>>Hey everyone. Welcome back to the Cube's live coverage of snowflake summit 22 live from Las Vegas. Caesar's forum. Lisa Martin here with Dave Valante. We've got a couple of guests here. We're gonna be talking about every day. AI. You wanna know what that means? You're in the right spot. Kurt UL joins us, the chief customer officer at data ICU and the mod Conn, the head of AI and ML strategy at snowflake guys. Great to have you on the program. >>It's wonderful to be here. Thank you so much. >>So we wanna understand Kurt what everyday AI means, but before we do that for the audience who might not be familiar with data, I could give them a little bit of an overview. What about what you guys do your mission and maybe a little bit about the partnership? >>Yeah, great. Uh, very happy to do so. And thanks so much for this opportunity. Um, well, data IKU, we are a collaborative platform, uh, for enterprise AI. And what that means is it's a software, you know, that sits on top of incredible infrastructure, notably snowflake that allows people from different backgrounds of data, analysts, data, scientists, data, engineers, all to come together, to work together, to build out machine learning models and ultimately the AI that's gonna be the future, uh, of their business. Um, and so we're very excited to, uh, to be here, uh, and you know, very proud to be a, a, a very close partner of snowflake. >>So Amad, what is Snowflake's AI strategy? Is it to, is it to partner? Where do, where do you pick up? And Frank said today, we, we're not doing it all. Yeah. The ecosystem by design. >>Yeah. Yeah, absolutely. So we believe in the best of breed look. Um, I think, um, we, we think that we're the best data platform and for data science and machine learning, we want our customers to really use the best tool for their use cases. Right. And, you know, data ICU is, is our leading partner in that space. And so, you know, when, when you talk about, uh, machine learning and data science, people talk about training a model, but it's really the difficult part and challenges are really, before you train the model, how do you get access to the right data? And then after you train the model, how do you then run the model? And then how do you manage the model? Uh, that's very, very important. And that's where our partnership with, with data, uh, IKU comes in place. Snowflake provides the platform that can process data at scale for the pre-processing bit and, and data IKU comes in and really, uh, simplifies the process for deploying the models and managing the model. >>Got it. Thank >>You. You talk about KD data. Aico talks about everyday AI. I wanna break that down. What do you mean by that? And how is this partnership with snowflake empowering you to deliver that to companies? >>Yeah, absolutely. So everyday AI for us is, uh, you know, kind of a future state that we are building towards where we believe that AI will become so pervasive in all of the business processes, all the decision making that organizations have to go through that it's no longer this special thing that we talk about. It's just the, the day to day life of, uh, of our businesses. And we can't do that without partners like snowflake and, uh, because they're bringing together all of that data and ensuring that there is the, uh, the computational horsepower behind that to drive that we heard that this morning in some of the keynote talking about that broad democratization and the, um, let's call it the, uh, you know, the pressure that that's going to put on the underlying infrastructure. Um, and so ultimately everyday AI for us is where companies own that AI capability. They're building it themselves very broad, uh, participation in the development of that. And all that work then is being pushed down into best of breed, uh, infrastructure, notably of course, snowflake. Well, >>You said push down, you, you guys, you there's a term in the industry push down optimization. What does that mean? How is it evolving? Why is it so important? >>So Amma, do you want to take a first step at that? >>Yeah, absolutely. So, I mean, when, when you're, you know, processing data, so saying data, um, before you train a, uh, a model, you have to do it at scale, that that, that data is, is coming from all different sources. It's human generated machine generated data, we're talking millions and billions of rows of data. Uh, and you have to make sense of it. You have to transform that data into the right kind of features into the right kind of signals that inform the machine learning model that you're trying to, uh, train. Uh, and so that's where, you know, any kind of large scale data processing is automatically pushed down by data IQ, into snowflakes, scalable infrastructure. Um, so you don't get into like memory issues. You don't get into, um, uh, situations where you're where your pipeline is running overnight, and it doesn't finish in time. Right? And so, uh, you can really take advantage of the scalable nature of cloud computing, uh, using Snowflake's infrastructure. So a lot of that processing is actually getting pushed down from data I could down into the scalable snowflake compute engine. How >>Does this affect the life of a data scientist? You always hear a data scientist spend 80% of the time wrangling data. Uh, I presume there's an infrastructure component around that you trying, we heard this morning, you're making infrastructure, my words, infrastructure, self serve, uh, does this directly address that problem and, and talk about that. And what else are you doing to address that 80% problem? >>It, it certainly does, right? Uh, that's how you solve for, uh, data scientists needing to have on demand access to computing resources, or of course, to the, uh, to the underlying data, um, is by ensuring that that work doesn't have to run on their laptop, doesn't have to run on some, you know, constrained, uh, physical machines, uh, in, in a data center somewhere. Instead it gets pushed down into snowflake and can be executed at scale with incredible parallelization. Now what's really, uh, I important is the ongoing development, uh, between the two products, uh, and within that technology. And so today snowflake, uh, announced the introduction of Python within snow park, um, which is really, really exciting, uh, because that really opens up this capability to a much wider audience. Now DataCo provides that both through a visual interface, um, in historically, uh, since last year through Java UDFs, but that's kind of the, the two extremes, right? You have people who don't code on one side, you know, very no code or a low code, uh, population, and then a very high code population. On the other side, this Python, uh, integration really allows us to, to touch really kind the, the fat center of the data science population, who, uh, who, for whom, you know, Python really is the lingua franca that they've been learning for, uh, for decades now. Sure. So >>Talking about the data scientist, I wanna elevate that a little bit because you both are enterprise customers, data ICO, and snowflake Kurt as the chief customer officer, obviously you're with customers all the time. If we look at the macro environment of all the challenges, companies have to be a data company these days, if you're not, you're not gonna be successful. It's how do we do that? Extract insights, value, action, take it. But I'm just curious if your customer conversations are elevating up to the C-suite or, or the board in terms of being able to get democratize access to data, to be competitive, new products, new services, we've seen tremendous momentum, um, on, on the, the part of customer's growth on the snowflake side. But what are you hearing from customers as they're dealing with some of these current macro pains? >>Yeah, no, I, I think it is the conversation today, uh, at that sea level is not only how do we, you know, leverage, uh, new infrastructure, right. You know, they they're, you know, most of them now are starting to have snowflake. I think Frank said, uh, you know, 50% of the, uh, fortune 500, so we can say most, um, have that in place. Um, but now the question is, how do we, how do we ensure that we're getting access to that data, to that, to that computational horsepower, to a broader group of people so that it becomes truly a transformational initiative and not just an it initiative, not just a technology initiative, but really a core business initiative. And that, that really has been a pivot. You know, I've been, you know, with my company now for almost eight years, right. Uh, and we've really seen a change in that discussion going from, you know, much more niche discussions at the team or departmental level now to truly corporate strategic level. How do we build AI into our corporate strategy? How do we really do that in practice? And >>We hear a lot about, Hey, I want to inject data into apps, AI, and machine intelligence into applications. And we've talked about, those are separate stacks. You got the data stack and analytics stack over here. You got the application development, stack the databases off in the corner. And so we see you guys bringing those worlds together. And my question is, what does that stack look like? I took a snapshot. I think it was Frank's presentation today. He had infrastructure at the lowest level live data. So infrastructure's cloud live data. That's multiple data sources coming in workload execution. You made some announcements there. Mm-hmm, <affirmative>, uh, to expend expand that application development. That's the tooling that is needed. Uh, and then marketplace, that's how you bring together this ecosystem. Yes. Monetization is how you turn data into data products and make money. Is that the stack, is that the new stack that's emerging here? Are you guys defining that? >>Absolutely. Absolutely. You talked about like the 80% of the time being spent by data scientists and part of that is actually discovering the right data. Right. Um, being able to give the right access to the right people and being able to go and discover that data. And so you, you, you go from that angle all the way to processing, training a model. And then all those predictions that are insights that are coming out of the model are being consumed downstream by data applications. And so the two major announcements I'm super excited about today is, is the ability to run Python, which is snow park, uh, in, in snowflake. Um, that will do, you know, you can now as a Python developer come and bring the processing to where the data lives rather than move the data out to where the processing lives. Right. Um, so both SQL developers, Python developers, fully enabled. Um, and then the predictions that are coming out of models that are being trained by data ICU are then being used downstream by these data applications for most of our customers. And so that's where number, the second announcement with streamlet is super exciting. I can write a complete data application without writing a single line of JavaScript CSS or HTML. I can write it completely in Python. It's it makes me super excited as, as a Python developer, myself >>And you guys have joint customers that are headed in this direction, doing this today. Where, where can you talk about >>That? Yeah, we do. Uh, you know, there's a few that we're very proud of. Um, you know, company, well known companies like, uh, like REI or emeritus. Um, but one that was mentioned today, uh, this morning by Frank again, uh, Novartis, uh, pharmaceutical company, you know, they have been extremely successful, uh, in accelerating their AI and ML development by expanding access to their data. And that's a combination of, uh, both the data ICU, uh, layer, you know, allowing for that work to be developed in that, uh, in that workspace. Um, but of course, without, you know, the, the underlying, uh, uh, platform of snowflake, right, they, they would not have been able to, to have re realized those, uh, those gains. And they were talking about, you know, very, very significant increases in inefficiency everything from data access to the actual model development to the deployment. Um, it's just really, really honestly inspiring to see. >>And it was great to see Novartis mentioned on the main stage, massive time to value there. We've actually got them on the program later this week. So that was great. Another joint customer, you mentioned re I we'll let you go, cuz you're off to do a, a session with re I, is that right? >>Yes, that's exactly right. So, uh, so we're going to be doing a fireside chat, uh, talking about, in fact, you know, much of the same, all of the success that they've had in accelerating their, uh, analytics, workflow development, uh, the actual development of AI capabilities within, uh, of course that, uh, that beloved brand. >>Excellent guys, thank you so much for joining Dave and me talking about everyday AI, what you're doing together, data ICO, and snowflake to empower organizations to actually achieve that and live it. We appreciate your insights. Thank you both. You guys. Thank you for having us for our guests and Dave ante. I'm Lisa Martin. You're watching the Cube's live coverage of snowflake summit 22 from Las Vegas. Stick around our next guest joins us momentarily.

(upbeat pop music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CubeConversation. >> Hello, everyone welcome to this CUBE Conversation here in Palo Alto, California, I'm John Furrier, co-host of theCUBE. We are here forth Inhi Cho Suh General Manager of IBM Watson, Customer Engagement, Former Cube alumni, I think she's been on dozens of times. Great to see you again. Welcome to our Palo Alto Studios. >> Yeah, great being here, John. >> So, we haven't chatted in awhile. IBM thing just happened, a little bit of a rainy event, here in February. Interesting change over since we last talked, but first give an update on what you're up to these days, what group are you leading, what's new? >> Okay, well first of all, I'm here based in California, which I'm excited about, and I lead our Watson West office, which is our Watson headquarters, here on the west coast, in downtown San Francisco, and we hosted our Think Conference, and at Think I lead with, in IBM, what we call our Watson Customer Engagement Business Unit, which is really the business applications, of how we apply Watson and other disruptive tech to a line of business audiences, both SAS and on premise software, so really excited about the areas of applying AI and machine learning as well as Blockchain to things like supply chain, and logistics, to order management, to next generation of retail. A lot of new, exciting areas. >> Yeah, we've had many conversations over the years from big data to as your career spanned across IBM, and you have a much more horizontal view of things, now. You're horizontally scalable, as we say in the cloud world. What's your observation of the trends these days? Because there's a lot waves. Actually, the waves that you guys announced, was the IBM, Watson NE ware and the cloud private ware. Marvin and I had an amazing conversation that video went viral. This is now getting a big tailwind for IBM. What's your thoughts in general about the overall ecosystem, because you're here in Silicon Valley, you've seen the big waves, you've got another big data world, cloud is here, multi cloud. What's your thoughts on the big mega-trends? >> Yeah, that's a good question. I think the first chapter of cloud, everyone ran to public cloud. When you look at it through the lens of enterprise, though, the hot topic right now in the second chapter is really about not just public cloud, but multi-cloud, hybrid cloud. Meaning, whether it's a private, public, it's about thinking about the applications and the nature of the applications and regardless of where the data sits, what are the implications of actually getting work done? Through, kind of, new container services, new ways of microservices in the development, of how APIs are integrated, and so, the hot topic right now is definitely hybrid cloud, multi cloud. And the work we've done to certify, what we call, IBM cloud private really enables us to not just take any business application to any cloud in our cloud, as well, but actually to enable Watson and Watson based applications also across multi cloud environments. >> So, chapter two, Jenny mentioned that in her key notes, I want to dig into that because we've been talking a lot about multi cloud architecture, and one of the big debates has been, in the industry, oh, don't pick a soul cloud. I've been writing a bunch of content about that at this DOD jedi deal with Amazon and Oracle, fighting for it out there, but that's also happening at the enterprise, but the reality is, everyone has multiple clouds. If you've got a sales force or if you've got this and that and the other thing, you probably have multiple clouds, so it's not so much soul cloud vs. as it is, workloads having a cloud for the right job and that seems to be validated at IBM Think, in talking to the top technical people and in the industry. They all say, pick the right cloud for the job. And we've heard that before in Big Data. Pick the right tool for the job. So, given that, workloads seem to be driving the demand for cloud. Since you're on the app side, how are you seeing that? Because the world's flipped. It used to be infrastructure and software enable the app's capabilities. Now the workloads have infrastructure as code, made with cloud, they're driving the requirements. This is a change over. >> It is a big change and part of, I would say, when people first ran to the cloud, and a lot of the public cloud services were digital SaaS services, where people were wanting to stitch multiple applications across clouds, and that became a challenge, so in this next iteration, that I'm seeing is, really, a couple things. One is, data gravity. So, where does the data actually reside, for the workload that's actually happening? Whether it's the transactions, whether it's customer information, whether it's product information, that's one piece. The second piece is a lot more analytics, right? And the spectrum of analytics running from traditional warehouse capabilities, to more, let's say, larger scale big data projects to full blown advanced algorithms and AI applications, is, people are saying, look, not only do I want to stitch these applications across multiple clouds; I also want to make sure I can actually tap into the data to apply new types of analytics and derive new services and new values out of relationships, understanding of how products are consumed, and so forth. So, for us, when we think about it is, we want to be able to enable that fluid understanding of data across the clouds, as well as protect and be thoughtful about the data privacy rights around it, compliance around GDPR, as well as how we think about the security aspects as well, for the enterprise. >> That is a great point. I think I want to drill down on the data piece, your background on data obviously is going to be key in your job now obviously, it's pretty obvious with Watson, but David Floyd, a wiki bonds research analyst, just posted a taxonomy of hybrid cloud research report that laid out the different kinds of cloud you could have. There's edge clouds, there's all kinds of things from public to edge, so when you look at that, you're thinking, okay, the data plain is the critical nature of the cloud. Now, depending on which cloud architecture for the use case, the workload, whatever, the data plain seems to be this magical opportunity. AI is going to have a big part of that. Can you just talk about how you guys see that evolving? Because, obviously, AI is a killer part of your strategy. This data piece is inter-operating across the clouds. >> Yes. >> Data management governs you're smiling, cause there's a killer answer coming. >> Totally. This is such a great set up. Actually, Ginni even said it in her keynote at Think, which was, you can't have an AI strategy without an information architecture strategy, which is an IA strategy, and information architecture is all about what you said: it's data preparation; understanding the foundation of it, making sure you've got the right governance structure, the integration of it, and then actually how you apply the more advanced analytics on top. So, information architecture and thinking about the data aspects in all kinds of data. Majority of the data actually sits behind, what I would say, the traditional public firewall. So, it sits behind the firewalls of our enterprise clients, like 80 plus percent of it, and then, many of the clients, we actually recently did a study, with about 5,000 senior executives, across many, many thousands of organizations, and 85% of them want to apply AI to improve their customer service, to improve the way they engage their clients and their products and services, so this is a huge opportunity right now for pretty much every organization to think through; kind of their data strategy. Their information architecture strategy, as part of their overall AI strategy. >> So, a question a got on twitter comes up a lot, and, also on my notes here, I wanted to ask you is, how can companies increase transparency trust and mitigate bias in AI? Because this comes up a lot and that's the questions that come in from the community is, Hey, I got my site, my apps running in Germany. I've got users over there, I'm global. I have to manage compliance, I got all this governess now, I'm over my shoulders, kind of a pain in the butt, but also I don't want to have the software be skewed on bias and other things, and then, I also get this whole Facebook dynamic going on, where it's like, I don't trust people holding my data. This is a big, huge issue. >> It is enormous. >> You guys are in the middle of it, what's your thoughts, what's the update, what's the dynamic and what's the solution? >> So, this is a big topic. I think we could do a whole episode just on this topic alone. So, trust and developing trust and transparency in AI should be a fundamental requirement across many, many different types of institutions. So, first of all, the responsibility doesn't sit only with the technology vendors; it's a shared responsibility across government institutions, the consumers, as well as the business leaders, in terms of how they're thinking about it. The more important piece, though, is when you think about the population that's available, that really understands AI, and they're actually coding and developing on it, is that we have to think about the diverse population that's participating in the governance of it, because you don't want just one tribe or one group that's coding and developing the algorithms, or deciding the decision models. >> Like the nerds or the geeks; they're a social aspect, society aspect as well, right? Social science. >> Exactly. I actually just did a recent conversational series with Northwestern Kellogg's business school, around the importance of developing trust and transparency, not only in the algorithms themselves, but the methodology of how you think about culture and value and ethics come into play through different lens, depending on the country you live in, as you kind of referenced, depending on your different values and religious backgrounds. It may because of different institutional and/or policy positions, depending on the nature, and so there has to be a general awareness of this that's thoughtful. Now, why I'm so excited about the work we're doing at IBM is we've actually launched a couple new initiatives. One is, what we call, AI OpenScale, which is really a platform and an opportunity to have the ability to begin to apply AI, see how AI operations and models function in production. We have methodologies in terms of engaging understanding fairness, so there's a 360 degree fairness kit, which is actually available in the open source world, there's a set of tools to understand and train people on recognizing bias, so even just definitions of, what do you mean by bias? It could be things like, group think, it could be, you're just self selecting on certain data sets to reinforce your hypotheses, it could be unconscious levels and it's not just traditionally socially oriented, types of bias. >> It could be data bias, too. It could be data bias, right? >> Totally. Machine generated biases in IOT world, also. >> So, contextual and behavioral biases kind of kick into play here. >> Yeah, but it starts with transparency trust. It also starts with thoughtful governance, it starts with understanding in your position on policy around data privacy, and those things are things that should be educational conversations across the entire industry. >> How far along are we on the progress bar there? I mean, it seems like it's early and we seem to be talking for awhile, but it seems even more early than most people think. Still a lot more work. Your thoughts on where the progress bar is on this whole mash up of tech and social issues around bias and data? Where are we? >> We're really at the early stages, and part of the reason we're at the early stages is I think people have, so far, really applied AI in very simple task oriented applications. The more, what we call, broad AI, meaning multi task work flow applications are starting, and we're also starting seeing in the enterprise. Now, in the enterprise world, you can still have bias, so, for example, when you talked about data bias, one of the simple examples I use is, think about loan approvals. If one of the criteria may be based on gender, you may have a sensitivity around the lack of women owned business leaders, and that could be a scoring algorithm that says, hey, maybe it's a higher risk when in fact, it's not necessarily a higher risk, it's just that the sampling is off, right. So, that would be a detection to say, hey maybe you have sensitivity around that data set, because you actually have an insufficient amount of data. So, part of data detection and understanding biases; where you have sampling of data that's incorrect, where your segmentation could be rethought, where it may just require an additional supervision or like decision making criteria as part of your governance process. >> This is actually a great area for young people to get involved, whether at their universities or curriculum, this kind of seems to be, whether it's political science and/or data science kind of coming together, you kind of have a mash. What's your advice to people watching that might be either in high school, college, or rethinking their career, because this seems to be hot area. >> It is a hot area. I would recommend it for every student at every age, quite frankly and we're at such an early stage that it's not too late to join and you're not too young nor are you too old to actually get in the industry, so that's point one. This is a great time for everyone to get involved. The second piece is, I would just start with online courses that are available, as well as participate in communities and companies like IBM, where we actually make available on a number of our web based applications, that you can actually do some online training and courses to understand the services that we have, to begin to understand the taxonomy and the language, so a very simple set, would be like, learn the language of AI first, and then, as you're learning coding, if you're more technically inclined, there's just a myriad of classes available. >> Final question, before I move on to the topic around inclusion and diversity, machine learning is impacting all verticals. I was just in an interview, talking with Don En-ju-bin-ski, she's got a company where it's neuroscience and machine learning coming together. Machine learning's being impacted all over. We mentioned basic data bias, and machine learning can help there. Machine learning meets blank every vertical, every market, is being impacted machine learning, which will trigger some of the things you're seeing on the app side. Your thoughts, looking at where you've come from in your career at IBM to now, just the evolution of what machine learning has enabled, your thoughts on the impact of machine learning. >> Oh, it's exciting and I'll give you a real simple example, so one of the great things my own team actually did was apply machine learning to, everyone loves the holiday shopping period, right? Between Thanksgiving to New Years, so we actually develop, what we call, Watson Order Optimizer and one of my favorite brands is REI, so the recreational equipment incorporated company, they actually applied our Watson Order Optimizer to optimize in real time. The best place, let's say you want to order a kayak or a T-shirt or a hiking boot, but the best way to create the algorithms to ship from different stores, and shipping from stores, for most retailers, is a high cost variable, because you don't know what the inventory positions are, you don't necessarily know the movement of traffic into that store, you may not even know what the price promotions are, so what was exciting about putting machine learning algorithms to this was, we could actually curate things like shipping and tax information, inventory positions of products in stores, pricing, a movement of goods as part of that calculation. So, this is like a set of business rules that are automatically developed, using Watson, in a way that would be almost impossible for any human to actually come up with all of the possible business roles, right? Because this is such a complex situation, and then you're trying to do it at the peak time, which is, like Black Friday, Cyber Monday Weekend, so we were able to actually apply Watson Machine Learning to create the business roles for when it should be shipped from a warehouse or a particular store. In order to meet the customer requirement, which is the fulfillment of that brand experienced, or the product experienced, so my view is, there are so many different places across the industry, that we could actually apply machine learning to, and my team is really excited about what we've been doing, especially in the next generation of supply chain. >> And it's also causing students to be really attracted to computer science, both men and women. My daughter, who is a senior at Berkeley, is interested in it, so you're starting to see the impact of machine learning is hitting all main stream, which is a good segue to my next question, we've been very passionate, I know it's one of your passions is inclusion and diversity or diversity and inclusion, there's always debates: D before I or I before D? Some say inclusion and diversity or diversity and inclusion. It's all the same thing, there's just a lot of effort going on to bring the tech industry up to par with the reality of the world, and so you have a study out. I've got a copy here. Talk about this study: Women in Leadership and the Priority Paradox. Talk about the study; what was behind it and what were some of the findings? >> Sure, and I'm excited that your daughter, that's a senior in college, is going to be another woman that's entering the workforce, and especially being in tech, so the priority paradox is that we actually looked at over 2,300 organizations, these are some of the top institutions around the world, that are curating and attracting the best talent and skills. Now, when you look at that population, we were surprised to find out that you would think by 2019-2018 that only 18% of those organizations actually had women in senior leadership positions, and what I categorize as senior leading positions, are in the see-swee, as vice presidents, maybe senior executives or senior managers; director level folks. So, that's one piece, which is, wow, given the size and the state where we are in the industry, only 18%: we could do better. Now, why do we believe that? The second piece is, you want the full population of the human capacity to think and creatively solve. Some of the world's biggest complex problems; you don't want a small population of the world trying to do this, so, the second piece of the paradox, which was the most surprising, is that 79% of these companies actually said that formalizing or prioritizing gender, fostering that kind of inclusive culture, was not a business priority, and that they had a harder time actually mapping that gap. Now, in the study, what we actually discovered though, was those companies, that did make it a priority, actually had first mover advantage, and making it a priority is quite simple. It's about understanding how to create that inclusive culture, to allow different perspectives and different experiences to be allowed in the co-creation and development. >> So, first mover advantage, in terms of what? >> Performance, actual business performance, so even though 80% of the organizations that we interviewed actually said that they've not made it a business priority, the 20% that did, we actually saw higher performance in their outcomes, in terms of business performance. >> So, this is actually a business benefit, too. I think your point is, the first mover advantage is saying, those companies that actually brought in the leadership to create that different perspective, had higher performance. >> Absolutely. >> We've talked about this before; one of the things I always say is that, tech is now mainstream, and it's 18% of the target audience of tech isn't the market, it's 50/50 or 51. Some say 51% women/men, so who's building the products for half the audience? So, again, this doesn't make any sense, so this is a good statistic. >> It is, and if you think about the students that are actually graduating out of graduate school, recently, there's actually more women graduating out of grad school than men. When you think about that population that's now entering the workforce, and what's actually happening through the pipeline, I think there's got to be thoughtful focus and programmatic improvements across the industry, around how to develop talent and make sure that different companies and organizations can move. Like you said, problem solve for creating new products that actually serve the world, not just serve certain populations, but also do it in a way that's thoughtful about, kind of, the makeup. >> And the mainstream and prep of tech obviously makes it more attractive, I mean, you're seeing a lot more women thinking about machines, like my daughter, the question is, how do they come in and not lose their footing, mentor-ship? So, what are the priorities that you see the industry needs to do? What are some of the imperatives to keep the pipeline and keep all the mentoring, obviously mentoring is hot, we see the networking built. >> Yeah, mentoring is huge. >> What's your thoughts on the best practices that you've been involved in? >> Some of the best practices we've actually done a number with an IBM, we've done a program called, Tech Re-Entry, so women that have decided to come back into the tech workforce, we actually have a 12 week internship program to do that. Another is a big initiative that we have around P-TECH, which is the next generation of workers aren't just going to have a formal college and or PHD masters type degrees. The next generation, which we're calling, is not necessarily a white collar, blue collar, what we're calling it is, new collar, meaning these are students that are able to combine their equivalent of a high school degree and early college education in one to be kind of, if you think about it, next generation of technical vocational schools, right? That quickly enter the workforce, are able to do jobs in terms of web development, in terms of cloud management, cloud services, it could be next generation of-- >> It's a huge skill gap opportunity, this is a big opportunity for people. >> It is, and we're seeing great adoption. We've seen it on a number of states across the US, this is an effort that we partner with, the states and the governors of each state, because public education has got to be done in a systematic way that you can actually sustain it for many, many years and this is something that we were excited about championing in the state of New York first. >> The ReEntry program and other things, I always tell myself, the technology is so new now you could level up a lot faster than, there's not that linear school kind of mentality, you don't need eight years to learn something. You could literally learn something pretty quickly these days because the gap between you and someone else is so short now, because it's all new skills. >> It's true, it's true. We talk about digital disruption through the lens of businesses, but there's a huge digital disruption through the lens of what you're talking about, which is our individual development and talent, and the ability to learn through so many different channels that's available now, and the focus around micro degrees, micro skills, micro certifications, there's so many ways for everyone to get involved, but I really do encourage everyone across every industry to have some knowledge and basis and understanding of tech, because tech will redefine how services and products are delivered across every category. >> And that's not male or female: that's just everyone. Again, back to technology for good, we can solve technology problems, You guys have been doing it at IBM, solve technology problems, but now the people problem is about getting people empowered, all gender, races, et cetera, the people getting the skills, getting employed, working for clouds, this is an opportunity. >> This is a huge opportunity. I think this is an exciting time. We feel like we're entering this next phase of, what I call, chapter two of cloud, this is chapter two of digital reinvention, of the enterprise, digital reinvention of the individual, actually, and it's an opportunity for every country, every population group to get involved, in so many new and creative ways, and we're at the early foundation stages in terms of both AI development, as well as new capabilities like Blockchain. So, it's an exciting time for everybody. >> Well, that's a whole nother topic. We'll have to bring you back, Inhi. Great to see you, in fact, welcome to Palo Alto. First time in our studio. Let's co-host something together, me and you. We'll do a series: John and Inhi. >> I would love that. That would be fun. I'm excited to be here. >> You can drop by our studio anytime now that you live in Palo Alto, we're neighbors. Inhi Cho Suh here, general manager IBM Watson, customer engagement, friend of theCUBE, here inside our studios, Palo Alto. I'm John Furrier, thanks for watching. (upbeat music)

(upbeat music) >> Live from San Francisco. It's theCUBE covering RSA Conference 2019, brought to you by Forescout. >> Hey welcome back everybody. Jeff Frick here with theCUBE. We're at RSA North America at the newly opened and finally finished Moscone Center. We're here in the Forescout booth, excited to be here. And we've got our next guest who's been coming to this show for a long, long time. He's Dan Burns, the CEO of Optiv. Dan, great to see you. >> Great to see you too, Jeff. Appreciate you having me on the show. >> So you said this is your 23rd RSA. >> Yeah, somewhere right around there. It's got to be and I don't think I've missed any in between. I've missed some Black Hats in there now and again but RSA is just one of those that that I feel like you got to go to. >> Right, right, so obviously the landscape has changed dramatically so we won't go all the way back 23 years. But in the last couple of years as things have really accelerated with the internet and IoT and OT and all these connected devices, autonomous cars. From a threat perspective and from where you sit in the captain's seat, what are you seeing? What are your, kind of your impressions? How are you helping people navigate this? >> Yeah I appreciate that question, Jeff. So it has changed dramatically. There's no doubt about it. So I got into security in 1996. And that was a long time ago so it's really in the infancy of security. And back in '96 when I remember really studying what security was, and by the way back then it was called information security. Now it's cyber security. But it was really straightforward and simple. There were probably two or three threats and vulnerabilities out there right? Some of the early on one so that's one part of the equation. The second part there were probably two or three regulations and standards out there. No more than that. And then when you went over to kind of the third part of the triad and you talk about vendors and technology there were maybe five or six right? You have McAfee, you have Check Point and you had some of the early, early stage companies that were really addressing kind of simplistic things, right? >> Right. >> Firewalling, URL filtering and things like that. And now you fast-forward to today and it's night and day, so much different. So today when we talk about threats and vulnerabilities there are hundreds of millions, if not billions, of threats and vulnerabilities. Number one, big problem. Number two, regulations standards. There's hundreds of them globally. And number three when you look at our great technology partners here and I think there's probably about 3,500 technology partners here on the floor today. Night and day >> Right. >> Nigh and day from '96 to 2019. And that's created a lot of issues, right? A lot of issues which I'm happy to talk about. >> Yeah, complexity and but you've been a great quote of one of the other things I saw doing the research for this interview. You talked about rationalization >> Yeah. >> and how does a CSO rationalize the world in which you just described because they can't hire their way out of it. They can't buy their way out of it. And at some point you're going to have to make trade-off decisions 'cause you can't use all the company's resources just for security. At the same time, you don't want to be in the cover of the Wall Street Journal tomorrow because you have a big breach that you just discovered. >> Yeah >> How do you help >> it's a balancing act >> How do you help them figure this, navigate these choppy waters? >> Yeah so we think Optiv is in a prime space to do that and place to do that. No doubt about it. So let's talk about the complexity that's out there. Now you look at the landscape. You look at the 25, 35 hundred different technology companies out there today. And when we talk to a typical client and we ask a question. How many vendors, how many OEMs do you have to deal with on an annual basis and the response, of course, depending on the size of the organization but let's just take your average small, mid-sized, enterprise client, the response is somewhere between 75 and 90 partners. And then of course we've got shot on our face. >> Just on the security side? >> Just on the security >> That's not counting all their CRM and all their >> That's not IT, that's not anything. That is just to solve >> 75? >> and build their own security programs. And the next response we get from them is we can't do it, we just can't do it. We spend about 90% of our time acting as if I'm the CSO right now, 90 plus percent of our time working with all of these wonderful, great technologies and partners just to establish those relationships and make sure we're going the right things by them and then by us. And so given this complexity in the marketplace, everything that's going on, it's just a prime scenario for what we call ourselves is a global cyber security solutions integrator, right? Being able to, for a lack of a better term, be the gatekeeper for our clients and help them navigate this complexity that's out there in the space. And so the value that we bring, I talk about it in terms of an equation, right? We're all mathematical in nature, typically people in cyber and so when I think about cyber, I think about equations. And the first equation I think abut is a very simplistic one. It's people, it's process and technology. And you need equal focus on all three of those parts of the equation to truly balance things in a matter where you're building a very effective security program. And historically CSOs have really leaned towards the technology side of that equation. >> Right. And now what we're seeing is a balance like we've got to worry about people, right? We've got to find people with that intelligence and knowledge and know-how and wherewithal, right? And we've got to find companies that have that process expertise, the processes, a means to an end. How do I get to a certain outcome? And so what we bring is the people process and technology. All sides of the equation with the ability in masses to help clients plan, build and run their entire security program or parts of it. >> So how, how is it changed with a couple things like cloud computing. >> Yeah. >> So now I'm sure the bad guys use the cloud just like the good guys use the cloud. So the type of scale and resources that they can bring to bear are significantly higher. Just the pure quantity of and variability using AI and machine learning and as we saw in the election really kind of simple Facebook targeting methods that most marketers use, that work at REI to get you to buy a sleeping bag if you looked at tents on your last way in. So how is the role of AI and machine learning now going to impact this balance? And then of course the other thing is all we see is so many open security jobs. You just can't hire enough people. They're just not there. So that's a whole kind of different level of pressure on the CSO. >> Yeah definitely no doubt about it. And there are few companies that can truly build that have enough budget to address cyber on their own. And those today are typically the large financial right? They're typically given massive budgets. >> Right. >> They have massive teams and they're able to minimize the partnerships and really handle a lot of their own stuff internally and go out for special things. But you look at the typical company, small, mid, even some of the large enterprise companies. No, they can't find the resources. They can't get the budget. They can't address everything. And to your point around digital transformation and what's going on in the world there. And that's probably what continues to support 3,500 technology companies out here. >> Right. >> Right? It's the continuous change >> Right. >> That we see in the industry every single day and of course cloud is one of the most recent transformations and obviously a real one which opens up other threat factors and other scenarios that create new vulnerabilities, and new threats and so that the problem just keeps getting bigger exponentially >> So you come in for another 20 years? Is that what you're saying? (laughing) >> How you're, come for another 20 years. I think though eventually, Jeff, I can remember I kind of poke fun at this a little bit. I can remember I think it was Palo Alto, there was a first company that said, hey we're a platform company. And I think that started happening whatever, it was roughly seven years ago. We're a platform company. And I can remember so many people kind of pooh-poohing that. Right, you're not a, nobody's a platform company. Fair enough, fair enough back then. But I'm going to say, fast-forward to today and that's what it's going to happen, have to happen in this industry, Jeff. >> Right, right. >> Eventually we will have to have some large platform companies that can address multiple things within a client's environment, right? And then there will always be the need to to fill gaps with some of the other great new emerging technologies out there so maybe we won't have 3,500 vendors in ten years. Maybe it's 2,000 so there will be consolidation. There will be the platform play >> Right. >> that happens. >> But then you have the addition of public cloud, right? So now a lot of, a lot of infrastructures, they've got some stuff in public cloud. They still have some stuff on their data center, right? So this is kind of hybrid world. Then you add the IoT thing and the OT connectivity back to the IT which is relatively new. So now if you've got this whole other threat factors that you never had to deal with before at all. It's the machines down on the factory floor. You had been pumping out widgets for a long time that are suddenly connected the infrastructure. So the environment that you're trying to apply security to is really evolving at a crazy pace. >> That is, it's a great industry to be in. (Jeff laughs) Every day I wake up, pitch myself I think all our guys do. >> Right. >> What's amazing, I don't see that slowing down, right? So I think that's why some of that balance continues to be there in the future. One of the things that we're seeing in our industry is companies really trying to take this inside-out approach as opposed to this outside-in approach. And I'll tell you the difference. The outside-in approach is it's all of this chaos, right? It's all the chaos that's behind us and we see it right here. It's everybody telling you what you need >> Right. >> and you build it, you building a security program around what's being fed to you externally as opposed to really taking a step back looking at your organization understanding what your company's initiatives and priorities are, right? And your own company's vision, mission and strategy. And I tell people all the time, I don't care if they're part of our company or any company, first thing you should do is understand the vision and the mission and the strategy of the organization you work for. And so that's part of the inside-out approach. Understanding what your company is trying to accomplish and is a security practitioner really wrapping your arms in your mind around that and supporting those initiatives and aligning your security initiatives to the business initiatives >> Right. >> And then doing it through a risk management type of program and feeding that risk management dashboard and information directly to the board >> Right. >> So. >> So I'm curious how the how you approach the kind of the changes now we have state-sponsored attackers. And how, what they're trying to get and why they're trying to get it has maybe changed and the value equation on your assets, that clearly some assets are super valuable and for some information and some things that are kind of classical but now we're seeing different motivations, political motivations, other types of motivations. So they're probably attacking different repositories of data that you maybe didn't think carry that type of value. Are you seeing >> Yeah. >> kind of a change in that both in the way the attacks are executed and what they're trying to get and the value they're trying to extract then just kind of a classic commercial ransomware or I'm just going to grab some money out of your account. >> Yeah I think, I think you are right. And it kind of goes back to the earlier part of the conversation, the number of devices that the attackers can attack are almost infinite right? >> Right. And especially with the edge right? With IoT it's created this thing we call the edge. Devices on street lights. Devices on meters. Devices here, devices there. >> Right, right. >> So the number of devices they can go for is ever increasing, right? which continues to support the need >> Right. and the cause that we all are a part of. And in the ways they're going to do that is going to change as well. There's no question about it. Yeah, so we've seen different ways of doing it. Yes there's no question about it. Back to the state-sponsored it's kind of stuff the way I look at cyber and probably one of my biggest personal concerns is I think about us, people and family right? We all have family is that cyber and ultimately cyber warfare has created this levity, or equalness in terms of countries, right? Where a country like the U.S. or Russia or somebody with massive resources around physical weapons are now no longer necessarily as powerful as they were. So brevity it's just created this field, leveling playing field. So countries like North Korea, countries like Afghanistan and others have a new opportunity to create a pretty bad situation. >> Right, right. And we haven't seen cyber warfare quote and unquote yet. We would call it something a little because they haven't really used it as a mass weapon of destruction but the threat of that being there >> Right. is creating a more of a even playing field. >> Right. >> And that's one of my biggest concerns like what's the next step there. >> Right, and the other thing is really the financial implications. If you don't do it right, it's beyond being embarrassed on the Wall Street Journal. But right GDPR regulations went into place last year. It's now the California data privacy law that's coming into place. >> Yeah. >> People are calling it kind of the GDPR of California. And that may take more of a national footprint as time moves on. It's weird on one hand we're kind of desensitized 'cause there's so many data breaches right? You can't keep track. We don't actually flip past that page on the wall. >> I can't keep track. But on the other hand there is this kind of this renewed, kind of consumer protection of my data that's now being codified into law with significant penalties. So I wonder how that plays into your kind of risk portfolio strategy of deciding how much to invest. How much you need to put into this effort because if you get in trouble, it's expensive. >> Yeah it is. So can be and it will be and it will get even more expensive. And we're still waiting for the lawmakers to levy some pretty heavy fines. We've seen a few but I think there's going to be more and I think you do have to pay more attention to regulations and compliance. But I think it is a balancing act. Back to our inside-out approach that I was talking about. A lot of companies when PCI came out, as you know, Jeff, a lot of companies were guiding their security program by PCI specifically >> Right. >> and only, and that's a very outside-in approach, right? That's not really accounting for the assets that you were talking about earlier. Not all of them. >> Right. >> Some of them. And so I think that's a great point, right? As a CSO, the first thing you've got to understand is what are your assets? What are you trying to protect? >> Right. And our friends here at Forescout do a great job of giving you the visualization of your network, understanding what your assets are. And then I think the next step is placing a dollar value on that. And not many people do that, right. They're, oh here's my assets. >> You're paying >> This one's kind of important >> This one's kind of important. But to get buy-in from the rest of your organization, you need to force the conversation with your counterparts, with your CFO, with your CMO, with anyone who's a partial owner of those assets >> Right. and make them put a dollar amount on. How much do you think that the data on the server is worth? How much do you think the data on this server, how much do you think, and inventory that is part of the asset inventory. And then I think you've got a much better argument as it relates to getting budget and getting buy-in. >> Right. >> Getting buy-in. And I see it a lot where CSOs tend to be, most tend to be a little bit introverted right? >> Right. >> They'd rather hang out there on the second floor and be there with their team. Take a look at the latest threats. Take a look at what's going on, with their (coughs) logs and their data and trying to solve really critical problems. But my recommendations to CSOs is man, build tight relationships across the entire organization and get out there, be out there, be visible. Get buy-in. Do lunch and learns on why cyber is so critical and how our employees can help us on this journey. >> Right, right. Dan you trip into a whole other category that we'll have to leave for next time which is, what is the value of that data 'cause I think that's changed quite a bit over the last little while. But thanks for taking a few minutes >> Absolutely, Jeff. and hopefully have a good 23rd RSA. >> Thank you very much. >> All right. >> I appreciate it. >> He's Dan, I'm Jeff. You're watching theCUBE. We're at RSA in North America at Moscone at the Forescout booth. Thanks for watching. See you next time. (upbeat music)