>>From San Francisco. It's the cube covering get lab commit 20, 20 Raji you buy get lab. >>Hi, I'm Stu Miniman and this is the cubes coverage of get lab commit 2020. We're here in San Francisco. It's a little bit chilly but uh, my first guests, uh, on the program are used to the weather cause they're coming to us from Wisconsin. Uh, both from Northwestern mutual, uh, sitting to my left here is Kyle person who is a senior engineer and sitting to his left is Sean who is also a senior engineer. Gentlemen, thanks so much for joining us. Thanks for having us. Alright. We thought, you know, both of us coming from colder climates that may be coming to San Francisco would be a little warmer, but they have hand warmers, they have warm drinks and it is the warmth of the community that will warm our innards. I'm short right there. It says get warm. That's what we're here to do. All right, Kyle, let's start with you. Northwestern mutual. I think most people are familiar with the organization, but give us a little bit of a, you know, how people should think of Northwestern mutual in 2020 and, uh, your roles. >>Yeah. So obviously we mean we're a large insurance company but also into financial services and products and we're really trying to become more of a digital company as well. We think that that's going to be a differentiator in the marketplace. You know, having apps that our customers can interact with, um, trying to speed up underwriting, things like that. So we're really just trying to be a technology company as much of an insurance company. Okay, >>great. And Sean, I understand you're, you're on the same team as Kyle helping you along with that digital transformation that that's been all the buzz for the last couple of years. Yeah. He can't get rid of me. We flew, you know, 1200 miles and I'm still sitting next to, uh, but yeah, at Northwestern mutual, I mean, going back a number of years now, the, the company started down this path of doing a digital transformation where we wanted to be, you know, a software company that is providing financial service and financial stability for our clients. So it was a big shift that we, we started, we knew we needed to modernize everything. So we started down that path. Great. So we had that. So Kyle, maybe if you, it can, you know, when did get lab enter the picture, what was kind of the initial use case and, uh, let's, let's go from there. >>Yeah, it was before my time. I'm, Chad has been there for a long time. Most of the ride, but uh, yeah, it's been several years and it's been, uh, you know, starting out with SCM, moving into CEI and then, you know, adopted sustainer journey that you hear about even in the keynote today. That's pretty much how we charted our course. Okay. >>So Sean, you've been there since the beginning of a, uh, to get lab usage? Pretty much it, it showed up a couple months before I got there. But, uh, going back to your early mid 22, yeah, 2015, uh, we had kind of a more of a pilot group of engineers that were, were starting out, you know, to get us down this path to where we wanted to go and they needed a new tool, something that worked better than what we currently had at an M and a, they settled on, on get lab because it provided, you know, one being open source was a huge selling point for us. Um, and it was just ever-growing. So it allowed our developers to really get going and get going much faster. Okay, great. And in the keynote, okay, Kyle, they were talking about how it's not just about the dev, the second the ops, but really not allowing everybody into the same tooling, even marketing and finance. What's kind of the breadth of the organization is it is mostly devs that dev and ops does security, you know, who, who's involved in using this tooling. >>It's everybody. We're a, we're figuring out our, you know, everyone's kinda got their own spin on things. So we're in that, um, classic position where I think we have the tooling sprawl that everyone talks about and we're, we're constantly evaluating, you know, how does Gilad fit into that picture? What do we bolt on? You know, we have the luxury of being able to integrate with other things as well. But then if certainly if we can get an economy of scale where we can just use get lab, it's to provide that seamless interface. That's something we always look to do too. All right. >>So Sean, my understanding is a NM is also using Coubernetties and that's something that you're involved in. So very money you bring us in people, you know, sometimes get misconstrued as to the scope and the purpose of, of Kubernetes. We've been at the cube con cloud date of con for a number of years, but uh, why don't you set the stage for us and kind of walk us through the, the what and the why of Kubernetes? Yeah. For us at least being able to leverage something like Kubernetes, which when you really back out and you know, do the 10,000 foot view, it's container management and being able to go into a more modern architecture. We're leveraging containers for pretty much whatever we can, or at least what makes sense. Um, and that's kind of how we started down the path with get lab moving into Kubernetes. You know, we were, we were trying to figure out like, where do we want to go so, you know, let's not just push the boat out a little, let's drop, kick the boat off the end of the pier and see where we end up. >>So we started working down that path of deploying get lab into Kubernetes cause it allowed us to easily expand and make the application highly available. So even if some easies go down in AWS, which knock on wood never happens. Uh, we're still good to go. Our users are, wouldn't even notice. Okay. Um, so you mentioned AWS. Is that your primary cloud, your only cloud? What, what is your cloud situation? Yeah, that's, that's a Northwestern mutual is public cloud. Okay, great. And speak a little bit to, you know, Amazon does have plenty of its own tooling. Uh, maybe a little bit about how get lab and, how those fit together for you. Um, well, I mean, we use get lab CIS specifically to be able to provision different services in one, not that we need as long as they fit near within our security requirements. And, you know, everything we do, we get vetted out through our internal processes, but it's really allowed our developers to move forward so much faster. >>You know, it's kind of gone are the days of, let me fill out a request for, you know, X, Y, Z and, you know, wait two as it goes through somebody's work queue and they eventually get around to it. Um, allowing our developers to just, you know, do their commits, get their, you know, peer review and just deploy and provision right away, allows us to get our applications to market just so much faster than even a few years ago. Alright. So Kyle, the two of you are presenting here at the show, uh, you know, we, we love, we heard GitLab talking on stages. You know, customers don't just use it, they commit, they add feedback in and they speak at the show. So maybe speak a little bit of, uh, you know, NMS, you know, involvement as to uh, you know, are you committing code and what, what are you speaking about? >>So we're here to speak about our journey on Kubernetes. I'm trans covering the application side and I'm going to talk about our, our dabble in Kubernetes CII. So we're, we're really looking to, um, we're looking for efficiencies I guess in, in density. That's a primary driver behind trying to explore the trail. But also, um, one of the things I'll talk about in the talk is, is boosting our security posture using Kubernetes. So a lot of times people are using API keys and they're getting stale and not being rotated. Uh, we can do some neat things with Kubernetes and it's native. I am offerings to boost our security posture by moving towards role based access and getting those credentials that are rotating and providing us, uh, you know, non stale sort of authentication credentials, things like that. >>Sean, yeah, pretty much covers it. Uh, uh, and beyond with the CIA, you know, being able to run and get lab itself within Qube and having the different components broken out and we're alive. It's, it, it's enabling us to limit any kind of attack plane that could exist. You know, it's, you have to get through a lot to even get to it. So it's really just been a huge, a huge plus for us. OK. I, I'd love to hear both. Both of you have been to these events a number of times. You're speaking to event. What, what, what's the value of coming to get loud commit? I mean, for me it's a, a huge networking thing and being able to relay our experiences that we've gone through to other people that are out in the community. I mean, I know lots of, you know, everyone wants to see, you know, what can you do in Kubernetes and like, here's some of the things that we've done. >>Here's some of the things that you know, works that didn't work. You know, we would recommend you going this kind of route if we were to start it over again. And beyond that, you know, meeting people from all over the world, like, uh, we were talking with some, uh, some guy, gentleman Nico from white duck who is from Germany. It's not something you get to do, you know, face to face all the time. Alright. Sean, can you share with our audience any of those? You know, if we could do it over again, we'd change something. Is it an organizational thing or technical piece or until don't don't use EFS for getting repo data. It will not end well for you can take away. All right. Kyle, how about you? You've been to a number of these shows, uh, you know, the networking, the piece or you know, what else, what, what, what, what for you personally and for NM, uh, you know, brings you back. >>Yeah. Networking is a big thing. I think it's also getting feedback on, you know, what we're doing. Does it make sense? Just like get lab is throwing things out there early, trying to tighten up that contribution loop. We want to get our ideas out there and then this is an opportunity for people to ask questions about our presentation. If maybe we're off in the wrong direction, maybe we can get that steered back on course. Um, you know, we're just really here to get the feedback. Yeah. I always love talking to people in the financial industry and you talk about open source, you know, if, if you went back, you know, five years ago, you'd probably get a little bit of sideways looks as to wait, you know, you're doing what, um, are we past that? Do do you feel are most people, uh, you know, really understanding where we are with, with cloud and open source in general that it, you know, it makes perfect sense for a financial institution to be part of it. >>I'd say at NM we, we've finally gotten past that curve and now we're, we're trying to, you know, make it even easier for our internal developers to easier participate in open source, you know, their internal products and contribute more to the community. Uh, we've completely done an about face from, you know, probably 15 years ago where it was open source. You wanted to, what to, yeah, let's go. How can we make things better? It's, it's all about, you know, our, our customers. So we want to make sure we create the best product and experience for them. That's awesome. Yeah, there's still some barriers there. I mean, it's all about managing risk, right? So you have to do things diligently and make sure that your bases are covered. And so it's not like it can be a free for all. We have to do our due diligence, but we, you know, we love to contribute. And we love to get up without their there as we can. All right. Well, Kyle and Sean, thank you so much for sharing with our audience. Best of luck with your presentations and, uh, have a great time at the show. Thank you. All right. Uh, thank you to, to NM for joining us. I'm Stu Miniman and thank you for watching the cube.

>> Announcer: Live from Orlando, Florida, it's theCUBE covering Grace Hopper Celebration of Women in Computing, brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's coverage of the Grace Hopper Conference here at the Orange County Convention Center, I'm your host Rebecca Knight. We are joined today by Emilia Sherifova, she is the VP of Architecture and Engineering at Northwestern Mutual, thanks so much for joining us Emilia. >> Thank you for having me. >> So I want to start off by talking about how you got to Northwestern Mutual. You came via an acquisition, you were CTO of LearnVest. What is LearnVest? >> LearnVest is a financial planning start up, it's a company that is bringing financial planning to the masses, it's a very mission driven organization. When Northwestern Mutual came as an interest to acquire us, we saw an incredible opportunity to partner with a Fortune 100 company, and tap into its client base of five million people, and bring sort of best in class digital experience and innovation, with best in class financial services products. >> Talk about that problem a little bit, in terms of bringing financial planning to the masses. Why don't the masses have financial planning? What's the disconnect? >> I think it's not easy, often it's a human driven problem. Often humans do not want to deal with their finances, as I know personally for myself, historically when I met with my financial advisor in the past I would get a plan and I wouldn't follow up on that. So building delightful experiences that engage our clients, with the combination of a financial planner that's prodding you, and giving you guidance. >> So there is a human there? >> Absolutely, there's no way to avoid a human. So it was that regional model of LearnVest to have the human help the robot part of it, and we are doing the same thing with Northwestern Mutual, where we're leveraging Northwestern Mutual best in class distribution work force and providing them tools to help them do their work best. >> I love the idea of a delightful experience when dealing with your finances, it seems antithetical. Give me some examples of what you mean by this. >> I think ability to give you a 360 view of your life, and give you a financial wellness score, for instance, after we've gotten a couple of data points about you, but also gathered some of the predictive data points that we know are probably true about you, and give you a score, one score, that gives you an idea what's the probability of you reaching your financial goal, or you retiring, or you going broke. So there is a way to do that in an easy, digestible and kind of delightful way where we're able to leverage technology and predictive capabilities to really push for financial security of our clients. >> And what is the customer response here? >> Customer response, it's been great. Now that we've rolled out a lot of these experiences for the customer base of Northwestern Mutual we have massive engagement with our customers, our traffic has gone dramatically up. >> So people are hungering for this? >> Absolutely, it's a much needed thing, and we're here to help them. >> So you've now been with Northwestern Mutual for a few years now, dividing your time between New York and Milwaukee, you're in both technology and financial services, both male dominated fields, can you describe a little bit about your career path, and how you got into it, and what you've learned along the way. >> Oh, absolutely. I'm originally from Russia, and I come from a family of engineers, so it was a somewhat natural path for me. I got into software engineering in the late 90s. My go to language initially was C programming, and I participated in the Y2K Challenge on Wall Street. >> Which seems so quaint! (laughs) >> And I've spent over a decade on Wall Street, building electronic trading systems, market data feeds. So I feel honored to have been able to pursue and have these possibilities, but I know how not easy it is, given what a male dominated world this is. >> Is it as bad as the headlines make it out to be? I mean, it really does, when you read it, it's sickening. The sexism, the biases, what's your experience been? >> I think I've been lucky enough to work in very supportive places, but I can tell you majority of teams that I've been part of are majority male, and whether my team mates want to be inclusive and engaging, when the majority is someone else that doesn't look like you, act like you, lean on similar defaults as you, it does not make for a very welcome environment. So I recognize that, and a big part of that, I feel, is having proper on boarding practices. Because on boarding often can happen, if you don't have a formal on boarding process, on boarding can happen in informal ways, and when it happens in informal ways, you tend to be attracted to the people who are like you, and you hang out with. So if you look at the technology world, it's dominated by mostly male. If you are in a start up world, it's mostly young males. And so I am determined to bring operational excellence and sustainability and diversity through strong operational practices, like ensuring that there is proper on boarding. Where for instance, a young mother who has a child has the potential to go home at 4 p.m., and cannot hang out with the guys and drink coke or beer at 7 p.m., to really understand the culture of the group that she joined. We want to make sure that she has sustainable, thoughtful on boarding practices, feeling like she's part of the organization. This is just one way of doing it. >> In terms of the on boarding, and I think you're absolutely right in a sense, that we do gravitate towards people who are just like us, look like us, talk like us, think like us, so are you pairing the new people with people who are not like them? >> Absolutely, but also actually I am pairing them with people who also recently just went through on boarding, that just join also fairly recently. That way they can explain the pitfalls that they gone through, and so we're definitely making sure we have these co pilots, but also rigorous processes to get people comfortable, whatever their background is. >> Now how many Grace Hoppers have you been to, Emilia? >> I have to say this is my first one. >> Your first one, you're a newbie! So what is your experience been so far. >> I am incredibly moved by the experience, actually. I have to say I've never seen so much energy before. I am moved by the stories that I have heard, incredibly inspired. I am inspired to keep pushing. I felt I could relate to a lot of presenters' backgrounds, I also came from a small town, that actually is not on the map, because it was a military town in the former Soviet Union, and a lot of stories of overcoming, and persisting, and ending up here, is what I can relate to. So I'm very excited, and very grateful, and I want to be here every year. >> So you'll be back? >> Totally! >> Great! Well, Emilia, thanks so much for joining us, it's been really fun talking to you. >> Thanks for having me. >> We'll be back with more from Grace Hopper just after this.

(upbeat intro music) >> Good afternoon, ladies and gentlemen and welcome back to fabulous Las Vegas, Nevada where we are theCUBE covering AWS re:Invent for the 10th year in a row. John Furrier, you've been here for all 10. How does this one stack up? >> It's feeling great. It's just back into the saddle of more people. Everyone's getting bigger and growing up. The companies that were originally on are getting stronger, bigger. They're doing takeovers in restaurants and still new players are coming in. More startups are coming in and taking care of what I call the (indistinct) on classic, all the primitives. And then you starting to see a lot more ecosystem platforms building on top of AWS. I call that NextGen Cloud, NextGen AWS. It's happening. It's happening right now. >> Best thing about all of these startups is they grow up, they mature, and we stay the same age, John. (John laughing) All right. All right. All right. Very excited to introduce you our next guest, he wears a lot of hats as the CEO, founder, and chairman at Reltio, please welcome Manish. Manish, welcome to the show. How is your show going so far? >> Well, thank you so much. You know, this is amazing. Just the energy, the number of people. You know, I was here last year, just after the pandemic, and I think it's almost double, if not more the number of people this year. >> John: Pushing 50,000. The high water mark was 65,000 in 2019. >> We should be doing like a Price Is Right sort of thing here on the show and figure out. >> Yeah, $1. >> Savannah: Yeah, yeah. (laughing) One guest, 80,000 guests. How many guests are here? Just in case the audience is not familiar, we know you're fast growing, very exciting business. Tell us what Reltio does. >> So, Reltio is a SaaS platform for data unification and we started Reltio in 2011. We have been serving some of the largest customers across industries like life sciences, healthcare, financial services, insurance, high tech, and retail. Those are, you know, some of the areas that we are focused on. The product capabilities are horizontal because we see the same data problem across every industry. Highly fragmented, highly siloed data that is slowing down the business for every organization out there. And that's the problem that we are solving. We are breaking down these silos, you know, one profile or one record, or one customer product supplier information record at a time, and bringing the acceleration of this unified data to every organization. >> This is the show Steam this year, Adam Celeste is going to be on stage talking about data end to end. Okay. Integrating in all aspects of a company. The word data analyst probably goes away pretty shortly. Everyone was going to be using data. This has been, and he talks about horizontal and vertical use cases. We've been saying that in theCUBE, I think it was about seven years ago, we first said we're going to start to see horizontally scalable data not just compute and cloud. This is now primetime conversation. Making that all work with governance is a real hard problem. Understanding the data. Companies have to put this horizontal and vertical capabilities in place together. >> Absolutely. You know, the data problem may be a horizontal problem, but every industry or vertical that you go into adds its own nuance or flavor to it. And that's why, you know, this has to be a combination of the horizontal and vertical. And we at Reltio thought about this for a while, where, you know, every time we enter a conversation, we are talking about patient data or physician data or client data and financial services or policy and customer information and insurance. But every time it's the number of silos that we encounter that is just an increasing number of applications, increasing number of third party data sources, and bringing that together in a manner where you can understand the semantics of it. Because, you know, every record is not created equal. Every piece of information is not created equal. But at the same time, you have to stitch it together in order to create that holistic, you know, the so-called 360 degree view. Because without that, the types of problems that you're trying to solve are not possible. Right? It's not possible to make those breakthroughs. And that's where I think the problem may be horizontal, but the application of the capabilities has to be verticalized. >> John: I'm smiling because, you know, when you're a founder like you are, and Dave, a lot here are at theCUBE, you're often misunderstood before people figure out what you do and why you started the company. And I can imagine, and knowing you and covering your company, that this is not just yesterday you came up with this idea that now everyone's talking about. There was probably moments in your history when you started, you're scratching it, "Hey the future's going to be this horizontal and vertical, especially where machine learning needs to know the data, the linguistics, whatever the data is, it's got to be very particular for the vertical, but you need to expand it." So when did you have the moment where people finally figured out like, what you guys doing is, like, relevant? I mean, now the whole world now sees- >> Savannah: Overnight success 11 years later. >> John: This shows the first time I've heard Amazon and the industry generally agree that horizontally scalable data systems with vertical value, that it's natural. We've been saying it for seven years on theCUBE. You've been doing the startup. >> Yeah. >> As a founder, you were there early. Now people are getting it. What's it like? Tell, take us through. When did you have the moment? When did you tipping point for the world getting it? >> Yeah, and you know, the key thing to remember is that, you know, not only have I been in this space for a long time but the experiences that we have gone through starting in 2011, there was a lot of focus on, you know, even AWS was at that point in time in the infancy stages. >> Yeah. >> And we said that we are going to set up a software as a service capability that runs only on public cloud because we had seen what customers had tried to do behind their firewalls and the types of hurdles that they had run into before. And while the concept was still in its nascent stages, but the directional signals, the fact that number of applications that you see in use today across any organization, that's growing. It used to be a case when in early 2000s, you know, this is early part of my career, where having six different applications across the enterprise landscape was considered complex. But now those same organizations are talking about 400, 500, a thousand different applications that they're using to run their business end to end. So, you know, this direction was clear. The need for digital transformation was becoming clear. And the fact that, you know, cloud was the only vehicle that you could use to solve these types of ad scale problems was also becoming clear. But what wasn't yet mainstream was this notion that, you know, if you're doing digital transformation, you need access to clean, consistent, trusted information. Or if you're doing machine learning or any kind of data analytics, you need similar kinds of trusted information. It wasn't a mainstream concept, but people were struggling with it because, you know, the whole notion of garbage in garbage out was becoming clearer to them as they started running into hurdles. And it's great to see that now, you know, after having gone through the transformation of, yes, we have provided the compute and the storage, but now we really need to unlock the value out of data that goes on this compute and storage. You know, it's great to see that even Amazon or AWS is talking about it. >> Well, as a founder, it's satisfying, and congratulations, we've been covering that. I got to ask, you mention this end to end. I like the example of in the 2006 applications considered complex, now hundreds and thousands of workloads are on an enterprise. Today we're going to hear more end to end data services on AWS and off AWS, hybrid or edge or whatever, that's happens. Now cross, it sounds like it's going to get more complex still. >> I mean... >> John: Right. I mean, that's not easy. >> Savannah: The gentle understatement of the century. I love that. Yes. >> If Adam's message is end to end, it's going to be more complex. How does it get easier? Because the enterprise, you know, the enterprise vendors love solving complexity with more complexity. That's the wrong answer. >> Well, you're absolutely right that things are going to get more complex. But you know, this is where, whether it is Amazon or you know, us, Reltio as a vendor coming in, the goal should always be what are we going to simplify for the customer? Because they are going to end up with a complex landscape on their hands anyway. Right? >> Savannah: Right. >> So that is where, what can be below the surface and simplified for the customers to use versus bringing their focus to the business value that they can get out of it. Unlocking that business value has to be the key aspect that we have to bring to the front. And, you know, that is where, yes, the landscape complexity may grow, but how is the solution making it simpler, easier, faster for you to get value out of the data that you're trying to work with? >> As a mission, that seems very clear and clean cut, but I'm curious, I can imagine there's so many different things that you're prioritizing when you're thinking about how to solve those problems. What is that decision matrix like for you? >> For us, it goes back to the core focus and the core problem that we are in the business of solving which is in a siloed, fragmented landscape, how can we create a single source of truth orientation that your business can depend on? If you're looking for the unified view of the customer, the product, the supplier, the location, the asset, all these are elements that are critical or crucial for you to run your business end to end. And we are there to provide that solution as Reltio to our customers. So, you know, we always, for our decision matrix have to go back to are we simplifying that problem for our customers and how much faster, easier, nimbler can it be, you know, both as a solution and also the time to value that it brings to the equation for the customer. >> Super important, end of the equation. Clearly you are on to something. You are not only a unicorn company, unicorn company being evaluated at over $1 billion latest evaluation, correct me if I'm wrong, is $1.7 billion as of last year. But you are also a centaur, which is seven times more rare than a unicorn, which for the audience maybe not familiar with the mythical creatures that define the Silicon Valley nomenclature in Lexicon. A centaur is a company with a hundred million in annual reoccurring revenue. How does it feel to be able to say that as a CEO or to hear me say that to you? >> Well, as a CEO, it's, you know, something that we have been working towards. the goal that we can deliver value to our customers, help every industry, you know, you just think about the types of products that you touch in a day, whether it's, you know, any healthcare related products that you're looking at. We are working with customers who are solving for the patient record to be unified with our platform. We are working with financial services companies who are helping you simplify how you do banking with them. We are working with retailers who are working in the area of, you know, leisure apparel or athletic goods and they are using our capabilities to simplify how they deliver better experience to you. So as I go across these industries, being able to influence and touch and simplify things overall for the customers that these companies are serving, that's an amazing feeling. And, you know, doing this while we are also making sure that we can build a durable business that has substantial revenue behind it- >> Savannah: Substantial. >> Gives us a lot of legs to stand on and talk about how we can change how the companies should run their entire data stack. >> And you're obviously a very efficient team practicing what you teach. You told me how many employees that you have? >> We have 450 employees across the globe. >> 450 employees and a hundred million in reoccurring revenue. It's pretty strong. It's pretty strong. >> Thank you. >> That's a quarter million in rev per employee. They're doing a pretty good job. That's absolutely fantastic. >> The cloud has been very successful, partnering with the cloud, a lot of leverage for the cloud. >> And that's been a part of our thesis from the very beginning that, you know, the capabilities that we build and bring to life have to be built on public cloud infrastructure. That's something that has been core to our innovation cycle because we look at it as a layer cake of innovation that we sit on and we can continue to drive faster value for our customers. >> John: Okay, so normally we do a bumper sticker. Tell me the bumper sticker for the show. We changed it to kind of modernize it called the Insta Challenge, Instagram challenge. Instagram has reels, short videos. What's the Instagram reel from your perspective? You have to do an Instagram reel right now about why this time in history, this time in for Amazon web services, this point for Reltio. Why is this moment in time important in the computer industry? Because, you know, we've reported, I put a story out, NextGen Clouds here. People are seeing their status go from ISV to ecosystem platforms on top of AWS. Your success has continued to grow. Something's going on. What's the Instagram reel about why this year's so important in the history of the cloud? >> Well, you know, just think about the overall macroeconomic conditions. You know, everybody's trying to think about where the next, you know, the set of growth is going to come from or how we are going to tackle, you know, what we have as challenges in front of us. And at the end of the day, most of the efficiency that came from applying new applications or, you know, buying new products in the application space has delivered its value. The next unlock is going to come from data. And that is the key that we have to think about because the traditional model of going across 500 different applications to run your business is no longer going to be a scalable model to work with. If you really want to move faster with your business, you have to think about how to use data as a strategic asset and think about things differently. And we are talking about delivering experience at the edge, delivering, you know, real time type of engagement with the customers that we work with. And that is where the entire data value proposition starts to deliver a whole new set of options to the customers. And that's something that we all have to think about differently. It's going to require a fundamentally different architecture, innovation, leading with data instead of thinking about the traditional landscape that we have been running with. >> Leading with data and transforming architecture. A couple themes we've had on the show lately already. >> John: Well I think there's been a great, I mean this is a great leadership example of what's going on in the industry. As young people are looking at their careers. I've talked with a lot of folks under 30, they're trying to figure out what's a good career path and they're looking at all this change in front of them. >> That's a great point, John. >> Whether it's a computer science student or someone in healthcare, these industries are being reinvented with data. What's your advice to those young, this up and coming generation that might not take the traditional path traveled 'cause it might not be there. What's your advice for those people making these career decisions? >> I think there are two things that are relevant to every career option out there. Knowledge and awareness of data and how to apply computing techniques to the data is key and relevant. It's the language that we all have to learn and be familiar with. Without that, you know, you'll be missing a key part of your arsenal that you will be required to bring to work but won't have access to if you're not well-versed or familiar with those two areas. So this is lingua franca that we all have to get used to. >> Data and computer technology applied to business or some application or some problem. >> Manish: Applied to business. You know, figuring out how to apply it to deliver business outcomes is the key thing to keep in mind. >> Okay. >> Yeah. Last question for you to wrap us up. It's obviously an exciting, thrilling, vibrant moment here on the show floor, but I'm curious because I can imagine some of your customers, especially given the scale that they're at, I mean we're talking about some Fortune 100s here, how are you delivering value in this uncertain market? I mean, I know you solved this baseline problem but I can imagine there's a little bit of frantic energy within your customer base. >> Manish: Yeah. You know, with data this has been a traditional challenge. Everybody talks about the motherhood and apple pie. If you have better data, you can drive better outcomes. But some of the work that we have been doing is quantifying, measuring those outcomes and translating what the dollar impact of that value is for each one of the customers. And this is where the work that we have done with large, you know, let's say life sciences companies like AstraZeneca or GSK or in financial services with companies like Northwestern Mutual or Fidelity or, you know, common household names like McDonald's where they're delivering their digital transformation with the data capabilities that we are helping build with them. That's the key part that's been, you know, extremely valuable. And that is where in each one of these situations, we are helping them measure what the ROI is at every turn. So being able to go into these discussions with the hard dollar ROI that you can expect out of it is the key thing that we are focused on. >> And that's so mission critical now and at any economic juncture. Just to echo that, I noticed that Forrester did an independent study looking at customers that invested in your MDM solution. 366% ROI and a total net present value of 13 million over three years. So you clearly deliver on what you just promised there with customers and brands that we touch in all of our everyday lives. Manish, thank you so much for being on the show with us today. You and Reltio are clearly crushing it. We can't wait to have you back hopefully for some more exciting updates at next year's AWS re:Invent. John, thanks for- >> Or sooner. >> Yeah, yeah. Or sooner or maybe in the studios or who knows, at one of the other fabulous events we'll all be at. I'm sure you'll be traveling around given the success that the company is seeing. And John, thanks for bringing the young folks into the conversation, was a really nice touch. >> We got skill gaps, we might as well solve that right now. >> Yeah. And I like to think that there are young minds watching theCUBE or at least watching, maybe their parents are- >> We're streaming to Twitch. All the gamers are watching this right now. Stop playing the video games. >> We have the hottest stream on Twitch right now if you're not already ready for it. John Furrier, Manish Sood, thank you so much for being on the show with us. Thank all of you at home or at the office or in outer space or wherever you happen to be tuned in to this fabulous live stream. You are watching theCUBE, the leader in high tech coverage. My name is Savannah Peterson. We're at AWS re:Invent here in Las Vegas where we'll have our head in the clouds all week.

(bright music) >> Hi, everyone. Welcome to theCUBE's coverage of WiDS 2022. I'm Lisa Martin, very happy to be covering this conference. I've got Vidya Setlur here with me, the director of Tableau Research. Vidya, welcome to the program. >> Thanks, Lisa. It's great to be here. >> So this is one of my favorite events. You're a keynote this year. You're going to be talking about what makes intelligent visual analytics tools really intelligent. Talk to me a little bit about some of the key takeaways that the audience is going to glean from your conversation. >> Yeah, definitely. I think we've reached a point where everybody understands that data is important, trying to understand that data is equally important. And we're also getting to that point where technology and AI is really picking up. Algorithms are getting better, computers are getting faster. And so there's a lot of dialogue and conversation around how AI can help with visual analysis to make our jobs easier, help us glean insights. So I thought it was a really timely point where we can really actually talk about it, and distilling into the specifics of how these tools can actually be intelligent beyond just the general buzz of AI. >> And that's a great point that you bring up. There's been a lot of buzz around AI for a long time. The organizations talk about it, software vendors talk about it being integrated into their technologies, but how can AI really help to make visual analytics interpretable in a way that makes sense for the data enthusiast and the business? >> Yeah, so to me, I think my point of view, which tends to be the general agreement among the research community, is AI is getting better. And there are certain types of algorithms, especially these repetitive tasks. We see this with even Instagram, right? You put a picture on Instagram, there are filters that can maybe make the image look better, some fun backgrounds. And those, generally speaking, are AI algorithms at work. So there are these simple, either fun ways or tasks that reduce friction where AI can play a role, and they tend to be really good with these repetitive tasks, right? If I had to upload a picture and constantly edit the background manually, that's a pain. So AI algorithms are really good at figuring out where people tend to do a particular task often, and that's a good place for these algorithms to come into play. But that being said, I think fundamentally speaking, there are going to be tasks where AI can't simply replace a human. Humans have a really strong visual system. We have a very highly cognitive system where we can glean insights and takeaways beyond just the pixels, or just the text. And so how do we actually design systems where algorithms augment a human, where a human can stay in the driver's seat, stay creative, but defer all these mundane or repetitive tasks that simply add friction to the computer? And that's what the keynote is about. >> And talk to me about when you're talking with organizations, where are they in terms of appetite to understand the benefits that natural language processing, AI and humans together, can have on visual analytics, and being able to interpret that data? >> Yeah. So I would say it's really moving fast. So three years ago, organizations were like AI, it's a great buzzword, we're weary because when rubber hits the road, it's really hard to take that into action. But now we're slowly seeing places where it can actually work. So organizations are really thirsty to figure out how do we actually add customer value? How do we actually build products where AI can move from a simple, cute proof of concept working in a lab to actual production? And that is where organizations are right now. And we've already seen that with various types of examples, like machine translation. You open up a Google page in Spanish, and you can hit auto translate and it will convert it into English. Now, is it perfect? Not, but is it good enough? Yes. And I think that's where AI algorithms are heading, and organizations are really trying to figure out what's in it for us, and what's in it for our customers. >> What are some of the cultural, anytime we talk about AI, we always talk about ethics. But what are some of the cultural, or the language specific challenges with respect to natural language techniques that organizations need to be aware of? >> Yeah, that's a great question, and it's a common question, and really important. So as I've said, these AI algorithms are only as good as the data that they're often trained on. And so it's really important, in addition to the cultural aspects of incorporating those into the techniques, is to really figure out what sort of biases come into play, right? So a simple example is there's sarcasm in language, and different cultures have different ways of interpreting it. There are subtleties in language, jokes. My kids have a certain type of language when they're talking with each other that I may not understand. So there's a whole complexity around cultural appropriation generations that, where language constantly evolves, as well as biases. For example, we've had conversations in the news where AI algorithms are trained on a particular data set for detecting crime. And there are hidden biases that go into play with that sort of data. So we're really, it's important to be acknowledged of where the data is, and what sorts of cultural biases come into play. But translation, simple language translation is already more or less a solved problem. But beyond the simple language translation, we also have to account for language subtleties as well. >> Right, and the subtleties can be very dramatic. When you're talking with organizations that are really looking to become data driven. Everybody talks about being data driven, and we hear it on the news all the time, it's mainstream. But what that actually really means, and how an organization actually delivers on that are two different things. When you're talking with customers that are, okay, we've got to talk about ethics. We know that there's biases and data. How do you help them get around that so that they can actually adopt that technology, and make it useful and impactful to the business? >> Yeah. So just as important as figuring out how AI algorithms can help an organization's business, it's equally important for an organization to be more data literate about the data that feeds into these algorithms. So making data as a first class citizen, and figuring out are there hidden biases? Is the data comprehensive enough? Acknowledging where there are limitations in the data and being completely transparent about that. And sharing that with customers, I think, is really key. And coming back to humans being in the driver's seat. If these experiences are designed where humans are, in fact, in the driver's seat, as a human, they can intervene and correct and repair the system if they do see certain types of oddities that come into play with these algorithms. >> Going to ask you in our final few minutes here, I know that you have a PhD in computer graphics from Northwestern, is it? >> Yep. >> Northwestern. >> Go Wildcats, yep. >> Were you always interested in STEM and data? Talk to me a little bit about your background. >> Yeah. I grew up in a family full of academics and female academics. And now, yes, I have boys, including my dog. Everybody's male, but I have a really strong vested interest in supporting women in STEM. And I actually would go further and say, STEAM. I think arts and science are both equally important. In fact, I would say that on our research team, there's a good representation of minorities and women. And data analysis and visual analysis, in particular, is a field that is very conducive for women in the field, because women tend to be naturally meticulous. They're very good at distilling what they're seeing. So I would argue that there are a host of disciplines in this space that make it equally exciting and conducive for women to jump in. >> I'm glad that you said that. That's actually quite exciting, and that's a real positive thing that's going on in the industry, and what you're seeing. So I'm looking forward to your keynote, and I'm sure the audience is as well. Vidya, it was a pleasure to have you on the program talking about intelligent visual analytics tools, and the opportunities that they bring to organizations. Thanks for your time. >> Thanks, Lisa. >> For Vidya Setlur, I'm Lisa Martin. You're watching theCUBE's coverage of WiDS conference 2022. Stick around, more great content coming up next. (bright music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome to this special noob digital event where we're focusing in on data ops data ops in Acton with generous support from friends at IBM let me set up the situation here there's a real problem going on in the industry and that's that people are not getting the most out of their data data is plentiful but insights perhaps aren't what's the reason for that well it's really a pretty complicated situation for a lot of organizations there's data silos there's challenges with skill sets and lack of skills there's tons of tools out there sort of a tools brief the data pipeline is not automated the business lines oftentimes don't feel as though they own the data so that creates some real concerns around data quality and a lot of finger-point quality the opportunity here is to really operationalize the data pipeline and infuse AI into that equation and really attack their cost-cutting and revenue generation opportunities that are there in front of you think about this virtually every application this decade is going to be infused with AI if it's not it's not going to be competitive and so we have organized a panel of great practitioners to really dig in to these issues first I want to introduce Victoria Stassi with who's an industry expert in a top at Northwestern you two'll very great to see you again thanks for coming on excellent nice to see you as well and Caitlin Alfre is the director of AI a vai accelerator and also part of the peak data officers organization at IBM who has actually eaten some of it his own practice what a creep let me say it that way Caitlin great to see you again and Steve Lewis good to see you again see vice president director of management associated a bank and Thompson thanks for coming on thanks Dave make speaker alright guys so you heard my authority with in terms of operationalizing getting the most insight hey data is wonderful insights aren't but getting insight in real time is critical in this decade each of you is a sense as to where you are on that journey or Victoria your taste because you're brand new to Northwestern Mutual but you have a lot of deep expertise in in health care and manufacturing financial services but where you see just the general industry climate and we'll talk about the journeys that you are on both personally and professionally so it's all fair sure I think right now right again just me going is you need to have speech insight right so as I experienced going through many organizations are all facing the same challenges today and a lot of those pounds is hard where do my to live is my data trust meaning has a bank curated has been Clinton's visit qualified has a big a lot of that is ready what we see often happen is businesses right they know their KPIs they know their business metrics but they can't find where that data Linda Barragan asked there's abundant data disparity all over the place but it is replicated because it's not well managed it's a lot of what governance in the platform of pools that governance to speak right offer fact it organizations pay is just that piece of it I can tell you where data is I can tell you what's trusted that when you can quickly access information and bring back answers to business questions that is one answer not many answers leaving the business to question what's the right path right which is the correct answer which which way do I go at the executive level that's the biggest challenge where we want the industry to go moving forward right is one breaking that down along that information to be published quickly and to an emailing data virtualization a lot of what you see today is most businesses right it takes time to build out large warehouses at an enterprise level we need to pivot quicker so a lot of what businesses are doing is we're leaning them towards taking advantage of data virtualization allowing them to connect to these data sources right to bring that information back quickly so they don't have to replicate that information across different systems or different applications right and then to be able to provide that those answers back quickly also allowing for seamless access to from the analysts that are running running full speed right try and find the answers as quickly as they find great okay and I want to get into that sort of how news Steve let me go to you one of the things that we talked about earlier was just infusing this this mindset of a data cult and thinking about data as a service so talk a little bit about how you got started what was the starting NICUs through that sure I think the biggest thing for us there is to change that mindset from data being just for reporting or things that have happened in the past to do some insights on us and some data that already existed well we've tried to shift the mentality there is to start to use data and use that into our actual applications so that we're providing those insight in real time through the applications as they're consumed helping with customer experience helping with our personalization and an optimization of our application the way we've started down that path or kind of the journey that we're still on was to get the foundation laid birch so part of that has been making sure we have access to all that data whether it's through virtualization like vic talked about or whether it's through having more of the the data selected in a data like that that where we have all of that foundational data available as opposed to waiting for people to ask for it that's been the biggest culture shift for us is having that availability of data to be ready to be able to provide those insights as opposed to having to make the businesses or the application or asked for that day Oh Kailyn when I first met into pulp andari the idea wobble he paid up there yeah I was asking him okay where does a what's the role of that at CBO and and he mentioned a number of things but two of the things that stood out is you got to understand how data affect the monetization of your company that doesn't mean you know selling the data what role does it play and help cut cost or ink revenue or productivity or no customer service etc the other thing he said was you've got a align with the lines of piss a little sounded good and this is several years ago and IBM took it upon itself Greek its own champagne I was gonna say you know dogfooding whatever but it's not easy just flip a switch and an infuse a I and automate the data pipeline you guys had to go you know some real of pain to get there and you did you were early on you took some arrows and now you're helping your customers better on thin debt but talk about some of the use cases that where you guys have applied this obviously the biggest organization you know one of the biggest in the world the real challenge is they're sure I'm happy today you know we've been on this journey for about four years now so we stood up our first book to get office 2016 and you're right it was all about getting what data strategy offered and executed internally and we want to be very transparent because as you've mentioned you know a lot of challenges possible think differently about the value and so as we wrote that data strategy at that time about coming to enterprise and then we quickly of pivoted to see the real opportunity and value of infusing AI across all of our needs were close to your question on a couple of specific use cases I'd say you know we invested that time getting that platform built and implemented and then we were able to take advantage of that one particular example that I've been really excited about I have a practitioner on my team who's a supply chain expert and a couple of years ago he started building out supply chain solution so that we can better mitigate our risk in the event of a natural disaster like the earthquake hurricane anywhere around the world and be cuz we invest at the time and getting the date of pipelines right getting that all of that were created and cleaned and the quality of it we were able to recently in recent weeks add the really critical Kovach 19 data and deliver that out to our employees internally for their preparation purposes make that available to our nonprofit partners and now we're starting to see our first customers take advantage too with the health and well-being of their employees mine so that's you know an example I think where and I'm seeing a lot of you know my clients I work with they invest in the data and AI readiness and then they're able to take advantage of all of that work work very quickly in an agile fashion just spin up those out well I think one of the keys there who Kaelin is that you know we can talk about that in a covet 19 contact but it's that's gonna carry through that that notion of of business resiliency is it's gonna live on you know in this post pivot world isn't it absolutely I think for all of us the importance of investing in the business continuity and resiliency type work so that we know what to do in the event of either natural disaster or something beyond you know it'll be grounded in that and I think it'll only become more important for us to be able to act quickly and so the investment in those platforms and approach that we're taking and you know I see many of us taking will really be grounded in that resiliency so Vic and Steve I want to dig into this a little bit because you know we use this concept of data op we're stealing from DevOps and there are similarities but there are also differences now let's talk about the data pipeline if you think about the data pipeline as a sort of quasi linear process where you're investing data and you might be using you know tools but whether it's Kafka or you know we have a favorite who will you have and then you're transforming that that data and then you got a you know discovery you got to do some some exploration you got to figure out your metadata catalog and then you're trying to analyze that data to get some insights and then you ultimately you want to operationalize it so you know and and you could come up with your own data pipeline but generally that sort of concept is is I think well accepted there's different roles and unlike DevOps where it might be the same developer who's actually implementing security policies picking it the operations in in data ops there might be different roles and fact very often are there's data science there's may be an IT role there's data engineering there's analysts etc so Vic I wonder if you could you could talk about the challenges in in managing and automating that data pipeline applying data ops and how practitioners can overcome them yeah I would say a perfect example would be a client that I was just recently working for where we actually took a team and we built up a team using agile methodologies that framework right we're rapidly ingesting data and then proving out data's fit for purpose right so often now we talk a lot about big data and that is really where a lot of industries are going they're trying to add an enrichment to their own data sources so what they're doing is they're purchasing these third-party data sets so in doing so right you make that initial purchase but what many companies are doing today is they have no real way to vet that so they'll purchase the information they aren't going to vet it upfront they're going to bring it into an environment there it's going to take them time to understand if the data is of quality or not and by the time they do typically the sales gone and done and they're not going to ask for anything back but we were able to do it the most recent claim was use an instructure data source right bring that and ingest that with modelers using this agile team right and within two weeks we were able to bring the data in from the third-party vendor what we considered rapid prototyping right be able to profile the data understand if the data is of quality or not and then quickly figure out that you know what the data's not so in doing that we were able to then contact the vendor back tell them you know it sorry the data set up to snuff we'd like our money back we're not gonna go forward with it that's enabling businesses to be smarter with what they're doing with 30 new purchases today as many businesses right now um as much as they want to rely on their own data right they actually want to rely on cross the data from third-party sources and that's really what data Ops is allowing us to do it's allowing us to think at a broader a higher level right what to bring the information what structures can we store them in that they don't necessarily have to be modeled because a modeler is great right but if we have to take time to model all the information before we even know we want to use it that's gonna slow the process now and that's slowing the business down the business is looking for us to speed up all of our processes a lot of what we heard in the past raised that IP tends to slow us down and that's where we're trying to change that perception in the industry is no we're actually here to speed you up we have all the tools and technologies to do so and they're only getting better I would say also on data scientists right that's another piece of the pie for us if we can bring the information in and we can quickly catalog it in a metadata and burn it bring in the information in the backend data data assets right and then supply that information back to scientists gone are the days where scientists are going and asking for connections to all these different data sources waiting days for access requests to be approved just to find out that once they figure out how it with them the relationship diagram right the design looks like in that back-end database how to get to it write the code to get to it and then figure out this is not the information I need that Sally next to me right fold me the wrong information that's where the catalog comes in that's where due to absent data governance having that catalog that metadata management platform available to you they can go into a catalog without having to request access to anything quickly and within five minutes they can see the structures what if the tables look like what did the fields look like are these are these the metrics I need to bring back answers to the business that's data apps it's allowing us to speed up all of that information you know taking stuff that took months now down two weeks down two days down two hours so Steve I wonder if you could pick up on that and just help us understand what data means you we talked about earlier in our previous conversation I mentioned it upfront is this notion of you know the demand for for data access is it was through the roof and and you've gone from that to sort of more of a self-service environment where it's not IT owning the data it's really the businesses owning the data but what what is what is all this data op stuff meaning in your world sure I think it's very similar it's it's how do we enable and get access to that clicker showing the right controls showing the right processes and and building that scalability and agility and into all of it so that we're we're doing this at scale it's much more rapidly available we can discover new data separately determine if it's right or or more importantly if it's wrong similar to what what Vic described it's it's how do we enable the business to make those right decisions on whether or not they're going down the right path whether they're not the catalog is a big part of that we've also introduced a lot of frameworks around scale so just the ability to rapidly ingest data and make that available has been a key for us we've also focused on a prototyping environment so that sandbox mentality of how do we rapidly stand those up for users and and still provide some controls but have provide that ability for people to do that that exploration what we're finding is that by providing the platform and and the foundational layers that were we're getting the use cases to sort of evolve and come out of that as opposed to having the use cases prior to then go build things from we're shifting the mentality within the organization to say we don't know what we need yet let's let's start to explore that's kind of that data scientist mentality and culture it more of a way of thinking as opposed to you know an actual project or implement well I think that that cultural aspect is important of course Caitlin you guys are an AI company or at least that you know part of what you do but you know you've you for four decades maybe centuries you've been organized around different things by factoring plant but sales channel or whatever it is but-but-but-but how has the chief data officer organization within IBM been able to transform itself and and really infuse a data culture across the entire company one of the approaches you know we've taken and we talk about sort of the blueprint to drive AI transformation so that we can achieve and deliver these really high value use cases we talked about the data the technology which we've just pressed on with organizational piece of it duration are so important the change management enabling and equipping our data stewards I'll give one a civic example that I've been really excited about when we were building our platform and starting to pull districting structured unstructured pull it in our ADA stewards are spending a lot of time manually tagging and creating business metadata about that data and we identified that that was a real pain point costing us a lot of money valuable resources so we started to automate the metadata and doing that in partnership with our deep learning practitioners and some of the models that they were able to build that capability we pushed out into our contacts our product last year and one of the really exciting things for me to see is our data stewards who be so value exporters and the skills that they bring have reported that you know it's really changed the way they're able to work it's really sped up their process it's enabled them to then move on to higher value to abilities and and business benefits so they're very happy from an organizational you know completion point of view so I think there's ways to identify those use cases particularly for taste you know we drove some significant productivity savings we also really empowered and hold our data stewards we really value to make their job you know easier more efficient and and help them move on to things that they are more you know excited about doing so I think that's that you know another example of approaching taken yes so the cultural piece the people piece is key we talked a little bit about the process I want to get into a little bit into the tech Steve I wonder if you could tell us you know what's it what's the tech we have this bevy of tools I mentioned a number of them upfront you've got different data stores you've got open source pooling you've got IBM tooling what are the critical components of the technology that people should be thinking about tapping in architecture from ingestion perspective we're trying to do a lot of and a Python framework and scaleable ingestion pipe frameworks on the catalog side I think what we've done is gone with IBM PAC which provides a platform for a lot of these tools to stay integrated together so things from the discovery of data sources the cataloging the documentation of those data sources and then all the way through the actual advanced analytics and Python models and our our models and the open source ID combined with the ability to do some data prep and refinery work having that all in an integrated platform was a key to us for us that the rollout and of more of these tools in bulk as opposed to having the point solutions so that's been a big focus area for us and then on the analytic side and the web versus IDE there's a lot of different components you can go into whether it's meal soft whether it's AWS and some of the native functionalities out there you mentioned before Kafka and Anissa streams and different streaming technologies those are all the ones that are kind of in our Ketil box that we're starting to look at so and one of the keys here is we're trying to make decisions in as close to real time as possible as opposed to the business having to wait you know weeks or months and then by the time they get insights it's late and really rearview mirror so Vic your focus you know in your career has been a lot on data data quality governance master data management data from a data quality standpoint as well what are some of the key tools that you're familiar with that you've used that really have enabled you operationalize that data pipeline you know I would say I'm definitely the IBM tools I have the most experience with that also informatica though as well those are to me the two top players IBM definitely has come to the table with a suite right like Steve said cloud pack for data is really a one-stop shop so that's allowing that quick seamless access for business user versus them having to go into some of the previous versions that IBM had rolled out where you're going into different user interfaces right to find your information and that can become clunky it can add the process it can also create almost like a bad taste and if in most people's mouths because they don't want to navigate from system to system to system just to get their information so cloud pack to me definitely brings everything to the table in one in a one-stop shop type of environment in for me also though is working on the same thing and I would tell you that they haven't come up with a solution that really comes close to what IBM is done with cloud pack for data I'd be interested to see if they can bring that on the horizon but really IBM suite of tools allows for profiling follow the analytics write metadata management access to db2 warehouse on cloud those are the tools that I've worked in my past to implement as well as cloud object store to bring all that together to provide that one stop that at Northwestern right we're working right now with belieber I think calibra is a great set it pool are great garments catalog right but that's really what it's truly made for is it's a governance catalog you have to bring some other pieces to the table in order for it to serve up all the cloud pack does today which is the advanced profiling the data virtualization that cloud pack enables today the machine learning at the level where you can actually work with our and Python code and you put our notebooks inside of pack that's some of this the pieces right that are missing in some of the under vent other vendor schools today so one of the things that you're hearing here is the theme of openness others addition we've talked about a lot of tools and not IBM tools all IBM tools there there are many but but people want to use what they want to use so Kaitlin from an IBM perspective what's your commitment the openness number one but also to you know we talked a lot about cloud packs but to simplify the experience for your client well and I thank Stephen Victoria for you know speaking to their experience I really appreciate feedback and part of our approach has been to really take one the challenges that we've had I mentioned some of the capabilities that we brought forward in our cloud platform data product one being you know automating metadata generation and that was something we had to solve for our own data challenges in need so we will continue to source you know our use cases from and grounded from a practitioner perspective of what we're trying to do and solve and build and the approach we've really been taking is co-creation line and that we roll these capability about the product and work with our customers like Stephen light victorious you really solicit feedback to product route our dev teams push that out and just be very open and transparent I mean we want to deliver a seamless experience we want to do it in partnership and continue to solicit feedback and improve and roll out so no I think that will that has been our approach will continue to be and really appreciate the partnerships that we've been able to foster so we don't have a ton of time but I want to go to practitioners on the panel and ask you about key key performance indicators when I think about DevOps one of the things that we're measuring is the elapsed time the deploy applications start finished where we're measuring the amount of rework that has to be done the the quality of the deliverable what are the KPIs Victoria that are indicators of success in operationalizing date the data pipeline well I would definitely say your ability to deliver quickly right so how fast can you deliver is that is that quicker than what you've been able to do in the past right what is the user experience like right so have you been able to measure what what the amount of time was right that users are spending to bring information to the table in the past versus have you been able to reduce that time to delivery right of information business answers to business questions those are the key performance indicators to me that tell you that the suite that we've put in place today right it's providing information quickly I can get my business answers quickly but quicker than I could before and the information is accurate so being able to measure is it quality that I've been giving that I've given back or is this not is it the wrong information and yet I've got to go back to the table and find where I need to gather that from from somewhere else that to me tells us okay you know what the tools we've put in place today my teams are working quicker they're answering the questions they need to accurately that is when we know we're on the right path Steve anything you add to that I think she covered a lot of the people components the around the data quality scoring right for all the different data attributes coming up with a metric around how to measure that and and then showing that trend over time to show that it's getting better the other one that we're doing is just around overall date availability how how much data are we providing to our users and and showing that trend so when I first started you know we had somewhere in the neighborhood of 500 files that had been brought into the warehouse and and had been published and available in the neighborhood of a couple thousand fields we've grown that into weave we have thousands of cables now available so it's it's been you know hundreds of percent in scale as far as just the availability of that data how much is out there how much is is ready and available for for people to just dig in and put into their their analytics and their models and get those back into the other application so that's another key metric that we're starting to track as well so last question so I said at the top that every application is gonna need to be infused with AI this decade otherwise that application not going to be as competitive as it could be and so for those that are maybe stuck in their journey don't really know where to get started I'll start with with Caitlin and go to Victoria and then and then even bring us home what advice would you give the people that need to get going on this my advice is I think you pull the folks that are either producing or accessing your data and figure out what the rate is between I mentioned some of the data management challenges we were seeing this these processes were taking weeks and prone to error highly manual so part was ripe for AI project so identifying those use cases I think that are really causing you know the most free work and and manual effort you can move really quickly and as you build this platform out you're able to spin those up on an accelerated fashion I think identifying that and figuring out the business impact are able to drive very early on you can get going and start really seeing the value great yeah I would actually say kids I hit it on the head but I would probably add to that right is the first and foremost in my opinion right the importance around this is data governance you need to implement a data governance at an enterprise level many organizations will do it but they'll have silos of governance you really need an interface I did a government's platform that consists of a true framework of an operational model model charters right you have data domain owners data domain stewards data custodians all that needs to be defined and while that may take some work in in the beginning right the payoff down the line is that much more it's it it's allowing your business to truly own the data once they own the data and they take part in classifying the data assets for technologists and for analysts right you can start to eliminate some of the technical debt that most organizations have acquired today they can start to look at what are some of the systems that we can turn off what are some of the systems that we see valium truly build out a capability matrix we can start mapping systems right to capabilities and start to say where do we have wares or redundancy right what can we get rid of that's the first piece of it and then the second piece of it is really leveraging the tools that are out there today the IBM tools some of the other tools out there as well that enable some of the newer next-generation capabilities like unit nai right for example allowing automation for automation which right for all of us means that a lot of the analysts that are in place today they can access the information quicker they can deliver the information accurately like we've been talking about because it's been classified that pre works being done it's never too late to start but once you start that it just really acts as a domino effect to everything else where you start to see everything else fall into place all right thank you and Steve bring us on but advice for your your peers that want to get started sure I think the key for me too is like like those guys have talked about I think all everything they said is valid and accurate thing I would add is is from a starting perspective if you haven't started start right don't don't try to overthink that over plan it it started just do something and and and start the show that progress and value the use cases will come even if you think you're not there yet it's amazing once you have the national components there how some of these things start to come out of the woodwork so so it started it going may have it have that iterative approach to this and an open mindset it's encourage exploration and enablement look your organization in the eye to say why are their silos why do these things like this what are our problem what are the things getting in our way and and focus and tackle those those areas as opposed to trying to put up more rails and more boundaries and kind of encourage that silo mentality really really look at how do you how do you focus on that enablement and then the last comment would just be on scale everything should be focused on scale what you think is a one-time process today you're gonna do it again we've all been there you're gonna do it a thousand times again so prepare for that prepare forever that you're gonna do everything a thousand times and and start to instill that culture within your organization a great advice guys data bringing machine intelligence an AI to really drive insights and scaling with a cloud operating model no matter where that data live it's really great to have have three such knowledgeable practitioners Caitlyn Toria and Steve thanks so much for coming on the cube and helping support this panel all right and thank you for watching everybody now remember this panel was part of the raw material that went into a crowd chat that we hosted on May 27th Crouch at net slash data ops so go check that out this is Dave Volante for the cube thanks for watching [Music]

>> From Cambridge, Massachusetts, it's theCUBE covering MIT Chief Data Officer and Information Quality Symposium 2019 brought to you by SiliconANGLE Media. (upbeat techno music) >> Hi everybody, welcome back to Cambridge, Massachusetts. You're watching theCUBE, the leader in tech coverage. We go out to the events. We extract the signal from the noise, and we're here at the MIT CDOIQ Conference, Chief Data Officer Information Quality Conference. It is the 13th year here at the Tang building. We've outgrown this building and have to move next year. It's fire marshal full. Gokula Mishra is here. He is the Senior Director of Global Data and Analytics and Supply Chain-- >> Formerly. Former, former Senior Director. >> Former! I'm sorry. It's former Senior Director of Global Data Analytics and Supply Chain at McDonald's. Oh, I didn't know that. I apologize my friend. Well, welcome back to theCUBE. We met when you were at Oracle doing data. So you've left that, you're on to your next big thing. >> Yes, thinking through it. >> Fantastic, now let's start with your career. You've had, so you just recently left McDonald's. I met you when you were at Oracle, so you cut over to the dark side for a while, and then before that, I mean, you've been a practitioner all your life, so take us through sort of your background. >> Yeah, I mean my beginning was really with a company called Tata Burroughs. Those days we did not have a lot of work getting done in India. We used to send people to U.S. so I was one of the pioneers of the whole industry, coming here and working on very interesting projects. But I was lucky to be working on mostly data analytics related work, joined a great company called CS Associates. I did my Master's at Northwestern. In fact, my thesis was intelligent databases. So, building AI into the databases and from there on I have been with Booz Allen, Oracle, HP, TransUnion, I also run my own company, and Sierra Atlantic, which is part of Hitachi, and McDonald's. >> Awesome, so let's talk about use of data. It's evolved dramatically as we know. One of the themes in this conference over the years has been sort of, I said yesterday, the Chief Data Officer role emerged from the ashes of sort of governance, kind of back office information quality compliance, and then ascended with the tailwind of the Big Data meme, and it's kind of come full circle. People are realizing actually to get value out of data, you have to have information quality. So those two worlds have collided together, and you've also seen the ascendancy of the Chief Digital Officer who has really taken a front and center role in some of the more strategic and revenue generating initiatives, and in some ways the Chief Data Officer has been a supporting role to that, providing the quality, providing the compliance, the governance, and the data modeling and analytics, and a component of it. First of all, is that a fair assessment? How do you see the way in which the use of data has evolved over the last 10 years? >> So to me, primarily, the use of data was, in my mind, mostly around financial reporting. So, anything that companies needed to run their company, any metrics they needed, any data they needed. So, if you look at all the reporting that used to happen it's primarily around metrics that are financials, whether it's around finances around operations, finances around marketing effort, finances around reporting if it's a public company reporting to the market. That's where the focus was, and so therefore a lot of the data that was not needed for financial reporting was what we call nowadays dark data. This is data we collect but don't do anything with it. Then, as the capability of the computing, and the storage, and new technologies, and new techniques evolve, and are able to handle more variety and more volume of data, then people quickly realize how much potential they have in the other data outside of the financial reporting data that they can utilize too. So, some of the pioneers leverage that and actually improved a lot in their efficiency of operations, came out with innovation. You know, GE comes to mind as one of the companies that actually leverage data early on, and number of other companies. Obviously, you look at today data has been, it's defining some of the multi-billion dollar company and all they have is data. >> Well, Facebook, Google, Amazon, Microsoft. >> Exactly. >> Apple, I mean Apple obviously makes stuff, but those other companies, they're data companies. I mean largely, and those five companies have the highest market value on the U.S. stock exchange. They've surpassed all the other big leaders, even Berkshire Hathaway. >> So now, what is happening is because the market changes, the forces that are changing the behavior of our consumers and customers, which I talked about which is everyone now is digitally engaging with each other. What that does is all the experiences now are being captured digitally, all the services are being captured digitally, all the products are creating a lot of digital exhaust of data and so now companies have to pay attention to engage with their customers and partners digitally. Therefore, they have to make sure that they're leveraging data and analytics in doing so. The other thing that has changed is the time to decision to the time to act on the data inside that you get is shrinking, and shrinking, and shrinking, so a lot more decision-making is now going real time. Therefore, you have a situation now, you have the capability, you have the technology, you have the data now, you have to make sure that you convert that in what I call programmatic kind of data decision-making. Obviously, there are people involved in more strategic decision-making. So, that's more manual, but at the operational level, it's going more programmatic decision-making. >> Okay, I want to talk, By the way, I've seen a stat, I don't know if you can confirm this, that 80% of the data that's out there today is dark data or it's data that's behind a firewall or not searchable, not open to Google's crawlers. So, there's a lot of value there-- >> So, I would say that percent is declining over time as companies have realized the value of data. So, more and more companies are removing the silos, bringing those dark data out. I think the key to that is companies being able to value their data, and as soon as they are able to value their data, they are able to leverage a lot of the data. I still believe there's a large percent still not used or accessed in companies. >> Well, and of course you talked a lot about data monetization. Doug Laney, who's an expert in that topic, we had Doug on a couple years ago when he, just after, he wrote Infonomics. He was on yesterday. He's got a very detailed prescription as to, he makes strong cases as to why data should be valued like an asset. I don't think anybody really disagrees with that, but then he gave kind of a how-to-do-it, which will, somewhat, make your eyes bleed, but it was really well thought out, as you know. But you talked a lot about data monetization, you talked about a number of ways in which data can contribute to monetization. Revenue, cost reduction, efficiency, risk, and innovation. Revenue and cost is obvious. I mean, that's where the starting point is. Efficiency is interesting. I look at efficiency as kind of a doing more with less but it's sort of a cost reduction, but explain why it's not in the cost bucket, it's different. >> So, it is first starts with doing what we do today cheaper, better, faster, and doing more comes after that because if you don't understand, and data is the way to understand how your current processes work, you will not take the first step. So, to take the first step is to understand how can I do this process faster, and then you focus on cheaper, and then you focus on better. Of course, faster is because of some of the market forces and customer behavior that's driving you to do that process faster. >> Okay, and then the other one was risk reduction. I think that makes a lot of sense here. Actually, let me go back. So, one of the key pieces of it, of efficiency is time to value. So, if you can compress the time, or accelerate the time and you get the value that means more cash in house faster, whether it's cost reduction or-- >> And the other aspect you look at is, can you automate more of the processes, and in that way it can be faster. >> And that hits the income statement as well because you're reducing headcount cost of your, maybe not reducing headcount cost, but you're getting more out of different, out ahead you're reallocating them to more strategic initiatives. Everybody says that but the reality is you hire less people because you just automated. And then, risk reduction, so the degree to which you can lower your expected loss. That's just instead thinking in insurance terms, that's tangible value so certainly to large corporations, but even midsize and small corporations. Innovation, I thought was a good one, but maybe you could use an example of, give us an example of how in your career you've seen data contribute to innovation. >> So, I'll give an example of oil and gas industry. If you look at speed of innovation in the oil and gas industry, they were all paper-based. I don't know how much you know about drilling. A lot of the assets that goes into figuring out where to drill, how to drill, and actually drilling and then taking the oil or gas out, and of course selling it to make money. All of those processes were paper based. So, if you can imagine trying to optimize a paper-based innovation, it's very hard. Not only that, it's very, very by itself because it's on paper, it's in someone's drawer or file. So, it's siloed by design and so one thing that the industry has gone through, they recognize that they have to optimize the processes to be better, to innovate, to find, for example, shale gas was a result output of digitizing the processes because otherwise you can't drill faster, cheaper, better to leverage the shale gas drilling that they did. So, the industry went through actually digitizing a lot of the paper assets. So, they went from not having data to knowingly creating the data that they can use to optimize the process and then in the process they're innovating new ways to drill the oil well cheaper, better, faster. >> In the early days of oil exploration in the U.S. go back to the Osage Indian tribe in northern Oklahoma, and they brilliantly, when they got shuttled around, they pushed him out of Kansas and they negotiated with the U.S. government that they maintain the mineral rights and so they became very, very wealthy. In fact, at one point they were the wealthiest per capita individuals in the entire world, and they used to hold auctions for various drilling rights. So, it was all gut feel, all the oil barons would train in, and they would have an auction, and it was, again, it was gut feel as to which areas were the best, and then of course they evolved, you remember it used to be you drill a little hole, no oil, drill a hole, no oil, drill a hole. >> You know how much that cost? >> Yeah, the expense is enormous right? >> It can vary from 10 to 20 million dollars. >> Just a giant expense. So, now today fast-forward to this century, and you're seeing much more sophisticated-- >> Yeah, I can give you another example in pharmaceutical. They develop new drugs, it's a long process. So, one of the initial process is to figure out what molecules this would be exploring in the next step, and you could have thousand different combination of molecules that could treat a particular condition, and now they with digitization and data analytics, they're able to do this in a virtual world, kind of creating a virtual lab where they can test out thousands of molecules. And then, once they can bring it down to a fewer, then the physical aspect of that starts. Think about innovation really shrinking their processes. >> All right, well I want to say this about clouds. You made the statement in your keynote that how many people out there think cloud is cheaper, or maybe you even said cheap, but cheaper I inferred cheaper than an on-prem, and so it was a loaded question so nobody put their hand up they're afraid, but I put my hand up because we don't have any IT. We used to have IT. It was a nightmare. So, for us it's better but in your experience, I think I'm inferring correctly that you had meant cheaper than on-prem, and certainly we talked to many practitioners who have large systems that when they lift and shift to the cloud, they don't change their operating model, they don't really change anything, they get a bill at the end of the month, and they go "What did this really do for us?" And I think that's what you mean-- >> So what I mean, let me make it clear, is that there are certain use cases that cloud is and, as you saw, that people did raise their hand saying "Yeah, I have use cases where cloud is cheaper." I think you need to look at the whole thing. Cost is one aspect. The flexibility and agility of being able to do things is another aspect. For example, if you have a situation where your stakeholder want to do something for three weeks, and they need five times the computing power, and the data that they are buying from outside to do that experiment. Now, imagine doing that in a physical war. It's going to take a long time just to procure and get the physical boxes, and then you'll be able to do it. In cloud, you can enable that, you can get GPUs depending on what problem we are trying to solve. That's another benefit. You can get the fit for purpose computing environment to that and so there are a lot of flexibility, agility all of that. It's a new way of managing it so people need to pay attention to the cost because it will add to the cost. The other thing I will point out is that if you go to the public cloud, because they make it cheaper, because they have hundreds and thousands of this canned CPU. This much computing power, this much memory, this much disk, this much connectivity, and they build thousands of them, and that's why it's cheaper. Well, if your need is something that's very unique and they don't have it, that's when it becomes a problem. Either you need more of those and the cost will be higher. So, now we are getting to the IOT war. The volume of data is growing so much, and the type of processing that you need to do is becoming more real-time, and you can't just move all this bulk of data, and then bring it back, and move the data back and forth. You need a special type of computing, which is at the, what Amazon calls it, adds computing. And the industry is kind of trying to design it. So, that is an example of hybrid computing evolving out of a cloud or out of the necessity that you need special purpose computing environment to deal with new situations, and all of it can't be in the cloud. >> I mean, I would argue, well I guess Microsoft with Azure Stack was kind of the first, although not really. Now, they're there but I would say Oracle, your former company, was the first one to say "Okay, we're going to put the exact same infrastructure on prem as we have in the public cloud." Oracle, I would say, was the first to truly do that-- >> They were doing hybrid computing. >> You now see Amazon with outposts has done the same, Google kind of has similar approach as Azure, and so it's clear that hybrid is here to stay, at least for some period of time. I think the cloud guys probably believe that ultimately it's all going to go to the cloud. We'll see it's going to be a long, long time before that happens. Okay! I'll give you last thoughts on this conference. You've been here before? Or is this your first one? >> This is my first one. >> Okay, so your takeaways, your thoughts, things you might-- >> I am very impressed. I'm a practitioner and finding so many practitioners coming from so many different backgrounds and industries. It's very, very enlightening to listen to their journey, their story, their learnings in terms of what works and what doesn't work. It is really invaluable. >> Yeah, I tell you this, it's always a highlight of our season and Gokula, thank you very much for coming on theCUBE. It was great to see you. >> Thank you. >> You're welcome. All right, keep it right there everybody. We'll be back with our next guest, Dave Vellante. Paul Gillin is in the house. You're watching theCUBE from MIT. Be right back! (upbeat techno music)

>> from Cambridge, Massachusetts. It's the Cube covering M. I. T. Chief Data officer and Information Quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to M. I. T. In Cambridge, Massachusetts. Everybody You're watching The Cube, the Leader and Live Tech coverage. My name is Dave Volante, and it's my pleasure to introduce Matt Kobe, who's the vice president of business strategy Analytics of Chicago Bulls. We love talking sports. We love talking data. Matt. Thanks for coming on. >> No problem getting a date. So talk about >> your role. Is the head of analytics for the Bulls? >> Sure. So I work exclusively on the business side of the operation. So we have a separate team that those the basketball side, which is kind of your players stuff. But on the business side, um, what we're focused on is really two things. One is being essentially internal consultants for the rest of the customer facing functions. So we work a lot with ticketing, allow its sponsorship, um, marketing digital, all of those folks that engage with our customer base and then on the backside back end of it, we're building out the technical infrastructure for the organization right. So everything from data warehouse to C. R M to email marketing All of that sits with my team. And so we were a lot of hats, which is exciting. But at the end of the day, we're trying to use data to enhance the customer and fan experience. Um and that's our aim. And that's what we're driving towards >> success in sports. In a larger respect. It's come down to don't be offended by this. Who's got the best geeks? So now your side of the house is not about like you say, player performance about the business performances. But that's it. That's a big part of getting the best players. I mean, if it's successful and all the nuances of the N B, A salary cap and everything else, but I think there is one, and so that makes it even more important. But you're helping fund. You know that in various ways, but so are the other two teams that completely separate. Is there a Chinese wall between them? Are you part of the sort of same group? >> Um, we're pretty separate. So the basketball folks do their thing. The business folks do their thing from an analytic standpoint. We meet and we collaborate on tools and other methods of actually doing the analysis. But in terms of, um, the analysis itself, there is a little bit of separation there, and mainly that is from priority standpoint. Obviously, the basketball stuff is the most important stuff. And so if we're working on both sides that we'd always be doing the basketball stuff and the business stuff needs to get done, >> drag you into exactly okay. But which came first? The chicken or the egg was It was the sort of post Moneyball activity applied to the N B. A. And I want to ask you a question about that. And then somebody said, Hey, we should do this for the business side. Or was the business side of sort of always there? >> I think I think, the business side and probably the last 5 to 7 years you've really seen it grown. So if you look at the N. B. A. I've been with the Bulls for five years. If you look at the N. B. A. 78 years ago, there was a handful of Business analytics teams and those those teams had one or two people at him. Now every single team in the NBA has some sort of business analytics team, and the average staff is seven. So my staff is six full time folks pushed myself, so we'll write it right at the average. And I think what you've seen is everything has become more complex in sports. Right? If you look at ticketing, you've got all the secondary markets. You have all this data flowing in, and they need someone to make sense of all that data. If you look at sponsorship sponsorship, his transition from selling a sign that sits on the side of the court for these truly integrated partnerships, where our partners are coming to us and saying, What do we get out of? This was our return. And so you're seeing a lot more part lot more collaboration between analytics and sponsorship to go back to those partners and say, Hey, here's what we delivered And so I think you it started on the basketball side, certainly because that's that's where the, you know that is the most important piece. But it quickly followed on the business side because they saw the value that that type of thinking can bring in the business. >> So I know this is not, you know, your swim lane, but But, you know, the lore of Billy Beane and Moneyball and all that, a sort of the starting point for sports analytics. Is that Is that Is that a fair characterization? Yeah. I mean, was that Was that really the main spring? >> I think it It probably started even before that. I think if you have got to see Billy being at the M I t Sports Analytics conference and him thought he always references kind of Bill James is first, and so I think it started. Baseball was I wouldn't say the easiest place to start, But it was. It's a one versus one, right? It's pitcher versus batter. In a lot of cases, basketball is a little bit more fluid. It's a team. Sport is a little harder, but I think as technology has advanced, there's been more and more opportunities to do the analytics on the basketball side and on the business side. I think what you're seeing is this huge. What we've heard the first day and 1/2 here, this huge influx of data, not nearly to the levels of the MasterCard's and others of the world. But as more and more things moved to the mobile phone, I think you're going to see this huge influx of data on the business side, and you're going to need the same systems in the same sort of approach to tackle it. >> S O. Bill James is the ultimate sports geek, and he's responsible for all these stats that, no, none of us understand. He's why we don't pay attention to batting average anymore. Of course, I still do. So let's talk about the business side of things. If you think about the business of baseball, you know it's all about maximizing the gate. Yeah, there's there's some revenue, a lot of revenue course from TV. But it's not like football, which is dominated by the by the TV. Basketball, I think, is probably a mix right. You got 80 whatever 82 game season, so filling up the stadium is important. Obviously, N v A has done a great job of of really getting it right. Free agency is like, fascinating. Now >> it's 12 months a year >> scored way. Talk about the NBA all the time and of course, you know, people like celebrities like LeBron have certainly helped, and now a whole batch of others. But what's the money side of the n ba look like? Where's the money coming from? >> Yeah, I mean, I think you certainly have broadcast right, but in many ways, like national broadcast sort of takes care of it itself. In some ways, from the standpoint of my team, doesn't have a lot of control over national broadcast money. That's a league level thing. And so the things that we have control over the two big buckets are ticketing and sponsorship. Those those are the two big buckets of revenue that my team spends a lot of time on. Ticketing is, is one that is important from the standpoint, as you say, which is like, How do we fill the building right? We've got 41 home game, supposed three preseason games. We got 44 events a year. Our goal is to fill the building for all 44 of those events. We do a pretty good job of doing it, but that has cascading effects into other revenue streams. Right, As you think about concessions and merchandise and sponsorship, it's a lot easier to spell spot cell of sponsorship when you're building is full, then if you're building isn't full. And so our focus is on. How do we? How do we fill the building in the most efficient way possible? And as you have things like the secondary market and people have access to tickets in different ways than they did 10 to 15 years ago, I think that becomes increasingly complex. Um, but that's the fun area that's like, That's where we spend a lot of time. There's the pricing, There's inventory management. It's a lot of, you know, is you look a traditional cpg. There's there's some of those same principles being applied, which is how do you are you looking airline right there? They're selling a plane. It's an asset you have to fill. We have ah, building. That's an asset we have to fill, and how do we fill it in the most optimal way? >> So the idea of surge pricing demand supply, But so several years ago, the Red Sox went to a tiered pricing. You guys do the same If the Sox are playing Kansas City Royals tickets way cheaper than if they're playing the Yankees. You guys do a similar. So >> we do it for single game tickets. So far are season ticket holders. It's the same price for every game, but on the price for primary tickets for single games, right? So if we're playing, you know this year will be the Clippers and the Lakers. That price is going to be much more expensive, so we dynamically price on a game to game basis. But our season ticket holders pay this. >> Why don't you do it for the season ticket holders? Um, just haven't gone there yet. >> Yeah, I mean, there's some teams have, right, so there's a few different approaches you convey. Lovely price. Those tickets, I think, for for us, the there's in years past. In the last few years, in particular, there's been a couple of flagship games, and then every other game feels similar. I think this will be the first year where you have 8 to 10 teams that really have a shot at winning the title, and so I think you'll see a more balanced schedule. Um, and so we've We've talked about it a lot. We just haven't gone to that made that move yet? >> Well, a season ticket holder that shares his tickets with seven other guys with red sauce. You could buy a BMW. You share the tickets, so but But I would love it if they didn't do the tiered. Pricing is a season ticket holder, so hope you hold off a while, but I don't know. It could maximize revenues if the Red Sox that was probably not a stupid thing is they're smart people. What about the sponsorships? Is fascinating about the partners looking for our ally. How are you measuring that? You're building your forging a tighter relationship, obviously, with the sponsors in these partners. Yeah, what's that are? Why look like it's >> measured? A variety of relies, largely based on the assets that they deliver. But I think every single partner we talk to these days, I also leave the sponsorship team. So I oversee. It's It's rare in sports, but I stayed over business strategy and Alex and sponsorship team. Um, it's not my title, but in practice, that's what I do. And I think everyone we talked to wants digital right? They want we've got over 25,000,000 social media followers with the Bulls, right? We've got 19,000,000 on Facebook alone. And so sponsors see those numbers and they know that we can deliver impression. They know we can deliver engagement and they want access to those channels. And so, from a return on, I always call a return on objectives, right? Return on investment is a little bit tricky, but return on objectives is if we're trying to reel brand awareness, we're gonna go back to them and say, Here's how many people came to our arena and saw your logo and saw the feature that you had on the scoreboard. If you're on our social media channels or a website, here's the number of impressions you got. Here is the number of engagements you got. I think where we're at now is Maura's Bad Morris. Still better, right? Everyone wants the big numbers. I think where you're starting to see it move, though, is that more isn't always better. We want the right folks engaging with our brands, and that's really what we're starting to think about is if you get 10,000,000 impressions, but they're 10,000,000 impressions to the wrong group of potential customers, that's not terribly helpful. for a brand. We're trying to work with our brands to reach the right demographics that they want to reach in order to actually build that brand awareness they want to build. >> What, What? Your primary social channels. Twitter, Obviously. >> So every platform has a different purpose way. Have Facebook, Twitter, instagram, Snapchat. We're in a week. We bow in in China and you know, every platform has a different function. Twitter's obviously more real time news. Um, you know the timeline stuff, it falls off really quick. Instagram is really the artistic piece of it on, and then Facebook is a blend of both, and so that's kind of how we deploy our channels. We have a whole social team that generates content and pushes that content out. But those are the channels we use and those air incredibly valuable. Now what you're starting to see is those channels are changing very rapidly, based on their own set of algorithms, of how they deliver content of fans. And so we're having to continue to adapt to those changing environments in those social >> show impressions. In the term, impressions varies by various platforms. So so I know. I know I'm more familiar with Twitter impressions. They have the definition. It's not just somebody who might have seen it. It's somebody that they believe actually spent a few seconds looking at. They have some algorithm to figure that out. Yeah. Is that a metric that you finding your brands are are buying into, for example? >> Yeah. I mean, I think certainly there they view it's kind of the old, you know, when you bought TV ads, it's how many households. So my commercial right, it's It's a similar type of metric of how many eyeballs saw a piece of content that we put out. I think we're the metrics. More people are starting to care about his engagements, which is how many of you actually engaged with that piece of content, whether it's a like a common a share, because then that's actual. Yeah, you might have seen it for three seconds, but we know how things work. You're scrolling pretty fast, But if you actually stopped to engage it with something, that's where I think brands are starting to see value. And as we think about our content, we have ah framework that our digital team uses. But one of the pillars of that is thumb stopping. We want to create content that is some stopping that people actually engage with. And that's been a big focus of ours. Last couple years, >> I presume. Using video, huge >> video We've got a whole graphics team that does custom graphics for whether it's stats or for history, historical anniversaries. We have a hole in house production team that does higher end, and then our digital team does more kind of straight from the phone raw footage. So we're using a variety of different mediums toe reach our fans >> that What's your background? How'd you get into all of this? >> I spent seven years in consulting, so I worked for Deloitte on their strategy group out of Chicago, And I worked for CPG companies like at the intersection of Retailer and CPG. So a lot of in store promotional work helping brands think through just General Revenue management, pricing strategy, promotional strategy and, um stumbled upon greatness with the Bulls job. A friend gave me the heads up that they were looking to fill this type of role and I was able to get my resume in the mix and I was lucky enough to get get the job, and it's been when I started. We're single, single, single, so it's a team of one. Five years later, we're a team of six, and we'll probably keep growing. So it's been an exciting ride and >> your background is >> maths. That's eyes business. Undergrad. And then I got a went Indian undergrad business and then went to Kellogg. Northwestern got an MBA on strategy, so that's my background. But it's, you know, I've dabbled in sports. I worked for the Chicago 2016 Olympic bid back in the day when I was at Deloitte. Um, and so it's been It's always been a dream of mine. I just never knew how I get there like I was wanted to work in sports. They just don't know the path. And I'm lucky enough to find the path a lot earlier than I thought. >> How about this conference? I know you have been the other M I T. Event. How about this one? How we found some of the key takeaways. Think you >> think it's been great because a lot of the conferences we go to our really sports focus? So you've got the M. I T Sports Analytics conference. You have seat. You have n b a type, um, programming that they put on. But it's nice to get out of sports and sort of see how other bigger industries are thinking about some of the problems specifically around data management and the influx of data and how they're thinking about it. It's always nice to kind of elevated. Just have some room to breathe and think and meet people that are not in sports and start to build those, you know, relationships and with thought leaders and things like that. So it's been great. It's my first time here. What are probably back >> good that Well, hopefully get to see a game, even though that stocks are playing that well. Thanks so much for coming in Cuba. No problems here on your own. You have me. It was great to have you. All right. Keep right, everybody. I'll be back with our next guest with Paul Gill on day Volante here in the house. You're watching the cue from M I T CEO. I cube. Right back

(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) >> Welcome to this special CUBEConversation here in the Palo Alto CUBE studio. I'm John Furrier, the co-host, theCUBE co-founder of SiliconANGLE Media. In theCUBE we're here with Alan Cohen, CUBE alumni, joining us today for a special segment on the future of technology and the impact to society. Always good to get Alan's commentary, he's the Chief Commercial Officer for Illumio, industry veteran, has been through many waves of innovation and now more than ever, this next wave of technology and the democratization of the global world is upon us. We're seeing signals out there like cryptocurrency and blockchain and bitcoin to the disruption of industries from media and entertainment, biotech among others. Technology is not just a corner industry, it's now pervasive and it's having some significant impacts and you're seeing that in the news whether it's Facebook trying to figure out who they are from a data standpoint to across the board every company. Alan, great to see you. >> Always great to be here, I always feel like, I can't tell whether I'm at the big desk at ESPN or I've got the desk chair at CNBC, but that's what it's like being on theCUBE. >> Great to have you on extracting the signal noises, a ton of noise out there, but one of things of the most important stories that we're tracking is, that's becoming very obvious, and you're seeing it everywhere from Meed to all aspects of technology. Is the impact of technology to people in society, okay you're seeing the election, we all know what that is, that's now a front and center in the big global conversation, the Russian's role of hacking, the weaponizing of data, Facebook's taking huge brand hits on that, to emerging startups, and the startup game that we're used to in Silicon Valley is changing. Just the dynamics, I mean cryptocurrency raises billions of dollars but yet (laughs) something like 10, 20% of it's been hacked and stolen. It's a really wild west kind of environment. >> Well it's a very different environment. John, you and I have been in the technology industry certainly for a whole bunch of lines under our eyes over the years have gone there. My friend Tom Friedman has this phrase that he says, "Everybody's connected and nobody's in control," so the difference is that, as you just said, the tech industry is not a separate industry. The tech industry is in every product and service. Cryptocurrency is like, the concept of that money is just code. You know, our products and services are just code, it raises a couple of really core issues. Like for us on the security point of view, if I don't trust people with the products they're selling me, that I feel like they're going to be hacked, including my personal data, so your product now includes my personal information, that's a real problem because that could actually melt down commerce in a real way. Obviously the election is if I don't trust the social systems around it, so I think we're all at an, and I'd like to say world is still kind of like iRobot moment, and if you remember iRobot, it's like, people build all these robots to serve humankind and then one day the robots wake up and they go, "We have our own point of view on how things are going to work" and they take over, and I think whether it's the debate about AI, whether cryptocurrency's good or bad, or more importantly, the products and services I use, which are now all digitally connected to me, whether I trust them or not is an issue that I think everyone in our industry has to take a step back because without that trust, a lot of these systems are going to stop growing. >> Chaos is an opportunity, I think that's been quoted many times, a variety-- >> You sound like Jeff Goldblum in like Jurassic Park, yeah. (laughing) >> So chaos is upon us, but this is an opportunity. The winds are shifting, and that's an opportunity for entrepreneurs. The technology industry has to start working for us but we've got to be mindful of these blind spots and the blind spots are technology for good not necessarily just for profits, so that also is a big story right now. We see things like AI for good, Intel has been doing a lot of work on that area, and you see stars dedicated to societal impact, then young millennials, you see the demographic shift where they want to work on stuff that empowers people and changes society so a whole kind of new generation revolution and kind of hippie moment, if you look at the 60s, what the 60s were, right? >> Well there's people out in the street protesting, right? There were a couple of million women out in the street this weekend, so we are in that kind of moment again, people are not happy with things. >> And I believe this is a signal of a renaissance, a change, a sea change at enormous levels, so I want to get your thoughts on this. As technology goes out in mainstream, certainly from a security standpoint, your business Illumio is in that now where there's not a lot of control, just like you were mentioning before we came on that all the spends happening but no one has more than 4% market share. These are dynamics and this is not just within one vertical. What's your take on this, how do you view this sea change that's upon us, this tech revolution? >> Well, you know, think about it. You and I grew up in the era where clients server took over from main frame, right? So remember there was this big company called IBM and they owned a lot of the industry, and then it blew up for client server and then there were thousands of companies and it consolidated its way down, but when those thousands of new companies, like you didn't know what was going to be Apollo and what was going to be Oracle right? Like you didn't know how that was going to work out, there was a lot of change and a lot of uncertainty. I think now we're seeing this on a scale like that's 10x of this that there's so much innovation and there's so much connectedness going on very rapidly, but no one is in control. In the security market, you know, what's happening in our world is like, people said, okay I have to reestablish control over my data, I've lost that control, and I've lost it for good reasons, meaning I've evolved to the cloud, I've evolved to the app economy, I've done all of these things, and I've lost it for bad reasons because like am I, like I'm not really running my data center the way I should. We're in the beginning of a move in of people kind of reasserting that control, but it's very hard to put the genie back in the bottle because the world itself is so much more dynamic and more distributed. >> It's interesting, I've been studying communities and online communities for over a decade in terms of dynamics. You know, from the infrastructural level, how packets move to a human interaction. It's interesting, you mentioned that we're all connected and no one's in control, but you now see a ground swell of organic self-forming networks where communities are starting to work together. You kind of think about the analog world when we grew up without computers and networks, you kind of knew everyone, you knew your neighbor, you knew who the town loony was, you kind of knew things and people watch each other's kids and parents sat from the porch, let the kid play, that's the way that I grew up, but it was still chaotic but yet somewhat controlled by the group. So I got to ask you, when you see things like cryptocurrency, things like KYC, know your customer, anti money laundering, which is, you know these are policy based things, but we're in a world now where, you know, people don't know who their neighbors are. You're starting to see a dynamic where people are-- >> Put the phone down. >> Asserting themselves to know their neighbor, to know their customer, to have a connected tissue with context and so your trust and reputation become super important. >> Well I think people are really, so like every time there is a shift in technology, there's scary stuff. There's the fuddy-duddy moment where people are saying, "Oh we can't use that," or "I don't know that," and you know, clearly we're in this kind of new kam-ree and explosion of this cloud mobile blah blah blah type of computing thing and ... Blah blah blah is always a good intersection when you don't have a term. Then things form around it, and just as you said, so if you think about 25 years ago, right, people created The WELL and there was community writing first bulletin boards and like now we have Facebook and you go through a couple of generations and for a while, things feel out of control and then it reforms. I personally am an optimist. Ultimately I believe in the inherent goodness of people, but inherent goodness leaves you open and then, you know, could be manipulated, and people figure these things out. Whether it's cryptocurrency or AI, they are really exciting technologies that don't have any ground rules, right? What's going to happen I believe is that people are going to reestablish ground rules, they're going to figure out some of the core issues, and some of these things may make it, and some of these things may not make it. Like cryptocurrency, like I don't know whether it makes it or not, but certainly the blockchain as a technology we're going to be incorporating in what we do, and maybe the blockchain replaces VPNs and last generation's way of protecting zeros and ones. If AI is figuring out how to read an MRI in five minutes, it's a good thing, and if the AI is teaching you how to exclude old folks for me finding jobs, it's a bad thing. I think as technology forms, there's always Spectre and 007, right? There's always good and bad sides and you know, I think if you believe-- >> I'm with you on that. I think value shifts and I think ultimately it's like however you want to look at it will shift to something, value activity will be somewhere else. Behind me in the bookshelf is a book called The World is Flat and you're quoted in it a lot as a futurist because you have inherently that kind of view, well that's not what you do for a living, but you're kind of in an opt-- >> Alan: Marketing, futurist, kind of same thing. >> Thomas Friedman, the book, that was a great book and at that time, it was game changing. If you take that premise into today where we are living in a flat world and look at cryptocurrency, and then over with the geo political landscape, I mean I just can't see why the Federal Reserve wouldn't reign in this cryptocurrency because if Japan's going to control a bunch of, or China, it's going to be some interesting conversations. I mean I would be like all over that if I was in the Federal Reserve. >> I think people-- Look, cryptocurrency's really interesting and I think people a little over-rotated. If you look at the amount of GDP that's invested in cryptocurrency, it's like, I don't know, there might've been, you know 20 years ago the same amount involved invested in Beanie Babies, right? I mean things show up for a while and the question is is it sustainable over time? Now I'm trained as an economist, you and I have had this conversation, so I don't know how you have a series of monetary without kind of governmental backing, I just don't understand. But I do understand that people find all kinds of interesting ways to trade, and if it's an exchange, like I mean what's the difference between gold and cryptocurrency? Somebody has ascribed a value to something that really has no efficacy outside of its usage. Yeah I mean you can make a filling or bracelets out of gold but it doesn't really mean anything except people agree to a unit of value. If people do that with cryptocurrency, it does have the ability to become a real currency. >> I want to pick your perspective on this being an economist, this is is the hottest area of cryptocurrency, it's also known as token economics, is a concept. >> Alan: Token economics. >> You know that's an area that theCUBE, with CUBE coins, experimenting with tokens. Tokens technically are used for things in mobile and whatnot but having a token as a utility in a network is kind of the whole concept, so the big trend that we're seeing and no one's really talking about this yet is instead of having a CTO, Chief Technology Officer, they're looking for a CEO, a Chief Economist Officer, because what you're seeing with the MVP economy we're living in and this gamification which became growth hack which didn't really help users, the notion of decentralized applications and token economics can open the door for some innovation around value and it's an economic problem, how you have a fiscal policy of your token, there's a monetary policy, what's it tied to? A product and a technology, so you now have a now a new, twisted, intertwined mechanism. >> Well you have it as part of this explosion, right? We're at a period of time, it feels like there's a great amount of uncertainly because everything's, you know, there's a lot of different forces and not everybody's in control of them, and you know, it's interesting. Google has this architecture, they call it BeyondCorp, where the concept is like networks are not trusted so I will just put my trust in this device, Duo Security's a great example of a company that's built a technology, a security technology around it which is completely antithetical to everything we know about networks and security. They're saying everything's the internet, I'll just protect the device that it's on. It's a kind of perfect architecture for a world like where nobody is in charge, so just isolate those, buy this, what is a device? It's a token too, it's a person, your iPhone's your personal token. Then over time, systems will form around it. I think we just have to, we always have to learn how to function in a different type of economy. I mean democracy was a new economy 250 years ago that kind of screwed around with most of the world, and a lot of people didn't think it would make it, in fact we went through two World War wars that it was a little on the edge whether democracy was going to make it and it seems to have done okay, like it was pretty good IPO to buy into. You know, in 1776. But it's always got risks and struggles with it. I think if, ultimately it comes together, it's whether a large group of people can find a way to function socially, economically, and with their personal safety in these systems. >> You bring up a great point, so I want to go to the next level in this conversation which is around-- >> Alan: You've got the wrong guy if you're going to the next level because I just tapped out. >> No, no, no we'll get you there. It's my job to get you there. The question is that everyone always wants to look at, whether it's someone looking at the industry or actors inside the industries across the board, mainly the tech and we'll talk about tech, is the question of are we innovating? You brought up some interesting nuances that we talk about with token economics. I mean Steve Jobs had the classic presentation where he had street signs, technology meets liberal arts. That's a mental image that people who know Steve Jobs, know Apple, was a key positioning point for Apple at that time which was let's make computers and technology connect with society, liberal arts. But we were just talking about is the business impact of technology, the economics, and that's just not like just some hand waving, making technology integrate with business. You're in the security business, There are some gamification technology, gamification that's business built into the products. So the question is, if we have the integration of business, technology, economics, policy, society rolling into the product definitions of innovation, does that change the lens and the aperture of what innovation is? >> I think it does, right? The IT industry's somewhere between three and four trillion dollars depends on how it counts in. It grows pretty slowly, it grows by a low single digit. That tells me as composite, like is that, that slow growth is a structural signal about how consumers of technology think in a macro sense. On a micro sense, things shift very rapidly, right? New platforms show up, new applications show up, all kinds of things show up. What I don't think we have done yet, to your point, is in this new integrated world, the role of technology is not just technology anymore. I don't think, you know you said you need Chief Economical Officer, what about Chief Political Officer? What about a Chief Social Officer? How many heads of HR make decisions about the insertion of systems into their business? And that's what this kind of iRobot concept is in my mind which is that you know, we are exceeding control of things that used to be done by human beings to systems and when you see control, the social mores, the political mores, the cultural mores, and the human emotional mores have to move with it. We don't tend to think about things like that. We're like, "I win and my competitors lose." Like technology used to be much more of a zero sum, my tech's better than yours. But the question is not just is my tech better than yours, is my customer better off in their industry for the consumption of my technology of inserting it into their offering or their service? You know what, that is probably going to be the next area of study. The other thing that's very important in whether, any of you have read Peter Thiel's book Zero to One, the nature of competition technology used to feel like a flat playing field and now the other thing that's rising is do you have super winners? And then what is the power of the super winners? So you mentioned whether it's Facebook or Google or Amazon or you know, or Microsoft, the FANG companies right? Their roles are so much more significant now than the Four Horsemen of the Nasdaq were in 2000 when you had Intel and Cisco and Oracle and Saht-in it's a different game. >> You're seeing that now. That's a good point, so you're reinforcing kind of this notion that the super players if you will are having an impact, you're mentioning the confluence of these new sectors, you know, government, policy, social are new areas. The question is, this sounds like a strategic imperative for the industry, and we're early so it's not like there's a silver bullet or is there, it doesn't sound like there, so to me that's not really in place yet, I mean. >> Oh no. We're not even in alpha. We have demo code for the new economy and we're trying to get the new model funded. >> John: That's the demo version, not the real version. It's the classic joke. >> Yeah this not the alpha or the beta version that like you're going to go launch it. If people think they're launching it, I think it's a little preliminary and you know, it's not just financial investment, it's like do I buy in? I'll tell you something that's really interesting. I've been visiting a bunch of our customers lately and the biggest change I'd say in the last two years is they now have to prove to their customers they're going to be good custodians of their data. Think about that, like you could go to any digital commerce you do, any website you use and you give them basically the ticket to the Furrier family privacy, you do, but you don't spend a lot of time questioning whether they're really going to protect your data. That has changed. And it's really changing in B2B and in government organizations. >> The role of data to us is regulation, GDPR in Europe, but this is a whole new dynamic. >> It's not just my data because I'm worried about my credit card getting hacked, I'm worried about my identity. Like am I going to show up as a meme in some social media feed that's substituted for the news? I don't want to use the FN word, but you know what I mean? It is a really brave new world. It's like a hyper-democracy and a hyper-risky state at the same time. >> We're living in an area of massive pioneering, new grounds, this is new territory so there's a lot of strategic imperatives that are yet not defined. So now let's take it to how people compete. We were talking before we came on camera, you mentioned the word we're in an MVP economy, minimum viable product concept, and you're seeing that being a standard operating procedure for essentially de-risking this challenge. The old way of you know, build it, ship it, will it work? We're seeing the impact from Hollywood to big tech companies to every industry. >> Well you've got a coffee mug for a company that does both. Amazon does MVP in entertainment, like we'll create one pilot and see if it goes as opposed to ordering a season for 17 million dollars to hey, let's try this feature and put it out on AWS. What's interesting is I don't think we've completely tilted but the question is will buyers of technology, of entertainment products, of any product start to say, "I'll try it." You know like, look, I've done four startups and I always know there's somebody I can go to get and try my early product. There are people that just have an appetite, right? The Jeffrey Moores, early adapter, all the way to the left of the-- >> They'll buy anything new. >> They'll try it, they're interested, they have the time and the resources, or they're just intellectually curious. But it was always a very small group of people in the IT industry. What I think that the MVP economy is starting to do is look, I Kickstarted my wallet. I don't know if I'm the only person who bought that skinny little wallet on Kickstarter, it doesn't matter to me, it had appeal. >> What's the impact of the MVP economy? Is it going to change to the competitive landscape like Peter Thiel was suggesting? Does it change the economics? Does it change the makeup of the team? All of the above? What's your thoughts on how this is going to impact? Certainly the encumbrance will seem to be impacted or not. >> I think two things happen. One, it attacks the structural way markets work. If you go back to classical economics, land, labor, and capital, and people who own those assets, now you add information as a fourth. If those guys were around now they would say that would be the fourth core asset, production, I'm sorry, means of production is the term. The people who can dominate that would dominate a market. Now that that's flattened out, you know, I think it pushes against the traditional structures and it allows new giants to kind of show up overnight. I mean the e-commerce market is rife with companies that have, like look at Stich Fix. A company driven by AI, fashions, tries to figure out what you like, sends it to you every month, just had a monster IPO. We invented, by the way the Spiegal Catalog, except like with a personal assistant and you know, it's changed that in just a short number of years. I think two things happen. One is you'll get new potential giants but certainly new players in the market quickly. Two, it'll force a change in the business model of every company. If you're in a cab in any city in the world, I'm not saying whether the app works there or not, Uber and Lyft has forced every cab company to show you here's the app to call the cab. They haven't quite caught up to the rest of the experience. What I think happens is ultimately, the larger players in an industry have to accommodate that model. For people like me, people who build companies or large technology companies, we may have to start thinking about MVPing of features early on, working with a small group, which is a little what the beta process is but now think about it as a commercial process. Nobody does it, but I bet sure a lot of people will be doing it in five years. >> I want to get your take on that approach because you're talking about really disrupting, re-imagining industry, the Spiegal catalog now becomes digital with technology, so the role of technology in business, we kind of talked about the intertwine of that and its nuance, it's going to get better in my opinion. But specifically the IT, the information technology industry is being disrupted. Used to be like a department, and the IT department will give you your phone on your desk, your PC on your desk or whatever, now that's being shattered and everyone that's participating in that IT industry is evolving. What's your take on the IT industry's disruption? >> Well look, it started 20 years ago when Marc Benioff and Salesforce decided to sell the sales forces instead of IT people, right? They went around to the end buyer. I don't think it's a new trend, I think a lot of technology leaders now figure out how to go to the business buyer directly and make their pitch and interestingly enough, the business buyer, if the IT team doesn't get on board, will do that. >> John: Because of cloud computing and ... >> Because of everything. The modern analog I think in our world is that the developers are increasingly in control. Like my friend Martin Casado up in Andreessen talks about this a lot. The traditional model on our industry is you build a product, you launch it, you launch your company, you work with the traditional analyst firms, you try to get a little bit of halo, you get customer references, those are the things you do and there was a very wall structured, for example, enterprise buying cycle. >> And playbook. >> Playbook, and there's the challenger sale and there's Jeffrey Moore and there's like seeing God. You've got your textbooks on how it's been done. As everything turns into code, the people who work with code for a living increasingly become the front end of your cycle and if you can get to them, that changes. Like I mean think about like, you know, Tom wrote about this actually in The World is Flat, like Linux started as a patchy. It didn't start with the IT department, it started with developers and there was the Linux foundation and now Linux is everything. >> There's a big enemy called the big mini computer, and not operating systems and work stations. >> Wiped out whole parts of Boston and other parts of the world, right? >> Exactly, that's why I moved out here. >> You filed client's server out here. >> I filed a smell of innovation. No but this is interesting because this location of industries is happening, so with that, so they also on the analog, so Martin's at Andreessen, so we'll do a little VC poke there at the VCs because we love them of course, they're being dislocated-- >> I don't (mumbles) my investors. >> Well no, their playbook is being challenged. Here's an example, go big or go home investment thesis seems not to be working. Where if you get too much cash on the front end, with the MVP economy we were just riffing on and with the big super powers, the Amazons and the Googles, you can't just go big or go home, you're going to be going home more than going big. >> I think they know that. I mean Dee-nuh Suss-man who's I think Chief Investment Officer at Nasdaq has a very well known talking line that there are half as many public companies as there were 10 years ago, so the exit scenario for our industry is a little bit different. We now have things like acqui-hires, right we have other models for monetization, but I think what the flip side of it is, we're in the-- >> Adapt or die because the value will shift. Liquidity's changing, which acqui-hires-- >> I think the investment community gets it completely and they spend a lot more time with the developer mindset. In fact I think there's been a doubling down focus on technical founders versus business founders for companies for just that reason because as everything turns to code, you got to hang out with the code community. I think there are actually-- >> You think there'll be more doubling down on technical founders? You do, okay. >> Yeah I think because that is ultimately the shift. There are business model shifts, but it's, you know, I mean like Uber was a business model shift, I mean the technology was the iPhone and GPS and they wrote an app for it, but it was a business model shift, so it can be a business model shift. >> And then scale. >> And then scale and then all of those other things. But I think if you don't think about developers when you're in our, and it's like we built Illumio because a developer could take the product and get started. I mean you can, developers actually can write security policy with our product because there's a class of customers, where as not everyone where that matters. There's other people where the security team is in charge or the infrastructure team is in charge but I think everything is based on zeros and ones and everything is based on code and if you're not sensitive to how code gets bought, consumed, I mean there's a GitHub economy which is I don't even have to write the code, I'll go look at your code and maybe use pieces of it, which has always been around. >> Software disruption is clear. Cloud computing is scale. Agile is fast, and with de-risking capabilities, but the craft is coming back and some will argue, we've talked about on theCUBE before is that, you know, the craftsmanship of software is moving to up the stack in every industry, so-- >> I think it's more like a sports league. I love the NBA, right? In the old days, your professional team, you'd scout people in college. Now they used to scout them in high school, now they're scouting kids in middle school. >> (laughs) That's sad. >> Well what it says is that you have to-- >> How can you tell? >> You know but they can, right? I think you know, your point about it craft, you're going to start tracking developers as they go through their career and invest and bet on them. >> Don't reveal our secrets to theCUBE. We have scouts everywhere, be careful out there. (laughs) >> But think about that, imagine it's like there's such a core focus on hiring from college, but we had an intern from high school two years ago. We hire freshman. >> Okay so let's go, I want to do a whole segment on this but I want to just get this point because we're both sports fans and we can riff on sports all day long. >> I'm just not getting the chance >> And the greatness of Tom Brady >> to talk about the Patriots. >> And Tom Brady's gotten his sixth finger attached to his hands for his sixth ring coming up. No but this is interesting. Sports is highly data driven. >> Alan: Yep. >> Okay and so what you're getting at here, with an MVP economy, token economics is more of a signal, not yet mainstream, but you can almost go there and think okay data driven gives you more accuracy so if you can bring data driven to the tech world, that's kind of an interesting point. What's your thoughts on that? >> Yeah I mean look, I think you have to track everything. You have to follow things, and by the way, we have great tools now, you can track people through LinkedIn. There's all kinds of vehicles to tracking individuals, you track products, you track everything, and you know look, we were talking about this before we went on the show right, people make decisions based on analytics increasingly. Now the craft part is what's interesting and I'm not the complete expert, I'm on the business side, I'm not an engineer by training, but look a lot of people understand a great developer is better than five bad developers. >> Well Mark Andris' 10x is a classic example of that. >> There's clearly a star system involved, so if I think in middle school or in high school, you're going to be a good developer, and I'm going to track your career through college and I'm going to try to figure out how to attach. That's why we started hiring freshmen. >> Well my good friend Dave Girouard started a company that does that, will fund the college education for people that they want to bet on. >> Sure, they're just taking an option in them. >> Yeah, option on their earnings. Exactly. >> They are. >> It sounds like token economics to me. (laughs) >> You know you can sell anything. We are in that economy, you can sell those pieces. The good news is I think it can be a great flattener, meaning that it can move things back more to a meritocracy because if I'm tracking people in high school, I'm not worrying whether they're going to go to Stanford or Harvard or Northwestern, right? I'm going to track their abilities in an era and it's interesting, speaking about craft, you know, what are internships? They're apprenticeships. I mean it is a little bit like a craft, right? Because you're basically apprenticing somebody for a future payout for them coming to work for you and being skilled because they don't know anything when they come and work, I shouldn't say that, they actually know a lot of things. >> Alan, great to have you on theCUBE as always, great to come in and get the update. We'll certainly do more but I'd like to do a segment on you on the startup scene and sort of the venture capital dynamics, we were tracking that as well, we've been putting a lot of content out there. We believe Silicon Valley's a great place. This mission's out there, we've been addressing them, but we really want to point the camera this year at some of the great stuff, so we're looking forward to having you come back in. My final question for you is a personal one. I love having these conversations because we can look back and also look forward. You do a lot of mentoring and you're also helping a lot of folks in the industry within just your realm but also startups and peers. What's your advice these days? Because there's a lot of things, we just kind of talked a lot of it. When people come to you for advice and say, "Alan, I got a career change," or "I'm looking at this new opportunity," or "Hey, I want to start a company," or "I started a company," how is your mentoring and your advisory roles going on these days? Can you share things that you're advising? Key points that people should be aware of. >> Well look, ultimately ... I never really thought about it, you just asked the question so, ultimately, I think to me it comes down to own your own fate. What it means is like do something that you're really passionate about, do something that's going to be unique. Don't be the 15th in any category. Jack Welch taught us a long time ago that the number one player in a market gets 70% of the economic value, so you don't want to play for sixth place. It's like Ricky Bobby said, if you're not first, you're last. (John chuckles) I mean you can't always be first, but you should play for that. I think for a lot of companies now, I think they have to make sure that, and people participating, make sure that you're not playing the old playbook, you're not fighting yesterday's battle. Rhett Butler in Gone With the Wind said, "There's a lot of money in building up an empire, "and there's even more money in tearing it down." There are people who enter markets to basically punish encumbrance, take share because of innovation, but I think the really inspirational is you know, look forward five years and find a practical but aggressive path to being part of that side of history. >> So are we building up or are we taking down? I mean it seems to me, if I'm not-- >> You're always doing both. The ocean is always fighting the mountains, right? That is the course of, right? And then new mountains come up and the water goes someplace else. We are taking down parts of the client server industry, the stack that you and I built a lot of our personal career of it, but we're building this new cloud and mobile stack at the same time. And you're point is we're building a new currency stack and we're going to have to build a new privacy stack. It's never, the greatest thing about our industry is there's always something to do. >> How has the environment of social media, things out there, we're theCUBE, we do our thing with events, and just in general, change the growth plans for individuals if you were, could speak to your 23 year old self right now, knowing what you know-- >> Oh I have one piece of advice I give everybody. Take as much risk as humanly possible in your career earlier on. There's a lot of people that have worked with me or worked for me over the years, you know people when they get into their 40s and they go, "I'm thinking about doing a startup," I go, "You know when you got two kids in college "and you're trying to fund your 401K, "working for less cash and more equity may not be "the most comfortable conversation in your household." It didn't work well in my household. I mean I'm like Benjamin Button. I started in big companies, I'm going to smaller companies. Some day it's just going to be me and a dog and one other guy. >> You went the wrong way. >> Yeah I went the wrong way and I took all the risk later. Now I was lucky in part that the transition worked. When I see younger folks, it's always like, do the riskiest thing humanly possible because the penalty is really small. You have to find a job in a year, right? But you know, you don't have the mortgage, and you don't have the kids to support. I think people have to build an arc around their careers that's suitable with their risk profile. Like maybe you don't buy into bitcoin at 19,000. Could be wrong, could be 50,000 sometime, but you know it's kind of 11 now and it's like-- >> Yeah don't go all in on 19, maybe take a little bit in. It's the play and run-- >> Dollar cost averaging over the years, that's my best fidelity advice. I think that's what's really important for people. >> What about the 45 year old executive out there, male or female obviously, the challenges of ageism? We're in economy, a gig economy, whatever you want to call, MVP economics, token economics, this is a new thing. Your advice to someone who's 45 who just says "Hey you're too old for our little hot startup." What should they do? >> Well being on the other side of that history I understand it firsthand. I think that you have an incumbent role in your career to constantly re-educate yourself. If you show up, whether you're a 25, 35, 45, 55, or 65, I hope I'm not working when I'm 75, but you never know right? (mumbles) >> You'll never stop working, that's my prediction. >> But you know have you mastered the new skills? Have you reinvented yourself along the way? I feel like I have a responsibility to feed the common household. My favorite part of my LinkedIn profile, it says, "Obedient worker bee at the Cohen household," because when I go home, I'm not in charge. I've always felt that it's up to me to make sure I'm not going to be irrelevant. That to me is, you know, that to me, I don't worry about ageism, I worry about did I-- >> John: Relevance. >> Yeah did I make myself self-obsolescent? I think if you're going to look at your career and you haven't looked at your career in 15 years and you're trying to do something, you may be starting from a deficit. So the question, what can I do? Before I make that jump, can I get involved, can I advise some small companies? Could I work part time and on the weekends and do some things so that when you finally make that transition, you have something to offer and you're relevant in the dialogue. I think that's, you know, nobody trains you, right? We're not good as an industry-- >> Having a good community, self-learning, growth mindset, always be relevant is not a bad strategy. >> Yeah, I mean because I find increasingly, I see people of all ages in companies. There is ageism, there is no doubt. There's financial ageism and then there's kind of psychological bias ageism, but if you keep yourself relevant and you are the up to speed in your thing, people will beat a path to want to work for you because there's still a skill gap in our industry-- >> And that's the key. >> Yeah, make sure that you're on the right side of that skill gap, and you will always have something to offer to people. >> Alan, great to have you come in the studio, great to see you, thanks for the commentary. It's a special CUBEConversation, we're talking about the future of technology impact the society and a range of topics that are emerging, we're on a pioneering, new generational shift and theCUBE is obviously covering the most important stories in Silicon Valley from figuring out what fake news is to impact to the humans around the world and again, we're doing our part to cover it. Alan Cohen, CUBEConversation, I'm John Furrier, thanks for watching. (upbeat music)

>> Commentator: Live from Palo Alto, it's theCUBE, covering Women Transforming Technology 2017, brought to you by VMware. >> Welcome back to theCUBE's coverage of Women Transforming Technology held at VMware. I'm your host Rebecca Knight. Joining me today is Cornelia Davis. She is the Senior Director of Technology at Pivotal which is the Palo Alto-based company that provides Agile development services on an open source platform. Thank you so much for joining us. >> Thank you for having me. I'm so happy to be here. >> So before the cameras were rolling, you started telling me a little bit about your personal story. You're a woman in tech who loves the tech, but you said for the past three years, you've also become an activist and an evangelist for getting more women into this business. Tell us about that transformation. >> Yes, I'll tell you a little bit about that story. I have the gray hair to prove it. I've been doing this for some time. I actually was a woman studying computer science back in the day where we were getting close to equity. >> Rebecca: There was a time when it was-- >> Yeah, there was so back in the '80s, I was majoring in computer science and I think that we were close to 40% at the time, although I have to say even before I was in college, I was always the girl who was out playing soccer with the boys at lunch time. Gender never really seemed to make much of a difference to me but anyway, I got a degree in computer science and then I spent 25 years in the industry and sure, there were times where I would notice that I was the only woman in the room. Actually I would say maybe three or four years ago, I went to a customer opening where they were catering to the developer community and in the room there were 250 developers, I was the only woman. I mean seriously, I was the only woman of 250 and I was like wow. But other than notice it and chuckle about it and even have some of those experiences where maybe somebody assumed that I was the HR person and not the technologist, those types of things, I never really did anything about it. And then about three years ago, I had the great fortune of meeting Robin Hauser Reynolds and Stacy Hartmann who are the two women behind the movie Code: Debugging the Gender Gap, you've seen it? >> Rebecca: Yes, yes. >> A fantastic film, a fantastic piece and had this opportunity to meet them and got involved in the film and Pivotal became a sponsor. They did some of the filming. They did some interviewing of people at Pivotal and it was through that experience and then I got to go to some of the screenings and participate in panels and so on and it was through that experience that I started to understand that it wasn't just curiosity, that it was actually declining, the numbers were declining and that it was a real serious problem. And so after being in the industry for 25 years and not really doing anything about it, I've become an activist and so I spend a lot of time jabbing on about this. I'll give you another example. Last year in January, Pivotal brought most of the company together here in the Bay Area. We brought about 1,200 people into the Bay Area for worldwide kickoff. And the very first talk that they had after our CEO spoke was a talk on diversity and they actually invited me to come up and speak about gender diversity or lack thereof in technology and talked about the Girls Who Code and some of those great programs out there. >> I want to get back to Girls Who Code because I know that you're passionate about it, but I want to also just get back to the moment that you described where you went from chuckling about being the only woman in the room and saying, "Oh it's not silly," to really feeling, "Hey this isn't right. "I want things to be different." What was that moment? Are you trying to recreate that moment for other women as a wake up call? How would you describe your activism? >> I don't know that it was a moment, but the thing that catalyzes me, the thing that makes me really passionate about doing this is that I have this tremendous opportunity. The way that I came into computing personally was at the end of my sophomore year in high school when we were signing up for classes the following year, I was looking at what might I sign up for and I signed up for a computer programming class and then I went off and I joked around that I went off and had a bitchin' summer. That's the stuff we said in the '80s. I went off and had a bitchin' summer. >> We should bring that word back. Let's do it, Cornelia. >> It's a good word. And I came back and had this computer class on my schedule and I was like, "Uh no, no, no, no. "There is no way I'm doing this." And I skipped class for the first two or three days and then I finally went and curiosity got the better of me. I tried it out and I was hooked. Literally that was the moment, not for my activism, but that was the moment where I had like, "Oh my gosh, this is going to change everything. "This is what want to I do." And that's what brought me to computing and that's what makes me an activist now because I didn't realize for those 25 years that other people didn't have those opportunities, that they were actually systemically being discouraged from having those opportunities and so I think that's at the core of my activism is I want people to have the opportunity because I love what I do so much and I think I was mentioning before before we started rolling the cameras that I've been a technologist my whole career. Occasionally I've branched off and tried to do maybe a little bit more leadership or a little bit more of that, but I love the tech so much and it's such a great wonderful career to be in, self-sustaining and all of those things, I want other people to have that opportunity. That's what gets me going. >> I was reading a bio where you're a self-described propeller head and you can find her knee deep in the code and now you want to inspire the next generation and so you've gotten involved with Girls Who Code. Tell us more. >> Yes so it wasn't actually through the film. I think it was just simply, it was serendipitous, right around the time that I was starting to awaken to what was going on in the industry. Working for Pivotal, Pivotal in our San Francisco office, it's a very cool office. It's very different from what I saw in most of my career which was cube farms. It's a very open floor plan, very hip, just a cool place to be. >> What the rest of us East Coasters envisions Silicon Valley to be. >> Yeah, it's really pretty cool. And so the Girls Who Code, for those of you who might be watching that don't know about the Girls Who Code, it's an organization that really targets high school girls and their flagship program is in the summer they have a seven-week immersion program where they bring girls in and they basically code, they learn to code from nine to five every day for seven weeks. It's a pretty intensive program. Well about three years ago, we weren't sponsoring at that level, but we would be a field trip location. One of our close partners, investors, customers, is General Electric. They hosted a group of these 20 girls in their San Ramon office. They came to us for a couple of summers as a field trip location and of course the girls loved it. They walk off the elevator there's snacks, there's drinks. We parent programmed with them. It's a really cool experience. And then last summer, we actually took the next step and hosted our own groups so we had a group of 20 young women who were here in our Palo Alto office for seven weeks learning to code and I had the wonderful opportunity to spend time with them several times throughout the summer and I actually commute to the Bay Area, not everyday but I commute to the Bay Area and the days that I was coming up here in part to see the girls, I'd wake up at four in the morning for my flight and I'd be like, "I get to spend time with the girls today," and I saw it. I saw the girls who in the first week were clearly there because their parents made them be there and they're sitting there like this and they've got the same attitude that I had when I was in high school the first three days like I am not doing this and the same people are standing up at the graduation ceremony at the end of the seven weeks saying, "This changed my life." And one of those young women I'm spending a little bit more time with is now a computer science major at Northwestern, early decision. It's just fantastic to see that light up. That's what gets me going. >> Now why high school? I get high school in the sense that they're old enough to take on a summer job like internship, but what is it about that age do you think that is so critical? >> Yeah so that age, I'll be honest with you, I think is almost too late for a lot of girls because we are able to reach, I just mentioned, that there were girls in there whose parents forced them into that. They had already self-selected out. Just like I had when I was in high school. I had self-selected out. I was way too cool to be in computing and so in some ways high school is a little bit too late. However, I think you nailed it, is that there's an opportunity there that they're mature enough that you can do something as immersive as a seven-week program and these girls are tremendous. These girls after a seven-week program are going back to their high schools and being the president of their Girls Who Code after school clubs and teaching them and I was just spending some time, we had a hangout with them recently where they said when their friends are asking, "What are you going to do this summer?" And the girls said, "I have no idea, "but you know what you should do "is you should do Girls Who Code." She said, "That's all I want to do. "I just want to do Girls Who Code all over again." And so I think you're right, I think it's opportunistic in that they're ready, but unfortunately I think it, like I said, it self-selects a lot of people out. I think fundamentally the thing that we need to do to reach the younger grades, the younger students, is it needs to be part of the curriculum. It absolutely 100% needs to be part of primary school curriculum so that they can get hooked and understand what it is before they self-select out because they're self-selecting out based on a perception and the image that they have of what it is, the Silicon Valley show, that's a perception. Sure it's satyr but young people see that and they don't see it as that. It just looks like something where there's a whole bunch of misbehaving men treating women poorly. >> So on that actually Cornelia, what do you make of the really distressing news that we're hearing that's not necessarily new, there has been the Uber bombshell of last week, but what we know about the culture here and maybe why there were so many women and it was almost 50/50 and then we started to see a drastic change and lower numbers of women in computer science and a lot of women just saying, "Ew, I don't want to be part of that. "I don't want that for my career." What do you say to them and what do you say to the men who are not even knowingly discouraging them from that kind of career? >> Oh, I love what you just said, not even knowingly. One of the things that I spend a lot of time talking with folks about every chance I get is implicit bias. I think that there's definitely overt sexism and in the last week we've seen that big in the news and that is a huge problem. I think I've heard statistics of whatever 60% of women have some level of relatively overt sexism, 100% of us get the implicit, the non-overt, and people who are well-meaning saying things, when they say for example, I was just chatting with a young lady a couple of weeks ago. She's a sophomore in college and she was telling me that last summer during her internship, within the first week or two, her boss was talking to her about her career plans moving forward and was already encouraging her to go more into management than into technology. This person was not evil, wasn't trying to keep women out of technology or keep women out of the most technical parts of a technology career, but he really genuinely believed that, "Maybe women are better at that and not so good at this," and it's really just our implicit biases. So I think that's a big part of it. And for the last year or two, I've been talking about implicit bias and I've been talking about the compensating mechanisms so first of all recognizing your implicit biases and then being conscious about them and then consciously combating them. I've become in the last several months, I would say six months, I've become more and more interested in the idea of how do we actually change those implicit biases. >> And this is men and women. It's not just the men here. >> No question because when I've had conversations where I've spoken for example on implicit bias, I've had women come up to me afterward and say, "I signed my son up for a coding camp. "I never even thought about signing up my daughter." >> Rebecca: Oh, that hurts. >> And I was like, "So you're signing her up now, right?" She's like, "Oh yeah, oh yeah, yeah, yeah." And so I think it's really interesting to start thinking about how do we actually get rid of them? It's one thing to recognize them and then fight them, but it's another thing to get rid of them. I think the only way we can get rid of them goes back to the statistics that we talked about early on which is I am surprised when I see a woman technologist. That's just the way our brains work. We categorize things. >> We have an idea in our head of what that person looks like. >> We put things in buckets. We wouldn't be able to function in this world with so many different inputs unless we put things into buckets and we just put things into buckets largely based on statistics. And so I'm becoming increasingly interested in really amplifying the voice of women in technology because when we hear women's voices in technology, women who are up there not talking about what we're talking about today which is the gender imbalance, but talking about the tech itself, then we start to normalize, then we start to re-categorize things in our brains so that we're not surprised when we hear a woman talking about something deeply technical or somebody who's doing particle physics or something like that, we're not surprised anymore and say, "Wow she's a rocket scientist," it's normal. That's what I'm interested in doing is getting that to be the norm, not the exception. I think the first step what I would say to people, what I do say to men and women across the industry is first of all recognize it and then let's see what we can do to change it. >> Cornelia Davis, thank you so much. That's good advice, that's good advice. And we'll be right back with theCUBE's coverage of Women Transforming Technology here at VMware. (modern techno music)