>>Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, or is modernized now. Yes, let's talk about that. And with me is Ravi, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >>Thank you so much. I'm so glad to be here with you. >>I asked you what the new requirements are around modern applications. I've seen some, you know, some of your comments, you gotta be flexible, distributed, multimodal, mobile edge. It, that those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >>Yeah, I think what has basically happened is that, uh, so far, uh, it's been a transition of sorts. And now we are come to a point where, uh, the tipping point and the tipping point has been, uh, uh, more because of COVID and there COVID has pushed us to a world where we are living, uh, in a sort of, uh, occasionally connected manner where our digital, uh, interactions, precede our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than, uh, in a digital manner, as opposed to sort of making a more specific human contact that has really been the, uh, sort of accelerant to this modernized. Now, as a team in this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. >>They're all sitting behind. Uh, they used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, uh, but they are all centralized still. Uh, but where our engagement happens with the data is, uh, at the edge, uh, at your point of convenience at your point of consumption, not where the data is actually sitting. So this has led to, uh, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? Uh, but it just basically comes down to the fact that the data needs to be where you are engaging with it. And that means if you are doing it on your mobile phone, or if you are sitting, uh, doing something in your body or traveling, or whether you are in a subway, whether you're in a plane or a ship, wherever the data needs to come to you, uh, and be available as opposed to every time you going to the data, which is centrally sitting in some place. >>And that is the fundamental shift in terms of how the modern architecture needs to think, uh, when they, when it comes to digital transformation and, uh, transitioning their old applications to, uh, the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Uh, otherwise people are basically waiting for that circle of death that we all know, uh, and blaming the networks and other pieces. The problem is actually, the data is not where you are engaging with. It has got to be fetched, you know, seven seas away. Um, and that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >>I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this, because date data by its very nature is distributed. It's always been distributed, but w w but distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, uh, of, uh, of a super rock solid database that can handle, you know, distributed data? Yes. >>So there are two issues that you're a little too over there with Forrest is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is, uh, like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data in one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, uh, when you have the data, you can first look at it to perform. >>Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five minutes. Again, this is a, there's a class of problem that we solve that same data. Now, eventually, without you ever having to, uh, sort of do a casting it to a different database, you can now do a solid, uh, acquire. These are classic sequel queries, which is our next magic. We are a no SQL database, but we have a full functional sequel. The sequel has been the language that has talked to data for 40 odd years successfully. Every other database has come and try to implement their own QL query language, but they've all failed only sequel as which stood the test of time of 40 odd years. >>Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is, uh, basically, uh, look at the data and any common tutorial, uh, any, uh, any which way you look at the data. All it will come, uh, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries, select star from where Canada stuff, because it's at an English level, it becomes easy to, so the same data, you didn't have to go move it to another database, do your, uh, sort of transformation of the data and all this stuff. Same day that you do this. >>Now, that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, but Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the ability to query the operational data in a different way. I'll talk budding. What was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. >>So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and find different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, uh, the database management system. And that's where the distributed, uh, platform that we have built enables us to get it to where you need the data to be, you know, in a classic way, we call it CDN in the data as in like content delivery networks. So far do static, uh, uh, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >>The first part of the, the answer to my question, are you saying you could do this without skiing with a no schema on, right? And then you can apply those techniques. >>Uh, fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read things. So because there is no schema, it is just a on document that is sitting inside. And Jason is the lingua franca of the web, as you very well know by now. So it just Jason that we manage, you can do key lookups of the Jason. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and the other sophisticated pieces of technology behind it. >>You can do searching on it, using the, um, the full textual analysis pipeline. You can do ad hoc wedding on the analytic side, and you can write your own custom logic on it using our eventing capabilities. So that's, that's what it allows because we keep the data in the native form of Jason. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing, uh, in the last 40 years because we developed various, uh, database systems and data processing systems of various points. In time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. >>We had queuing systems, all the systems, if you want to use any one of them, our answer has always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this is not one to fly instead, bring the logic to the data. So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this, >>As you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >>Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data casting because it required you to have it in seven schema in one sense at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably flooded, but it not really, uh, how do you say, um, keep to the promise that it actually meant to be? So that's why it was a swamp I need, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it, and you create different types of indexes to manage it. You distribute the index, you distribute the data you have, um, like we were discussing, you have acid semantics on top of, and when you, when you put all these things together, uh, it's, it's, it's a tough proposition, but they have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >>So you predicted the trend around multimodal and converged, uh, databases. Um, you kind of led Couchbase through that. I want to, I always ask this question because it's clearly a trend in the industry and it, it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, we have a little teeny scissors and a knife. That's not that sharp. How do you respond to that? Uh, >>A great one. Um, my answer is always, I use another analogy to tackle that, but is that, have you ever accused a smartphone of being a Swiss army knife? No. No. Nobody does that because it's actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Like as in a moment, it could be a Tom, Tom telling you all the directions, the next one, it's your PDA. >>Third one, it's a fantastic phone. Uh, four, it's a beautiful camera, which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment is a video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just taught that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, they missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app is the economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get the alert saying that today you got to leave home at eight 15 for your nine o'clock meeting. >>And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's gone there's notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place without that, you couldn't even do this simple function, uh, in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build, because half the time you're running sideline to sideline, just, you know, um, integrating data from one system to the other. >>So I love the analogy with the smartphone. I w I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? And so, so, but, but is there, is that a fair, where, in other words, those specialized databases, they say there still is a place for them, but they're getting >>Absolutely, absolutely great analogy and a great extension to the question. That's, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of the music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they haven't, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. >>Yes, it's 90% there or 80% there. It depends on your audio file mess of your, uh, I mean, you don't experience the super specialized ones do not go away. You know, there are, there are places where, uh, the specialized use cases will demand a separate system to exist, but even there that has got to be very closed. Um, how do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that, oh, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car and walk into my living room, that's same songs should continue and play in my living room speakers. Then it's a world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >>I love, I love that example too. When I was a kid, we used to go to Twitter, et cetera. And we'd to play around with, we take off the big four foot speakers. Those stores are out of business too. Absolutely. Um, now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi. >>I believe so. Uh, because I think, uh, what had happened was the relational systems. Uh, I've been where the norm, they rule the roost, if you will, for the last 40 odd years, and then gain this no sequel movement, which was almost as though a rebellion from the relational world, we all inhibited, uh, uh, because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee, they required your DBA and your data architect. And you have to call them just to add one column and stuff like that. And the world had moved on. This was the world of blogs and tweets and, uh, you know, um, mashups and, um, uh, uh, a different generation of digital behavior, digital, native people now, um, who are operating in these and the, the applications, the, the consumer facing applications. >>We are living in this world. And yet the enterprise ones were still living in the, um, in the other, the other side of the divide. So all came this solution to say that we don't need SQL. Actually, the problem was never sequel. No sequel was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations, and the inability for these, the system to scale, the relational systems were built like, uh, airplanes, which is that if, uh, San Francisco Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set in from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the alarm to somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. >>These are called vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world, uh, is make the system how it is only scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guests. I'll add one more coach to it, one more car to it. And the better part of the way we have done this year is that, and we have super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have ID only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. >>You can attach the kind of coaches we call this multi-dimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it quite. So that's the beauty of this architecture. Now, why is that important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because you would say that I cannot run this analytical Barre because then my operational workload will suffer. Then my friend, then we'll slow down millions of customers that impacted that problem. We will solve the same data in which you can do analytical buddy, an operational query because they're separated by these cars, right? As in like we, we fence the, the, the resources, so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or equity. >>And then yet you can run this analytical body, which will take a couple of minutes to run one, not impeding the other. So that's in one sense, sort of the, part of the, um, uh, problems that we have solved here is that relational versus, uh, uh, the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same quality language on top. Y it's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, uh, the internal combustion engine, uh, I think gas, uh, you says, these are the issues we really wanted to solve. Um, so solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, or that are for your shifters. >>Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So, uh, even when you feed people the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blue harder to go fast and lean back for, for it to, you know, uh, to apply a break that's, that's how we seem to define, uh, design software. Instead, we should be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, uh, and the gas bottle and the, um, and the gear shifter is by putting cul back on underneath the surface, we have completely solved, uh, the relational, uh, uh, limitations of schema, as well as scalability. >>So in, in, in that way, and by bringing back the classic acid capabilities, which is what relational systems, uh, we accounted on and being able to do that with the sequel programming language, we call it like multi-state SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up the salt in the modern times, but rather than get, um, sort of pedantic about whether it's, we have no SQL or sequel or new sequel, or, uh, you know, any of that sort of, uh, jargon, oriented debate, uh, this, these are the debates of computer science that they are actually, uh, and they were the solve and they have solved them with, uh, the latest release of $7, which we released a few months ago. >>Right, right. Last July, Ravi, we got to leave it there. I, I love the examples and the analogies. I can't wait to be face to face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >>Fantastic. Thanks for the time. And the Aboriginal Dan was, I mean, very insightful questions really appreciate it. Thank you. >>Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

>> Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, is modernize now. Yes, let's talk about that. And with me is Ravi mayor him, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >> Thank you so much. I'm so glad to be here with you. >> I want to ask you what the new requirements are around modern applications. I've seen some of your comments, you got to be flexible, distributed, multimodal, mobile, edge. Those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >> Yeah, I think what has basically happened is that so far it's been a transition of sorts. And now we are come to a point where that tipping point and that tipping point has been more because of COVID and there are COVID has pushed us to a world where we are living in a in a sort of occasionally connected manner where our digital interactions precede, our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than in a digital manner, as opposed to sort of making a more specific human contact. That does really been the sort of accelerant to this modernize Now, as a team. In this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. They're all sitting behind. They used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, but they are all centralized still, but where our engagement happens with the data is at the edge at your point of convenience, at your point of consumption, not where the data is actually sitting. So this has led to, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? But it just basically comes down to the fact that the data needs to be there, if you are engaging with it. And that means if you are doing it on your mobile phone, or if you're sitting, but doing something in your while you're traveling, or whether you're in a subway, whether you're in a plane or a ship, wherever the data needs to come to you and be available, as opposed to every time you going to the data, which is centrally sitting in some place. And that is the fundamental shift in terms of how the modern architecture needs to think when they, when it comes to digital transformation and, transitioning their old applications to the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Otherwise, people are basically waiting for that circle of death that we all know, and blaming the networks and other pieces. The problem was actually, the data is not where you are engaging with it. It's got to be fetched, you know, seven sea's away. And that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >> I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this because date data by its very nature is distributed. It's always been distributed, but with the distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, of, of a super rock solid database that can handle, you know, distributed data? >> Yes. So there are two issues that you alluded little too over there. The first is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data. In one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, when you have the data, you can first look at it to perform. Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five milliseconds, this is, this is a class of problem that we solve that same data. Now, eventually, without you ever having to sort of do a casting it to a different database, you can now do solid queries. Our classic SQL queries, which is our next magic. We are a no SQL database, but we have a full functional SQL. The SQL has been the language that has talked to data for 40 odd years successfully. Every other database has come and tried to implement their own QL query language, but they've all failed only SQL has stood the test of time of 40 odd years. Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is basically a look at the data and any common editorial, any, any which way you look at the data, all it will come, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries select star from where, kind of stuff, because it's at an English level becomes easy to so the same day that you didn't have to go move it to another database, do your sort of transformation of the data and all the stuff, same day that you do this. Now that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, what Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the, your ability to query the operational data in a different way. And talk querying, what was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and apply different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, the database management system. And that's where the distributed platform that we have built enables us to get it to where you need the data to be, you know, in the classic way we call it CDN'ing the data as in like content delivery networks. So far do static, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >> And on the first part of, of the, the, the answer to my question, are you saying you could do this without scheme with a no schema on, right? And then you can apply those techniques. >> Fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read thing. So, because there is no schema, it is just a Json document that is sitting inside. And Json is the lingua franca of the web, as you very well know by now. So it just Json that we manage, you can do key value look ups of the Json. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and other sophisticated pieces of technology behind it. You can do searching on it, using the, the full textual analysis pipeline. You can do ad hoc webbing on the analytics side, and you can write your own custom logic on it using or inventing capabilities. So that's, that's what it allows because we keep the data in the native form of Json. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, bring, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing in the last 40 years, because we developed various database systems and data processing systems at various points in time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. We had queuing systems, all these systems, if you want to use any one of them are answered. It always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this, it's not going to fly instead, bring the logic to the data, right? So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this. >> But as you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >> Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data recasting because it required you to have it in seven schema in one sense at, at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably related, but it not really, how do you say keep to the promise that it actually meant to be? So that's why it was a swamp I mean, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it. And you create different types of indexes to manage it. You distribute the index, you distribute the data you have, like we were discussing, you have ACID semantics on top of, and when you, when you put all these things together, it's, it's, it's a tough proposition, but we have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >> So you predicted the trend around multimodal and converged databases. You kind of led Couchbase through that. I, I want, I always ask this question because it's clearly a trend in the industry and it, and it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, where you have have a little teeny scissors and a knife, that's not that sharp. How, how do you respond to that? >> A great one. My answer is always, I use another analogy to tackle that, and is that, have you ever accused a smartphone of being a Swiss army knife? - No. No. >> Nobody does. That because it actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Right? As in a moment, it could be a TomTom, telling you all the directions, the next one, it's your PDA. Third one. It's a fantastic phone. Four. It's a beautiful camera which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment, it's the video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just thought that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, he missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app based economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get an alert saying that today you got to leave home at >> 8: 15 for your nine o'clock meeting. And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's got this notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place. Without that, you couldn't even do this simple function in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build. Because half the time you're running sideline to sideline, just, you know, integrating data from one system to the other. >> So I love the analogy with the smartphone. I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? So, so, but, but is there, is that a fair and where, in other words, those specialized databases, they say there still is a place for them, but they're getting. >> Absolutely, absolutely great analogy and a great extension to the question. That's like, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of my music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they, I mean, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. Yes, it's 90% there or 80% there. It depends on your audio file-ness of your, I mean, your experience super specialized ones do not go away. You know, there are, there are places where the specialized use cases will demand a separate system to exist. But even there that has got to be very closed. How do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that all, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car, walk into my living room, that same songs should continue and play in my living room speakers. Then it's a connected world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >> I love, I love that example too. When I was a kid, we used to go to Tweeter, et cetera. And we used to play around with three, take home, big four foot speakers. Those stores are out of business too. Absolutely. And now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi? >> I believe so, because I think what had happened was relational systems. I've mean where the norm, they rule the roost, if you will, for the last 40 odd years and then gain this no SQL movement, which was almost as though a rebellion from the relational world, we all inhabited because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee. They required your DBA and your data architect. And you had to call them just to add one column and stuff like that. And the world had moved on. This was a world of blogs and tweets and, you know, mashups and a different generation of digital behavior, There are digital, native people now who are operating in these and the, the applications, the, the consumer facing applications. We are living in this world. And yet the enterprise ones were still living in the, in the other, the other side of the divide. So out came this solution to say that we don't need SQL. Actually the problem was never SQL. No SQL was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations and the inability for these, the system to scale, the relational systems were built like airplanes, which is that if a San Francisco, Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the allowance that you'll somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. These are all vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world is make the system horizontally scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guest. I'll add one more coach to it, one more car to it. And the better part of the way we have done this here is that, and we are super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have, I need only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. You can attach the kind of coaches we call this multidimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it, right? So that's the beauty of this architecture. Now, why is that architecture important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because, you would say that I cannot run this analytical query because then my operational workload will suffer. Then my front end, then we'll slow down millions of customers that impacted that problem. They'll solve the same data once again, do analytical query, an operational query because they're separated by these cars, right? As in like we, we, we fence the, the, the resources so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or a query. And then yet you can run this analytical query, which will take a couple of minutes to them. One, not impeding the other. So that's in one sense, sort of the part of the problems that we have solved it here is that relational versus the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same query language on top. Why? It's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, the internal combustion engine the gas, you says, these were the issues we really wanted to solve. So solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, over there your gear shifters. Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So even when you feed people, the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blow harder to go fast. And they lean back for, for it to, you know, to apply a break that's, that's how we seem to define design software. Instead, we shouldn't be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, and the gas pedal and the, and the gear shifters by putting SQL back on underneath the surface, we have completely solved the relational limitations of schema, as well as scalability. So in, in, in that way, and by bringing back the classic ACID capabilities, which is what relational systems we accounted on, and being able to do that with the SQL programming language, we call it like multi-statement SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up to solve in the modern times, rather than get sort of pedantic about whether it's we have no SQL or SQL or new SQL, or, you know, any of that sort of jargon oriented debate. This is, these are the debates of computer science that they are actually, and they were the solve, and they have solved them with the latest release of 7.0, which we released a few months ago. >> Right, right. Last July, Ravi, we got got to leave it there. I love the examples and the analogies. I can't wait to be face-to-face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >> Fantastic. Thanks for the time. And the opportunity I was, I mean, very insightful questions really appreciate it. - Thank you. >> Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

>> (lively music) >> Narrator: 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 2021, the digital event experience. I'm your host, Lisa Martin. I've got an alumni joining me and a brand new guest to the CUBE please welcome Paul Papas, the Global Managing Partner, for IBM Global Business Services, this is transformation services. Paul, welcome back to the virtual CUBE. >> Thanks Lisa great to be here with you today. And Dominique Dubois is here as well. She is the Global Strategy and Offerings Leader in business transformation services or BTS at IBM. Dominique, welcome to the program. >> Thanks Lisa, great to be here. So, we're going to be talking about accelerating business transformation with intelligent workflows. We're going to break through all that, but Paul we're going to start with you. Since we last got together with IBM, a lot has changed so much transformation, so much acceleration of transformation. Talk to me from your perspective, how have you seen the way that businesses running change and what some of the changes in the future are going to be? >> Well, you hit on two key words there Lisa and thanks so much for that question. Two key words that you hit on were change and acceleration. And that's exactly what we see. We were seeing this before the pandemic and if anything, with the pandemic did when things started started kind of spreading around the world late or early last year, around January, February timeframe we saw that word acceleration really take hold. Every one of our clients were looking for new ways to accelerate the change that they had already planned to adapt to this new, this new normal or this new abnormal, depending on how you view it. In fact, we did a study recently, an IBV study that's our Institute of Business Value and found that six out of 10 organizations were accelerating all of their transformation initiatives they had already planned. And that's exactly what we're seeing happening right now in all parts of the world and across all industries. This acceleration to transform. >> So, one of the things that we've talked about for years, Paul, before the pandemic was even a thing, is that there was a lot of perceived technical barriers in terms of like the tech maturity for organizations and employees being opposed to change. People obviously it can be a challenge. They're used to doing things the way they are. But as you just said, in that IBV survey, nearly 60% of businesses say we have to accelerate our transformation due to COVID, probably initially to survive and then thrive. Talk to me about some of those, those barriers that were there a little over a year ago and how businesses 60 plus percent of them have moved those out of the way. >> You know at IBM we've got a 109 year history of being a technology innovation company. And the rate of pace of technical change is always increasing. It's something that we love and that we're comfortable with. But the rate and pace of change is always unsettling. And there's always a human element for change. And the human element is always the rate, the rate setter in terms of the amount of change that you can have in an organization. Our former chairman Ginni Rometty, used to say that growth and comfort cannot co-exist. And it's so true because changing is uncomfortable. It's unsettling. It can be, it can be nerve-racking. It can instill fear and fear can be paralyzing in terms of driving change. And what we also see is there's a disconnect, a lot of times and that IBV study that I was referring to before, we saw results coming back where 78% of executives feel that they have provided the training and enablement to help their employees transform to new required skills and new ways of working but only half of the people surveyed felt the same way. Similarly, we saw a disconnect in terms of companies feeling that they're providing the right level of health and wellness support during the pandemic. And only half of the employees responded back they feel that they're getting that level of support. So, the people change aspect of doing a transformation or adapting to new circumstances is always the most critical component and always the hardest component. And when we talk about helping our clients do that in IBM that's our service as organization. That's the organization that Dominique Dubois is representing here today. I'm responsible for business transformation services within our organization. We help our clients adapt using new technologies, transforming the way they work, but also addressing the people change elements that could be so difficult and hitting them head on so that they can make sure that they can survive and thrive in a meaningful and lasting way in this new world. >> One of the hardest things is that cultural transformation regardless of a pandemic. So, I can't imagine I'd love to get one more thing, Paul from you before we head over to Dominique. IBM is on 109 year old organization. Talk to me about the IBM pledge. This is something that came up last year, huge organization massive changes last year, not just the work from home that the mental concerns and issues that people had. What did IBM do like as a grassroots effort that went viral? >> Yeah, so, it's really great. So, when the pandemic started, we all have to shift it, We all have to shift to working from home. And as you mentioned, IBM's 109 year old company, we have over 300,000 employees working in 170 countries. So, we had to move this entire workforce. It's 370,000 humans to working in a new way that many of which have never done before. And when we started experiencing, the minute we did that, within a few weeks, my team and I were talking Dominique is on my team and we were having conversations where we were feeling really exhausted. Just a few weeks into this and it was because we were constantly on Webex, we were constantly connected and we're all used to working really hard. We travel a lot, we're always with our clients. So, it wasn't that, you have a team that is adapting to like working more hours or longer hours, but this was fundamentally different. And we saw that with schools shutting down and lock downs happening in different of the world the home life balance was getting immediately difficult to impossible to deal with. We have people that are taking care of elderly parents, people that are homeschooling children, other personal life situations that everyone had to navigate in the middle of a pandemic locked at home with different restrictions on when you can go out and get things done. So, we got together as a group and we just started talking about how can we help? How can we help make life just a little bit easier for all of our people? And we started writing down some things that we would, we would commit to doing with each other. How we would address each other. And when that gave birth to was what we call the IBM Work From Home Pledge. And it's a set of principles, all grounded in the belief that, if we act this way, we might just be able to make life just a little bit easier for each other and it's grounded in empathy. And there are parts of the Plex that are pledging to be kind. Recognizing that in this new digital world that we're showing up on camera inside of everyone's home. We're guests in each other's homes. So, let's make sure that we act appropriately as guests at each other's home. So, if children run into the frame during the middle of a meeting or dog started barking during the middle of a meeting, just roll with it. Don't call out attention to it. Don't make people feel self-conscious about it. Pledged the support so your fellow IBM by making time for personal needs. So, if someone has to, do homeschooling in the middle of the day, like Dominique's got triplets she's got to do homeschooling in the middle of the day. Block that time off and we will respect that time on your calendar. And just work around it and just deal with it. There are other things like respecting that camera ready time. As someone who's now been on camera every day it feels like for the last 14 months we want to respect the time that people when they have their cameras off. And not pressure them to put their cameras on saying things like, Hey, I can't see you. There's no reason to add more pressure to everyone's life, if someone's camera's off, it's all for a reason. And then other things like pledging to checking on each other, pledging to set boundaries and tend to our own self-care. So, we published that as a group, we just again and we put it on a Slack channel. So it's kind of our communication method inside the company. It was just intended to be for my organization but it started going viral and tens of thousands of IBM members started taking, started taking the pledge and ultimately caught the attention of our CEO and he loved it, shared it with his leadership team, which I'm a part of. And then also then went on LinkedIn and publicly took the pledge as well. Which then also got more excitement and interaction with other companies as well. So, grassroots effort all grounded in showing empathy and helping to make life just a little bit easier for everyone. >> So important, I'm going to look that up and I'm going to tell you as a person who speaks with many tech companies a week. A lot of businesses could take a lead from that and it gets really important and we are inviting each other into our homes and I see you're a big Broadway fan I'll have to ask you that after we wrap (giggles) Dominique I don't know how you're doing any of this with triplets. I only have two dogs (Dominique laughs) but I'd love to know this sense of urgency, that is everywhere you're living it. Paul talked about it with respect to the acceleration of transformation. How from your lens is IBM and IBM helping customers address the urgency, the need to pivot, the need to accelerate, the need to survive and thrive with respect to digital transformation actually getting it done? >> Right, thanks Lisa, so true our clients are really needing to and ready to move with haste. That that sense of urgency can be felt I think across every country, every market, every industry. And so we're really helping our clients accelerate their digital transformations and we do that through something that we call intelligent workflows. And so workflows in and of themselves are basically how organizations get work done. But intelligent workflows are how we infuse; predictive properties, automation, transparency, agility, end to end across a workflow. So, pulling those processes together so they're not solid anymore and infusing. So, simply put we bring intelligent workflows to our clients and it fundamentally reinvents how they're getting work done from a digital perspective, from a predictive perspective, from a transparency perspective. And I think what really stands apart when we deliver this with our clients in partnership with our clients is how it not only delivers value to the bottom line, to the top line it also actually delivers greater value to their employees, to the customers, to the partner to their broader ecosystem. And intelligent workflows are really made up of three core elements. The first is around better utilizing data. So, aggregating, analyzing, getting deeper insight out of data, and then using that insight not just for employees to make better decisions, but actually to support for emerging technologies to leverage. So we talked about AI, automation, IOT, blockchain, all of these technologies require vast amounts of data. And what we're able to bring both on the internal and external source from a data perspective really underpins what these emerging technologies can do. And then the third area is skills. Our skills that we bring to the table, but also our clients deep, deep expertise, partner expertise, expertise from the ecosystem at large and pulling all of that together, is how we're really able to help our clients accelerate their digital transformations because we're helping them shift, from a set of siloed static processes to an end-to-end workflow. We're helping them make fewer predictions based on the past historical data and actually taking more real-time action with real time insights. So, it really is a fundamental shift and how your work is getting done to really being able to provide that emerging technologies, data, deep skills-based end to end workflow. >> That word fundamental has such gravity. and I know we say data has gravity being fundamental in such an incredibly dynamic time is really challenging but I was looking through some of the notes that you guys provided me with. And in terms of what you just talked about, Dominique versus making a change to a silo, the benefits and making changes to a spectrum of integrated processes the values can be huge. In fact, I was reading that changing a single process like billing, for example might deliver up to 20% improved results. But integrating across multiple processes, like billing, collections, organizations can achieve double that up to 40%. And then there's more taking the intelligent workflow across all lead to cash. This was huge. Clients can get 50 to 70% more value from that. So that just shows that fundamental impact that intelligent workflows can make. >> Right, I mean, it really is when we see it really is about unlocking exponential value. So, when you think about crossing end to end workflow but also, really enhancing what clients are doing and what companies are doing today with those exponential technologies from kind of single use the automation POC here and AI application POC here, actually integrating those technologies together and applying them at scale. When I think intelligent workflows I think acceleration. I think exponential value. But I also really think about at scale. Because it's really the ability to apply these technologies the expertise at scale that allows us to start to unlock a lot of that value. >> So let's go over Paul, in the last few minutes that we have here I want to talk about IBM garage and how this is helping clients to really transform those workflows. Talk to me a little bit about what IBM garage is. I know it's not IBM garage band and I know it's been around since before the pandemic but help us understand what that is and how it's delivering value to customers. >> Well, first I'm going to be the first to invite you to join the IBM garage band, Lisa so we'd love to have you >> I'm in. no musical experience required... >> I like to sing, all right I mean (laughs) We're ready, we're ready for. So, let me talk to you about IBM garage and I do want to key on two words that Dominique was mentioning speed and scale. Because that's what our clients are really looking for when they're doing transformations around intelligent workflows. How can you transform at scale, but do that with speed. And that really becomes the critical issue. As Dominique mentioned, there's a lot of companies that can help you do a proof of concept do something in a few weeks that you can test an idea out and have something that's kind of like a throw away piece of work that maybe proves a point or just proves a point. But even if it does prove the point at that point you'd have to restart a new, to try to get something that you could actually scale either in the production technology environment or scale as a change across an organization. And that's where IBM garage comes in. It's all a way of helping our clients co-create, co-execute and then cooperate, innovating at scale. So, we use methods like design thinking inside of IBM we've trained several hundred thousand people on design thinking methods. We use technologies like neural and other things that help our clients co-create in a dynamic environment. And what's amazing for me is that, the cause of the way we were, we were doing work with clients in a garage with using IBM garage in a garage environment before the pandemic. And one of our clients Frito-Lay of North America, is an example where we've helped them innovate at scale and speed using IBM garage over a long period of time. And when the pandemic hit, we in fact were running 11 garages across 11 different workflow areas for them the pandemic hit and everyone was sent home. So, we all instantly overnight had to work from home together with relay. And what was great is that we were able to quickly adapt the garage method to working in a virtual world. To being able to run that same type of innovation and then use that innovation at scale in a virtual world, we did that overnight. And since that time which happened, that happened back in March of last year throughout the pandemic, we've run over 1500 different garage engagements with all of our clients all around the world in a virtual, in a virtual environment. It's just an incredible way, like I said to help our clients innovate at scale. >> That's fantastic, go ahead Dominique. >> Oh, sorry, was just said it's a great example, we partnered with FlightSafety International, they train pilots. And I think a great example of that speed and scale right is in less than 12 weeks due to the garage methodology and the partnership with FlightSafety, we created with them and launched an adaptive learning solution. So, a platform as well as a complete change to their training workflow such that they had personalized kind of real-time next best training for how they train their pilots for simulators. So, reducing their cycle time but also improving the training that their pilots get, which as people who normally travel, it's really important to us and everyone else. So, just a really good example, less than 12 weeks start to start to finish. >> Right, talk about acceleration. Paul, last question for you, we've got about 30 seconds left I know this is an ecosystem effort of IBM, it's ecosystem partners, it's Alliance partners. How are you helping align right partner with the right customer, the right use case? >> Yeah, it's great. And our CEO Arvind Krishna has really ushered in this era where we are all about the open ecosystem here at IBM and working with our ecosystem partners. In our services business we have partnerships with all the major, all the major technology players. We have a 45 year relationship with SAP. We've done more SAP S 400 implementations than anyone in the world. We've got the longest standing consulting relationship with Salesforce, we've got a unique relationship with Adobe, they're only services and technology partner in the ecosystem. And we just recently won three, procedures Partner Awards, with them and most recently we announced a partnership with Celonis which is an incredible process execution software company, process mining software company that's going to help us transform intelligent workflows in an accelerated way, embedded in our garage environment. So, ecosystem is critical to our success but more importantly, it's critical to our client success. We know that no one alone has the answers and no one alone can help anyone change. So, with this open ecosystem approach that we take and global business services and our business transformation services organization, we're able to make sure that we bring our clients the best of everyone's capabilities. Whether it's our technology, partners, our services IBM's own technology capabilities, all in the mix, all orchestrated in service to our client's needs all with the goal of driving superior business outcomes for them. >> And helping those customers in any industry to accelerate their business transformation with those intelligent workloads and a very dynamic time. This is a topic we could keep talking about unfortunately, we are out of time but thank you both for stopping by and sharing with me what's going on with respect to intelligent workflows. How the incremental exponential value it's helping organizations to deliver and all the work that IBM is doing to enable its customers to be thrivers of tomorrow. We appreciate talking to you >> Paul: Thanks Lisa. >> Dominique: Thank you >> For Paul Papas and Dominique Dubois I'm Lisa Martin. You're watching the CUBE's coverage of IBM Think the digital event experience. (gentle music)

(lively 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 2021, the digital event experience. I'm your host, Lisa Martin. I've got an alumni joining me and a brand new guest to the CUBE please welcome Paul Papas, the Global Managing Partner, for IBM Global Business Services, this is transformation services. Paul, welcome back to the virtual CUBE. >> Thanks Lisa great to be here with you today. And Dominique Dubois is here as well. She is the Global Strategy and Offerings Leader in business transformation services or BTS at IBM. Dominique, welcome to the program. >> Thanks Lisa, great to be here. So, we're going to be talking about accelerating business transformation with intelligent workflows. We're going to break through all that, but Paul we're going to start with you. Since we last got together with IBM, a lot has changed so much transformation, so much acceleration of transformation. Talk to me from your perspective, how have you seen the way that businesses running change and what some of the changes in the future are going to be? >> Well, you hit on two key words there Lisa and thanks so much for that question. Two key words that you hit on were change and acceleration. And that's exactly what we see. We were seeing this before the pandemic and if anything, with the pandemic did when things started started kind of spreading around the world late or early last year, around January, February timeframe we saw that word acceleration really take hold. Every one of our clients were looking for new ways to accelerate the change that they had already planned to adapt to this new, this new normal or this new abnormal, depending on how you view it. In fact, we did a study recently, an IBV study that's our Institute of Business Value and found that six out of 10 organizations were accelerating all of their transformation initiatives they had already planned. And that's exactly what we're seeing happening right now in all parts of the world and across all industries. This acceleration to transform. >> So, one of the things that we've talked about for years, Paul, before the pandemic was even a thing, is that there was a lot of perceived technical barriers in terms of like the tech maturity for organizations and employees being opposed to change. People obviously it can be a challenge. They're used to doing things the way they are. But as you just said, in that IBV survey, nearly 60% of businesses say we have to accelerate our transformation due to COVID, probably initially to survive and then thrive. Talk to me about some of those, those barriers that were there a little over a year ago and how businesses 60 plus percent of them have moved those out of the way. >> You know at IBM we've got 109 year history of being a technology innovation company. And the rate of pace of technical change is always increasing. It's something that we love and that we're comfortable with. But the rate and pace of change is always unsettling. And there's always a human element for change. And the human element is always the rate, the rate setter in terms of the amount of change that you can have in an organization. Our former chairman Ginni Rometty, used to say that growth and comfort cannot co-exist. And it's so true because changing is uncomfortable. It's unsettling. It can be, it can be nerve-racking. It can instill fear and fear can be paralyzing in terms of driving change. And what we also see is there's a disconnect, a lot of times and that IBV study that I was referring to before, we saw results coming back where 78% of executives feel that they have provided the training and enablement to help their employees transform to new required skills and new ways of working but only half of the people surveyed felt the same way. Similarly, we saw a disconnect in terms of companies feeling that they're providing the right level of health and wellness support during the pandemic. And only half of the employees responded back they feel that they're getting that level of support. So, the people change aspect of may doing a transformation or adapting to new circumstances is always the most critical component and always the hardest component. And when we talk about helping our clients do that in IBM that's our service as organization. That's the organization that Dominique Dubois are representing here today. I'm responsible for business transformation services within our organization. We help our clients adapt using new technologies, transforming the way they work, but also addressing the people change elements that could be so difficult and hitting them head on so that they can make sure that they can survive and thrive in a meaningful and lasting way in this new world. >> One of the hardest things is that cultural transformation regardless of a pandemic. So, I can't imagine I'd love to get one more thing, Paul from you before we head over to Dominique. IBM is on 109 year old organization. Talk to me about the IBM pledge. This is something that came up last year, huge organization massive changes last year, not just the work from home that the mental concerns and issues that people had. What did IBM do like as a grassroots effort that went viral? >> Yeah, so, it's really great. So, when the pandemic started, we all have to shift it, We all have to shift to working from home. And as you mentioned, IBM's 109 year old company, we have over 300,000 employees working in 170 countries. So, we had to move this entire workforce. It's 370,000 humans to working in a new way that many of which have never done before. And when we started experiencing, the minute we did that, within a few weeks, my team and I were talking Dominique is on my team and we were having conversations where we were feeling really exhausted. Just a few weeks into this and it was because we were constantly on Webex, we were constantly connected and we're all used to working really hard. We travel a lot, we're always with our clients. So, it wasn't that, you have a team that is adapting to like working more hours or longer hours, but this was fundamentally different. And we saw that with schools shutting down and lock downs happening in different of the world the home life balance was getting immediately difficult to impossible to deal with. We have people that are taking care of elderly parents, people that are homeschooling children, other personal life situations that everyone had to navigate in the middle of a pandemic locked at home with different restrictions on when you can go out and get things done. So, we got together as a group and we just started talking about how can we help? How can we help make life just a little bit easier for all of our people? And we started writing down some things that we would, we would commit to doing with each other. How we would address each other. And when that gave birth to was what we call the IBM Work From Home Pledge. And it's a set of principles, all grounded in the belief that, if we act this way, we might just be able to make life just a little bit easier for each other and it's grounded in empathy. And there are parts of the Plex that are pledging to be kind. Recognizing that in this new digital world that we're showing up on camera inside of everyone's home. We're guests in each other's homes. So, let's make sure that we act appropriately as guests at each other's home. So, if children run into the frame during the middle of a meeting or dog started barking during the middle of a meeting, just roll with it. Don't call out attention to it. Don't make people feel self-conscious about it. Pledged the support so your fellow IBM by making time for personal needs. So, if someone has to, do homeschooling in the middle of the day, like Dominique's got triplets she's got to do homeschooling in the middle of the day. Block that time off and we will respect that time on your calendar. And just work around it and just deal with it. There are other things like respecting that camera ready time. As someone who's now been on camera every day it feels like for the last 14 months we want to respect the time that people when they have their cameras off. And not pressure them to put their cameras on saying things like, Hey, I can't see you. There's no reason to add more pressure to everyone's life, if someone's camera's off, it's all for a reason. And then other things like pledging to checking on each other, pledging to set boundaries and tend to our own self-care. So, we published that as a group, we just again and we put it on a Slack channel. So it's kind of our communication method inside the company. It was just intended to be for my organization but it started going viral and tens of thousands of IBM members started taking, started taking the pledge and ultimately caught the attention of our CEO and he loved it, shared it with his leadership team, which I'm a part of. And then also then went on LinkedIn and publicly took the pledge as well. Which then also got more excitement and interaction with other companies as well. So, grassroots effort all grounded in showing empathy and helping to make life just a little bit easier for everyone. >> So important, I'm going to look that up and I'm going to tell you as a person who speaks with many tech companies a week. A lot of businesses could take a lead from that and it gets really important and we are inviting each other into our homes and I see you're a big Broadway fan I'll have to ask you that after we wrap (giggles) Dominique I don't know how you're doing any of this with triplets. I only have two dogs (Dominique laughs) but I'd love to know this sense of urgency, that is everywhere you're living it. Paul talked about it with respect to the acceleration of transformation. How from your lens is IBM and IBM helping customers address the urgency, the need to pivot, the need to accelerate, the need to survive and thrive with respect to digital transformation actually getting it done? >> Right, thanks Lisa, so true our clients are really needing to and ready to move with haste. That that sense of urgency can be felt I think across every country, every market, every industry. And so we're really helping our clients accelerate their digital transformations and we do that through something that we call intelligent workflows. And so workflows in and of themselves are basically how organizations get work done. But intelligent workflows are how we infuse; predictive properties, automation, transparency, agility, end to end across a workflow. So, pulling those processes together so they're not solid anymore and infusing. So, simply put we bring intelligent workflows to our clients and it fundamentally reinvents how they're getting work done from a digital perspective, from a predictive perspective, from a transparency perspective. And I think what really stands apart when we deliver this with our clients in partnership with our clients is how it not only delivers value to the bottom line, to the top line it also actually delivers greater value to their employees, to the customers, to the partner to their broader ecosystem. And intelligent workflows are really made up of three core elements. The first is around better utilizing data. So, aggregating, analyzing, getting deeper insight out of data, and then using that insight not just for employees to make better decisions, but actually to support for emerging technologies to leverage. So we talked about AI, automation, IOT, blockchain, all of these technologies require vast amounts of data. And what we're able to bring both on the internal and external source from a data perspective really underpins what these emerging technologies can do. And then the third area is skills. Our skills that we bring to the table, but also our clients deep, deep expertise, partner expertise, expertise from the ecosystem at large and pulling all of that together, is how we're really able to help our clients accelerate their digital transformations because we're helping them shift, from a set of siloed static processes to an end-to-end workflow. We're helping them make fewer predictions based on the past historical data and actually taking more real-time action with real time insights. So, it really is a fundamental shift and how your work is getting done to really being able to provide that emerging technologies, data, deep skills-based end to end workflow. >> That word fundamental has such gravity. and I know we say data has gravity being fundamental in such an incredibly dynamic time is really challenging but I was looking through some of the notes that you guys provided me with. And in terms of what you just talked about, Dominique versus making a change to a silo, the benefits and making changes to a spectrum of integrated processes the values can be huge. In fact, I was reading that changing a single process like billing, for example might deliver up to 20% improved results. But integrating across multiple processes, like billing, collections, organizations can achieve double that up to 40%. And then there's more taking the intelligent workflow across all lead to cash. This was huge. Clients can get 50 to 70% more value from that. So that just shows that fundamental impact that intelligent workflows can make. >> Right, I mean, it really is when we see it really is about unlocking exponential value. So, when you think about crossing end to end workflow but also, really enhancing what clients are doing and what companies are doing today with those exponential technologies from kind of single use the automation POC here and AI application POC here, actually integrating those technologies together and applying them at scale. When I think intelligent workflows I think acceleration. I think exponential value. But I also really think about at scale. Because it's really the ability to apply these technologies the expertise at scale that allows us to start to unlock a lot of that value. >> So let's go over Paul, in the last few minutes that we have here I want to talk about IBM garage and how this is helping clients to really transform those workflows. Talk to me a little bit about what IBM garage is. I know it's not IBM garage band and I know it's been around since before the pandemic but help us understand what that is and how it's delivering value to customers. >> Well, first I'm going to be the first to invite you to join the IBM garage band, Lisa so we'd love to have you >> I'm in. no musical experience required... >> I like to sing, all right I mean (laughs) We're ready, we're ready for. So, let me talk to you about IBM garage and I do want to key on two words that Dominique was mentioning speed and scale. Because that's what our clients are really looking for when they're doing transformations around intelligent workflows. How can you transform at scale, but do that with speed. And that really becomes the critical issue. As Dominique mentioned, there's a lot of companies that can help you do a proof of concept do something in a few weeks that you can test an idea out and have something that's kind of like a throw away piece of work that maybe proves a point or just proves a point. But even if it does prove the point at that point you'd have to restart a new, to try to get something that you could actually scale either in the production technology environment or scale as a change across an organization. And that's where IBM garage comes in. It's all a way of helping our clients co-create, co-execute and then cooperate, innovating at scale. So, we use methods like design thinking inside of IBM we've trained several hundred thousand people on design thinking methods. We use technologies like neural and other things that help our clients co-create in a dynamic environment. And what's amazing for me is that, the cause of the way we were, we were doing work with clients in a garage with using IBM garage in a garage environment before the pandemic. And one of our clients Frito-Lay of North America, is an example where we've helped them innovate at scale and speed using IBM garage over a long period of time. And when the pandemic hit, we in fact were running 11 garages across 11 different workflow areas for them the pandemic hit and everyone was sent home. So, we all instantly overnight had to work from home together with relay. And what was great is that we were able to quickly adapt the garage method to working in a virtual world. To being able to run that same type of innovation and then use that innovation at scale in a virtual world, we did that overnight. And since that time which happened, that happened back in March of last year throughout the pandemic, we've run over 1500 different garage engagements with all of our clients all around the world in a virtual, in a virtual environment. It's just an incredible way, like I said to help our clients innovate at scale. >> That's fantastic, go ahead Dominique. >> Oh, sorry, was just said it's a great example, we partnered with FlightSafety International, they train pilots. And I think a great example of that speed and scale right is in less than 12 weeks due to the garage methodology and the partnership with FlightSafety, we created with them and launched an adaptive learning solution. So, a platform as well as a complete change to their training workflow such that they had personalized kind of real-time next best training for how they train their pilots for simulators. So, reducing their cycle time but also improving the training that their pilots get, which as people who normally travel, it's really important to us and everyone else. So, just a really good example, less than 12 weeks start to start to finish. >> Right, talk about acceleration. Paul, last question for you, we've got about 30 seconds left I know this is an ecosystem effort of IBM, it's ecosystem partners, it's Alliance partners. How are you helping align right partner with the right customer, the right use case? >> Yeah, it's great. And our CEO Arvind Krishna has really ushered in this era where we are all about the open ecosystem here at IBM and working with our ecosystem partners. In our services business we have partnerships with all the major, all the major technology players. We have a 45 year relationship with SAP. We've done more SAP S 400 implementations than anyone in the world. We've got the longest standing consulting relationship with Salesforce, we've got a unique relationship with Adobe, they're only services and technology partner in the ecosystem. And we just recently won three, procedures Partner Awards, with them and most recently we announced a partnership with Celonis which is an incredible process execution software company, process mining software company that's going to help us transform intelligent workflows in an accelerated way, embedded in our garage environment. So, ecosystem is critical to our success but more importantly, it's critical to our client success. We know that no one alone has the answers and no one alone can help anyone change. So, with this open ecosystem approach that we take and global business services and our business transformation services organization, we're able to make sure that we bring our clients the best of everyone's capabilities. Whether it's our technology, partners, our services IBM's own technology capabilities, all in the mix, all orchestrated in service to our client's needs all with the goal of driving superior business outcomes for them. >> And helping those customers in any industry to accelerate their business transformation with those intelligent workloads and a very dynamic time. This is a topic we could keep talking about unfortunately, we are out of time but thank you both for stopping by and sharing with me what's going on with respect to intelligent workflows. How the incremental exponential value it's helping organizations to deliver and all the work that IBM is doing to enable its customers to be thrivers of tomorrow. We appreciate talking to you >> Thanks Lisa. >> Thank you >> For Paul Papas and Dominique Dubois I'm Lisa Martin. You're watching the CUBE's coverage of IBM Think the digital event experience. (gentle music)

>> Narrator: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. >> Welcome back to IBM's Machine Learning Everywhere. Build your ladder to AI, along with Dave Vellante, John Walls here, wrapping up here in New York City. Just about done with the programming here in Midtown. Dave, let's just take a step back. We've heard a lot, seen a lot, talked to a lot of folks today. First off, tell me, AI. We've heard some optimistic outlooks, some, I wouldn't say pessimistic, but some folks saying, "Eh, hold off." Not as daunting as some might think. So just your take on the artificial intelligence conversation we've heard so far today. >> I think generally, John, that people don't realize what's coming. I think the industry, in general, our industry, technology industry, the consumers of technology, the businesses that are out there, they're steeped in the past, that's what they know. They know what they've done, they know the history and they're looking at that as past equals prologue. Everybody knows that's not the case, but I think it's hard for people to envision what's coming, and what the potential of AI is. Having said that, Jennifer Shin is a near-term pessimist on the potential for AI, and rightly so. There are a lot of implementation challenges. But as we said at the open, I'm very convinced that we are now entering a new era. The Hadoop big data industry is going to pale in comparison to what we're seeing. And we're already seeing very clear glimpses of it. The obvious things are Airbnb and Uber, and the disruptions that are going on with Netflix and over-the-top programming, and how Google has changed advertising, and how Amazon is changing and has changed retail. But what you can see, and again, the best examples are Apple getting into financial services, moving into healthcare, trying to solve that problem. Amazon buying a grocer. The rumor that I heard about Amazon potentially buying Nordstrom, which my wife said is a horrible idea. (John laughs) But think about the fact that they can do that is a function of, that they are a digital-first company. Are built around data, and they can take those data models and they can apply it to different places. Who would have thought, for example, that Alexa would be so successful? That Siri is not so great? >> Alexa's become our best friend. >> And it came out of the blue. And it seems like Google has a pretty competitive piece there, but I can almost guarantee that doing this with our thumbs is not the way in which we're going to communicate in the future. It's going to be some kind of natural language interface that's going to rely on artificial intelligence and machine learning and the like. And so, I think it's hard for people to envision what's coming, other than fast forward where machines take over the world and Stephen Hawking and Elon Musk say, "Hey, we should be concerned." Maybe they're right, not in the next 10 years. >> You mentioned Jennifer, we were talking about her and the influencer panel, and we've heard from others as well, it's a combination of human intelligence and artificial intelligence. That combination's more powerful than just artificial intelligence, and so, there is a human component to this. So, for those who might be on the edge of their seat a little bit, or looking at this from a slightly more concerning perspective, maybe not the case. Maybe not necessary, is what you're thinking. >> I guess at the end of the day, the question is, "Is the world going to be a better place with all this AI? "Are we going to be more prosperous, more productive, "healthier, safer on the roads?" I am an optimist, I come down on the side of yes. I would not want to go back to the days where I didn't have GPS. That's worth it to me. >> Can you imagine, right? If you did that now, you go back five years, just five years from where we are now, back to where we were. Waze was nowhere, right? >> All the downside of these things, I feel is offset by that. And I do think it's incumbent upon the industry to try to deal with the problem, especially with young people, the blue light problem. >> John: The addictive issue. >> That's right. But I feel like those downsides are manageable, and the upsides are of enough value that society is going to continue to move forward. And I do think that humans and machines are going to continue to coexist, at least in the near- to mid- reasonable long-term. But the question is, "What can machines "do that humans can't do?" And "What can humans do that machines can't do?" And the answer to that changes every year. It's like I said earlier, not too long ago, machines couldn't climb stairs. They can now, robots can climb stairs. Can they negotiate? Can they identify cats? Who would've imagined that all these cats on the Internet would've led to facial recognition technology. It's improving very, very rapidly. So, I guess my point is that that is changing very rapidly, and there's no question it's going to have an impact on society and an impact on jobs, and all those other negative things that people talk about. To me, the key is, how do we embrace that and turn it into an opportunity? And it's about education, it's about creativity, it's about having multi-talented disciplines that you can tap. So we talked about this earlier, not just being an expert in marketing, but being an expert in marketing with digital as an understanding in your toolbox. So it's that two-tool star that I think is going to emerge. And maybe it's more than two tools. So that's how I see it shaping up. And the last thing is disruption, we talked a lot about disruption. I don't think there's any industry that's safe. Colin was saying, "Well, certain industries "that are highly regulated-" In some respects, I can see those taking longer. But I see those as the most ripe for disruption. Financial services, healthcare. Can't we solve the HIPAA challenge? We can't get access to our own healthcare information. Well, things like artificial intelligence and blockchain, we were talking off-camera about blockchain, those things, I think, can help solve the challenge of, maybe I can carry around my health profile, my medical records. I don't have access to them, it's hard to get them. So can things like artificial intelligence improve our lives? I think there's no question about it. >> What about, on the other side of the coin, if you will, the misuse concerns? There are a lot of great applications. There are a lot of great services. As you pointed out, a lot of positive, a lot of upside here. But as opportunities become available and technology develops, that you run the risk of somebody crossing the line for nefarious means. And there's a lot more at stake now because there's a lot more of us out there, if you will. So, how do you balance that? >> There's no question that's going to happen. And it has to be managed. But even if you could stop it, I would say you shouldn't because the benefits are going to outweigh the risks. And again, the question we asked the panelists, "How far can we take machines? "How far can we go?" That's question number one, number two is, "How far should we go?" We're not even close to the "should we go" yet. We're still on the, "How far can we go?" Jennifer was pointing out, I can't get my password reset 'cause I got to call somebody. That problem will be solved. >> So, you're saying it's more of a practical consideration now than an ethical one, right now? >> Right now. Moreso, and there's certainly still ethical considerations, don't get me wrong, but I see light at the end of the privacy tunnel, I see artificial intelligence as, well, analytics is helping us solve credit card fraud and things of that nature. Autonomous vehicles are just fascinating, right? Both culturally, we talked about that, you know, we learned how to drive a stick shift. (both laugh) It's a funny story you told me. >> Not going to worry about that anymore, right? >> But it was an exciting time in our lives, so there's a cultural downside of that. I don't know what the highway death toll number is, but it's enormous. If cell phones caused that many deaths, we wouldn't be using them. So that's a problem that I think things like artificial intelligence and machine intelligence can solve. And then the other big thing that we talked about is, I see a huge gap between traditional companies and these born-in-the-cloud, born-data-oriented companies. We talked about the top five companies by market cap. Microsoft, Amazon, Facebook, Alphabet, which is Google, who am I missing? >> John: Apple. >> Apple, right. And those are pretty much very much data companies. Apple's got the data from the phones, Google, we know where they get their data, et cetera, et cetera. Traditional companies, however, their data resides in silos. Jennifer talked about this, Craig, as well as Colin. Data resides in silos, it's hard to get to. It's a very human-driven business and the data is bolted on. With the companies that we just talked about, it's a data-driven business, and the humans have expertise to exploit that data, which is very important. So there's a giant skills gap in existing companies. There's data silos. The other thing we touched on this is, where does innovation come from? Innovation drives value drives disruption. So the innovation comes from data. He or she who has the best data wins. It comes from artificial intelligence, and the ability to apply artificial intelligence and machine learning. And I think something that we take for granted a lot, but it's cloud economics. And it's more than just, and somebody, one of the folks mentioned this on the interview, it's more than just putting stuff in the cloud. It's certainly managed services, that's part of it. But it's also economies of scale. It's marginal economics that are essentially zero. It's speed, it's low latency. It's, and again, global scale. You combine those things, data, artificial intelligence, and cloud economics, that's where the innovation is going to come from. And if you think about what Uber's done, what Airbnb have done, where Waze came from, they were picking and choosing from the best digital services out there, and then developing their own software from this, what I say my colleague Dave Misheloff calls this matrix. And, just to repeat, that matrix is, the vertical matrix is industries. The horizontal matrix are technology platforms, cloud, data, mobile, social, security, et cetera. They're building companies on top of that matrix. So, it's how you leverage the matrix is going to determine your future. Whether or not you get disrupted, whether your the disruptor or the disruptee. It's not just about, we talked about this at the open. Cloud, SaaS, mobile, social, big data. They're kind of yesterday's news. It's now new artificial intelligence, machine intelligence, deep learning, machine learning, cognitive. We're still trying to figure out the parlance. You could feel the changes coming. I think this matrix idea is very powerful, and how that gets leveraged in organizations ultimately will determine the levels of disruption. But every single industry is at risk. Because every single industry is going digital, digital allows you to traverse industries. We've said it many times today. Amazon went from bookseller to content producer to grocer- >> John: To grocer now, right? >> To maybe high-end retailer. Content company, Apple with Apple Pay and companies getting into healthcare, trying to solve healthcare problems. The future of warfare, you live in the Beltway. The future of warfare and cybersecurity are just coming together. One of the biggest issues I think we face as a country is we have fake news, we're seeing the weaponization of social media, as James Scott said on theCUBE. So, all these things are coming together that I think are going to make the last 10 years look tame. >> Let's just switch over to the currency of AI, data. And we've talked to, Sam Lightstone today was talking about the database querying that they've developed with the Plex product. Some fascinating capabilities now that make it a lot richer, a lot more meaningful, a lot more relevant. And that seems to be, really, an integral step to making that stuff come alive and really making it applicable to improving your business. Because they've come up with some fantastic new ways to squeeze data that's relevant out, and get it out to the user. >> Well, if you think about what I was saying earlier about data as a foundational core and human expertise around it, versus what most companies are, is human expertise with data bolted on or data in silos. What was interesting about Queryplex, I think they called it, is it essentially virtualizes the data. Well, what does that mean? That means i can have data in place, but I can have access to that data, I can democratize that data, make it accessible to people so that they can become data-driven, data is the core. Now, what I don't know, and I don't know enough, just heard about it today, I missed that announcement, I think they announced it a year ago. He mentioned DB2, he mentioned Netezza. Most of the world is not on DB2 and Netezza even though IBM customers are. I think they can get to Hadoop data stores and other data stores, I just don't know how wide that goes, what the standards look like. He joked about the standards as, the great thing about standards is- >> There are a lot of 'em. (laughs) >> There's always another one you can pick if this one fails. And he's right about that. So, that was very interesting. And so, this is again, the question, can traditional companies close that machine learning, machine intelligence, AI gap? Close being, close the gap that the big five have created. And even the small guys, small guys like Uber and Airbnb, and so forth, but even those guys are getting disrupted. The Airbnbs and the Ubers, right? Again, blockchain comes in and you say, "Why do I need a trusted third party called Uber? "Why can't I do this on the blockchain?" I predict you're going to see even those guys get disrupted. And I'll say something else, it's hard to imagine that a Google or a Facebook can be unseated. But I feel like we may be entering an era where this is their peak. Could be wrong, I'm an Apple customer. I don't know, I'm not as enthralled as I used to be. They got trillions in the bank. But is it possible that opensource and blockchain and the citizen developer, the weekend and nighttime developers, can actually attack that engine of growth for the last 10 years, 20 years, and really break that monopoly? The Internet has basically become an oligopoly where five companies, six companies, whatever, 10 companies kind of control things. Is it possible that opensource software, AI, cryptography, all this activity could challenge the status quo? Being in this business as long as I have, things never stay the same. Leaders come, leaders go. >> I just want to say, never say never. You don't know. >> So, it brings it back to IBM, which is interesting to me. It was funny, I was asking Rob Thomas a question about disruption, and I think he misinterpreted it. I think he was thinking that I was saying, "Hey, you're going to get disrupted by all these little guys." IBM's been getting disrupted for years. They know how to reinvent. A lot of people criticize IBM, how many quarters they haven't had growth, blah, blah, blah, but IBM's made some big, big bets on the future. People criticizing Watson, but it's going to be really interesting to see how all this investment that IBM has made is going to pay off. They were early on. People in the Valley like to say, "Well, the Facebooks, and even Amazon, "Google, they got the best AI. "IBM is not there with them." But think about what IBM is trying to do versus what Google is doing. They're very consumer-oriented, solving consumer problems. Consumers have really led the consumerization of IT, that's true, but none of those guys are trying to solve cancer. So IBM is talking about some big, hairy, audacious goals. And I'm not as pessimistic as some others you've seen in the trade press, it's popular to do. So, bringing it back to IBM, I saw IBM as trying to disrupt itself. The challenge IBM has, is it's got a lot of legacy software products that have purchased over the years. And it's got to figure out how to get through those. So, things like Queryplex allow them to create abstraction layers. Things like Bluemix allow them to bring together their hundreds and hundreds and hundreds of SaaS applications. That takes time, but I do see IBM making some big investments to disrupt themselves. They've got a huge analytics business. We've been covering them for quite some time now. They're a leader, if not the leader, in that business. So, their challenge is, "Okay, how do we now "apply all these technologies to help "our customers create innovation?" What I like about the IBM story is they're not out saying, "We're going to go disrupt industries." Silicon Valley has a bifurcated disruption agenda. On the one hand, they're trying to, cloud, and SaaS, and mobile, and social, very disruptive technologies. On the other hand, is Silicon Valley going to disrupt financial services, healthcare, government, education? I think they have plans to do so. Are they going to be able to execute that dual disruption agenda? Or are the consumers of AI and the doers of AI going to be the ones who actually do the disrupting? We'll see, I mean, Uber's obviously disrupted taxis, Silicon Valley company. Is that too much to ask Silicon Valley to do? That's going to be interesting to see. So, my point is, IBM is not trying to disrupt its customers' businesses, and it can point to Amazon trying to do that. Rather, it's saying, "We're going to enable you." So it could be really interesting to see what happens. You're down in DC, Jeff Bezos spent a lot of time there at the Washington Post. >> We just want the headquarters, that's all we want. We just want the headquarters. >> Well, to the point, if you've got such a growing company monopoly, maybe you should set up an HQ2 in DC. >> Three of the 20, right, for a DC base? >> Yeah, he was saying the other day that, maybe we should think about enhancing, he didn't call it social security, but the government, essentially, helping people plan for retirement and the like. I heard that and said, "Whoa, is he basically "telling us he's going to put us all out of jobs?" (both laugh) So, that, if I'm a customer of Amazon's, I'm kind of scary. So, one of the things they should absolutely do is spin out AWS, I think that helps solve that problem. But, back to IBM, Ginni Rometty was very clear at the World of Watson conference, the inaugural one, that we are not out trying to compete with our customers. I would think that resonates to a lot of people. >> Well, to be continued, right? Next month, back with IBM again? Right, three days? >> Yeah, I think third week in March. Monday, Tuesday, Wednesday, theCUBE's going to be there. Next week we're in the Bahamas. This week, actually. >> Not as a group taking vacation. Actually a working expedition. >> No, it's that blockchain conference. Actually, it's this week, what am I saying next week? >> Although I'm happy to volunteer to grip on that shoot, by the way. >> Flying out tomorrow, it's happening fast. >> Well, enjoyed this, always good to spend time with you. And good to spend time with you as well. So, you've been watching theCUBE, machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Have a good one. (techno music)

>> Announcer: Live, from Silicon Valley, it's theCUBE, covering Google Cloud Next '17. (funky techno music) >> Welcome back to our live coverage here of Google Next 2017, an event that last year was focused only on Google Cloud. They've actually expanded a bit, they're talking about G Suite, talking about some of the devices, and they bring in a really broad and diverse community, so when I talk to the Google people, it's not one show, it's a handful of shows. I went to the analyst event. My guest for this segment is Chris Wahl, who came in through the community event. So, excited to get that angle. Chris, thanks so much for doing the drive with me from San Francisco down to Palo Alto. For those of us not in the area, it's a 45 minute drive, it's not too bad. It's a beautiful, sunny day. It's great to catch up with you and thanks for coming. >> Always glad to be on, love being a CUBE Alumni, so, I think it's my third time. >> Wow, a three-time Alumni. It's like if you've been a host of Saturday Night Live for like seven times, you know you get the special jacket. - Automatically. >> Things like that. You're getting up there. Three times. It's like, you're not quite in Pak Elsinger area, but you have passed, you've been on more than Andy Jassey now. >> Wow, cool. >> I think that that's pretty impressive. >> Bucket list, accomplished. >> Exactly, so, what brings you to the Google event and tell us a little bit about the community event. >> Yeah, to be honest, I thought it was a spam email at first. I just got an invite saying, hey, we have this Google event going on, and I'm not really plugged in to the Google Universe too much. So I said, cool, I'm interested, I'll take a look. Got invited out by Sarah Novotny to a community focus day. >> Host: Sarah's awesome. Also a CUBE Alum, of course. >> Yeah, Alum, and ran OSCON I think, as a boarder or some kind of management facility for quite a while. So yeah, the Google Cloud Next is this week but on Tuesday. They actually had a bunch of influencers, evangelists, community members, out to spend time with all sorts of Google-y Google-ers, talking around what their vision is around kind of bridging the gap to the enterprise, what their thought around Kubernetes, and just really the community in general were. Which was kind of cool because it was all fresh and clean and new for me. So, it was really great to taste the Kool-Aid, and see how delicious it could be. >> Yeah, so I'm curious what your take is. I remember I did a panel at Interop a couple of years ago, and it was like, basically, hyper-scale, you're-not-Google, so what do you need to do, how do you do it, do you just use Google stuff, can you code like Google, can you act like Google, or are you just an enterprise and you're forced to live in the past. >> I think over the last couple of years, the idea of the Sight Reliability Engineers come out and been more focused on the enterprise and kind of dovetailed into the Dev-Op story. So, it was really interesting to hear, not only trying to talk to the enterprise, but also how they're trying to get the enterprise to kind of stop being the traditional enterprise that it's been. Which I think entirely, it's something that we practitioners have always been trying to do. No one wants to be on-call all the time and fixing these flaming disasters and things like that. But at the same time, you have to recognize that moving that much intrinsic culture poison from one side to the next is hard. They're admitting that too, it's like, we wold love for you guys to be more Google-y, and to use the tools that we have here, but we're not sure you even know what the tools are or how to use them, or what kind of documentation is necessary, or what meet-ups we can go to find my people, you know, the practitioners. >> I want to channel our friends, the Geek Whisperers, and alright Chris, so how did you transition out of being a VMware guy to someone that does cool and interesting things now, because VMware is no longer the coolness. >> That's been the vibe, yeah. It's something I personally have been trying to, I don't think in any technology you want to be that technology specific. VMware, love it, have been doing it for 12 something years, but you don't just want to be pigeon-holed in that kind of silo. Which is actually why I wanted to come out and talk with the folks at Google around what they're doing to build a community. I think it was Sam something-or-other-- >> Host: Sam Ramji. >> Sam Ramji actually came up and said, you know, as long as we're going to exist as a company, we're going to have this community day. It's the first one they've done, and they plan to do it basically infinitely forever, because they realized they had the analysts, and things like that out there, they had all the engineers and developers, but what were they missing? The folk in the trenches that are trying to adopt and use this sort of technology. I like that aspect of it. There weren't any huge, mind-shattering results that were out there, except for I think, me personally, I like that Google kind of admitted that yeah, they hadn't been doing the best job around interfacing with the community and getting IT practitioners and operation-centric folks into the fold, welcoming into the bosom of Google, and that they were trying to work on that. And it's like, okay, awesome. Let's have a conversation, which the other half of the day was an un-conference, where we literally broke up into groups, that we decided ourselves as like a democracy of Google decision-making. We formed eight different groups. Some focused on containers, I actually sat in in a two hour session where we just kind of riffed on abstraction layers and where we should we start working. Is it at the container level, is it at the hypervisor level, is it at the virtual machine level? And it was neat because everyone had a completely different idea and background around that. I felt like I was an alien in that conversation for a lot of it 'cause they're working on solving problems that are totally alien to my world. So I liked all that. >> It's an interesting crowd when the server-less stuff got talked about in the keynote today-- >> Yes! >> There was a big clap and I loved Brian Stevens. He's like, functions are just fragments of code, and they get applause, you know, he's kind of like-- (Chris laughs) >> It's like either remark, I got applause for that. >> Yeah, yeah, it's pretty funny. But you know, that's the kind of people that come to this show, right? So, you checked out a thing called, what was it, Code Labs or something like that? Maybe you could talk a little bit about that. >> Yeah, yeah, there was, I had some notes there that I'd written down. Certification in Code Labs, specifically. So Code Labs was interesting 'cause it's a place that you can, you have to book it in advance, like a day in advance, and from about 11 to seven each day, they just have Google-y Google-ers, you know, very Google-y people out there that say alright, here's all our various APIs, such as the new one where you can query a video and say I'm looking for, I think in the keynote, they had "find me baseball" in this video, and it actually shows you in the timeline where baseball occurs. There's also things to do image tagging and things like that. And, I don't know, it might be difficult to grasp that API interaction at first. And so you can sit down, and they'll show you how to write code in the languages of your choice. Obviously Go is very prominent. I'm a PowerShell developer, so it's like, alright, how would you write that in Curl, and that's maybe our bridge to one another, since I don't know Go and they don't know PowerShell, or the person I was working with. So that was cool, to hear how they approach those things, because I've typically done it as an Ops person. I'm typically looking at it from the perspective of I'm trying to automate some task and feed it into an orchestration engine. And I'm not super deep on APIs in general, I like them, but ... That was cool, I liked that you're basically getting to meet with really, really awesome engineers and SREs to pick their brain and their vast decades of experience on writing code. To work with APIs and things that are Google-centric. So that was awesome. >> So it sounds like you didn't feel like this was a marketing show, right, - [Chris] No! >> that they bring in the engineers, the technical people, I mean it's not far being from San Franscisco from the Google-Plex, the Mothership is nearby. >> Thats's a good point because a lot of these shows have just become a sales pitch in a wolf's clothing or a conference clothing, and this was ... I've never met so many really, really talented engineers all concentrated in one spot. I mean, you've got the rock stars that I think everybody knows, like Sarah, and Kelsey, that are very available and personable, but you also have a whole army of people that have a huge amount of passion around writing code and understand what your problems are and wanting to talk to you. I felt like a person, which I've been a Google customer since, I guess, Google came out, you know, Google apps and things like that. This is really the first time I really started putting faces to the technical practitioners that work there, and they're really interested and excited with what my mundane kind of problems. So, that's kind of cool. >> Yeah, I found they're definitely, they're listening, they're talking, it's really good, because right, we at our firm, we've used Google for a while and it's like, oh wait I have a challenge. Who do I call, who do I email? Nope, you should just watch the YouTube video and use it. C'mon, aren't you smart enough to use these things right? You know, was kind of how we all felt for a while. Interesting. Kinder, gentler Google than we've knew in the past? >> They had the Google leaders circle and the various groups that you could join online, but it was just, you can't fake that kind of raw passion, and I sat down with some of the SREs at the community day, and it was really just, talk to me about what you do, and why, and what tools you use, and what can we do to be better? More specifically, the Dev Rel, the developer relations folks were just awesome. And they're like, is our title threatening? What meet-up should we go to? What can we do to make your life better? And I just kind of, at first, said a few comments and realized, no, this is real. They want to know my day one and day two operations, so that they can find the right tools, or if there isn't one, build one. And I don't know, that's great. I've never seen that at a conference before. So I'm hooked. I definitely plan to go again. >> Alright, so anything you didn't see that you were hoping to see, follow-up that you want to have, other cool stuff going on that you want to share? >> I almost want to do like a plea to Google that throughout the community today and at the conference, there's been a lot of commentary and some, kind of some references to, oh we don't want to tell you how to do things, we don't want to tell you how to build architecture in a certain way. Please do tell me how to do those things. At least give me a reference architecture, or some example environments, because I feel like a lot of it is just, here's some cool things you can do, kind of in isolation. Or here are some things with Kubernetes that kind of exist outside of reality. I'm looking for, alright, I don't have any of that stuff, how do I onboard into that? Here's a white paper, and that kind of jazz. >> Yeah, and we saw, you know, I hate to always bring up AWS, but AWS went from here's this giant toolbox with all these things to right, here's some services, here are some tracks, here are some, not wizards, but you know, templates you can follow for certain things. Here are people that are probably similar to you and, boy, with Google with their AI and ML and all their things that they can do to help us sort out all the TLAs that they've got to. (Chris laughs) You know, they should be able to help going forward because, yeah, Google should be able to personalize all that to be able to work a little bit better for us as opposed to us having to just kind of figure it out a little bit. I know you played with the Google Cloud a little bit yourself-- - Yeah. >> And it wasn't as simple as you were hoping, right? >> It was hard. (both laugh) I mean-- >> Host: C'mon, if you can't figure it out, you know-- >> I don't feel like I'm the sharpest tool in the shed, but I was like, I'm kind of the representative layman ops person, and it felt very convoluted, complex, the documentation was fragmented. I'm like, just give me the wizard so that I can start fishing for myself. I just do that first hit for free, and then I'll take care of it beyond that. So, that would be my one ask to Google as a whole, but otherwise I think the tooling and the people, and the culture are all there, it's just build a few more things and I think we've got some interesting entanglements at the enterprise level once that's done. >> Okay, want to give me the final word, what's going on with you other than, your hometown, your new hometown of Austin, Texas. South By coming, so I know there's a lot of music and fun going on but, what's happening in your world, what's happening with Rubrik? >> Oh yeah, I'll mention South By, definitely will be there, I will not be available online or anything. I'm going to be going into sequester mode and just listen to music with my co-host actually. If you listen to the Datanauts podcast, with Ethan Banks, he's going to come by. So, we'll be at the show I guess if you want to hang out with us, hit us up. Otherwise, Rubrik's been awesome. It's definitely a rocket ship ride and it was actually dove-tailed into quite a few conversations I had while at Google Next. Because movement of data into and around clouds is non-trivial, so that's where the Cloud Data Management world that we're in, kind of fits into that equation, and why I personally wanted to go to this show, but also professionally I thought that there'd be some inroads there to discuss with the other practitioners. >> Absolutely, the whole infrastructure side and how that plays in the public cloud, how it plays with Sass, there's a lot of those discussions going on. Congrats, you guys have been growing some good buzz. You guys have been hiring, too, so check Chris out for all that. We'll be back, lots more coverage here of the Google Cloud Next 2017, you're watching theCUBE. (funky techno music)

>>live from the Frederick P Rose Hall, home of jazz at Lincoln center in New York, New York. It's the queue at IBM Z. Next redefining digital business. Brought to you by headline sponsor. IBM. >>Hey everyone. We are here live in New York city for the IBM Z system. Special presentation of the cube. I'm John furrier, cofounder SiliconANGLE at my coast. Dave Alante co founder Wiki bond.org. Dave, we are here with gathering Corine, vice president of the Z systems technology. Welcome to the cube. Great to have you. >>Thank you. I'm really glad to be here. It's an exciting day for us. >>We had a great conversation last night. I wanted to just get you introduced to the crowd one year overseeing a lot of the technology side of it. You're involved in the announcement, but uh, you're super technical and uh, and, and the speeds and feeds of this thing are out there. It's in the news, it's in the press, but it's not really getting the justice. And we were talking earlier on our intro about how the main frame is back in modernize, but it's not your grandfather's mainframe. Tell us what's different, what's the performance tech involved, why is it different and what should people be aware of? >>Sure. So this machine really is unmatched. We have tremendous scale performance in multiple dimensions that we can talk through. The IO subsystem provides tremendous value security that's unmatched. So many of the features and attributes to the system just cannot be compared to other platforms. And the Z 13 what we're announcing today evolves and improves so many of those attributes. We really designed the system to support transaction growth from mobility, to do analytics in the system, integrated with the data and the transactions that we can drive insights when they really matter and support it. Cloud delivery. >>So there's two, two threads that are out there in the news that we've wanted to pivot on. One is the digital business model, and that's out in the press release is all the IBM marketing and action digital business. We believe as transformers, that's pretty much something that's gonna be transformative. But performance with the cloud has been touted, Hey, basically unlimited performance with cloud. Think of compute as a not a scarce resource anymore. How do you guys see that? Cause you guys are now pushing performance to a whole nother level. Why can't I just get scale out saying or scale out infrastructure, build data centers. What is this fitted with that mindset or is it, >>yeah, so I, there's, there's performance in so many different dimensions and I'll can talk you through a few of them. So at the, at the heart of the technology in this system, we have tremendous value in from the processor up. So starting at the base technology, we build the microprocessor in 22 nanometer technology, eight cores per chip. We've got four layers of cash integrate on this. More cash that can be accessed from these processor cores then can compare to anything else. Tremendous value. Don't have to go out through IO to memory as frequently as you would have to in other environments. We also have an iOS SIS subsystem that has hundreds of additional processing cores that allows you to drive workload fast through that. Um, so I think it's the, it's, it's the, the, the scale of this system that can allow you to do things in a single footprint that you have to do with a variety of distributed environments separately coupled with unique security features, embedded encryption capability on the processor, PCIE attached, tamper resistance, cryptography, compression engines as so many of these technologies that come together to build a system. >>So IBM went to the, went to the, went to the woodshed back and took all the good technology from the back room cobbled together. Cause you guys have done some pretty amazing things in the, what they call proprietary days, been mainframe back in the sixties seventies eighties and client server a lot of innovation. So you guys, is that true? Would that be an accurate statement? You guys kind of cobbled together and engineered this system with the best >>engineered from, from from soup to nuts, from the casters up. We live, we literally have made innovations at almost every level here in the system. Now it's evolved from previous generations and we have tremendous capabilities in the prior ones as well. But you see across almost every dimension we have improved performance scape scalability capability. Um, and we've done that while opening up the platform. So some of the new capabilities that we're discussing today include enterprise Linux. So Linux on the platform run Linux on many platforms. Linux is Linux, but it's even better on the Z 13 because now you have the scalability, the security, the availability behind it and new open support, we're announcing KVM will be supported on this platform later this year we have OpenStack supported, we're developing an ecosystem around this. We have renouncing Postgres, Docker, no JS support on the mainframe. And that's tremendously exciting because now we're really broadening a user base and allowing users to do a lot more with Linux on the main. >>So one of the big themes that we're hearing today is bringing marrying analytics and transaction systems together. You guys are very excited about that. Uh, one of the, even even the New York times article referenced this, people are somewhat confused about this because other people talk about doing it. We go to the Hadoop world, you know, we talked big data, spark in memory databases, SAP doing their stuff with Hannah. What's different about what Z systems are doing? >>That's a great question. So today many users are moving data off of platforms, including the mainframe to do their analytics. Moving back on this ETL process, extract, transform load. It's incredibly expensive, cumbersome copies of that data. You have redundancy, you have security risk, tremendous complexity to manage. And it's totally unnecessary today because you can do that analytics now on the system Z platform, driving tremendous capability insights that can be done within the transaction and integrated where the transactions and the data live. So much more value to do that. And we've built up a portfolio of capabilities and some of them are new. We're an announcing as part of today's event as well that can allow us to do transformation of the data analytics of that data. And it, and it's, it's at every level, right? We have embedded analytics, accelerators in the process or a new engine we call Cindy single instruction. Multiple data allows you to do, uh, a mathematical, uh, vector processing. >>Let's drill down on that. I want to get your particular on this. You have the in process or stuff is compelling to me. I like, I want to drill down on that. Get technical. Right now all the rage is in memory in memory. She's not even on the big data. Spark has got traction for the analytics. DTL thing is a huge problem. I think that's 100% accurate across the board. We hear that all the time. But what's going on in the process server because you guys have advanced not just in memory, it's in processor. What is that architecture, what are the, some of the tech features and why is that different than just saying, Hey, I'm doing a lot of in memory. >>So, so the process or has um, a deeper and richer cash hierarchy, um, than, than we see in other environments. That means we have four layers of cash. Two of those cash layers are embedded within the processor core itself. They're private to the core. The next layer is on the processor chip and it's shared amongst all those cores. And the fourth layer on a herder, right, is on a separate chip. It's huge. It's embedded DRAM technology. It's a tremendously large cash and we've expanded that, which means you don't have to go out to memory nearly as frequently because you, >>you stayed in the yard that stayed in the yard today in memory is state of the art today. You guys have taken it advanced inside the core. What kind of performances that dude, what's the, what's the advantage? >>There's huge performance advantages to that. We see, we see, we can do, uh, analytics. Numbers are something like 17 times faster than comparable solutions. Being able to bring those analytics into the system for insights when you need them, right? To be able to do faster of scoring of transactions, to be able to do faster fraud detection with so many applications. So many industries are looking to be able to bring these insights faster, more co-located with the data and not have to wait the latency associated with moving data off and, and, and doing some sort of analysis on data that's stale. How that's not interesting. We really want to be able to to integrate that where the data and the transactions live and we can now do that on the. >>So in memory obviously is awesome, right? You can go much faster. A best IO is no IO as gene Amdahl would say, but if something goes wrong and you have to flush the memory in reload >>everything, it's problematic. How does IBM address that? So to minimize that problem relative to we hear you hear complaints and other architectures that that that's problematic. How do you solve that problem or have you solved that problem? >>Well, you know, I think it's a combination of, of the cash, the memory and the analytics capabilities, the resiliency of the system. So you worry about machines going down, failures and we've built in security, reliability, redundancy at every level to prevent failures. We have diagnostic capabilities, things like the IBM Z aware solution, right? This is a solution that's been used to monitor the system behavior so that you can identify anomalous behaviors before you have a problem that's been available with cos. now we're extending that to Linux for the first time. We have solutions like disaster recovery, continuous availability solutions like the GDPs, uh, it's now extended to be a virtual appliance for Linux. So I, there's so many features and functions. This system allow you to have a much more robust, capable, >>popular is Linux. Can you quantify that? You guys talk a lot about Linux and can you give us some percentage? >>Linux has been around for 15 years on the mainframe and um, we have a very good user adoption. We're, we're, we're seeing a large fraction of our clients are running Linux either all by itself or in concert with Zoes. >>So double digit workloads. >>Yeah, it's a very, it's a very significant fraction of the myths in the field today. >>God, I don't want to get a personal perspective from you on some things. One, you went, uh, you have an applied physics degree from Yale, master's from an applied physics from Stanford, PhD, applied physics from Stanford and all the congratulations by the way, you're super smart means you, it means you can get to the schools you means you're, you're smart. But the rage is software defined, right? So I want you to tell us from your perspective being in applied physics, the advances in Silicon is really being engineered now. So is it the combination of that software defined? What's your perspective? What should people know about the tech at the physics side of it? Cause you can't change physics know the other day, but Silicon is doing some good stuff. So talk about that, that convergence between the physics, Silicon and software. >>Yeah, that's a, that's a great question. So I think what sets us apart here with the mainframe is our ability to integrate across that stack. So you're right, Silicon Silicon piece of 22 nanometers Silicon, we can all do similar things with it, but when you co optimize what you do with that Silicon with high-performance system design, with innovations at every level, from where operating systems software, you can build an end to end solution that's unmatched. And with an IBM, we, we, we do that. We really have an opportunity to collaborate across the stack. So can we put things in the operating system? It can take advantage of something that's in that hardware and being able to do that gives us a unique opportunity. And we've done that here, right? Whether it's the Cyndi accelerator and having our software capabilities or see Plex optimizes a Java, be able to take advantage of what's in that, uh, in that microprocessor, we see that with new instructions that we offer here that can be taken advantage of compilers that optimize for what's in the technology. So I think it's that, it's that co optimization across the stack. You're right, software as a user, you see the software, you see the solution, you see the capability at the machine. But to get that you need the infrastructure underneath it, you need the capabilities that can be exploited by the software. And that's why that, >>and we're seeing that in dev ops right now with the dev ops movement. You're seeing, I want to abstract away the complexities of infrastructure and have software be more optimized. And here you guys are changing the state of the art in with the in-memory to in processor architecture, but also you're enabling developers and software to work effectively. >>Right? And I think about cloud service delivery, right? You know, and we would love to be able to offer end users it as a service so we can access the mainframe. All of those qualities of service that we know and love about the mainframe without the complexity and can do that. Technologies like Zoes connect and Blumix with system Z mobile first platform, allowing you to connect from systems, engagements, the six systems of Rutgers deploy Z services. So you can, we were trying to help our clients to be able to not be cost centers for their, uh, for their firms but to provide value added services. And that can be done with the capabilities on the main. >>So no, Docker, OpenStack KVM, obviously we talked about Linux. What does that mean from a business standpoint, from the perspective of running applications? Can you sort of walk us through what you expect clients to do or what >>it's, it's, it's all about standardization and really expanding an ecosystem for users on the platform. And we want anybody running Linux anywhere to be able to run it on, run their applications, develop their applications on the mainframe. And to be able to take advantage of the consolidation opportunities driven by the scale the platform and be able to drive unmatched end to end security solutions on this plot. Right? It's, it's a combination of enabling an ecosystem to be able to do what users expect to be able to do. And that ecosystem continues to evolve. It's very rapidly changing. We know we have to respond, but we want to make sure that we are providing the capabilities that developers and users expect on the platform. And I think we've taken a tremendous leap at the Z 13 to be able to do that. >>So obviously Linux opened up. That was the starting point. Right? Um, what do you expect with the sort of new open innovations? Will you pull in more workloads, more applications or, >>I certainly believe we will. And you know, new workloads on the platform. This is, this is a, an evolution for us and we continue to see the opportunity to bring new workloads to the platform. Things, support of, of, of Linux. And the expanding ecosystem there helps us to do that effectively. We see that, whether it's um, the, the, the transaction growth from mobile and being able to say, what does that mean for the mainframe? How can we not just respond to that but take advantage, enable new opportunities there. And I, so I think absolutely Linux will help us to grow workloads to get into new spaces and really continue to modernize the mainframe. >>John and I were talking at the open Paul Moritz at the time, CEO of VMware in 2009. So we are going to build a software mainframe. Um, interesting, very bold statement. Don't, where's he working on pivotal? Do you have a software mainframe? Have you already built it? >>I don't think you can have software that running on something. And so the mainframe is not a piece of hardware. The mainframe's a solution. It's a platform that includes technology, infrastructure, hardware and the software capabilities that run on it. And as I said, I think it's the integration that the co optimization across that really provides value to clients. I don't know how you can have a software solution without some fundamental infrastructure that gives you the qualities of service. That's so much of the inherent security availability. All of that is >>that's a marketing. It didn't, it didn't pan out. The vision was beautiful and putting a great PowerPoint together. he went to pivotal now, but I think what's happening is what you're, what you're talking about is it's distributed mainframe capability. The scale out open source movement has driven the wannabe mainframe market to explode. And so what now you look at Amazon, you can Google look at these, these power data centers. They are mainframes. In essence, they are centralized places. Well, they want to say the cloud is a software mainframe. Software runs on these data centers. So instead of having rack and stack, uh, three x86 processors, you just drop into mainframe or God box as I call it. And you have this monster box that's highly optimized and then you could have clusters of other stuff around it. Your argument is the integration is what, what makes the difference that end. And so Amazon makes their own gear, right? We know that now they don't do open compute. They're making their own gear. So people who want to be Amazon would probably go to some kind of hybrid mainframe. Like they're not making their own. 70 makes sense of that cause Amazon, I mean they purpose built their own boxes. They are building their own point though, right? I mean to the outside of the box. Right. >>The way I see it as is for for mission critical applications where you cannot support any downtime, you want to have a system that's built from the ground up for pure availability for security and we have that right? We have a system that you can prevent failures, right? We have redundancy at so many levels. We have, we have, you know, if a transaction, different model rate, you win when you take money out of your account or when you transfer money more potently into your account, you need to make sure it's there, right? You want to know that with a hundred percent confidence and to do that I would expect you feel more confident running that >>credit card transactions, same game all over again. Mission critical versus non mission critical, I mean internet of things. But what's not mission critical is my follow up question here of things. Some sensors data that's passive. I, if it's running my airplane, ass running your temperature. Oh, you're down for 10 minutes. I mean, yeah, >>there were some times that we would accept, accepts and downs. >>Lumpy. No, it's really about lumpy SLA performing. Amazon gets away with that because the economics are fantastic, right? So you can't be lumpy and bank transaction. What about costs versus, Oh mainframe. So expensive, so expensive. You guys put out some TCO data that suggest it's less expensive. Help us get through that. >>Yeah, so, so I think when we look at total cost of ownership, we're often looking at the savings to administration and the management of the complexity of sprawl. And with the mainframe, because you have such scale and what you can include in it in a single footprint, you can now consolidate so much into this literally very small environment and the cost savings because of the integration capabilities, because of the performance that you can contain within this box, you see end-to-end cost savings for our clients. And in that, that the break even point is not so large. Right. And so you talked about mission critical. If you're doing your mission critical work on your mainframe and you have other things that you need to do that aren't, you don't consider perhaps as mission critical, you have an opportunity to consolidate. You can do that all on the same platform. You're, you're not, you know, we, we can run with tremendous utilization. You can, you want to use these machines for all their work. >>So sorry. So a follow up on that. So the stickiness then AKA lock-in used to be, I got a bunch of COBOL code that won't run anywhere else. He got me, I got to keep buying Mayfair. I was just saying now the stickiness is for the types of workloads that your clients are running. It is cheaper. That's your, >>it's cheaper. And I think it has unmatched capability, availability, security features that you can't find in other solutions. >>And if you had to, in theory you could replicate it, but it would just be so expensive with people. >>In theory, I, okay. But I think some the fundamental technologies and solutions across that stack, who else can do that? Right. Okay. Can integrate solutions in the hardware and all the way up that stack. And, and I, I don't know anyone else, >>tell me what, tell me what, in your opinion, what gets you most excited about this technology platform? I mean, is there a couple things? Just are one thing saying >>that is so game changing. I'm super excited by this. Um, I can't sleep at night. I'm intoxicated technically. I mean, what gets you jazzed up on this? >>Well, I, I'll tell you, it's, today's a really proud day. I have to say being here and being a part of this launch, you know, personally having been a part of the development, been an IBM for 15 years. I spent the last eight years doing hardware development, including building components and key parts of the system. And now to see us bring that to market and with the value that I know we're bringing to clients, it's, it get, I, I get a little choked up. I truly, honestly, I truly, honestly feel really, really proud about what we've done. Um, so in terms of what is most exciting, um, I think the analytics story is incredibly powerful and I think being able to take a bunch of the technologies that we've built up over time, including some of the new capabilities like in database transformation and advanced analytics that we'll be continuing to roll out over the course of this year. I think this can be really transformative and I think we can help our clients to take advantage of that. I think they will see tremendous value to their business. We'll be able to do things that we simply couldn't do with the old model of moving data off and, and having the latency that comes with that. So I'm really excited about that >>nice platform, not just a repackaging of mainframe. Okay, great. So second, final question from me I want to ask you is two perspectives on, um, the environment, the society we live in. So first let's talk it CIO, CEO, what mindset should they be in as this new transformation? The digital businesses upon them and they have the ability to rearchitect now with mainframe and cloud and data centers. What should they be thinking about as someone who has a PhD in applied physics, been working on this killer system? What is the, what's the moonshot for that CIO and, and how should they be thinking about their architecture right now? >>So I think CEO's need to be thinking about what is a good solution for the variety of problems that they have in their shops and not segment those as we've often seen. Um, you have the x86 distributed world and maybe you have a main frame this and that. I begin to think about this more holistically about the set of challenges you need to go address as a business. And what capabilities do you want to bring to bear to solve those problems? I think that when you think about it that way, you get away from good enough solutions. You get away from some of this, um, mindset that you have about this only plays over there. And this only plays over there. And I think you open yourself up for new possibilities that can drive tremendous value to their businesses. And we can think differently about how to use technology, drive efficiency, drive performance, and real value. >>Last night at dinner, we, we all, we all have families and kids. Um, and you know, even there's a lot of talk about software driving the world these days. And it is, software's amazing. It's great. Best time to be a software developer. Since I've been programming since I was in college and, and it's so much so awesome with open source. However, there's a real culture hacker culture now with hardware. So, um, what's your advice to young people out there? You know, middle schoolers or parents that have kids in middle school for women, young girls, young boys with this. Now you've got drones, you've got hackers, raspberry pie, these kinds of things are going on. You've got kind of this Homebrew computer mindset. These young kids, they don't even know what Apple butter >>I would say it is, it is so exciting. Uh, the, the, the engineering world, the technology challenges, hardware or software. And I wouldn't even differentiate. I think we have a tremendous opportunity to do new and exciting things here. Um, I would say to young girls and boys don't opt out too soon. That means take your classes, studying math and science in school and keep it as an option because you might find when you're in high school or college or beyond, that you really want to do this cool stuff. And if you haven't taken the basics, you, you find yourselves not in a position to be able to, to, to, to team and build great things and deliver new products and provide a lot of value. So I think it's a really exciting area. And I've been >>it's a research as I'm seeing like this. I mean I went to the 30th anniversary for apples Macintosh in Cupertino last year and that whole Homebrew computer club was a hacker culture. You know, the misfits, if you will. And a coder camp. >>I think that think there are people who grow up in, always know that they want to be the engineer, the software developer. And that's great. And then there are others of us, and I'll put myself in that in that space that you may have a lot of different interests. And what has drawn me to engineering and to the, the work that we do here is has been the, the ability to solve tough problems, to, to do something you've never, no one has ever done before, to team with fantastically smart people and to build new technology. I think it's an incredibly exciting space and I encourage people to think about that opportunity >>from a person who has a PhD in applied physics. That's awesome. Thank Kevin. Thanks for joining us here inside the queue, VP of systems. Again, great time to be a software build. Great time to be making hardware and solutions. This is the cue. We're excited to be live in New York city. I'm John furry with Dave Alante. We'll be right back. This rep break.