(upbeat music) >> Hello friends and welcome back to theCUBE's Live coverage of AWS re:Invent 2022 from the Venetian Expo in Vegas, baby. This show is absolutely packed. Lisa Martin with Dave Vellante, Dave this is day two, but really full day one of our wall to wall coverage on theCUBE. We've had great conversations the last half day this morning already, we've been talking with a lot of companies, a lot of Amazonians and some Amazonians that have left and gone on to interesting more things, which is what we're going to talk about next. >> Well, I'm excited about this segment because it's a really interesting space. You've got a search company who's gotten into observability and security and through our ETR partner our research, we do quarterly research and Elastic off the charts. Obviously they're the public company, so you can see how well they're doing. But the spending momentum on this platform is very, very strong and it has been consistently for quite some time. So really excited to learn more. >> The voice of the customer speaking loudly, from Elastic, its Chief Product Officer joins us, Ken Exner. Ken, welcome to the program. Hi, thank you, good to be here. >> Dave Vellante: Hey Ken. >> So a lot of us know about Elastic from Elastic Search but it's so much more than that these days. Talk about Elastic, what's going on now? What's the current product strategy? What's your vision? >> Yeah. So people know Elastic from the ELK Stack, you know Elastic Search, Logstash, Kibana. Very, very popular open source projects. They've been used by millions of developers for years and years. But one of the things that we started noticing over the years is that people were using it for all kinds of different use cases beyond just traditional search. So people started using Elastic Search to search through operational data, search through logs, search through all kinds of other types of data just to find different answers. And what we started realizing is the customers were taking us into different spaces. They took us into log analytics they started building log management solutions. And we said, cool, we can actually help these customers by providing solutions that already do this for them. So it took us into observability, they took us into security, and we started building solutions for security and observability based on what customers were starting to do with the platform. So customers can still use the platform for any number of different use cases for how do you get answers added data or they can use our pre-built packaged solutions for observability and security. >> So you were a longtime Amazonian. >> I was. I was. >> Talk a little bit about some of the things that you did there and what attracted you to Elastic? 'Cause it's only been a couple months, right? >> I've been here three months, I think three months as of yesterday. And I was at AWS for 16 years. So I was there a long, long time. I was there pretty much from the beginning. I was hired as one of the first product managers in AWS. Adam Selipsky hired me. And it was a great run. I had a ton of fun, I learned a lot. But you know, after 16 years I was kind of itching to do something new and it was going to take something special because I had a great gig and enjoyed the team at AWS. But I saw in Elastic sort of a great foundational technology they had a lot of momentum, a huge community behind it. I saw the business opportunity where they were going. I saw, you know the business opportunity of observability and security. These are massive industries with tons of business problems. Customers are excited about trying to get more answers out of data about their operational environment. And I saw, you know, that customers were struggling with their operating environments and things were becoming increasingly complicated. We used to talk in AWS about, you know how customers want to move from monolithic applications to monoliths, but one of the secrets was that things were increasingly complicated. Suddenly people had all these different microservices they had all these different managed services and their operating environment got complicated became this constellation of different systems, all emitting data. So companies like Elastic were helping people find answers in that data, find the problems with their systems so helping tame that complexity. So I saw that opportunity and I said I want to jump on that. Great foundational technology, good community and building solutions that actually helped solve real problems. >> Right. >> So, before you joined you probably looked back, and said, let think about the market, what's happening in the market space. What were the big trends that you saw that sort of informed your decision? >> Well, just sort of the mountain of data that was sort of emerging. Adam Selipsky in his talk this morning began by talking about how data is just multiplying constant. And I saw this, I saw how much data businesses were drowning in. Operational data, security data. You know, if you're trying to secure your business you have all these different endpoints you have all these different devices, you have different systems that you need to monitor all tons of data. And companies like Elastic were helping companies sort of manage that complexity, helping them find answers in that. So, when you're trying to track intruders or trying to track you know, malicious activity, there's a ton of different systems you need to pay attention to. And you know, there's a bunch of data. It's different devices, laptops and phone devices and stuff that you need to pay attention to. And you find correlations across that to figure out what is going on in your network, what is going on in your business. And that was exciting to me. This is a company sort of tackling one of the hardest problems which is helping you understand your operating environment, helping you understand and secure your business. >> So everybody's getting into observability. >> Yep. >> Right, it's a very crowded space right now. First of all, you know it's like overnight it just became the hottest thing going. VCs were throwing money at it. Why was that and how were you guys different? >> Well, we began by focusing on log analytics because that was the core of what we were doing. But customers started using it beyond log analytics and started using it for APM and started using it for performance data. And what we realized is that we could do all this for customers. So we ended up, sort of overnight over the course of three years building that a complete observe observability suite. So you can do APM, you can do profiling, you can do tracing, sort of distributed tracing, you can do synthetic monitoring everything you want to do, wheel user wondering. >> Metrics? >> All of it, metrics, all of it. And you can use the same system for this. So this was sort of a powerful concept, not only is it the best in leading log system, it also provides everything you need for complete observability. And because it's based on this open platform you can extend it to a number of different scenarios. So this is important, a lot of the different observability companies provide you something that's sort of packaged and as long as you're trying to do what it wants to support, it's great. But with Elastic, you have this flexible data architecture that you can use for anything. So companies use it to monitor assembly lines, they use it to monitor dish networks, for example use it to not only manage their fleet of servers they also use it to manage all their devices. So 25 million desktop devices. So, you know, observability systems like that that can do a number of different scenarios, I think that's a powerful thing. It's not just about how do you manage your servers how do you manage the things that are simple. It's how do you manage anything? How do you get observability into anything. >> Multiple use cases. >> Sorry, when you say complete, okay you talked about all the different APM, log analytics tracing, metrics, and also end-to-end. >> Ken Exner: End-to-end, yeah. >> Could you talk about that component of complete? >> So, if you're trying to find an issue like you have some metric that goes into alarm. You want to have a metric system that has alarming. Once that metric goes in alarm you're going to want to dig into your log. So you're going to want it to take you to the area of your logs that has that issue. Once you gets to there, you're going to want to find the trace ID that takes you to your traces and looks at sort of profiling, distributed tracing information. So a system that can do all of that end-to-end is a powerful solution. So it not only helps you track things end-to-end across the different signals that you're monitoring, but it actually helps you remediate more quickly. And the other thing that Elastic does that is unique is a lot of ML in this. So not only helping you find the information but surfacing things before you even know of them. So anomaly detection for example, helps you know about something before you even realize that there was an issue. So you should pay attention to this because it's anomalous. So a lot of systems help you find something if you know what to look for. But we're trying to help you not only find the things that you know to look for, but help you find the things that you didn't even think to know about. >> And it's fair to say one of your differentiators is you're open, open source. I mean, maybe talk about the ELK stack a little bit and how that plays. >> Yeah, well, so the great thing about this is we've extended that openness to both security and to observability. An example of this on the security side is all the detection rules that you use for looking for intrusion all the detection rules are open source and there's an entire community around this. So if you wanted to create a detection rule you can publish an open source, there's a bunch in GitHub you can benefit from what the community is doing as well. So in the world of security you want to be supported by the entire community, everyone looking for the same kind of issues. And there's an entire community around Elastic that is helping support these detection rules. So that approach, you know wanting to focus on community is differentiating for us. Not just, we got you covered as long you use things from us you can use it from the entire community. >> Well there implies the name Elastic. >> Yeah >> Talk a little bit about the influence that the customer has in the product roadmap and the direction. You've talked a little bit in the beginning about customers were leading us in different directions. It sounds very Amazonian in terms of following the customers where they go. >> It does, it actually does, it was one of the things that resonated for me personally is the journey that Elastic took to observability and security was customer led. So, we started looking at what customers were doing and realized that they were taking us into log analytics they were taking us into APM, they were taking us into these different solutions, and yeah, it is an Amazonian thing, so it resonated for me personally. And they're going to continue taking us in new places. Like we love seeing all the novel things that customers do with the platform and it's sort of one of the hallmarks of a great platform is you can have all kinds of novel things that, novel use cases for how people use your platform and we'll continue to see things and we may get taken into other solutions as well as we start seeing things emerge, like common patterns. But for now we're really excited about security and observability. >> So what do you see, so security's a big space, right? >> Yep. >> You see the optiv taxonomy and it makes your eyes bleed 'cause there's so many tools in there. Where do you fit in that taxonomy? How do you see and think about the security space and the opportunity for your customers? >> Yeah, so we began with logs in the security space as well. So SIEM, which is intrusion detection is based on aggregating a bunch of logs and helping you do threat hunting on those logs. So looking for patterns of malicious behavior or intrusion. So we started there and we did both detections as well as just ad hoc threat hunting. But then we started expanding into endpoint protection. So if we were going to have agents on all these different devices they were gathering logs, what if we also started providing remediation. So if you had malicious activity that was happening on one of the servers, don't just grab the information quarantine it, isolate it. So that took us into sort of endpoint protection or XDR. And then beyond that, we recently got into cloud security as well. So similar to observability, we started with logs but expanded to a full suite so that you can do everything. You can have both endpoint protection, you can have cloud security, all of it from one solution. >> Security is a very crowded market as well. What's your superpower? >> Ken Exner: What's our super power? >> Yeah. >> I think it, a lot of it is just the openness. It's the open platform, there's the community around it. People know and love the, the Elastic Search ELK stack and use it, we go into businesses all the time and they're familiar, their security engineers are using our product for searching through logs. So they're familiar with the product already and the community behind it. So they were excited about being able to use detection rules from other businesses and stay on top of that and be part of that community. The transparency of that is important to the customers. So if you're trying to be the most secure place, the most secure business, you want to basically invest in a community that's going to support that and not be alone in that. >> Right, absolutely, so much that rides on that. Favorite customer example that you think really articulates the value of Elastic, its openness, its transparency. >> Well, there's a customer Dish Media Dish Networks that's going to present here at re:Invent tomorrow at 1:45 at Mandalay Bay. I'm excited about their example because they use it to manage, I think it's 10 billion records a day across 25 million devices. So it illustrates the scale that we can support for managing observability for a company but also just sort of the unique use cases. We can use this for set top boxes for all their customers and they can track the performance that those customers are having. It's a unique case that a lot of vendors couldn't support but we can support because of the openness of the platform, the open data architecture that we have. So I think it illustrates the scale that we support, the elasticity, but also the openness of the data platform. >> Awesome and folks can catch that tomorrow, 1:45 PM at the Mandalay Bay. Last question for you, Ken, is you have a bumper sticker. >> Ken Exner: A bumper sticker? >> A bumper sticker you're going to put it on your fancy sexy new car and it's about elastic, what does it say? >> Helping you get answers out of data. So yeah. >> Love it, love it. Brilliant. >> Ken Exner: Thank you. >> Short and sweet. Ken, it's been a pleasure. >> It's been a pleasure being here, thank you. >> Thank you so much for sharing your journey with us as an Amazonian now into Elastic what Elastic is doing from a product perspective. We will keep our eyes peeled as Dave was saying. >> Ken Exner: Fantastic. >> The data show is really strong spending momentum so well done. >> Thank you very much, good to meet you. >> Our pleasure. For our guest and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

(slow music) >> Welcome back to theCUBE's ongoing coverage of VMworld 2021, the second year in a row we've done this virtually. My name is Dave Vellante and long-time VMware technologist and new CTO Kit Colbert is here. Kit, welcome. Good to see you again. >> Thanks, Dave. Super excited to be here. >> So let's talk about your new role. You've been at VMware. You've touched all the bases so to speak (Kit chuckles) and, you know, love the career evolution. You're ready for this job. So tell us about that role. >> Well, I hope so. I don't know. It's definitely a big step up. Been here at VMware for 18 years now, which, if know Silicon Valley, you know that's a long time. It's probably like four or five normal Silicon Valley lifetime's in terms of stints at a company. But I love it. I love the company. I love the culture. I love the technology and I'm super passionate, super excited about it. And so, you know, previously I was CTO for one of our business groups and focused on a specific set of our products and services. But now, as the corporate CTO, I really am overseeing all of VMware R&D. In the sense of really trying to drive a whole bunch of core engineering transformations, right, where we've talked a lot about our shift toward becoming a SaaS company. So, you know, a cloud services company. And so there's a lot of changes we got to make internally. Technologies, platform services we need to build out, you know, the sort of culture aspects of it again. And so, you know, I'm kind of sitting at the center of that and, I'll be honest, it's big, there's a lot of stuff to go and do, but I am just super excited about it. Wake up every day, really excited to meet a whole bunch of new people across the organization and to learn all the cool things we're doing. Well, you know, I'll say it again, like the level of innovation happening inside VMware is just insane. And it's really cool now that I get kind of more of a front and center row to see everything that's happening. >> And when I was preparing for the interview with Raghu, you know, I've been following VMware for a long time, and I sort of noted that it's like the fourth, you know, wave of executive management and I sort of went back and said, okay, yes, we know it started with, you know, Workstation. Okay, fine. But then really quickly went into really changing the way in which we think about servers, and server utilization, and driving. I remember the first time I ever saw a demo, I said, "Wow, this is going to be completely game-changing." And then thought about the era of the software-defined data center, fine-tuning the cloud strategy, and then this explosion of innovation, whether it was this sort of NSX piece, the acquisitions you've made around security, again, more cloud expansion. And now you're laying out sort of this Switzerland from Multi-Cloud combined with, as you're pointing out, this as a service model. So when you think about the technical vision of the company transforming into a cloud and subscription model, what does that mean from a sort of architectural standpoint >> Yeah. >> Or a mindset perspective? >> Oh yeah. Both great questions and both sort of key focus areas for me, and by the way, it's something I've been thinking about for quite a while, right? Yeah, so you're right. Like we are on our third or fourth lap of the track depending on how you count. But I also think that this notion of getting into Multi-Cloud, of becoming a real cloud services company is going to be probably the biggest one for us. And the biggest transformation that we're going to have to make, you know, we did extend from core compute virtualization to network and storage with the software-defined data center. But now these things I think are a bit more fundamental. So, you know, how are we thinking about it? Well, we're thinking about it in a few different ways. I do think, as you mentioned, the mindset is definitely the most important thing. This notion that, you know, we no longer really have product teams purely, they should be thinking of themselves as service teams and the idea being that they are operating and accountable for the availability of their cloud service. And so this means we really needed to step up our game, and we have in terms of the types of tooling that we built, but really it's about getting these developers engaged with that, to know that, hey, like what matters most of all right now is that service availability, in addition to things like security, compliance, et cetera. But we have monitoring systems to tell you, hey, like there's a problem. And that you need to go jump on those things immediately. This is not like, you know, a normal bug that comes in, oh, I'll get to it tomorrow or whatever. It's like, no, no, you got to step up and really get there immediately. And so there is that big mindset shift and that's something we've been driving for the past few years, but we need to continue to push there. And as part of that, you know, what we've seen is that a lot of our individual teams have gone out and build like really great cloud services, but what we really want to build to enable us to accelerate that, is a platform, a true, you know, SaaS platform and leveraging all these great capabilities that we have to help all of our teams go faster. And so it gets to things like standardization and really raising the bar across the board to allow all these teams to focus on what makes their products or services unique and differentiated rather than, you know, just doing the basic blocking and tackling. So those are couple of things I'm really focused on. Both driving the mindset shift. You know, as I was taking on this role, I did a lot of reading on other CTOs and, you know, how do they view their roles within their companies? And one of the things I did hear there was that the CTO is kind of the, I don't know if the keeper is the right word, but the keeper of the engineering culture, right, that you want to really be a steward for that to help take it forward in the right sort of directions that aligned with the strategic direction of the business. And so that's a big aspect for what I'm thinking about. And the second one in the SaaS platform, one of the really interesting things about this reorg that we've done internally is, that traditionally CTO is kind of focused, you know, outbound, maybe a little bit inbound, but typically don't have large engineering organizations, but here, what we want to do, because the SaaS platform is so important to us. We did centralize it within the office of the CTO. And so now, you know, my customers, from an engineering standpoint, are all the internal business units. So a lot of really big changes inside VMware, but I think this is the sort of stuff we need to do to help us really accelerate toward the multi-cloud vision that we're painting. >> Well, VMware has always had a superstrong engineering culture, and I liked the way you phrase that, "The steward of the engineering culture," when you think about a product mindset, 'course correct me, if I'm off here, but when you're building a product and you're making that thing rock-solid, you know, Maritz used to talk about the hardened top. And so it seems to me that the services mindset expands the mind a little bit in terms of what other services can I integrate to make my service better, whether that's a machine, intelligence service, or a security service or, you know, the dozens of other services that you guys are now building, the combination of that innovation has like a step function and a lever on top of the sort of traditional product mindset. >> Yeah, I think you're absolutely right there's a ton of like really fundamental mental mindset shifts, right? That are a part of that. And the integration piece you mentioned, super critical, but I also think it's actually taking a step back and looking at the life cycle more holistically. When you're thinking about a product, you're thinking about, okay, I'ma get the bits together, I'm going to ship it out. But then it's really up to the customer to go deploy that, to operate it, to, you know, deal with problems and bugs that come up. And when you're delivering a cloud service, those are all problems that you, as the application creator, have to deal with. And so you've got to be on top of all those things. And, you know, if you design something in such a way that it becomes kind of hard to debug at runtime, well, that's going to directly impact your availability, that might have, you know, contractual obligations with an SLA impact to a customer. So there's some really big implications there that I think traditionally product teams didn't always fully think through, but now that they sort of have to with like a cloud service. The other point, I think that's really important there, is the notion of simplicity and ease of use. Experience is always important, right? Customer experience, user experience, but it gets even more magnified in a SaaS type of environment because the idea is that you shouldn't have to talk to anybody. You, as a user, should be able to go and call an API and start using this thing, right, and swipe a credit card and you're good to go. And so, you know, that sort of maniacal focus on how you just remove roadblocks, remove any unnecessary things between that customer and getting the value that they're looking for. So in general, the thing that I really love about SaaS and cloud services is that they really align incentives very well. What you want to do, as an application builder, as a solution builder, really aligns well with what customers are looking for. And you can get that feedback very, very rapidly, which allows for much quicker evolution of the underlying product and application. >> So one of the other things I learned from my interview with Raghu, and I couldn't go deep into it, I did a little bit with Sumit, but I wonder if I get your perspectives as well. I always talk about this abstraction layer across clouds, hybrid, multi-cloud, edge, abstracting, you know, the underlying complexity, and Raghu, it's nuance, but he said, "Okay, but the thing is, we're not trying to limit access to the primitives. We want to allow developers to go there to the extent." And my takeaway was okay, but the abstraction is you want to be that single management layer with access to the deep primitives and APIs of the respective clouds. But simplify, to your point, across those estates at the management layer, maybe you could add some color to that. >> Yeah, you know, it's a really interesting question. But let me tell you about how we think about it because you're right. In that, you know, the abstractions can sometimes find the underlying primitives and capabilities. And so Raghu getting at, hey, like we don't necessarily force you one way or the other. And here's the way to think about it, is that it's really about delivering optionality. And we do that through offering these abstractions at different layers. So to your point, Dave, like we have a management capabilities that can enable you to manage consistently across all types of clouds, public, private, edge, et cetera, irrespective of what that underlying infrastructure is. And so you'll look at things that are like our vRealize suite of products, or CloudHealth, or Tanzu, Tanzu Mission Control is really focused on that one as well. But then we also have our infrastructure layer. That's what we're doing with VMware Cloud. And this notion of delivering consistent infrastructure. Now, even though the core, sort of IIS layer, is more consistent, you still get great flexibility in terms of the higher-level services. If you want to use a database from one of the public clouds, or a messaging system, or streaming service, or, you know, AI, whatever it is, you still got that sort of optionality as well. And so the reason that we offer these different things is because customers are just in different places. As a matter of fact, a single customer may have all of those different use cases, right? They may have some apps where they're moving from on-prem into the cloud. They want to do that very quickly. So, boom, we can just do it really fast with VMware Cloud, consistent infrastructure. We can VMotion that thing up in the Cloud, great. But for other ones, maybe a modern app they're building, and maybe a team has chosen to use native AWS for that, but they want to leverage Kubernetes. So there you could put in a Tanzu Mission Control to give them that, you know, consistent management across sites, or leverage CloudHealth to understand costs and to really enable the application teams to manage costs on their own. So, you know, I always go back to that concept of optionality, like we offer sort of these different levels of abstraction, and it really depends on what the use case is because the reality is, especially for a complex enterprise, they're likely going to have all of those use cases. >> You know, I want to stay on optionality for a moment because you're essentially becoming a cloud company. I'm expanding the definition of cloud, which I think is appropriate 'cause the cloud is expanding. It's going on-prem, it's going out to the edge, there's hybrid connections, across clouds, et cetera. And when you look at the public cloud players, they all are deep into what I'll call data management. I'm not even sure what that term means anymore sometimes, but certainly they all own, own, databases, but they also offer databases from folks. I go back to something Maritz said with the software mainframe that we want to be able to run any workload, you know, anywhere and have high reliability, recovery, you know, lowest costs, et cetera. So you're going to run those workloads. Project Monterey is about supporting new workloads, but it doesn't seem like you have aspirations to own sort of the database layer, for example, what's your philosophy around that? >> Yeah. Not generally. I mean, we do have some solutions like Greenplum, for instance, that play in that space, more of a data warehouse solution, but generally speaking, you're absolutely right. You know, VMware success was built through tight partnerships. We have a very, very broad partner network. And of course, we see hyperscalers as great partners as well. And so, I think if we get back to like, what's the core of VMware, it really is providing those powerful abstractions in the right places, at the infrastructure level, at the management level, and so forth. But yeah, we're not trying to necessarily compete with everyone, reinvent the world. And by the way, if I just take a step back, when we talk to customers, what really drives them toward using multiple clouds is the fact that they want to get after these, what we call, best of breed cloud services, that many of the different public clouds offer databases and AI and ML systems. And for each app team, the exact one that perfectly meets their needs may be different, right? Maybe on one conference is another cloud. And so that is really the optionality that we want to optimize for when we talk to those customers. They want the easiest way of getting that app onto that cloud, so we can take advantage of that cloud service, but what they worry about is the lack of consistency there. And that goes across the board. You know, if something fails at 2:00 am, and you have to wake up and go fix it. Do you have like the right sort of tooling in place, if it's fails on one cloud versus another, do you have to like, you know, scramble to figure out which tools to go use, you know, which dashboard to look at? It's like, no, that you want kind of a consistent one. When you think about, from a security perspective, how do you drive a secure software supply chain? How do you prevent the types of attacks that we've seen in the past few years? Where people insert malicious code into your supply chain and now you're running with hack code out there. And if you have different teams doing different things across different clouds, well, that's going to just open up sort of a can of worm of different possibilities there for hackers to get in. So that's why this consistency is so important. And so, you know, I guess, if we refine the optionality a little bit, that point, it's about getting optionality around cloud services and then like those are the things that really differentiate. And so, you know, we're not trynna compete with that. We're saying, hey, like we want to bring customers to those and give them the best experience that they can, irrespective of whether that's in the public cloud, or on-prem, or even at the edge. >> And that's a huge technical challenge and amazing value for customers. I want to ask you, there's a lot of talk about ESG today. How does that fit into the CTO mindset? >> Yeah. >> Is it a bolt-on, is it a fundamental component? >> Yeah. Yeah, so ESG is talking about environment, sustainability, and governance. And so, you know, it's not an environment, excuse me, equity, (Kit chuckles) equity, sustainability, and governance. Getting my acronyms wrong, which as the technologist, really a faux pas, but any case, equity, sustainability, and governance. And the idea there is that if we look at the core values for VMware, this is something that's hugely important. And something that we've actually been focused on for quite a while. We now have a whole team focused on this, really being a force multiplier to help keep us honest across VMware, to help ensure equity, and in many different ways, that we have or continue to increase, for instance, the amount of female representation within our organization, or underrepresented minorities or communities, ensuring that, you know, pay is equal across the company. You know, these different sorts of things, but also around sustainability. They actually have a number of folks working very closely with our teams to drive sustainability into our products. You know, vSphere is great because it reduces the amount of physical servers you need. So by definition reduces the carbon footprint there. But now, you know, taking a step further. We have cloud partners that we're working with to ensure that they have net-zero carbon emissions, you know, using 100% renewables by 2030. And in fact, that's something that, we ourselves, have signed up for, you know, today we are carbon-neutral, but what we want to get to is to be net carbon zero by 2030, which is an absolutely huge lift. And that's, by the way, not just for VMware, our operations, our offices, but also for our supply chain as well. And so, you know, when you look across, you know, as well as efforts around diversity and inclusion, this is something that is very core to what we do as a company, but it's also a personal passion of mine. The ESG office actually lives within my organization. And it does that because what I view the office of the CTO as being is really a force multiplier, as I said before, like, yes, the team is located here, but their purview is across all of engineering. And in fact, all of VMware. So I think, you know, when we look at this, it's about getting the best talent we have, very diverse talent, increasing our ability to deliver innovative products, but also doing so in a way that's good for the planet, that is sustainable. And that is giving back to the community. >> You know, by the way, I don't think that was faux pas. (Kit laughs) 'Cause a lot of times, people use environmental, social, and governance, and your equity piece would fall into the S in that equation, the social responsibility, you know, components. So I think you've just done an interesting twist on the acronym. So no mistake there. (Dave chuckles) Just another way to look at it. >> Yup, yup, yup. >> So you're now deep into the CTO role. What should we look for in the, you know, coming months and years? How should we >> Hmm. >> Kind of evaluate progress? What are those sort of milestones that we should be looking at? >> Yeah, so about a month or so into the job now, and so still getting my arms wrapped around, but, you know, I'm looking at measuring success in a few different ways. First of all, as I said before, the ESG component and in diversity, equity inclusion in particular, in terms of our workforce, extraordinarily important to me and something we're going to be really pushing hard on, you know, as we all know, you know, women, underrepresented minorities, not very well represented, in general, in Silicon Valley. So something that we all need to step up on. And so we're going to be putting a lot of effort in there, and that will actually help drive, as I said before, all of these innovations, this fundamental shift in mindset, I mean, that requires diverse perspectives. It requires pushing us out of our comfort zone, but the net result of that, so that what you're going to see, is a much faster cadence of releases of innovation coming from VMware. So there's some just insanely exciting things (Kit laughs) that are happening in the labs right now that we're cooking up. But, you know, as we start making this shift, we're going to be delivering those faster and faster to our customers and our partners. >> You know, I'm interested to hear that it's a passion of yours. There was an article, I think it was last week, in "The Wall Street Journal," it was an insert section on "Women in the Workforce," and there was a stat in there, which I thought was pretty interesting. I'll run it by and you see what you think, you know, it was talking about COVID, and post COVID,and the stresses. And it's interesting to me because a lot of executives, and pfft, you know, I'm with them, said, "Hey, work from home. This a beautiful thing. It's good for business too, because, you know, everybody's more productive," but you have this perpetual workday now. It's like we never sleep. It bleeds in the weekends. And the stat from Qualtrics, which was published in the journal, I think it said, "30% of working women said that their mental health has declined since COVID." And that number was only 15% for working men, is still notable, but half. And so, you know, one has to question maybe that perpetual work week and, you know, maybe there's a benefit from business productivity, but then there's the other side of that as well. And a lot of women have left the workforce, a lot of previously working moms. And so there's an untapped labor pool there, and there's this huge labor shortage. And so these are important issues, but they're not easy ones to solve, are they? >> No, no, no. It's something we've been putting a lot of thought into at VMware. So we do have a flexible program that we're rolling out in terms of work. People can come into the office if they want to, of course, you know, where we have offices where it's safe to do so, where the government has allowed that, and people can have an actual desk there, or sometimes they can say, "Hey, I only want to come in once or twice a week." And then we say, "Okay, we'll have some floating desks that you can take." And others are saying, "I want to be fully remote." So we give people a pretty broad range in terms of how they want to address that. But I do think, to your point though, and this is something I've been really trying to do already is to create a more inclusive environment by doing a number of different things. And so it's being thoughtful around when you're sending emails. 'Cause like my sort of schedule is, I do tend to like fire off emails late at night after the kids are in bed, I get a little quiet time, some thinking time, but I make it very clear that I'm not expecting an immediate response. Don't worry about it. This is my work time. Doesn't have to be your work time. And so really setting those, I guess, boundaries, if you will, explicitly and kind of the expectations maybe is a better term, setting that explicitly, trying to schedule meetings, not at times where you're going to have to drop the kids off at school or pick them (indistinct) and to take over your life. And so we really try to emphasize boundaries and really setting those things appropriately. But honestly, it's something that we're still working on and I'm still learning. And so I'd love to get feedback from folks, but those are some of the early thinkings. But I would say that we at VMware are taking it very, very seriously and really supporting our employees in terms of navigating that work-life balance. >> Well Kit, congratulations on the new role and it's great to see you again. I hope next year we can be face-to-face, always a pleasure to have you on theCUBE. >> Thanks, Dave. Appreciated being here. >> All right, and thank you for watching theCUBE's continuous coverage of VMworld 2021, the virtual edition. Keep it right there for more right after this. (slow music)

>>As we've been reporting, the pandemic has called CSOs to really shift their spending priorities towards securing remote workers. Almost overnight. Zero trust has gone from buzzword to mandate. What's more as we wrote in our recent cybersecurity breaking analysis, not only Maseca pro secured increasingly distributed workforce, but now they have to be wary of software updates in the digital supply chain, including the very patches designed to protect them against cyber attacks. Hello everyone. And welcome to this Q conversation. My name is Dave Vellante and I'm pleased to welcome Derek manky. Who's chief security insights, and global threat alliances for four guard labs with fresh data from its global threat landscape report. Derek. Welcome. Great to see you. >>Thanks so much for, for the invitation to speak. It's always a pleasure. Multicover yeah, >>You're welcome. So first I wonder if you could explain for the audience, what is for guard labs and what's its relationship to fortunate? >>Right. So 40 grand labs is, is our global sockets, our global threat intelligence operation center. It never sleeps, and this is the beat. Um, you know, it's, it's been here since inception at port in it. So it's it's 20, 21 years in the making, since Fortinet was founded, uh, we have built this in-house, uh, so we don't go yum technology. We built everything from the ground up, including creating our own training programs for our, our analysts. We're following malware, following exploits. We even have a unique program that I created back in 2006 to ethical hacking program. And it's a zero-day research. So we try to meet the hackers, the bad guys to their game. And we of course do that responsibly to work with vendors, to close schools and create virtual patches. Um, and, but, you know, so it's, it's everything from, uh, customer protection first and foremost, to following, uh, the threat landscape and cyber. It's very important to understand who they are, what they're doing, who they're, uh, what they're targeting, what tools are they using? >>Yeah, that's great. Some serious DNA and skills in that group. And it's, it's critical because like you said, you can, you can minimize the spread of those malware very, very quickly. So what, what now you have, uh, the global threat landscape report. We're going to talk about that, but what exactly is that? >>Right? So this a global threat landscape report, it's a summary of, uh, all, all the data that we collect over a period of time. So we released this, that biannually two times a year. Um, cyber crime is changing very fast, as you can imagine. So, uh, while we do release security blogs, and, uh, what we call threat signals for breaking security events, we have a lot of other vehicles to release threat intelligence, but this threat landscape report is truly global. It looks at all of our global data. So we have over 5 million censorship worldwide in 40 guard labs, we're processing. I know it seems like a very large amount, but North of a hundred billion, uh, threat events in just one day. And we have to take the task of taking all of that data and put that onto scale for half a year and compile that into something, um, that is, uh, the, you know, that that's digestible. That's a, a very tough task, as you can imagine, so that, you know, we have to work with a huge technologies back to machine learning and artificial intelligence automation. And of course our analyst view to do that. >>Yeah. So this year, of course, there's like the every year is a battle, but this year was an extra battle. Can you explain what you saw in terms of the hacker dynamics over the past? Let's say 12 months. I know you do this twice a year, but what trends did you see evolving throughout the year and what have you seen with the way that attackers have exploited this expanded attack surface outside of corporate network? >>Yeah, it was quite interesting last year. It certainly was not normal. Like we all say, um, and that was no exception for cybersecurity. You know, if we look at cyber criminals and how they pivoted and adapted to the scrap threat landscape, cyber cyber criminals are always trying to take advantage of the weakest link of the chain. They're trying to always prey off here and ride waves of global trends and themes. We've seen this before in, uh, natural disasters as an example, you know, um, trying to do charity kind of scams and campaigns. And they're usually limited to a region where that incident happened and they usually live about two to three weeks, maybe a month at the most. And then they'll move on to the next to the next trip. That's braking, of course, because COVID is so global and dominant. Um, we saw attacks coming in from, uh, well over 40 different languages as an example, um, in regions all across the world that wasn't lasting two to three weeks and it lasted for the better part of a year. >>And of course, what they're, they're using this as a vehicle, right? Not preying on the fear. They're doing everything from initial lockdown, uh, fishing. We were as COVID-19 movers to, um, uh, lay off notices then to phase one, reopenings all the way up to fast forward to where we are today with vaccine rollover development. So there's always that new flavor and theme that they were rolling out, but because it was so successful for them, they were able to, they didn't have to innovate too much, right. They didn't have to expand and shifted to new to new trends. And themes are really developed on new rats families as an example, or a new sophisticated malware. That was the first half of the year and the second half of the year. Um, of course people started to experience COVID fatigue, right? Um, people started to become, we did a lot of education around this. >>People started to become more aware of this threat. And so, um, cyber criminals have started to, um, as we expected, started to become more sophisticated with their attacks. We saw an expansion in different ransomware families. We saw more of a shift of focus on, on, um, uh, you know, targeting the digital supply chain as an example. And so that, that was, that was really towards Q4. Uh, so it, it was a long lived lead year with success on the Google themes, um, targeting healthcare as an example, a lot of, um, a lot of the organizations that were, you know, really in a vulnerable position, I would say >>So, okay. I want to clarify something because my assumption was that they actually did really increase the sophistication, but it sounds like that was kind of a first half trends. Not only did they have to adapt and not have to, but they adapt it to these new vulnerabilities. Uh, my sense was that when you talk about the digital supply chain, that that was a fairly sophisticated attack. Am I, am I getting that right? That they did their sort of their, their, their increased sophistication in the first half, and then they sort of deployed it, did it, uh, w what actually happened there from your data? >>Well, if we look at, so generally there's two types of attacks that we look at, we look at the, uh, the premeditated sophisticated attacks that can have, um, you know, a lot of ramp up work on their end, a lot of time developing the, the, the, the weaponization phase. So developing, uh, the exploits of the sophisticated malware that they're gonna use for the campaign reconnaissance, understanding the targets, where platforms are developed, um, the blueprinting that DNA of, of, of the supply chain, those take time. Um, in fact years, even if we look back to, um, uh, 10 plus years ago with the Stuxnet attacks, as an example that was on, uh, nuclear centrifuges, um, and that, that had four different zero-day weapons at the time. That was very sophisticated, that took over two years to develop as an example. So some of these can take years of time to develop, but they're, they're, uh, very specific in terms of the targets are going to go after obviously the ROI from their end. >>Uh, the other type of attack that we see is as ongoing, um, these broad, wide sweeping attacks, and the reality for those ones is they don't unfortunately need to be too sophisticated. And those ones were the ones I was talking about that were really just playing on the cool, the deem, and they still do today with the vaccine road and development. Uh, but, but it's really because they're just playing on, on, um, you know, social engineering, um, using, uh, topical themes. And in fact, the weapons they're using these vulnerabilities are from our research data. And this was highlighted actually the first pop landscape before last year, uh, on average were two to three years old. So we're not talking about fresh vulnerabilities. You've got to patch right away. I mean, these are things that should have been patched two years ago, but they're still unfortunately having success with that. >>So you mentioned stuck next Stuxnet as the former sort of example, of one of the types of attacks that you see. And I always felt like that was a watershed moment. One of the most sophisticated, if not the most sophisticated attack that we'd ever seen. When I talk to CSOs about the recent government hack, they, they, they suggest I infer maybe they don't suggest it. I infer that it was of similar sophistication. It was maybe thousands of people working on this for years and years and years. Is that, is that accurate or not necessarily? >>Yeah, there's definitely a, there's definitely some comparisons there. Uh, you know, one of the largest things is, uh, both attacks used digital circuits certificate personation, so they're digitally signed. So, you know, of course that whole technology using cryptography is designed by design, uh, to say that, you know, this piece of software installed in your system, hassles certificate is coming from the source. It's legitimate. Of course, if that's compromised, that's all out of the window. And, um, yeah, this is what we saw in both attacks. In fact, you know, stocks in that they also had digitally designed, uh, certificates that were compromised. So when it gets to that level of students or, uh, sophistication, that means definitely that there's a target that there has been usually months of, of, uh, homework done by cyber criminals, for reconnaissance to be able to weaponize that. >>W w what did you see with respect to ransomware? What were the trends there over the past 12 months? I've heard some data and it's pretty scary, but what did you see? >>Yeah, so we're actually, ransomware is always the thorn in our side, and it's going to continue to be so, um, you know, in fact, uh, ransomware is not a new itself. It was actually first created in 1989, and they demanded ransom payments through snail mail. This was to appeal a box, obviously that, that, that didn't take off. Wasn't a successful on the internet was porn at the time. But if you look at it now, of course, over the last 10 years, really, that's where it ran. The ransomware model has been, uh, you know, lucrative, right? I mean, it's been, um, using, uh, by force encrypting data on systems, so that users had to, if they were forced to pay the ransom because they wanted access to their data back data was the target currency for ransomware. That's shifted now. And that's actually been a big pivotal over the last year or so, because again, before it was this let's cast a wide net, in fact, as many people as we can random, um, and try to see if we can hold some of their data for ransom. >>Some people that data may be valuable, it may not be valuable. Um, and that model still exists. Uh, and we see that, but really the big shift that we saw last year and the threat landscape before it was a shift to targeted rats. So again, the sophistication is starting to rise because they're not just going out to random data. They're going out to data that they know is valuable to large organizations, and they're taking that a step further now. So there's various ransomware families. We saw that have now reverted to extortion and blackmail, right? So they're taking that data, encrypting it and saying, unless you pay us as large sum of money, we're going to release this to the public or sell it to a buyer on the dark web. And of course you can imagine the amount of, um, you know, damages that can happen from that. The other thing we're seeing is, is a target of going to revenue services, right? So if they can cripple networks, it's essentially a denial of service. They know that the company is going to be bleeding, you know, X, millions of dollars a day, so they can demand Y million dollars of ransom payments, and that's effectively what's happening. So it's, again, becoming more targeted, uh, and more sophisticated. And unfortunately the ransom is going up. >>So they go to where the money is. And of course your job is to, it's a lower the ROI for them, a constant challenge. Um, we talked about some of the attack vectors, uh, that you saw this year that, that cyber criminals are targeting. I wonder if, if, you know, given the work from home, if things like IOT devices and cameras and, you know, thermostats, uh, with 75% of the work force at home, is this infrastructure more vulnerable? I guess, of course it is. But what did you see there in terms of attacks on those devices? >>Yeah, so, uh, um, uh, you know, unfortunately the attack surface as we call it, uh, so the amount of target points is expanding. It's not shifting, it's expanding. We still see, um, I saw, I mentioned earlier vulnerabilities from two years ago that are being used in some cases, you know, over the holidays where e-commerce means we saw e-commerce heavily under attack in e-commerce has spikes since last summer, right. It's been a huge amount of traffic increase everybody's shopping from home. And, uh, those vulnerabilities going after a shopping cart, plugins, as an example, are five to six years old. So we still have this theme of old vulnerabilities are still new in a sense being attacked, but we're also now seeing this complication of, yeah, as you said, IOT, uh, B roll out everywhere, the really quick shift to work from home. Uh, we really have to treat this as if you guys, as the, uh, distributed branch model for enterprise, right. >>And it's really now the secure branch. How do we take, um, um, you know, any of these devices on, on those networks and secure them, uh, because yeah, if you look at the, what we highlighted in our landscape report and the top 10 attacks that we're seeing, so hacking attacks hacking in tabs, this is who our IPS triggers. You know, we're seeing attempts to go after IOT devices. Uh, right now they're mostly, uh, favoring, uh, well in terms of targets, um, consumer grade routers. Uh, but they're also looking at, um, uh, DVR devices as an example for, uh, you know, home entertainment systems, uh, network attached storage as well, and IP security cameras, um, some of the newer devices, uh, what, the quote unquote smart devices that are now on, you know, virtual assistance and home networks. Uh, we actually released a predictions piece at the end of last year as well. So this is what we call the new intelligent edge. And that's what I think is we're really going to see this year in terms of what's ahead. Um, cause we always have to look ahead and prepare for that. But yeah, right now, unfortunately, the story is, all of this is still happening. IOT is being targeted. Of course they're being targeted because they're easy targets. Um, it's like for cybercriminals, it's like shooting fish in a barrel. There's not just one, but there's multiple vulnerabilities, security holes associated with these devices, easy entry points into networks. >>I mean, it's, um, I mean, attackers they're, they're highly capable. They're organized, they're well-funded they move fast, they're they're agile, uh, and they follow the money. As we were saying, uh, you, you mentioned, you know, co vaccines and, you know, big pharma healthcare, uh, where >>Did you see advanced, persistent >>Threat groups really targeting? Were there any patterns that emerged in terms of other industry types or organizations being targeted? >>Yeah. So just to be clear again, when we talk about AP teams, um, uh, advanced, specific correct group, the groups themselves they're targeting, these are usually the more sophisticated groups, of course. So going back to that theme, these are usually the target, the, um, the premeditated targeted attacks usually points to nation state. Um, sometimes of course there's overlap. They can be affiliated with cyber crime, cyber crime, uh, uh, groups are typically, um, looking at some other targets for ROI, uh, bio there's there's a blend, right? So as an example, if we're looking at the, uh, apt groups I had last year, absolutely. Number one I would say would be healthcare. Healthcare was one of those, and it's, it's, it's, uh, you know, very unfortunate, but obviously with the shift that was happening at a pop up medical facilities, there's a big, a rush to change networks, uh, for a good cause of course, but with that game, um, you know, uh, security holes and concerns the targets and, and that's what we saw IPT groups targeting was going after those and, and ransomware and the cyber crime shrine followed as well. Right? Because if you can follow, uh, those critical networks and crippled them on from cybercriminals point of view, you can, you can expect them to pay the ransom because they think that they need to buy in order to, um, get those systems back online. Uh, in fact, last year or two, unfortunately we saw the first, um, uh, death that was caused because of a denial of service attack in healthcare, right. Facilities were weren't available because of the cyber attack. Patients had to be diverted and didn't make it on the way. >>All right. Jericho, sufficiently bummed out. So maybe in the time remaining, we can talk about remediation strategies. You know, we know there's no silver bullet in security. Uh, but what approaches are you recommending for organizations? How are you consulting with folks? >>Sure. Yeah. So a couple of things, um, good news is there's a lot that we can do about this, right? And, um, and, and basic measures go a long way. So a couple of things just to get out of the way I call it housekeeping, cyber hygiene, but it's always worth reminding. So when we talk about keeping security patches up to date, we always have to talk about that because that is reality as et cetera, these, these vulnerabilities that are still being successful are five to six years old in some cases, the majority two years old. Um, so being able to do that, manage that from an organization's point of view, really treat the new work from home. I don't like to call it a work from home. So the reality is it's work from anywhere a lot of the times for some people. So really treat that as, as the, um, as a secure branch, uh, methodology, doing things like segmentations on network, secure wifi access, multi-factor authentication is a huge muscle, right? >>So using multi-factor authentication because passwords are dead, um, using things like, uh, XDR. So Xers is a combination of detection and response for end points. This is a mass centralized management thing, right? So, uh, endpoint detection and response, as an example, those are all, uh, you know, good security things. So of course having security inspection, that that's what we do. So good threat intelligence baked into your security solution. That's supported by labs angles. So, uh, that's, uh, you know, uh, antivirus, intrusion prevention, web filtering, sandbox, and so forth, but then it gets that that's the security stack beyond that it gets into the end user, right? Everybody has a responsibility. This is that supply chain. We talked about. The supply chain is, is, is a target for attackers attackers have their own supply chain as well. And we're also part of that supply chain, right? The end users where we're constantly fished for social engineering. So using phishing campaigns against employees to better do training and awareness is always recommended to, um, so that's what we can do, obviously that's, what's recommended to secure, uh, via the endpoints in the secure branch there's things we're also doing in the industry, um, to fight back against that with prime as well. >>Well, I, I want to actually talk about that and talk about ecosystems and collaboration, because while you have competitors, you all want the same thing. You, SecOps teams are like superheroes in my book. I mean, they're trying to save the world from the bad guys. And I remember I was talking to Robert Gates on the cube a couple of years ago, a former defense secretary. And I said, yeah, but don't, we have like the best security people and can't we go on the offensive and weaponize that ourselves. Of course, there's examples of that. Us. Government's pretty good at it, even though they won't admit it. But his answer to me was, yeah, we gotta be careful because we have a lot more to lose than many countries. So I thought that was pretty interesting, but how do you collaborate with whether it's the U S government or other governments or other other competitors even, or your ecosystem? Maybe you could talk about that a little bit. >>Yeah. Th th this is what, this is what makes me tick. I love working with industry. I've actually built programs for 15 years of collaboration in the industry. Um, so, you know, we, we need, I always say we can't win this war alone. You actually hit on this point earlier, you talked about following and trying to disrupt the ROI of cybercriminals. Absolutely. That is our target, right. We're always looking at how we can disrupt their business model. Uh, and, and in order, there's obviously a lot of different ways to do that, right? So a couple of things we do is resiliency. That's what we just talked about increasing the security stack so that they go knocking on someone else's door. But beyond that, uh, it comes down to private, private sector collaborations. So, uh, we, we, uh, co-founder of the cyber threat Alliance in 2014 as an example, this was our fierce competitors coming in to work with us to share intelligence, because like you said, um, competitors in the space, but we need to work together to do the better fight. >>And so this is a Venn diagram. What's compared notes, let's team up, uh, when there's a breaking attack and make sure that we have the intelligence so that we can still remain competitive on the technology stack to gradation the solutions themselves. Uh, but let's, let's level the playing field here because cybercriminals moved out, uh, you know, um, uh, that, that there's no borders and they move with great agility. So, uh, that's one thing we do in the private private sector. Uh, there's also, uh, public private sector relationships, right? So we're working with Interpol as an example, Interfor project gateway, and that's when we find attribution. So it's not just the, what are these people doing like infrastructure, but who, who are they, where are they operating? What, what events tools are they creating? We've actually worked on cases that are led down to, um, uh, warrants and arrests, you know, and in some cases, one case with a $60 million business email compromise fraud scam, the great news is if you look at the industry as a whole, uh, over the last three to four months has been for take downs, a motet net Walker, uh, um, there's also IE Gregor, uh, recently as well too. >>And, and Ian Gregor they're actually going in and arresting the affiliates. So not just the CEO or the King, kind of these organizations, but the people who are distributing the ransomware themselves. And that was a unprecedented step, really important. So you really start to paint a picture of this, again, supply chain, this ecosystem of cyber criminals and how we can hit them, where it hurts on all angles. I've most recently, um, I've been heavily involved with the world economic forum. Uh, so I'm, co-author of a report from last year of the partnership on cyber crime. And, uh, this is really not just the pro uh, private, private sector, but the private and public sector working together. We know a lot about cybercriminals. We can't arrest them. Uh, we can't take servers offline from the data centers, but working together, we can have that whole, you know, that holistic effect. >>Great. Thank you for that, Derek. What if people want, want to go deeper? Uh, I know you guys mentioned that you do blogs, but are there other resources that, that they can tap? Yeah, absolutely. So, >>Uh, everything you can see is on our threat research blog on, uh, so 40 net blog, it's under expired research. We also put out, uh, playbooks, w we're doing blah, this is more for the, um, the heroes as he called them the security operation centers. Uh, we're doing playbooks on the aggressors. And so this is a playbook on the offense, on the offense. What are they up to? How are they doing that? That's on 40 guard.com. Uh, we also release, uh, threat signals there. So, um, we typically release, uh, about 50 of those a year, and those are all, um, our, our insights and views into specific attacks that are now >>Well, Derek Mackie, thanks so much for joining us today. And thanks for the work that you and your teams do. Very important. >>Thanks. It's yeah, it's a pleasure. And, uh, rest assured we will still be there 24 seven, three 65. >>Good to know. Good to know. And thank you for watching everybody. This is Dave Volante for the cube. We'll see you next time.

>>As we've been reporting, the pandemic has called CSOs to really shift their spending priorities towards securing remote workers. Almost overnight. Zero trust has gone from buzzword to mandate. What's more as we wrote in our recent cybersecurity breaking analysis, not only Maseca pro secured increasingly distributed workforce, but now they have to be wary of software updates in the digital supply chain, including the very patches designed to protect them against cyber attacks. Hello everyone. And welcome to this Q conversation. My name is Dave Vellante and I'm pleased to welcome Derek manky. Who's chief security insights, and global threat alliances for four guard labs with fresh data from its global threat landscape report. Derek. Welcome. Great to see you. >>Thanks so much for, for the invitation to speak. It's always a pleasure. Multicover yeah, >>You're welcome. So first I wonder if you could explain for the audience, what is for guard labs and what's its relationship to fortunate? >>Right. So 40 grand labs is, is our global sockets, our global threat intelligence operation center. It never sleeps, and this is the beat. Um, you know, it's, it's been here since inception at port in it. So it's it's 20, 21 years in the making, since Fortinet was founded, uh, we have built this in-house, uh, so we don't go yum technology. We built everything from the ground up, including creating our own training programs for our, our analysts. We're following malware, following exploits. We even have a unique program that I created back in 2006 to ethical hacking program. And it's a zero-day research. So we try to meet the hackers, the bad guys to their game. And we of course do that responsibly to work with vendors, to close schools and create virtual patches. Um, and, but, you know, so it's, it's everything from, uh, customer protection first and foremost, to following, uh, the threat landscape and cyber. It's very important to understand who they are, what they're doing, who they're, uh, what they're targeting, what tools are they using? >>Yeah, that's great. Some serious DNA and skills in that group. And it's, it's critical because like you said, you can, you can minimize the spread of those malware very, very quickly. So what, what now you have, uh, the global threat landscape report. We're going to talk about that, but what exactly is that? >>Right? So this a global threat landscape report, it's a summary of, uh, all, all the data that we collect over a period of time. So we released this, that biannually two times a year. Um, cyber crime is changing very fast, as you can imagine. So, uh, while we do release security blogs, and, uh, what we call threat signals for breaking security events, we have a lot of other vehicles to release threat intelligence, but this threat landscape report is truly global. It looks at all of our global data. So we have over 5 million censorship worldwide in 40 guard labs, we're processing. I know it seems like a very large amount, but North of a hundred billion, uh, threat events in just one day. And we have to take the task of taking all of that data and put that onto scale for half a year and compile that into something, um, that is, uh, the, you know, that that's digestible. That's a, a very tough task, as you can imagine, so that, you know, we have to work with a huge technologies back to machine learning and artificial intelligence automation. And of course our analyst view to do that. >>Yeah. So this year, of course, there's like the every year is a battle, but this year was an extra battle. Can you explain what you saw in terms of the hacker dynamics over the past? Let's say 12 months. I know you do this twice a year, but what trends did you see evolving throughout the year and what have you seen with the way that attackers have exploited this expanded attack surface outside of corporate network? >>Yeah, it was quite interesting last year. It certainly was not normal. Like we all say, um, and that was no exception for cybersecurity. You know, if we look at cyber criminals and how they pivoted and adapted to the scrap threat landscape, cyber cyber criminals are always trying to take advantage of the weakest link of the chain. They're trying to always prey off here and ride waves of global trends and themes. We've seen this before in, uh, natural disasters as an example, you know, um, trying to do charity kind of scams and campaigns. And they're usually limited to a region where that incident happened and they usually live about two to three weeks, maybe a month at the most. And then they'll move on to the next to the next trip. That's braking, of course, because COVID is so global and dominant. Um, we saw attacks coming in from, uh, well over 40 different languages as an example, um, in regions all across the world that wasn't lasting two to three weeks and it lasted for the better part of a year. >>And of course, what they're, they're using this as a vehicle, right? Not preying on the fear. They're doing everything from initial lockdown, uh, fishing. We were as COVID-19 movers to, um, uh, lay off notices then to phase one, reopenings all the way up to fast forward to where we are today with vaccine rollover development. So there's always that new flavor and theme that they were rolling out, but because it was so successful for them, they were able to, they didn't have to innovate too much, right. They didn't have to expand and shifted to new to new trends. And themes are really developed on new rats families as an example, or a new sophisticated malware. That was the first half of the year and the second half of the year. Um, of course people started to experience COVID fatigue, right? Um, people started to become, we did a lot of education around this. >>People started to become more aware of this threat. And so, um, cyber criminals have started to, um, as we expected, started to become more sophisticated with their attacks. We saw an expansion in different ransomware families. We saw more of a shift of focus on, on, um, uh, you know, targeting the digital supply chain as an example. And so that, that was, that was really towards Q4. Uh, so it, it was a long lived lead year with success on the Google themes, um, targeting healthcare as an example, a lot of, um, a lot of the organizations that were, you know, really in a vulnerable position, I would say >>So, okay. I want to clarify something because my assumption was that they actually did really increase the sophistication, but it sounds like that was kind of a first half trends. Not only did they have to adapt and not have to, but they adapt it to these new vulnerabilities. Uh, my sense was that when you talk about the digital supply chain, that that was a fairly sophisticated attack. Am I, am I getting that right? That they did their sort of their, their, their increased sophistication in the first half, and then they sort of deployed it, did it, uh, w what actually happened there from your data? >>Well, if we look at, so generally there's two types of attacks that we look at, we look at the, uh, the premeditated sophisticated attacks that can have, um, you know, a lot of ramp up work on their end, a lot of time developing the, the, the, the weaponization phase. So developing, uh, the exploits of the sophisticated malware that they're gonna use for the campaign reconnaissance, understanding the targets, where platforms are developed, um, the blueprinting that DNA of, of, of the supply chain, those take time. Um, in fact years, even if we look back to, um, uh, 10 plus years ago with the Stuxnet attacks, as an example that was on, uh, nuclear centrifuges, um, and that, that had four different zero-day weapons at the time. That was very sophisticated, that took over two years to develop as an example. So some of these can take years of time to develop, but they're, they're, uh, very specific in terms of the targets are going to go after obviously the ROI from their end. >>Uh, the other type of attack that we see is as ongoing, um, these broad, wide sweeping attacks, and the reality for those ones is they don't unfortunately need to be too sophisticated. And those ones were the ones I was talking about that were really just playing on the cool, the deem, and they still do today with the vaccine road and development. Uh, but, but it's really because they're just playing on, on, um, you know, social engineering, um, using, uh, topical themes. And in fact, the weapons they're using these vulnerabilities are from our research data. And this was highlighted actually the first pop landscape before last year, uh, on average were two to three years old. So we're not talking about fresh vulnerabilities. You've got to patch right away. I mean, these are things that should have been patched two years ago, but they're still unfortunately having success with that. >>So you mentioned stuck next Stuxnet as the former sort of example, of one of the types of attacks that you see. And I always felt like that was a watershed moment. One of the most sophisticated, if not the most sophisticated attack that we'd ever seen. When I talk to CSOs about the recent government hack, they, they, they suggest I infer maybe they don't suggest it. I infer that it was of similar sophistication. It was maybe thousands of people working on this for years and years and years. Is that, is that accurate or not necessarily? >>Yeah, there's definitely a, there's definitely some comparisons there. Uh, you know, one of the largest things is, uh, both attacks used digital circuits certificate personation, so they're digitally signed. So, you know, of course that whole technology using cryptography is designed by design, uh, to say that, you know, this piece of software installed in your system, hassles certificate is coming from the source. It's legitimate. Of course, if that's compromised, that's all out of the window. And, um, yeah, this is what we saw in both attacks. In fact, you know, stocks in that they also had digitally designed, uh, certificates that were compromised. So when it gets to that level of students or, uh, sophistication, that means definitely that there's a target that there has been usually months of, of, uh, homework done by cyber criminals, for reconnaissance to be able to weaponize that. >>W w what did you see with respect to ransomware? What were the trends there over the past 12 months? I've heard some data and it's pretty scary, but what did you see? >>Yeah, so we're actually, ransomware is always the thorn in our side, and it's going to continue to be so, um, you know, in fact, uh, ransomware is not a new itself. It was actually first created in 1989, and they demanded ransom payments through snail mail. This was to appeal a box, obviously that, that, that didn't take off. Wasn't a successful on the internet was porn at the time. But if you look at it now, of course, over the last 10 years, really, that's where it ran. The ransomware model has been, uh, you know, lucrative, right? I mean, it's been, um, using, uh, by force encrypting data on systems, so that users had to, if they were forced to pay the ransom because they wanted access to their data back data was the target currency for ransomware. That's shifted now. And that's actually been a big pivotal over the last year or so, because again, before it was this let's cast a wide net, in fact, as many people as we can random, um, and try to see if we can hold some of their data for ransom. >>Some people that data may be valuable, it may not be valuable. Um, and that model still exists. Uh, and we see that, but really the big shift that we saw last year and the threat landscape before it was a shift to targeted rats. So again, the sophistication is starting to rise because they're not just going out to random data. They're going out to data that they know is valuable to large organizations, and they're taking that a step further now. So there's various ransomware families. We saw that have now reverted to extortion and blackmail, right? So they're taking that data, encrypting it and saying, unless you pay us as large sum of money, we're going to release this to the public or sell it to a buyer on the dark web. And of course you can imagine the amount of, um, you know, damages that can happen from that. The other thing we're seeing is, is a target of going to revenue services, right? So if they can cripple networks, it's essentially a denial of service. They know that the company is going to be bleeding, you know, X, millions of dollars a day, so they can demand Y million dollars of ransom payments, and that's effectively what's happening. So it's, again, becoming more targeted, uh, and more sophisticated. And unfortunately the ransom is going up. >>So they go to where the money is. And of course your job is to, it's a lower the ROI for them, a constant challenge. Um, we talked about some of the attack vectors, uh, that you saw this year that, that cyber criminals are targeting. I wonder if, if, you know, given the work from home, if things like IOT devices and cameras and, you know, thermostats, uh, with 75% of the work force at home, is this infrastructure more vulnerable? I guess, of course it is. But what did you see there in terms of attacks on those devices? >>Yeah, so, uh, um, uh, you know, unfortunately the attack surface as we call it, uh, so the amount of target points is expanding. It's not shifting, it's expanding. We still see, um, I saw, I mentioned earlier vulnerabilities from two years ago that are being used in some cases, you know, over the holidays where e-commerce means we saw e-commerce heavily under attack in e-commerce has spikes since last summer, right. It's been a huge amount of traffic increase everybody's shopping from home. And, uh, those vulnerabilities going after a shopping cart, plugins, as an example, are five to six years old. So we still have this theme of old vulnerabilities are still new in a sense being attacked, but we're also now seeing this complication of, yeah, as you said, IOT, uh, B roll out everywhere, the really quick shift to work from home. Uh, we really have to treat this as if you guys, as the, uh, distributed branch model for enterprise, right. >>And it's really now the secure branch. How do we take, um, um, you know, any of these devices on, on those networks and secure them, uh, because yeah, if you look at the, what we highlighted in our landscape report and the top 10 attacks that we're seeing, so hacking attacks hacking in tabs, this is who our IPS triggers. You know, we're seeing attempts to go after IOT devices. Uh, right now they're mostly, uh, favoring, uh, well in terms of targets, um, consumer grade routers. Uh, but they're also looking at, um, uh, DVR devices as an example for, uh, you know, home entertainment systems, uh, network attached storage as well, and IP security cameras, um, some of the newer devices, uh, what, the quote unquote smart devices that are now on, you know, virtual assistance and home networks. Uh, we actually released a predictions piece at the end of last year as well. So this is what we call the new intelligent edge. And that's what I think is we're really going to see this year in terms of what's ahead. Um, cause we always have to look ahead and prepare for that. But yeah, right now, unfortunately, the story is, all of this is still happening. IOT is being targeted. Of course they're being targeted because they're easy targets. Um, it's like for cybercriminals, it's like shooting fish in a barrel. There's not just one, but there's multiple vulnerabilities, security holes associated with these devices, easy entry points into networks. >>I mean, it's, um, I mean, attackers they're, they're highly capable. They're organized, they're well-funded they move fast, they're they're agile, uh, and they follow the money. As we were saying, uh, you, you mentioned, you know, co vaccines and, you know, big pharma healthcare, uh, where >>Did you see advanced, persistent >>Threat groups really targeting? Were there any patterns that emerged in terms of other industry types or organizations being targeted? >>Yeah. So just to be clear again, when we talk about AP teams, um, uh, advanced, specific correct group, the groups themselves they're targeting, these are usually the more sophisticated groups, of course. So going back to that theme, these are usually the target, the, um, the premeditated targeted attacks usually points to nation state. Um, sometimes of course there's overlap. They can be affiliated with cyber crime, cyber crime, uh, uh, groups are typically, um, looking at some other targets for ROI, uh, bio there's there's a blend, right? So as an example, if we're looking at the, uh, apt groups I had last year, absolutely. Number one I would say would be healthcare. Healthcare was one of those, and it's, it's, it's, uh, you know, very unfortunate, but obviously with the shift that was happening at a pop up medical facilities, there's a big, a rush to change networks, uh, for a good cause of course, but with that game, um, you know, uh, security holes and concerns the targets and, and that's what we saw IPT groups targeting was going after those and, and ransomware and the cyber crime shrine followed as well. Right? Because if you can follow, uh, those critical networks and crippled them on from cybercriminals point of view, you can, you can expect them to pay the ransom because they think that they need to buy in order to, um, get those systems back online. Uh, in fact, last year or two, unfortunately we saw the first, um, uh, death that was caused because of a denial of service attack in healthcare, right. Facilities were weren't available because of the cyber attack. Patients had to be diverted and didn't make it on the way. >>All right. Jericho, sufficiently bummed out. So maybe in the time remaining, we can talk about remediation strategies. You know, we know there's no silver bullet in security. Uh, but what approaches are you recommending for organizations? How are you consulting with folks? >>Sure. Yeah. So a couple of things, um, good news is there's a lot that we can do about this, right? And, um, and, and basic measures go a long way. So a couple of things just to get out of the way I call it housekeeping, cyber hygiene, but it's always worth reminding. So when we talk about keeping security patches up to date, we always have to talk about that because that is reality as et cetera, these, these vulnerabilities that are still being successful are five to six years old in some cases, the majority two years old. Um, so being able to do that, manage that from an organization's point of view, really treat the new work from home. I don't like to call it a work from home. So the reality is it's work from anywhere a lot of the times for some people. So really treat that as, as the, um, as a secure branch, uh, methodology, doing things like segmentations on network, secure wifi access, multi-factor authentication is a huge muscle, right? >>So using multi-factor authentication because passwords are dead, um, using things like, uh, XDR. So Xers is a combination of detection and response for end points. This is a mass centralized management thing, right? So, uh, endpoint detection and response, as an example, those are all, uh, you know, good security things. So of course having security inspection, that that's what we do. So good threat intelligence baked into your security solution. That's supported by labs angles. So, uh, that's, uh, you know, uh, antivirus, intrusion prevention, web filtering, sandbox, and so forth, but then it gets that that's the security stack beyond that it gets into the end user, right? Everybody has a responsibility. This is that supply chain. We talked about. The supply chain is, is, is a target for attackers attackers have their own supply chain as well. And we're also part of that supply chain, right? The end users where we're constantly fished for social engineering. So using phishing campaigns against employees to better do training and awareness is always recommended to, um, so that's what we can do, obviously that's, what's recommended to secure, uh, via the endpoints in the secure branch there's things we're also doing in the industry, um, to fight back against that with prime as well. >>Well, I, I want to actually talk about that and talk about ecosystems and collaboration, because while you have competitors, you all want the same thing. You, SecOps teams are like superheroes in my book. I mean, they're trying to save the world from the bad guys. And I remember I was talking to Robert Gates on the cube a couple of years ago, a former defense secretary. And I said, yeah, but don't, we have like the best security people and can't we go on the offensive and weaponize that ourselves. Of course, there's examples of that. Us. Government's pretty good at it, even though they won't admit it. But his answer to me was, yeah, we gotta be careful because we have a lot more to lose than many countries. So I thought that was pretty interesting, but how do you collaborate with whether it's the U S government or other governments or other other competitors even, or your ecosystem? Maybe you could talk about that a little bit. >>Yeah. Th th this is what, this is what makes me tick. I love working with industry. I've actually built programs for 15 years of collaboration in the industry. Um, so, you know, we, we need, I always say we can't win this war alone. You actually hit on this point earlier, you talked about following and trying to disrupt the ROI of cybercriminals. Absolutely. That is our target, right. We're always looking at how we can disrupt their business model. Uh, and, and in order, there's obviously a lot of different ways to do that, right? So a couple of things we do is resiliency. That's what we just talked about increasing the security stack so that they go knocking on someone else's door. But beyond that, uh, it comes down to private, private sector collaborations. So, uh, we, we, uh, co-founder of the cyber threat Alliance in 2014 as an example, this was our fierce competitors coming in to work with us to share intelligence, because like you said, um, competitors in the space, but we need to work together to do the better fight. >>And so this is a Venn diagram. What's compared notes, let's team up, uh, when there's a breaking attack and make sure that we have the intelligence so that we can still remain competitive on the technology stack to gradation the solutions themselves. Uh, but let's, let's level the playing field here because cybercriminals moved out, uh, you know, um, uh, that, that there's no borders and they move with great agility. So, uh, that's one thing we do in the private private sector. Uh, there's also, uh, public private sector relationships, right? So we're working with Interpol as an example, Interfor project gateway, and that's when we find attribution. So it's not just the, what are these people doing like infrastructure, but who, who are they, where are they operating? What, what events tools are they creating? We've actually worked on cases that are led down to, um, uh, warrants and arrests, you know, and in some cases, one case with a $60 million business email compromise fraud scam, the great news is if you look at the industry as a whole, uh, over the last three to four months has been for take downs, a motet net Walker, uh, um, there's also IE Gregor, uh, recently as well too. >>And, and Ian Gregor they're actually going in and arresting the affiliates. So not just the CEO or the King, kind of these organizations, but the people who are distributing the ransomware themselves. And that was a unprecedented step, really important. So you really start to paint a picture of this, again, supply chain, this ecosystem of cyber criminals and how we can hit them, where it hurts on all angles. I've most recently, um, I've been heavily involved with the world economic forum. Uh, so I'm, co-author of a report from last year of the partnership on cyber crime. And, uh, this is really not just the pro uh, private, private sector, but the private and public sector working together. We know a lot about cybercriminals. We can't arrest them. Uh, we can't take servers offline from the data centers, but working together, we can have that whole, you know, that holistic effect. >>Great. Thank you for that, Derek. What if people want, want to go deeper? Uh, I know you guys mentioned that you do blogs, but are there other resources that, that they can tap? Yeah, absolutely. So, >>Uh, everything you can see is on our threat research blog on, uh, so 40 net blog, it's under expired research. We also put out, uh, playbooks, w we're doing blah, this is more for the, um, the heroes as he called them the security operation centers. Uh, we're doing playbooks on the aggressors. And so this is a playbook on the offense, on the offense. What are they up to? How are they doing that? That's on 40 guard.com. Uh, we also release, uh, threat signals there. So, um, we typically release, uh, about 50 of those a year, and those are all, um, our, our insights and views into specific attacks that are now >>Well, Derek Mackie, thanks so much for joining us today. And thanks for the work that you and your teams do. Very important. >>Thanks. It's yeah, it's a pleasure. And, uh, rest assured we will still be there 24 seven, three 65. >>Good to know. Good to know. And thank you for watching everybody. This is Dave Volante for the cube. We'll see you next time.

from London England it's the cube covering Koopa inspire 19 emia taught to you by Koopa hey welcome to the cube Lisa Martin coming to you from London I'm at Koopa inspire 19 pleased to be joined by one of Koopas spent setters hit me here is alla Williams the managing director and chief procurement officer at Barclays al welcome to the cube Thank You Lisa thanks for having me so Barclays is a three hundred plus year old Bank three hundred and thirty five years I think I also was was headquartered in London I didn't know this until he did some research Barclays is the pioneer of the ATM yes and a credit card in the UK credit card why first credit card in the UK and the pioneer in inventor of the ATM correct yes so when we think of an organization that is three hundred and thirty five years old we think how agile is that organization how transformative can it be talk to me about what it's like at Barclays from a digital perspective before we get into some of the procurement stuff which or not but tight and culture like that's a great question right could you think about a three hundred thirty-five year old Bank how innovative could it can it be right how agile can it be and the market in the the sector we work in requires us to be very agile because banking is a disrupted sector especially on the retail and consumer side expectations around technology and mobile capabilities and digital transformation are the most significant they've ever been in this sector and so for Barclays it's it's absolutely key that we deliver on those capabilities both in terms of our front office for our consumers and our corporate clients as well as for our own employees within the bank how influential is the consumer side because as consumers we are so used to being able to get anything that we want we can buy products and services we can pay bills with a click or swipe on the on the business side it's harder for businesses to transform and innovate it's a lot of other risks and security issues how influential is Barclays Barclays is your retail your consumer business in terms of your b2b work and that's a great question because I think the the experiences that shape people's expectations come from their interactions in retail and consumer when it comes to b2b and traditionally business-to-business commerce and financial transactions haven't been nearly as sophisticated streamlined or frictionless you know as you would in a consumer model so the expectations are built on the consumer side in consumer to business type models and then the business and business models been playing catch-up for the last several years as a result talk to me about now the role of finance leaders I was reading surgery that Kupa did recently have 253 uk-based financial decision makers and a big number of them I think it was 96 percent said we don't have complete visibility of all of our spent there's a big opportunity there to work with a company like Cooper but talk to me about how the role of the chief procurement officer is changing you've been doing it for quite a while you're a veteran right some of the trends that you have seen that you've really jumped on and said this is the direction we need to be going in right so I've been the chief procurement officer of Barclays for two and half years and the CEO of a large global technology company for nine years before that so I think the the the role of the chief procurement officer has changed significantly over the course of the last say 10 years five years two years we're at a point now where the chief procurement officer is seen as a source of and the organization of procurement is seen as a source of innovation it's seen as a source of capacity creation for the the the organization for the company and it's also seen as sort of a steward of the portfolio of spins for that particular organization to ensure we're maximizing the utility and value of that spend and of that supply chain so the expectations for procurement have tripled quadrupled or more fold in the last you know four or five years some of the interesting things that we're hearing from Koopa and from their customers and partners today is beyond simply initiative to simply but beyond you know dramatically improving procurement and invoicing and dispensing and leveraging the platform as one source for visibility of all that spent but it's being transformative to completely other areas like I was hearing a story of a customer who redefined procurement and is actually positively impacting corporate sustainability yes Wow so talk to me a little bit about I know one of the things that you really thrive on is competition how are you leveraging that and maybe your old American football days to build and maybe foster a sense of collaborative competition within your team to transform procurement at Barclays yes so I think that whether it's in sport or whether it's in business I think the concept of teams is key and effective teams are built on trust they're built on empowerment they they're built on collaboration open communication limited asymmetry and information as it's passed and that's all about kind of driving agility for whether you're on this on the football field American footballer or other football or on or in a business environment of business context so you know it's really and as a CEO and for all of the leaders on my team it's also about being a player coach and knowing when you need to be a player when you need to sort of roll up sleeves contribute in a particular area or particular solving a particular problem but more importantly when you need to be coached and and help those players sort of and those team members in on the team sort of step up to the challenge and coach them to be more success see Bennet Berkeley's a couple years now talk to me about your use case the purpose has with Koopa what are you guys doing together and what are some of the transformations that both internally and externally you've been able to achieve yeah so the relationship with coop has been great again I joined to make a couple of years ago one of the sort of first pillars associated with our overall transformation journey of centralizing procurement from five different procurement or six different procurement organizations really to moving to strategic locations to building out a new organization structure and operating model for for procurement I won't go into all that but one of the key pillars was around technology and we didn't have a common procure-to-pay or source to pay capability that extended or threaded throughout the bank for managing and supply chain so early on when I joined Barclays partnered partnering with Koopa working both of our teams working very effectively together to deploy sort of country by country and region by region we're now in 11 countries with the Koopa source to pay platform we're going to point to six more by the end of this calendar year and over 95% of our spend is flowing through Koopa as a multinational banks so it's been a significant component of our overall transformation journey for for Barclays and part of that transformation journey the technology piece is important that all a lot of its cultural we talked about a history of a three hundred and thirty-five year old organization but also going from five different procurement organizations down to one using a central platform that's challenging to get folks on board right being comfortable with change is your spirit of competitiveness was that a facilitator of getting adoption so that you could get them well I think so I mean I think to get the most out of teams and the most out of any organization large or small you need to galvanize around a common set of goals and objectives the the adage we ought to be pulling on the rope together to achieve achieve the end result and I think in the case of the sort of our Koopa journey both in terms of its strategy and overall deployment it was something more or less our entire procurement organization was able able to galvanize around and in feel like they were a part of and it it created an identity for us within Barclays as a procurement organization as well and kind of put his front and center with our business units and our stakeholders in a way we had we'd never been before so in terms of procurement having a seat at the board table is that something now that you have the ability to do with Barclays and be much more of a strategic driver of business yeah and look at Barclays compared to some of my other experiences it's not an it's not an issue of not having a seat at the table we might have a seat at too many tables sometimes there's a lot of attention on procurement within Barclays to help it deliver on its strategic objectives so with that seat comes a lot of responsibility so I often will coach my teams to ensure that they understand kind of that that that component of it's not just about having a seat at the table it's about what we're going to contribute what are we going to do differently when we're at that table when we're helping shape the decisions for the organization and what are the accountabilities and responsibilities that will pick up as a result and deliver on those promises that's absolutely critical one of the things that was talked about this morning is to trust Rob Bernstein talked about it they also had a guest speaker Rachel Botsman who's a trusted expert it was such an interesting conversation you know we talked about any chuck event that the cube goes to you always talk about trust got to have trust in the data you gotta have trust in your suppliers but what they were talking about here was really being an enabler of trust but cooper really working to earn the trust of its customers tell me about how has earned your trust and also allowed you to have those better discussions at the board table so that you have marked trusted relationships with your executive and your peer team yeah I mean it all starts for Barclays at the very top of the house in front office because we're in the business of trust I mean Bank a bank is in the business of trust that's what we deliver and promise to our consumers and our corporate clients and I think you know within procurement we need to make sure we're sort of delivering on that same promise around around trust and building trust with our teams and with our suppliers in the case of Kupa frankly it was about asking them to ensure they appropriately set expectations with me with my team in terms of what we could or couldn't do with the capability right don't over promise and under deliver but actually be very prudent and practical about what we're gonna be able to get done and then deliver on those promises to the best of your ability but if something and I always do if something goes so not according to plan right it's be open communicative and direct with the issue and how we're going to address it that to me is how we build trust in any team and that's how we built trust with Kupa through our transformation over the last two years that's critical mister your point no deployment probably ever goes perfectly according to plan there are always things that happen whatever it is software hardware is that we're talking about and I think for companies to address that confront it help the customer through those challenges to me that's more valuable I'm saying everything went beautifully was flawless that's not reality right I completely agree and I think that's that's what separates good from great companies to write is their ability to build that trust whether it be within their supply chain with their clients with their employees and look it's it's a journey it's not something you're one and done and you can say okay we've got the trust you can lose it as easy as you can obtain it and you have to keep a focus on on those trusting relationships should think about that we've earned this trust but we have to focus on it so we don't lose it so we grow X having the focus on that because you're right whether it's a deployment of software it's not one and it's the same thing with any sort of trusted relationship right it's maintaining that it's ensuring that there's value right being delivered on both sides that's right tell me a little bit about your ability Barclays ability as a spend setter in this program that Cooper has to influence technology directions like they talk a lot about the community all the insights that they're able to deliver to the community because of the community as Burton is able to be a strategic her gir with Cooper rather than just a customer yeah Phil we are I mean Rob and his team Raja Ravi the entire crew are very receptive and they're very collaborative in hearing from an organization like Barclays now look I'll be the first to admit Barclays and in banking and banking specifically in the UK it's a different animal than many other companies and sectors that kupo would work in so what might work for other companies doesn't always work for us and kind of flipping that around there's certain things that we need from Koopa that that we've been able to partner with them to deliver over the course of the last two years and the relationship of coop has been fantastic they hear us they listen to us they help us understand what the solution can do what it can't do or won't be able to do in the near term and then how do we augment that in the right way so we don't create cottage industries of activity with Impa cure med when we could be leveraging the capability of ghupat to deliver on those services right so you mentioned a little bit about what's next for you guys in terms of rolling out the deployment a little bit more broadly last question for you is some of the news that came out today with the expansion of Koopa pay with American Express for example and just some of the other innovations that Koopa is making what are some of your thoughts what are some of the things that excite you about the direction are going in well yes so on the Koopa pay front I'm actually going to be on stage with Ravi tomorrow talking about Koopa pay because Mark Lee card is also a key component of that capability for the first virtual card that they integrated probably I believe it was yeah and and so so I think about payments is sort of the one not the only but one of the next frontiers from a source to pay or a procurement perspective and it's about how do we innovate in the payment space to get away from having that through the old traditional methods of adding suppliers you know detailed information to our vendor masters so that we can then eventually get an invoice and then reconcile payment remittance to invoices and sort of work through there's a lot of cost in that a lot of time and very little speed we want to move the dial on speed the value we want to move the dial on efficiencies and eventually get to a point where we can offer things like early payment discounts so by having control over our our payment process and that's where Koopa pay and the Barclaycard partnership with Koopa pay is really played a key role in making that happen so in q1 we made our commitment to deploy Koopa pay in q1 after we're through some of our deployments through the rest of this year on the base of the platform and look forward to continuing that journey next year on the payment side one last thing that just popped up I was doing some research and the b2c side is transformed much faster a lot of demand from the consumers we talked about that a moment ago do you see what the direction could the pay is going in with Barclays card for example as bringing in some of the consumer implements to start facilitating the acceleration that's needed there and I think yes I think that's exactly right because again when you think about the consumer side of payments or use it we're all using our phones we're using other digital means we're using wearables we're using different ways of buying and paying especially in retail and the first question we have to ask ourselves why can't those innovations be applied in a b2b space now kupah pay is I think a start of sort of that journey and certainly not the end you know destination but certainly I think it sets us off in the right direction yeah we as consumers are quite demanding yes I'll thank you for doing you on the cube ensuring the Barclays spends that our success rate good luck tomorrow in your keynote thank you for having me thank you pleasure I'm Lisa Martin you're watching the cube from cuca inspire London 19 thanks for watching

>> Announcer: Live from Boston, Massachusetts. It's theCUBE! Covering ZertoCON 2018. Brought to you by Zerto. >> This is theCUBE. I'm Paul Gillin here on the ground in Boston at ZertoCON 2018, the final day of Zerto's user conference. Here in Boston and joining me now is Avi Raichel. (laughing) I hope I didn't butcher that too badly. Avi Raichel, who is Chief Information Officer at Zerto, and welcome, thanks for joining us. >> Thanks for having me, Paul. >> CIO at a software company is a bit of an unusual position, right? Because you're a user of the product, so you have to give unvarnished feedback to your developers, but you also have input into how the product is built. How does a CIO interact with the product side of the business? >> Yeah, you're spot-on Paul. As the CIO I've got a unique point of view within the management team of the company, because I have the same role as the customers we're trying to sell to. So yeah, the product team and the marketing team and the sales team can use me as a sounding board to see that we're taking the product to the right direction. And of course we use it, so we can give honest feedback on how it is working. >> Do you see CIOs becoming increasingly involved in the resilience issue? Is this becoming a C-level issue? >> Yeah, I believe it is, and this because of the digital transformation. Because almost any organization in the world is going through a digital transformation, one way of another I.T is becoming more and more critical part of the business. So once so much relies on I.T, I.T resilience becomes a significant issue because, let's say your health care provider now, a lot of your operations are digitalized. Can you really afford the down time? What does it mean? So this is become a strategic risk for organisation. You can talk about significant revenue leakage, about huge brand, reputation if you have problems. So, I.T resilience is become massive, every organization needs to have. >> A lot at stake. Talk about your own internal I.T infrastructure. Your use of clouds, and where are your data centers located? >> So, like most organizations, we are in this landscape of hybrid-multi cloud I.T infrastructure. We have few data centers, we have small data center that is on-prem. We have the Colo data center, and of course we use the public clouds and modern, one of them, and I think this will be the reality going forward. I think once the hype of the cloud settled down, everybody understands that you don't put everything in one single cloud. And I.T needs to enter this hybrid reality, and part of I.T resiliency is this workload mobility, and having the option to move workloads between the data centers. >> So what is the business case that you would make for going multi-cloud? >> I think there are a couple of reasons. One, is one of the barriers to going in to the cloud, the public cloud, one of the concerns is the locking. I think organization needs to worry that the putting all the eggs in one basket, and when you craft cloud strategy, one of the things you need to pay attention to is do you have an exit strategy? What happens if tomorrow, prices are not to your liking, or, you don't feel you're getting the right operational level? So you need to be able to move, and make sure this is not a one-way journey that leaves you in doubt. Without options. That is one aspect. The second aspect is, in the end, you will find that many workloads are optimized for a specific cloud. Some things can run better on the dual, some things can run better on the beleur, so, or on Google, so by natural evolvement, you will find yourself with several clouds. And of course, there are workloads that in the end, shouldn't go to the cloud. That's part of the hype, is over, that not everything is cloud-ready, and sometimes you should leave some workloads on-prem or in a Colo data center. That creates this hybrid reality. >> Are you able to dynamically shift workloads between cloud providers, or do you pretty much assign a workload to a specific provider and leave it there? >> You can. You can move them. That's one of the nice things that a Zerto product has not between all the providers in the world, but with the Zerto product, you can actually mobilize from your data center, or to a Dual or AWSM between them. So this is something that, you definitely can do. And I really feel that the next few years we will see more and more of that, as you will start to have smart placement of clouds, depending on commercials, quotas, and optimization. >> Of course, you're a user of your company's product. What features do you find most useful in running the I.T operations at Zerto? >> So, I think that the product started of course from V.R with continuous data protection. And that is the basic usage, and for that this is a great product that can give you the peace of mind that your infrastructure is resilient, and in case something happens, you can recover within, I feel, seconds, within few minutes, which is great. But I also find this workload mobility feature very useful. Just last month, we've mobilized about 50 workloads from our on-premise data center to the Colo data center, and it was just a few hours of work, with the Zerto product and without any downtime, which is fantastic. >> We're just two days out as we record this, two days out from the implementation of the general data protection regulation in Europe, which you do a lot of business in Europe, how has that impacted your role as a CIO? >> I think as a CIO, all these regulations are something that you must address, and you must have an honest look at at all the data flows within the organization, and make sure that you're complying with this regulation, so this is an initiative we took on in the last few months, together with our legal department, to map everything, and make sure that once the regulations go live, we are ready. >> One of that tiny percentage who expects to be ready in time? >> I think at this stage nobody can really say exactly what ready means, because this is new, but we're making an honest effort to complete the mapping, and to make sure that the way we understand, and what our consultants tell us, that the regulations means, that we are complying, with, we feel we are relatively in a good place. This is not something organizations should ignore. >> Right. So as you talk to your peers, to other CIOs and other organizations, what do you see as being the principal priorities they have, over the next 12 months or so? >> So, I think that CIOs, still like I said, with digital transformation, where technology is the engine, driving force will be the customer experience, employee experience. I think everybody is still early-on with the journey to the cloud, and this is something that will still take few years, until everybody completes the journey. Cyber is, of course, strategic risk for companies, so almost all CIOs deal with developing and building security program that tackles not only predict and protect, but also the detect and response capabilities that the organization require. I think finding ways to leverage the data assets within the organization is a great opportunity for I.T. And I think there is a number of technologies that are reaching their maturity stage, things like Artificial Intelligence, machine learning, blockchain and I.O.T. So, CIOs should find a way, the right use case, to implement those technology for the organization. Not for the sake of implementating cool technologies, but because they really bring disruptive innovation that can generate significant business value. >> Very exciting time and also a risky time. >> Yup, of course. >> Avi Raichel, thankyou for joining us on theCUBE. >> Thank you very much. >> We'll be right back. From ZertoCON in Boston, I'm Paul Gillin, this is theCUBE. (upbeat techno music)

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, its theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer event in San Francisco, Spring, 2018. About 100 people, predominantly practitioners, which is a pretty unique event. Not a lot of vendors, a couple of them around, but really a lot of people that are out in the wild doing this work. We're really excited to have a return guest. We last saw him at Spark Summit East 2017. Can you believe I keep all these shows straight? I do not. Alfred Essa, he is the VP, Analytics and R&D at McGraw-Hill Education. Alfred, great to see you again. >> Great being here, thank you. >> Absolutely, so last time we were talking it was Spark Summit, it was all about data in motion and data on the fly, and real-time analytics. You talked a lot about trying to apply these types of new-edge technologies and cutting-edge things to actually education. What a concept, to use artificial intelligence, a machine learning for people learning. Give us a quick update on that journey, how's it been progressing? >> Yeah, the journey progresses. We recently have a new CEO come on board, started two weeks ago. Nana Banerjee, very interesting background. PhD in mathematics and his area of expertise is Data Analytics. It just confirms the direction of McGraw-Hill Education that our future is deeply embedded in data and analytics. >> Right. It's funny, there's a often quoted kind of fact that if somebody came from a time machine from, let's just pick 1849, here in San Francisco, everything would look different except for Market Street and the schools. The way we get around is different. >> Right. >> The things we do to earn a living are different. The way we get around is different, but the schools are just slow to change. Education, ironically, has been slow to adopt new technology. You guys are trying to really change that paradigm and bring the best and latest in cutting edge to help people learn better. Why do you think it's taken education so long and must just see nothing but opportunity ahead for you. >> Yeah, I think the... It was sort of a paradox in the 70s and 80s when it came to IT. I think we have something similar going on. Economists noticed that we were investing lots and lots of money, billions of dollars, in information technology, but there were no productivity gains. So this was somewhat of a paradox. When, and why are we not seeing productivity gains based on those investments? It turned out that the productivity gains did appear and trail, and it was because just investment in technology in itself is not sufficient. You have to also have business process transformation. >> Jeff Frick: Right. >> So I think what we're seeing is, we are at that cusp where people recognize that technology can make a difference, but it's not technology alone. Faculty have to teach differently, students have to understand what they need to do. It's a similar business transformation in education that I think we're starting to see now occur. >> Yeah it's great, 'cause I think the old way is clearly not the way for the way forward. That's, I think, pretty clear. Let's dig into some of these topics, 'cause you're a super smart guy. One thing's talk about is this algorithmic transparency. A lot of stuff in the news going on, of course we have all the stuff with self-driving cars where there's these black box machine learning algorithms, and artificial intelligence, or augmented intelligence, bunch of stuff goes in and out pops either a chihuahua or a blueberry muffin. Sometimes it's hard to tell the difference. Really, it's important to open up the black box. To open up so you can at least explain to some level of, what was the method that took these inputs and derived this outpout. People don't necessarily want to open up the black box, so kind of what is the state that you're seeing? >> Yeah, so I think this is an area where not only is it necessary that we have algorithmic transparency, but I think those companies and organizations that are transparent, I think that will become a competitive advantage. That's how we view algorithms. Specifically, I think in the world of machine learning and artificial intelligence, there's skepticism, and that skepticism is justified. What are these machines? They're making decisions, making judgments. Just because it's a machine, doesn't mean it can't be biased. We know it can be. >> Right, right. >> I think there are techniques. For example, in the case of machine learning, what the machines learns, it learns the algorithm, and those rules are embedded in parameters. I sort of think of it as gears in the black box, or in the box. >> Jeff Frick: Right. >> What we should be able to do is allow our customers, academic researchers, users, to understand at whatever level they need to understand and want to understand >> Right. >> What the gears do and how they work. >> Jeff Frick: Right. >> Fundamental, I think for us, is we believe that the smarter our customers are and the smarter our users are, and one of the ways in which they can become smarter is understanding how these algorithms work. >> Jeff Frick: Right. >> We think that that will allow us to gain a greater market share. So what we see is that our customers are becoming smarter. They're asking more questions and I think this is just the beginning. >> Jeff Frick: Right. >> We definitely see this as an area that we want to distinguish ourselves. >> So how do you draw lines, right? Because there's a lot of big science underneath those algorithms. To different degrees, some of it might be relatively easy to explain as a simple formula, other stuff maybe is going into some crazy, statistical process that most layman, or business, or stakeholders may or may not understand. Is there a way you slice it? Is there kind of wars of magnitude in how much you expose, and the way you expose within that box? >> Yeah, I think there is a tension. The tension traditionally, I think organizations think of algorithms like they think of everything else, as intellectual property. We want to lock down our intellectual property, we don't want to expose that to our competitors. I think... I think that's... We do need to have intellectual property, however, I think many organizations get locked into a mental model, which I don't think is just the right one. I think we can, and we want our customers to understand how our algorithm works. We also collaborate quite a bit with academic researchers. We want validation from the academic research community that yeah, the stuff that you're building is in fact based on learning science. That it has warrant. That when you make claims that it works, yes, we can validate that. Now, where I think... Based on the research that we do, things that we publish, our collaboration with researchers, we are exposing and letting the world know how we do things. At the same time, it's very, very difficult to build an engineer, an architect, scalable solutions that implement those algorithms for millions of users. That's not trivial. >> Right, right, right. >> Even if we give away quite a bit of our secret sauce, it's not easy to implement that. >> Jeff Frick: Right. >> At the same time, I believe and we believe, that it's good to be chased by our competition. We're just going to go faster. Being more open also creates excitement and an ecosystem around our products and solutions, and it just makes us go faster. >> Right, which gives to another transition point, which would you talk about kind of the old mental model of closed IP systems, and we're seeing that just get crushed with open source. Not only open source movements around specific applications, and like, we saw you at Spark Summit, which is an open source project. Even within what you would think for sure has got to be core IP, like Facebook opening up their hardware spec for their data centers, again. I think what's interesting, 'cause you said the mental model. I love that because the ethos of open source, by rule, is that all the smartest people are not inside your four walls. >> Exactly. >> There's more of them outside the four walls regardless of how big your four walls are, so it's more of a significant mental shift to embrace, adopt, and engage that community from a much bigger accumulative brain power than trying to just trying to hire the smartest, and keep it all inside. How is that impacting your world, how's that impacting education, how can you bring that power to bear within your products? >> Yeah, I think... You were in effect quoting, I think it was Bill Joy saying, one of the founders of Sun Microsystems, they're always, you have smart people in your organization, there are always more smarter people outside your organization, right? How can we entice, lure, and collaborate with the best and the brightest? One of the ways we're doing that is around analytics, and data, and learning science. We've put together a advisory board of learning science researchers. These are the best and brightest learning science researcher, data scientists, learning scientists, they're on our advisory board and they help and set, give us guidance on our research portfolio. That research portfolio is, it's not blue sky research, we're on Google and Facebook, but it's very much applied research. We try to take the no-knowns in learning science and we go through a very quick iterative, innovative pipeline where we do research, move a subset of those to product validation, and then another subset of that to product development. This is under the guidance, and advice, and collaboration with the academic research community. >> Right, right. You guys are at an interesting spot, because people learn one way, and you've mentioned a couple times this interview, using good learning science is the way that people learn. Machines learn a completely different way because of the way they're built and what they do well, and what they don't do so well. Again, I joked before about the chihuahua and the blueberry muffin, which is still one of my favorite pictures, if you haven't seen it, go find it on the internet. You'll laugh and smile I promise. You guys are really trying to bring together the latter to really help the former. Where do those things intersect, where do they clash, how do you meld those two methodologies together? >> Yeah, it's a very interesting question. I think where they do overlap quite a bit is... in many ways machines learn the way we learn. What do I mean by that? Machine learning and deep learning, the way machines learn is... By making errors. There's something, a technical concept in machine learning called a loss function, or a cost function. It's basically the difference between your predicted output and ground truth, and then there's some sort of optimizer that says "Okay, you didn't quite get it right. "Try again." Make this adjustment. >> Get a little closer. >> That's how machines learn, they're making lots and lots of errors, and there's something behind the scenes called the optimizer, which is giving the machine feedback. That's how humans learn. It's by making errors and getting lots and lots of feedback. That's one of the things that's been absent in traditional schooling. You have a lecture mode, and then a test. >> Jeff Frick: Right. >> So what we're trying to do is incorporate what's called formative assessment, this is just feedback. Make errors, practice. You're not going to learn something, especially something that's complicated, the first time. You need to practice, practice, practice. Need lots and lots of feedback. That's very much how we learn and how machines learn. Now, the differences are, technologically and state of knowledge, machines can now do many things really well but there's still some things and many things, that humans are really good at. What we're trying to do is not have machines replace humans, but have augmented intelligence. Unify things that machines can do really well, bring that to bear in the case of learning, also insights that we provide. Instructors, advisors. I think this is the great promise now of combining the best of machine intelligence and human intelligence. >> Right, which is great. We had Gary Kasparov on and it comes up time and time again. The machine is not better than a person, but a machine and a person together are better than a person or a machine to really add that context. >> Yeah, and that dynamics of, how do you set up the context so that both are working in tandem in the combination. >> Right, right. Alright Alfred, I think we'll leave it there 'cause I think there's not a better lesson that we could extract from our time together. I thank you for taking a few minutes out of your day, and great to catch up again. >> Thank you very much. >> Alright, he's Alfred, I'm Jeff. You're watching theCUBE from the Corinium Chief Analytics Officer event in downtown San Francisco. Thanks for watching. (energetic music)

(upbeat music) >> From the Corinium Chief Analytics Officer Conference, Spring, San Francisco. It's theCUBE! (computerized thrum) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer event in Spring 2018. Really, a ton of practitioners for such a very small event. Super, super intimate, super, super customer stories and practitioners, so we're really excited to have our next guest. She's Kirtida Parikh, she's the Head of Enterprise Business Analytics for Silicon Valley Bank. Welcome. >> Thank you. Good to be here. >> So, what do you think of the show? It's kind of an interesting little event. >> I personally do think that they do an amazing job of organizing this particular event, and out of all the events throughout the year I try to choose and come to this event. >> Right, very good. So, you were just on a panel. >> Kirtida: Yes. >> With a bunch of practitioners. For the folks that didn't attend the panel, what were some of the interesting things that came out of it? Some surprises? >> I think one of the main surprises that I had as one of the panel members is the audience, and the audience actually did say that not 99% of the people have issues working with other virtual teams within the bank, or within their own organization. And many people have tried to figure out how to work together, and that was a very pleasant surprise to me. >> And they're working better together. >> Absolutely. >> From what you said before we turned on the cameras. >> It's a higher productivity when you try to work things out together. >> What's going to happen to shadow IT if the IT department is suddenly easier to work with? >> (laughing) Well, I don't think it is either the department or a person that is difficult to work with. It's, I think, more of a clash of cultures between the two groups. And IT does need, for their own right reasons, to have a process in place and go by the rules so that they can keep the company safe from compliance and regulation perspective. >> Right. >> Whereas analytics, by nature, needs to be creative and has to focus on time to market. And they have to be agile and work really fast enough, and so they can't have the bandwidth to follow the process. So it's more of a clash of two cultures. >> Jeff: Right. >> And I think we need to open up the boundaries and think about virtual efforts to be able to get something done. >> That's interesting, because we always talk about people, process, and tech. And they're called "tech conferences," they're not called "process tech conferences." >> Yeah. >> And so there's a lot of focus on the technology and the new shiny object. >> Mm-hmm (affirmative). >> Whether it's Hadoop, or big data, or Spark, or, you know, all this fun stuff. But as you just said, really, the harder part is the people and the process. >> People. >> And as you just said, culture really is derived from the processes and the responsibilities that you have under your jurisdiction, I guess, so. >> Absolutely. And I personally feel technology is not an end by itself. It's a means to an end. >> Right, right. >> And so the success of a company is how you embrace. How people embrace technology leads to results. >> Right. >> It's neither technology nor people on their own, it's how they embrace technology is what leads to success. >> So I wonder if you can share some insight from your experience at Silicon Valley Bank? You're the head of the analytics group. You know, banks are interesting to me because banks have been data-driven forever, right? >> They have to be. >> There isn't really any money in a room somewhere. It's numbers on a page and numbers on a database. >> Kirtida: Mm-hmm (affirmative). >> And all your products are pretty digital, so, when you start to bring more advanced analytics and you try to change the culture a little bit and run it through the, overused, "digital transformation." What are some of the things you're looking at? How are they transformational? What's kind of the acceptance in the broader team, as you said, when there can be some culture clash, and you have regulation and you're a regulated industry and there's real issues and barriers that you have to overcome? >> Right. So, barriers are always there in any organization, in any industry, particularly when you are introducing a totally new way of making decisions. And when the company is very successful based on making intuition-based decisions, it's hard for you to sell the idea that, no, I can give you information, and that will expedite your decision-making process. So, I think when I joined the bank, I didn't realize, but 99% of my job was to be the change agent. (laughing) >> (laughing) Not an easy job. >> And a storyteller. >> Right, right. >> Because unless you tell the story and sell the idea, you are not able to bring the change. >> Jeff: Right. >> So, yes, there are barriers, and there are always going to be barriers. But I personally like challenges, so I embrace the challenges and try to overcome. So what I ended up doing is, I started thinking about where can I have IT add value, and where are the opportunities where I can value them? So instead of me going to the business and talking to them about what we can do together, I brought that team member along with me. So that visibility and transparency made them feel valued, and they were more than willing to partner with me, and so that changed the landscape to work with IT. But on the other hand, from the business side, I personally think that unless you have one or two examples, and one of my first examples was a business process. And it used to take a number of hours, and I reduced it to leave it only 10% of that time. And they said, oh, wow, that does make sense. What can we do more? Can we partner on this? So initially, first quarter, I had 20 questions and requests, and the second quarter... First whole year we had only twenty questions and requests, and the following quarter we had 200 of them. >> Wow. So when you're looking for an opportunity to apply your skills, your knowledge to bring some change to your organization, how much of it is you kind of searching for inefficiencies, say in the internal business process, versus maybe a business stakeholder saying, wow, you know, if we could only do X. Or I have this problem, can you help me find the root cause? Silicon Valley Bank's such a unique institution, because it's got a couple of segments that it really focuses on. >> Kirtida: Mm-hmm (affirmative). >> Obviously in tech, a little-known wine business. I think you guys do a lot of investing there. >> Yes. >> Because tech guys like to open wineries. >> Tech banking. >> (laughing) So you've got some really small specialty segments. So how did you find some of those early opportunities? >> You see, when you do something and it's successful, it's a two-edged sword. Things keep coming, and the demand grows exponentially fast, it's an exponential growth rate. So what we had to do was really focus on what matters the most, and that came only from two-way communication with the business as well as with the executive team. So if the executive team, we realize that this is the revenue-generating opportunities, here is where we can make a difference, we focus on it and show them the value. Or, if it is a process that really needed some attention, and we could benefit from cost effectiveness, so there was kind of an RY framework where we focus on it. But, to be very honest, we didn't have to look far to look for opportunities, just because revenue is the main focus for business as well as executives. >> Right, right, right. >> So it was a two-way communication that helped us really identify, but I didn't have to hunt for opportunities because, you know, that's where your experience come into play. >> Right, right. So, I'm just curious on the revenue side, the question always comes up, how do I get started, how do we get started, how do we get early wins to build momentum in my company? So was it customer retention, was it cross-selling? I mean, what were some of the things that you saw that were revenue-tied, and everybody likes being tied to revenue, where you thought you could have some success? >> So, my idea of really making a difference is very simple. What does the business focus on? How does a bank operate? They have to get new clients, and increase the size of the cake, or the size of the clientele that they have. So, acquisition is one area. >> Jeff: Okay. >> The second is, once you have them, how can you have them deepen their relationship with you so that the switching cost to another bank is higher? >> Jeff: Right. >> And the third is, once they're with you, you also want to retain them in many different ways by increasing client satisfaction. And then, of course, cost effectiveness. How do you plan your staffing needs and capacity? So, I started in each of those areas at least taking up one or two business questions and showing them the value. And now it's covering all those spectrum of businesses. >> That's great. So now you've got more inbound opportunities for places to apply your analytics than you probably have people to apply them. (laughing) >> (laughing) Yes. That's a good problem to have. >> That's a good problem to have. Well, I'd just love to get your take, too, on kind of the higher level view of the democratization of the data. Of the data itself, of the tools to operate the data, and then, of course, hopefully if you've democratized the access and the tools, hopefully when somebody finds something, they actually have the power to implement it. So how have you seen that environment change, not specifically at Silicon Valley Bank, but generically over the last couple years within your career? >> Well, I personally think that, in my career, in different organizations, democratization is a necessity. It's no longer a topic of discussion. It is something you have to do. Because analytics in general is an enabler community, and you can have as many enablers as you have the people who are users. So, how do you really create analytic center of excellence by giving them the ropes and tools to fish for themselves, or to find their own insights and create their own stories. >> Jeff: Right. >> So what I did, and this worked really well, is create a virtual team of analytic center of excellence where it's not only my team members, but it's some other pockets of analytics teams, but at the same time, the users themselves. >> Jeff: Right. >> And they become the advocates of what you do, and as far as tools are concerned, you know, we used to have an era where you have IT control tools to be able to democratize and give the insights, and now it is user-driven tools. So we did move from one end of the spectrum to the other end of the spectrum, so that it becomes easy for the user to actually grasp the insights. >> Right, right. And still maintain control and governance and all that kind of stuff, yeah. >> Oh, yeah. Security, information security control is a big one, and we can maintain that. >> Right, right. >> And as far as the governance and the data, I mean, they're not pulling their own definitions and other things. It's based off of information foundation, which is solid and scalable. >> Which is solid. Okay, so, going to give you the last word. You've said the word "story" at least four times. >> Uh-huh. (laughing) >> Maybe more since we sat down, we'll have to check the transcript. I wonder if you could expand a little bit on how valuable storytelling is in this whole process. I think it gets left off a lot, right? >> Mm-hmm (affirmative). >> People want to focus on the math and focus on the technology, and focus on the wiz-bang and the flashing lights and the datacenter, but you keep saying "story." Why do you keep saying story? Why is story so important? >> You have multiple stakeholders. First thing is the executive team, they do not have the time. I mean, they are focusing on so many different aspects that they don't have the time enough for anybody to go through the whole textbook, or whole chapter. So if you can tell them story in 30 seconds in an elevator, or three minutes in a hallway, and then request for 30 minutes, you are bound to get some time with them. And in that short time, would you rather show them the value that you can bring to the table, or would you show them how the sausage is being made? >> Jeff: Right. >> And so that's where one type of storytelling is important, to sell the idea. The second is the working team, who we are working with. And I have seen that unless you tell your story and sell the story, you can't get their buy-in, and the virtual team effort that I was talking about fails miserably. So that's another area where you need to tell the story. >> Jeff: Right. >> And the third is, once you have an analytic product, then how do you get adopters? So to tell the adopter what is in there for them is a storytelling too. >> Right, right. Small detail. >> Yeah. >> Actually getting people to use it for their benefit. >> (laughing) >> All right, well I think this is so important, because as you mentioned a number of times, it's about people, and people working together, teams working together in this collaborative effort to make it happen. As somebody else said, it's a team sport. >> And you know, the interesting that I have seen is now that I come to these conferences, there are five people, at least, in different five companies, they said they've hired a journalist on their team because they realized the storytelling is so important. >> Jeff: Really? >> Yeah, so the hybrid function analytics, we say, requires data engineers, data scientists, statisticians, communicators, storyweavers and tellers, which is a journalist, and then a change agent and project manager. >> That's why they bring theCUBE. >> (laughing) >> Trying to tell the story. So, thank you for sharing your story. >> Thank you so much. >> We really appreciate the time. All right. >> Kirtida: Take care. >> You're watching theCUBE from the Corinium Chief Analytics Officer Summit in San Francisco. Thanks for watching. (computerized music)

>> From the Corinium Chief Analytics Officer Conference Spring San Francisco, it's The Cube >> Hey welcome back, Jeff Frick with The Cube We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring event. We go to Chief Data Officer, this is Chief Analytics Officer. There's so much activity around big data and analytics and this one is really focused on the practitioners. Relatively small event, and we're excited to have another practitioner here today and it's Kevin Bates. He's the VP of Enterprise Data Strategy Execution for Fannie Mae. Kevin, welcome. >> It's a mouthful. Thank you. >> You've got it all. You've got strategy, which is good, and then you've got execution. And you've been at a big Fannie Mae for 15 years according to your LinkedIn, so you've seen a lot of changes. Give us kind of your perspective as this train keeps rolling down the tracks. >> OK. Yeah, so it's been a wild ride I've been there, like you say, for 15 years. When I started off there I was writing code, working on their underwriting systems. And I've been in different divisions including the credit loss division, which had a pretty exciting couple of years back around 2008. >> More exciting than you care to - >> Well, there was certainly a lot going on. Data's been sort of a consistent theme throughout my career, so the data, Fannie Mae not unlike most companies, is really the blood that keeps the entire organism functioning. So over the past few years I've actually moved into the Enterprise Data Division of the company where I have responsibility for delivery, operations, platforms, the whole 9 yards. And that's really given me the unique view of what the company does. It's given me the opportunity to touch most of the different business areas and learn a lot about what we need to do better. >> So how is the perspective changed around the data? Before data was almost a liability because you had to store it, keep it, manage it, and take good care of it. Now it's a core asset and we see the valuations up and down. One on one probably the driver of some of the crazy valuations that you see in a lot of the companies. So how has that added to change and what have you done to take advantage of that shift in attitude? >> Sure, it's a great question. So I think the data has always been the life blood and key ingredient to success for the company, but the techniques of managing the data have changed for sure, and with that the culture has to change and how you think about the data has to change. If you go back 10 years ago all of our data was stored in our data center, which means that we had to pay for all of those servers, and every time data kept getting bigger we had to buy more servers and it almost became like a bad thing. >> That's what I said, almost like a liability >> That's right And as we've certainly started adopting the cloud and technologies associated with the cloud you may step into that thinking "OK, now I don't have to manage my own data center I'll let Amazon or whoever do it for me." But it's much more fundamental than that because as you start embracing the cloud and now storage is no longer a limitation and compute is no longer a limitation the numbers of tools that you use is no longer really a limitation. So as an organization you have to change your way of thinking from "I'm going to limit the number of business intelligence tools that my users can take advantage of" to "How can I support them to use whatever tools they want?" So the mentality around the data I think really goes to how can I make sure the right data is available at the right time with the right quality checks so that everybody can say "yep, I can hang my hat on that data" but then get out of the way and let them self serve from there. It's very challenging, there's a lot of new tools and technologies involved. >> And that's a huge piece of the old innovation game to have the right data for the right people with the right tools and let more people play with it. But you've got this other pesky thing like governance. You've got a lot of legal restrictions and regulations and compliances. So how do you fold that into opening up the goodies, if you will. >> So I think one effort we have is we're building a platform we call the Enterprise Data Infrastructure so for that 85 percent of data at Fannie Mae what we do is loans, we create securities from the loans. And there's liabilities. There's a pretty finite set of data areas that are pretty much consistent at Fannie Mae and everybody uses those data sets. So taking those and calling them enterprise data sets that will be centralized they will be presented to our customers in a uniform way with all of the data quality checks in place. That's the big effort. It means that you're standardizing your data. You're performing a consistent data quality approach on that data and then you're making it available through any number of consumption patterns so that can be applications needed, so I'm integrating applications. It could be warehousing analytics. But it's the same data and it comes from that promise that we've tagged it enterprise data and we've done that good stuff to make sure that it's good, that it's healthy. That we know where we stand so if it's not a good data set we know how to tag it and make it such. For all the other data around we have to let our business partners be accountable for how they're enriching that data and innovating and so forth. But governance is not a - I think in the past another part of your question, governance used to be more of a, slow everybody down but if we can incorporate governance and have implied governance in the platform and then allow the customers to self serve off of that platform, governance becomes really that universal good. That thing that allows you to be confident that you can take the data and innovate with that data. >> So I'm curious how much of the value add now comes from the not enterprise data. The outside the core which you've had forever. What's the increasing importance and overlay of that exterior data to your enterprise data to drive more value out of your enterprise data? >> So that enterprise data like I say may be the 85%, it's just the facts. These are the loans we brought in. Here's how we can aggregate risk or how we can aggregate what we call UPB, or the value of our loans. That is pretty generic and it's intended to be. The third party data sets that our business partners may bring in that they bump up against that data can give them strategic advantages. Also the data that those businesses generate our business lines generate within their local applications which we would not call enterprise data, that's very much their special sauce. That's something that the broader organization doesn't need. Those things are all really what our data scientists and our business people combine to create the value added reports that they use for decisioning and so forth. >> And then I'm curious how the big data and the analytics environment has changed from the old day where you had some PHds and some super bright guys that ran super hard algorithms and it was on Mahogany Row and you put in the request and maybe from down high someday you'll get your request versus really trying to enable a broader set of analysts to have access to that data with a much broader set of tools, enabling a bunch of tools versus picking the one or two winners that are very expensive, you got to limit the seats et cetera. How has that changed the culture of the company as well as the way that you are able to deliver products and deliver new applications if you will? >> So I think that's a work in progress. We still have all the PHds and they still really call the shots. They're the ones that get the call from the Executive Vice President and they want to see something today that tells them what decision they should make. We have to enable them. They were enabled in the past by having people basically hustle to get them what they need. The big change we're trying to make now is to present the data in a common platform where they really can take it and run with it so there is a change in how we're delivering our systems to make sure we have the lowest level of granularity. That we have real time data. there's no longer waiting. And the technology tools that have come out in the past 10 years have enabled that. It's not just about implementing that, making it available to all those Phds. There's another population of analysts that is now empowered where they were not before. The guys that suffered just using excel or access databases that were I would call them not the power users but the empowered analysts. The ones who know the data, know how to query data but they're not hard core quants and they're not developers. Those guys have access to a plethora of tools now that were never available before that allow them to wrangle data from 20 different data sets, align it, ask questions of it. And they're really focused on operations and running our systems in a smoother, lower cost way. So I think the granularity, the timing, and support for that explosion of tools we'll still have the big, heavy SAS and R users that are the quants. I think that's the combination everything has to be supported and we'll support it better with higher quality, with more recent data, but the culture change isn't going to happen even in a few years. It will be a longer term path for larger organizations to really see maybe possibilities where they can restructure themselves based on technology. Right now the technologies are early enough and young enough that I think they're going to wait and see. >> Obviously you have a ton of legacy systems, you have all these tools. You have that core set, your enterprise data that doesn't really change that much. What's the objective down the road? Are you looking to expand on that core set? Is it such a fixture that you can't do anything with it in terms of flexibility? Where do you go from here? if we were to sit down three years from now what are we going to be talking about? >> So two things. One, I hope I'll be looking back with excitement at my huge success at transforming those legacy systems. In particular we have what we call the legacy warehouses that have been around well over 20 years that are limited and have not been updated because we've been trying to retire them for many years. Folding all of that into my core enterprise data infrastructure that will be fully aligned on terminology, on near-real time, all those things. That will be a huge success, I'll be looking back and glowing about how we did that and how we've empowered the business with that core data set that is uniquely available on this platform. They don't need to go anywhere else to find it. The other thing I think we'll see is enabling analysts to utilize cloud-based assets and really be successful working both with our on-premises data center, our own data center-supported applications but also starting to move their heavy running quantitative modeling and all the sorts of things they do into the data lake which will be cloud based and really enabling that as a true kind of empowerment for them so they can use a different sent of tools. They can move all that heavy lifting and the servers they sometimes bring down right now move it into an environment where they can really manage their own performance. I think those are going to be the two big changes three years from now that will feel like we're in the next generation. >> All right. Kevin Bates, projecting the future so we look forward to that day. Thanks for taking a few minutes out of your day. >> Thank you. >> All right, thanks. He's Kevin, I'm Jeff. You're watching The Cube from the Corinium Chief Analytics Officer Event in San Francisco. Thanks for watching. (music)

>> Announcer: From the Corinium Chief Analytics Officer Conference Spring, San Francisco It's theCUBE. >> Hey welcome back, everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring Event about a hundred CAO's as opposed to CDO's talking about big data, transformation and analytics and the role of analytics and a lot of practitioners are really excited to have our next guest. He's up from Mexico City, it's Jose Murillo. He's the chief analytics officer from Banorte. Jose, great to see you. >> Thank you for having me, Jeff. >> Absolutely, so for people that aren't familiar with Banorte give us a quick overview. >> Banorte's the second largest financial group in Mexico. We, for the last, during the last three years were able to leapfrog city bank. >> Congratulations, and as we were talking before we turned the cameras on, you and your project had a big part of that. So before we get in it, you are a chief analytics officer. How did you come in, what's the reporting structure, how do you work within the broader spectrum of the bank? >> Well I moved to Banorte like about five years ago from, I was working at the central bank where I spent about 10 years in the MPC, the Monitor Policy Committee, and I was invited by initially by the president of the board and when the new chief operating officer was named he invited me to, to lead a new analytics business unit that he wanted to create. And that's the way that I arrived there. >> Okay so you report in to the COO. >> He's the COO/CFO, so he's not only a very smart guy but a very powerful guy running the organization. >> And does the CIO also report to him? >> The CIO, the CDO, the CMO report to him. >> Okay so you have a CDO as well Chief Data Officer. >> We have a CDO who I work very close with him. >> We could go for a long time I might not let you leave for lunch. So I'm just curious on the relationship between the CDO and the CAO, the data officer and the analytics officer. We often hear one or the other, it's very seldom that I've heard both. So how do you guys divide and conquer your responsibilities? How do you parse that out? >> I guess he provides the foundation that we need to find analytics projects that are going to transform the financial group and he has been a very good partner in providing the data that we need and basically what we do as the CAO we find those opportunities to improve the efficiency, to bring the customer to the center, and be able to deliver value to our stakeholders. >> Right, so he's really kind of giving you the infrastructure if you will, of making that data available, getting it to you from all various sources, et cetera, that then you can use for your analytics magic on top. >> Exactly >> Okay, so that's very good, so when we sat down you said an exciting report has come out from, I believe it was HBR, about the tremendous ROI that you guys have realized. So you tell the story better than I, what did they find in your recent article? >> Well in the recent article from the Harvard Business Review is how Banorte has made its analytics business unit pay off. And what we have found in the past two and a half years is we've been able to deliver massive value and by now we have surpassed a billion dollars in net income creation. From analytics projects made on cost saving strategies and revenue generating projects. >> So you paid for yourself just barely >> Yeah. >> No I mean that's such a great story, just barely 'cause it's so it's so important. So as you said, that billion dollars have been realized both in cost savings but more importantly on incremental revenue and that's really the most important thing. >> Exactly >> So how are you measuring that ROI? >> So basically the way we measure it is on cost saving strategies that are related to a risk operational and financial cost. It's the contemporary news effect. And that can be audited. And on the other side, on revenue generating projects, the way we do it is we estimate the customer lifetime value, which is nothing else than the net present value of the relationship with our customers, so we need to estimate survival rates plus the depth of the relationship with our customers. >> So I just love, so you're doing all kinds of projects, you're measuring the value of the projects. What are some of the projects that had a high ROI that you would've never guessed that you guys applied some analytics to and said wow, terrific value relative to what we expected. >> Let me tell you about two types of projects. The first project that we started on was on cost of risk cutting strategies. And we delivered massive value and very quickly. So that helped us gain credibility. And the way we do it, we did it, is like to analyze a dicing of the data where we had excessive cost of risk. And in the first year, actually, that was the first quarter of Operations, we yielded about a 25% incremental value to the credit card business. And after that, we start to work with them and started the discovery data process. And from there, we were able to optimize analytically the cross cell process. And that's a project that has already a three year maturity. And by this time, we are able to sell, without having any bricks or mortars, about 25% of the credit cards sold by the financial group. If we were a territory within the financial group, we would be the largest one with 400 basis points lower on cost of risk, 30% more on activation rates. And it's no surprise that the acquisition cost is 30% less, vis-a-vis our most efficient channel. >> Right, I just want to keep digging down into this, Jose, there's a lot of this stuff to go. I mean, you've been issuing cards forever. So was it just a better way to score customers, was it a better way to avoid the big fraud customers, was it a better way to steal customers maybe from a competitor with a competitive rate that you can afford, I mean, what are some of the factors that allowed you to grow this business in such a big way? >> I guess it's something that has been improving during the first three years. The first thing is that we made like, a very simple cascade on seeing why we were not that efficient cross cell process. And we kind of fixed every part of it. Like on the income estimation models that we had, and we partner with the risk department to improve them. Up to the information that we had on our customers to contact them, and we partner with data governance to improve those. And finally, on the delivery process and all the engaging process with the customers. And it seemed that we were going to find something that was going to be more costly, but it was something that we had at the center of the customers so that it was more likely for them to go and pick up the card and we deliver it to their homes. And finally, that process was much more efficient and the gains that we had, we shared them with our customers. And after three years, we've done things with artificial intelligence to have much better scripts so that we are better able to serve our customers. We do a lot of experimentation, experimentation that we didn't do before. And we use some concepts from behavioral economics to try to explain much better the value proposition to our customers. >> So I just, I love this point, is that it was a bunch of small, it was optimizing lots of little steps and little pieces of the pie that added up to such a significant thing, it wasn't like this magic AI pixie dust. >> Initially, it as a big bang, and then it has been something incremental that has since, it's a project that at the end of the day, we own, and it's something that we are tracking. We are willing to put all the effort to have all the incremental efficiency within the process. >> So people, process, and technology, we talk about, those are the three pieces always to drive organizational change. And usually, the technology is the easy part, the hard part is the people and the process. So as you and your team have started to work with the various lines of businesses for all these different pieces. Promotional piece, customary attention piece, risk and governance piece, cross sale pice, how has their attitude towards your group changed over time as you've started to deliver insight and all this incremental deltas into their business. >> I guess you are hitting just on the spot. Building the models is the easy part. The hard part is to build the consensus around, to change a process that has run for 20 years, there's a lot of inertia. >> Right, right. >> And there are a lot of silos within organizations. So initially, I guess, the credibility that we gained initially helped us move faster. And at the end of the day, I think what happens is the way that we are set up is that the incentives are very well aligned within the different units that need to interact in the sense that we are a unit that is sponsored by the, corporately sponsored, and we make it easier for our partners to attain their goals. So that's, and they don't share the cost of us, so that helps. >> And those are the goals they already had. So you're basically helping them achieve their objectives that they already had better and more efficiently. >> Yeah, and you are pointing out correctly, it's the people, and besides the math, it's a highly, you could say diplomatic or political position in the sense that you need to have all the different partners and stakeholders aligned to change something that has been running for 20 years. >> Right, right. And i just love it, it's a ton of little marginal improvements across a wide variety of tough points, it's so impactful. So as you look forward now, is there another big bang out there, or do you just see kind of this constant march of incremental improvement, and, or are you just going to start getting into more different businesses or kind of different areas in the bank to apply the same process, where do you go next? >> Well, we started with the credit card business, but we moved toward the verticals within the financial group. From mortgages, auto loans, payroll loans, to we are working with the insurance company, the long term savings company. So we've increased the scope of the group. And we moved not only from cost to revenue generating projects. And so far, it has been, we have been on an exponential increase of our impact, I guess that's the big question. The first, we were able to do 46 times our cost. The second year, we made 106 times our cost, the third year, we are close to 200 times our cost with an incremental base. And so far, we've been on this increasing slide. At some point, it's, I guess, we are going to decelerate, but so far, we haven't hit the point. >> Right, the law of big numbers, eventually, you got to, eventually, you'll slow down a little bit. All right, well Jose, I'll give you the last word before we sign off here. Kind of tips and tricks that you would share with a peer if we're sitting around on a Friday afternoon on a back porch. You know, as you've gone through this journey, three and a half years and really sold you and your vision into the company, what would you share with a peer that's kind of starting this journey or starting to run into some of the early hurdles to get past. >> I guess there are two things that I could share. And once you have built a group like this and you have already, the incentives aligned and you have support from the top in the sense that they know that there's no other way they want really to compete and be successful, and suppose that you have all these preconditions set up and suddenly, you have a bunch of really smart people that are coming to a company, so you need to focus on ROI, high ROI projects. I;s very easy to get distracted on non-impactful projects. And I guess, the most important thing is that you have to learn to say no to a lot of things. >> Speaking my language, I love it. Learn to say no, it's the most important thing you'll ever, all right, well Jose, thanks for spending a few minutes and congratulations on all your success, what a great story. >> Thank you for having me, Jeff. >> Absolutely, he's Jose, I'm Jeff, you're watching theCUBE from the Corinium Chief Analytics Officer Summit in downtown San Francisco. (electronic music)

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, it's theCUBE. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer Symposium or Summit in San Francisco at the Parc 55 Hotel. We came up here last year. It's a really small event, very intimate, but a lot of practitioners sharing best practices and we're excited to have a really data-driven company represented, see Scott Zoldi, Chief Analytics Officer from FICO, Scott, great to see you. >> It's great to be here, thanks Jim. >> Absolutely. So, before we jump into it, I was just kind of curious. One of the things that comes up all the time, when we do Chief Data Officer and there's this whole structuring of how do people integrate data organizationally? Does it report to the CIO, the CEO? So, how have you guys done it, where do you report into in the FICO? >> So at FICO, when we work with data, it's generally going up through our CIO, but as part of that we have both the Chief Analytics Officer and the Chief Technology Officer that are also part of that responsibility of ensuring that we organize the data correctly, we have the proper governance in place, right, and the proper sort of concerns around privacy and security in place. >> Right, so you guys have been in the data business forever, I mean, data is your business, so when you hear all this talk about digital transformation and becoming more data-driven as a company, how does that impact a company like FICO? You guys have been doing this forever. What kind of opportunities are there to take, kind of, analytics to the next level? >> For us, I think it's really exciting. So, you're right, we've been at it for 60 years, right? And analytics is at the core of our business, and operationalizing out the data and around bringing better analytics into play. And now there's this new term, you know, Operationalizing Analytics. And so as we look at digital, we look at all the different types of data that are available to decisions and all the computation power that we have available today, it's really exciting now, to see the types of decisions that can be made with all the data and different types of analytics that are available today. >> Right, so what are some of those nuanced decisions? 'Cause, you know, from the outside world looking in, we see, kind of binary decisions, you know either I get approved for the card or not, or I get the unfortunate, you know you card didn't get through, we had a fraud event, I got to call and tell them please turn my card back on. Seems very binary, so as you get beyond the really simple binary, what are some of the things that you guys have been able to do with the business, having a much more obviously nuanced and rich set of data from which to work? >> So one of the things that we focus on is really around having a profile of each and every customer so we can make a better behavioral decision. So we're trying to understand behavior, ultimately, and that behavior can be manifested in terms of making a fraud decision, or a credit decision. But it's really around personalized analytics, essentially like an analytics of one, that allows us to understand that customer very, very well to make a decision around, what is the next sort of opportunity from a business perspective, a retention perspective, or improving that customer experience. Right, and then how much is it is your driving, could you talk about the operationalizing this? So there's operationalizing it inside the computers and the machines that are making judgements, and scoring things, and passing out decisions, versus more the human factor, the human touch. How do you divide which goes where? And how do you prioritize so that more people get more data from which to work with and make decisions, versus just the ones that are driven inside of an algorithm, inside of a machine? >> Yeah, it's a great point, because a lot of times organizations want to apply analytics to the data they have, but they haven't given a thought to the entire operization of that. So we generally look at it in four parts. One is around data, what is the data we need to make a decision, 'cause decisions always come first, business decisions. Where is that data, how do we gather it and then make it available? Next stage, what are the analytics that we want to apply? And that involves the time that we need to make a decision and how to make that decision over time. And then comes the people part, right? What is the process to work with that score, record the use of, let's say, an analytic, what was the outcome, was it more positive or based on using that analytic, right? And incorporating that back to make a change to the business over time, make actions over time in terms of improving that process, and that's a continual sort of process that you have to have when you operationalize analytics. Otherwise, this could be a one-off sort of analytic adventure, but not part of the core business. >> Right, and you don't want that. Now what about the other data, you know third-party data that you've brought in that isn't kind of part your guys' core? Obviously you have a huge corpus of your own internal data and through your partner financial institutions, but have you started to pull in more kind of third-party data, social data, other types of things to help you build that behavioral model? >> It kind of depends on the business that we're in and the region that we're in. Some regions, for example, outside the United States they're taking much more advantage of social data and social media, and even mobile data to make, let's say, credit decisions. But we generally are finding that most organizations aren't even looking that up, they already have it housed appropriately and to the maximum extent, and so that's usually where our focus is. Right, so to shift gears about the inside, and there's an interesting term, explainable AI, I've never heard that phrase, so what exactly, when you guys talk about explainable AI, what does that mean? Yeah, so machine-learning is kind of a very, very hot topic today and it's one that is focused on development of machine-learning models that learn relationships in data. And it means that you can leverage algorithms to make decisions based on collecting all this information. Now, the challenge is that these algorithms are much more intelligent than a human being, they're superhuman, but generally they're very difficult to understand how they made the decision, and how they came up with a score. So, explainable AI is around deconstructing and analyzing that model so we can provide examples and reasons for why the model scored the way it did. And that's actually paramount, because today we need to provide explanations as part of regulatory concerns around the use of these models, and so it's a very core part of that fact that as we operationalize analytics, and we use things like machine-learning and artificial intelligence, that explainability, the ability to say why did this model score me this way, is at front and center so we can have that dialogue with a customer and they can understand the reasons, and maybe improve the outcome in the future. >> Right, and was that driven primarily by regulations or because it just makes sense to be able to pull back the onion? On the other hand, as you said, the way machines learn and the way machines operate is very different than the way humans calculate, so maybe, I don't know if there's just some stuff in there that's just not going to make sense to a person. So how do you kind of square that circle? >> So, for us our journey to explainable AI started in the early 90s, so it's always been core to our business because, as you say, it makes common sense that you need to be able to explain that score, and if you're going to have a conversation with the customer. You know, since that time, machine-learning's become much more mainstream. There's over 2,000 start-up companies today all trying to apply machine-learning and AI. >> Right. >> And that's where regulation is coming in, because in the early days we used explainable AI to make sure we understood what the model did, how to explain it to our governance teams, how to explain it to our customers, and the customers explain it to their clients, right? Today, it's around having regulation to make sure that machine-learning and artificial intelligence is used responsibly in business. >> Yeah, it's pretty amazing, and that's why I think we hear so much about augmented intelligence as opposed to artificial intelligence, there's nothing artificial about it. It's very different, but it really is trying to add to, you know, provide a little bit more data, a little bit more structure, more context to people that are trying to make decisions. >> And that's critically important because, you know, very often, the AI or machine-learning will make a decision differently than we will, so it can add some level of insight to us, but we always need that human factor in there to kind of validate the reasons, the explanations, and then make sure that we have that kind of human judgment that's running alongside. >> Right, right. So I can't believe I'm going to sit here and say that it's, whatever it is, May 15th today, the year's almost halfway over. But what are some of your priorities for the balance of the year, what are some of the things you are working on as you look forward? Obviously, FICO's a big data-driven company, you guys have a ton of data, you're in a ton of transactions so you've got kind of a front edge of this whole process. What are you looking at, what are some of your short-term priorities, mid-term priorities, as you move through the balance of the year and into next year? >> So number one is around explainable AI, right? And really helping organizations get that ability to explain their models. We're also focused very much around bringing more of the unsupervised analytic technologies to the market. So, very often when you build a model, you have a set of data and a set of outcomes, and you train that model, and you have a model that makes prediction. But more and more, we have parts of our businesses today that where unsupervised analytic models are much more important, in areas like-- >> What does that mean, unsupervised analytics models? >> So, essentially what it means is we're trying to look for patterns that are not normal, unlike any other customers. So if you think about a money launderer, there's going to be very few people that will behave like a money launderer, or an insider, or something along those lines. And so, by building really, really good models of predicting normal behavior any deviation or a mis-prediction from that model could point to something that's very abnormal, and something that should be investigated. And very often, we use those in areas of cyber-security crimes, blatant money laundering, insider fraud, in areas like that where you're not going to have a lot of outcome data, of data to train on, but you need to still make the decisions. >> Wow. Which is really hard for a computer, right? That's the opposite of the types of problems that they like. They like a lot of, a lot of, of revs. >> Correct, so that's why the focus is on understanding good behavior really, really well. And anything different than what it thinks is good could be potentially valuable. >> Alright, Scott, well keep track of all of our scores, we all depend on it. (laughs) >> Scott: We all do. >> Thanks for taking a few minutes out of your day. >> Scott: Appreciate it. >> Alright, he's Scott, I'm Jeff, you are watching theCUBE from San Francisco. Thanks for watching. (upbeat electronic music)

>> Announcer: From the Corinium Chief Analytics Officer Conference. Spring, San Francisco, it's theCUBE! >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring event 2018. About 100 people, really intimate, a lot of practitioners sharing best practices about how they got started, how are they really leveraging data and becoming digitally transformed, analytically driven, data driven. We're excited to have Vishal Morde. He's the VP of Data Science at Barclays, welcome. >> Glad to be here, yeah. >> Absolutely. So we were just talking about Philly, you're back in Delaware, and you actually had a session yesterday talking about Barclays journey. So I was wondering if you could share some of the highlights of that story with us. >> Absolutely, so I had a talk, I opened the conference with data science journey at Barclays. And, we have been on this journey for five years now where we transform our data and analytics practices and really harness the power of Big Data, Machine Learning, and advanced analytics. And the whole idea was to use this power of, newly found power that we have, to make the customer journey better. Better through predictive models, better through deeper and richer consumer insights and better through more personalized customer experience. So that is the sole bet. >> Now it's interesting because we think of financial services as being a data driven, organization already. You guys are way ahead Obviously Wall Street's trading on microseconds. What was different about this digital transformation than what you've been doing for the past? >> I think the key was, we do have all the data in the world. If you think about it, banks know everything about you, right? We have our demographic data, behaviors data. From very granular credit card transactions data, we have your attitudal data, but what we quickly found out that we did not have a strategy to use that data well. To improve our our productivity, profitability of a business and make the customer experience better. So what we did was step one was developing a comprehensive data strategy and that was all about organizing, democratizing, and monetizing our data assets. And step towards, then we went about the monetization part in a very disciplined way. We built a data science lab where we can quickly do a lot of rapid prototyping, look at any idea in machine learning data science, incubate it, validate it, and finally, it was ready for production. >> So I'm curious on that first stage, so you've got all this data, you've been collecting it forever, suddenly now you're going to take an organized approach to it. What'd you find in that first step when you actually tried to put a little synthesis and process around what you already had? >> Well the biggest challenge was, the data came from different sources. So we do have a lot of internal data assets, but we are in the business where we do have to get a lot of external data. Think about credit bureau's, right? Also we have a co-brand business, where we work with partners like Uber, imagine the kind of data we get from them, we have data from American Airlines. So our idea was to create a data governance structure of, we formed a Chief Data Office, the officer forum, we got all the people across our organization to understand the value of data. We are a data driven company as you said but, it took us a while to take that approach and importance of data, and then, data analytics need to be embedded in the organizational DNA, and that's what we're going to focus on first. Data awareness of importance of data, importance of governance as well, and then we could think about democratizing and monetizing, organization's the key for us. >> Right, right, well so how did you organize, how has the Chief Data Officer, what did he or she, who did he or she report to, how did you organize? >> Right, so it was directly reporting to our CEO. >> Jeff: Into the CEO, not into the CIO? >> Not into the CIO. We had a technology office, we do kind of, have a line-of-sight or adopted line with technology, and we made sure that that office has a lot of high-level organization buy-in, they are given budgets to make sure the data governance was in place, key was to get data ownership going. We were using a lot of data, but there was no data ownership. And that was the key, once we know that, who actually owned this data, then you can establish a governance framework, then you can establish how you use this data, and then, how to be monetized. >> So who owned it before you went through this exercise, just kind of, it was just kind of there? >> Yeah, there wasn't a clear ownership, and that's the key for us. Once you establish ownership, then it becomes an asset, we were not treating data as an asset, so there was a change in, kind of mindset, that we had to go through, that data is an asset, and it was used as a means to an end, rather than an asset. >> Right, well what about the conflict with the governance people, I'm sure there was a lot of wait, wait, wait, we just can't open this up to anybody, I'm sure it's a pretty interesting discussion because you have to open it up to more people, but you still have to obviously follow the regs. >> Right, and that's where there are a lot of interesting advancement in data science, where, in the area of data governance, there are new tools out there which lets you track who's actually accessing your data. Once we had that infrastructure, then you can start figuring out okay, how do we allow access, how do we actually proliferate that data across different levels of the organization? Because data needs to be in the hands of decision makers, no matter who they are, could be our CEO, to somebody who's taking our phone calls. So that democratization piece became so important, then we can think about how do you-- you can't directly jump into monetization phase before you get your, all the ducks in order. >> So what was the hardest part, the biggest challenge, of that first phase in organizing the data? >> Creating that 360 degree view on our customers, we had a lot of interesting internal data assets, but we were missing big pieces of the puzzles, where we're looking at, you're trying to create a 360 degree view on a customer, it does take a while to get that right, and that's where the data, setting up the data governance piece, setting up the CDO office, those are the more painful, more difficult challenges, but they lay the foundation for all the the work that we wanted to do, and it allowed to us to kind of think through more methodically about our problems and establish a foundation that we can now, we can take any idea and use it, and monetize it for you. >> So it's interesting you, you said you've been on this journey for five years, so, from zero to a hundred, where are you on your journey do you think? >> Right, I think we're just barely scratching the surface, (both laughing) - I knew you were going to say that >> Because I do feel that, the data science field itself is evolving, I look at data science as like ever-evolving, ever-mutating kind of beast, right? And we just started our journey, I think we are off to a good start, we have really good use-cases, we have starting using the data well, we have established importance of data, and now we are operationalized on the machine learning data science projects as well. So that's been great, but I do feel there's a lot of untapped potential in this, and I think it'll only get better. >> What about on the democratization, we just, in the keynote today there was a very large retailer, I think he said he had 50 PhDs on staff and 150 data centers this is a multi-billion dollar retailer. How do you guys deal with resource constraints of your own data science team versus PhDs, and trying to democratize the decision making out to a much broader set of people? >> So I think the way we've thought about this is think big, but start small. And what we did was, created a data science lab, so what it allowed is to kind of, and it was the cross-functional team of data scientists, data engineers, software developers kind of working together, and that is a primary group. And they were equally supported by your info-sec guys, or data governance folks, so, they're a good support group as well. And with that cross-functional team, now we are able to move from generating an idea, to incubating it, making sure it has a true commercial value and once we establish that, then we'll even move forward operationalization, so it was more surgical approach rather than spending millions and millions of dollars on something that we're not really sure about. So that did help us to manage a resource constraint now, only the successful concepts were actually taken through operationalization, and we before, we truly knew the bottom line impact, we could know that, here's what it means for us, and for consumers, so that's the approach that we took. >> So, we're going to leave it there, but I want to give you the last word, what advice would give for a peer, not in the financial services industry, they're not watching this. (both laugh) But you know, in terms of doing this journey, 'cause it's obviously, it's a big investment, you've been at it for five years, you're saying you barely are getting started, you're in financial services, which is at it's base, basically an information technology industry. What advice do you give your peers, how do they get started, what do they do in the dark days, what's the biggest challenge? >> Yeah, I feel like my strong belief is, data science is a team sport, right? A lot of people come and ask me: how do we find these unicorn data scientist, and my answer always being that, they don't exist, they're figments of imagination. So it's much better to take cross-functional team, with a complimentary kind of skill set, and get them work together, how do you fit different pieces of the puzzle together, will determine the success of the program. Rather than trying to go really big into something, so that's, the team sport is the key concept here, and if I can get the word out across, that'll be really valuable. >> Alright, well thanks for sharin' that, very useful piece of insight! >> Vishal: Absolutely! >> Alright thanks Vishal, I'm Jeff Frick, you are watching theCUBE, from the Corinium Chief Analytic Officer summit, San Francisco, 2018, at the Parc 55, thanks for watching! (bubbly music plays)

(techno music) >> Announcer: From the Corinium Chief Analytics Officer Conference Spring San Francisco. It's theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Parc 55 Hotel at the Corinium Chief Analytics Officer Spring 2018 event, about 100 people, pretty intimate affair. A lot of practitioners here talking about the challenges of Big Data and the challenges of Analytics. We're really excited to have a very special Cube guest. I think he was the first guy to launch his company on theCUBE. It was Big Data New York City 2013. I remember it distinctly. It's Prakash Nanduri, the co-founder and CEO of Paxata. Great to see you. >> Great seeing you. Thank you for having me back. >> Absolutely. You know we got so much mileage out of that clip. We put it on all of our promotional materials. You going to launch your company? Launch your company on theCUBE. >> You know it seems just like yesterday but it's been a long ride and it's been a fantastic ride. >> So give us just a quick general update on the company, where you guys are now, how things are going. >> Things are going fantastic. We continue to grow. If you recall, when we launched, we launched the whole notion of democratization of information in the enterprise with self service data prep. We have gone onto now delivered real value to some of the largest brands in the world. We're very proud that 2017 was the year when massive amount of adoption of Paxata's adaptive information platform was taken across multiple industries, financial services, retail, CPG, high tech, in the OIT space. So, we just keep growing and it's the usual challenges of managing growth and managing, you know, the change in the company as you, as you grow from being a small start-up to know being a real company. >> Right, right. There's good problems and bad problems. Those are the good problems. >> Yes, yes. >> So, you know, we do so many shows and there's two big themes over and over and over like digital transformation which gets way over used and then innovation and how do you find a culture of innovation. In doing literally thousands of these interviews, to me it seems pretty simple. It is about democratization. If you give more people the data, more people the tools to work with the data, and more people the power to do something once they find something in the data, and open that up to a broader set of people, they're going to find innovations, simply the fact of doing it. But the reality is those three simple steps aren't necessarily very easy to execute. >> You're spot on, you're spot on. I like to say that when we talk about digital transformation the real focus should be on the deed . And it really centers around data and it centers around the whole notion of democratization, right? The challenge always in large enterprises is democratization without governance becomes chaos. And we always need to focus on democratization. We need to focus on data because as we all know data is the new oil, all of that, and governance becomes a critical piece too. But as you recall, when we launched Paxata, the entire vision from day one has been while the entire focus around digitization covers many things right? It covers people processes. It covers applications. It's a very large topic, the whole digital transformation of enterprise. But the core foundation to digital transformation, data democratization governance, but the key issue is the companies that are going to succeed are the companies that turn data into information that's relevant for every digital transformation effort. >> Right, right. >> Because if you do not turn raw data into information, you're just dealing with raw data which is not useful >> Jeff: Right >> And it will not be democratized. >> Jeff: Right >> Because the business will only consume the information that is contextual to their need, the information that's complete and the information that is clean. >> Right, right. >> So that's really what we're driving towards. >> And that's interesting 'cause the data, there's so many more sources of data, right? There's data that you control. There's structured data, unstructured data. You know, I used to joke, just the first question when you'd ask people "Where's your data?", half the time they couldn't even, they couldn't even get beyond that step. And that's before you start talking about cleaning it and making it ready and making it available. Before you even start to get into governance and rights and access so it's a really complicated puzzle to solve on the backend. >> I think it starts with first focusing on what are the business outcomes we are driving with digital transformation. When you double-click on digital transformation and then you start focusing on data and information, there's a few things that come to fore. First of all, how do I leverage information to improve productivity in my company? There's multiple areas, whether it is marketing or supply chain or whatever. The second notion is how do I ensure that I can actually transform the culture in my company and attract the brightest and the best by giving them the the environment where democratization of information is actually reality, where people feel like they're empowered to access data and turn it into information and then be able to do really interesting things. Because people are not interested on being subservient to somebody who gives them the data. They want to be saying "Give it to me. "I'm smart enough. "I know analytics. "I think analytically and I want to drive my career forward." So the second thing is the cultural aspect to it. And the last thing, which is really important is every company, regardless of whether you're making toothpicks or turbines, you are looking to monetize data. So it's about productivity. It's about cultural change and attracting of talent. And it's about monetization. And when it comes to monetization of data, you cannot be satisfied with only covering enterprise data which is sitting in my enterprise systems. You have to be able to focus on, oh, how can I leverage the IOT data that's being generated from my products or widgets. How can I generate social immobile? How can I consume that? How can I bring all of this together and get the most complete insight that I need for my decision-making process? >> Right. So, I'm just curious, how do you see it your customers? So this is the chief analytics officer, we go to chief data officer, I mean, there's all these chief something officers that want to get involved in data and marketing is much more involved with it. Forget about manufacturing. So when you see successful cultural change, what drives that? Who are the people that are successful and what is the secret to driving the cultural change that we are going to be data-driven, we are going to give you the tools, we are going to make the investment to turn data which historically was even arguably a liability 'cause it had to buy a bunch o' servers to stick it on, into that now being an asset that drives actionable outcomes? >> You know, recently I was having this exact discussion with the CEO of one of the largest financial institutions in the world. This gentleman is running a very large financial services firm, is dealing with all the potential disruption where they're seeing completely new type of PINTEC products coming in, the whole notion of blockchain et cetera coming in. Everything is changing. Everything looks very dramatic. And what we started talking about is the first thing as the CEO that we always focus on is do we have the right people? And do we have the people that are motivated and driven to basically go and disrupt and change? For those people, you need to be able to give them the right kind of tools, the right kind of environment to empower them. This doesn't start with lip service. It doesn't start about us saying "We're going to be on a digital transformation journey" but at the same time, your data is completely in silos. It's locked up. There is 15,000 checks and balances before I can even access a simple piece of data and third, even when I get access to it, it's too little, too late or it's garbage in, garbage out. And that's not the culture. So first, it needs to be CEO drive, top down. We are going to go through digital transformation which means we are going to go through a democratization effort which means we are going to look at data and information as an asset and that means we are not only going to be able to harness these assets, but we're also going to monetize these assets. How are we going to do it? It depends very much on the business you're in, the vertical industry you play in, and your strengths and weaknesses. So each company has to look at it from their perspective. There's no one size fits all for everyone. >> Jeff: Right. >> There are some companies that have fantastic cultures of empowerment and openness but they may not have the right innovation or the right kind of product innovation skills in place. So it's about looking at data across the board. First from your culture and your empowerment, second about democratization of information which is where a company like Paxata comes in, and third, along with democratization, you have to focus on governance because we are for-profit companies. We have a fiducial responsibility to our customers and our regulators and therefore we cannot have democratization without governance. >> Right, right >> And that's really what our biggest differentiation is. >> And then what about just in terms of the political play inside the company. You know, on one hand, used to be if you held the information, you had the power. And now that's changed really 'cause there's so much information. It's really, if you are the conduit of information to help people make better decisions, that's actually a better position to be. But I'm sure there's got to be some conflicts going through digital transformation where I, you know, I was the keeper of the kingdom and now you want to open that up. Conversely, it must just be transformational for the people on the front lines that finally get the data that they've been looking for to run the analysis that they want to rather than waiting for the weekly reports to come down from on high. >> You bet. You know what I like to say is that if you've been in a company for 10, 15 years and if you felt like a particular aspect, purely selfishly, you felt a particular aspect was job security, that is exactly what's going to likely make you lose your job today. What you thought 10 years ago was your job security, that's exactly what's going to make you lose your job today. So if you do not disrupt yourself, somebody else will. So it's either transform yourself or not. Now this whole notion of politics and you know, struggle within the company, it's been there for as long as, humans generally go towards entropy. So, if you have three humans, you have all sort of issues. >> Jeff: Right, right. >> The issue starts frankly with leadership. It starts with the CEO coming down and not only putting an edict down on how things will be done but actually walking the walk with talking the talk. If, as a CEO, you're not transparent, it you're not trusting your people, if you're not sharing information which could be confidential, but you mention that it's confidential but you have to keep this confidential. If you trust your people, you give them the ability to, I think it's a culture change thing. And the second thing is incentivisation. You have to be able to focus on giving people the ability to say "by sharing my data, "I actually become a hero." >> Right, right. >> By giving them the actual credit for actually delivering the data to achieve an outcome. And that takes a lot of work. But if you do not actually drive the cultural change, you will not drive the digital transformation and you will not drive the democratization of information. >> And have you seen people try to do it without making the commitment? Have you seen 'em pay the lip service, spend a few bucks, start a project but then ultimately they, they hamstring themselves 'cause they're not actually behind it? >> Look, I mean, there's many instances where companies start on digital transformation or they start jumping into cool terms like AI or machine-learning, and there's a small group of people who are kind of the elites that go in and do this. And they're given all the kind of attention et cetera. Two things happen. Because these people who are quote, unquote, the elite team, either they are smart but they're not able to scale across the organization or many times, they're so good, they leave. So that transformation doesn't really get democratized. So it is really important from day one to start a culture where you're not going to have a small group of exclusive data scientists. You can have those people but you need to have a broader democratization focus. So what I have seen is many of the siloed, small, tight, mini science projects end up failing. They fail because number one, either the business outcome is not clearly identified early on or two, it's not scalable across the enterprise. >> Jeff: Right. >> And a majority of these exercises fail because the whole information foundation that is taking raw data turning it into clean, complete, potential consumable information, to feed across the organization, not just for one siloed group, not just one data science team. But how do you do that across the company? That's what you need to think from day one. When you do these siloed things, these departmental things, a lot of times they can fail. Now, it's important to say "I will start with a couple of test cases" >> Jeff: Right, right. >> "But I'm going to expand it across "from the beginning to think through that." >> So I'm just curious, your perspective, is there some departments that are the ripest for being that leading edge of the digital transformation in terms of, they've got the data, they've got the right attitude, they're just a short step away. Where have you seen the great place to succeed when you're starting on kind of a smaller PLC, I don't know if you'd say PLC, project or department level? >> So, it's funny but you will hear this, it's not rocket science. Always they say, follow the money. So, in a business, there are three incentives, making more money, saving money, or staying out of jail. (laughs) >> Those are good. I don't know if I'd put them in that order but >> Exactly, and you know what? Depending on who are you are, you may have a different order but staying out of jail if pretty high on my list. >> Jeff: I'm with you on that one. >> So, what are the ambiants? Risk and compliance. Right? >> Jeff: Right, right. >> That's one of those things where you absolutely have to deliver. You absolutely have to do it. It's significantly high cost. It's very data and analytic centric and if you find a smart way to do it, you can dramatically reduce your cost. You can significantly increase your quality and you can significantly increase the volume of your insights and your reporting, thereby achieving all the risk and compliance requirements but doing it in a smarter way and a less expensive way. >> Right. >> That's where incentives have really been high. Second, in making money, it always comes down to sales and marketing and customer success. Those are the three things, sales, marketing, and customer success. So most of our customers who have been widely successful, are the ones who have basically been able to go and say "You know what? "It used to take us eight months "to be able to even figure out a customer list "for a particular region. "Now it takes us two days because of Paxata "and because of the data prep capabilities "and the governance aspects." That's the power that you can deliver today. And when you see one person who's a line of business person who says "Oh my God. "What used to take me eight months, "now it's done in half a day". Or "What use to take me 22 days to create a report, "is now done in 45 minutes." All of a sudden, you will not have a small kind of trickle down, you will have a tsunami of democratization with governance. That's what we've seen in our customers. >> Right, right. I love it. And this is just so classic too. I always like to joke, you know, back in the day, you would run your business based on reports from old data. Now we want to run your business with stuff you can actually take action on now. >> Exactly. I mean, this is public, Shameek Kundu, the chief data officer of Standard Chartered Bank and Michael Gorriz who's the global CIO of Standard Chartered Bank, they have embraced the notion that information democratization in the bank is a foundational element to the digital transformation of Standard Chartered. They are very forward thinking and they're looking at how do I democratize information for all our 87,500 employees while we maintain governance? And another major thing that they are looking at is they know that the data that they need to manipulate and turn into information is not sitting only on premise. >> Right, right. >> It's sitting across a multi-cloud world and that's why they've embraced the Paxata information platform to be their information fabric for a multi-cloud hybrid world. And this is where we see successes and we're seeing more and more of this, because it starts with the people. It starts with the line of business outcomes and then it starts with looking at it from scale. >> Alright, Prakash, well always great to catch up and enjoy really watching the success of the company grow since you launched it many moons ago in New York City >> yes Fantastic. Always a pleasure to come back here. Thank you so much. >> Alright. Thank you. He's Prakash, I'm Jeff Frick. You're watching theCUBE from downtown San Francisco. Thanks for watching. (techno music)

>> Host: Live from Atlanta, Georgia, it's theCUBE. Covering IFS World Conference 2018, brought to you by IFS. >> Welcome back to theCUBE's live coverage of IFS World Conference here in Atlanta, Georgia. I'm your host, Rebecca Knight. We have a three-guest panel with us today. We have Amy Eager. She is the technical solution architect APS10 evangelist. Mark Large, the head of business systems at Volac, and Jay Gnuse, who is the information technology director at Chief industries. Jay, Mark, Amy, thanks so much for joining us. >> Absolutely. >> Thanks. >> So, tell me Amy, you are an evangelist. I love that you have that in your title, very cool. Tell me a little bit about what you do as a technical solution architect. >> So, I really have sort of a dual role. And technical solution architect is something very important to me, because I have the luxury of working with our prospective customers. So folks that haven't joined the IFS family yet, as well as our existing customers. So, for example I've known Jay for over 10 plus years, and we've worked on various technology aspects together. When customers upgrade and they need new hardware, and all those types of things I provide advice on that. But being a product evangelist, which is my second time. I'm very honored I was the APS9 evangelist, and the applications 10 evangelist, and that means that I get to go out and spread the word about all the benefits that IFS Applications 10 is going to bring to our customer base. So it's very exciting. >> So, you were on the main stage this morning, you're going to be back this afternoon. What are some of your favorite features that you sing from the rooftops? >> Well, I think really we got to see a great look at some of that this morning. The fact that you can interact with a business application simply by typing a few words in, and talking to the arena bot. I think that is really next generation, and going to take us to the next level. Obviously we see more and more of our users wanting to interact with their business applications the way they do with their phones and their consumer apps. So I think that's really very exciting, that certainly would be one feature, and just continuing to extend our user experience through what you saw this morning and the IFS arena client. Really putting the information right in the hands of the users. >> And then in an intuitive way, an easy way. >> Yes, absolutely, if it's not easy they're not using it so. >> That's a very good point, good point. So you both have these buttons on that says, "I'm an early adopter of IFS Applications 10". >> Yes. >> I want to hear from both of you, why were you an early adopter? We'll start with you Mark. >> Well, we were on Apps8 before we joined Apps10, and we did a lot of development work with IFS on Apps8, and it sort of came to fruition within nine, so within nine, a lot of the work we did was available. We didn't upgrade to nine, so when the opportunity with ten came along, we said let's go for it. We have a very good relationship with IFS, so we trust each other, which is key to any upgrade program. And there's so many new features that we can really benefit from as a business. >> And do you feel almost a little bit of an ownership in the sense that you, because you were with Apps9, and you may have even helped IFS sort of think about what could be next. >> Yes, yeah, so we definitely own the system. And in working with R&D they listened to what we're saying, so that, you know, for a software provider, that's a real key thing that they listen to their customers. IFS has definitely done that on Apps10, especially with the arena client, which launched today, and the arena bot, which I was one of the first people to use, so it's a very good tool. >> Very cool. Jay, how about you, why did your company choose to be an early adopter? >> Yeah, we've really got a great team at Chief, so we knew that we could find the problems and create the cases accordingly that are needed, so that IFS could fix them. And then we also have some very creative people on our team that can come up with enhancements, or small tweaks to the program to help and shape it for the future. So that was one of the reasons. We also are really excited about the training that we received, and that was something we were looking forward to, and sure enough we did get a lot of training, lot of questions asked, so we feel like we know IFS better now than we did before thanks to all the training with it, especially with R&D. >> So what are some of the benefits that you're already seeing? So, IFS Apps10 is going to be out to the general market later this year, or early next? >> Amy: Just shortly following the conference. >> Oh, excellent, okay. But you've been playing around with it for a while now, what are some of the benefits that you're seeing? >> One of the features that we like is the ability to edit customer order lines. Multiple lines at the same time, that's a nice new features that makes it a little quicker for our end users. Another one is the part cost history details. We should be able to save some data space by using that one. We also like the ability to move functional objects in the equipment object navigator. That's easier now, before we had left over objects sitting out there, and now we can move them. So that's nice. I really like the ability to pin records on the detail screen. It's kind of hard to describe how it works, but once you see it, it's easy and it's a very nice feature. And then, of course, the manufacturing visualizer is huge for us. With it, our customers have to make changes, and may be late in the game, and with that, we can see what's going on on the shop floor, and we can adjust accordingly. >> Yeah. >> How about you, Jay? I mean, sorry, Mark. (laughing) >> Yeah, so we, that's been a gamechanger for the business, we've gone from the webportal for booking holidays to the arena client, you can do it in the pub, or anywhere on the go, basically. Which is really good. >> I like being in the pub. >> Yeah, the pub's good. >> That works for me. >> And then um, Wadaco, which is the warehouse data collection system, which is all about scanning and handling units, and we manufacture lots of different products for the human and animal markets, and we have a sashay and then a box and an outer box and then onto a pallet, that's lots of transactions, but in Wadaco now, handing an issue you can do it as one transaction, so instead of ten transactions, you've got one transaction. That's a massive gamechanger for the way in which we use the system. >> One of the things we've heard a lot in the keynote, and also just in these interviews that I'm doing here, is just how customer-centric IFS is. And you're customers, so can you tell us a little bit about how, how has this company, earned your trust? What is it, what is it doing that's so special than other companies, that its competitors aren't? >> It's the relationship, it's all down to relationships. We have four values in our company, performance, ambition, collaboration, and trust. And actually, if you take all those four key values, IFS follows them, we work together well with best of breed system, we collaborate well. We're both ambitious businesses, and we trust each other. So if you've got all those four things, you've got a win. >> Right, right, right. So what do you want to see out of the next Woco. So we're at Woco 2018, I know it's only day one, but when you think about, you are valued and dear customers to IFS, so what do you want to see with this relationship going forward? >> Well I think the title this year, the what's coming next, is really important, and it's so great to be able to see all the new features that they're rolling out, and what they're doing, so looking forward to that again next time. >> And it's also the expansion of the arena client, you know, it's quite small at the moment, and there's all the updates coming, we're getting to see them firsthand, but this time next year or in 18 months, the arena client will be massive. And it will be the future. >> It's true, it's true. And how much are you talking with each other? I mean, that's one of the things that we've heard, too, is that customers, you know, it's almost like companies don't want their clients talking to each other but that's not so at Woco, that we want the feedback, want the interaction. What's the experience, can you describe what it's like? >> Well, to me, I mean, it really is sort of the foundation of IFS. I really believe that, I see that, I think we all do. The relationships that we have with our customers, that is something unique. This is not a vendor supplier customer type relationship. We are partners in this, and I think another unique aspect that I would love to point out is the fact that, not only do we want our customers talking to each other, we have perspective customers here. This is not a situation where it's all just long-term customers, we've all known each other. We have perspective customers here that are really gaining insight into what it means to be part of the IFS family. And that's certainly, I'm very proud to say, I've been with IFS for over 20 years, and that's something that has always been the case with our organization, and it means a lot to us for our customers to feel that way as well. >> So, now that the software has been deployed, how are you keeping, how can customers keep it up to date? >> Well, this is another aspect that we, we really introduced this in IFS Applications 9, but it continues on with IFS Applications 10, of course, which are quarterly updates. So, through the lifecycle of a release, an update will come out every quarter, they are cumulative updates, excuse me, so if you see update four comes out, and there isn't anything that you need, then you skip that, maybe you skip the next one, but six comes by and you say, there's something there that I want to apply, it's like applying a patch. So they can really stay current, and that's our driver. And another unique thing about this is not only are we introducing fixes and corrections, we know those exist in software, but we also try and introduce little feature, functional enhancements in these updates as well. So there could be a new feature available to you, or a new lobby, or something new with the bot perhaps coming in an update, so, it's very important to us to not only, of course, deliver fixes and patches when needed, but also functional enhancements throughout the lifecycle of the product. >> And have you experienced this yet? >> Yeah, even on the beta version, we've been taking updates, so we're on update four at the moment, so, uh, and update five's ready. We're going to make the decision if we want to take it now or we wait til RTM which is July for us. Which is quite soon. >> Right, right, no, it's just around the corner. Exactly. And so, I assume you'll be early adopters of Apps 11, too. >> Mark: Maybe. (laughing) There's no reason why not. >> Right, exactly. Well, Jay, Mark, Amy, thank you so much for joining us. It's been a really great conversation here. >> Amy: Thank you so much, thank you guys. >> Jay: Thank you. >> Mark: Thank you. >> I'm Rebecca Knight, we'll have more from IFS WOCO 2018 just after this.

hey welcome back here ready Jeff Rick here with the key we're at the chief data scientist USA conference in downtown San Francisco and we're really excited to have a representative from IBM Sam Lightstone distinguished engineer from IBM join us Sam great to se you thank you very much pleasure to be here absolutely so we cover a ton of IBM events we're at world of Watson world lots of developer conference big the big event in New York earlier this year around strata so you know we're big fans of all the things that IBM is doing and in Rob Thomas and the SPARC group so I could go on and on but we won't go there we'll talk about what you were talking about earlier today and kind of let the cat out of the bag which is always exciting breaking news or breaking Bay there I don't know exactly how we would describe it but you talked about something new IBM data confluence yeah you could share this what's that all about yeah so it's a it's a whole new idea a whole new paradigm that were that we were incubating right now inside of IBM and it's not yet available but we're hoping to start trials in January ish timeframe but it comes from a realization that so much data is about to come upon us from distributed data sources you know everybody's got not only your cell phone but increasingly data is coming from Internet of Things you're gonna have data coming from your car data come from your glasses some smart meters on your house and it's deluge of data and the way that people like to do data science on this data today is they pull this data from these devices and put it into a central repository which is which is a perfectly legitimate strategy but it means that you're creating copies of the data and there's a certain complexity of dragging that data through the internet into some central repository so the idea that we had with data confluence is to leave the data where it is and create and allow the data all these different data sources if you can imagine cars you can imagine cell phones or smart meters on buildings allow them to find one another and collaborate on data science problems like a computational mesh so that we can bring hundreds thousands millions of microprocessors to bear on the data where it lives without moving it around and our theory is not only is that simpler for everyone because the data doesn't have to move around but we can actually bring more computation to bear because every one of those data sources has compute and has persistence and you can multiply the the opportunities right and you took a chance you ran a live demo which is you know always risky business at any anything but but there was a really interesting because concepts that you highlighted kind of organically forming adapting constellation right of these of these sources and the example you use they were solar panels but for them to do this kind of automatically if you will as opposed to someone going in and scripting and building the structure because tomorrow as you demonstrated in your demo you might want to add more or add more so exactly that dynamic functions are pretty pretty interesting yeah and it's a very powerful concept and a very necessary concept and the reason it's so necessary is these devices could be anywhere right and you could have most your devices in New York but a few of them in the Yukon or Alaska or something and you don't want them to all be equally connected right so it's important to be sensitive to create this network that is sort of geospatially aware and connectivity aware not not just sort of hard-coded you know so that so one aspect of that is to be sensitive to network latency and topology that's one reason why it has to be automatic the other reason has to be automatic is if you really want this to scale to thousands of devices you can't have some programmer trying to figure out who connects to what right it's just too hard right so making it really adaptive and automatic is super important another thing that's really important for the Internet of Things is depending on the on the circumstance but if you can imagine cell phones for example you can have a network of thousands millions of phones but at any point in time somebody some of those funds are gonna be turned off so the network has to be adaptive to the possibility that devices go offline right are there intentionally like a phone perhaps unintentionally because they break you know if you have a device on a smart meter it may simply break and then that particular device is offline for a period of time right so the network has to be resilient to that and that's part of what we've been building in particular using technology that we incubated in our UK labs in Hursley so it's it's been a great collaboration across IBM this is not just you know one you know one set of people in one lab but actually a corporate collaboration and really our goal is to make this as you say automatic but I would I would say beyond automatic to make it resilient right there's got to be resilient and fault tolerant because the complexities that we could be dealing with are just too large for human being to deal with right and clearly and distributed right that's the big thing guys we're leveraging IBM bluemix cloud you know all this stuff doesn't happen with with cloud capabilities and the demo you did here you were here the data center was concerned San Jose and the actual data elements were in in Toronto so just you know Amazon and Microsoft and Google are always you know get talked about a lot it within the cloud space but really iBM is making major players and it if not in that top three certainly right there in the fourth position as a leader in cloud and then what this cloud enables and then really kind of with the whole cognitive push you know that's a priority for Ginni and the team to really bring more intelligence he's exactly right and what data confluence you know what we're hoping not only to tap in to data science on distributed systems for IOT and also for enterprise use cases as well but really to take it to the next level of hybrid cloud because these data sources could be in the cloud and they could be on-premises they could be anywhere in the world and you can mix and match and that's really a very powerful capability for our customers many companies now struggling as their data is now part cloud and part on-premises right and in the compute as well right you could deal shift exactly compute from the edge to the cloud you know a dynamic fashion based on what the kind of optimal solution is or as you said sometimes over the edges off lined and you can't do it there it's exactly right so kind of a cool story you said this came out of a out of something called blue unicorn what is blue you know fantastic so blue unicorn was an initiative that a few of us got together on inside of IBM you probably know some of these folks Rob Thomas so I think you've interviewed gears from Karachay Leah and myself and the three of us got together and we said you know we want to find a more effective way to tap in to the creative juices of our staff we got some of the greatest minds in the world working at IBM we hire brilliant people PhDs masters of the top schools all over the world and all too often we hire these people and we tell them what they should be working on that wouldn't it be better if we could find a repeatable process for them to come to us and say here's the next big innovation that IBM ssin should have and blue unicorn came out of that desire to tap into and and nurture this creative passion of of our staff and was really designed almost like an internal VC initiative so people would would come to us with proposals and we've got those proposals we start out with hundreds and feted it down to dozens that down to just a small few that we would fund from the ones that we funded you know that would go through periodic reviews until eventually we ended up with a very small set that are still being incubated and and did a confluence happen to have been one of those projects awesome so it's different than kind of the 10% thing this is actually almost like an internal you you put your proposal together you pitch it whereas if it was an internal VC you get funded and then you go do that with your team right one thing I would say is one of the you know as we're setting up we're trying to find ways to make it work make it efficient one of the best filtering factors that we came up with is that people had to show us running code before it was funded right right and that was amazing because that meant people had to work nights and weekends they had to have that level of passion and commitment for their idea to get to that level of vetting and that was incredible that that definitely filtered the people who were super passionate about what they were doing and the people just said yeah I'd like to tinker and that was tremendous okay and then you're here at the show melting a small show tight group kind of multi industry any good takeaway surprises from the last couple days here at the chief data science USA show you know it's been an amazing conference actually and some great speakers some great insights I think one of the most useful insights for me was was I was curious to hear from this audience what is the duration of data that is important to them do they need to see data from the last hour the last month the last year the last 10 years and of course it does vary from problem to problem but many people said you know for the work that I do I need about three months to build a model and then once I have a model I'm really looking at the last two to four weeks of data to gain data science insight and that was a very important point for me especially as we continue on our work on analytics data science and IBM it's very important for us to understand the range of data that that people are using shorter than you seem sure yeah it's shorter because I know certainly in the data warehousing space that I've been working a lot of my career in people do data analytics on you know six months a year or three years right so this is this is it definitely is somewhat of a shift and it tells us something about our society that things are moving faster and the idea that's older than six months is is usually not as interesting anymore yeah really shows kind of the dynamic real-time nature it's not this is analyzing just the old stuff is interesting but not nearly as interesting as being on top of where's the spark stream somebody's other thing is funny Beth Comstock kicked off the GU minds and machines event a couple days ago she said we even walk faster in cities they've done so everything is continuing to speed up right all right so you're from now you're back here what are we gonna be talking about Wow okay well you know we just launched a few months or a few weeks ago actually the the Watson Data Platform a huge event for us and it really is for us the foundation the data foundation of all the cognitive computing that we're that IBM is coming out with it's gonna bring together data science and data storage and collaboration across you know amongst analysts and data scientists together all all one platform for all your data needs I'm hoping that a year from now I'm going to speak to you about how data confluence is a core part of that of that platform and we're gonna be raeng analytics on millions of devices all over the world all right Sam well thanks for taking a few minutes I know you gotta go catch an airplane for stopping by and sharing your insight thank you all right Sam lights on I'm Jeff Creek you're watching the cube thanks for watching

>> live from Boston, Massachusetts. It's the Cube covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day Volante and Stew Minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. Stillman and I have pleased to have Jean Kolker on a Cuba lem. Uh, he's IBM vice president and chief data officer of the Global Technology Services division. And Seth Dobrin who's the Director of Digital Strategies. That Monsanto. You may have seen them in the news lately. Gentlemen. Welcome to the Cube, Jean. Welcome back. Good to see you guys again. Thanks. Thank you. So let's start with the customer. Seth, Let's, uh, tell us about what you're doing here, and then we'll get into your role. >> Yes. So, you know, the CDO summit has been going on for a couple of years now, and I've been lucky enoughto be participating for a couple of a year and 1/2 or so, Um, and you know, really, the nice thing about the summit is is the interaction with piers, um, and the interaction and networking with people who are facing similar challenges from a similar perspective. >> Yes, kind of a relatively new Roland topic, one that's evolved, Gene. We talked about this before, but now you've come from industry into, ah, non regulated environment. Now what's happened like >> so I think the deal is that way. We're developing some approaches, and we get in some successes in regulated environment. Right? And now I feel with And we were being client off IBM for years, right? Using their technology's approaches. Right? So and now I feel it's time for me personally to move on something different and tried to serve our power. I mean, IBM clients respected off in this striking from healthcare, but their approaches, you know, and what IBM can do for clients go across the different industries, right? And doing it. That skill that's very beneficial, I think, for >> clients. So Monsanto obviously guys do a lot of stuff in the physical world. Yeah, you're the head of digital strategy. So what does that entail? What is Monte Santo doing for digital? >> Yes, so, you know, for as head of digital strategies for Monsanto, really? My role is to number one. Help Monsanto internally reposition itself so that we behave and act like a digital companies, so leveraging data and analytics and also the cultural shifts associated with being more digital, which is that whole kind like you start out this conversation with the whole customer first approach. So what is the real impact toe? What we're doing to our customers on driving that and then based on on those things, how can we create new business opportunities for us as a company? Um, and how can we even create new adjacent markets or new revenues in adjacent areas based on technologies and things we already have existing within the company? >> It was the scope of analytics, customer engagement of digital experiences, all of the above, so that the scope is >> really looking at our portfolio across the gamut on DH, seeing how we can better serve our customers and society leveraging what we're doing today. So it's really leveraging the re use factor of the whole digital concept. Right? So we have analytics for geospatial, right? Big part of agriculture is geospatial. Are there other adjacent areas that we could apply some of that technology? Some of that learning? Can we monetize those data? We monetize the the outputs of those models based on that, Or is there just a whole new way of doing business as a company? Because we're in this digital era >> this way? Talked about a lot of the companies that have CEOs today are highly regulated. What are you learning from them? What's what's different? Kind of a new organization. You know, it might be an opportunity for you that they don't have. And, you know, do you have a CDO yet or is that something you're planning on having? >> Yes, So we don't have a CDO We do have someone acts as an essential. he's a defacto CEO, he has all of the data organizations on his team. Um, it's very recent for Monsanto, Um, and and so I think, you know, in terms of from the regular, what can we learn from, you know, there there are. It's about half financial people have non financial people, are half heavily regulated industries, and I think, you know, on the surface you would. You would think that, you know, there was not a lot of overlap, but I think the level of rigor that needs to go into governance in a financial institution that same thought process. Khun really be used as a way Teo really enable Maur R and D. Mohr you know, growth centered companies to be able to use data more broadly and so thinking of governance not as as a roadblock or inhibitor, but really thinking about governance is an enabler. How does it enable us to be more agile as it enable us to beam or innovative? Right? If if people in the company there's data that people could get access to by unknown process of known condition, right, good, bad, ugly. As long as people know they can do things more quickly because the data is there, it's available. It's curated. And if they shouldn't have access it under their current situation, what do they need to do to be able to access that data? Right. So if I would need If I'm a data scientist and I want to access data about my customers, what can I can't? What can and can't I do with that data? Number one doesn't have to be DEA Nana Mayes, right? Or if I want to access in, it's current form. What steps do I need to go through? What types of approval do I need to do to do to access that data? So it's really about removing roadblocks through governance instead of putting him in place. >> Gina, I'm curious. You know, we've been digging into you know, IBM has a very multifaceted role here. You know how much of this is platforms? How much of it is? You know, education and services. How much of it is, you know, being part of the data that your your customers you're using? >> Uh so I think actually, that different approaches to this issues. My take is basically we need Teo. I think that with even cognitive here, right and data is new natural resource worldwide, right? So data service, cognitive za za service. I think this is where you know IBM is coming from. And the BM is, you know, tradition. It was not like that, but it's under a lot of transformation as we speak. A lot of new people coming in a lot off innovation happening as we speak along. This line's off new times because cognitive with something, really you right, and it's just getting started. Data's a service is really new. It's just getting started. So there's a lot to do. And I think my role specifically global technology services is you know, ah, largest by having your union that IBM, you're 30 plus 1,000,000,000 answered You okay? And we support a lot of different industries basically going across all different types of industries how to transition from offerings to new business offerings, service, integrated services. I think that's the key for us. >> Just curious, you know? Where's Monsanto with kind of the adoption of cognitive, You know what? Where are you in that journey? >> Um, so we are actually a fairly advanced in the journey In terms of using analytics. I wouldn't say that we're using cognitive per se. Um, we do use a lot of machine learning. We have some applications that on the back end run on a I So some form of artificial or formal artificial intelligence, that machine learning. Um, we haven't really gotten into what, you know, what? IBM defined his cognitive in terms of systems that you can interact with in a natural, normal course of doing voice on DH that you spend a whole lot of time constantly teaching. But we do use like I said, artificial intelligence. >> Jean I'm interested in the organizational aspects. So we have Inderpal on before. He's the global CDO, your divisional CDO you've got a matrix into your leadership within the Global Services division as well as into the chief date officer for all of IBM. Okay, Sounds sounds reasonable. He laid out for us a really excellent sort of set of a framework, if you will. This is interval. Yeah, I understand your data strategy. Identify your data store says, make those data sources trusted. And then those air sequential activities. And in parallel, uh, you have to partner with line of business. And then you got to get into the human resource planning and development piece that has to start right away. So that's the framework. Sensible framework. A lot of thought, I'm sure, went into it and a lot of depth and meaning behind it. How does that framework translate into the division? Is it's sort of a plug and play and or is there their divisional goals that are create dissonance? Can you >> basically, you know, I'm only 100 plus days in my journey with an IBM right? But I can feel that the global technology services is transforming itself into integrated services business. Okay, so it's thiss framework you just described is very applicable to this, right? So basically what we're trying to do, we're trying to become I mean, it was the case before for many industries, for many of our clients. But we I want to transform ourselves into trusted broker. So what they need to do and this framework help is helping tremendously, because again, there's things we can do in concert, you know, one after another, right to control other and things we can do in parallel. So we trying those things to be put on the agenda for our global technology services, okay. And and this is new for them in some respects. But some respects it's kind of what they were doing before, but with new emphasis on data's A service cognitive as a service, you know, major thing for one of the major things for global technology services delivery. So cognitive delivery. That's kind of new type off business offerings which we need to work on how to make it truly, you know, once a sense, you know, automated another sense, you know, cognitive and deliver to our clients some you value and on value compared to what was done up until recently. What >> do you mean by cognitive delivery? Explained that. >> Yeah, so basically in in plain English. So what's right now happening? Usually when you have a large systems  computer IT system, which are basically supporting lot of in this is a lot of organizations corporations, right? You know, it's really done like this. So it's people run technology assistant, okay? And you know what Of decisions off course being made by people, But some of the decisions can be, you know, simple decisions. Right? Decisions, which can be automated, can standardize, normalize can be done now by technology, okay and people going to be used for more complex decisions, right? It's basically you're going toe. It turned from people around technology assisted toa technology to technology around people assisted. OK, that's very different. Very proposition, right? So, again, it's not about eliminating jobs, it's very different. It's taken off, you know, routine and automata ble part off the business right to technology and given options and, you know, basically options to choose for more complex decision making to people. That's kind of I would say approach. >> It's about scale and the scale to, of course, IBM. When when Gerstner made the decision, Tio so organized as a services company, IBM came became a global leader, if not the global leader but a services business. Hard to scale. You could scare with bodies, and the bigger it gets, the more complicated it gets, the more expensive it gets. So you saying, If I understand correctly, the IBM is using cognitive and software essentially to scale its services business where possible, assisted by humans. >> So that's exactly the deal. So and this is very different. Very proposition, toe say, compared what was happening recently or earlier? Always. You know other. You know, players. We're not building your shiny and much more powerful and cognitive, you know, empowered mouse trap. No, we're trying to become trusted broker, OK, and how to do that at scale. That's an open, interesting question, but we think that this transition from you know people around technology assisted Teo technology around people assisted. That's the way to go. >> So what does that mean to you? How does that resonate? >> Yeah, you know, I think it brings up a good point actually, you know, if you think of the whole litany of the scope of of analytics, you have everything from kind of describing what happened in the past All that to cognitive. Um, and I think you need to I understand the power of each of those and what they shouldn't should be used for. A lot of people talk. You talk. People talk a lot about predictive analytics, right? And when you hear predictive analytics, that's really where you start doing things that fully automate processes that really enable you to replace decisions that people make right, I think. But those air mohr transactional type decisions, right? More binary type decisions. As you get into things where you can apply binary or I'm sorry, you can apply cognitive. You're moving away from those mohr binary decisions. There's more transactional decisions, and you're moving mohr towards a situation where, yes, the system, the silicon brain right, is giving you some advice on the types of decisions that you should make, based on the amount of information that it could absorb that you can't even fathom absorbing. But they're still needs really some human judgment involved, right? Some some understanding of the contacts outside of what? The computer, Khun Gay. And I think that's really where something like cognitive comes in. And so you talk about, you know, in this in this move to have, you know, computer run, human assisted right. There's a whole lot of descriptive and predictive and even prescriptive analytics that are going on before you get to that cognitive decision but enables the people to make more value added decisions, right? So really enabling the people to truly add value toe. What the data and the analytics have said instead of thinking about it, is replacing people because you're never going to replace you. Never gonna replace people. You know, I think I've heard people at some of these conferences talking about, Well, no cognitive and a I is going to get rid of data scientist. I don't I don't buy that. I think it's really gonna enable data scientist to do more valuable, more incredible things >> than they could do today way. Talked about this a lot to do. I mean, machines, through the course of history, have always replaced human tasks, right, and it's all about you know, what's next for the human and I mean, you know, with physical labor, you know, driving stakes or whatever it is. You know, we've seen that. But now, for the first time ever, you're seeing cognitive, cognitive assisted, you know, functions come into play and it's it's new. It's a new innovation curve. It's not Moore's law anymore. That's driving innovation. It's how we interact with systems and cognitive systems one >> tonight. And I think, you know, I think you hit on a good point there when you said in driving innovation, you know, I've run, you know, large scale, automated process is where the goal was to reduce the number of people involved. And those were like you said, physical task that people are doing we're talking about here is replacing intellectual tasks, right or not replacing but freeing up the intellectual capacity that is going into solving intellectual tasks to enable that capacity to focus on more innovative things, right? We can teach a computer, Teo, explain ah, an area to us or give us some advice on something. I don't know that in the next 10 years, we're gonna be able to teach a computer to innovate, and we can free up the smart minds today that are focusing on How do we make a decision? Two. How do we be more innovative in leveraging this decision and applying this decision? That's a huge win, and it's not about replacing that person. It's about freeing their time up to do more valuable things. >> Yes, sure. So, for example, from my previous experience writing healthcare So physicians, right now you know, basically, it's basically impossible for human individuals, right to keep up with spaced of changes and innovations happening in health care and and by medical areas. Right? So in a few years it looks like there was some numbers that estimate that in three days you're going to, you know, have much more information for several years produced during three days. What was done by several years prior to that point. So it's basically becomes inhuman to keep up with all these innovations, right? Because of that decision is going to be not, you know, optimal decisions. So what we'd like to be doing right toe empower individuals make this decision more, you know, correctly, it was alternatives, right? That's about empowering people. It's not about just taken, which is can be done through this process is all this information and get in the routine stuff out of their plate, which is completely full. >> There was a stat. I think it was last year at IBM Insight. Exact numbers, but it's something like a physician would have to read 1,500 periodic ALS a week just to keep up with the new data innovations. I mean, that's virtually impossible. That something that you're obviously pointing, pointing Watson that, I mean, But there are mundane examples, right? So you go to the airport now, you don't need a person that the agent to give you. Ah, boarding pass. It's on your phone already. You get there. Okay, so that's that's That's a mundane example we're talking about set significantly more complicated things. And so what's The gate is the gate. Creativity is it is an education, you know, because these are step functions in value creation. >> You know, I think that's ah, what? The gate is a question I haven't really thought too much about. You know, when I approach it, you know the thinking Mohr from you know, not so much. What's the gate? But where? Where can this ad the most value um So maybe maybe I have thought about it. And the gate is value, um, and and its value both in terms of, you know, like the physician example where, you know, physicians, looking at images. And I mean, I don't even know what the error rate is when someone evaluates and memory or something. And I probably don't want Oh, right. So, getting some advice there, the value may not be monetary, but to me, it's a lot more than monetary, right. If I'm a patient on DH, there's a lot of examples like that. And other places, you know, that are in various industries. That I think that's that's the gate >> is why the value you just hit on you because you are a heat seeking value missile inside of your organisation. What? So what skill sets do you have? Where did you come from? That you have this capability? Was your experience, your education, your fortitude, >> While the answer's yes, tell all of them. Um, you know, I'm a scientist by training my backgrounds in statistical genetics. Um, and I've kind of worked through the business. I came up through the RND organization with him on Santo over the last. Almost exactly 10 years now, Andi, I've had lots of opportunities to leverage. Um, you know, Data and analytics have changed how the company operates on. I'm lucky because I'm in a company right now. That is extremely science driven, right? Monsanto is a science based company. And so being in a company like that, you don't face to your question about financial industry. I don't think you face the same barriers and Monsanto about using data and analytics in the same way you may in a financial types that you've got company >> within my experience. 50% of diagnosis being proven incorrect. Okay, so 50% 05 0/2 summation. You go to your physician twice. Once you on average, you get in wrong diagnosis. We don't know which one, by the way. Definitely need some someone. Garrett A cz Individuals as humans, we do need some help. Us cognitive, and it goes across different industries. Right, technologist? So if your server is down, you know you shouldn't worry about it because there is like system, you know, Abbas system enough, right? So think about how you can do that scale, and then, you know start imagined future, which going to be very empowering. >> So I used to get a second opinion, and now the opinion comprises thousands, millions, maybe tens of millions of opinions. Is that right? >> It's a try exactly and scale ofthe data accumulation, which you're going to help us to solve. This problem is enormous. So we need to keep up with that scale, you know, and do it properly exactly for business. Very proposition. >> Let's talk about the role of the CDO and where you see that evolving how it relates to the role of the CIA. We've had this conversation frequently, but is I'm wondering if the narratives changing right? Because it was. It's been fuzzy when we first met a couple years ago that that was still a hot topic. When I first started covering this. This this topic, it was really fuzzy. Has it come in two more clarity lately in terms of the role of the CDO versus the CIA over the CTO, its chief digital officer, we starting to see these roles? Are they more than just sort of buzzwords or grey? You know, areas. >> I think there's some clarity happening already. So, for example, there is much more acceptance for cheap date. Office of Chief Analytics Officer Teo, Chief Digital officer. Right, in addition to CEO. So basically station similar to what was with Serious 20 plus years ago and CEO Row in one sentence from my viewpoint would be How you going using leverage in it. Empower your business. Very proposition with CDO is the same was data how using data leverage and data, your date and your client's data. You, Khun, bring new value to your clients and businesses. That's kind ofthe I would say differential >> last word, you know, And you think you know I'm not a CDO. But if you think about the concept of establishing a role like that, I think I think the name is great because that what it demonstrates is support from leadership, that this is important. And I think even if you don't have the name in the organization like it, like in Monsanto, you know, we still have that executive management level support to the data and analytics, our first class citizens and their important, and we're going to run our business that way. I think that's really what's important is are you able to build the culture that enable you to leverage the maximum capability Data and analytics. That's really what matters. >> All right, We'll leave it there. Seth Gene, thank you very much for coming that you really appreciate your time. Thank you. Alright. Keep it right there, Buddy Stew and I'll be back. This is the IBM Chief Data Officer Summit. We're live from Boston right back.