>> [Announcer] From San Francisco, it's theCUBE covering OSIsoft PIWORLD 2018, brought to you by OSIsoft. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE, we're in downtown San Francisco at the OSIsoft PIWorld 2018. They've been doing it for like 28 years, it's amazing. We've never been here before, it's our first time and really these guys are all about OT, operational transactions. We talk about IoT and industrial IoT, they're doing it here. They're doing it for real and they've been doing it for decades so we're excited to have our next two guests. Tyler Duncan, he's a Technologist from Dell, Tyler, great to see you. >> Hi, thank you. >> He's joined by Ed Watson, the global account manager for channels for Osisoft. Or OSIsoft, excuse me. >> Glad to be here. Thanks, Jeff. >> I assume Dell's one of your accounts. >> Dell is one of my accounts as well as Nokia so-- >> Oh, very good. >> So there's a big nexus there. >> Yep, and we're looking forward to Dell Technology World next week, I think. >> Next week, yeah. >> I think it's the first Dell Technology not Dell EMC World with-- >> That's right. >> I don't know how many people are going to be there, 50,000 or something? >> There'll be a lot. >> There'll be a lot. (laughs) But that's all right, but we're here today... >> Yeah. >> And we're talking about industrial IoT and really what OSIsoft's been doing for a number of years, but what's interesting to me is from the IT side, we kind of look at industrial IoT as just kind of getting here and it's still kind of a new opportunity and looking at things like 5G and looking at things like IPE, ya know, all these sensors are now going to have IP connections on them. So, there's a whole new opportunity to marry the IT and the OT together. The nasty thing is we want to move it out of those clean pristine data centers and get it out to the edge of the nasty oil fields and the nasty wind turbine fields and crazy turbines and these things, so, Edge, what's special about the Edge? What are you guys doing to take care of the special things on the Edge? >> Well, a couple things, I think being out there in the nasty environments is where the money is. So, trying to collect data from the remote assets that really aren't connected right now. In terms of the Edge, you have a variety of small gateways that you can collect the data but what we see now is a move toward more compute at the Edge and that's where Dell comes in. >> Yeah, so I'm part of Dell's Extreme Scale and Structure Group, ESI, and specifically I'm part of our modular data center team. What that means is that for us we are helping to deploy compute out at the Edge and also at the core, but the challenges at the Edge is, you mentioned the kind of the dirty area, well, we can actually change that environment so that's it's not a dirty environment anymore. It's a different set of challenges. It may be more that it's remote, it's lights out, I don't have people there to maintain it, things like that, so it's not necessarily that it's dirty or ruggedized or that's it's high temperature or extreme environments, it just may be remote. >> Right, there's always this kind of balance in terms of, I assume it's all application specific as to what can you process there, what do you have to send back to process, there's always this nasty thing called latency and the speed of the light that just gets in the way all the time. So, how are you redesigning systems? How are you thinking about how much computing store do you put out on the Edge? How do you break up that you send back to central processing? How much do you have to keep? You know we all want to keep everything, it's probably a little bit more practical if you're keepin' it back in the data center versus you're tryin' to store it at the Edge. So how are you looking at some of these factors in designing these solutions? >> [Ed] Well, Jeff, those are good points. And where OSIsoft PI comes in, for the modular data center is to collect all the power cooling and IT data, aggregate it, send to the Cloud what needs to be sent to the Cloud, but enable Dell and their customers to make decisions right there on the Edge. So, if you're using modular data center or Telecom for cell towers or autonomous vehicles for AR VR, what we provide for Dell is a way to manage those modular data centers and when you're talking geographically dispersed modular data centers, it can be a real challenge. >> Yeah, and I think to add to that, there's, when we start lookin' at the Edge and the data that's there, I look at it as kind of two different purposes. There's one of why is that compute there in the first place. We're not defining that, we're just trying to enable our customers to be able to deploy compute however they need. Now when we start looking at our control system and the software monitoring analytics, absolutely. And what we are doing is we want to make sure that when we are capturing that data, we are capturing the right amount of data, but we're also creating the right tools and hooks in place in order to be able to update those data models as time goes on. >> [Jeff] Right. >> So, that we don't have worry about if we got it right on day one. It's updateable and we know that the right solution for one customer and the right data is not necessarily the right data for the next customer. >> [Jeff] Right. >> So we're not going to make the assumptions that we have it all figured out. We're just trying to design the solution so that it's flexible enough to allow customers to do whatever they need to do. >> I'm just curious in terms of, it's obviously important enough to give you guys your own name, Extreme Scale. What is Extreme Scale? 'Cause you said it isn't necessarily because it's dirty data and hardened and kind of environmentally. What makes an Extreme Scale opportunity for you that maybe some of your cohorts will bring you guys into an opportunity? >> Yeah so I think for the Extreme Scale part of it is, it is just doing the right engineering effort, provide the right solution for a customer. As opposed to something that is more of a product base that is bought off of dell.com. >> [Jeff] Okay. >> Everything we do is solution based and so it's listening to the customer, what their challenges are and trying to, again, provide that right solution. There are probably different levels of what's the right level of customization based off of how much that customer is buying. And sometimes that is adding things, sometimes it's taking things away, sometimes it's the remote location or sometimes it's a traditional data center. So our scrimpt scale infrastructure encompasses a lot of different verticals-- >> And are most of solutions that you develop kind of very customer specific or is there, you kind of come up with a solution that's more of an industry specific versus a customer specific? >> Yeah, we do, I would say everything we do is very customer specific. That's what our branch of Dell does. That said, as we start looking at more of the, what we're calling the Edge. I think ther6e are things that have to have a little more of a blend of that kind of product analysis, or that look from a product side. I'm no longer know that I'm deploying 40 megawatts in a particular location on the map, instead I'm deploying 10,000 locations all over the world and I need a solution that works in all of those. It has to be a little more product based in some of those, but still customized for our customers. >> And Jeff, we talked a little bit about scale. It's one thing to have scale in a data center. It's another thing to have scale across the globe. And, this is where PI excels, in that ability to manage that scale. >> Right, and then how exciting is it for you guys? You've been at it awhile, but it's not that long that we've had things like at Dupe and we've had things like Flink and we've had things like Spark, and kind of these new age applications for streaming data. But, you guys were extracting value from these systems and making course corrections 30 years ago. So how are some of these new technologies impacting your guys' ability to deliver value to your customers? >> Well I think the ecosystem itself is very good, because it allows customers to collect data in a way that they want to. Our ability to enable our customers to take data out of PI and put it into the Dupe, or put it into a data lake or an SAP HANA really adds significant value in today's ecosystem. >> It's pretty interesting, because I look around the room at all your sponsors, a lot of familiar names, a lot of new names as well, but in our world in the IT space that we cover, it's funny we've never been here before, we cover a lot of big shows like at Dell Technology World, so you guys have been doing your thing, has an ecosystem always been important for OSIsoft? It's very, very important for all the tech companies we cover, has it always been important for you? Or is it a relatively new development? >> I think it's always been important. I think it's more so now. No one company can do it all. We provide the data infrastructure and then allow our partners and clients to build solutions on top of it. And I think that's what sustains us through the years. >> Final thoughts on what's going on here today and over the last couple of days. Any surprises, hall chatter that you can share that you weren't expecting or really validates what's going on in this space. A lot of activity going on, I love all the signs over the building. This is the infrastructure that makes the rest of the world go whether it's power, transportation, what do we have behind us? Distribution, I mean it's really pretty phenomenal the industries you guys cover. >> Yeah and you know a lot of the sessions are videotaped so you can see Tyler from last year when he gave a presentation. This year Ebay, PayPal are giving presentations. And it's just a very exciting time in the data center industry. >> And I'll say on our side maybe not as much of a surprise, but also hearing the kind of the customer feedback on things that Dell and OSIsoft have partnered together and we work together on things like a Redfish connector in order to be able to, from an agnostic standpoint, be able to pull data from any server that's out there, regardless of brand, we're full support of that. But, to be able to do that in an automatic way that with their connector so that whenever I go and search for my range of IP addresses, it finds all the devices, brings all that data in, organizes it, and makes it ready for me to be able to use. That's a big thing and that's... They've been doing connectors for a while, but that's a new thing as far as being able to bring that and do that for servers. That, if I have 100,000 servers, I can't manually go get all those and bring them in. >> Right, right. >> So, being able to do that in an automatic way is a great enablement for the Edge. >> Yeah, it's a really refreshing kind of point of view. We usually look at it from the other side, from IT really starting to get together with the OT. Coming at it from the OT side where you have such an established customer base, such an established history and solution set and then again marrying that back to the IT and some of the newer things that are happening and that's exciting times. >> Yeah, absolutely. >> Yeah. >> Well thanks for spending a few minutes with us. And congratulations on the success of the show. >> Thank you. >> Thank you. >> Alright, he's Tyler, he's Ed, I'm Jeff. You're watching theCUBE from downtown San Francisco at OSIsoft PI WORLD 2018, thanks for watching. (light techno music)

>> Announcer: From San Francisco, it's theCUBE covering OSIsoft PIWORLD 2018, brought to you by OSIsoft. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE, we're in downtown San Francisco at the OSIsoft PIWorld 2018. They've been doing it for like 28 years, it's amazing. We've never been here before, it's our first time and really these guys are all about OT, operational transactions. We talk about IoT and industrial IoT, they're doing it here. They're doing it for real and they've been doing it for decades so we're excited to have our next two guests. Tyler Duncan, he's a Technologist from Dell, Tyler, great to see you. >> Hi, thank you. >> He's joined by Ed Watson, the global account manager for channels for Osisoft. Or OSIsoft, excuse me. >> Glad to be here. Thanks, Jeff. >> I assume Dell's one of your accounts. >> Dell is one of my accounts as well as Nokia so-- >> Oh, very good. >> So there's a big nexus there. >> Yep, and we're looking forward to Dell Technology World next week, I think. >> Next week, yeah. >> I think it's the first Dell Technology not Dell EMC World with-- >> That's right. >> I don't know how many people are going to be there, 50,000 or something? >> There'll be a lot. >> There'll be a lot. (laughs) But that's all right, but we're here today... >> Yeah. >> And we're talking about industrial IoT and really what OSIsoft's been doing for a number of years, but what's interesting to me is from the IT side, we kind of look at industrial IoT as just kind of getting here and it's still kind of a new opportunity and looking at things like 5G and looking at things like IPE, ya know, all these sensors are now going to have IP connections on them. So, there's a whole new opportunity to marry the IT and the OT together. The nasty thing is we want to move it out of those clean pristine data centers and get it out to the edge of the nasty oil fields and the nasty wind turbine fields and crazy turbines and these things, so, Edge, what's special about the Edge? What are you guys doing to take care of the special things on the Edge? >> Well, a couple things, I think being out there in the nasty environments is where the money is. So, trying to collect data from the remote assets that really aren't connected right now. In terms of the Edge, you have a variety of small gateways that you can collect the data but what we see now is a move toward more compute at the Edge and that's where Dell comes in. >> Yeah, so I'm part of Dell's Extreme Scale and Structure Group, ESI, and specifically I'm part of our modular data center team. What that means is that for us we are helping to deploy compute out at the Edge and also at the core, but the challenges at the Edge is, you mentioned the kind of the dirty area, well, we can actually change that environment so that's it's not a dirty environment anymore. It's a different set of challenges. It may be more that it's remote, it's lights out, I don't have people there to maintain it, things like that, so it's not necessarily that it's dirty or ruggedized or that's it's high temperature or extreme environments, it just may be remote. >> Right, there's always this kind of balance in terms of, I assume it's all application specific as to what can you process there, what do you have to send back to process, there's always this nasty thing called latency and the speed of the light that just gets in the way all the time. So, how are you redesigning systems? How are you thinking about how much computing store do you put out on the Edge? How do you break up that you send back to central processing? How much do you have to keep? You know we all want to keep everything, it's probably a little bit more practical if you're keepin' it back in the data center versus you're tryin' to store it at the Edge. So how are you looking at some of these factors in designing these solutions? >> Ed: Well, Jeff, those are good points. And where OSIsoft PI comes in, for the modular data center is to collect all the power cooling and IT data, aggregate it, send to the Cloud what needs to be sent to the Cloud, but enable Dell and their customers to make decisions right there on the Edge. So, if you're using modular data center or Telecom for cell towers or autonomous vehicles for AR VR, what we provide for Dell is a way to manage those modular data centers and when you're talking geographically dispersed modular data centers, it can be a real challenge. >> Yeah, and I think to add to that, there's, when we start lookin' at the Edge and the data that's there, I look at it as kind of two different purposes. There's one of why is that compute there in the first place. We're not defining that, we're just trying to enable our customers to be able to deploy compute however they need. Now when we start looking at our control system and the software monitoring analytics, absolutely. And what we are doing is we want to make sure that when we are capturing that data, we are capturing the right amount of data, but we're also creating the right tools and hooks in place in order to be able to update those data models as time goes on. >> Jeff: Right. >> So, that we don't have worry about if we got it right on day one. It's updateable and we know that the right solution for one customer and the right data is not necessarily the right data for the next customer. >> Jeff: Right. >> So we're not going to make the assumptions that we have it all figured out. We're just trying to design the solution so that it's flexible enough to allow customers to do whatever they need to do. >> I'm just curious in terms of, it's obviously important enough to give you guys your own name, Extreme Scale. What is Extreme Scale? 'Cause you said it isn't necessarily because it's dirty data and hardened and kind of environmentally. What makes an Extreme Scale opportunity for you that maybe some of your cohorts will bring you guys into an opportunity? >> Yeah so I think for the Extreme Scale part of it is, it is just doing the right engineering effort, provide the right solution for a customer. As opposed to something that is more of a product base that is bought off of dell.com. >> Jeff: Okay. >> Everything we do is solution based and so it's listening to the customer, what their challenges are and trying to, again, provide that right solution. There are probably different levels of what's the right level of customization based off of how much that customer is buying. And sometimes that is adding things, sometimes it's taking things away, sometimes it's the remote location or sometimes it's a traditional data center. So our scrimpt scale infrastructure encompasses a lot of different verticals-- >> And are most of solutions that you develop kind of very customer specific or is there, you kind of come up with a solution that's more of an industry specific versus a customer specific? >> Yeah, we do, I would say everything we do is very customer specific. That's what our branch of Dell does. That said, as we start looking at more of the, what we're calling the Edge. I think ther6e are things that have to have a little more of a blend of that kind of product analysis, or that look from a product side. I'm no longer know that I'm deploying 40 megawatts in a particular location on the map, instead I'm deploying 10,000 locations all over the world and I need a solution that works in all of those. It has to be a little more product based in some of those, but still customized for our customers. >> And Jeff, we talked a little bit about scale. It's one thing to have scale in a data center. It's another thing to have scale across the globe. And, this is where PI excels, in that ability to manage that scale. >> Right, and then how exciting is it for you guys? You've been at it awhile, but it's not that long that we've had things like at Dupe and we've had things like Flink and we've had things like Spark, and kind of these new age applications for streaming data. But, you guys were extracting value from these systems and making course corrections 30 years ago. So how are some of these new technologies impacting your guys' ability to deliver value to your customers? >> Well I think the ecosystem itself is very good, because it allows customers to collect data in a way that they want to. Our ability to enable our customers to take data out of PI and put it into the Dupe, or put it into a data lake or an SAP HANA really adds significant value in today's ecosystem. >> It's pretty interesting, because I look around the room at all your sponsors, a lot of familiar names, a lot of new names as well, but in our world in the IT space that we cover, it's funny we've never been here before, we cover a lot of big shows like at Dell Technology World, so you guys have been doing your thing, has an ecosystem always been important for OSIsoft? It's very, very important for all the tech companies we cover, has it always been important for you? Or is it a relatively new development? >> I think it's always been important. I think it's more so now. No one company can do it all. We provide the data infrastructure and then allow our partners and clients to build solutions on top of it. And I think that's what sustains us through the years. >> Final thoughts on what's going on here today and over the last couple of days. Any surprises, hall chatter that you can share that you weren't expecting or really validates what's going on in this space. A lot of activity going on, I love all the signs over the building. This is the infrastructure that makes the rest of the world go whether it's power, transportation, what do we have behind us? Distribution, I mean it's really pretty phenomenal the industries you guys cover. >> Yeah and you know a lot of the sessions are videotaped so you can see Tyler from last year when he gave a presentation. This year Ebay, PayPal are giving presentations. And it's just a very exciting time in the data center industry. >> And I'll say on our side maybe not as much of a surprise, but also hearing the kind of the customer feedback on things that Dell and OSIsoft have partnered together and we work together on things like a Redfish connector in order to be able to, from an agnostic standpoint, be able to pull data from any server that's out there, regardless of brand, we're full support of that. But, to be able to do that in an automatic way that with their connector so that whenever I go and search for my range of IP addresses, it finds all the devices, brings all that data in, organizes it, and makes it ready for me to be able to use. That's a big thing and that's... They've been doing connectors for a while, but that's a new thing as far as being able to bring that and do that for servers. That, if I have 100,000 servers, I can't manually go get all those and bring them in. >> Right, right. >> So, being able to do that in an automatic way is a great enablement for the Edge. >> Yeah, it's a really refreshing kind of point of view. We usually look at it from the other side, from IT really starting to get together with the OT. Coming at it from the OT side where you have such an established customer base, such an established history and solution set and then again marrying that back to the IT and some of the newer things that are happening and that's exciting times. >> Yeah, absolutely. >> Yeah. >> Well thanks for spending a few minutes with us. And congratulations on the success of the show. >> Thank you. >> Thank you. >> Alright, he's Tyler, he's Ed, I'm Jeff. You're watching theCUBE from downtown San Francisco at OSIsoft PI WORLD 2018, thanks for watching. (light techno music)

I from Las Vegas Nevada it's the cube covering IBM world of Watson 2016 brought to you by IBM now here are your hosts John furrier and Dave vellante hey welcome back everyone we're here live in Las Vegas for IBM's world of Watson at the mandalay bay here this is the cube SiliconANGLE media's flagship program we go out to the events and extract the signal-to-noise I'm John Ford SiliconANGLE i'm here with dave vellante my co-host chief researcher red Wikibon calm and our next guest is inderpal bhandari who's the chief global chief data officer for IBM welcome to the cube welcome back thank you thank you meet you you have in common with Dave at the last event 10 years Papa John was just honest we just talked about the ten year anniversary of I OD information on demand and Dave's joke why thought was telling we'll set up the says that ten years ago different data conversation how do you get rid of it is I don't want the compliance and liability now it shifted to a much more organic innovative exciting yeah I need a value add what's the shift what's the big change in 10 years what besides the obvious of the Watson vision how did what it move so fast or too slow what's your take on this ya know so David used to be viewed as exhaust right the tribe is something to get rid of like you pointed out and now it's much more to an asset and in fact you know people are even talking about about quantifying it as an asset so that you can reflect it on the balance sheet and stuff like that so it certainly moved a long long way and I think part of it has to do with the fact that we are inundated with data and data does contain valuable information and to the extent that you're able to glean it and act on it efficiently and quickly and accurately it leads to a competitive advantage what's the landscape for architects out there because a lot of things that we hear is that ok i buy the day they I got a digital transformation ok but now I got to get put the data to work so I need to have it all categorized what's the setup is there a general architecture philosophy that you could share with companies that are trying to set themselves up for some baseline foundational sets of building blocks I mean I think they buy the Watson dream that's a little Headroom I just want to start in kindergarten or in little league or whatever metaphor we want to use any to baseline what's today what's the building blocks approach the building blocks approach I mean from a if you're talking about a pure technical architectural that kind of approach that's one thing if you're really going after a methodology that's going to allow you to create value from data I would back you up further I would say that you want to start with the business itself and gaining an understanding of how the business is going to go about monetizing itself not its data but you know what is the businesses monetization strategy how does the business plan to make money over the next few years not how it makes money today but over the next few years how it plans to make money that's the right starting point once you've understood that then it's basically reflecting on how data is best used in service of that and then that leads you down to the architecture the technologies the people you need the skills makes the process Tanner intuitive the way it used to be the ivory tower or we would convene and dictate policy and schemas on databases and say this is how you do it you're saying the opposite business you is going to go in and own the road map if you will the business it's a business roadmap and then figure it out yeah go back then go back well that's that's really the better way to address it than my way so the framework that we talked about in in Boston and now and just you're like the professor I'm the student so and I've been out speaking to other cheap date officers about it it's spot on this framework so let me briefly summarize it and we can I heard you not rebuilding it to me babe I'm saying this is Allah Falls framework I've stolen it but with no shame no kidding and so again we're doing a live TV it's you know he can source your head I will give him credit so but you have said they're there are two parallel and three sequential activities that have to take place for data opposite of chief data officer the two parallel our partnership with the line of business and get the skill sets right the three sequential are the thing you just mentioned how you going to monetize data access to data data sources and Trust trust the data okay so great framework and I'd say I've tested it some CEOs have said to me well I geeza that's actually better than the framework I had so they've sort of evolved as I said you're welcome and oh okay but now so let's drill into that a little bit maybe starting with the monetization piece in the early days Jonna when people are talking about Big Data it was the the mistake people made was I got to sell the data monetize the data itself not necessarily it's what you're saying yes yes I think that's the common pitfall with that when you start thinking about monetization and you're the chief data officer your brain naturally goes to well how do I monetize the data that's the wrong question the question really is how is the business planning to monetize itself what is the monetization strategy for the overall business and once you understand that then you kind of back into what data is needed to support it and that's really kind of the sets the staff the strategy in place and then the next two steps off well then how do you govern that data so it's fit for the purpose of that business lead that you just identified and finally what data is so critical that you want to centralize it and make sure that it's completely trusted so you back into those three those three steps so thinking about data sources you know people always say well should you start with internal should you start with external and the answer presumably is it depends it depends on the business so how do you how do you actually go through that decision tree what's that process like yeah I mean if you know you start with the monetization strategy of the company so for example I'll use IBM a banana and the case of IBM took me the first few months to understand that our monetization strategy was around cognitive business specifically making enterprises into cognitive businesses and so then the strategy that we have internally for IBM's data is to enable cognition within within IBM the enterprise and move forward with that and then that becomes a showcase for our customers because it is after all such a good example of a complex enterprise and so backing you know backing in from that strategy it becomes clear what are some of the critical data elements that you need to master that you need to trust that you need to centralize and you need to govern very very rigorously so that's basically how I approached it did I answer your question daivam do you get so so you touched on the on the second part I want to drill into the the third sequential activities which which is sources so i did so you did we just talk about this well the sources i mean if you had something add to that yes in terms of the i think you mentioned the internal versus external so one thing else i'll mention especially if you kind of take that 10-year outlook that we were talking about 10 years ago serials had very internal outlook in terms of the data was all internal business data today it's much more external as well there's a lot more exogenous data that we have to handle and validity and that's because we're making use of a lot more unstructured data so things like news feeds press releases articles that have just been written all our fair game to amplify the view that you have about some entity so for example if we're dealing with a new supplier you know previously we might gather some information by talking with them now we'd also be able to look at essentially everything that's out there about them and factor that in so it is a there's an element of the exogenous data that's brought to bear and then that obviously becomes part of the realm of the CDO as well to make sure that that data is available and you unusable by the business is John Kelly said something go ahead sorry well Jeff Jonas would say that's the observation space right that you want to have the news feeds it's extra metadata that could change the alchemy if you will of whatever the mix of the data is that kind of well yeah I would say you might even go further than just metadata i would say that in some some sense it's part of your intrinsic data set because you know it gives you additional information about the entities that you're collecting data on and that measuring the John Kelly in the keynote this morning he made two statements he said one is in three to five years every health care practitioners going to going to want to consult Watson and then he also said same thing for MA because watch is going to know every public piece of data about every single company right so it's would seem that within the three to five year time frame that the shift is going to be increasingly toward external data sources not necessarily the value in the lever points but in terms of the volume certainly of data is that fair I think it's a it's a fair statement I mean I think if you think of it in the healthcare context if you know a patient comes in and there's a doctor or a practitioner that's examining the patient right there they're generating some data based on their interaction but then if you think about the exogenous data that's relevant and pertinent to that case that could involve you know thousands of journals and articles and so you know your example of essentially saying that the external data could be far greater than the internal data out say we're already there okay and then the third sequential piece is trust are you gonna be able to trust the trust we talk a lot about we were down to Big Data NYC the same week you guys made your big announcement the data works everybody talks about data Lakes we joke gets the data swamp and can't really trust the data yeah we further away from a single version of the truth than we ever were so how are you dealing with that problem internally at IBM and what's the focus is it more on reporting is it more on supporting lines of business in product yeah the focus internal within IBM is in terms of driving cognition at the way I would describe it is at points where today we have significant human judgment being exercised to make decisions and that's you know thousands of points in our enterprise or complicated enterprise like IBM's and each of those decision points is actually an opportunity to inject cognitive technology and play and then bring to bear and augmented intelligence to those decisions that you know a factors in the exogenous data so leaving a much better informed decision but also them a much more accurate decision okay the two parallel activities let's start with the first one line of business you know relationships sounds like bromide why is it not just sort of a trite throwaway statement what where's the detail behind that so the detail behind that if you go back to the very first and the most important step and this whole thing with regard to the monetization strategy of the company understanding that if you don't have those deep relationships with the lines of business there's no way that you'll be able to understand the monetization strategy of the business so that's why that's a concurrent activity that has to start on day one otherwise you won't even get past the you know that that very first first base in terms of understanding what the monetization strategies are for the business and that can only really come by working directly with the business units meeting with their leadership understanding their business so you have to do that due diligence and that's where that partnership becomes critical then as you move on as you progress to that sequence you need them again so for instance once you understood the strategy and now you understood what data you need to follow that strategy and to govern it you need their help in governing the business because in many cases the businesses may be the ones collecting the data or at least controlling the source systems for that data so that partnership then just gets deeper and deeper and deeper as you move forward in that program I love the conscience of monetizing earlier and this some tweets going around you know what's holding it back cost of building it obviously and manageability but I want to bring that back and bring a developer perspective here because a lot of emphasis is on developing apps where the data is now part of the development process I wrote a blog post in 2008 saying that dated some new development kit radical at the time but reality it came out to be true and that they're looking at data as library of value to tap into so if stuffs annandale they could be sitting there for years but I could pull something out and be very relevant in context in real time and change the game on some insight and the insight economy is bob was saying so what is your strategy for IBM 21 on board more developer goodness and to how do you talk to customers were really trying to figure out a developer strategy so they can build apps and not to go back and rewrite it make it certainly mobile first etc but what's how does a date of first appt get built and I should developers be programming with you I'll give you a way to think about it right i mean and going back again to that ten-year paradigm shift right so ten years ago if somebody wanted to write an application and put it on the internet and it was based on data the hardest part was getting hold of the data because it was just very very difficult for them to get all of it to access the data and then those who did manage to get all of the data they were very successful in being able to utilize it so now with the the paradigm shift that's happened now is the approaches that you make the data available to developers and so they don't have to go through that work both in terms of accessing collecting finding that data then cleaning it it's also significant and so time consuming that it could put put back there their whole process of eventually getting to the app so to the extent that you have large stores of data that are ready to go and you can then make that available to a body of developers it just unleashes it's like having a library of code available is it all the hard work and I think that's a good way to look at it I mean that's think that's a very good way to look at it because you've also got technologies like the deep learning technologies where you can essentially train them with data so you don't need to write the code they get trained to later so I see a DevOps of data means like an agile meets I'm again you're right a lot of the cleaning and this is where you no more noise we all know that problem or data creates more noise better cleaning tools so however you can automate that yes seems to be the secret differentiator it's an accelerator it's amazing accelerator for development if you have good sets of data that are available for them to used so I want to round out my my little framework here your frame working with my my learnings for the fifth one being skills yes so this is complicated because it involves organization skills changes as pepper going through the lava here we try to get her on the cube Dave home to think the pamper okay babe yeah so should I take over pepper you want to go see pepper I want to see pepper on the cube hey sorry exact dress but so a lot of issues there there's reporting structures so what do you mean when you talk about sort of the skill sets and rescaling so and I'll describe to you a little bit about the organization that I have at IBM as an example some of that carries over and some of that doesn't the reason I say that is again I mean the skills piece there are some generic skill sets that you need for to be achieved data officer to be a successful chief data officer in an enterprise there is one pillar that I have in my organization is around data science data engineering DevOps deep learning and these are the folks who are adept at those technologies and approaches and methodologies and they can take those and apply them to the enterprise so in a sense these are the more technical people then another pillar that's again pretty generic and you have to have it is the information and data governance pillow so that anything that's flowing any data that's flowing through the data platform that I spoke off in the first pillar that those that that data is governed and fit for purpose so they have to worry about that as soon as any data is you even think of introducing that into the platform these folks have to be on that and they're essentially governing it making sure that people have the right access security the quality is good its improving there's a path to improving it and so forth I think those are some fairly generic you know skill sets that we have to get in the case of the first pillar what's difficult is that there aren't that many people with those skills and so it's hard to find that talent and so the sooner you get on it so that would that's the biggest barrier in the case of the second pillar what's the most difficult piece there is you need people who can walk the balance between monetization and governance too much governance and you essentially slow everything down and nothing moved a cuff and you're handcuffed and then you know if it's too much monetization you might run aground because you you ignored some major regulation so walking that loss of market value yeah that's what you have to really get ahead of your skis as they say and have a faceplant you'll try too hard to live boost mobile web startups like Twitter that's big cock rock concert with Twitter Facebook if you try to monetize too early yes you lose the flywheel effect of value absolutely so walking that balance is critical so that's that that's really finding the skill set to be able to do that that's that's what what's at play in that second or the third one is if you are applying it to an enterprise you have to integrate these you know this platform into the workflow off the enterprise itself otherwise you're not going to create any impact because that's where the impact gets created right that's basically where the data is that the tip of the spear to so to speak so you it's going to create value and in a large enterprise which has legacy systems which are silos which is acquiring companies and so on and so forth that's enough itself a significant job and that skill set is that's a handicapped because if you have that kind of siloed mentality you don't get the benefits of the data sharing right so what's that what's said how much how much effort would it take I'm just kind of painting that picture kind of like out there like well a lot of massively hard ya know that that's you know a lot of you know a lot of people think that data mining is all about my data you know this is my data I'm not going to give it to you the one of the functions of the chief data office is to change that mindset yeah and to stop making use of the data in a broader context than just a departmental siloed type of approach and now some data can legitimately be used only departmentally but the moment you need two or more department start using that data I mean it's essentially corporate data so are those roles a shared service everybody see that works it maybe varies but is it a shared service that reports into the chief data officer or is it embedded into the business those those skill sets that you talked about I think those skill sets are definitely part of the chief data officer you know organization now it's interesting you mentioned that about embedding them and the business units now in a in a large enterprise a complicated enterprise like IBM the different business units and that potentially have different business objectives and so forth you know you you do need a chief data officer role for each of these business units and that's something that I've been advocating that's my fault pillar and we are setting that up and then within the context of IBM so that they serve the business unit but they essentially reporting to me so that they can make use of the overall corporate structure you do their performance review the performance review is done by the business unit it is ok but the functional direction is given by me ok so I get back to still go either way oh yes that's a balance loon yeah absolutely under a lot of time for sure i'll get back to this data mining because you bring up a good point we can maybe continue on our next time we talk but data monies were all the cutting edge kind of best practices are were arsed work what we're relations are still there technically if you're here but that the dynamic of data mining is is that you're assuming no new data so with if you have a lot of data coming in most of the best data mining techniques are like a corpus you attack it and learned but if the pile of data is getting bigger faster that you could date a mine it what good is against or initial circular hole I'm going to again you know just take you back 10 years from now and now right and the differences between the two so it's very interesting points that you bring up I'll give you an example from 10 years ago this data mining example not ten years ago actually my first go-around at IBM so it's like 94 yeah one of the things I've done was we had a program a computer program that every team in the National Basketball Association started using and this was a classic data mining program it would look at the data and find insights and present them and one of the insights that it came up with and this was for a critical playoff game it told the coach you got to play your backup point guard and your backup forward now think about that which same coach would actually go with that so it's very hard for them to believe that they don't know if it's right or wrong in my own insurance and the way we got around that was we essentially pointed back to the snippets of video where those circumstances occurred and now the coach could see what is going on make a you know an informed decision flash forward to now the systems we have now can actually look at all that context all at once what's happening in the video what's happening in the audio also the data can piece together the context so data mining is very different today than what it was them now it's all about weaving the context and the story together and serving it up yeah what happened what's happening and what's going to happen kinda is the theaters of yes there are in sight writing what happened it's easy just yeah look at the data and spit out some insight what's happening now is a bit harder in memory I think that's the difference between cognition as it away versus data mining as you know we understood a few years ago great cartridge we can go for another hour but do we ever get enough love to follow up on some of the deep learning maybe come down to armonk next time we're in this certainly on the sports data we have a whole program on sports data so we love the sports with the ESPN of tech and bringing you all the action right here yes I did Doug before Moneyball you know my mistake was letting right yeah yeah right the next algorithm but that's okay you know we put a little foot mark on the cube notes for that thank you very much thank you appreciate okay live in Mandalay Bay we're right back with more live coverage I'm Sean for a table on thing great back today I am helping people

hi from Las Vegas Nevada it's the cube covering IBM world of Watson 2016 brought to you by IBM now here are your hosts John Fourier as Dave Volante hey welcome back everyone we are here live at the Mandalay Bay at the IBM world of Watson this is Silicon angles cube our flagship program we go out to the events and extract the signal from the noise I'm John Fourier with my co-host Dave allanté for the two days of wall-to-wall coverage our next guest is michelle fools so who's the chief marketing officer for IBM knew the company fairly new within the past year yes welcome to the queue last month I think you check all these new hires a lot of new blood coming inside me but this is a theme we heard from Staples to be agile to be fast you're new what's what's your impressions and what's your mandate for the branding the IBM strong brand but yes what's the future look well look I'm I'm thrilled to be here and I'm thrilled to be here because this is an extraordinary company that makes real difference in the world right and that I think you feel it here at the world of Watson in the sort of everyday ways that Watson and IBM touches consumers such as end-users makes their health better you know allows them to have greater experiences so so that's incredible to be part of my kind of company having said that and exactly to your point it's a time of acceleration and change for everyone in IBM is not immune to that and so my mandate here in my remit here and coming in and being a huge fan of what IBM has to say well how do we sharpen our messaging how do we always feel like a challenger brand you know how do we think about what Watson can do for people what the cloud can do what our services business can do and how is that distinctive and differentiated from everybody else out there and I think we have an incredible amount of assets to play with that's got to be through the line you know it's no longer the case that we can have a message on TV and that you know attracts the world the digital experiences are having every single day when they're clicking through on an ad when they're chatting with somebody when their car call center when they have a sales interaction is that differentiated message that brand resident all the way through second thing is marketing's become much more of a science you know and that to me is super exciting I've been a CEO most of my career and you know that the notion that marketing has to drive revenue that marketing has to drive retention and loyalty and expansion that we can come to the table with much more science in terms of what things are most effective in making sure that more clients love us more deeply for longer I'm gonna ask you the question because we had we've had many conversations with Kevin he was just here he was on last year Bob Lord the new chief digital officer we talked to your customers kind of the proof points in today's market is about transparency and if you're not a digital company how could you expect customers to to work with them so this has been a big theme for IBM you guys are hyper focused on being a digital company yes yes and how does it affect the brand a brand contract with the users what's your thoughts on that well first of all Bob Lord is awesome we've known each other for 10 years so it's so wonderful to be working with him again and Dave Kenny as well I think that the at the end of the day consumers have experiences and and you know think of every business you know out there as a consumer and they're having experiences all the time their expectations are being shaped by the fact that they go on Amazon and get prime delivery right their expectations are being shaped by they can go on Netflix and get you know personalized recommendations for them or Spotify and so our job of course and we have some of the greatest technical minds in the world it's to make sure that every experience lines up with the highest of their expectations and so much of that is digital and so my passion my background is entirely in the digital space I have a CEO of Travelocity and then CEO of gilt chief marketing a digital officer at Citigroup so the notion that you know the world's greatest digital experiences is something I'm very passionate about you mentioned Zelda so big TV ads and you think of the smarter planet which was so effective but it was a big TV campaign so you do what's the what's the sort of strategy that you're envisioning is in sort of digital breadcrumbs maybe you could talk about deadly yeah well think about Watson it's a perfect place to think about the Watson branding what does Watson really mean right Watson is and Ginni has said this so well of course it's cognitive and but at the end of the day it's about helping people make better decisions and so you can do some advertising with Watson and Bob Dylan and Watson and you know the young young girl with Serena and and you can get that messaging high but then you've got to bring it all the way through so that's why it's something like this is so powerful to see Woodside up their alley or all these companies talking about staples how they are using Watson embedded in their processes their tools to make their end-users experiences better and how nobody else could do this for them the way Watson's doing it that's taking a brand on high and advertising message on high and delivering value for businesses for patients for consumers all the way through that's what we have to do I got to ask you about that ad advertising trends I so we all see ad blocker in the news digital is a completely different new infrastructure expanded dynamic with social what not you can talk about Bob and I were talking last night about it too you Trevor you know banner ads are all out there impression base and then coded URLs to a landing page email marketing not gonna go away anytime soon but it's changing rapidly we have now new channels yeah what's your thoughts because this is now a new kind of ROI equation is there any thoughts on how you look at that and is it going to integrate into the top level campaigns how are you looking at the new digital that the cutting-edge digital stuff huge amounts of thoughts on this topic so I think you know if you think back 15 20 years ago there were always something called market mix modelling which helps advertisers and marketers to understand the effectiveness of their TV campaigns and frankly not too dissimilar from Nielsen you know there were so there was art and science at best in it and then all of a sudden the digital world evolved and you could get at a tactical level very very clear about attribution and whether you drove something and the challenge for us now is much more sophisticated models that are multi-touch attribution because the reality is an average consumer doesn't do one thing or have one interaction with a brand they're gonna see a TV show and watch a commercial while they're watching that commercial that business user or that end consumer is on their iPad or on their phone they're seeing a digital ad the next day at work they're being retargeted because they were aughts company they search for something they see a search campaign our job is to connect those dots and understand what really moves that consumer that business user to take an action and there are many sophisticated multi-touch attribution models where you model you know a standard set of behaviors and you test correlations against a bunch of different behaviors so you understand of what I did all the money I spent what really drove impact and by cohort I think that's the other credit there's no more the sense of sort of aggregated everything you really have to break it out yeah I didn't space my cohort to see what moves me and improve that experience right which has been you you get the example in the day of the Hilton retirees you already know that the retard the hotel was full so so obviously Watson plays a role in them Satyam plays a role in that so it's all about data it's all about you know that's where I think Watson can be extraordinarily helpful so if you think about the tool as a marketer has they're becoming more and more sophisticated and retargeting with something out of 10 years ago whenever was introduced that helped all of us a little bit and getting that message but it is only as good as the API is behind it and the the experience behind it when now when I was at gilt I was CEO of gilt we would put over a thousand products on sale every day that would be sold out by the next day sales down this 24-hour flash sale we had to get really really good at knowing how to how to retarget because last thing you want is to retarget something that sold out right or gone the next day and understand the user that was in and out and they're coming back and of course in that cohort that's where Watson to me is very exciting and you probably saw this in some of the demos of where Watson can help marketers you know where Watson can can really understand what are the drivers of behavior and what is likely to drive the highest purpose why were you so successful at guild and and how are the challenges different years because there's a sort of relatively more narrow community or city group to I was called the chief marketing and digital officer at Citigroup and and you know a tremendous budget and a lot of transactions you have to drive every day a lot of people you want to open credit cards and bank accounts so around the world I think that the the relentless focus on on marketing being art and science you know art and science and I think that's you know that passion for analytics passion for measurement having been CEO that passion for being able to say this is what we're doing and this is what we're driving so you've been kind of a data geek in your career you mentioned the financial services you can't to measure everything but back to the ad question you know the old saying used to be wasting half my advertise I just don't know which half yeah and my archives is wasted but now for the first time in the history of business in the modern era you measure everything online that's right so does that change your view and the prism of how you look at the business cuz you mentioned multi-touch yeah so now does that change the accountability for the suppliers I mean at agencies doing the big campaign I think it changes the game for all of us and there's no destination this is every day you can get better at optimizing your budget and and I would be the first to tell you as much of a sort of engineering and data geek because I've always been and deep-fried in the reality is there is art even in those attribution models what look back windows you choose etc that you know you're making decisions as a company but once you make those decisions you can start arraying all of your campaigns and saying what really moved the needle what was the most effective it's not an indictment that say what are we can do differently tomorrow you know the best marketers are always optimizing they're always figuring out at what point in the final can we get better tomorrow well in answer about talent because that's one of the things that we always talk about and also get your thoughts on Women in Technology scheme we were just at Grace Hopper last week and we started to fellowship called the tech truth and we're doing it's real passion area for us we have a site up QP 65 net / women in tech all women interviews we're really trying it the word out but this is now a big issue because now it's not stem anymore it's team arts is in there and we were also talking to the virtual reality augmented reality user experience is now potentially going to come into the immersion students and there's not enough artists yeah so you starting to see a combination of new discipline talents that are needed in the professions as well as the role of women in technology yeah your thoughts on that because this isn't you've been very successful what's your view on that at what's your thoughts about thank you for what you're doing right it takes a lot of people up there saying that this is important to make a difference so most of all thank you you know I think that this this is obviously a place I've been passion about forever I remember being a and being pregnant and that becoming this huge you know issue a news story and you're trying to juggle it right and how could a woman CEO be pregnant so it's so funny how people ridiculous took attention but but I think that the point is that the the advantage as a company has when there are great women in engineering and great women in data science and great women and user experience and design are just palpable they're probable in a variety of ways right when the team thinks differently the team is more creative the team is more open to new ideas the output for the customers are better right I mean they just saw a snapchat today just announced that in 2013 70% of their users were women so all the early adopters were women you know now it's balance but the early the early crowd were women and so we have got to figure out how to break some of the minds now I'm incredibly encouraged though while we still have a long way to go the numbers would suggest that we're having the conversation more and more and women are starting to see other women like them that they want to be it's a global narrative which is good why we're putting some journalists on there and funding it as and just as a fellowship because this it's a global story yeah okay and the power women I mean it's like there are real coders and this real talent coming in and the big theme that came out of that was is that 50% of the consumers of product are women's but therefore they should have some women features and related some vibe in there not just a male software driven concept well and should too when a powerful individual male individual like Satya steps in it and and you know understands what the mistaken and someone like refer to his speech two years ago where he said that you should just bad karma don't speak up and opening up transparency he got some heat yeah but that talk as you probably know but my opinion it's it's it's a positive step when an individual like that it was powerful and opening transparency within their company yeah that's it is that great networking I host a core I've been doing this for a year years with a good friend of mine Susan line from AOL we host a quarterly breakfast for women in tech every every quarter in New York City and we've been doing it for a long time it's amazing when those women come together the conversations we have the discussions we have how to help each other and support each other and so that's that's a real passion we were lost in a few weeks ago for the data science summit which Babu Chiana was hosting in and one of the folks was hosting the data divas breakfast we a couple there were a couple day two dudes who walked in and it was interesting yeah the perspectives 25 percent of the women or the chief data officer were women mm-hmm which was an interesting discussion as well so great 1,000 men at 15 you know as you see that techno but it's certainly changing when I get back to the mentoring thing because one of the things that we're all so passionate about is you've been a pioneer okay so now there's now an onboarding of new talent new personas new professions are being developed because we're seeing a new type of developer we're seeing new types of I would say artists becoming either CG so there's new tech careers that weren't around and a lot of the new jobs that are going to be coming online haven't even been invented yet right so you see cognition and what cognitive is enabling is a new application of skills yep can your thoughts on that because this is an onboarding opportunity so this could change the the number of percentage of women is diverse when you think about what I mean it's clear your notion of steam right your notion of stem that is a male and female phenomena and that is what this country needs it's what this world needs more of and so there's a policy and education obligation and all of us have to the next generation to say let's make sure we're doing right by them in terms of education and job opportunities when you think about onboarding I mean to me that the biggest thing about onboarding is the world is so much more interconnected than it used to be if you're a marketer it's not just art or science you have to do both it's a right brain left brain connectivity and I think 1020 years ago you could grow up in a discipline that was functional and maybe siloed and maybe you were great at left brain or great at right brain and the world demands so much more it's a faster pace it's an accelerated pace and the interconnection is critical and I've one of the things we're doing is we're putting together these diamond teams and I think it's going to really help lead the industry diamond teams are when you have on every small agile marketing team and analytics head a product marketing had a portfolio marketing had a design or a social expert these small pods that work on campaigns gone are the days that you could say designer designs it product comes up with the concept then it goes so it's design team then it goes to a production team then it goes to an analytics team we're forcing this issue by putting these teams together and saying you work together every day you'll get a good sense of where the specialty is and how you learn how to make your own discipline better because you've got the analytics person asked a question about media buying and media planning advertising as we're seeing this new real-time wet web yeah world mobile world go out the old days of planned media buyers placed the advertisement was a pacing item for execution yep now things you mentioned in the guild flash sales so now you're seeing new everyday flash opportunities to glob on to an opportunity to be engagement yeah and create a campaign on the fly yes and a vision of you guys I mean do you see that and does it change the cadence of how you guys do your execution of course of course that's one of the reasons we're moving to this diamond team and agile I think agile will ultimately be as impactful to marketing as it was to engineering and development and so I think the of course and that has to start with great modeling and great attribution because you have to know where things are performing so that you can iterate all the time I mean I believe in a world where you don't have marketing budgets and I know that sounds insane but I believe in a world where you set target and ranges on what you think you're gonna spend at the beginning of the year and every week like an accordion you're optimizing spend shipping code you've been marketing you should be doing like code so much of marketing is its episodic you boom and then it dies in a moment it's gone to the next one and you're talking about something that's I love that you know the personas to your point are much more fluid as well you got Millennials just creating their own vocations yes well this is where I think consumer companies have led the path and you know if you think about a lot of b2b companies we've had this aggregated CIO type buyer and now we've got to get much more sophisticated about what does the developer want you know what's important to the developer the messaging the tools the capabilities the user experience what about the marketer you know what the person in financial services and so both industry and professional discipline and you know schooling now with Watson you don't have to guess what they want you can actually just ask them yeah well you can actually the huge advantage you got you observe the observation space is now addressable right so you pull that in and say and that's super important even the stereotype of the persona is changing you've been saying all week that the developer is increasingly becoming business oriented maybe they don't they want they don't want to go back and get their MBA but they want to learn about capex versus op X and that's relevant to them and they to be a revolutionary you have to understand the impact right and and and they want to ship code they want to change the world I mean that is every engineering team I've ever worked at the time only worked with I mean I've been as close to engineering as from day one of the internet or early on in the internet great engineers are revolutionaries they want to change the world and they change the world they want to have a broader and broader understanding of what levers are at their disposal and I will say that I you know and I am one of the reasons I came to yam is I am passionate about this point technology cannot be in the hands of a few companies on the west coast who are trying to control and dominate the experience technology has to exist for all those amazing developers everywhere in the world who will make a difference to end user this is IBM strategy you actually have a big presence on the west coast also in Germany so you guys are going to where the action centers ours but not trying to just be so Malory point is what exactly because my point is IBM has always been there for making businesses stronger and better we don't monetize their data that's not our thing our thing is to use our cloud our cognitive capabilities and Watson to make actual businesses better so that ultimately consumers have better health care and better results I know you're new on the job silence this is not a trick question just kind of a more conversational as you talk to Bob lower Bob Chiana Jeanne yeah what's the promise of the brand and you used to be back in the days when you know Bob piano we talk about when we I worked at IBM in the 80s co-op student and it was you'll never get fired for buying IBM mainframe the kind of concept but it's evolved and I'll see we see a smarter plan what's the brand promise now you guys talk about what's the brainstorm on its head I think that I think the greatest innovators the world the most passionate business leaders of tomorrow come to IBM to make the world better and I I believe this is a brand for the forward the forward lookers the risk takers the you know the makers I think that you come to IBM because there's extraordinary assets and industry knowledge real humans real relationships that we exist to make your business better not our business will be a vibrato be exist to make your business better that has always been where IBM has been strong you know it's interesting that brings up a good point and just riffing on that Dave and I were just observing you know at the Grace Hopper with our tech truth mentorship which is promoting the intersection of Technology and social justice you're seeing that mission of Technology business value and social justice as an integral part of strategies because now the consumer access the consumerization of business yeah software based is now part of that feedback you're not doing good Millennials demand it I mean Millennials now when you look at the research in the next generation high Millennials are very very you know they want to know what are you doing for the world I mean who could do a 60 minute show besides IBM who could have who could be on 60 minutes changing cancer changing cancer outcomes for people beside IBM that that is an extraordinary testament to what the brand is and how it comes to life every day and that's important for Millennials we had Mary click-clack Clinton yesterday she is so impressive we're talking about how though these ozone layer is getting smaller these are us problems it can be solved they have to be so climate change can be solved so the whole getting the data and she's weather compass oh she's got a visit view on that is interesting her point is if we know what the problems are we as a community global society could actually solve them completely and it's an you know the more we make this a political and we say here is a problem and we have the data and we have the tools we have the people and capabilities to solve it that is where IBM Stan's tallest well I think with Watson use its focused on some big hairy problems to start with and now you're knocking off some some of the you know maybe more mundane but obviously significant to a marketer incredible that a company can start with the hardest most complicated problems the world has and actually make a difference my final question when I asked Mary this yesterday and she kind of talked about if she could have the magic Watson algorithm to just do something magical her and what would it be and she said I'll send Watson to the archives of all the weather data going back to World War two just compile it all and bring it back or addressability so the question is if you could have a Magic Watson algorithm for your chief marketing officer job what would you assign it to do like what would it be it's like first task well first of all reaction of course I'm a mom of six year olds an eight year old and so I want Watson to optimize my time no but a chief marketing officer I mean I think it really does go back to getting Watson's help in understanding how we use a dollar better how we use a dollar smarter how we affect more customers and and and connect connects with more customers in the way we you know we communicate the way we engage the way we've put our programs out that would be extraordinary and that's possible that's becoming more and more possible you know bringing science into the art of marketing I think will have great impact on what we're doing in also just the world I mean nobody wants to have you know maybe targeted ten times for something that's sold out well we asked one more time here so I got some more couple of questions because it's not getting the hook yet I gotta ask you see you mentioned Travelocity you know the web you've been through the web 1.2.0 yeah yeah so on so URLs and managing URLs was a great tracking mechanism from the old impressions weren't working and go to call to action get that look right there but now we different where that world is kind of like become critical infrastructure for managing technology since you're kind of geeking out with us here what's your view of the API economy because now apps don't use URLs they use tokens they use api's they use new push notification based stuff what sure how does api's change the marketing opportunities both right it's clearly changes the engineering environment and sort of opens up the world of possibilities in terms of who you partner with and how etc and I think it changes the marketing world too and entirely right you think about the API economy and the access you have to new ways of doing business new potential partnerships new ways of understanding data you know that that is absolutely you know at the fore of a lot of our thinking it might change the agency relationships to if they got to be more technical in changing as much as fast as companies are and they have to you know they are an extension they're your best you should be able to look in a room of agency and your team and not know who is who when you can tell who is who you have a problem and so agencies themselves have to become you know way more scientific harder-hitting faster pace and outcomes orient and somebody sees now are saying you know what pay me on outcomes I love that I love that mode to say we're in the boat with you pay me on outcome and the big s eyes are right there - absolutely yes Michele Palooza new chief marketing officer at IBM changing the game bring in some great mojo to IBM they're lucky to have you great conversations and thanks for coming on the cube live at Mandalay Bay this is silicon angles the cube I'm John four with Dave Volante be right back with more after this short break

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

(upbeat music) >> And, welcome back to theCube's coverage of Red Hat Summit, 2021. I'm John Furrier, host of theCube. We've got a great segment here on how Red Hat is working with telcos and the disruption in the telco cloud. We've got a great guest Cube alumni, Darrell Jordan Smith, senior vice president of industries and global accounts at Red Hat. Darrell, great to see you. Thanks for coming back on theCube. >> Oh, it's been, it's great to be here and I'm really excited about having the opportunity to talk to you today. >> Yeah, we're not in person, in real life's coming back soon. Although I hear Mobile World Congress, might be in person this year, looking like it's good. A lot of people are going to be virtual and activating I know. A lot to talk about. This is probably one of the most important topics in the industry because when you talk about telco industry, you're really talking about the edge. You're talking about 5G, talking about industrial benefits for business, because it's not just edge for connectivity access. We're talking about innovative things from self-driving cars to business benefits. It's not just consumer, it's really bringing that together. You guys are really leading with the cloud-native platform from REL, OpenShift managed services. Everything about the cloud-native underpinnings, you guys have been successful as a company. But now in your area, telco is being disrupted. You're leading the way >> Absolutely. Give us your take on this, this is super exciting. >> Well, it's actually one of the most exciting times. I've been in the industry for 30 years. I'm probably aging myself now, but in the telecommunications industry, this for me, is the most exciting. It's where, you know, technology is actually going to visibly change, the way, that everyone interacts with the network. And with the applications that are being developed out there on, on our platform. and, you know, as you mentioned, IoT, and a number of the other AI and ML innovations, that are occurring in the marketplace. We're going to see a new wave of applications and innovation. >> What's the key delivery workload you're seeing, with 5G environment. Obviously it's not just, you know 5G in the sense of thinking about mobile phones or mobile computers as they are now. It's not just that consumer, "Hey surf the web and check your email and get an app and download and, and communicate". It's bigger than that now. Can you tell us, where you see the workloads coming in on the 5G environment? >> You, you hit the nail on the head. The, the, the, the killer application, isn't the user or the consumer and the way that we traditionally have known it. Because you might be able to download a video and that might take 20 seconds less, but you're not going to pay an awful lot more money for that. The real opportunity around 5G, is the industrial applications. Things like connected car. You know automotive driving, factory floor automation. How you actually interface digitally with your bank. How we're doing all sorts of things, more intelligently at the edge of the network, using artificial intelligence and machine learning. So all of those things are going to deliver a new experience, for everyone that interacts with the network and the telcos are at the heart of it. >> You know, I want to get into the real kind of underpinnings, of what's going on with the innovations happening. You just kind of laid out kind of the implications of the use cases and the target application workloads, but there's kind of two big things going on with the edge and 5G. One is under the hood networking, you know, what's going on with the moving the packets around the workload, throughput, bandwidth, et cetera, and all that, that goes on under the hood. And then there's the domain expertise in the data, where AI and machine learning have to kind of weave in. So let's take the first part, first. OpenShift is out there. Red Hat's got a lot of products, but you have to nail the networking requirements and cloud native with containerization, because at large scales, not just packets, it's all kinds of things going on, security, managing compute at the edge. There's a lot of things under the hood, if you will, from a networking perspective. >> Could you share what Red Hat's doing in that area? >> Yep, so, so that's a very good question, in that we've been building on our experience with OpenStack and the last time I was on theCube, I talked about, you know, people virtualizing network applications and network services. We're taking a lot of that knowledge, that we've learned from OpenStack and we're bringing that into the container based world. So we're looking at how we accelerate packets. We're looking at how we build cloud-native applications, on bare metal, in order to drive that level of performance. We're looking at actually how we do, the certification around these applications and services, because they may be sitting in different applets across the cloud. And in some instances running on multiple clouds, at the same time. So we're building on our experience from OpenStack. We're bringing all of that into OpenShift, our container based environment. With all of the tooling necessary to make that effective. >> It's interesting with all the automation going on and certainly with the edge developing nicely, the way you're describing it, it's certainly disrupting the telco cloud. You have an operator mindset a cloud-native operator thinking, kind of, I mean it's distributed computing. We know that, but it's hybrid. So it's essentially cloud operations. So there's an operator mindset here, that's just different. Could you just share quickly, before we move on to the next segment, what's different about this operating model, for the, these new kinds of operators. As, as you guys have been saying, the CIO is the new cloud operator. That's the skill set they have to be thinking. And certainly IT, to anyone else provisioning and managing infrastructure has to think like an operator, what's your view? >> Exactly. They certainly do need to think like an operator. They need to look at how they automate a lot of these functions, because they're actually deployed in many different places, all at the same time. They have to live independently of each other, that's what cloud-native actually really is. So the whole, the whole notion of five nines and vertically orientated stacks of five nines availability that's kind of going out the window. We're looking at application availability, across a hybrid cloud environment and making sure the application can live and sustain itself. So operators as part of OpenShift is one element of that, operations in terms of management and orchestration and all the tooling that we actually also provide as Red Hat, but also in conjunction with a big partner ecosystem, such as companies like Netcracker, for example, or IBM as another example. Or Ericsson bringing their automation tool sets and their orchestration tool sets, to that whole equation, to address exactly that problem. >> Yeah. You bring up the ecosystem and this is really an interesting point. I want to, just hit on that real quick, because it reminds me of the days, when we had this massive innovation wave in the nineties. During that era, the client server movement, really was about multi-vendor, right? And that, you start to see that now and where this ties into here I think, is and I want to get your reaction to this is that, you know, moving to the cloud was all about to 2015, moved to the cloud, move to the cloud, cloud-native. Now it's all about not only being agile and better performance, but you're going to have smaller footprints, with more security requirements, more net, enterprise requirements. This is now, it's more complicated. So you have to kind of make the complication go away. And now you have more people in the ecosystem, filling in these white spaces. So, you have to be performance and purpose built, if you will. I hate to use that word, but, or, or at least performing and agile, smaller footprint, greater security, enabling other people to participate. That's a requirement. Can you share your reactions to that? >> Well, that's core of what we do at Red Hat. I mean, we take open source community software, into a hardened distribution, fit for the telecommunications marketplace. So we're very adapt to working with communities and third parties. That ecosystem is really important to us. We're investing hundreds of engineers, literally hundreds of engineers, working with our ecosystem partners, to make sure that their application is services certified running on our platform. But also importantly, is certified to be running in conjunction with other cloud-native applications that sit under the same cloud. So that, that is not trivial to achieve, in any stretch of the imagination. And a lot of IT technology skills come to bear. And as you mentioned earlier a lot of networking skills, things that we've learned, and we build with a lot of these traditional vendors as we bring that to the marketplace. >> You know, I've been saying on theCube, I think five years ago, I started talking about this and it was kind of a loose formulation. I want to get your reaction, because you brought up ecosystem. Now saying, you know, you're going to see the big clouds develop obviously Amazon and Microsoft came in after and now Google and others. And then I said, there's going to be a huge wave of, of what I call secondary clouds. And you see companies, like Snowflake building on top of Amazon. And so you start to see the power law, of new cloud service providers emerging, that can either sit and work with, across multiple clouds, either one cloud or others, that's now multi-cloud and hybrid. But this rise of the new, more CSPs, more cloud service providers. This is a huge part of your area right now because some call that telco, telco cloud, edge hits that. What is Red Hat doing in this cloud service provider market specifically? How do you help them? If I'm a cloud service provider, what do I get in working with Red Hat? How do I be successful? Because it's very easy to be a cloud service provider now more than ever. What do I do? How do you help? How do you help me? >> Well, we, we, we offer a, a platform called OpenShift which is our containerized based platform, but it's not just a container. It involves huge amounts of tooling associated with operating it, developing in and around it. So the, the concept that we have, is that you can bring those applications, develop them once, on one, one single platform, and run it on premise. You can run it natively as a service in Microsoft's environment. You can actually run it natively as a service in Amazon's environment. You can run it natively in IBM's environment. You can build an application once and run it in all of them, depending on what you want to achieve and who actually provides you the best zoning, the best terms and conditions, the best, the best tooling in terms of other services, such as an AI, associated with that. So it's all about developing it once, certifying it once, but deploying it in many, many different locations, leveraging the largest possible developer ecosystem, to drive innovation through applications on that common platform. >> So the assumption there, is that's going to drive down costs. Can you tell me about why the benefits, the economics are there? Talk about the economics. >> Well, Yeah, so, so, A, it does drive down costs and that's an important aspect but more importantly, it drives up agility, so time to market advantage is actually attainable for you. So many of the telcos when they deploy a network service, traditionally it would take them literally, maybe a year to roll it all out. They have to do it in days, they have to do updates in real time, in day two operations, in literally minutes. So we were building the fabric necessary, in order to enable those applications and services to occur. And as you move into the edge of the network and you look at things like private 5G networks, service providers or telcos, in this instance, will be able to deliver services all the way out to the edge, into that private 5G environment and operate that, in conjunction with those enterprise clients. >> So OpenShift allows me if I get this right, from the CSP to run, have a horizontally scalable organization. Okay. And from a unification platform standpoint. Okay. Whether it's 5G and other functions, is that correct? >> Darrell: That's correct. >> Okay. So you've got that. Now I want to come in and bring in the top of the stack with the other element that's been been a big conversation here at Red Hat Summit and in the industry. That is AI and the use of data. One of the things that's emerging is the ability to have both the horizontal scale, as well as the specialism of the data and have that domain expertise. You're in the industries for Red Hat. This is important because you're going to have, one industry is going to have different jargon, different language, different data, different KPIs. So you got to have that domain expertise, to enable the ability, to, to write the apps and also enable AI. Can you comment on how that works and what's Red Hat do in there? >> So, so, so, we, we're developing OpenShift and a number of our, other technologies, to be fit for the edge of the network, where a lot of these AI applications will reside, because you want them at the closest to the client or the, or the application itself, where it needs to reside. We're, we're creating that edge fabric, if you like. The next generation of hybrid cloud is really going to be, in my view at the edge. We're enabling a lot of the service providers to go after that, but we're also igniting by industry. You mentioned different industries. So if I look at, for example, manufacturing with MindSphere, we recently announced with Siemens, how they do at the edge of the network, factory automation, collecting telemetry, doing real-time data and analytics, looking at materials going through the factory floor, in order to get a better quality result, with lower, lower levels of imperfections, as they run through that system. It's just one industry and they have, their own private and favorite AI platforms and data sets they want to work with. With their own data scientists who understand that, that, that ecosystem inherently. You can move that to healthcare. And you can imagine, you know, how you actually interface with your healthcare professionals here in North America, but also around the world. How those applications and services and what the AI needs to do, in terms of understanding x-rays and looking at, you know common errors associated with different x-rays, so, so our practitioner can make a more specific diagnosis, faster, saving money and potentially lives as well. So different, different vertical markets in this space, have different AI and ML requirements and needs, different data sciences and different data models. And what we're seeing is an ecosystem of companies, that are starting up there in that space, you know, we have Watson as part of IBM, but you have Perceptor Labs, you have H2O and a number of other, very very important AI based companies in that ecosystem. >> Yeah. And you've got the horizontal scalability of the control plane then in the platform, if you will, that gives us cross-organizational leverage and enable that, that vertical domain expertise. >> Exactly. And you'd want to build an AI application, that might run on a factory floor for certain reasons, it's location and what they're actually physically building. You might want to run that on premise. You might actually want to put it in the IBM cloud, or in Zuora or into AWS. You develop it once to OpenShift, you can deploy it in all of those as a service, sitting natively in those environments. >> Darrell, great chat. You got a lot going on. telco cloud, there is a lot of cloud-native disruption going on. It's a challenge and an opportunity. And some people have to be on the right side of history, on this one, if they're going to get it right. We'll know, and the scoreboard will be very clear, 'cause this is a shift, it's a shift. So again, you hit all the key points that I wanted to get out, but I want to ask you two more areas that are hot here at Red Hat Summit 21, as well, again as well in the industry. I want to get your reaction and thoughts on. And they are DevSecOps and automation. Okay. Two areas everyone's talking about, DevOps, which we know is infrastructure as code, programmability, under the hood, modern application development, all good. You add the second there, security, DevSecOps, it's critical. Automation is continuing to be the benefits of cloud-native. So DevSecOps and automation, what's your take, and how's that impact the telco world and your world? >> You can't, you can't operate a network without having security in place. You're talking about very sensitive data. You're talking about applications that could be real-time critical And this is actually, even lifesaving or life threatening, if you don't get them right. So the acquisition that Red Hat recently made around StackRox, really helps us, make that next level of transition into that space. And we're looking at about how we go about securing containers, in a cloud-native environment. As you can imagine, there'll be many many thousands, tens of thousands of containers running. If one is actually misbehaving for want of a better term, that creates a security risk and a security loophole. We're shoring that up. That's important for the deployment OpenShift in the telco domain and other domains. In terms of automation, if you can't do it at scale and if you look at 5G and you look at the radios at the edge of the network and how you're going to provision those services. You're talking about hundreds of thousands of nodes, hundreds of thousands. So you have to automate a lot of those processes, otherwise you can't scale to meet the opportunity. You can't physically deploy. >> You know, Darrell this is a great conversation, you know as a student of history and Dave Vellante and I always kind of joke about that. And you've been in and around the industry for a long time. Telcos have been balancing this evolution of digital business for many, many decades. And now with cloud-native, it's finally a time where you're startin' to see, that it's just the same game, now, new infrastructure. You know, video, voice, text, data, all now happening, all transformed and going digital, all the way, all aspects of it. In your opinion, how should telcos be thinking about, as they put their plans in place for next generation? Because you know, the world is, is now cloud-native. There's a huge surface here of opportunities, different ecosystem relationships. The power dynamics are shifting. It's, it's really a time where there will be winners and there will be losers. What's your, what's your view on on how the telco industry needs to Cloudify, and how to be positioned for success? >> So, so one of the things I, I truly believe very deeply, that the telcos need to create a platform, horizontal platform that attracts developer and ecosystems to their platform, because innovation is going to sit elsewhere. Then you know, there might be a killer application that one telco might create, but in reality, most of those innovations, the most of those disruptors are going to occur from outside of that telco company. So you want to create an environment, where you're easy to engage and you've got maximum sets of tools and versatility and agility in order to attract that innovation. If you attract the innovation, you're going to ignite the business opportunity that 5G and 6G and beyond is going to actually provide you, or enable your business to drive. And you've really got to unlock that innovation. And you can only unlock it, in our view at Red Hat innovation, if you're open. You know, you follow open standards, you're using open systems and open source, is a method or a tool, that you guys, if you're a telco I would ask, you guys need to leverage and harness. >> Yeah. And there's a lot. And there's a lot of upside there if you get that right. >> Yes. >> There's plenty of upside. A lot of leverage, a lot of assets, take advantage of the whole offline, online, coming back together. We are living in a hybrid world, certainly with the pandemic. We've seen what that means. It's put a spotlight, on critical infrastructure and the critical shifts. If you had to kind of get pinned down Darrell, how would you describe that learnings from the pandemic. As folks start to come out of the pandemic, there is a light at the end of the tunnel. As we come out of this pandemic, companies want a growth strategy. Want to be positioned for success. What's your learning coming out of the pandemic? >> So from, from my perspective, which really kind of in one respect was, was very admirable, but, in another respect is actually deeply, a lot of gratitude, is the fact that the telecommunications companies, because of their carrier grade capabilities and their operational prowess, were able to keep their networks up and running and they had to move significant capacity from major cities to rural areas, because everyone was working from home. And in many different countries around the world, they did that extremely, extremely well. And their networks held up. I don't know, and maybe someone will correct me and email me, but I don't know one telco had a huge network outage, through this pandemic. And that kept us connected. It kept us working. And it also, what I also learned is, that in certain countries, particularly Latam, where they have a very large prepaid market. They were worried that the prepaid market in the pandemic would go down, because they felt that people would have less money to spend. And therefore they wouldn't top up their phones as much. The opposite effect occurred. They saw prepaid grow. And that really taught me, that, that connectivity is critical, in times of stress, that we are also, where everyone's going through. So, I think there were some key learnings there. >> Yeah, I think you're right on the money there. It's like they pulled the curtain back of all the FUD and said, you know, necessity's the mother of invention. And when you look at what happened and what had to happen, to survive in the pandemic and be functional, you're, you nailed it. The network stability, the resilience, but also the new capabilities that were needed, had to be delivered in an agile way. And I think, you know, it's pretty much a forcing function, for all the projects that are on the table, to know which ones to double down on. So, I think you pretty much nailed it. >> Thank you. Darrell Jordan Smith, senior vice president of industries and global accounts for Red Hat, theCube alumni. Thanks for that insight. Thanks for sharing. Great conversation around telcos and telco clouds and all the edge opportunities. Thanks for coming on. >> Thank you, John. >> Okay. It's theCube's coverage of Red Hat Summit 21. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>From around the globe. It's the Cube with digital coverage of IBM think 2021 brought to you by IBM. Welcome back to the cubes coverage of IBM Think 2021 virtual. I'm john ferrier host of the cube. Got a great story here. Navigating Covid 19 with Watson advertising and weather channel conversations. Sherry back steen. Who's the gM of Watson advertising in the weather company. Sherry, thanks for coming on the cube. My favorite part of IBM think is to talk about the tech and also the weather company innovations. Thanks for coming on. >>Hi, happy to be here, john >>So COVID-19 obviously some impact for people that working at home. Um normally you guys have been doing a lot of innovation around weather weather data um certainly huge part of it. Right. And so lots been changing with AI and the weather company and IBM so let's first start before we jump in, just a little background about what your team has created because a lot of fascinating things here. Go ahead. >>Yeah. So when the pandemic started, you know we looked at the data that we were seeing and of course in weather accuracy and accurate data is really important trusted data. And so we created a COVID-19 hub on our weather channel app and on weather.com. And essentially what it was is an aggregated area where consumers could get the most up to date information on covid cases, deaths in their area, trends see heat maps uh information from the C. D. C. And what was unique about it. It was to a local level. Right so state level information is helpful but we know that consumers uh me included. I need information around what's happening around me. And so we were able to bring this down to a county level which we thought was really helpful for consumers >>share as watching sports on tv. And recently, a few months ago, the Masters was on and you saw people getting back into real life, It's almost like a weather forecast. Now. You want to know what's going on in the pandemic. People are sharing that. They're getting the vaccine. Um, really interesting. And so I want to understand how this all came together with you guys. Is was it something that has a weather data, a bunch of geeks saying, hey, we should do this for companies, but take us to the thought process with their team. Was it like you saw this as value? How did you get to this? Because this is an interesting user benefit. I want to know the weather, I want to know if it's safe. These are kind of a psychology of a user expectation. How did you guys connect the dots here for this project? >>Well, we certainly do have a very passionate team of people, um some weather geeks included, um and you're absolutely right watching the Masters a few months ago was amazing to see, you know, some sense of normality happening here. But you know, we looked at, you know, IBM, the weather company, like, how do we help during this pandemic? And when we thought about it, we looked at there's an amazing gap of information. And as the weather channel, you know, what we do is bring together data, give people insights and help them make decisions with that. And so it was really part of our mission. It's always been that way to give information to keep people safe. And so all we did is took a different data set and provided the same thing. And so in this case, the covid data set, which we actually had to, you know, aggregate from different sources whether it was the C. D. C. The World Health Organization uh State governments or county governments to provide this to consumers. But it was really really natural for us because we know what consumers want. You know we all want information around where we live, right? And then we want to see like where our friends live, where our relatives live to make sure that they're okay. And then that enables people to make the decisions that are right for their family. And so it was really really natural for us to do that. And then of course we have the technology to be able to scale to hundreds of millions of people. Which is really important. >>It's not obvious until you actually think about that. It's so obvious. Congratulations. What a great innovation. What were the biggest challenges you guys had to face and how did you overcome it? Because I'm curious. I see you've got a lot of, lot of large scale data dealing with diversity of data with weather. What was the challenges with Covid? And how did you overcome it? >>So again, without a doubt it was the data because you're looking at one, we wanted that county level data. So you're looking at multiple sources. So how do we aggregate this data? So first finding that trusted source that that we could use. But then how do you pull it in in an automated way? And the challenge was it with the State Department, the county departments that data came in all kinds of formats. Some counties used maps, some use charts, some use pds to get that information. So we had to pull all this unstructured data, uh, and then that data was updated at different times. So some counties did it twice a day, some did it once day, different time zones. So that really made it challenging. And so then, you know, so what we did is this is where the power of A I really helps because a I can take all of that data, bring in and organize it and then we could put it back out to the consumer in a very digestible way. And so we were able to do that. We built an automated pipeline around that so we can make sure that it was updated. It was fresh and timely, which was really important. But without a doubt looking at that structured data and unstructured data and really helping it to make sense to the consumer was the biggest challenge. And what's interesting about it. Normally it would take us months to do something like that. I challenged the team to say we don't have months, we have days. They turned that around in eight days, which was just an amazing herculean feat. But that's really just the power of, as you said, passionate people coming together to do something so meaningful. >>I love the COVID-19 success stories when people rally around their passion and also their expertise. What was the technology to the team used? Because the theme here at IBM think is transformation innovation, scale. How did you move so fast to make that happen? >>So we move fast by our Ai capabilities and then using IBM cloud and so really there's four key components are like four teams that worked on it. So first there was the weather company team um and because we are a consumer division of IBM, we know what consumers want. So we understand the user experience and the design, but we also know how to build an A. P. I. That can scale because you're talking about being able to scale not only in a weather platform. So in the midst of covid weather still happened, so we still had severe weather record breaking hurricane season. And so those A. P. S. Have to scale to that volume. Then the second team was the AI team. So that used the Watson AI team mixed with the weather Ai team to again bring in that data to organize that data. Um And we used Watson NLP so natural natural language processing in order to create that automated pipeline. Then we had the corralled infrastructure so that platform team that built that architecture and that data repository on IBM cloud. And then the last team was our data privacy office. So making sure that that data was trusted that we have permission to use it uh and just know really that data governance. So it's all of that technology and all of those teams coming together to build this hub for consumers. Um And it worked I mean we would have about four million consumers looking at that hub every single day. Um and even like a year later we still have a couple million people that access that information. So it's really kind of become more like the weather checking the weather's come that daily habit. >>That's awesome. And I gotta I gotta imagine that these discoveries and innovations that was part of this transformation at scale have helped other ways outside the pandemic and you share how this is connected to um other benefits outside the pandemic. >>Yeah so absolutely um you know ai for businesses part of IBM strategy and so really helping organizations to help predict um you know to help take workloads and automate them. So they're high valued employees can work on you know other work. And also you know to bring that personalization to customers. You know, it's really a i when I look at it for my own part of a IBM with the weather company, three things where I'm using this technology. So the first one is around advertising. So the advertising industry is at a really um you know, pivotal part right now, a lot of turmoil and challenges because of privacy legislation because big tech companies are um you know, getting rid of tracking pixels that we normally use to drive the business. So we've created a suite of AI solutions for publishers for you know, different players within the ad tech space, um which is really important because it protects the open web, so like getting covid information or weather information, all of that is free information to the public. We just ask that you underwrite it by seeing advertising so we can keep it free. So those products protect the open red. So really, really important. Then on the consumer side of my business, within the weather channel, we actually used Watson Ai um to connect health with weather. So we know that there's that connection, some health um you know, issues that people have can be impacted by weather, like allergies and flew. So we've actually used Watson Ai to build a um Risk of flu that goes 15 days out. So we can tell people in your local area this one actually goes down to the zip code level, um the risk of flu in your area or the risk of allergies. So help to manage your symptoms, take your prescription. So, um that's a really interesting way. We're using AI and of course weather dot com and our apps are on IBM cloud, so we have this strong infrastructure to support that. And then lastly, you know, our weather forecasting has always been rooted in a i you take 100 different weather models, you apply ai to that to get the best and most accurate forecasts that you deliver. Um and so we are using these technologies every day to, you know, move our business forward and to provide, you know, weather services for people. >>I just love the automation and as users have smartphones and more instrumentation on their bodies, whether it's wearables, people will plan their day around the weather, and retail shops will have a benefit knowing what the stock and or not have on hand and how to adjust that. This, the classic edge computing paradigm, fascinating impact. You wouldn't think about that, but that's a pretty big deal. People are planning >>around >>the weather data and making that available is critical. >>Oh, absolutely. You know, every business needs a weather strategy because whether it impacts your supply chain, um agriculture, should I be watering today or not even around, you know, um, if you think about energy and power lines, you know, the vegetation growth over power lines can bring power lines down and it's a disruption, you know, to customers and power. So there's just when you start thinking about it, you're like, wow, whether really impacts every business, um, not to say just consumers in general and their daily lives. >>And uh, and there's a lot of cloud scale to that can help companies whether it's um be part of a better planet or smarter planet as it's been called, and help with with global warming. I mean, you think about this is all kind of been contextually relevant now more than ever. Super exciting. Um Great stuff. I want to get your take on outside of um the IBM response to the pandemic more broadly outside of the weather. What are you guys doing um to help? Are you guys doing anything else with industry? How could you talk a little bit more about IBM s response more broadly to the pandemic? >>Yeah so IBM has been you know working with government academia, industry is really from the beginning uh in several different ways. Um you know the first one of the first things we did is it opened up our intellectual property. So R. I. P. And our technology our supercomputing To help researchers really try to understand COVID-19 some of the treatments and possible cures so that's been really beneficial as it relates to that. Um Some other things though, that we're doing as well is we created a chat bots that companies and clients could use and this chat but could either be used to help train teachers because they have to work remotely or help other workers as well. Um and also the chatbots was helping as companies started to re enter back to the workforce and getting back to the office. So the chatbots been really helpful there. Um and then, you know, one of the things that we've been doing on the advertising side is we actually have helped the ad council with their vaccine campaign. Um It's up to you is the name of the campaign and we delivered a ad unit that can dynamically assemble a creative in real time to make sure that the right message was getting out the right time to the right person. So it's really helped to maximize that campaign to reach people um and encourage them if it's the right thing for them, you know where the vaccines are available. Um and that you know, they could take those. So a lot of great work that's going on within IBM. Um and actually the most recent thing just actually in the past month is we release the Digital Health Pass in cooperation with the state of new york. Um and this is a fantastic tool because it is a way for individuals to keep their private information around their vaccines or you know, some of the Covid test they've been having on a mobile device that's secure and we think that this is going to be really important as cities start to reopen um to have that information easily accessible. >>Uh sure, great insight, um great innovation navigating Covid 19 a lot of innovation transformation at IBM and obviously Watson and the weather company using AI and also, you know, when we come out of Covid post, post Covid as real life comes back, we're still going to be impacted. We're gonna have new innovations, new expectations, tracking, understanding what's going on, not just the weather. So thanks >>for absolutely great >>work. Um, awesome. Thank you. >>Great. Thanks john good to see you. >>Okay. This is the cubes coverage of IBM. Think I'm john for a host of the cube. Thanks for watching. Yeah.

>> Announcer: From around the globe. It's theCUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Oh, welcome back to theCUBE's coverage of IBM Think 2021 virtual, I'm John Furrier, your host of theCUBE. We've got a great story here. Navigating COVID-19 with Watson advertising and weather channel conversations, Sheri Bachstein, who's the GM of Watson Advertising in the weather company. Sheri, thanks for coming on theCUBE. My favorite part of IBM Think is to talk about the tech and also the weather company innovations. Thanks for coming on. >> Hi, happy to be here John. >> So COVID-19 obviously some impact for people that working at home. Normally you guys have been doing a lot of innovation around weather, weather data, certainly huge part of it. And so lots been changing with AI and the weather company and IBM, so let's first start before we jump in just a little background about what your team has created because a lot of fascinating things here. Go ahead. >> Yeah, so when the pandemic started, we looked at the data that we were seeing and of course in weather accuracy and accurate data is really important trusted data. And so we created a COVID-19 hub on our weather channel app and on weather.com and essentially what it was is an aggregated area where consumers could get the most up-to-date information on COVID cases, deaths in their area, trends see heat maps, information from the CDC. And what was unique about it, it was to a local level, right? So state level information is helpful, but we know that consumers me included. I need information around what's happening around me. And so we were able to bring this down to a County level which we thought was really helpful for consumers >> Sheri's watching sports on TV. And recently a few months ago, the masters was on and you saw people getting back into real life. It's almost like a weather forecast. Now you want to know what's going on in the pandemic. People are sharing that they're getting the vaccine, really interesting. And so I want to understand how this all came together with you guys. Was it something that as a weather data and a bunch of geeks saying, Hey, we should do this for companies but take us to thought process 113. Was it like you saw this as value? How did you get to this? Because this is an interesting user benefit. I want to know the weather. I want to know if it's safe. These are kind of a psychology of a user expectation. How did you guys connect the dots here for this project? >> Well, we certainly do have a very passionate team of people some weather geeks included and you're absolutely right. Watching the masters a few months ago was amazing to see some sense of normality happening here. But we looked at IBM and the weather company like how do we help during this pandemic? And when we thought about it we looked at there's an amazing gap of information. And as the weather channel, what we do is bring together data give people insights and help them make decisions with that. And so it was really part of our mission. It's always been that way to give information to keep people safe. And so all we did is took a different data set and provided the same thing. And so in this case, the COVID data set which we actually had to aggregate from different sources whether it was the CDC, the world health organization, a state governments, our County governments to provide this to consumers. But it was really, really natural for us because we know what consumers want. We all want information around where we live, right? And then we want to see like where our friends live, where our relatives live to make sure that they're okay. And then if that enables people to make the decisions that are right for their family. And so it was really, really natural for us to do that. And then of course we have the technology to be able to scale to hundreds of millions of people, which is really important. >> Yeah, it's not obvious until you actually think about it, then it's so obvious. Congratulations, what a great innovation what were the biggest challenges you guys had to face and how did you overcome it? Because I'm curious, I see you got a lot of large scale data dealing with diversity of data with weather. What was the challenges with COVID and how did you overcome it? >> So again, without a doubt it was the data, because you're looking at one, we wanted that County level data. So you're looking at multiple sources. So how do we aggregate this data? So first finding that trusted source that we could use but then how do you pull it in, in an automated way? And the challenge was it with the state departments, the County departments, that data came in, all kinds of formats. Some counties used maps, some use charts some use PDFs to get that information. So we had to pull all this unstructured data and then that data was updated at different times. So some counties did it twice a day some did it once a day, different time zones. So that really made it challenging. And so then, so what we did is this is where the power of AI really helps, because AI can take all of that data bring it in, organize it, and then we could put it back out to the consumer in a very digestible way. And so we were able to do that. We built an automated pipeline around that so we can make sure that it was updated. It was fresh and timely, which was really important but without a doubt, looking at that structured data and unstructured data and really helping it to make sense to the consumer was the biggest challenge. And I'll, what's interesting about it. Normally it would take us months to do something like that. I challenged the team to say, we don't have months. We have days. They turned that around in eight days which was just an amazing Herculean feat but that's really just the power of as you said, passionate people coming together to do something so meaningful. >> I love the COVID-19 success stories when people rally around their passion and also their expertise, what was the technology did the team use? Because the theme here at IBM Think is, transformation, innovation, scale. How did you move so fast to make that happen? >> So we moved fast by our AI capabilities and then using IBM cloud. And so really there's four key components or like four teams that worked on it. So first there was the weather company team. And because we are a consumer division of IBM we know what consumers want. So we understand the user experience and the design but we also know how the build an API that can scale because you're talking about being able to scale not only in a weather platform. So in the midst of COVID weather still happen. So we still had severe weather record breaking hurricane season. And so those APIs have to scale to that volume. Then the second team was the AI team. So that used the Watson AI team mixed with the weather AI team to again bring in that data to organize that data. And we use Watson NLP. So natural language processing in order to create that automated pipeline. Then we had the collateral infrastructure. So that platform team that built that architecture and that data repository on IBM cloud. And then the last team was our data privacy office. So making sure that that data was trusted that we have permission to use it and just really that data governance. So it was all of that technology and all of those teams coming together to build this hub for consumers. And it worked, I mean we would have about 4 million consumers looking at that hub every single day. And even like a year later, we still have a couple million people that access that information. So it's really kind of become more like the weather checking the weather, that daily habit. >> That's awesome. And I got to imagine that these discoveries and these innovations that was part of this transformation that scale I've helped other ways outside of the pandemic. Can you share how this is connected to other benefits outside the pandemic? >> Yeah, so absolutely, AI for business is part of IBM strategy. And so really helping organizations to help predict, to help take workloads and automate them. So they're high valued employees can work on other work and also to bring that personalization to customers is really AI. When I look at it for my own part of a IBM with the weather company, three things where I'm using this technology. So the first one is around advertising. So the advertising industry is at a really pivotal part right now, a lot of turmoil and challenges because of privacy legislation because big tech companies are getting rid of tracking pixels that we normally use to drive the business. So we've created a suite of AI solutions for publishers, for different players within the ad tech space which is really important because it protects the open web. So like getting COVID information or weather information all of that is free information to the public. We just ask that you underwrite it by saying advertising so we can keep it free. So those products protect the open read. So really, really important. Then on the consumer side of my business within the weather channel we actually use Watson AI to connect health with weather. So we know that there's that connection. Some health issues that people have can be impacted by weather like allergies and flu. So we've actually used Watson AI to build a risk of flu that goes 15 days out. So we can tell people in your local area this one actually goes down to the zip code level the risk of flu in your area or the risk of allergies. So it help to manage your symptoms, take your prescription. So that's a really interesting way we're using AI and of course, weather.com and our apps are an IBM cloud. So we have this strong infrastructure to support that. And then lastly our weather forecasting has always been rooted in AI. You take a hundred different weather models you apply AI to that to get the best and most accurate forecast that you deliver. And so we are using these technologies every day to move our business forward and to provide weather services for people. >> I just love the automation as users have smartphones and more instrumentation on their bodies, whether it's wearables, people will plan their day around the weather and retail shops will have a benefit knowing what to stock or not have on hand and how to adjust that this the classic edge computing paradigm, fascinating impact. You wouldn't think about that, but that's a pretty big deal. People are planning around the weather data and making that available as critical. >> Oh, absolutely. Every business needs a weather strategy because whether it impacts your supply chain, agriculture should I be watering today or not, even around if you think about energy and power lines, the vegetation growth of our power lines can bring power lines down and it's a disruption, to customers and power. So there's just, when you start thinking about it you're like, wow, weather really impacts every business not to say just consumers in general and their daily life. >> Yeah, and there's a lot of cloud scale too, that can help companies whether it's be part of better planet or smarter planet as it's been called and help with, with global warming. I mean, you think about this is all kind of been contextually relevant now more than ever super exciting, great stuff. I want to get your take on outside of the IBM response to the pandemic, more broadly outside of the weather. What are you guys doing to help? Are you guys doing anything else with industry? How could you, talk a little bit more about IBM's response more broadly to the pandemic? >> Yeah, so IBM has been working with government academia industries really from the beginning in several different ways. The first, one of the first things we did is it opened up our intellectual property. So our IP and our technology, our super computing to help researchers, really try to understand COVID-19, some of the treatments and possible cures. So that's been really beneficial as it relates to that. Some other things though that we're doing as well is we created a Chatbot that companies and clients could use. And this Chatbot could either be used to help train teachers because they have to work remotely or help other workers as well. And also the Chatbot was helping as companies started to reenter back to the workforce and getting back to the office. So the Chatbot has been really helpful there. And then one of the things that we've been doing on the advertising side is we actually have helped the ad council with their vaccine campaign. It's up to you as the name of the campaign. And we delivered a ad unit that can dynamically assemble a creative in real time to make sure that the right message was getting out the right time to the right person. So it's really helped to maximize that campaign to reach people. And they encourage them if it's the right thing for them, where the vaccines are available and that they could take those. So a lot of great work that's going on within IBM and actually the most recent thing just actually in the past month is we released the digital health pass in cooperation with the state of New York. And this is a fantastic tool because it is a way for individuals to keep their private information around their vaccines, or some of the COVID tests they've been having on a mobile device that's secure. And we think that this is going to be really important as cities start to reopen to have that information easily accessible. >> Awesome Sheri, great insight, great innovation navigating COVID-19, lots of innovation transformation at IBM and obviously Watson and the weather company using AI. And also, when we come out of COVID post COVID, as real life comes back, we're still going to be impacted. We're going to have new innovations, new expectations, tracking, understanding what's going on not just the weather. So thanks for doing that great work. Awesome, thank you. >> Great, thanks John. Good to see you. >> This is theCUBE's coverage of IBM Think, I'm John Furrier, the host of theCUBE. Thanks for watching. (upbeat music)

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

>> Live from Las Vegas, it's theCUBE, covering Dell Technologies World 2019. Brought to you by Dell Technologies and it's ecosystem partners. >> Welcome to Vegas! Lisa Martin with John Furrier. You're watching us on theCUBE live. The end of Day One of our three days of coverage of Dell Technologies World. Can you hear the music? The party's already getting started. We have more content to bring you. Please welcome a couple of guests from DXE Technology, Kevin Johnston, Chief Sales and Revenue Officer, Cloud and Platform Service. Kevin, it's great to have you. >> Thank you very much. Glad to be here. >> Our pleasure. We've got Graham Stringer, Managing Director of Workplace and Mobility for DXE Americas. >> Thank you. Good to be here as well. >> Yeah, you waited just in time for the concert, guys! >> We did. >> Just in time. Here we go. >> All right, so, Kevin, let's go ahead and start with you. Give our audience and understanding of DXE. What you guys do, who you are, all that good stuff. >> Yeah, okay. That's great. So DXE was formed two years ago as a result of the merger of legacy HP Enterprise Services Business and CSC. DXE was formed really for the purpose of helping our large enterprise clients accelerate their digital transformation. So we're about a $22 billion IT services company, really aligned with our partners helping our clients transform digitally. >> And you guys were on the cloud early, too. There's a lot of devops going on. >> Yep. >> You guys had your hands in all the clouds. >> We have. >> What's your take on, here at Dell Technologies World, Microsoft's partnering with VMware? >> Yeah, so we would share a lot of beliefs with Dell Technology and VMware in particular, in that multi-cloud is a real thing. And we see multi-cloud, especially for the large enterprise clients, really being an answer for quite some number of years to come. We also believe that a large percentage of application portfolios will migrate to cloud. Whether it's private clouds or public clouds, and that there's a lot of work to be done to transform those applications to really take advantage of cloud native features. >> So last year's theme of Dell Technologies World was Make It Real, 'It' being digital transformation, security transformation, IT transformation, and workforce workplace automation. Graham, I'd love to get your perspectives on workplace mobility and some of the things that were announced this morning with Unified Workspace, Workspace ONE, and recognizing, hey, for our customers to transform digitally successfully, we've got to make sure that their are people are successful, and their people are highly distributed. What are some of the things that you heard this morning that are exciting, aligning with some of the trends that you're seeing in the workplace? >> Well the big trend that we're seeing is the role that HR is now playing in digital transformation of the workplace. If you go back two, three, four years, it was very IT centric. Conversations were predominantly with the CIO. We're now seeing 30, 40% of organizations or more engaging at the HR level. We did a recent project with one of the big retailers in the industry and right out of the bat, this chief HR officer was engaged right from the get-go. They want to know that their employees are going to experience work very differently. So that's one of the big trends we're seeing emerging. >> When did this shift happen? When was this going on? Past year, two years? Because this is a shift. >> I would say the shift has definitely happened the last couple of years. Millennials are having a huge impact. You're getting quite the cross-pollination of a lot of different generations. Millennials are now having an enormous impact. If you look at outlets like Glassdoor, millennials want to know when they go to an organization can I bring my own device? Am I going to have a great workplace experience? And you can't stick with a very traditional, legacy way of delivering IT where everything was shift left and you got to a point where everybody hated each other. >> That's a problem for productivity. >> Yes, a very big problem for productivity, absolutely. >> Talk about some of the challenges that customers have overcome with digital transformation, as it starts to become less of a buzz word and actually more of a reality and strategic imperative that has some visibility at the unit economics and value. >> Yeah, I think every large enterprise client we talk to has a digital transformation agenda of some sort and at some varying place along the path to trying to adopt a new business model or adapt to a different business process, so the challenges that we see with these clients in general is how do we scale? So I have legacy IT that won't disappear overnight and I have all the possibilities of digitally enabling or bringing new digital technologies that enable these processes or models. So this is a challenge: how to enable digital at scale where traditional and digital have to live together for some period of time. >> And it's not just a tech challenge, it's culture, too. How far has tech come because you've mentioned containers with legacy? That has been a great message to IT is I can put a container around it and hold onto it for a little while longer, I don't have to kill it, and make the changes to cloud-native. >> For the tech guys, there's been a lot of fun things and containers probably is the bridge for legacy apps into cloud for sure. For the rest of the folks, for the normal people, the way work gets done and the way to rethink how to do work in the mix of IT or technology into business is just different. >> Graham's point is beautiful because the expectation of the employee or the worker whether they're in the firm or outside the firm, outside in or inside out, however they look at it, is the new experience they want. So the expectations are changing. What's the biggest thing, we saw some stats on stage about remote working, three places, two places, I mean, hell, I'm always on the road. What is some of the expectations that you're seeing? Obviously millennials and some of the older folks. >> They want to see IT delivered in the way they want to receive it. That's one of the biggest trends we're seeing. So for Millennials, my son's kind of in that age category, right, they love to text. To pick up a phone for a younger generation is a little bit foreign. You go and deal with baby boomers, they want to be dealt with in a much different manner. So you've got that whole change, and then you've got the whole notion now of work is changing; where do I work, the ability to basically work 24/7, wherever I want, however I want, using whatever device that I want. And that of course is now creating a whole new set of challenges for IT, particularly around security. >> But employee experience is absolutely fundamental to a business' success; their ability to delight costumers, their ability to deliver outcome, so it's really pretty core. Talk to us about those conversations that you're having with customers. Are they understanding how significant that employee experience is to bottom line business outcomes differentiation? >> Very much so. We're working right now with a large manufacturing firm and they're doing not just an inside out, but outside in, so they're actually coming to watch. It's part of a workplace strategy to look at it from the outside as well. In other words, how can our client take innovation to their suppliers, their customers, to demonstrate that they understand it? So that's extremely exciting when we see that they're not just focused on their own employees and the experience germane to them. >> One thing I might add is that maybe less so from a user experience per say, but the individuals as an employee. So the shift to digital and the skill shift that's required to go with that is really probably the most monumental change that all of us technology companies and the business part of our large enterprise clients is dealing with. Whether it's a skills gap or whether it's a culture gap, this idea of just simply waterfall to agile and the way to think about that or silo versus end-to-end as just simple ways to think differently about how to go faster. So the experience, how you recruit, whose going to make it, who can be trained, and then where you need to be able to source the new talent from as well. >> I totally agree with you. We do hundreds of shows a year, this is our tenth year doing theCUBE, that is the number one things that we hear over and over again from practitioners and customers and from people working. It's not the check, you can always get a check solution, it's the cultural and the skills gap. Both are huge problems. >> And this is part of the digital at scale point. So we'll hire something in the neighborhood of six to eight thousand digital skills people. We're just about to close on active position of Luxoft, an agile devops digital company. We'll bring another 13,000 in. But if you think about the normal large enterprise and what you need to do to be able to have the university networks and to be able to really source that scale in order to effect the transformations that business need to make to stay competitive. >> And the other point, the engagements have changed too. I'm sure you guys have seen your end but every IT or CIO we talk to says, "I outsourced everything decades ago and now I've got a couple guys running the show. Now I need to have a hundred x more people coding and building core competency." That's still going to need to engage people in the channel or our service providers but they need to build core talent in house. It's swinging back and they don't know what to do. (laughter) Is that why they call you guys? Is that how you guys get involved? >> We'll help train. We'll help clients think through what does an IT or business organization need to look like profile wise, skill wise, operating model wise, and in many cases it's I have my digital model but I still have my traditional model that needs to coexist with it and then here's where the opportunities are for people to develop career paths and progress. >> Kevin, talk about the sweet spot of your engagements that you're doing right now. Where's the heart of your business? Is it someone whose really hurting, needs an aspirin, they've got a headache, is it a problem? Is it an opportunity? Is it a growth issue? Where do you see the spectrum of your engagements? >> We kind of find clients in one of three spots normally. "Hey, I know I need to do something but I'm not sure what it is, can you help me figure out to get started?" So more design thinking, problem solving. We have other clients at the other end of the spectrum who are, "Hey, I've got this figured out. I need a partner to help me execute it's scale. And I know the model that I want to do, I know the business reason for doing it." And then we have a lot of folks that are in the middle, which is, "I've started, I've got a few hundred AWS accounts. I got private clouds sitting idle. Someone help me." Or, "I've got security issues, compliance issues." >> So they're in the middle of the journey and they just need a little reboot or a kickstart. >> They need help scaling. >> They ran out of gas. (laughter) >> And how are you working with Dell Technologies and their companies, Dell EMC, if they were to do that? >> The partnership with Dell Technologies, VMware, are really center to how we go to market. DXE is one of the top few partners largest in the ecosystem. The breadth of our portfolios are extremely complementary, whether it's things like device as a service or multi- and hybrid cloud, or pivotal and devops. So the breadth of the portfolios max up really well which makes it the impact potential for our clients even more important. Dell Technology broadly is really one of the few partners that we're shoulder-to-shoulder going with to the market as well. >> Awesome. Great stuff. What's the biggest learnings you guys can share with the audience that you gathered over your multiple engagements holistically across your client base? That's learnings, that could be a best practice, or just either some scar tissue or revelations or epiphanies. Share some experience here. >> I think one of the big learnings we're seeing is the shift now to very much business outcome driven decision making. If you go back to your point about the big ITO outsourcing days, that was all about just strictly driving cost out, and that's why you got to that point where everybody was left hating each other. Now it's about business outcomes. You've got the impact of Millennials, you've got organizations wanting to create a new and better experience for the employees and they're coming to us to say, "How do we accomplish that?" We've got an organization we're working with right now, they're trying to elevate themselves to be one of the top 50 best places to work for in the US. How do they arrive at that? For them, that's their barometer and so it's not about driving costs out, it's really achieving that overall experience and enhance a business outcome. >> So they're betting on productivity gains from morale and happy workers. >> Right. And also they're recognizing the downstream impact on their customers, productivity, the level of employee engagement, right? I mean those are the things that the organization knows that if they hit on those, I mean the sky's the limit. >> Right. Anything on your end? Learnings? >> Yeah, I would say the "don't understand the talent" challenge. The ability to pivot from here's the way we all know and are familiar with doing things to the new way. There will be a big talent challenge. The other thing is the operating model from an IT standpoint. Traditional IT operating model operates at a particular speed, cloud operates at a different speed. And the tools, the talents, the skills that go with that are just completely different. And then I think the last thing is just it seems maybe surprising, but compliance at scale and at speed. So security and regulatory compliance, we see that falling over all the time. >> Great practice you guys. I've been following you guys for many years, you've got a great organization, lots of smart people there we've interviewed many times. My final question is a tech question: what technologies do you guys like that you think is ready for prime time or almost ready for prime time worth having customer keep focusing on and which one's a little more over hyped and out of reach at the moment? >> I'll take a stab at that. If you look at today's Wall Street Journal, Deloitte talks to I believe the figure they quoted was roughly 25% of organizations are doing AI in some form already, PoC or at least are committing to it in terms of strategy. We're seeing that inside DXE as well. AI is now being incorporated into our workplace offerings. The potential for that is enormous, it's real. The technology in the last couple of years, particularly with cloud computing, has really enabled it. When you look at platforms like Watson, these are capabilities that just weren't there 10, 12, 15 years ago, and now the impact that it can have on the workplace, help lines, chats, chatbots, and so forth, is enormous and it's real. Five, 10 years ago it definitely was not in it's maturity. >> Okay, over hyped. >> What's over hyped? I don't know, what comes to mind for you? >> Or maybe I'll rephrase it differently: not yet ready for prime time, but looks good on the fairway but not yet known. . . >> I think for me through workplace, IoT has still got a ways to go. AI and analytics is definitely there. IoT I would say is a little bit behind. I'm sure that Kevin has cloud and platform thoughts. >> Yeah, I would say from an over hyped standpoint, we've seen a lot of companies, large enterprises, legacy application portfolios think they're going to refactor all their applications and cloud native everything. So it feels that people are now kind of getting past that point, but we still see that idea a lot. I think the opportunity that is really in front of us, and you kind of called out, containers. Legacy applications into cloud feel like a remaining frontier for the large enterprise. We think containers and the idea of autonomous, continue optimization, financial performance, is a way to make apps run in cloud financially and performance wise in a way that we don't see a lot of companies fully solving for that yet. >> Awesome. >> A lot of work to do, a lot of opportunity. Kevin, Graham, thank you so much for sharing some of your time and thoughts and insights with John and me on theCUBE this afternoon. >> Very good. >> Thank you. >> We appreciate it. For John Furrier, I'm Lisa Martin, and you've been watching theCUBE live from Vegas. Day One of our coverage of Dell Technologies World is now in the books. Thanks for watching. (upbeat techno music)

>> Live from Midtown Manhattan, it's theCUBE. Covering BigData New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. (calm electronic music) >> Basil Faruqui, who's the Solutions Marketing Manger at BMC, welcome to theCUBE. >> Thank you, good to be back on theCUBE. >> So first of all, heard you guys had a tough time in Houston, so hope everything's gettin' better, and best wishes to everyone down in-- >> We're definitely in recovery mode now. >> Yeah and so hopefully that can get straightened out quick. What's going on with BMC? Give us a quick update in context to BigData NYC. What's happening, what is BMC doing in the big data space now, the AI space now, the IOT space now, the cloud space? >> So like you said that, you know, the data link space, the IOT space, the AI space, there are four components of this entire picture that literally haven't changed since the beginning of computing. If you look at those four components of a data pipeline it's ingestion, storage, processing, and analytics. What keeps changing around it, is the infrastructure, the types of data, the volume of data, and the applications that surround it. And the rate of change has picked up immensely over the last few years with Hadoop coming in to the picture, public cloud providers pushing it. It's obviously creating a number of challenges, but one of the biggest challenges that we are seeing in the market, and we're helping costumers address, is a challenge of automating this and, obviously, the benefit of automation is in scalability as well and reliability. So when you look at this rather simple data pipeline, which is now becoming more and more complex, how do you automate all of this from a single point of control? How do you continue to absorb new technologies, and not re-architect our automation strategy every time, whether it's it Hadoop, whether it's bringing in machine learning from a cloud provider? And that is the issue we've been solving for customers-- >> Alright let me jump into it. So, first of all, you mention some things that never change, ingestion, storage, and what's the third one? >> Ingestion, storage, processing and eventually analytics. >> And analytics. >> Okay so that's cool, totally buy that. Now if your move and say, hey okay, if you believe that standard, but now in the modern era that we live in, which is complex, you want breath of data, but also you want the specialization when you get down to machine limits highly bounded, that's where the automation is right now. We see the trend essentially making that automation more broader as it goes into the customer environments. >> Correct >> How do you architect that? If I'm a CXO, or I'm a CDO, what's in it for me? How do I architect this? 'Cause that's really the number one thing, as I know what the building blocks are, but they've changed in their dynamics to the market place. >> So the way I look at it, is that what defines success and failure, and particularly in big data projects, is your ability to scale. If you start a pilot, and you spend three months on it, and you deliver some results, but if you cannot roll it out worldwide, nationwide, whatever it is, essentially the project has failed. The analogy I often given is Walmart has been testing the pick-up tower, I don't know if you've seen. So this is basically a giant ATM for you to go pick up an order that you placed online. They're testing this at about a hundred stores today. Now if that's a success, and Walmart wants to roll this out nation wide, how much time do you think their IT department's going to have? Is this a five year project, a ten year project? No, and the management's going to want this done six months, ten months. So essentially, this is where automation becomes extremely crucial because it is now allowing you to deliver speed to market and without automation, you are not going to be able to get to an operational stage in a repeatable and reliable manner. >> But you're describing a very complex automation scenario. How can you automate in a hurry without sacrificing the details of what needs to be? In other words, there would seem to call for repurposing or reusing prior automation scripts and rules, so forth. How can the Walmart's of the world do that fast, but also do it well? >> Yeah so we do it, we go about it in two ways. One is that out of the box we provide a lot of pre-built integrations to some of the most commonly used systems in an enterprise. All the way from the Mainframes, Oracles, SAPs, Hadoop, Tableaus of the world, they're all available out of the box for you to quickly reuse these objects and build an automated data pipeline. The other challenge we saw, and particularly when we entered the big data space four years ago was that the automation was something that was considered close to the project becoming operational. Okay, and that's where a lot of rework happened because developers had been writing their own scripts using point solutions, so we said alright, it's time to shift automation left, and allow companies to build automations and artifact very early in the developmental life cycle. About a month ago, we released what we call Control-M Workbench, its essentially a community edition of Control-M, targeted towards developers so that instead of writing their own scripts, they can use Control-M in a completely offline manner, without having to connect to an enterprise system. As they build, and test, and iterate, they're using Control-M to do that. So as the application progresses through the development life cycle, and all of that work can then translate easily into an enterprise edition of Control-M. >> Just want to quickly define what shift left means for the folks that might not know software methodologies, they don't think >> Yeah, so. of left political, left or right. >> So, we're not shifting Control-M-- >> Alt-left, alt-right, I mean, this is software development, so quickly take a minute and explain what shift left means, and the importance of it. >> Correct, so if you think of software development as a straight line continuum, you've got, you will start with building some code, you will do some testing, then unit testing, then user acceptance testing. As it moves along this chain, there was a point right before production where all of the automation used to happen. Developers would come in and deliver the application to Ops and Ops would say, well hang on a second, all this Crontab, and these other point solutions we've been using for automation, that's not what we use in production, and we need you to now go right in-- >> So test early and often. >> Test early and often. So the challenge was the developers, the tools they used were not the tools that were being used on the production end of the site. And there was good reason for it, because developers don't need something really heavy and with all the bells and whistles early in the development lifecycle. Now Control-M Workbench is a very light version, which is targeted at developers and focuses on the needs that they have when they're building and developing it. So as the application progresses-- >> How much are you seeing waterfall-- >> But how much can they, go ahead. >> How much are you seeing waterfall, and then people shifting left becoming more prominent now? What percentage of your customers have moved to Agile, and shifting left percentage wise? >> So we survey our customers on a regular basis, and the last survey showed that eighty percent of the customers have either implemented a more continuous integration delivery type of framework, or are in the process of doing it, And that's the other-- >> And getting close to a 100 as possible, pretty much. >> Yeah, exactly. The tipping point is reached. >> And what is driving. >> What is driving all is the need from the business. The days of the five year implementation timelines are gone. This is something that you need to deliver every week, two weeks, and iteration. >> Iteration, yeah, yeah. And we have also innovated in that space, and the approach we call jobs as code, where you can build entire complex data pipelines in code format, so that you can enable the automation in a continuous integration and delivery framework. >> I have one quick question, Jim, and I'll let you take the floor and get a word in soon, but I have one final question on this BMC methodology thing. You guys have a history, obviously BMC goes way back. Remember Max Watson CEO, and Bob Beach, back in '97 we used to chat with him, dominated that landscape. But we're kind of going back to a systems mindset. The question for you is, how do you view the issue of this holy grail, the promised land of AI and machine learning, where end-to-end visibility is really the goal, right? At the same time, you want bounded experiences at root level so automation can kick in to enable more activity. So there's a trade-off between going for the end-to-end visibility out of the gate, but also having bounded visibility and data to automate. How do you guys look at that market? Because customers want the end-to-end promise, but they don't want to try to get there too fast. There's a diseconomies of scale potentially. How do you talk about that? >> Correct. >> And that's exactly the approach we've taken with Control-M Workbench, the Community Edition, because earlier on you don't need capabilities like SLA management and forecasting and automated promotion between environments. Developers want to be able to quickly build and test and show value, okay, and they don't need something that is with all the bells and whistles. We're allowing you to handle that piece, in that manner, through Control-M Workbench. As things progress and the application progresses, the needs change as well. Well now I'm closer to delivering this to the business, I need to be able to manage this within an SLA, I need to be able to manage this end-to-end and connect this to other systems of record, and streaming data, and clickstream data, all of that. So that, we believe that it doesn't have to be a trade off, that you don't have to compromise speed and quality for end-to-end visibility and enterprise grade automation. >> You mentioned trade offs, so the Control-M Workbench, the developer can use it offline, so what amount of testing can they possibly do on a complex data pipeline automation when the tool's offline? I mean it seems like the more development they do offline, the greater the risk that it simply won't work when they go into production. Give us a sense for how they mitigate, the mitigation risk in using Control-M Workbench. >> Sure, so we spend a lot of time observing how developers work, right? And very early in the development stage, all they're doing is working off of their Mac or their laptop, and they're not really connected to any. And that is where they end up writing a lot of scripts, because whatever code business logic they've written, the way they're going to make it run is by writing scripts. And that, essentially, becomes the problem, because then you have scripts managing more scripts, and as the application progresses, you have this complex web of scripts and Crontabs and maybe some opensource solutions, trying to simply make all of this run. And by doing this on an offline manner, that doesn't mean that they're losing all of the other Control-M capabilities. Simply, as the application progresses, whatever automation that the builtin Control-M can seamlessly now flow into the next stage. So when you are ready to take an application into production, there's essentially no rework required from an automation perspective. All of that, that was built, can now be translated into the enterprise-grade Control M, and that's where operations can then go in and add the other artifacts, such as SLA management and forecasting and other things that are important from an operational perspective. >> I'd like to get both your perspectives, 'cause, so you're like an analyst here, so Jim, I want you guys to comment. My question to both of you would be, lookin' at this time in history, obviously in the BMC side we mention some of the history, you guys are transforming on a new journey in extending that capability of this world. Jim, you're covering state-of-the-art AI machine learning. What's your take of this space now? Strata Data, which is now Hadoop World, which is Cloud Air went public, Hortonworks is now public, kind of the big, the Hadoop guys kind of grew up, but the world has changed around them, it's not just about Hadoop anymore. So I'd like to get your thoughts on this kind of perspective, that we're seeing a much broader picture in big data in NYC, versus the Strata Hadoop show, which seems to be losing steam, but I mean in terms of the focus. The bigger focus is much broader, horizontally scalable. And your thoughts on the ecosystem right now? >> Let the Basil answer fist, unless Basil wants me to go first. >> I think that the reason the focus is changing, is because of where the projects are in their lifecycle. Now what we're seeing is most companies are grappling with, how do I take this to the next level? How do I scale? How do I go from just proving out one or two use cases to making the entire organization data driven, and really inject data driven decision making in all facets of decision making? So that is, I believe what's driving the change that we're seeing, that now you've gone from Strata Hadoop to being Strata Data, and focus on that element. And, like I said earlier, the difference between success and failure is your ability to scale and operationalize. Take machine learning for an example. >> Good, that's where there's no, it's not a hype market, it's show me the meat on the bone, show me scale, I got operational concerns of security and what not. >> And machine learning, that's one of the hottest topics. A recent survey I read, which pulled a number of data scientists, it revealed that they spent about less than 3% of their time in training the data models, and about 80% of their time in data manipulation, data transformation and enrichment. That is obviously not the best use of a data scientist's time, and that is exactly one of the problems we're solving for our customers around the world. >> That needs to be automated to the hilt. To help them >> Correct. to be more productive, to deliver faster results. >> Ecosystem perspective, Jim, what's your thoughts? >> Yeah, everything that Basil said, and I'll just point out that many of the core uses cases for AI are automation of the data pipeline. It's driving machine learning driven predictions, classifications, abstractions and so forth, into the data pipeline, into the application pipeline to drive results in a way that is contextually and environmentally aware of what's goin' on. The history, historical data, what's goin' on in terms of current streaming data, to drive optimal outcomes, using predictive models and so forth, in line to applications. So really, fundamentally then, what's goin' on is that automation is an artifact that needs to be driven into your application architecture as a repurposable resource for a variety of-- >> Do customers even know what to automate? I mean, that's the question, what do I-- >> You're automating human judgment. You're automating effort, like the judgments that a working data engineer makes to prepare data for modeling and whatever. More and more that can be automated, 'cause those are pattern structured activities that have been mastered by smart people over many years. >> I mean we just had a customer on with a Glass'Gim CSK, with that scale, and his attitude is, we see the results from the users, then we double down and pay for it and automate it. So the automation question, it's an option question, it's a rhetorical question, but it just begs the question, which is who's writing the algorithms as machines get smarter and start throwing off their own real-time data? What are you looking at? How do you determine? You're going to need machine learning for machine learning? Are you going to need AI for AI? Who writes the algorithms >> It's actually, that's. for the algorithm? >> Automated machine learning is a hot, hot not only research focus, but we're seeing it more and more solution providers, like Microsoft and Google and others, are goin' deep down, doubling down in investments in exactly that area. That's a productivity play for data scientists. >> I think the data markets going to change radically in my opinion. I see you're startin' to some things with blockchain and some other things that are interesting. Data sovereignty, data governance are huge issues. Basil, just give your final thoughts for this segment as we wrap this up. Final thoughts on data and BMC, what should people know about BMC right now? Because people might have a historical view of BMC. What's the latest, what should they know? What's the new Instagram picture of BMC? What should they know about you guys? >> So I think what I would say people should know about BMC is that all the work that we've done over the last 25 years, in virtually every platform that came before Hadoop, we have now innovated to take this into things like big data and cloud platforms. So when you are choosing Control-M as a platform for automation, you are choosing a very, very mature solution, an example of which is Navistar. Their CIO's actually speaking at the Keno tomorrow. They've had Control-M for 15, 20 years, and they've automated virtually every business function through Control-M. And when they started their predictive maintenance project, where they're ingesting data from about 300,000 vehicles today to figure out when this vehicle might break, and to predict maintenance on it. When they started their journey, they said that they always knew that they were going to use Control-M for it, because that was the enterprise standard, and they knew that they could simply now extend that capability into this area. And when they started about three, four years ago, they were ingesting data from about 100,000 vehicles. That has now scaled to over 325,000 vehicles, and they have no had to re-architect their strategy as they grow and scale. So I would say that is one of the key messages that we are taking to market, is that we are bringing innovation that spans over 25 years, and evolving it-- >> Modernizing it, basically. >> Modernizing it, and bringing it to newer platforms. >> Well congratulations, I wouldn't call that a pivot, I'd call it an extensibility issue, kind of modernizing kind of the core things. >> Absolutely. >> Thanks for coming and sharing the BMC perspective inside theCUBE here, on BigData NYC, this is the theCUBE, I'm John Furrier. Jim Kobielus here in New York city. More live coverage, for three days we'll be here, today, tomorrow and Thursday, and BigData NYC, more coverage after this short break. (calm electronic music) (vibrant electronic music)

- [Announcer] Live, from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media, and its ecosystem partners. - Hello everyone, welcome back to CUBE NYC. This is a CUBE special presentation of something that we've done now for the past couple of years. IBM has sponsored an influencer panel on some of the hottest topics in the industry, and of course, there's no hotter topic right now than AI. So, we've got nine of the top influencers in the AI space, and we're in Hell's Kitchen, and it's going to get hot in here. (laughing) And these guys, we're going to cover the gamut. So, first of all, folks, thanks so much for joining us today, really, as John said earlier, we love the collaboration with you all, and we'll definitely see you on social after the fact. I'm Dave Vellante, with my cohost for this session, Peter Burris, and again, thank you to IBM for sponsoring this and organizing this. IBM has a big event down here, in conjunction with Strata, called Change the Game, Winning with AI. We run theCUBE NYC, we've been here all week. So, here's the format. I'm going to kick it off, and then we'll see where it goes. So, I'm going to introduce each of the panelists, and then ask you guys to answer a question, I'm sorry, first, tell us a little bit about yourself, briefly, and then answer one of the following questions. Two big themes that have come up this week. One has been, because this is our ninth year covering what used to be Hadoop World, which kind of morphed into big data. Question is, AI, big data, same wine, new bottle? Or is it really substantive, and driving business value? So, that's one question to ponder. The other one is, you've heard the term, the phrase, data is the new oil. Is data really the new oil? Wonder what you think about that? Okay, so, Chris Penn, let's start with you. Chris is cofounder of Trust Insight, long time CUBE alum, and friend. Thanks for coming on. Tell us a little bit about yourself, and then pick one of those questions. - Sure, we're a data science consulting firm. We're an IBM business partner. When it comes to "data is the new oil," I love that expression because it's completely accurate. Crude oil is useless, you have to extract it out of the ground, refine it, and then bring it to distribution. Data is the same way, where you have to have developers and data architects get the data out. You need data scientists and tools, like Watson Studio, to refine it, and then you need to put it into production, and that's where marketing technologists, technologists, business analytics folks, and tools like Watson Machine Learning help bring the data and make it useful. - Okay, great, thank you. Tony Flath is a tech and media consultant, focus on cloud and cyber security, welcome. - Thank you. - Tell us a little bit about yourself and your thoughts on one of those questions. - Sure thing, well, thanks so much for having us on this show, really appreciate it. My background is in cloud, cyber security, and certainly in emerging tech with artificial intelligence. Certainly touched it from a cyber security play, how you can use machine learning, machine control, for better controlling security across the gamut. But I'll touch on your question about wine, is it a new bottle, new wine? Where does this come from, from artificial intelligence? And I really see it as a whole new wine that is coming along. When you look at emerging technology, and you look at all the deep learning that's happening, it's going just beyond being able to machine learn and know what's happening, it's making some meaning to that data. And things are being done with that data, from robotics, from automation, from all kinds of different things, where we're at a point in society where data, our technology is getting beyond us. Prior to this, it's always been command and control. You control data from a keyboard. Well, this is passing us. So, my passion and perspective on this is, the humanization of it, of IT. How do you ensure that people are in that process, right? - Excellent, and we're going to come back and talk about that. - Thanks so much. - Carla Gentry, @DataNerd? Great to see you live, as opposed to just in the ether on Twitter. Data scientist, and owner of Analytical Solution. Welcome, your thoughts? - Thank you for having us. Mine is, is data the new oil? And I'd like to rephrase that is, data equals human lives. So, with all the other artificial intelligence and everything that's going on, and all the algorithms and models that's being created, we have to think about things being biased, being fair, and understand that this data has impacts on people's lives. - Great. Steve Ardire, my paisan. - Paisan. - AI startup adviser, welcome, thanks for coming to theCUBE. - Thanks Dave. So, uh, my first career was geology, and I view AI as the new oil, but data is the new oil, but AI is the refinery. I've used that many times before. In fact, really, I've moved from just AI to augmented intelligence. So, augmented intelligence is really the way forward. This was a presentation I gave at IBM Think last spring, has almost 100,000 impressions right now, and the fundamental reason why is machines can attend to vastly more information than humans, but you still need humans in the loop, and we can talk about what they're bringing in terms of common sense reasoning, because big data does the who, what, when, and where, but not the why, and why is really the Holy Grail for causal analysis and reasoning. - Excellent, Bob Hayes, Business Over Broadway, welcome, great to see you again. - Thanks for having me. So, my background is in psychology, industrial psychology, and I'm interested in things like customer experience, data science, machine learning, so forth. And I'll answer the question around big data versus AI. And I think there's other terms we could talk about, big data, data science, machine learning, AI. And to me, it's kind of all the same. It's always been about analytics, and getting value from your data, big, small, what have you. And there's subtle differences among those terms. Machine learning is just about making a prediction, and knowing if things are classified correctly. Data science is more about understanding why things work, and understanding maybe the ethics behind it, what variables are predicting that outcome. But still, it's all the same thing, it's all about using data in a way that we can get value from that, as a society, in residences. - Excellent, thank you. Theo Lau, founder of Unconventional Ventures. What's your story? - Yeah, so, my background is driving technology innovation. So, together with my partner, what our work does is we work with organizations to try to help them leverage technology to drive systematic financial wellness. We connect founders, startup founders, with funders, we help them get money in the ecosystem. We also work with them to look at, how do we leverage emerging technology to do something good for the society. So, very much on point to what Bob was saying about. So when I look at AI, it is not new, right, it's been around for quite a while. But what's different is the amount of technological power that we have allow us to do so much more than what we were able to do before. And so, what my mantra is, great ideas can come from anywhere in the society, but it's our job to be able to leverage technology to shine a spotlight on people who can use this to do something different, to help seniors in our country to do better in their financial planning. - Okay, so, in your mind, it's not just a same wine, new bottle, it's more substantive than that. - [Theo] It's more substantive, it's a much better bottle. - Karen Lopez, senior project manager for Architect InfoAdvisors, welcome. - Thank you. So, I'm DataChick on twitter, and so that kind of tells my focus is that I'm here, I also call myself a data evangelist, and that means I'm there at organizations helping stand up for the data, because to me, that's the proxy for standing up for the people, and the places and the events that that data describes. That means I have a focus on security, data privacy and protection as well. And I'm going to kind of combine your two questions about whether data is the new wine bottle, I think is the combination. Oh, see, now I'm talking about alcohol. (laughing) But anyway, you know, all analogies are imperfect, so whether we say it's the new wine, or, you know, same wine, or whether it's oil, is that the analogy's good for both of them, but unlike oil, the amount of data's just growing like crazy, and the oil, we know at some point, I kind of doubt that we're going to hit peak data where we have not enough data, like we're going to do with oil. But that says to me that, how did we get here with big data, with machine learning and AI? And from my point of view, as someone who's been focused on data for 35 years, we have hit this perfect storm of open source technologies, cloud architectures and cloud services, data innovation, that if we didn't have those, we wouldn't be talking about large machine learning and deep learning-type things. So, because we have all these things coming together at the same time, we're now at explosions of data, which means we also have to protect them, and protect the people from doing harm with data, we need to do data for good things, and all of that. - Great, definite differences, we're not running out of data, data's like the terrible tribbles. (laughing) - Yes, but it's very cuddly, data is. - Yeah, cuddly data. Mark Lynd, founder of Relevant Track? - That's right. - I like the name. What's your story? - Well, thank you, and it actually plays into what my interest is. It's mainly around AI in enterprise operations and cyber security. You know, these teams that are in enterprise operations both, it can be sales, marketing, all the way through the organization, as well as cyber security, they're often under-sourced. And they need, what Steve pointed out, they need augmented intelligence, they need to take AI, the big data, all the information they have, and make use of that in a way where they're able to, even though they're under-sourced, make some use and some value for the organization, you know, make better use of the resources they have to grow and support the strategic goals of the organization. And oftentimes, when you get to budgeting, it doesn't really align, you know, you're short people, you're short time, but the data continues to grow, as Karen pointed out. So, when you take those together, using AI to augment, provided augmented intelligence, to help them get through that data, make real tangible decisions based on information versus just raw data, especially around cyber security, which is a big hit right now, is really a great place to be, and there's a lot of stuff going on, and a lot of exciting stuff in that area. - Great, thank you. Kevin L. Jackson, author and founder of GovCloud. GovCloud, that's big. - Yeah, GovCloud Network. Thank you very much for having me on the show. Up and working on cloud computing, initially in the federal government, with the intelligence community, as they adopted cloud computing for a lot of the nation's major missions. And what has happened is now I'm working a lot with commercial organizations and with the security of that data. And I'm going to sort of, on your questions, piggyback on Karen. There was a time when you would get a couple of bottles of wine, and they would come in, and you would savor that wine, and sip it, and it would take a few days to get through it, and you would enjoy it. The problem now is that you don't get a couple of bottles of wine into your house, you get two or three tankers of data. So, it's not that it's a new wine, you're just getting a lot of it. And the infrastructures that you need, before you could have a couple of computers, and a couple of people, now you need cloud, you need automated infrastructures, you need huge capabilities, and artificial intelligence and AI, it's what we can use as the tool on top of these huge infrastructures to drink that, you know. - Fire hose of wine. - Fire hose of wine. (laughs) - Everybody's having a good time. - Everybody's having a great time. (laughs) - Yeah, things are booming right now. Excellent, well, thank you all for those intros. Peter, I want to ask you a question. So, I heard there's some similarities and some definite differences with regard to data being the new oil. You have a perspective on this, and I wonder if you could inject it into the conversation. - Sure, so, the perspective that we take in a lot of conversations, a lot of folks here in theCUBE, what we've learned, and I'll kind of answer both questions a little bit. First off, on the question of data as the new oil, we definitely think that data is the new asset that business is going to be built on, in fact, our perspective is that there really is a difference between business and digital business, and that difference is data as an asset. And if you want to understand data transformation, you understand the degree to which businesses reinstitutionalizing work, reorganizing its people, reestablishing its mission around what you can do with data as an asset. The difference between data and oil is that oil still follows the economics of scarcity. Data is one of those things, you can copy it, you can share it, you can easily corrupt it, you can mess it up, you can do all kinds of awful things with it if you're not careful. And it's that core fundamental proposition that as an asset, when we think about cyber security, we think, in many respects, that is the approach to how we can go about privatizing data so that we can predict who's actually going to be able to appropriate returns on it. So, it's a good analogy, but as you said, it's not entirely perfect, but it's not perfect in a really fundamental way. It's not following the laws of scarcity, and that has an enormous effect. - In other words, I could put oil in my car, or I could put oil in my house, but I can't put the same oil in both. - Can't put it in both places. And now, the issue of the wine, I think it's, we think that it is, in fact, it is a new wine, and very simple abstraction, or generalization we come up with is the issue of agency. That analytics has historically not taken on agency, it hasn't acted on behalf of the brand. AI is going to act on behalf of the brand. Now, you're going to need both of them, you can't separate them. - A lot of implications there in terms of bias. - Absolutely. - In terms of privacy. You have a thought, here, Chris? - Well, the scarcity is our compute power, and our ability for us to process it. I mean, it's the same as oil, there's a ton of oil under the ground, right, we can't get to it as efficiently, or without severe environmental consequences to use it. Yeah, when you use it, it's transformed, but our scarcity is compute power, and our ability to use it intelligently. - Or even when you find it. I have data, I can apply it to six different applications, I have oil, I can apply it to one, and that's going to matter in how we think about work. - But one thing I'd like to add, sort of, you're talking about data as an asset. The issue we're having right now is we're trying to learn how to manage that asset. Artificial intelligence is a way of managing that asset, and that's important if you're going to use and leverage big data. - Yeah, but see, everybody's talking about the quantity, the quantity, it's not always the quantity. You know, we can have just oodles and oodles of data, but if it's not clean data, if it's not alphanumeric data, which is what's needed for machine learning. So, having lots of data is great, but you have to think about the signal versus the noise. So, sometimes you get so much data, you're looking at over-fitting, sometimes you get so much data, you're looking at biases within the data. So, it's not the amount of data, it's the, now that we have all of this data, making sure that we look at relevant data, to make sure we look at clean data. - One more thought, and we have a lot to cover, I want to get inside your big brain. - I was just thinking about it from a cyber security perspective, one of my customers, they were looking at the data that just comes from the perimeter, your firewalls, routers, all of that, and then not even looking internally, just the perimeter alone, and the amount of data being pulled off of those. And then trying to correlate that data so it makes some type of business sense, or they can determine if there's incidents that may happen, and take a predictive action, or threats that might be there because they haven't taken a certain action prior, it's overwhelming to them. So, having AI now, to be able to go through the logs to look at, and there's so many different types of data that come to those logs, but being able to pull that information, as well as looking at end points, and all that, and people's houses, which are an extension of the network oftentimes, it's an amazing amount of data, and they're only looking at a small portion today because they know, there's not enough resources, there's not enough trained people to do all that work. So, AI is doing a wonderful way of doing that. And some of the tools now are starting to mature and be sophisticated enough where they provide that augmented intelligence that Steve talked about earlier. - So, it's complicated. There's infrastructure, there's security, there's a lot of software, there's skills, and on and on. At IBM Think this year, Ginni Rometty talked about, there were a couple of themes, one was augmented intelligence, that was something that was clear. She also talked a lot about privacy, and you own your data, etc. One of the things that struck me was her discussion about incumbent disruptors. So, if you look at the top five companies, roughly, Facebook with fake news has dropped down a little bit, but top five companies in terms of market cap in the US. They're data companies, all right. Apple just hit a trillion, Amazon, Google, etc. How do those incumbents close the gap? Is that concept of incumbent disruptors actually something that is being put into practice? I mean, you guys work with a lot of practitioners. How are they going to close that gap with the data haves, meaning data at their core of their business, versus the data have-nots, it's not that they don't have a lot of data, but it's in silos, it's hard to get to? - Yeah, I got one more thing, so, you know, these companies, and whoever's going to be big next is, you have a digital persona, whether you want it or not. So, if you live in a farm out in the middle of Oklahoma, you still have a digital persona, people are collecting data on you, they're putting profiles of you, and the big companies know about you, and people that first interact with you, they're going to know that you have this digital persona. Personal AI, when AI from these companies could be used simply and easily, from a personal deal, to fill in those gaps, and to have a digital persona that supports your family, your growth, both personal and professional growth, and those type of things, there's a lot of applications for AI on a personal, enterprise, even small business, that have not been done yet, but the data is being collected now. So, you talk about the oil, the oil is being built right now, lots, and lots, and lots of it. It's the applications to use that, and turn that into something personally, professionally, educationally, powerful, that's what's missing. But it's coming. - Thank you, so, I'll add to that, and in answer to your question you raised. So, one example we always used in banking is, if you look at the big banks, right, and then you look at from a consumer perspective, and there's a lot of talk about Amazon being a bank. But the thing is, Amazon doesn't need to be a bank, they provide banking services, from a consumer perspective they don't really care if you're a bank or you're not a bank, but what's different between Amazon and some of the banks is that Amazon, like you say, has a lot of data, and they know how to make use of the data to offer something as relevant that consumers want. Whereas banks, they have a lot of data, but they're all silos, right. So, it's not just a matter of whether or not you have the data, it's also, can you actually access it and make something useful out of it so that you can create something that consumers want? Because otherwise, you're just a pipe. - Totally agree, like, when you look at it from a perspective of, there's a lot of terms out there, digital transformation is thrown out so much, right, and go to cloud, and you migrate to cloud, and you're going to take everything over, but really, when you look at it, and you both touched on it, it's the economics. You have to look at the data from an economics perspective, and how do you make some kind of way to take this data meaningful to your customers, that's going to work effectively for them, that they're going to drive? So, when you look at the big, big cloud providers, I think the push in things that's going to happen in the next few years is there's just going to be a bigger migration to public cloud. So then, between those, they have to differentiate themselves. Obvious is artificial intelligence, in a way that makes it easy to aggregate data from across platforms, to aggregate data from multi-cloud, effectively. To use that data in a meaningful way that's going to drive, not only better decisions for your business, and better outcomes, but drives our opportunities for customers, drives opportunities for employees and how they work. We're at a really interesting point in technology where we get to tell technology what to do. It's going beyond us, it's no longer what we're telling it to do, it's going to go beyond us. So, how we effectively manage that is going to be where we see that data flow, and those big five or big four, really take that to the next level. - Now, one of the things that Ginni Rometty said was, I forget the exact step, but it was like, 80% of the data, is not searchable. Kind of implying that it's sitting somewhere behind a firewall, presumably on somebody's premises. So, it was kind of interesting. You're talking about, certainly, a lot of momentum for public cloud, but at the same time, a lot of data is going to stay where it is. - Yeah, we're assuming that a lot of this data is just sitting there, available and ready, and we look at the desperate, or disparate kind of database situation, where you have 29 databases, and two of them have unique quantifiers that tie together, and the rest of them don't. So, there's nothing that you can do with that data. So, artificial intelligence is just that, it's artificial intelligence, so, they know, that's machine learning, that's natural language, that's classification, there's a lot of different parts of that that are moving, but we also have to have IT, good data infrastructure, master data management, compliance, there's so many moving parts to this, that it's not just about the data anymore. - I want to ask Steve to chime in here, go ahead. - Yeah, so, we also have to change the mentality that it's not just enterprise data. There's data on the web, the biggest thing is Internet of Things, the amount of sensor data will make the current data look like chump change. So, data is moving faster, okay. And this is where the sophistication of machine learning needs to kick in, going from just mostly supervised-learning today, to unsupervised learning. And in order to really get into, as I said, big data, and credible AI does the who, what, where, when, and how, but not the why. And this is really the Holy Grail to crack, and it's actually under a new moniker, it's called explainable AI, because it moves beyond just correlation into root cause analysis. Once we have that, then you have the means to be able to tap into augmented intelligence, where humans are working with the machines. - Karen, please. - Yeah, so, one of the things, like what Carla was saying, and what a lot of us had said, I like to think of the advent of ML technologies and AI are going to help me as a data architect to love my data better, right? So, that includes protecting it, but also, when you say that 80% of the data is unsearchable, it's not just an access problem, it's that no one knows what it was, what the sovereignty was, what the metadata was, what the quality was, or why there's huge anomalies in it. So, my favorite story about this is, in the 1980s, about, I forget the exact number, but like, 8 million children disappeared out of the US in April, at April 15th. And that was when the IRS enacted a rule that, in order to have a dependent, a deduction for a dependent on your tax returns, they had to have a valid social security number, and people who had accidentally miscounted their children and over-claimed them, (laughter) over the years them, stopped doing that. Well, some days it does feel like you have eight children running around. (laughter) - Agreed. - When, when that rule came about, literally, and they're not all children, because they're dependents, but literally millions of children disappeared off the face of the earth in April, but if you were doing analytics, or AI and ML, and you don't know that this anomaly happened, I can imagine in a hundred years, someone is saying some catastrophic event happened in April, 1983. (laughter) And what caused that, was it healthcare? Was it a meteor? Was it the clown attacking them? - That's where I was going. - Right. So, those are really important things that I want to use AI and ML to help me, not only document and capture that stuff, but to provide that information to the people, the data scientists and the analysts that are using the data. - Great story, thank you. Bob, you got a thought? You got the mic, go, jump in here. - Well, yeah, I do have a thought, actually. I was talking about, what Karen was talking about. I think it's really important that, not only that we understand AI, and machine learning, and data science, but that the regular folks and companies understand that, at the basic level. Because those are the people who will ask the questions, or who know what questions to ask of the data. And if they don't have the tools, and the knowledge of how to get access to that data, or even how to pose a question, then that data is going to be less valuable, I think, to companies. And the more that everybody knows about data, even people in congress. Remember when Zuckerberg talked about? (laughter) - That was scary. - How do you make money? It's like, we all know this. But, we need to educate the masses on just basic data analytics. - We could have an hour-long panel on that. - Yeah, absolutely. - Peter, you and I were talking about, we had a couple of questions, sort of, how far can we take artificial intelligence? How far should we? You know, so that brings in to the conversation of ethics, and bias, why don't you pick it up? - Yeah, so, one of the crucial things that we all are implying is that, at some point in time, AI is going to become a feature of the operations of our homes, our businesses. And as these technologies get more powerful, and they diffuse, and know about how to use them, diffuses more broadly, and you put more options into the hands of more people, the question slowly starts to turn from can we do it, to should we do it? And, one of the issues that I introduce is that I think the difference between big data and AI, specifically, is this notion of agency. The AI will act on behalf of, perhaps you, or it will act on behalf of your business. And that conversation is not being had, today. It's being had in arguments between Elon Musk and Mark Zuckerberg, which pretty quickly get pretty boring. (laughing) At the end of the day, the real question is, should this machine, whether in concert with others, or not, be acting on behalf of me, on behalf of my business, or, and when I say on behalf of me, I'm also talking about privacy. Because Facebook is acting on behalf of me, it's not just what's going on in my home. So, the question of, can it be done? A lot of things can be done, and an increasing number of things will be able to be done. We got to start having a conversation about should it be done? - So, humans exhibit tribal behavior, they exhibit bias. Their machine's going to pick that up, go ahead, please. - Yeah, one thing that sort of tag onto agency of artificial intelligence. Every industry, every business is now about identifying information and data sources, and their appropriate sinks, and learning how to draw value out of connecting the sources with the sinks. Artificial intelligence enables you to identify those sources and sinks, and when it gets agency, it will be able to make decisions on your behalf about what data is good, what data means, and who it should be. - What actions are good. - Well, what actions are good. - And what data was used to make those actions. - Absolutely. - And was that the right data, and is there bias of data? And all the way down, all the turtles down. - So, all this, the data pedigree will be driven by the agency of artificial intelligence, and this is a big issue. - It's really fundamental to understand and educate people on, there are four fundamental types of bias, so there's, in machine learning, there's intentional bias, "Hey, we're going to make "the algorithm generate a certain outcome "regardless of what the data says." There's the source of the data itself, historical data that's trained on the models built on flawed data, the model will behave in a flawed way. There's target source, which is, for example, we know that if you pull data from a certain social network, that network itself has an inherent bias. No matter how representative you try to make the data, it's still going to have flaws in it. Or, if you pull healthcare data about, for example, African-Americans from the US healthcare system, because of societal biases, that data will always be flawed. And then there's tool bias, there's limitations to what the tools can do, and so we will intentionally exclude some kinds of data, or not use it because we don't know how to, our tools are not able to, and if we don't teach people what those biases are, they won't know to look for them, and I know. - Yeah, it's like, one of the things that we were talking about before, I mean, artificial intelligence is not going to just create itself, it's lines of code, it's input, and it spits out output. So, if it learns from these learning sets, we don't want AI to become another buzzword. We don't want everybody to be an "AR guru" that has no idea what AI is. It takes months, and months, and months for these machines to learn. These learning sets are so very important, because that input is how this machine, think of it as your child, and that's basically the way artificial intelligence is learning, like your child. You're feeding it these learning sets, and then eventually it will make its own decisions. So, we know from some of us having children that you teach them the best that you can, but then later on, when they're doing their own thing, they're really, it's like a little myna bird, they've heard everything that you've said. (laughing) Not only the things that you said to them directly, but the things that you said indirectly. - Well, there are some very good AI researchers that might disagree with that metaphor, exactly. (laughing) But, having said that, what I think is very interesting about this conversation is that this notion of bias, one of the things that fascinates me about where AI goes, are we going to find a situation where tribalism more deeply infects business? Because we know that human beings do not seek out the best information, they seek out information that reinforces their beliefs. And that happens in business today. My line of business versus your line of business, engineering versus sales, that happens today, but it happens at a planning level, and when we start talking about AI, we have to put the appropriate dampers, understand the biases, so that we don't end up with deep tribalism inside of business. Because AI could have the deleterious effect that it actually starts ripping apart organizations. - Well, input is data, and then the output is, could be a lot of things. - Could be a lot of things. - And that's where I said data equals human lives. So that we look at the case in New York where the penal system was using this artificial intelligence to make choices on people that were released from prison, and they saw that that was a miserable failure, because that people that release actually re-offended, some committed murder and other things. So, I mean, it's, it's more than what anybody really thinks. It's not just, oh, well, we'll just train the machines, and a couple of weeks later they're good, we never have to touch them again. These things have to be continuously tweaked. So, just because you built an algorithm or a model doesn't mean you're done. You got to go back later, and continue to tweak these models. - Mark, you got the mic. - Yeah, no, I think one thing we've talked a lot about the data that's collected, but what about the data that's not collected? Incomplete profiles, incomplete datasets, that's a form of bias, and sometimes that's the worst. Because they'll fill that in, right, and then you can get some bias, but there's also a real issue for that around cyber security. Logs are not always complete, things are not always done, and when things are doing that, people make assumptions based on what they've collected, not what they didn't collect. So, when they're looking at this, and they're using the AI on it, that's only on the data collected, not on that that wasn't collected. So, if something is down for a little while, and no data's collected off that, the assumption is, well, it was down, or it was impacted, or there was a breach, or whatever, it could be any of those. So, you got to, there's still this human need, there's still the need for humans to look at the data and realize that there is the bias in there, there is, we're just looking at what data was collected, and you're going to have to make your own thoughts around that, and assumptions on how to actually use that data before you go make those decisions that can impact lots of people, at a human level, enterprise's profitability, things like that. And too often, people think of AI, when it comes out of there, that's the word. Well, it's not the word. - Can I ask a question about this? - Please. - Does that mean that we shouldn't act? - It does not. - Okay. - So, where's the fine line? - Yeah, I think. - Going back to this notion of can we do it, or should we do it? Should we act? - Yeah, I think you should do it, but you should use it for what it is. It's augmenting, it's helping you, assisting you to make a valued or good decision. And hopefully it's a better decision than you would've made without it. - I think it's great, I think also, your answer's right too, that you have to iterate faster, and faster, and faster, and discover sources of information, or sources of data that you're not currently using, and, that's why this thing starts getting really important. - I think you touch on a really good point about, should you or shouldn't you? You look at Google, and you look at the data that they've been using, and some of that out there, from a digital twin perspective, is not being approved, or not authorized, and even once they've made changes, it's still floating around out there. Where do you know where it is? So, there's this dilemma of, how do you have a digital twin that you want to have, and is going to work for you, and is going to do things for you to make your life easier, to do these things, mundane tasks, whatever? But how do you also control it to do things you don't want it to do? - Ad-based business models are inherently evil. (laughing) - Well, there's incentives to appropriate our data, and so, are things like blockchain potentially going to give users the ability to control their data? We'll see. - No, I, I'm sorry, but that's actually a really important point. The idea of consensus algorithms, whether it's blockchain or not, blockchain includes games, and something along those lines, whether it's Byzantine fault tolerance, or whether it's Paxos, consensus-based algorithms are going to be really, really important. Parts of this conversation, because the data's going to be more distributed, and you're going to have more elements participating in it. And so, something that allows, especially in the machine-to-machine world, which is a lot of what we're talking about right here, you may not have blockchain, because there's no need for a sense of incentive, which is what blockchain can help provide. - And there's no middleman. - And, well, all right, but there's really, the thing that makes blockchain so powerful is it liberates new classes of applications. But for a lot of the stuff that we're talking about, you can use a very powerful consensus algorithm without having a game side, and do some really amazing things at scale. - So, looking at blockchain, that's a great thing to bring up, right. I think what's inherently wrong with the way we do things today, and the whole overall design of technology, whether it be on-prem, or off-prem, is both the lock and key is behind the same wall. Whether that wall is in a cloud, or behind a firewall. So, really, when there is an audit, or when there is a forensics, it always comes down to a sysadmin, or something else, and the system administrator will have the finger pointed at them, because it all resides, you can edit it, you can augment it, or you can do things with it that you can't really determine. Now, take, as an example, blockchain, where you've got really the source of truth. Now you can take and have the lock in one place, and the key in another place. So that's certainly going to be interesting to see how that unfolds. - So, one of the things, it's good that, we've hit a lot of buzzwords, right now, right? (laughing) AI, and ML, block. - Bingo. - We got the blockchain bingo, yeah, yeah. So, one of the things is, you also brought up, I mean, ethics and everything, and one of the things that I've noticed over the last year or so is that, as I attend briefings or demos, everyone is now claiming that their product is AI or ML-enabled, or blockchain-enabled. And when you try to get answers to the questions, what you really find out is that some things are being pushed as, because they have if-then statements somewhere in their code, and therefore that's artificial intelligence or machine learning. - [Peter] At least it's not "go-to." (laughing) - Yeah, you're that experienced as well. (laughing) So, I mean, this is part of the thing you try to do as a practitioner, as an analyst, as an influencer, is trying to, you know, the hype of it all. And recently, I attended one where they said they use blockchain, and I couldn't figure it out, and it turns out they use GUIDs to identify things, and that's not blockchain, it's an identifier. (laughing) So, one of the ethics things that I think we, as an enterprise community, have to deal with, is the over-promising of AI, and ML, and deep learning, and recognition. It's not, I don't really consider it visual recognition services if they just look for red pixels. I mean, that's not quite the same thing. Yet, this is also making things much harder for your average CIO, or worse, CFO, to understand whether they're getting any value from these technologies. - Old bottle. - Old bottle, right. - And I wonder if the data companies, like that you talked about, or the top five, I'm more concerned about their nearly, or actual $1 trillion valuations having an impact on their ability of other companies to disrupt or enter into the field more so than their data technologies. Again, we're coming to another perfect storm of the companies that have data as their asset, even though it's still not on their financial statements, which is another indicator whether it's really an asset, is that, do we need to think about the terms of AI, about whose hands it's in, and who's, like, once one large trillion-dollar company decides that you are not a profitable company, how many other companies are going to buy that data and make that decision about you? - Well, and for the first time in business history, I think, this is true, we're seeing, because of digital, because it's data, you're seeing tech companies traverse industries, get into, whether it's content, or music, or publishing, or groceries, and that's powerful, and that's awful scary. - If you're a manger, one of the things your ownership is asking you to do is to reduce asset specificities, so that their capital could be applied to more productive uses. Data reduces asset specificities. It brings into question the whole notion of vertical industry. You're absolutely right. But you know, one quick question I got for you, playing off of this is, again, it goes back to this notion of can we do it, and should we do it? I find it interesting, if you look at those top five, all data companies, but all of them are very different business models, or they can classify the two different business models. Apple is transactional, Microsoft is transactional, Google is ad-based, Facebook is ad-based, before the fake news stuff. Amazon's kind of playing it both sides. - Yeah, they're kind of all on a collision course though, aren't they? - But, well, that's what's going to be interesting. I think, at some point in time, the "can we do it, should we do it" question is, brands are going to be identified by whether or not they have gone through that process of thinking about, should we do it, and say no. Apple is clearly, for example, incorporating that into their brand. - Well, Silicon Valley, broadly defined, if I include Seattle, and maybe Armlock, not so much IBM. But they've got a dual disruption agenda, they've always disrupted horizontal tech. Now they're disrupting vertical industries. - I was actually just going to pick up on what she was talking about, we were talking about buzzword, right. So, one we haven't heard yet is voice. Voice is another big buzzword right now, when you couple that with IoT and AI, here you go, bingo, do I got three points? (laughing) Voice recognition, voice technology, so all of the smart speakers, if you think about that in the world, there are 7,000 languages being spoken, but yet if you look at Google Home, you look at Siri, you look at any of the devices, I would challenge you, it would have a lot of problem understanding my accent, and even when my British accent creeps out, or it would have trouble understanding seniors, because the way they talk, it's very different than a typical 25-year-old person living in Silicon Valley, right. So, how do we solve that, especially going forward? We're seeing voice technology is going to be so more prominent in our homes, we're going to have it in the cars, we have it in the kitchen, it does everything, it listens to everything that we are talking about, not talking about, and records it. And to your point, is it going to start making decisions on our behalf, but then my question is, how much does it actually understand us? - So, I just want one short story. Siri can't translate a word that I ask it to translate into French, because my phone's set to Canadian English, and that's not supported. So I live in a bilingual French English country, and it can't translate. - But what this is really bringing up is if you look at society, and culture, what's legal, what's ethical, changes across the years. What was right 200 years ago is not right now, and what was right 50 years ago is not right now. - It changes across countries. - It changes across countries, it changes across regions. So, what does this mean when our AI has agency? How do we make ethical AI if we don't even know how to manage the change of what's right and what's wrong in human society? - One of the most important questions we have to worry about, right? - Absolutely. - But it also says one more thing, just before we go on. It also says that the issue of economies of scale, in the cloud. - Yes. - Are going to be strongly impacted, not just by how big you can build your data centers, but some of those regulatory issues that are going to influence strongly what constitutes good experience, good law, good acting on my behalf, agency. - And one thing that's underappreciated in the marketplace right now is the impact of data sovereignty, if you get back to data, countries are now recognizing the importance of managing that data, and they're implementing data sovereignty rules. Everyone talks about California issuing a new law that's aligned with GDPR, and you know what that meant. There are 30 other states in the United States alone that are modifying their laws to address this issue. - Steve. - So, um, so, we got a number of years, no matter what Ray Kurzweil says, until we get to artificial general intelligence. - The singularity's not so near? (laughing) - You know that he's changed the date over the last 10 years. - I did know it. - Quite a bit. And I don't even prognosticate where it's going to be. But really, where we're at right now, I keep coming back to, is that's why augmented intelligence is really going to be the new rage, humans working with machines. One of the hot topics, and the reason I chose to speak about it is, is the future of work. I don't care if you're a millennial, mid-career, or a baby boomer, people are paranoid. As machines get smarter, if your job is routine cognitive, yes, you have a higher propensity to be automated. So, this really shifts a number of things. A, you have to be a lifelong learner, you've got to learn new skillsets. And the dynamics are changing fast. Now, this is also a great equalizer for emerging startups, and even in SMBs. As the AI improves, they can become more nimble. So back to your point regarding colossal trillion dollar, wait a second, there's going to be quite a sea change going on right now, and regarding demographics, in 2020, millennials take over as the majority of the workforce, by 2025 it's 75%. - Great news. (laughing) - As a baby boomer, I try my damnedest to stay relevant. - Yeah, surround yourself with millennials is the takeaway there. - Or retire. (laughs) - Not yet. - One thing I think, this goes back to what Karen was saying, if you want a basic standard to put around the stuff, look at the old ISO 38500 framework. Business strategy, technology strategy. You have risk, compliance, change management, operations, and most importantly, the balance sheet in the financials. AI and what Tony was saying, digital transformation, if it's of meaning, it belongs on a balance sheet, and should factor into how you value your company. All the cyber security, and all of the compliance, and all of the regulation, is all stuff, this framework exists, so look it up, and every time you start some kind of new machine learning project, or data sense project, say, have we checked the box on each of these standards that's within this machine? And if you haven't, maybe slow down and do your homework. - To see a day when data is going to be valued on the balance sheet. - It is. - It's already valued as part of the current, but it's good will. - Certainly market value, as we were just talking about. - Well, we're talking about all of the companies that have opted in, right. There's tens of thousands of small businesses just in this region alone that are opt-out. They're small family businesses, or businesses that really aren't even technology-aware. But data's being collected about them, it's being on Yelp, they're being rated, they're being reviewed, the success to their business is out of their hands. And I think what's really going to be interesting is, you look at the big data, you look at AI, you look at things like that, blockchain may even be a potential for some of that, because of mutability, but it's when all of those businesses, when the technology becomes a cost, it's cost-prohibitive now, for a lot of them, or they just don't want to do it, and they're proudly opt-out. In fact, we talked about that last night at dinner. But when they opt-in, the company that can do that, and can reach out to them in a way that is economically feasible, and bring them back in, where they control their data, where they control their information, and they do it in such a way where it helps them build their business, and it may be a generational business that's been passed on. Those kind of things are going to make a big impact, not only on the cloud, but the data being stored in the cloud, the AI, the applications that you talked about earlier, we talked about that. And that's where this bias, and some of these other things are going to have a tremendous impact if they're not dealt with now, at least ethically. - Well, I feel like we just got started, we're out of time. Time for a couple more comments, and then officially we have to wrap up. - Yeah, I had one thing to say, I mean, really, Henry Ford, and the creation of the automobile, back in the early 1900s, changed everything, because now we're no longer stuck in the country, we can get away from our parents, we can date without grandma and grandpa setting on the porch with us. (laughing) We can take long trips, so now we're looked at, we've sprawled out, we're not all living in the country anymore, and it changed America. So, AI has that same capabilities, it will automate mundane routine tasks that nobody wanted to do anyway. So, a lot of that will change things, but it's not going to be any different than the way things changed in the early 1900s. - It's like you were saying, constant reinvention. - I think that's a great point, let me make one observation on that. Every period of significant industrial change was preceded by the formation, a period of formation of new assets that nobody knew what to do with. Whether it was, what do we do, you know, industrial manufacturing, it was row houses with long shafts tied to an engine that was coal-fired, and drove a bunch of looms. Same thing, railroads, large factories for Henry Ford, before he figured out how to do an information-based notion of mass production. This is the period of asset formation for the next generation of social structures. - Those ship-makers are going to be all over these cars, I mean, you're going to have augmented reality right there, on your windshield. - Karen, bring it home. Give us the drop-the-mic moment. (laughing) - No pressure. - Your AV guys are not happy with that. So, I think the, it all comes down to, it's a people problem, a challenge, let's say that. The whole AI ML thing, people, it's a legal compliance thing. Enterprises are going to struggle with trying to meet five billion different types of compliance rules around data and its uses, about enforcement, because ROI is going to make risk of incarceration as well as return on investment, and we'll have to manage both of those. I think businesses are struggling with a lot of this complexity, and you just opened a whole bunch of questions that we didn't really have solid, "Oh, you can fix it by doing this." So, it's important that we think of this new world of data focus, data-driven, everything like that, is that the entire IT and business community needs to realize that focusing on data means we have to change how we do things and how we think about it, but we also have some of the same old challenges there. - Well, I have a feeling we're going to be talking about this for quite some time. What a great way to wrap up CUBE NYC here, our third day of activities down here at 37 Pillars, or Mercantile 37. Thank you all so much for joining us today. - Thank you. - Really, wonderful insights, really appreciate it, now, all this content is going to be available on theCUBE.net. We are exposing our video cloud, and our video search engine, so you'll be able to search our entire corpus of data. I can't wait to start searching and clipping up this session. Again, thank you so much, and thank you for watching. We'll see you next time.

>> Live from New York, it's theCUBE covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. (techy music) >> Hello, everyone, welcome to this CUBE special presentation here in New York City for CUBENYC. I'm John Furrier with Dave Vellante. This is our ninth year covering the big data industry, starting with Hadoop World and evolved over the years. This is our ninth year, Dave. We've been covering Hadoop World, Hadoop Summit, Strata Conference, Strata Hadoop. Now it's called Strata Data, I don't know what Strata O'Reilly's going to call it next. As you all know, theCUBE has been present for the creation at the Hadoop big data ecosystem. We're here for our ninth year, certainly a lot's changed. AI's the center of the conversation, and certainly we've seen some horses come in, some haven't come in, and trends have emerged, some gone away, your thoughts. Nine years covering big data. >> Well, John, I remember fondly, vividly, the call that I got. I was in Dallas at a storage networking world show and you called and said, "Hey, we're doing "Hadoop World, get over there," and of course, Hadoop, big data, was the new, hot thing. I told everybody, "I'm leaving." Most of the people said, "What's Hadoop?" Right, so we came, we started covering, it was people like Jeff Hammerbacher, Amr Awadallah, Doug Cutting, who invented Hadoop, Mike Olson, you know, head of Cloudera at the time, and people like Abi Mehda, who at the time was at B of A, and some of the things we learned then that were profound-- >> Yeah. >> As much as Hadoop is sort of on the back burner now and people really aren't talking about it, some of the things that are profound about Hadoop, really, were the idea, the notion of bringing five megabytes of code to a petabyte of data, for example, or the notion of no schema on write. You know, put it into the database and then figure it out. >> Unstructured data. >> Right. >> Object storage. >> And so, that created a state of innovation, of funding. We were talking last night about, you know, many, many years ago at this event this time of the year, concurrent with Strata you would have VCs all over the place. There really aren't a lot of VCs here this year, not a lot of VC parties-- >> Mm-hm. >> As there used to be, so that somewhat waned, but some of the things that we talked about back then, we said that big money and big data is going to be made by the practitioners, not by the vendors, and that's proved true. I mean... >> Yeah. >> The big three Hadoop distro vendors, Cloudera, Hortonworks, and MapR, you know, Cloudera's $2.5 billion valuation, you know, not bad, but it's not a $30, $40 billion value company. The other thing we said is there will be no Red Hat of big data. You said, "Well, the only Red Hat of big data might be "Red Hat," and so, (chuckles) that's basically proved true. >> Yeah. >> And so, I think if we look back we always talked about Hadoop and big data being a reduction, the ROI was a reduction on investment. >> Yeah. >> It was a way to have a cheaper data warehouse, and that's essentially-- Well, what did we get right and wrong? I mean, let's look at some of the trends. I mean, first of all, I think we got pretty much everything right, as you know. We tend to make the calls pretty accurately with theCUBE. Got a lot of data, we look, we have the analytics in our own system, plus we have the research team digging in, so you know, we pretty much get, do a good job. I think one thing that we predicted was that Hadoop certainly would change the game, and that did. We also predicted that there wouldn't be a Red Hat for Hadoop, that was a production. The other prediction was is that we said Hadoop won't kill data warehouses, it didn't, and then data lakes came along. You know my position on data lakes. >> Yeah. >> I've always hated the term. I always liked data ocean because I think it was much more fluidity of the data, so I think we got that one right and data lakes still doesn't look like it's going to be panning out well. I mean, most people that deploy data lakes, it's really either not a core thing or as part of something else and it's turning into a data swamp, so I think the data lake piece is not panning out the way it, people thought it would be. I think one thing we did get right, also, is that data would be the center of the value proposition, and it continues and remains to be, and I think we're seeing that now, and we said data's the development kit back in 2010 when we said data's going to be part of programming. >> Some of the other things, our early data, and we went out and we talked to a lot of practitioners who are the, it was hard to find in the early days. They were just a select few, I mean, other than inside of Google and Yahoo! But what they told us is that things like SQL and the enterprise data warehouse were key components on their big data strategy, so to your point, you know, it wasn't going to kill the EDW, but it was going to surround it. The other thing we called was cloud. Four years ago our data showed clearly that much of this work, the modeling, the big data wrangling, et cetera, was being done in the cloud, and Cloudera, Hortonworks, and MapR, none of them at the time really had a cloud strategy. Today that's all they're talking about is cloud and hybrid cloud. >> Well, it's interesting, I think it was like four years ago, I think, Dave, when we actually were riffing on the notion of, you know, Cloudera's name. It's called Cloudera, you know. If you spell it out, in Cloudera we're in a cloud era, and I think we were very aggressive at that point. I think Amr Awadallah even made a comment on Twitter. He was like, "I don't understand "where you guys are coming from." We were actually saying at the time that Cloudera should actually leverage more cloud at that time, and they didn't. They stayed on their IPO track and they had to because they had everything betted on Impala and this data model that they had and being the business model, and then they went public, but I think clearly cloud is now part of Cloudera's story, and I think that's a good call, and it's not too late for them. It never was too late, but you know, Cloudera has executed. I mean, if you look at what's happened with Cloudera, they were the only game in town. When we started theCUBE we were in their office, as most people know in this industry, that we were there with Cloudera when they had like 17 employees. I thought Cloudera was going to run the table, but then what happened was Hortonworks came out of the Yahoo! That, I think, changed the game and I think in that competitive battle between Hortonworks and Cloudera, in my opinion, changed the industry, because if Hortonworks did not come out of Yahoo! Cloudera would've had an uncontested run. I think the landscape of the ecosystem would look completely different had Hortonworks not competed, because you think about, Dave, they had that competitive battle for years. The Hortonworks-Cloudera battle, and I think it changed the industry. I think it couldn't been a different outcome. If Hortonworks wasn't there, I think Cloudera probably would've taken Hadoop and making it so much more, and I think they wouldn't gotten more done. >> Yeah, and I think the other point we have to make here is complexity really hurt the Hadoop ecosystem, and it was just bespoke, new projects coming out all the time, and you had Cloudera, Hortonworks, and maybe to a lesser extent MapR, doing a lot of the heavy lifting, particularly, you know, Hortonworks and Cloudera. They had to invest a lot of their R&D in making these systems work and integrating them, and you know, complexity just really broke the back of the Hadoop ecosystem, and so then Spark came in, everybody said, "Oh, Spark's going to basically replace Hadoop." You know, yes and no, the people who got Hadoop right, you know, embraced it and they still use it. Spark definitely simplified things, but now the conversation has turned to AI, John. So, I got to ask you, I'm going to use your line on you in kind of the ask-me-anything segment here. AI, is it same wine, new bottle, or is it really substantively different in your opinion? >> I think it's substantively different. I don't think it's the same wine in a new bottle. I'll tell you... Well, it's kind of, it's like the bad wine... (laughs) Is going to be kind of blended in with the good wine, which is now AI. If you look at this industry, the big data industry, if you look at what O'Reilly did with this conference. I think O'Reilly really has not done a good job with the conference of big data. I think they blew it, I think that they made it a, you know, monetization, closed system when the big data business could've been all about AI in a much deeper way. I think AI is subordinate to cloud, and you mentioned cloud earlier. If you look at all the action within the AI segment, Diane Greene talking about it at Google Next, Amazon, AI is a software layer substrate that will be underpinned by the cloud. Cloud will drive more action, you need more compute, that drives more data, more data drives the machine learning, machine learning drives the AI, so I think AI is always going to be dependent upon cloud ends or some sort of high compute resource base, and all the cloud analytics are feeding into these AI models, so I think cloud takes over AI, no doubt, and I think this whole ecosystem of big data gets subsumed under either an AWS, VMworld, Google, and Microsoft Cloud show, and then also I think specialization around data science is going to go off on its own. So, I think you're going to see the breakup of the big data industry as we know it today. Strata Hadoop, Strata Data Conference, that thing's going to crumble into multiple, fractured ecosystems. >> It's already starting to be forked. I think the other thing I want to say about Hadoop is that it actually brought such great awareness to the notion of data, putting data at the core of your company, data and data value, the ability to understand how data at least contributes to the monetization of your company. AI would not be possible without the data. Right, and we've talked about this before. You call it the innovation sandwich. The innovation sandwich, last decade, last three decades, has been Moore's law. The innovation sandwich going forward is data, machine intelligence applied to that data, and cloud for scale, and that's the sandwich of innovation over the next 10 to 20 years. >> Yeah, and I think data is everywhere, so this idea of being a categorical industry segment is a little bit off, I mean, although I know data warehouse is kind of its own category and you're seeing that, but I don't think it's like a Magic Quadrant anymore. Every quadrant has data. >> Mm-hm. >> So, I think data's fundamental, and I think that's why it's going to become a layer within a control plane of either cloud or some other system, I think. I think that's pretty clear, there's no, like, one. You can't buy big data, you can't buy AI. I think you can have AI, you know, things like TensorFlow, but it's going to be a completely... Every layer of the stack is going to be impacted by AI and data. >> And I think the big players are going to infuse their applications and their databases with machine intelligence. You're going to see this, you're certainly, you know, seeing it with IBM, the sort of Watson heavy lift. Clearly Google, Amazon, you know, Facebook, Alibaba, and Microsoft, they're infusing AI throughout their entire set of cloud services and applications and infrastructure, and I think that's good news for the practitioners. People aren't... Most companies aren't going to build their own AI, they're going to buy AI, and that's how they close the gap between the sort of data haves and the data have-nots, and again, I want to emphasize that the fundamental difference, to me anyway, is having data at the core. If you look at the top five companies in terms of market value, US companies, Facebook maybe not so much anymore because of the fake news, though Facebook will be back with it's two billion users, but Apple, Google, Facebook, Amazon, who am I... And Microsoft, those five have put data at the core and they're the most valuable companies in the stock market from a market cap standpoint, why? Because it's a recognition that that intangible value of the data is actually quite valuable, and even though banks and financial institutions are data companies, their data lives in silos. So, these five have put data at the center, surrounded it with human expertise, as opposed to having humans at the center and having data all over the place. So, how do they, how do these companies close the gap? How do the companies in the flyover states close the gap? The way they close the gap, in my view, is they buy technologies that have AI infused in it, and I think the last thing I'll say is I see cloud as the substrate, and AI, and blockchain and other services, as the automation layer on top of it. I think that's going to be the big tailwind for innovation over the next decade. >> Yeah, and obviously the theme of machine learning drives a lot of the conversations here, and that's essentially never going to go away. Machine learning is the core of AI, and I would argue that AI truly doesn't even exist yet. It's machine learning really driving the value, but to put a validation on the fact that cloud is going to be driving AI business is some of the terms in popular conversations we're hearing here in New York around this event and topic, CUBENYC and Strata Conference, is you're hearing Kubernetes and blockchain, and you know, these automation, AI operation kind of conversations. That's an IT conversation, (chuckles) so you know, that's interesting. You've got IT, really, with storage. You've got to store the data, so you can't not talk about workloads and how the data moves with workloads, so you're starting to see data and workloads kind of be tossed in the same conversation, that's a cloud conversation. That is all about multi-cloud. That's why you're seeing Kubernetes, a term I never thought I would be saying at a big data show, but Kubernetes is going to be key for moving workloads around, of which there's data involved. (chuckles) Instrumenting the workloads, data inside the workloads, data driving data. This is where AI and machine learning's going to play, so again, cloud subsumes AI, that's the story, and I think that's going to be the big trend. >> Well, and I think you're right, now. I mean, that's why you're hearing the messaging of hybrid cloud and from the big distro vendors, and the other thing is you're hearing from a lot of the no-SQL database guys, they're bringing ACID compliance, they're bringing enterprise-grade capability, so you're seeing the world is hybrid. You're seeing those two worlds come together, so... >> Their worlds, it's getting leveled in the playing field out there. It's all about enterprise, B2B, AI, cloud, and data. That's theCUBE bringing you the data here. New York City, CUBENYC, that's the hashtag. Stay with us for more coverage live in New York after this short break. (techy music)

For Las Vegas, it's the cube covering vm world 2018, brought to you by vm ware and its ecosystem partners. Hello everyone. Welcome back to the cubes exclusive coverage live coverage here in Las Vegas for Vm world 2018. I'm John furrier with Dave Volante cohost of the cube. Our next guest ed walls. Cube alumni is also the general manager of storage and software defined infrastructure for IBM. Great to see you. Yeah, likewise. So we just were talking to Michael Dell earlier and again, the open ecosystem of vm world again, continues to be a little bit more better vibes this year. It seems to be an uplift around, hey, on premise as is working now, it seems to have some visibility. It's not gloom and doom for all this data center, but certainly some work going on. You guys were out front and software defined infrastructure and storage from so leader. Yep. What's the update? What's going on? Well, I think the, uh, the upbeat here that we're seeing from vm world is the same. We're seeing across the industry there You don't wear a boom happening and being data driven, is a, almost a renaissance. which plays a storage heavily. You do have to have right infrastructure to pull this off either on premises or in a hybrid or a multicloud at ECC. I like Lvm, we're kind of clear, you know the nomenclature around hybrid is homogeneous across the cloud and multicloud is all different types, which we see both so I think it's helping our business. You're seeing us grow since I've been You're seeing a large explosion and what we're doing on flash mostly because the here a growing pretty consistently above market. performance does matter. We did a major new and non. So you've seen our, our innovation pipelines alive and really kicking in and it really plays what clients are trying to really be more data driven of the drivers of your growth suddenly successes. What are the key drivers? Yeah, so I think it has to come down to offerings if you don't have the right offerings to help people to get. We use the term, you know, during the cloud, but it's really turning more of a journey to being data driven and it has to be in a cloud context and that is all about having the right offerings. We do focus in different areas for innovation, what we're doing, our flash, and it's not just flashed for individual raise, but where that's going as far as how you're going to access the data, you know, it's very different and modernizing the cognitive infrastructure or what you're going to do in these new cloud apps, data center compared to what you can do on ai infrastructure, but they need different things. But it's all about being more data driven, being more agile, but also really taking the complexity down. So, and I think when we're doing the exact same thing, all the announcements this week, we're all about making it easier to be data driven regardless of where you're at, which is what we're seeing and security to continues to pound this at the core, which is you're going to see the same thing from ibm. We're going to talk about technology innovation. We talked about the industry context, but it has to be the trust and security at the core and that's where I think that's why ibm has been doing so well. You know, it's interesting when I've been in this business a long time, you've been in the storage business awhile. Me, me a little longer than you guys, but John and I were talking about this the other day, the stores, everybody's been saying infrastructure is dead. I've been hearing that for 35 years. Tommy Rosen believe infrastructure matters, but it's consistently a growth market and a 60 percent plus gross margin product. Now you've got so many people don't understand that. I hear people that well, how come the VC's are putting so much money into storage? Backup is now data particular booming. Yep. Lot of companies say, well, we're going to divest of this or that or get out. You know, you've seen some companies say, oh well you're going to go here. It's just a consistent performer. Your thoughts. So I agree and and decided just storage, but I would say in most stores plays into that, right? It's all about being data driven and eventually it has to land someplace for performance, but it's the agility piece is that you're seeing us in the other industry. Leaders really drive that you can You mentioned backup, you know, actually leverage this data in different ways. so we'll talk about storage rays need to be flexible and fast, but backup ends up really turning into an interesting segment which you're seeing us lead in is how do you get more effective? All those copies of data instead of being some, you know, insurance policy, you hope you never get to and it might take you a long time to get the data back. Now these instantaneous copies for recovery, but also for how do you leverage that for being data driven. So all of a sudden if you're trying to be doing digital transformation, you have to deal with the infrastructure on prem or in the cloud. You're looking for ways to do that easier. We use it on a journey to cloud. It seems more and more it's a journey to being data driven, but it has to be in cloud context, but it plays in a storage or ugly it. You need the right infrastructure to build on to be honest. Sure. Yeah. The data driven thing and you're pounding that Mr Hardy, which I love by the way, it's not new for you guys, but one of the things we've been talking about from day one, you know our wrap data's at the center of the value proposition, but we were talking early on years ago about data being a real part of the development process now apps, so one of things gelsinger kind of talked about apps are now the new networks or something around networking networks and they also sent security. NSX has putting security of around the apps. Decoupling that from say network security. She's starting to see data in the APP component. Highly coupled with the application, new models around how that's stored and retrieved. Yep. Service Meshes and things going on around the application. How has that changed or have you been vectoring to that place where IBM. So one of the investment themes is how to do that and there's a couple different reasons. How do you become more data driven, so all that data, how do you use it to get better insights, right? Uh, and then all the data is typically on, you know, in your infrastructure, a lot of this on prem or in the cloud. So one is getting more insights, right? So you need to flexibly go after those copies. And then, uh, the other thing is how to use it to do better APP development. So you want to do Dev ops, which is how do you just get better quality, faster delivery for a pipeline. But a lot of times if you can't bring the data, bringing, like even if you do a test of a mobile app, you need to test it with the production data sets. So how do you do that? Flexibility and data is a hardest part of all this stuff. So we're doing on ai, how do you get application driven to go after it and the right performance. But we see Dev ops, you can see across our entire product line an API layer, yes, we have apis across all of our storage for our products and software, but a separate API layer to allow you to do a lot of these things with, not to replace any devops tools put to enable those devops tools or Coobernetti's or whatever orchestration engine to drive the flexibility of your will. I'll say mission critical, either primary storage or secondary storage cause. So programmability is critical through the apis infrastructure's code. If you don't have the right API layer and we play grandma will. That's exactly okay. And it's in place across our entire portfolio today. So you don't have to have an API for this device or that device or that backup? No, it's an API layer that covers them all. Voices you guys perfectly for the growth of containers, Coobernetti's and service measures. So as Dev ops starts to move up the stack, you need to have an under the hood programmable model and that is the software defined. So you guys are saying you have that today. Okay. So let's go. It was a big. We had major investments to get there and also do it in such a way that it's a consistent api layer that is abstracted away from the infrastructure you never want to give in general, you don't want get developers access to lower end technology and your system of record data, but you do need to give them access and have the rights, you know, security rounded access control. But it has to be api driven because sometimes there's not a human. It's, it's Dev ops. Okay, so let's stay on that for a secretary we made because I had. You're known as a business whiz kid. Oh, okay. But, but people don't realize how technical you are, which probably drives a lot of the people that work for you crazy. But when you think about the ascendancy of virtualization, it changed the way in which, for example, you had to do data protection and then one of your old companies, you know, you're popularize a source side district location, which made a lot of sense because you were taking 10 servers down to one. Right? As you go into this world of cloud and multicloud. Yep. You were just touching upon architecturally some of the things you What are the key components sort of architecturally that you've been driving have to do with microservices, etc. in your r and d pipeline, which we've noticed over the last two years that you've accelerated at ibm? So we talked about this data driven multi cloud architecture and the only reason for it, it's not a how do you go across a very broad portfolio that ibm storage has and how do you have a way to say we're going to be able to give you a modern, agile and flexible infrastructure that allows you to participate in modernizing your existing environment or allow you to ai, you know, lilly industry leading type, the ai technologies or these new apps. But hoW do you give clients a way to say this portfolio allows you to do that across your entire portfolio? So one is this api layers. You need to not only have rich apis around the storage self, but a lot of times you don't want to give those to developers or other people. So we need a separate api layer, which we put across both our primary storage and secondary storage. That was one that gives you the agility to do almost anything and it, and it doesn't compete with all these orchestration or dev ops tools. It enables them, it's the last mile if you would a second thing, you need to be software defined that gives you a way to api but also literally be able to move things, a flexibility but also investment protection and then you need some core innovation, right? So we still make it a lot of hardware, so we're making flash technologies that keep the low latency at workload with encryption on the card, at line speed with ddup, etc. All the things on the card. So you'll see us innovating on the technology side, but it's also having the agility and a and a flexibility. So you'll see that as a theme and we can have it in. We see clients adopting it in three areas. It's either modernizing traditional, I would say this is all about modernizing nutritional application more agile mode, private cloud or multi cloud. infrastructure and how to make it faster, The other thing is ai in it. It is huge right now. Getting insights right? So how did you machine learning deep learning, true ai on prem or in the cloud and this different technologies to get that insight. So you saw what we did. The largest ai supercomputer in the world was designed to use 100 percent ibm storage, actually ibm systems with a power, a power ai environments. That's a great point about this community is really an it footprint kind of app and its operation. So ai maybe ai ops but they're not. ai is not a core competency like tensor flow. So they need simplicity tools to do that easier. Right. So and I think that's what vm ware is doing with a lot of their announcements, just making it easier to deploy, but that's where ibm's been doing. Driving that pretty aggressively with our software side, our cloud side, but also what we're doing in infrastructure itself aliens and I was saying I think ai is not core comps in this community. essentially in this ai. So we just had an argument, not argument and discussion on the cube yesterday with jim colby, And he had brought up a good point about ai operations. Ai's going to automate a lot of things, but I was saying that's under the hood is a general purpose. Ai tensor flows, all these cools that developers want. Those are the guys who want programmable infrastructure. The guys who want ai ops is going to be Infrastructure guys. So really both are very important. under the hood, making things work faster. A very important. But there's different personas at the mechanic fix the engine. We do you get your ai, is it, is it sort of homegrown and your where's it come from? division is a little bit from the watson group, So, uh, we like our clusters. So we have obviously watson, which is very high end of what we do on a cognitive and since you're very deep learning, but we'll use all the open source tools. different than normal ai or machine learning, So what you're seeing, what we're doing in our, um, we did the latest product launch our flashlights from 9,100, which is granted hardware, innovation and vme through and through. We talk about ai infused, so it allows you to have better service and support. So what are we using machine learning, you know, we say deep learning of all the coal home data allows you to do a lot of analysis. Yes. It's an ibm's cloud. Yes. We use some of the things that we're using in watson, but it's all the tools you also see in our power ai for systems or what we're doing. Icp, ibm will go all the way to say here's all the open source tools you want to but you're wrestling with all the open source will help you put together a tool use mr. Customer, without limiting it, but allows you to kind of move forward Let's move forward to. You'd be data driven, and said, are wrestling with that technology. will make it easier to deploy. So you see that in our storage but in systems and really kind of an ibm theme if you would. So cloud, native compute foundation, I already asked you a question on the customer impact and you know, we cover the linux foundation heavily, cncf, you know, doing all this coobernetti's so it's been great being there from the beginning. So, but a lot of, I've noticed a pattern, there's a lot of talking about new players seeing their software defined infrastructure that would find storage. you mentioned earlier here that you guys made a significant investment. So the question is how do customers, potential buyers of this journey of going digital data, data driven, how do they determine the people who are saying it and actually doing it? What's required? I mean because that's ultimately the trying to squint through the noise as dave says, what, who's got what? So you've got multiple years doing this. If someone says hey, I got them to solidify storage. I just launched last week. Yeah, they might not have the trajectory. So how do customers test? Who's got the real deal? It's actually a real. You can. I'm looking at the floor across the way. So you have to get past the hype, right? So, so this is where I like being ibm, so why didn't I do the ceo the next gig or why did they come? and I thInk I think storage or infrastructure is a, I use the term Big boy, big girl gaMe. I think it's more than just building the next era is how to bring all this together and make it easier for clients, deliberate in the way they want. So I think the will see a lot of these point products people come up and say, I am the leader of this or a leader at that. And you saw a couple of stores guy say I'm the best ai, look the my benchmark within video, right? Um, but then you'll look at like an ibm, maybe not aS aggressive and the marketing of the infrastructure, but we're building the largest, you know, super cues, ai in the world. And we can take the exact same technology and give you a quarter rack or half rack or full rack of the exact same technology knowing you can scale it. So sometimes you have to say is, you know, it's not just a, the point, hey, um, you kind of get dig under the, I would say the, uh, culture of question you could customer what's the investment you've made? I mean, going ask like how many years you've been doing it, how does the customer and truly know someone's really going to be software defined and positioned for that data driven integration, that holistic package. Is there a, well, why don't we be, you know, so software defined, you know, the easy thing is, and this gets a little technical, but just, okay, show me your proposal and tell me how I can run the exact same infrastructure in a cloud of my choice. And you know, either on prem or not or on software is that software to find. And there's a lot of [inaudible] then Now that is self is not maybe the biggest thing, you know, that not suffered a fine. but who can you partner with, um, that can help you. Not only, okay, I'm going to get your next array. That's kind of a tactical, but who can I partner with to kind of go forward and you can't partner with 20 different firms. Who can you partner with to kind of get you from where you are to kind of where you want to get to. So I guess my questions are, I would start asking him how can you help me in a broader scope? And that's where ibm does well. The cloud is a good one. Can you run the storage on premise and on any cloud seamlessly route? What we did in the last launches flashes now you 100. We also say it's multi cloud enabled and what we did is we put all the software in the bundle. We have these validated designs that show you how to exactly do all the major use cases in any cloud and allow you to actually show you test environments. You do this cookbook at works but also the software go try it. But it's kinda how do you make it real compared to put a bunch of clouds and a configuration. But then it's the use cases. So we do a lot with the leaders and laggards, I mean we're with our leaders, but then we have some people that are really struggling to keep up and some of it is just showing the way forward and it's a, you gotta have a broad enough portfolio Otherwise you end up with a point product here and that point park there to get them over time, the right location. and I think that's where no one has enough team to keep up. But you also look at the developers and our developers abstracted away and does the storage to the performance and versatile for developers without even touching it back to your infrastructure as code comment. Oh well now the next one. So can I run, you know, you know, tell me whatever technology you ansible, if you're gonna run a whatever a workflow you got to use for your pipeline, will it work for the api? Should write without opening up the whole storage api. You can, you can drive the car, not even look at the engine. That's the key storage. Right? And it has to be that way. And then can I get all the key abilities in the same function? So it's a good question. I'll think about it more. well, I didn't mean to put you on the spot there. It's just good. It's good. Question number one question we get from people is how do you tell the pretenders when the players, that's ultimately what customers are. And they do bake offs when you see bake off. We had to, we had an auto nation earlier. Listen less, right? But what's the new bakeoff just run code on it. Right? So the world's changing and thanks for. Come on real quick. What's your thoughts on vm world 2018 this year? What's the vibe that you're feeling? What's the overall sentiment? What's your view? I think it's overall very positive. You mentioned early on it feels like an uplifting. I think you're seeing that across infrastructure and infrastructure does matter. I think uh, you know, there's big data economy, you, you hear a boom and it's going pretty well. So I think it's exciting and I think you feel among the ecosystem, any predictions for next year? What we'll see because we like to roll the predictions from next year, predictions for 2018 next year at vm world. What? What's going to happen? Oh geez. I think more of the same. I think you're gonna see even more and more how to be data driven. Yeah. The clouds have given during the cloud, but how to be more data driven and we're going to make it even easier and easier for our clients to do that and the ecosystems driving that and I think you can see more and more of that. Here he goes. This is robin. Thanks for coming on this cubes. Coverage here live in las vegas. I'm john furrier. dave. A lot. They stayed with us. We're on only day two. We've got three days of wall to wall coverage, two sets, tons of interviews, a lot of great people, a lot of great content. A lot of data we've got that data was sharing with you here. We're data driven on the cube. Stay with us. We'll be back after this short break. Okay.