>> Announcer: TheCUBE presents Ignite22, brought to you by Palo Alto Networks. >> Hey, welcome back to Vegas. Great to have you. We're pleased that you're watching theCUBE. Lisa Martin and Dave Vellante. Day two of theCUBE's coverage of Palo Alto Ignite22 from the MGM Grand. Dave, we're going to be talking about data. >> You know I love data. >> I do know you love data. >> Survey data- >> There is a great new survey that Palo Alto Networks just published yesterday, "What's next in cyber?" We're going to be digging through it with their CMO. Who better to talk about data with than a CMO that has a PhD in machine learning? We're very pleased to welcome to the program, Zeynep Ozdemir, CMO of Palo Alto Networks. Great to have you. Thank you for joining us. >> It's a pleasure to be here. >> First, I got to ask you about your PhD. Your background as a CMO is so interesting and unique. Give me a little bit of a history on that. >> Oh, absolutely, yes. Yes, I admit that I'm a little bit of an untraditional marketing leader. I spent probably the first half of my career as a software engineer and a research scientist in the area of machine learning and speech signal processing, which is very uncommon, I admit that. Honestly, it has actually helped me immensely in my current role. I mean, you know, you've spoken to Lee Klarich, I think a little while ago. We have a very tight and close partnership with product and engineering teams at Palo Alto Networks. And, you know, cybersecurity is a very complex topic. And we're at a critical juncture right now where all of these new technologies, AI, machine learning, cloud computing, are going to really transform the industry. And I think that I'm very lucky, as somebody who's very technically competent in all of those areas, to partner with the best people and the leading company right now. So, I'm very happy that my technical background is actually helping in this journey. >> Dave: Oh, wait, aren't you like a molecular biologist, or something? >> A reformed molecular...yes. >> Yes. >> Okay. Whoa, okay. (group laughs) >> But >> Math guy over here. >> Yeah. You guys just, the story that I tease is... the amount of data in there is unbelievable. This has just started in August, so a few months ago. >> Zeynep: Yeah. >> Fresh data. You surveyed 1300 CXOs globally. >> Zeynep: That's right. >> Across industries and organizations are saying, you know, hybrid work and remote work became status quo like that. >> Yes. >> Couple years ago everyone shifted to multicloud and of course the cyber criminals are sophisticated, and they're motivated, and they're well funded. >> Zeynep: That's right. >> What are some of the things that you think that the survey really demonstrated that validate the direction that Palo Alto Networks is going in? >> That's right. That's right. So we do these surveys because first and foremost, we have to make sure we're aligned with our customers in terms of our product strategy and the direction. And we have to confirm and validate our very strong opinions about the future of the cybersecurity industry. So, but this time when we did this survey, we just saw some great insights, and we decided we want to share it with the broader industry because we obviously want to drive thought leadership and make sure everybody is in the same level field. Some interesting and significant results with this one. So, as you said, this was 1300 C level cybersecurity decision makers and executives across the world. So we had participants from Europe, from Japan, from Asia Pacific, Latin America, in addition to North America. So one of the most significant stats or data points that we've seen was the fact that out of everybody interviewed, 96% of participants had experienced one or more cybersecurity breaches in the past 12 months. That was more than what we expected, to be honest with you. And then 57% of them actually experienced three or more. So those stats are really worth sharing in terms of where the state of cybersecurity is. What also was personally interesting to me was 33% of them actually experienced an operational disruption as a result of a breach, which is a big number. It's one third of participants. So all of these were very interesting. We asked them more detailed questions around you know, how many...like obviously all of them are trying to respond to this situation. They're trying different technologies, different tools and it seems like they're in a point where they're almost have too many tools and technologies because, you know, when you have too many tools and technologies, there's the operational overhead of integrating them. It creates blind spots between them because those tools aren't really communicating with each other. So what we heard from the responders was that on average they were on like 32 tools, 22% was on 50 or more tools, which is crazy. But what the question we asked them was, you know, are you, are you looking to consolidate? Are you looking to go more tools or less tools? Like what are your thoughts on that? And a significant majority of them, like about 77% said they are actively trying to reduce the number of technologies that they're trying to use because they want to actually achieve better security outcomes. >> I wonder if you could comment on this. So early on in the pandemic, we have a partner, survey partner ETR, Enterprise Technology Research. And we saw a real shift of course, 'cause of hybrid work toward endpoint security, cloud security, they were rearchitecting their networks, a new focus on, you know, different thinking about network security and identity. >> Yeah. >> You play in all of those in partner for identity. >> Zeynep: Yeah. >> I almost, my question is, is was there kind of a knee jerk reaction to get point tools to plug some of those holes? >> Zeynep: Yes. >> And now they're...'cause we said at the time, this is a permanent shift in thinking. What we didn't think through it's coming to focus here at this conference is, okay, we did that, but now we created another problem. >> Zeynep: Yeah. Yeah. >> Now we're- >> Yes, yes. You're very right. I think, and it's very natural to do this, right? >> Sure. >> Every time a problem pops up, you want to fix it as quickly as possible. And you look... you survey who can help you with that. And then you kind of get going because cybersecurity is one of those areas where you can't really wait and do, you know, take time to fix those problems. So that happened a lot and it is happening. But what happened as a result of that. For example, I'll give you a data point from the actual survey that answers this very question. When we asked these executives what keeps them like up at night, like what's their biggest concern? A significant majority of them said, oh we're having difficulty with data management. And what that means is that all these tools that they've deployed, they're generating a lot of insights and data, but they're disconnected, right? So there is no one place where you can say, look at it holistically and come to conclusions very fast about how threat actors are moving in an organization. So that's a direct result of this proliferation of tools, if you will. And you're right. And it will...it's a natural thing to deploy products very quickly. But then you have to take a step back and say, how do I make this more effective? How do I bring things together, bring all my data together to be able to get to threats detect threats much faster? >> An unintended consequence of that quick fix. >> And become cyber resilient. We've been hearing a lot about cyber resiliency. >> Yes, yes. >> Recently and something that I was noting in the survey is only 25% of execs said, yeah, our cyber resilience and readiness is high. And you found that there was a lack of alignment between the boards and the executive levels. And we actually spoke with I think BJ yesterday on how are you guys and even some of your partners >> Yeah. >> How are you helping facilitate that alignment? We know security's always a board level- >> Zeynep: Yes. >> Conversation, but the lack of alignment was kind of surprising to me. >> Yeah. Well I think the good news is that I think we... cybersecurity is taking its place in board discussions more and more. Whether there's alignment or not, at least it's a topic, right? >> Yeah. That was also out of the survey that we saw. I think yes, we have a lot of, a big role to play in helping security executives communicate better with boards and c-level executives in their organizations. Because as we said, it's a very complex topic, and it has to be taken from two angles. When there's...it's a board level discussion. One, how are you reducing risk and making sure that you're resilient. Two, how do you think about return on investment and you know, what's the right level of investment and is that investment going to get us the return that we need? >> What do you think of this? So there's another interesting stat here. What keeps executives up at night? >> Mmhm. >> You mentioned difficulty of data management. Normally, the CISO response to what's your number one problem is lack of talent. >> Zeynep: Number three there, yes. Yeah. >> And it is maybe somewhat related to difficulty of data management, but maybe people have realized, you know what? I'm never going to solve this problem by throwing bodies at it. >> Yeah. >> I got to think of a better way to consolidate my data. Maybe partner with a company that can help me do that. And then the second one was scared of being left behind changes in the tech stack. So we're moving so fast to digitize. >> Zeynep: Yes. >> And security's still an afterthought. And so it's almost as though they're kind of rethinking the problems 'cause they know that they can't just solve the issue by throwing, you know, more hires at it 'cause they can't find the people. >> That is...you're absolutely spot on. The thing about cybersecurity skills gap, it's a reality. It's very real. It's a hard place to be. It's hard to ramp up sometimes. Also, there's a lot of turnover. But you're right in the sense that a lot of the manual work that is needed for cybersecurity, it's actually more sort of much easier to tackle with machines- >> Yeah. >> Than humans. It's a funny double click on the stat you just gave. In North America, the responders when we asked them like how they're coping with the skills shortage, they said we're automating more. So we're using more AI, we're using more process automation to make sure we do the heavy lifting with machines and then only present to the people what they're very good at, is making judgements, right? Very sort of like last minute judgment calls. In the other parts of the world, the top answer to that question is how you're tackling cybersecurity skill shortage was, we're actually trying to provide higher wages and better benefits to the existing p... so there's a little bit of a gap between the two. But I think, I think the world is moving towards the former, which is let's do as much as we can with AI and machines and automation in general and then let's make sure we're more in an automation assisted world versus a human first world. >> We also saw on the survey that ransomware was, you know, the big concern in the United States. Not as much, not that it's not a concern >> Lisa: Yeah. >> In other parts of the world. >> Zeynep: Yeah. >> But it wasn't number one. Why do you think that is? Is it 'cause maybe the US has more to lose? Is it, you know, more high profile or- >> Yeah. Look, I mean, yes you're right? So most responders said number one is ransomware. That's my biggest concern going into 2023. And it was for JAPAC and I think EMEA, Europe, it was supply chain attacks. >> Dave: Right. >> So I think US has been hit hard by ransomware in the past year. I think it's like fresh memory and that's why it rose to the top in various verticals. So I'm not surprised with that outcome. I think supply chain is more of a... we've, you know, we've been hit hard globally by that, and it's very new. >> Lisa: Yeah. >> So I think a lot of the European and JAPAC responders are responding to it from a perspective of, this is a problem I still don't know how to solve. You know, like, and it's like I need the right infrastructure to...and I need the right visibility into my software supply chain. It's very top of mind. So those were some of the differences, but you're right. That was a very interesting regional distinction as well. >> How do you take this data and then bring it back to your customers to kind of close the loop? Do you do that? Do you say, okay, hey, we're going to share this data with you, get realtime feedback- >> Zeynep: Yes. >> Dave: We often like to do that with data- >> Zeynep: Absolutely. >> Say okay...'cause you know, when you do a survey like this, you're like, oh, I wish we asked A, B and C. But it gives you, informs you as to where to double click. Is there a system to do that? Or process to do that? >> Yes. Our hope and goal is to do this every year and see how things are changing and then do some historical analysis as to how things are changing as well. But as I said in the very beginning, I think we take this and we say, okay, there's a lot of alignment in these areas, especially for us for our products to see if where our products are deployed to see if some of those numbers vary, you know, per product. Because we address as a company, we address a lot of these concerns. So then it's very encouraging to say, okay, with certain customers, we're going to go, we're going to have develop certain metrics and we're going to measure how much of a difference we're making with these stats. >> Well, I mean, if you can show that you're consolidating- >> Yeah. >> You know, the number of tools and show the business impact- >> Right. >> Exactly. >> Home run. >> Exactly. Yes- >> Speaking of business outcomes, you know, we have so many conversations around everything needs to be outcome-based. Can security become an enabler of business outcomes for organizations? >> Absolutely. Security has to be an enabler. So it's, you know, back to the security lagging behind the evolution of the digital transformation, I don't think it's possible to move fast without having security move fast with digital transformation. I don't think anybody would raise their hands and say, I'm just going to have the most creative, most interesting digital transformation journey. But, you know, security is say, so I think we're past that point where I think generally people do agree that security has to run as fast as digital transformation and really enable those business outcomes that everybody's proud of. So Yes. Yes it is. >> So...sorry. So chicken and egg, digital transformation, cyber transformation. >> Zeynep: Yes. >> Lisa: How are they related? Is one digital leading? >> They are two halves of the perfect solution. They have to coexist because otherwise if you're taking a lot of risk with your digital transformation, is it really worth going through a digital transformation? >> Yeah. >> Yeah. >> So there's a board over here. I'm looking at it and it started out blank. >> Yes. >> And it's what's next in cyber and basically- >> That's this. Yes. >> People can come through and they can write down, and there's some great stuff in there: 5G, cloud native, some technical stuff, automated meantime to repair or to remediation. >> Yeah. >> Somebody wrote AWS. The AWS guys left their mark, which is kind of cool. >> Zeynep: That's great. >> And so I'm wondering, so we always talk about... we just talked about earlier that cyber is a board...has become a board level you know, issue. I think even go back mid last decade, it was really starting to gain strength. What I'm looking for, and I dunno if there's anything in here that suggests this is going beyond the board. So it becomes this top down thing, not just the the SOC, not just the, you know, IT, not just the board. Now it's top down maybe it's bottom up, middle out. The awareness across the organization. >> Zeynep: Absolutely. >> And that's something that I think is that is a next big thing in cyber. I believe it's coming. >> Cybersecurity awareness is a topic. And you know, there are companies who do that, who actually educate just all of us who work for corporations on the best way to tackle, especially when the human is the source and the reason knowingly or unknowing, mostly unknowingly of cyber attacks. Their education and awareness is critical in preventing a lot of this...before our, you know tools even get in. So I agree with you that there is a cybersecurity awareness as a topic is going to be very, very popular in the future. >> Lena Smart is the CISO of MongoDB does... I forget what she calls it, but she basically takes the top security people in the company like the super geeks and puts 'em with those that know nothing about security, and they start having conversations. >> Zeynep: Yeah. >> And then so they can sort of be empathic to each other's point of view. >> Zeynep: Absolutely. >> And that's how she gets the organization to become cyber aware. >> Yes. >> It's brilliant. >> It is. >> So simple. >> Exactly. Well that's the beauty in it is the simplicity. >> Yeah. And there are programs just to put a plug. There are programs where you can simulate, for example, phishing attacks with your, you know employee base and your workforce. And then teach them at that moment when they fall for it, you know, what they should have done. >> I think I can make a family game night. >> Yeah. Yeah. (group laughs) >> I'm serious. That's a good little exercise For everybody. >> Yes. Yeah, exactly. >> It really is. Especially as the sophistication and smishing gets more and more common these days. Where can folks go to get their hands on this juicy survey that we just unpacked? >> We have it online, so if you go to the Palo Alto Networks website, there's a big link to the survey from there. So for sure there's a summary version that you can come in and you can have access to all the stats. >> Excellent. Zeynep, it's been such a pleasure having you on the program dissecting what's keeping CXOs up at night, what Palo Alto Networks is doing to really help organizations digitally transform cyber transformation and achieve that nirvana of cyber resilience. We appreciate so much your insights. >> Thanks very much. It's been the pleasure. >> Dave: Good to have you. >> Thank you >> Zeynep Ozdemir and Dave Vellante. I'm Lisa Martin. You're watching theCUBE, the leader in live and emerging tech coverage. (upbeat music)

>> Narrator: Live from New York City, it's theCUBE covering IBM Data Science For All brought to you by IBM. >> Welcome back here on theCUBE. We are live in New York continuing our coverage here for Data Science for All where all things happen. Big things are happening. In fact, there's a huge event tonight I'm going to tell you about a little bit later on, but Tricia Wang who is our next guest is a part of that panel discussion that you'll want to tune in for live on ibmgo.com. 6 o'clock, but more on that a little bit later on. Along with Dave Vellante, John Walls here, and Tricia Wang now joins us. A first ever for us. How are you doing? >> Good. >> A global tech ethnographer. >> You said it correctly, yay! >> I learned a long time ago when you're not sure slow down. >> A plus already. >> Slow down and breathe. >> Slow down. >> You did a good job. Want to do it one more time? >> A global tech ethnographer. >> Tricia: Good job. >> Studying ethnography and putting ethnography into practice. How about that? >> Really great. >> That's taking on the challenge stretch. >> Now say it 10 times faster in a row. >> How about when we're done? Also co-founder of Sudden Compass. So first off, let's tell our viewers a little bit about Sudden Compass. Then I want to get into the ethnography and how that relates to tech. So let's go first off about Sudden Compass and the origins there. >> So Sudden Compass, we're a consulting firm based in New York City, and we help our partners embrace and understand the complexity of their customers. So whenever there are, wherever there's data and wherever there's people, we are there to help them make sure that they can understand their customers at the end of the day. And customers are really the most unpredictable, the most unknown, and the most difficult to quantify thing for any business. We see a lot of our partners really investing in big data data science tools and they're hiring the most amazing data scientists, but we saw them still struggling to make the right decisions, they still weren't getting their ROI, and they certainly weren't growing their customer base. And what we are helping them do is to say, "Look, you can't just rely only on data science. "You can't put it all into only the tool. "You have to think about how to operationalize that "and build a culture around it "and get the right skillsets in place, "and incorporate what we call the thick data, "which is the stuff that's very difficult to quantify, "the unknown, "and then you can figure out "how to best mathematically scale your data models "when it's actually based on real human behavior, "which is what the practice of ethnography is there to help "is to help you understand what do humans actually do, "what is unquantifiable. "And then once you find out those unquantifiable bits "you then have the art and science of figuring out "how do you scale it into a data model." >> Yeah, see that's what I find fascinating about this is that you've got hard and fast, right, data, objective, black and white, very clear, and then you've got people, you know? We all react differently. We have different influences, and different biases, and prejudices, and all that stuff, aptitudes. So you are meshing this art and science. >> Tricia: Absolutely. >> And what is that telling you then about how best to your clients and how to use data (mumbles)? >> Well, we tell our clients that because people are, there are biases, and people are not objective and there's emotions, that all ends up in the data set. To think that your data set, your quantitative data set, is free of biases and has some kind of been scrubbed of emotion is a total fallacy and it's something that needs to be corrected, because that means decision makers are making decisions based off of numbers thinking that they're objective when in fact they contain all the biases of the very complexity of the humans that they're serving. So, there is an art and science of making sure that when you capture that complexity ... We're saying, "Don't scrub it away." Traditional marketing wants to say, "Put your customers in boxes. "Put them in segments. "Use demographic variables like education, income. "Then you can just put everyone in a box, "figure out where you want to target, "figure out the right channels, "and you buy against that and you reach them." That's not how it works anymore. Customers now are moving faster than corporations. The new net worth customer of today has multiple identities is better understood when in relationship to other people. And we're not saying get rid of the data science. We're saying absolutely have it. You need to have scale. What is thick data going to offer you? Not scale, but it will offer you depth. So, that's why you need to combine both to be able to make effective decisions. >> So, I presume you work with a lot of big consumer brands. Is that a safe assumption? >> Absolutely. >> Okay. So, we work with a lot of big tech brands, like IBM and others, and they tend to move at the speed of the CIO, which tends to be really slow and really risk averse, and they're afraid to over rotate and get ahead over their skis. What do you tell folks like that? Is that a mistake being so cautious in this digital age? >> Well, I think the new CIO is on the cutting edge. I was just at Constellation Research Annual Conference in Half Moon Bay at-- >> Our friend Ray Wang. >> Yeah, Ray Wang. And I just spoke about this at their Constellation Connected Enterprise where they had the most, I would have to say the most amazing forward thinking collection of CIOs, CTOs, CDOs all in one room. And the conversation there was like, "We cannot afford to be slow anymore. "We have to be on the edge "of helping our companies push the ground." So, investing in tools is not enough. It is no longer enough to be the buyer, and to just have a relationship with your vendor and assume that they will help you deliver all the understanding. So, CIOs and CTOs need to ensure that their teams are diverse, multi-functional, and that they're totally integrated embedded into the business. And I don't mean just involve a business analyst as if that's cutting edge. I'm saying, "No, you need to make sure that every team "has qualitative people, "and that they're embedded and working closely together." The problem is we don't teach these skills. We're not graduating data scientists or ethnographers who even want to talk to each other. In fact, each side thinks the other side is useless. We're saying, "No, "we need to be able to have these skills "being taught within companies." And you don't need to hire a PhD data scientist or a PhD ethnographer. What we're saying is that these skills can be taught. We need to teach people to be data literate. You've hired the right experts, you have bought the right tools, but we now need to make sure that we're creating data literacy among decision makers so that we can turn these data into insights and then into action. >> Let's peel that a little bit. Data literate, you're talking about creativity, visualization, combining different perspectives? Where should the educational focus be? >> The educational focus should be on one storytelling. Right now, you cannot just be assuming that you can have a decision maker make a decision based on a number or some long PowerPoint report. We have to teach people how to tell compelling stories with data. And when I say data I'm talking about it needs the human component and it needs the numbers. And so one of the things that I saw, this is really close to my heart, was when I was at Nokia, and I remember I spent a decade understanding China. I really understood China. And when I finally had the insight where I was like, "Look, after spending 10 years there, "following 100 to 200 families around, "I had the insight back in 2009 that look, "your company is about to go out of business because "people don't want to buy your feature phones anymore. "They're going to want to buy smartphones." But, I only had qualitative data, and I needed to work alongside the business analysts and the data scientists. I needed access to their data sets, but I needed us to play together and to be on a team together so that I could scale my insights into quantitative models. And the problem was that, your question is, "What does that look like?" That looks like sitting on a team, having a mandate to say, "You have to play together, "and be able to tell an effective story "to the management and to leadership." But back then they were saying, "No, "we don't even consider your data set "to be worthwhile to even look at." >> We love our candy bar phone, right? It's a killer. >> Tricia: And we love our numbers. We love our surveys that tell us-- >> Market share was great. >> Market share is great. We've done all of the analysis. >> Forget the razor. >> Exactly. I'm like, "Look, of course your market share was great, "because your surveys were optimized "for your existing business model." So, big data is great if you want to optimize your supply chain or in systems that are very contained and quantifiable that's more or less fine. You can get optimization. You can get that one to two to five percent. But if you really want to grow your company and you want to ensure its longevity, you cannot just rely on your quantitative data to tell you how to do that. You actually need thick data for discovery, because you need to find the unknown. >> One of the things you talk about your passion is to understand how human perspectives shape the technology we build and how we use it. >> Tricia: Yes, you're speaking my language. >> Okay, so when you think about the development of the iPhone, it wasn't a bunch of surveys that led Steve Jobs to develop the iPhone. I guess the question is does technology lead and shape human perspectives or do human perspectives shape technology? >> Well, it's a dialectical relationship. It's like does a hamburger ... Does a bun shape the burger or does the bun shape the burger? You would never think of asking someone who loves a hamburger that question, because they both shape each other. >> Okay. (laughing) >> So, it's symbiote here, totally symbiotic. >> Surprise answer. You weren't expecting that. >> No, but it is kind of ... Okay, so you're saying it's not a chicken and egg, it's both. >> Absolutely. And the best companies are attuned to both. The best companies know that. The most powerful companies of the 21st century are obsessed with their customers and they're going to do a great job at leveraging human models to be scaled into data models, and that gap is going to be very, very narrow. You get big data. We're going to see more AI or ML disasters when their data models are really far from their actual human models. That's how we get disasters like Tesco or Target, or even when Google misidentified black people as gorillas. It's because their model of their data was so far from the understanding of humans. And the best companies of the future are going to know how to close that gap, and that means they will have the thick data and big data closely integrated. >> Who's doing that today? It seems like there are no ethics in AI. People are aggressively AI for profit and not really thinking about the human impacts and the societal impacts. >> Let's look at IBM. They're doing it. I would say that some of the most innovative projects that are happening at IBM with Watson, where people are using AI to solve meaningful social problems. I don't think that has to be-- >> Like IBM For Social Good. >> Exactly, but it's also, it's not just experimental. I think IBM is doing really great stuff using Watson to understand, identify skin cancer, or looking at the ways that people are using AI to understand eye diseases, things that you can do at scale. But also businesses are also figuring out how to use AI for actually doing better things. I think some of the most interesting ... We're going to see more examples of people using AI for solving meaningful social problems and making a profit at the same time. I think one really great example is WorkIt is they're using AI. They're actually working with Watson. Watson is who they hired to create their engine where union workers can ask questions of Watson that they may not want to ask or may be too costly to ask. So you can be like, "If I want to take one day off, "will this affect my contract or my job?" That's a very meaningful social problem that unions are now working with, and I think that's a really great example of how Watson is really pushing the edge to solve meaningful social problems at the same time. >> I worry sometimes that that's like the little device that you put in your car for the insurance company to see how you drive. >> How do you brake? How do you drive? >> Do people trust feeding that data to Watson because they're afraid Big Brother is watching? >> That's why we always have to have human intelligence working with machine intelligence. This idea of AI versus humans is a false binary, and I don't even know why we're engaging in those kinds of questions. We're not clearly, but there are people who are talking about it as if it's one or the other, and I find it to be a total waste of time. It's like clearly the best AI systems will be integrated with human intelligence, and we need the human training the data with machine learning systems. >> Alright, I'll play the yeah but. >> You're going to play the what? >> Yeah but! >> Yeah but! (crosstalk) >> That machines are replacing humans in cognitive functions. You walk into an airport and there are kiosks. People are losing jobs. >> Right, no that's real. >> So okay, so that's real. >> That is real. >> You agree with that. >> Job loss is real and job replacement is real. >> And I presume you agree that education is at least a part the answer, and training people differently than-- >> Tricia: Absolutely. >> Just straight reading, writing, and arithmetic, but thoughts on that. >> Well what I mean is that, yes, AI is replacing jobs, but the fact that we're treating AI as some kind of rogue machine that is operating on its own without human guidance, that's not happening, and that's not happening right now, and that's not happening in application. And what is more meaningful to talk about is how do we make sure that humans are more involved with the machines, that we always have a human in the loop, and that they're always making sure that they're training in a way where it's bringing up these ethical questions that are very important that you just raised. >> Right, well, and of course a lot of AI people would say is about prediction and then automation. So think about some of the brands that you serve, consult with, don't they want the machines to make certain decisions for them so that they can affect an outcome? >> I think that people want machines to surface things that is very difficult for humans to do. So if a machine can efficiently surface here is a pattern that's going on then that is very helpful. I think we have companies that are saying, "We can automate your decisions," but when you actually look at what they can automate it's in very contained, quantifiable systems. It's around systems around their supply chain or logistics. But, you really do not want your machine automating any decision when it really affects people, in particular your customers. >> Okay, so maybe changing the air pressure somewhere on a widget that's fine, but not-- >> Right, but you still need someone checking that, because will that air pressure create some unintended consequences later on? There's always some kind of human oversight. >> So I was looking at your website, and I always look for, I'm intrigued by interesting, curious thoughts. >> Tricia: Okay, I have a crazy website. >> No, it's very good, but back in your favorite quotes, "Rather have a question I can't answer "than an answer I can't question." So, how do you bring that kind of there's no fear of failure to the boardroom, to people who have to make big leaps and big decisions and enter this digital transformative world? >> I think that a lot of companies are so fearful of what's going to happen next, and that fear can oftentimes corner them into asking small questions and acting small where they're just asking how do we optimize something? That's really essentially what they're asking. "How do we optimize X? "How do we optimize this business?" What they're not really asking are the hard questions, the right questions, the discovery level questions that are very difficult to answer that no big data set can answer. And those are questions ... The questions about the unknown are the most difficult, but that's where you're going to get growth, because when something is unknown that means you have not either quantified it yet or you haven't found the relationship yet in your data set, and that's your competitive advantage. And that's where the boardroom really needs to set the mandate to say, "Look, I don't want you guys only answering "downstream, company-centric questions like, "'How do we optimize XYZ?"'" which is still important to answer. We're saying you absolutely need to pay attention to that, but you also need to ask upstream very customer-centric questions. And that's very difficult, because all day you're operating inside a company . You have to then step outside of your shoes and leave the building and see the world from a customer's perspective or from even a non existing customer's perspective, which is even more difficult. >> The whole know your customer meme has taken off in a big way right now, but I do feel like the pendulum is swinging. Well, I'm sanguined toward AI. It seems to me that ... It used to be that brands had all the power. They had all the knowledge, they knew the pricing, and the consumers knew nothing. The Internet changed all that. I feel like digital transformation and all this AI is an attempt to create that asymmetry again back in favor of the brand. I see people getting very aggressive toward, certainly you see this with Amazon, Amazon I think knows more about me than I know about myself. Should we be concerned about that and who protects the consumer, or is just maybe the benefits outweigh the risks there? >> I think that's such an important question you're asking and it's totally important. A really great TED talk just went up by Zeynep Tufekci where she talks about the most brilliant data scientists, the most brilliant minds of our day, are working on ad tech platforms that are now being created to essentially do what Kenyatta Jeez calls advertising terrorism, which is that all of this data is being collected so that advertisers have this information about us that could be used to create the future forms of surveillance. And that's why we need organizations to ask the kind of questions that you did. So two organizations that I think are doing a really great job to look at are Data & Society. Founder is Danah Boyd. Based in New York City. This is where I'm an affiliate. And they have all these programs that really look at digital privacy, identity, ramifications of all these things we're looking at with AI systems. Really great set of researchers. And then Vint Cerf (mumbles) co-founded People-Centered Internet. And I think this is another organization that we really should be looking at, it's based on the West Coast, where they're also asking similar questions of like instead of just looking at the Internet as a one-to-one model, what is the Internet doing for communities, and how do we make sure we leverage the role of communities to protect what the original founders of the Internet created? >> Right, Danah Boyd, CUBE alum. Shout out to Jeff Hammerbacher, founder of Cloudera, the originator of the greatest minds of my generation are trying to get people to click on ads. Quit Cloudera and now is working at Mount Sinai as an MD, amazing, trying to solve cancer. >> John: A lot of CUBE alums out there. >> Yeah. >> And now we have another one. >> Woo-hoo! >> Tricia, thank you for being with us. >> You're welcome. >> Fascinating stuff. >> Thanks for being on. >> It really is. >> Great questions. >> Nice to really just change the lens a little bit, look through it a different way. Tricia, by the way, part of a panel tonight with Michael Li and Nir Kaldero who we had earlier on theCUBE, 6 o'clock to 7:15 live on ibmgo.com. Nate Silver also joining the conversation, so be sure to tune in for that live tonight 6 o'clock. Back with more of theCUBE though right after this. (techno music)