and, and I think what we really need to do is to focus on helping clients because many of them are working through those early cases right now, identify use cases that work and without commitment to open standards, using exactly the same standard across like what you've got on your open retail initiative, which we're going to do, I think is going to be really important to help you out scale, but I wanted to just add one more thing, Dave, if you if you permit me. >> Yeah. >> Evaristus: In this COVID era, one of the things that we've been able to do for customers, which has been really helpful, is providing free technology for 90 days to enable them to work in an offline situation to work away from the office. One example, for example, is the just the ability to transfer files and bandwidth, new bandwidth is an issue because the parents and the kids are all working from home, we have a protocol, IBM Aspera, which will make available customers for 90 days at no cost. You don't need to give us your credit card, just log on and use it to improve the way that you work. So your bandwidth feels as if you are in the office. We have what's an assistant that is now helping clients in more than 18 countries that keep the same thing, basically providing COVID information. So those are all available. There's a slew of offerings that we have. We just want listeners to know that they can go on the IBM website and they can gain those offerings they can deploy and use them now. >> That's huge. I knew about the 90 day program, I didn't realize a sparrow was part of that and that's really important because you're like, Okay, how am I going to get this file there? And so thank you for, for sharing that and guys, great conversation. You know, hopefully next year, we could be face to face even if we still have to be socially distant, but it was really a pleasure having you on. Thanks so much. Stay safe, and good stuff. I appreciate it. >> Evaristus: Thank you very much, Dave. Thank you, Kit. Thank you. >> Thank you, thank you. >> All right, and thank you for watching everybody. This is Dave Volante for theCUBE, our wall to wall coverage of the IBM Think 2020 Digital Event Experience. We'll be right back right after this short break. (upbeat music)

>> Announcer: From theCUBE Studios in Palo Alto and Boston, it's theCUBE, covering IBM Think, brought to you by IBM. >> Welcome back, I'm Stu Miniman, and this is theCUBE's coverage of IBM Think 2020, the digital experience. Welcome to the program, Dinesh Nirmal, who's the chief product officer for Cloud Paks inside IBM. Dinesh, nice to see you, thanks so much for joining us. >> Thank you Stu, really appreciate you taking the time. >> All right, so, I've been to many IBM shows, and of course, I'm an analyst in the cloud space, so I'm familiar with IBM Cloud Paks, but maybe just refresh our audience minds here, what they are, how long have they been around for, what clouds do they live on, and maybe what's new in 2020 that if somebody had looked at this in the past that they might not know about IBM Cloud Pak? >> Yeah, so thanks Stu. So to start with, let me say that Cloud Paks are cloud agnostic. So, the whole goal is that you build once and it can run anywhere. That is the basic mantra, or principle, that we want to build Cloud Paks with. So they are, look at them as a set of micro services containerized in a form that it can run on any public cloud or behind a firewall. So that's the whole premise of Cloud Paks. So, when you go back to Cloud Paks, it's an integrated set of services that solve a specific set of business problems and also accelerates building each set of applications and solutions. That's what Cloud Paks brings. So, especially in this environment Stu, think about it. If I'm an enterprise, my goal is how can I accelerate and how can I automate? Those are the two key things that comes to my mind if I am a C-level exec at an enterprise. So, Cloud Paks enables that, meaning you already have a set of stitched together services that accelerates the application development. It automates a lot of things for you. So you today have a lot of applications running on multiple clouds or behind the firewall. How do you manage those, right? Cloud Paks will help. So, let me give you one example since you asked specifically on Cloud Paks. Let's take Cloud Pak for Data. The set of services that is available in Cloud Pak for Data will make it easier for all the way from ingest to visualization. There's a set of services that you can use, so you don't have to go build a service or a product or user product for ingest, then use another product for ETL, use another product for building models, another product to manage those models. The Cloud Pak for Data will solve all the problems end to end. It's a rich set of services that will give you all the value that you need all the way from ingest to visualization. And with any personas, whether you are a data engineer, data scientist, or you are a business analyst, you all can collaborate through the Cloud Paks. So that's the two minute answer to your question what Cloud Paks is. >> Awesome, thanks Dinesh. Yeah, I guess you pointed out something right at the beginning there. I hear IBM Cloud Pak and I think IBM Cloud. But you said specifically this is really cloud agnostic. So this week is Think, last week I was covering Red Hat Summit, so I heard a lot about multicloud deployments, talked to the rail team, talked to the open chip team. So, help me understand where do Cloud Pak fit when we're talking about these multicloud employments? And is there some connection with the partnership that, of course, IBM has with Red Hat? >> Of course, so all Cloud Paks are optimized for OpenShift, meaning how do we use the set of services that OpenShift gives, the container management that OpenShift provides? So as we build containers or micro services, how do we make sure that we are optimizing or taking advantage of OpenShift? So, for example, the set of services like logging, monitoring, security, all those services metering that comes from OpenShift is what we are using as Cloud Pak. So Cloud Paks are optimized for OpenShift. From an automation perspective, how do we use Ansible, right? So, all the value that Red Hat and OpenShift brings is what Cloud Pak is built on. So if you look at as a layer as a Lego, the base Lego is OpenShift and rail. And then on top of it sit Cloud Paks, and applications and solutions on top of it. So it's, if I look at layer base, the base Lego layer is OpenShift and Red Hat rail. >> Well, great, that's super important because, one of the things we've been looking at for a while is, you talk about hybrid cloud, you talk about multicloud, and often it's not that platform, that infrastructure discussion, but the biggest challenge for companies today is how do I build new applications, how do I modernize what I have? So, sounds like this is exactly where you're targeting to help people through that transformation that they're going through. >> Yeah, exactly Stu, because if you look at it, in the past products were siloed. You build a product, you use a set of specs to build it. It was siloed. And customers becomes the software integrators, or system integrators, where they have to take the different products, put it together. So even if I am focused on the data space, or AI space, before I had to bring in three or four or five different products, make it all work together to build a model, deploy the model, manage the model, the lifecycle of the model, the lifecycle of the data. But the Cloud Paks bring it all in one box, where out of the box you are ready to go. So your time to value is much more higher with Cloud Paks because you already get a set of stitched together services that gets working right out of the box. >> So, I love the idea of out of the box. When I think of cloud native, modern application development, simplicity is not the first thing I think of, Dinesh. So, help me understand. So many customers, it's the tools, the skillsets, they don't necessarily have the experience. How is what your product set and your team's doing, help customers that deal with the ever-changing landscape and the complexity that they are faced with? >> Yeah, so the honest truth, Stu, is that enterprise applications are not an app that you create and put it on iPhone, right? I mean, it is much more complex, because it's dealing with hundreds of millions of people trying to transact with the system. You need to make sure there is a disaster recovery backup, scalability, elasticity, all those things, security, obviously, very critical piece, and multitenancy. All those things has to come together in an enterprise application. So, when people talk about simplicity, it comes at a price. So, what Cloud Paks has done, is that we have really focused on the user experience and design piece. So, you as an end-user has a great experience using the integrated set of services. The complexity piece will still be there, to some extent, because you're building a very complex multitenant enterprise application, but how do we make it easier for a developer or a data scientist to collaborate or reuse the assets, find the data much more easier, or trust the data much more easier than before? Use AI to predict a lot of the things, including bias detection, all those things. So, we are making a lot of the development, automation and acceleration easier. The complexity part will be there still, because enterprise applications tend to be complex by nature. But we are making it much more easier for you to develop, deploy, manage and govern what you are building. >> Yeah, so, how does Cloud Paks allow you to really work with the customers, focus on things like innovation, showing them the latest in the IBM software portfolio? >> Yeah, so the first piece is that we made it much more easier for the different personas to collaborate. So in the past, what is the biggest challenge, me as a data scientist had? Me as a data scientist, the biggest challenge was that getting access to the data, trusted data. Now we have put some governance around it, where by which you can get data, trusted data, much more easier using Cloud Pak for Data. Governance around the data, meaning if you have a CDO, you want to see who is using the data, how clean is the data, right? A lot of times he data might not be clean, so we want to make sure we can help with that. Now, let me move into the the line of business piece, not just the data. If I am an LOB, and I want to use, automate a lot of the process I have in today, in my enterprise, and not go through the every process automation, and go through your superior or supervisor to get approval, how do we use AI in the business process automation also? So those kind of things, you will get through Cloud Paks. Now, the other piece of Cloud Pak, if I am an IT space, right? The day-two operations, scalability, security, delivery of the software, backup and restore, how do we automate and help with that, the storage layer? Those are day-two operations. So, we are taking it all the way from day one, meaning the whole experience of setting it up, to day two, where enterprise is really worried about, making it seamless and easy using Cloud Paks, I go back to what I said in the beginning, which is out of the accelerate and automate, a lot of the work that enterprise have to do today, much more easier. >> Okay, we talked earlier in the discussion about that this can be used across multiple clouded environments. My understanding, you mentioned one of the IBM Cloud Paks, one for data. There's a number of different Cloud Paks out there. How does that work from a customer's standpoint? Do I have to choose a Cloud Pak or a specific cloud? Is it a license that goes across all of my environments? Help me understand how this deployment mechanism and its support and maintenance works. >> Right, so we have the base, obviously. I said look at it as a modular Lego model. The base is obviously open chipped and rail. On top of its cells sits a bedrock, we call, which is a common set of services and the logic to expand. On top of it sits Cloud Pak for Data, Cloud Pak for Security, Cloud Pak for Applications, there's Cloud Pak for Multicloud Management, there's Cloud Pak for Integration. So there is total of six Cloud Paks that's available, but you can pick and choose which Cloud Pak you want. So let's say you are a CDO, or you are an enterprise who want to focus on data and AI, you can just pick Cloud Pak for Data. Or let's say you are a Cloud Pak based on processes, BPM decision rules, you can go without platform automation, which gives you the set of tools. But the biggest benefits too, is that all these Cloud Paks are a set of integrated services that can all work together, sits optimized on top of open chipped. So, all of a sudden, you'll need Cloud Pak for Data, and now you want to do data, but now you want to expand it into your line of business, and you want Cloud Pak for Automation, you can bring that in. Now those two Cloud Paks works together well. Now you want to bring in Cloud Pak for Multicloud Management, because you have data, or applications running on multiple clouds, so now you can bring Cloud Pak for MCM, which is multicloud management, and those three work together. So it's all a set of integrated set of services that is optimized on top of OpenShift, which makes it much more easier for customers to bring the rich set of services together and accelerate and automate their lifecycle journey within the enterprise. >> Great, last question for you Dinesh. What new in 2020, what should customers be looking at today? Would love if you can give a little bit of guidance as to where customers should be looking at for things that might be coming a little bit down the line here, and if they want to learn more about IBM Cloud Paks, where should they be looking? >> Yeah, they want to learn more, there's www.ibm.com/cloudpaks. There's a place to go. There, all the details around Cloud Paks are there. You can also get in touch with me, and I can definitely take you to more detail. But what is coming is that, look, so we have a set of Cloud Paks, but we want to expand and make it extensible. So how do we, already it's built on an open platform, but how do we make sure our partners and ISPs can come and build on top of the base-cloud part? So that's the focus going to be, as each Cloud Pak innovate and add more value within those Cloud Paks. We also want to expand it so that our partners and our ISPs and GSIs can build on top of it. So this year, the focus is continuously innovate across the Cloud Paks, but also make it much more extensible for third parties to come and build more value on top of the Cloud Pak itself. That's one area we are focusing on. The other area's MCM, right? Multicloud management, because there is tremendous appetite for customers to move data or applications on cloud, and not only on one cloud, hybrid cloud. So how do you manage that, right? So multicloud management definitely helps on that perspective. So our focus this year is going to be one, make it extensible, make it more open, but at the same time continuously innovate on every single Cloud Pak to make that journey for customers on automating and accelerating application development easier. >> All right, well Dinesh, thank you so much. Yeah, the things that you talked about, that absolutely top of mind for customers that we talked to. Multicloud management, as you said, it was the ACM, the Advanced Cluster Management, that we heard about from the Red Hat team last week at Summit. So thank you so much for the updates. Definitely exciting to watch Cloud Pak, how you're helping customers deal with that huge, it's the opportunity but also the challenge of building their next applications, modernizing what they're doing without, still having to think about what they have from (faintly speaking), so thanks so much, great to talk with you. >> Well, thanks Stu, great talking. >> All right, lots more coverage from IBM Think 2020, the digital experience. I'm Stu Miniman, and as always, thank you for watching theCUBE. (upbeat music)

>> Narrator: Live from Miami, Florida, it's The Cube. Covering IBM's Data in AI Forum, brought to you by IBM. >> Welcome back to Miami, everybody. You're watching The Cube, the leader in live tech coverage. We're here covering the IBM Data and AI Forum. Scott Buckles is here to my right. He's the business unit executive at IBM and long time Cube alum, Daniel Hernandez is the Vice President of Data and AI group. Good to see you guys, thanks for coming on. >> Thanks for having us. >> Good to see you. >> You're very welcome. We're going to talk about data ops, kind of accelerating the journey to AI around data ops, but what is data ops and how does it fit into AI? Daniel, we'll start with you. >> There's no AI without data. You've got data science to help you build AI. You've got dev ops to help you build apps. You've got nothing to basically help you prepare data for AI. Data ops is the equivalent of dev ops, but for delivering AI ready data. >> So, how are you, Scott, dealing with this topic with customers, is it resonating? Are they leaning into it, or are they saying, "what?" >> No, it's absolutely resonating. We have a lot of customers that are doing a lot of good things on the data science side. But, trying to get the right data at the right people, and do it fast, is a huge problem. They're finding they're spending too much time prepping data, getting the data into the models, and they're not spending enough time failing fast with some of those models, or getting the models that they need to put in production into production fast enough. So, this absolutely resonates with them because I think it's been confusing for a long time. >> So, AI's scary to a lot of people, right? It's a complicated situation, right? And how do you make it less scary? >> Talk about problems that can be solved with it, basically. You want a better customer experience in your contact center, you want a similarly amazing experience when they're interacting with you on the web. How do you do that? AI is simply a way to get it done, and a way to get it done exceptionally well. So, that's how I like to talk about it. I don't start with here's AI, tell me what problems you can solve. Here are the problems you've got, and where appropriate, here's where AI can help. >> So what are some of your favorite problems that you guys are solving with customers. >> Customer and employee care, which, basically, is any business that does business has customers. Customer and employee care are huge a problem space. Catching bad people, financial crimes investigation is a huge one. Fraud, KYC AML as an example. >> National security, things like that, right? >> Yeah. >> You spend all your time with customers, what else? >> Well, customer experience is probably the one that we're seeing the most. The other is being more efficient. Helping businesses solve those problems quicker, faster. Try to find new avenues for revenue. How to cut costs out of their organization, out of their run time. Those are the ones that we see the most. >> So when you say customer experience, immediately chat bots jumps into my head. But I know we're talking more than, sort of a, transcends chat bots, but double click on customer experience, how are people applying machine intelligence to improve customer experience? >> Well, when I think of it, I think about if you call in to Delta, and you have one bad experience, or your airline, whatever that airline may be, that that customer experience could lead to losing that customer forever, and there used to be an old adage that you have one bad experience and you tell 10 people about it, you have a good one, and you tell one person, or two peoples. So, getting the right data to have that experience is where it becomes a challenge and we've seen instances where customers, or excuse me, organizations are literally trying to find the data on the screen while the customer is on hold. So, they're saying, "can I put you on hold?" and they're trying to go out and find it. So, being able to automate finding that data, getting it in the right hands, to the right people, at the right time, in moment's notice, is a great opportunity for AI and machine learning, and that's an example of how we do it. >> So, from a technical standpoint, Daniel, you guys have this IBM Cloud Pak for Data that's going to magic data virtualization thing. Let's take an example that Scott just gave us, think of an airline. I love my mobile app, I can do everything on my mobile app, except there are certain things I can't do, I have to go to the website. There are certain things I have to do with e-commerce that I have to go to the website that I can't do. Sometimes watching a movie, I can't order a movie from the app, I have to go to website, the URL, and order it there and put it on my watch list. So, I presume that there's some technical debt in each of those platforms, and there's no way to get the data from here, and the data from here talking to each other. Is that the kind of problem that you're solving? >> Yes, and in this particular case, you're actually touching on what we mean by customer and employee care everywhere. The interaction you have on your phone should be the same as the interaction and the kind of response on the web, which should be the same, if not better, when you're talking to a human being. How do you have the exceptional customer and employee care, all channels. Today, say the art is, I've got a specific experience for my phone, a specific experience for my website, a specific, different experience in my contact center. The whole work we're doing around Watson Assistant, and it as a virtual assistant, is to be that nervous system that underpins all channels, and with Cloud Pak for Data, we can deliver it anywhere. You want to run your contact center on an IBM Cloud? Great. You want to run it on Amazon, Azure, Google, your own private center, or everything in between, great. Cloud Pak for Data is how you get Watson Assistant, the rest of Watson and our data stack anywhere you want, so you can deliver that same consistent, amazing experience, all channels, anywhere. >> And I know the tone of my question was somewhat negative, but I'm actually optimistic, and there's a couple examples I'll give. I remember Bill Belichick one time said, "Agh, the weather, it can't ever get the weather right," this is probably five, six years ago. Actually, they do pretty well with the weather compared to 10 or 15 years ago. The other is fraud detection. In the last 10 years, fraud detection has become so much better in terms of just the time it takes to identify a fraud, and the number of false positives. Even in the last, I'd say, 12 to 18 months, false positives are way down. I think that's machine intelligence, right? >> I mean, if you're using business rules, they're not way down. They're still way up. If you're using more sophisticated techniques, that are depending upon the operational data to be trained, then they should be way down. But, there is still a lot of these systems that are based on old school business rules that can't keep up. They're producing alerts that, in many cases, are ignored, and because they're ignored, you're susceptible to bad issues. With, especially AI based techniques for fraud detection, you better have good data to train this stuff, which gets back to the whole data ops thing, and training those with good data, which data ops can help you get done. >> And a key part to data ops is the people and the process. It's not just about automating things and automating the data to get it in the right place. You have to modernize those business processes and have the right skills to be able to do that as well. Otherwise, you're not going to make the progress. You're not going to reap the benefits. >> Well, that was actually my next question. What about the people and the process? We were talking before, off camera, about our PA, and he's saying "pave the cow path." But sometimes you actually have to re-engineer the process and you might not have the skill set. So it's people and process, and then technology you lay in. And we've always talked about this, technology is always going to change. Smart technologists will figure it out. But, the people and the process, that's the hardest part. What are you seeing in the field? >> We see a lot of customers struggling with the people and process side, for a variety of reasons. The technology seems to be the focus, but when we talk to customers, we spend a lot of time saying, "well, what needs to change in your business process "when this happens? "How do those business rules need to change "so you don't get those false positives?" Because it doesn't matter at the end of the day. >> So, can we go back to the business rules thing? So, it sounds like the business rules are sort of an outdated, policy based, rigid sort of structure that's enforced no matter what. Versus machine intelligence, which can interpret situations on the fly, but can you add some color to that and explain the difference between what you call sort of business rules based versus AI based. >> So the AI based ones, in this particular case, probably classic statistical machine learning techniques, to do something like know who I am, right? My name is Danny Hernandez, if you were to Google Danny Hernandez, the number one search result is going to be a rapper. There is a rapper that actually just recently came out, he's not even that good, but he's a new one. A statistical machine learning technique would be able to say, "all right, given Daniel "and the context information I know about him, "when I look for Daniel Hernandez, "and I supplement the identity with that "contextual information, it means it's one of "the six that work at IBM." Right? >> Not the rapper. >> Not the rapper. >> Not the rapper. >> Exactly. I don't mind being matched with a rapper, but match me with a good rapper. >> All you've got to do is search Daniel Hernandez and The Cube and you'll find him. >> Ha, right. Bingo. Actually that's true. So, in any case, the AI based techniques basically allow you to isolate who I am, based on more features that you know about me, so that you get me right. Because if you can't even start there, with whom are you transacting, you're not going to have any hope of detecting fraud. Either that, or you're going to get false positives because you're going to associate me with someone that I'm not, and then it's just going to make me upset, because when you should be transacting with me, you're not because you're saying I'm someone I'm not. >> So, that ties back to what we were saying before, know you're customer and anti money laundering. Which, of course, was big, and still is, during the crypto craze. Maybe crypto is not as crazy, but that was a big deal when you had bitcoin at whatever it was. What are some practical applications for KYC AML that you're seeing in the field today? >> I think that what we see a lot of, what we're applying in my business is automating the discovery of data and learning about the lineage of that data. Where did it come from? This was a problem that was really hard to solve 18 months ago, because it took a lot of man power to do it. And as soon as you did it once, it was outdated. So, we've recently released some capabilities within Watson Knowledge Catalog that really help automate that, so that as the data continues to grow, and continues to change, as it always does, that rather than having two, three hundred business analysts or data stewards trying to go figure that out, machine learning can go do that for you. >> So, all the big banks are glomming on to this? >> Absolutely. >> So think about any customer onboarding, right? You better know who your customer is, and you better have provisions around anti money laundering. Otherwise, there's going to be some very serious downside risk. It's just one example of many, for sure. >> Let's talk about some of the data challenges because we talked a lot about digital, digital business, I've always said the difference between a business and a digital business is how they use data. So, what are some of the challenging issues that customers are facing, and particularly, incumbents, Ginni Rometty used the term a couple of events ago, and it might have even been World of Watson, incumbent disruptors, maybe that was the first think, which I thought was a very poignant term. So, what are some of the data challenges that these incumbents are facing, and how is IMB helping solve them? >> For us, one of them that we see is just understanding where their data is. There is a lot of dark data out there that they haven't discovered yet. And what impact is that having on their analytics, what opportunities aren't they taking advantage of, and what risks are they being exposed to by that being out there. Unstructured data is another big part of it as well. Structured data is sort of the easy answer to solving the data problem, >> [Daniel Hernandez] But still hard. >> But still hard. Unstructured data is something that almost feels like an afterthought a lot of times. But, the opportunities and risks there are equally, if not greater, to your business. >> So yeah, what you're saying it's an afterthought, because a lot of times people are saying, "that's too hard." >> Scott Buckles: Right. >> Forget it. >> Scott Buckles: Right. Right. Absolutely. >> Because there's gold in them there hills, right? >> Scott Buckles: Yeah, absolutely. >> So, how does IBM help solve that problem? Is it tooling, is it discovery tooling? >> Well, yeah, so we recently released a product called InstaScan, that helps you to go discover unstructured data within any cloud environment. So, that was released a couple months ago, that's a huge opportunity that we see where customers can actually go and discover that dark data, discover those risks. And then combine that with some of the capabilities that we do with structured data too, so you have a holistic view of where your data is, and start tying that together. >> If I could add, any company that has any operating history is going to have a pretty complex data environment. Any company that wants to employ AI has a fundamental choice. Either I bring my AI to the data, or I bring my data to the AI. Our competition demand that you bring your data to the AI, which is expensive, hard, often impossible. So, if you have any desire to employ this stuff, you had better take the I'm going to bring my AI to the data approach, or be prepared to deal with a multi-year deployment for this stuff. So, that principle difference in how we think about the problem, means that we can help our customers apply AI to problem sets that they otherwise couldn't because they would have to move. And in many cases, they're just abandoning projects all together because of that. >> So, now we're starting to get into sort of data strategy. So, let's talk about data strategy. So, it starts with, I guess, understanding the value of your data. >> [Daniel Hernandez] Start with understanding what you got. >> Yeah, what data do I have. What's the value of that data? How do I get to that data? You just mentioned you can't have a strategy that says, "okay, move all the data into some God box." >> Good luck. >> Yeah. That won't work. So, do customers have coherent data strategies? Are they formulating? Where are we on that maturity curve? >> Absolutely, I think the advent of the CDO role, as the Chief Data Officer role, has really helped bring the awareness that you have to have that enterprise data strategy. >> So, that's a sign. If there's a CDO in the house. >> There's someone working on enterprise, yeah, absolutely. >> So, it's really their role, the CDO's role, to construct the data strategy. >> Absolutely. And one of the challenges that we see, though, in that, is that because it is a new role, is like going back to Daniel's historical operational stuff, right? There's a lot of things you have to sort out within your data strategy of who owns the data, right? Regardless of where it sits within an enterprise, and how are you applying that strategy to those data assets across the business. And that's not an easy challenge. That goes back to the people process side of it. >> Well, right. I bet you if I asked Jim Cavanaugh what's IBM's data strategy, I bet you he'd have a really coherent answer. But I bet you if I asked Scott Hebner, the CMO of the data and AI group, I bet you I'd get a somewhat different answer. And so, there's multiple data strategies, but I guess it's (mumbles) job to make sure that they are coherent and tie in, right? >> Absolutely. >> Am I getting this? >> Absolutely. >> Quick study. >> So, what's IBM's data strategy? (laughs) >> Data is good. >> Data is good. Bring AI to the data. >> Look, I mean, data and AI, that's the name of the business, that's the name of the portfolio that represents our philosophy. No AI without data, increasingly, not a lot of value of data without AI. We have to help our customers understand this, that's a skill, education, point of view problem, and we have to deliver technology that actually works in the wild, in their environment, not as we want them to be, but as they are. Which is often messy. But I think that's our fun. It's the reason we've been here for a while. >> All right, I'll give you guys a last word, we got to run, but both Scott and Daniel, take aways from the event today, things that you're excited about, things that you learned. Just give us the bumper sticker. >> For me, you talk about whether people recognize the need for a data strategy in their role. For me, it's people being pumped about that, being excited about it, recognizing it, and wanting to solve those problems and leverage the capabilities that are out there. >> We've seen a lot of that today. >> Absolutely. And we're at a great time and place where the capabilities and the technologies with machine learning and AI are applicable and real, that they're solving those problems. So, I think that gets everybody excited, which is cool. >> Bring it home, Daniel. >> Excitement, a ton of experimentation with AI, some real issues that are getting in the way of full-scale deployments, a methodology data ops, to deal with those real hardcore data problems in the enterprise, resonating, a technology stack that allows you to implement that as a company is, through Cloud Pak for Data, no matter where they want to run is what they need, and I'm happy we're able to deliver it to them. >> Great. Great segment, guys. Thanks for coming. >> Awesome. Thank you. >> Data, applying AI to that data, scaling with the cloud, that's the innovation cocktail that we talk about all the time on The Cube. Scaling data your way, this is Dave Vellante and we're in Miami at the AI and Data Forum, brought to you by IBM. We'll be right back right after this short break. (upbeat music)

>> Narrator: Live from Miami, Florida. It's theCUBE. Covering IBM's data and AI forum. Brought to you by IBM. >> Welcome back to the port of Miami everybody. This is theCube, the leader in live tech coverage. We're here covering the IBM AI and data forum. Of course, the centerpiece of IBM's AI platform is Watson. Beth Smith is here, she's the GM of IBM Watson. Beth, good to see you again. >> You too. Always good to be with theCUBE. >> So, awesome. Love it. So give us the update on Watson. You know, it's beyond Jeopardy. >> Yeah, yeah. >> Oh, wow. >> That was a long time ago now. (laughs) >> Right, but that's what a lot of people think of, when they think of Watson. What, how should we think about Watson today? >> So first of all, focus Watson on being ready for business. And then, a lot of people ask me, "So what is it?" And I often describe it as a set of tools, to help you do your own AI and ML. A set of applications that are AI applications. Where we have prebuilt it for you, around a use case. And there is examples where it gets embedded in a different application or system that may have existed already. In all of those cases, Watson is here, tuned to business enterprise, how to help people operational-wise, AI. So they can get the full benefit, because at the end of the day it's about those business outcomes. >> Okay, so the tools are for the super geeks, (Beth laughs) who actually want to go in and build the real AI. >> (laughs) That's right, that's right. >> The APPS are, okay. It's prebuilt, right? Go ahead and apply it. >> That's right. >> And the embedded is, we don't even know we're using it, right? >> That's right, or you may. Like, QRadar with Watson has an example of using Watson inside of it. Or, OpenPages with Watson. So sometimes you know you're using it. Sometimes you don't. >> So, how's the mix? I mean, in terms of the adoption of Watson? Are there enough like, super techies out there, who are absorbing this stuff? Or is it mostly packaged APPS? Is it a mix? >> So it is a mix, but we know that data science skills are limited. I mean, they're coveted, right? And so those are the geeks, as you say, that are using the tool chain as a part of it. And we see that in a lot of customers and a lot of industries around the world. And then from a packaged APP standpoint, the biggest use case of adoption is really around customer care, customer service, customer engagement. That kind of thing. And we see that as well. All around the world, all different industries. Lots of great adoption. Watson Assistant is our flagship in that. >> So, in terms of, if you think about these digital initiatives, we talked about digital transformation, >> Yup. >> Last few years, we kind of started in 2016 in earnest, it's real when you talk to customers. And there was a ton of experimentation going on. It was almost like spaghetti. Throw against the wall and see what sticks. Are you seeing people starting to place their bets on AI, Narrowing their scope, and really driving you know, specific business value now? >> Beth: Yeah. >> Or is it still kind of all over the place? >> Well, there's a lot of studies that says about 51% or so still stuck in experimentation. But I would tell you in most of those cases even, they have a nice pilot that's in production, that's doing a part of the business. So, 'cause people understand while they may be interested in the sexiness of the technology, they really want to be able to get the business outcomes. So yes, I would tell 'ya that things have kind of been guided, focused towards the use cases and patterns that are the most common. You know, and we see that. Like I mentioned, customer care. We see it in, how do you help knowledge workers? So you think of all those business documents, and papers and everything that exists. How do you assist those knowledge workers? Whether or not it's an attorney or an engineer, or a mortgage loan advisor. So you see that kind of use case, and then you see customers that are building their own. Focused in on, you know, how do they optimize or automate, or predict something in a particular line of business? >> So you mentioned Watson Assistant. So tell us more about Watson Assistant, and how has that affected adoption? >> So Watson Assistant as I said, it is our flagship around customer care. And just to give you a little bit of a data point, Watson Assistant now, through our public cloud, SaaS version, converses with 82 million end users a month. So it's great adoption. And this is, this is enabling customers. Customers of our customers, to be able to get self-service help in what they're doing. And Watson Assistant, you know, a lot of people want to talk about it being a chat bot. And you can do simple chat bots with it. But it's to sophisticated assistance as well. 'Cause it shows up to do work. It's there to do a task. It's to help you deal with your bank account, or whatever it is you're trying to do, and whatever company you're interacting with. >> So chat bots is kind of a, (laughs) bit of a pejorative. But you're talking about digital systems, it's like a super chat bot, right? >> Beth: Yeah. I saw a stat the other day that there's going to be, by I don't know, 2025, whatever. There's going to be more money spent on chat bot development, or digital assistance, than there is on mobile development. And I don't know if that's true or not, >> Beth: Mhm, wow. But it's kind of an interesting thing. So what are you seeing there? I mean, again I think chat bots, people think, oh, I got to talk into a bot. But a lot of times you don't know you're, >> Beth: That's right. >> so they're getting, they're getting better. I liken it to fraud detection. You know, 10 years ago fraud detection was like, six months later you'll, >> Right. >> you'll get a call. >> Exactly. >> And so chat bots are just going to get better and better and better, and now there's this super category that maybe we can define here. >> That's right. >> What is that all about? >> That's right. And actually I would tell you, they kind of, they can become the brain behind something that's happening. So just earlier today I was, I was with a customer and talking about their email CRM system, and Watson Assistant is behind that. So chat bots aren't just about what you may see in a little window. They're really about understanding user intent, guiding the user through what they're trying to either find out or do, and taking the action as a part of it. And that's why we talk about it being more than chat bots. 'Cause it's more than a FAQ interchange. >> Yes, okay. So it's software, >> Beth: Yes. >> that actually does, performs tasks. >> Beth: Yes. >> Probably could call other software, >> Beth: Absolutely. >> to actually take action. >> That's right. >> I mean, I see. We think of this as systems of agency, actually. Making, sort of, >> That's right. >> decisions and then I guess, the third piece of that is, having some kind of human interaction, where appropriate, right? >> That's right. >> What do you see in terms of, you know, infusing humans into the equation? >> So, well a couple of things. So one of the things that Watson Assistant will do, is if it realizes that it's not the expert on whatever it is, then it will pass over to an expert. And think of that expert as a human agent. And while it's doing that, so you may be in the queue, because that human person is tied up, you can continue to do other things with it, while you're waiting to actually talk to the person. So that's a way that the human is in the loop. I would tell you there's also examples of how the agents are being assisted in the background. So they have the interaction directly with the user, but Watson Assistant is helping them, be able to get to more information quicker, and narrow in on what the topic is. >> So you guys talk about the AI ladder, >> Beth: Mhm. >> Sort of, Rob talked about that this morning. My first version of the AI ladder was building blocks. It was like data and AI analytics, ML, and then AI on top of that. >> Beth: Yup. >> I said AI. Data and IA. >> Beth: Yup. >> Information Architecture. Now you use verbs. Sort of, to describe it. >> Beth: Yup. Which is actually more powerful. Collect, organize, analyze and infuse. Now infuse is like the Holy Grail, right? 'Cause that's operationalizing and being able to scale AI. >> Beth: That's right. >> What can you tell us about how successful companies are infusing AI, and what is IBM doing to help them? >> So, I'm glad you picked up first of all, that these are verbs and it's about action. And action leads to outcome, which is, I think, critical. And I would also tell you yes, infuse is, you know, the Holy Grail of the whole thing. Because that's about injecting it into business processes, into workflows, into how things are done. So you can then see examples of how attorneys may be able to get through their legal prep process in just a few minutes, versus 10, 15 hours on certain things. You can see conversion rates of, from a sales standpoint, improve significantly. A number of different things. We've also got it as a part of supply chain optimization, understanding a little bit more about both inventory, but also where the goods are along the way. And particularly when you think about a very complicated thing, there could be a lot of different goods in various points of transit. >> You know, I was sort of joking. Not joking, but mentioning Jeopardy at first. 'Cause a lot of people associate Watson with Jeopardy. >> Beth: Right. >> I can't remember the first time I saw that. It had to be the mid part of the last decade. What was it? >> Beth: February of 2011. >> 2011, okay I thought I even saw demos before that. I'm actually sure I did. Like in, back in some lab in IBM. And of course, the potential like, blew your mind. >> Right. >> I suspect you guys didn't even know what you had at the time. You were like, "Okay, we're going to go change the world." And you know, when you drive up and down 101 in Silicone Valley, it's like, "Oh, Watson this, Watson that." You know, you get the consumer guys, doing facial recognition, ad serving. You know, serving up fake news, you know. All kinds of applications. But IBM started to do something different. You're trying to really change business. Did you have any clue as to what you had at the time? And then how much of a challenge you were taking on, and then bring us to where we are now, and what do you see as a potential for the next 10 years? >> So, of course we had a clue. So let me start there. (Dave laughs) But with that, I think the possibilities of it weren't completely understood. There's no question in my mind about that. And what the early days were, were understanding, okay, what is that business application? What's the pattern that's going to come about as a part of it? And I think we made tremendous progress on that along the way. I would tell you now, you mentioned operationalizing stuff, and you know, now it's about, how do we help companies have it more throughout their company? Through different lines of business, how does it tie to various things that are important to us? And so that brings in things like trust, explainablity, the ethics of what it's doing. Bias detection and mitigation. And I actually believe a lot of that, and the operationalizing it within the processes, is where we're going to head, going forward. Of course there'll continue to be advancements on the features and the capabilities, but it's going to be about that. >> Alright, I'm going to ask you the it's depends question. (Beth laughs) So I know that's your answer, but at the macro, can machines make better diagnosis than doctors today, and if not, when will they be able to, in your view? >> So I would actually tell you that today they cannot, but what they can do is help the doctor make a better diagnosis than she would have done by herself. And because it comes back to this point of, you know, how the machine can process so much information, and help the expert, in this case the doctor's the expert, it could be an attorney, it could be an engineer, whatever. Help that expert be able to augment the knowledge that he or she has as a part of it. So, and that's where I think it is. And I think that's where it will be for my lifetime. >> So, there's no question in your mind that machines today, AI today, is helping make better diagnosis, it's just within augmented or attended type of approach. >> Absolutely. >> And I want to talk about Watson Anywhere. >> Beth: Okay, great. >> So we saw some discussion in the key notes and some demos. My understanding is, you could bring Watson Anywhere, to the data. >> That's right. >> You don't have to move the data around. Why is that important? Give us the update on Watson Anywhere. >> So first of all, this is the biggest requirement I had since I joined the Watson team, three and a half years ago. Was please can I have Watson on-prem, can I have Watson in my company data center, etcetera. And you know, we needed to instead, really focus in on what these patterns and use cases were, and we needed some help in the platform. And so thanks to Cloud Pak for data, and the underlying Red Hat OpenShift and container platform, we now are enabled to truly take Watson anywhere. So you can have it on premise, you can have it on the other public clouds, and this is important, because like you said, it's important because of where your data is. But it's also important because the workloads of today and tomorrow are very complex. And what's on cloud today, may be on premise tomorrow, may be in a different cloud. And as that moves around, you also want to protect the investment of what you're doing, as you have Watson customize for what your business needs are. >> Do you think you timed it right? I mean, you kind of did. All this talk about multicloud now. You really didn't hear much about it four or five years ago. For awhile I thought you were trying to juice your cloud business. Saying, "You want, if you want Watson, you got to go to the IBM cloud." Was there some of that, or was it really just, "Hey, now the timing's right." Where clients are demanding it, and hybrid and multicloud and on-prem situations? >> Well look, we know that cloud and AI go hand in hand. So there was a lot of positive with that. But it really was this technology point, because had I taken it anywhere three and a half years ago, what would've happened is, every deployment would've been a unique environment, a unique stack. We needed to get to a point that was a modern day, you know, infrastructure, if you will. And that's what we get now, with a container based platform. >> So you're able to scale it, such that every instance isn't a snowflake, >> That's right. >> that requires customization. >> That's right. So then I can invest in the enhancements to the actual capabilities it is there to do, not supporting multiple platform instantiations, under the covers. >> Well, okay. So you guys are making that transparent to the customer. How much of an engineering challenge is that? Can you share that with us? You got to run on this cloud, on that cloud, or on forever? >> Well, now because of Cloud Pak for data, and then what we have with OpenShift and Kubernetes and containers, it becomes, well, you know, there's still some technical work, my engineering team would tell you it was a lie. But it's simple now, it's straightforward. It's a lot of portability and flexibility. In the past, it would've been every combination of whatever people were trying to do, and we would not have had the benefit of what that now gives you. >> And what's the technical enable there? Is it sort of open API's? Architecture that allows for the interconnectivity? >> So, but inside of Watson? Or the overall platform? >> The overall platform. >> So I would say, it's been, at it's, at it's core it's what containers bring. >> Okay, really. So it's that, it's that. It's the marriage of your tech, >> Yeah. >> with the container wave. >> That's right. That's right. Which is why the timing was critical now, right? So you go back, yes they existed, but it really hadn't matured to a point of broad adoption. And that's where we are now. >> Yeah, the adoption of containers, Kubernetes, you know, micro services. >> Right, exactly. Now it's on a very steep curve. >> Exactly. >> Alright, give your last word on, big take away, from this event. What do you hearing, you know, what are you, some of the things you're most excited about? >> So first of all, that we have all of these clients and partners here, and all the buzz that you see. And that we've gotten. And then the other thing that I would tell you is, the great client examples. And what they're bragging on, because they are getting business outcomes. And they're getting better outcomes than they thought they would achieve. >> IBM knows how to throw an event. (Beth laughs) Beth, thanks so much for coming to theCUBE. >> Thank you, good to >> Appreciate it. >> see you again. >> Alright, great to see you. Keep it right there everybody, we'll be back. This is theCUBE live, from the IBM Data Forum in Miami, we'll be right back. (upbeat instrumental music)