>> from Palo Alto, California It's the Cube covering the EM Where women Transforming technology twenty nineteen. Brought to You by VM Wear >> Hi Lisa Martin with the Cube on the ground at the end. Where in Palo Alto, California, for the fourth annual Women Transforming Technology, even W. T. Squared on event that is near and dear to my heart. Excited to welcome back to the Cube pretty much. Rog Lachman, engineering leader, blocked in at the end where pretty much It's so great to have you back on the Cube. Thank you, Lisa. It's amazing to be here, and I can't believe it's been a year, a year. And so last year, when Protein was here, she launched her book. Nevertheless, she persistent love the title You just Did a session, which we'LL get to in a second, but I'd love to get your your experiences in the last year about the book launch. What's the feedback? Ben? What are some of the things that have made me feel great and surprised you at the same time? It's been fantastic. I wasn't expecting that when I started to write this book. It was more like I want to impact one woman's life. But what was interesting is I delivered around twenty twenty five talks last year. My calendar's booked for this year, but every time I go give a talk, my Lincoln goes crazy and I'm connecting with all these women and men. And it's just fantastic because they're basically resonating with everything I talk about in the book. I spoke at the Federal Reserve. Wow, I was like, This is a book on tech and they were like, No, this impacts all of us And I spoke to a group of lawyers and actually, law firms have fifty fifty when they get into law, right when they get into whenever I mean live, I'm not that familiar with it. But getting to partner is where they don't have equality or diversity, and it's resonated. So now I'm like, maybe I should just take the word check out What? You It's been impactful. And so last year was all about companies, so I did. You know, I spoke at uber I spoken Veum, where spoken nutanix it's looking a lot of these companies last year. This year is all about schools, fantastic schools of all different type, so I you know, I've done a talk at San Jose State. I went to CMU. They invited me over Carnegie Mellon. I supported the robotics team, which is all girls team. Nice. And it was fantastic because these girls high school kids were designing robots. They were driving these robots. They were coding and programming these robots and was an all girls team. And I asked them, I said, But you're excluding the men and the boys and they said no. When it's a combined boy girls team, the women end up the girls and organizing the men of the boys are actually writing the code. They're doing the drilling there, doing all that. And so the girls don't get to do any of that. And I was looking at just the competition and as watching these teams, the boy girl steams and those were all organizing. And I thought, this is exactly what happens in the workforce. You're right. Yeah. We come into the workforce, were busy organizing, coordinating and all that, and the men are driving the charge. And that's why these kids where this is at high school, Yeah, thirteen to seventeen, where this is becoming part of their cultural upbringing. Exactly. Pretty. In great. Yes, yes. And a very young age. So that was fascinating. I think that surprised me. You know, you were asking me what surprised you that surprised me. And what also surprised me was the confidence. Though these girls were doing all these things. I've never built a robot. I would love to. I haven't built a robot, and they were doing all these amazing things, and I thought, Oh, my God, >> they're like, >> confident women. But they were not. And it was because they felt that there was too much to lose. They don't want to take risks, they don't want to fail. And it was that impostor syndrome coming back so that conditioning happens way more impossible syndrome is something that I didn't even know what it wass until maybe the last five or six years suddenly even just seeing that a very terse description of anyone Oh, my goodness, it's not just me. And that's really a challenge that I think the more the more it's brought to light, the more people like yourself share stories. But also what your book is doing is it's not just like you were surprised to find out It's not just a tech. This is every industry, Yes, but his pulse syndrome is something that maybe people consider it a mental health issue and which is so taboo to talk about. But I just think it's so important to go. You're not alone. Yeah, vast majority men, women, whatever culture probably have that. Let's talk about that. Let's share stories. So that your point saying why I was surprised that these young girls had no confidence. Maybe we can help. Yes, like opening up. You know, I'm sharing it being authentic. Yeah. So I'm looking at my second book, which basically says what the *** happens in middle school? Because what happens is somewhere in middle school, girls drop out, so I don't know what it is. I think it's Instagram or Facebook or boys or sex. I don't know what it is, but something happens there. And so this year of my focus is girls and you know, young girls in schools and colleges. And I'm trying to get as much research as I can in that space to see what is going on there, because that totally surprised me. So are you kind of casting a wide net and terms like as you're. Nevertheless, she persisted. Feedback has shown you it's obviously this is a pervasive, yes issue cross industry. This is a global pandemic, yes, but it's your seeing how it's starting really early. Tell me a little bit about some of the things that we can look forward to in that book. So one thing that's important is bravery, Which reshma So Johnny, who's the CEO off girls code? She has this beautiful quote, she says. We raise our voice to be brave, and we'd raise our girls to be perfect, pretty telling. And so we want to be perfect. We won't have the perfect hair, the perfect bodies. We want a perfect partner. That never happens. But we want all that and because we want to be perfect, we don't want to take risks, and we're afraid to fail. So I want to focus on that. I want to talk to parents. I want to talk to the kids. I want to talk to teachers, even professors, and find out what exactly it is like. What is that conditioning that happens, like, why do we raise our girls to be perfect because that impacts us at every step of our lives. Not even careers. It's our lives. Exactly. It impacts us because we just can't take that risk. That's so fascinating. So you had a session here about persistent and inclusive leadership at W T squared forth and you will tell me a little bit about that session today. What were some of the things that came up that you just said? Yes, we're on the right track here. So I started off with a very depressing note, which is twenty eighty five. That's how long it's gonna take for us to see equality. But I talked about what we can do to get to twenty twenty five because I'm impatient. I don't want to wait twenty eighty five I'LL be dead by them. We know you're persistent book title. You know, my daughter will be in the seventies. I just don't want that for her. So, through my research, what I found is we need not only women to lean in. You know, we've have cheryl sound. We're talking about how women need to lean in, and it's all about the women. And the onus is on the woman the burdens on the woman. But we actually need society. Selena. We need organizations to lean in, and we need to hold them accountable. And that's where we're going to start seeing that changes doing that. So if you take the m r. I. You know, I've been with him for ten years, and I always ask myself, Why am I still here? One of the things we're trying to do is trying to take the Cirrus early this morning rail Farrell talked about like on the panel. He said, We are now Our bonuses are tied to, you know, domestic confusion, like we're way have to hire, you know, not just gender, right, Like underrepresented communities as well. We need to hire from there, and they're taking this seriously. So they're actually making this kind of mandatory in some sense, which, you know, it kind of sucks in some ways that it has to be about the story that weighing they're putting a stake in the ground and tying it to executive compensation. Yes, it's pretty bold. Yes. So organizations are leaning in, and we need more of that to happen. Yeah. So what are some of the things that you think could, based on the first *** thing you talked about the second one that you think could help some of the women that are intact that are leaving at an alarming rate for various reasons, whether it's family obligations or they just find this is not an environment that's good for me mentally. What are some of the things that you would advise of women in that particular situation? First thing is that it's to be equal partnership at home. A lot of women leave because they don't have that. They don't have that support on having that conversation or picking the right partner. And if you do pick the wrong partner, it's having that conversation. So if you have equal partnership at home, then it's both a careers that's important. So you find that a lot of women leave tech or leave any industry because they go have babies, and that happens. But it's just not even that, like once they get past that, they come backto work. It's not satisfying because they don't get exciting projects to work on that you don't get strategic projects, they don't have sponsors, which is so important toward the success, and they they're you know, people don't take a risk on them, and they don't take a risk. And so these are some of those things that I would really advice women. And, you know, my talk actually talked about that. Talked about how to get mail allies, how to get sponsors. Like what? You need to actually get people to sponsor you. Don't talk to me a little bit more about that. We talk about mentors a lot. But I did talk this morning with one of our guests about the difference between a sponsor and a mentor. I'd love you to give Sarah some of your advice on how women can find those sponsors. And actually, we activate that relationship. So mentors, uh, talk to you and sponsors talk about okay. And the way to get a sponsor is a is. You do great work. You do excellent work. Whatever you do, do it well. And the second thing is B is brag about it. Talk about it. Humble bragging, Yeah. Humble bragging talkabout it showcases demo it and do it with people who matter in organizations, people who can notice your work building that brand exactly. And you find that women are all the men toward and under sponsored. Interesting, Yes. How do you advise that they change that? There was a Harvard study on this. They found that men tend to find mentors are also sponsors. So what they do is, you know, I like you to stick pad girl singer, he says. Andy Grove was his mentor, but Andy Grove was also his sponsor in many ways, in for his career at Intel, he was a sponsor and a mental. What women tend to do is we find out like even me, like I have female spot him. Mentors were not in my organization, and they do not have the authority to advocate for me. They don't They're not sitting in an important meeting and saying, Oh, patina needs that project for team needs to get promoted. And so I'm not finding the right mentors who can also be my sponsors, or I'm not finding this one says right, and that's happens to us all the time. And so the way we have to switch this is, you know, mentors, a great let's have mentors. But let's laser focus on sponsors, and I've always said this all of last year. I'm like the key to your cell. Success is sponsorship, and I see that now. I am in an organization when my boss is my sponsor, which is amazing, because every time I go into a meeting with him, he says, This is about pretty much grew up. This is a pretty mers group. It's not me asking him. He's basically saying It's pretty nose grow, which is amazing to hear because I know he's my mentor in sponsor as well. And it's funny when I gave him a copy of my book and I signed it and I said, And he's been my sponsor to be more for like ten years I said, Thank you for being my sponsor and he looked at me. He said, Oh, I never realized it was your sponsor So that's another thing is men themselves don't know they're in this powerful position to have an impact, and they don't know that they are sponsors as well. And so we need. We need women to Fox and sponsors. I always say find sponsors. Mentorship is great, but focus of sponsors Look, I think it's an important message to get across and something I imagine we might be reading about in your next book to come. I know. Yeah, well, we'LL see. Artie, thank you so much for stopping by the Cube. It's great to talk to you and to hear some of the really interesting things that you've learned from nevertheless you persistent and excited to hear about book number two and that comes out. You got a combined studio. I'd love to thank you and thank you. I'm Lisa Martin. You're watching the queue from BM Where? At the fourth Annual Women Transforming Technology event. Thanks for watching.

>> Live from San Francisco, it's theCUBE covering IBM Think 2019, brought to you by IBM. >> Welcome back everyone, live here in Moscone North in San Francisco, it's theCUBE's exclusive coverage of IBM Think 2019. I'm John Furrier, Dave Vellante. We're bringing down all the action, four days of live coverage. We've got two great guests here, Elenita Elinon, Executive Director of Quantitative Research at JP Morgan Chase, and John Thomas, Distinguished Engineer and Director of the Data Science Elite Team... great team, elite data science team at IBM, and of course, JP Morgan Chase, great innovator. Welcome to theCUBE. >> Welcome. >> Thank you very much. >> Thank you, thank you, guys. >> So I like to dig in, great use case here real customer on the cutting edge, JP Morgan Chase, known for being on the bleeding edge sometimes, but financial, money, speed... time is money, insights is money. >> Absolutely. Yes. >> Tell us what you do at the Quantitative Group. >> Well, first of all, thank you very much for having me here, I'm quite honored. I hope you get something valuable out of what I say here. At the moment, I have two hats on, I am co-head of Quantitative Research Analytics. It's a very small SWAT, very well selected group of technologists who are also physicists and mathematicians, statisticians, high-performance compute experts, machine learning experts, and we help the larger organization of Quantitative Research which is about 700-plus strong, as well as some other technology organizations in the firm to use the latest, greatest technologies. And how we do this is we actually go in there, we're very hands-on, we're working with the systems, we're working with the tools, and we're applying it to real use cases and real business problems that we see in Quantitative Research, and we prove out the technology. We make sure that we're going to save millions of dollars using this thing, or we're going to be able to execute a lot on this particular business that was difficult to execute on before because we didn't have the right compute behind it. So we go in there, we try out these various technologies, we have lots of partnerships with the different vendors, and IBM's been obviously one of few, very major vendors that we work with, and we find the ones that work. We have an influencing role as well in the organization, so we go out and tell people, "Hey, look, "this particular tool, perfect for this type of problem. "You should try it out." We help them set it up. They can't figure out the technology? We help them out. We're kind of like what I said, we're a SWAT team, very small compared to the rest of the organization, but we add a lot of value. >> You guys are the brain trust too. You've got the math skills, you've got the quantitative modeling going on, and it's a competitive advantage for your business. This is like a key thing, a lot of new things are emerging. One of things we're seeing here in the industry, certainly at this show, it's not your yesterday's machine learning. There's certainly math involved, you've got cognition and math kind of coming together, deterministic, non-deterministic elements, you guys are seeing these front edge, the problems, opportunities, for you guys. How do you see that world evolving because you got the classic math, school of math machine learning, and then the school of learning machines coming together? What kind of problems do you see these things, this kind of new model attacking? >> So we're making a very, very large investment in machine learning and data science as a whole in the organization. You probably heard in the press that we've brought in the Head of Machine Learning from CMU, Manuela Veloso. She's now heading up the AI Research Organization, JP Morgan, and she's making herself very available to the rest of the firm, setting strategies, trying different things out, partnering with the businesses, and making sure that she understands the use case of where machine learning will be a success. We've also put a lot of investments in tooling and hiring the right kinds of people from the right kinds of universities. My organization, we're changing the focus in our recruiting efforts to bring in more data science and machine learning. But, I think the most important thing, in addition to all that investment is that we, first and foremost, understand our own problems, we work with researchers, we work with IBM, we work with the vendors, and say, "Okay, this is the types of problems, "what is the best thing to throw at it?" And then we PoC, we prove it out, we look for the small wins, we try to strategize, and then we come up with the recommendations for a full-out, scalable architecture. >> John, talk about the IBM Elite Program. You guys roll your sleeves up. It's a service that you guys provide with your top clients. You bring in the best and you just jump in, co-create opportunities together, solving problems. >> That is exactly right. >> How does this work? What's your relationship with JP Morgan Chase? What specific use case are you going after? What are the opportunities? >> Yeah, so the Data Science Elite Team was setup to really help our top clients in their AI journey, in terms of bringing skills, tools, expertise to work collaboratively with clients like JP Morgan Chase. It's been a great partnership working with Elenita and her team. We've had some very interesting use cases related to her model risk management platform, and some interesting challenges in that space about how do you apply machine learning and deep learning to solve those problems. >> So what exactly is model risk management? How does that all work? >> Good question. (laughing) That's why we're building a very large platform around it. So model risk is one of several types of risk that we worry about and keep us awake at night. There's a long history of risk management in the banks. Of course, there's credit risk, there's market risk, these are all very well-known, very quantified risks. Model risk isn't a number, right? You can't say, "this model, which is some stochastic model "it's going to cost us X million dollars today," right? We currently... it's so somewhat new, and at the moment, it's more prescriptive and things like, you can't do that, or you can use that model in this context, or you can't use it for this type of trade. It's very difficult to automate that type of model risk in the banks, so I'm attempting to put together a platform that captures all of the prescriptive, and the conditions, and the restrictions around what to do, and what to use models for in the bank. Making sure that we actually know this in real time, or at least when the trade is being booked, We have an awareness of where these models are getting somewhat abused, right? We look out for those types of situations, and we make sure that we alert the correct stakeholders, and they do something about it. >> So in essence, you're governing the application of the model, and then learning as you go on, in terms of-- >> That's the second phase. So we do want to learn at the moment, what's in production today. Morpheus running in production, it's running against all of the trading systems in the firm, inside the investment bank. We want to make sure that as these trades are getting booked from day to day, we understand which ones are risky, and we flag those. There's no learning yet in that, but what we've worked with John on are the potential uses of machine learning to help us manage all those risks because it's difficult. There's a lot of data out there. I was just saying, "I don't want our Quants to do stupid things," 'cause there's too much stupidity happening right now. We're looking at emails, we're looking at data that doesn't make sense, so Morpheus is an attempt to make all of that understandable, and make the whole workflow efficient. >> So it's financial programming in a way, that's come with a whole scale of computing, a model gone astray could be very dangerous? >> Absolutely. >> This is what you're getting at right? >> It will cost real money to the firm. This is all the use-- >> So a model to watch the model? So policing the models, kind of watching-- >> Yes, another model. >> When you have to isolate the contribution of the model not like you saying before, "Are there market risks "or other types of risks--" >> Correct. >> You isolate it to the narrow component. >> And there's a lot of work. We work with the Model Governance Organization, another several hundred person organization, and that's all they do. They figure out, they review the models, they understand what the risk of the models are. Now, it's the job of my team to take what they say, which could be very easy to interpret or very hard, and there's a little bit of NLP that I think is potentially useful there, to convert what they say about a model, and what controls around the model are to something that we can systematize and run everyday, and possibly even in real time. >> This is really about getting it right and not letting it get out of control, but also this is where the scale comes in so when you get the model right, you can deploy it, manage it in a way that helps the business, versus if someone throws the wrong number in there, or the classic "we've got a model for that." >> Right, exactly. (laughing) There's two things here, right? There's the ability to monitor a model such that we don't pay fines, and we don't go out of compliance, and there's the ability to use the model exactly to the extreme where we're still within compliance, and make money, right? 'Cause we want to use these models and make our business stronger. >> There's consequences too, I mean, if it's an opportunity, there's upside, it's a problem, there's downside. You guys look at the quantification of those kinds of consequences where the risk management comes in? >> Yeah, absolutely. And there's real money that's at stake here, right? If the regulators decide that a model's too risky, you have to set aside a certain amount of capital so that you're basically protecting your investors and your business, and the stakeholders. If that's done incorrectly, we end up putting a lot more capital in reserve than we should be, and that's a bad thing. So quantifying the risks correctly and accurately is a very important part of what we do. >> So a lot of skillsets obviously, and I always say, "In the money business, you want the best nerds." Don't hate me for saying that... the smartest people. What are some of the challenges that are unique to model risk management that you might not see in sort of other risk management approaches? >> There are some technical challenges, right? The volume of data that you're dealing with is very large. If you are building... so at the very simplistic level, you have classification problems that you're addressing with data that might not actually be all there, so that is one. When you get into time series analysis for exposure prediction and so on, these are complex problems to handle. The training time for these models, especially deep learning models, if you are doing time series analysis, can be pretty challenging. Data volume, training time for models, how do you turn this around quickly? We use a combination of technologies for some of these use cases. Watson Studio running on power hardware with GPUs. So the idea here is you can cut down your model training time dramatically and we saw that as part of the-- >> Talk about how that works because this is something that we're seeing people move from manual to automated machine learning and deep learning, it give you augmented assistance to get this to the market. How does it actually work? >> So there is a training part of this, and then there is the operationalizing part of this, right? At the training part itself, you have a challenge, which is you're dealing with very large data volumes, you're dealing with training times that need to be shrunk down. And having a platform that allows you to do that, so you build models quickly, your data science folks can iterate through model creation very quickly is essential. But then, once the models have been built, how do you operationalize those models? How do you actually invoke the models at scale? How do you do workflow management of those models? How do you make sure that a certain exposure model is not thrashing some other models that are also essential to the business? How do you do policies and workflow management? >> And on top of that, we need to be very transparent, right? If the model is used to make certain decisions that have obvious impact financially on the bottom line, and an auditor comes back and says, "Okay, you made this trade so and so, why? What was happening at that time?" So we need to be able to capture and snapshot and understand what the model was doing at that particular instant in time, and go back and understand the inputs that went into that model and made it operate the way it did. >> It can't be a black box. >> It cannot be, yeah. >> Holistically, you got to look at the time series in real time, when things were happening and happened, happening, and then holistically tie that together. Is that kind of the impact analysis? >> We have to make our regulars happy. (laughing) That's number one, and we have to make our traders happy. We, as quantitative researchers, we're the ones that give them the hard math and the models, and then they use it. They use their own skillsets too to apply them, but-- >> What's the biggest needs that your stakeholders on the trading side want, and what's the needs on the compliance side, the traders want more, they want to move quickly? >> They're coming from different sides of it. Traders want to make more money, right? And they want to make decisions quickly. They want all the tools to tell them what to do, and for them to exercise whatever they normally exercise-- >> They want a competitive advantage. >> They want that competitive advantage, and they're also... we've got algo-trades as well, we want to have the best algo behind our trading. >> And the regulator side, we just want to make sure laws aren't broken, that there's auditing-- >> We use the phrase, "model explainability," right? Can you explain how the model came to a conclusion, right? Can you make sure that there is no bias in the model? How can you ensure the models are fair? And if you can detect there is a drift, what do you do to correct that? So that is very important. >> Do you have means of detecting sort of misuse of the model? Is that part of the governance process? >> That is exactly what Morpheus is doing. The unique thing about Morpheus is that we're tied into the risk management systems in the investment bank. We're actually running the same exact code that's pricing these trades, and what that brings is the ability to really understand pretty much the full stack trace of what's going into the price of a trade. We also have captured the restrictions and the conditions. It's in the Python script, it's essentially Python. And we can marry the two, and we can do all the checks that the governance person indicated we should be doing, and so we know, okay, if this trade is operating beyond maturity or a certain maturity, or beyond a certain expiry, we'll know that, and then we'll tag that information. >> And just for clarification, Morpheus is the name of the platform that does the-- >> Morpheus is the name of the model risk platform that I'm building out, yes. >> A final question for you, what's the biggest challenge that you guys have seen from a complexity standpoint that you're solving? What's the big complex... You don't want to just be rubber-stamping models. You want to solve big problems. What are the big problems that you guys are going after? >> I have many big problems. (laughing) >> Opportunities. >> The one that is right now facing me, is the problem of metadata, data ingestion, getting disparate sources, getting different disparate data from different sources. One source calls it a delta, this other source calls it something else. We've got a strategic data warehouse, that's supposed to take all of these exposures and make sense out of it. I'm in the middle because they're there, probably at the ten-year roadmap, who knows? And I have a one-month roadmap, I have something that was due last week and I need to come up with these regulatory reports today. So what I end up doing is a mix of a tactical strategic data ingestion, and I have to make sense of the data that I'm getting. So I need tools out there that will help support that type of data ingestion problem that will also lead the way towards the more strategic one, where we're better integrated with this-- >> John, talk about how you solve the problems? What are some of the things that you guys do? Give the plug for IBM real quick, 'cause I know you guys got the Studio. Explain how you guys are helping and working with JP Morgan Chase. >> Yeah, I touched upon this briefly earlier, which is from the model training perspective, Watson Studio running on Power hardware is very powerful, in terms of cutting down training time, right? But you've got to go beyond model building to how do you operationalize these models? How do I deploy these models at scale? How do I define workload management policies for these models, and connecting to their backbone. So that is part of this, and model explainability, we touched upon that, to eliminate this problem of how do I ingest data from different sources without having to manually oversee all of that. We need to manually apply auto-classification at the time of ingestion. Can I capture metadata around the model and reconcile data from different data sources as the data is being brought in? And can I apply ML to solve that problem, right? There is multiple applications of ML along this workflow. >> Talk about real quick, comment before we break, I want to get this in, machine learning has been around for a while now with compute and scale. It really is a renaissance in AI, it's great things are happening. But what feeds machine learning is data, the cleaner the data, the better the AI, the better the machine learning, so data cleanliness now has to be more real-time, it's less of a cleaning group, right? It used to be clean the data, bring it in, wrangle it, now you got to be much more agile, use speed of compute to make sure that you're qualifying data before it comes in, these machine learning. How do you guys see that rolling out, is that impacting you now? Are you thinking about it? How should people think about data quality as an input in machine learning? >> Well, I think the whole problem of setting up an application properly for data science and machine learning is really making sure that from the beginning, you're designing, and you're thinking about all of these problems of data quality, if it's the speed of ingestion, the speed of publication, all of that stuff. You need to think about the beginning, set yourself up to have the right elements, and it may not all be built out, and that's been a big strategy I've had with Morpheus. I've had a very small team working on it, but we think ahead and we put elements of the right components in place so data quality is just one of those things, and we're always trying to find the right tool sets that will enable use to do that better, faster, quicker. One of the things I'd like to do is to upscale and uplift the skillsets on my team, so that we are building the right things in the system from the beginning. >> A lot of that's math too, right? I mean, you talk about classification, getting that right upfront. Mathematics is-- >> And we'll continue to partner with Elenita and her team on this, and this helps us shape the direction in which our data science offerings go because we need to address complex enterprise challenges. >> I think you guys are really onto something big. I love the elite program, but I think having the small team, thinking about the model, thinking about the business model, the team model before you build the technology build-out, is super important, that seems to be the new model versus the old days, build some great technology and then, we'll put a team around it. So you see the world kind of being a little bit more... it's easier to build out and acquire technology, than to get it right, that seems to be the trend here. Congratulations. >> Thank you. >> Thanks for coming on. I appreciate it. theCUBE here, CUBE Conversations here. We're live in San Francisco, IBM Think. I'm John Furrier, Dave Vellante, stay with us for more day two coverage. Four days we'll be here in the hallway and lobby of Moscone North, stay with us.

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's theCUBE covering autonomous vehicles. Brought to you by Western Digital. (upbeat electronic music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are at the Autotech Council Autonomous Vehicles event here at Western Digital. It's part of our ongoing work that we're doing with Western Digital about #datamakespossible and all the really innovative and interesting things that are going on that at the end of the day, there's some data that's driving it all and this is a really crazy and interesting space. So we're excited for our next guest. He's Mark DeSantis. He's the CEO of RoadBotics. Mark, great to see you. >> Welcome. >> Thanks, thanks for having me, Jeff. >> So just to give the quick overview of what is RoadBotics all about? >> Sure, we use a simple cellphone as a data collection device. You put that in the windshield, you drive, it records all the video and all that video gets uploaded to the Cloud and we assess the road's surface meter by meter. Our customers would be Public Works departments at the little town to a big city or even a state, and we apply the same principles that a pavement engineer would apply when they look at a piece of pavement. Looking for all the different subtle little features so that they can get, first of all, get an assessment of the road and then they can do capital planning and fix those roads and do a lot of things that they can't do right now. >> So I think the economics of roads and condition of roads, roads in general, right? We don't think about them much until they're closed, they're being fixed, they're broken up, there's a pothole. >> Mark: Yeah. >> But it's really a complex system and a really high value system that needs ongoing maintenance. >> That's right. I always use the example of the Romans who built a 50,000 mile road network across Europe, the Middle East, and Africa. Some of those roads, like the Appian Way, are still used today. They were very good road builders and they understand the importance of roads. Regrettably, we take our roads for granted. The American Society for Civil Engineers annually rates infrastructure and we're rated about 28% of our nation's 11 million lane miles as poor. Unfortunately, that's- >> Jeff: 28%? >> 28%. And that really means that you need to invest, we'll need to invest at least a million to two million bucks a mile to get those roads back into shape. So we take our roads for granted. I'm enjoying this conference and there's one point that I want to make that I think is very poignant, is the AV revolution will also require a revolution in the maintenance and sustenance of our road network, not just the United States but everywhere in the world. >> So it's interesting, and doing some research before we got together in terms of the active maintenance that's not only required to keep a road in good shape but if you keep the active maintenance in position, those roads will last a very long time. And you made an interesting comment that now the autonomous vehicles, it's actually more important for those vehicles, not only for jolting the electronics around that they're carrying, but also for everything to work the way it's supposed to work according to the algorithms. >> Andrew Ang, who's an eminent computer scientist, machine learning, we were spun out of Carnegie Mellon and he was a graduate of that program, recognized early on that the quality of the roads made all the difference in the world for these vehicles to move around. We, in turn, were spun out of Carnegie Mellon, out of that same group of AV researchers, and in fact, the impetus for the technology was to be able to use the sensing technology that allows a vehicle to move around to assess the quality of roads. And it's road inspection, really, is an important part of road maintenance. The ability to go look at an asset. Interestingly, it's an asset whose challenge is not the fact that it can't be inspected, it's the sheer size of the asset. When you're talking about a small town that might have a 60-mile road network, most and the vast majority of inspection is visual inspection. That means somebody in a car riding very slowly looking down and they'll do that for tens, thousands, hundreds of thousands of miles, very hard to do. Our system makes all that very, much more efficient. The interesting thing about autonomous vehicles is they'll have the capacity to use that data to do that very assessment. So for our company, we ultimately see us embedded in the vehicle itself, but for the time being, cellphones work fine. >> Right. So I'm just curious, what are some of those leading indicator data points? Because obviously we know the pothole. >> Mark: Yeah. >> By then things have gone too far but what are some of the subtle things that maybe I might see but I'm not really looking at? (laughs) >> Well, I think I've changed you right now and you don't know it. You're never going to look at a road the same- >> Oh, I told you, I told you. (laughs) >> After you hear me talk for the next three minutes. I don't look at roads the same and I'm not a civil engineer nor am I a pavement engineer, but as the CEO of this company I had to learn a lot about those two disciplines. And in fact, when you look at a piece of asphalt, you're actually looking for things like alligator cracks, which sort of looks like the back of an alligator's skin. Block cracks, edge cracks, rutting, a whole bunch of things that pavement engineers, frankly, and there is a discipline called pavement engineering, where they look for. And those features determine the state of that road and also dictate what repairs will be done. Concrete pavement has a similar set of characteristics. So what we're looking for when we look at a road is, I always say that, people say, "Well, you're the pothole company." If all you see are potholes, you don't have a business. And the reason is, potholes are at the end of a long process of degradation. So when you see a pothole, there are two problems. One is, you can certain blow out a tire or break an axle on that pothole but also it's indicative of a deeper problem which means the surface of the road has been penetrated which means you to dig up that road and replace it. So if you can see features that are predictive of a road that's just about to go bad, make small fixes, you can extend the useful life of that asset indefinitely. >> Right. So before I let you go, unfortunately, we're just short on time. >> Mark: Yeah. >> I would love to learn about roads. I told you, I skateboard so I pay a lot of attention to smooth roads. >> Mark: (laughs) And you'll pay even more now. >> Now I'll pay even more and call the city. (chuckles) But I want to pivot off what happened at Carnegie Mellon and obviously academic institutions are a huge part of this revolution. >> Yeah, yeah. >> There's a lot of work going on. We're close to Stanford and Berkeley here. Talk a little bit about what happens... It's happening at Carnegie Mellon and I think specifically you came out of the Robotics Institute in something called the Traffic21 project. >> Yeah, Traffic21 is funded by some local private interests who believed that the various technologies that are, really, CMU is known for around computer science, robots, engineering, could be instrumental in bringing about this AV revolution. And as a consequence of that, they developed a program early on to try to bring these technologies together. Uber came along and literally hired 27 of those researchers. Argo, now... Argo, Ford's autonomous vehicle now, is big in Pittsburgh as well. On any given day, by my estimate, it's not an official estimate here, there are about 400 autonomous vehicles, Ford and Uber vehicles, on Pittsburgh's streets every single day. It's an eerie experience being driven around by a completely autonomous Uber vehicle, believe me. >> I've been in a couple. It's interesting and we did a thing with a company called Phantom. They're the ones that step if your Uber gets stuck. >> Oh, yeah. >> Which is interesting. (laughs) So really interesting times and exciting and I will go and pay closer attention for the alligator patterns (laughs) on my route home tonight. (laughs) All right, Mark, thanks for stopping by and sharing the insight. >> Thanks again, Jeff. Appreciate you having me. >> All right, he's Mark, I'm Jeff. You're watching theCUBE from the Autotech Council Autonomous Vehicles event in Milpitas, California. Thanks for watching. (upbeat electronic music)

>> Announcer: Live from midtown Manhattan, it's the Cube. Covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Welcome back everyone, we are here live in New York City, in Manhattan for BigData NYC, our event we've been doing for five years in conjunction with Strata Data which is formerly Strata Hadoop, which was formerly Strata Conference, formerly Hadoop World. We've been covering the big data space going on ten years now. This is the Cube. I'm here with Aaron Kalb, whose Head of Product and co-founder at Alation. Welcome to the cube. >> Aaron Kalb: Thank you so much for having me. >> Great to have you on, so co-founder head of product, love these conversations because you're also co-founder, so it's your company, you got a lot of equity interest in that, but also head of product you get to have the 20 mile stare, on what the future looks, while inventing it today, bringing it to market. So you guys have an interesting take on the collaboration of data. Talk about what the means, what's the motivation behind that positioning, what's the core thesis around Alation? >> Totally so the thing we've observed is a lot of people working in the data space, are concerned about the data itself. How can we make it cheaper to store, faster to process. And we're really concerned with the human side of it. Data's only valuable if it's used by people, how do we help people find the data, understand the data, trust in the data, and that involves a mix of algorithmic approaches and also human collaboration, both human to human and human to computer to get that all organized. >> John Furrier: It's interesting you have a symbolics background from Stanford, worked at Apple, involved in Siri, all this kind of futuristic stuff. You can't go a day without hearing about Alexia is going to have voice-activated, you've got Siri. AI is taking a really big part of this. Obviously all of the hype right now, but what it means is the software is going to play a key role as an interface. And this symbolic systems almost brings on this neural network kind of vibe, where objects, data, plays a critical role. >> Oh, absolutely, yeah, and in the early days when we were co-founding the company, we talked about what is Siri for the enterprise? Right, I was you know very excited to work on Siri, and it's really a kind of fun gimmick, and it's really useful when you're in the car, your hands are covered in cookie dough, but if you could answer questions like what was revenue last quarter in the UK and get the right answer fast, and have that dialogue, oh do you mean fiscal quarter or calendar quarter. Do you mean UK including Ireland, or whatever it is. That would really enable better decisions and a better outcome. >> I was worried that Siri might do something here. Hey Siri, oh there it is, okay be careful, I don't want it to answer and take over my job. >> (laughs) >> Automation will take away the job, maybe Siri will be doing interviews. Okay let's take a step back. You guys are doing well as a start up, you've got some great funding, great investors. How are you guys doing on the product? Give us a quick highlight on where you guys are, obviously this is BigData NYC a lot going on, it's Manhattan, you've got financial services, big industry here. You've got the Strata Data event which is the classic Hadoop industry that's morphed into data. Which really is overlapping with cloud, IoTs application developments all kind of coming together. How do you guys fit into that world? >> Yeah, absolutely, so the idea of the data lake is kind of interesting. Psychologically it's sort of a hoarder mentality, oh everything I've ever had I want to keep in the attic, because I might need it one day. Great opportunity to evolve these new streams of data, with IoT and what not, but just cause you can get to it physically doesn't mean it's easy to find the thing you want, the needle in all that big haystack and to distinguish from among all the different assets that are available, which is the one that is actually trustworthy for your need. So we find that all these trends make the need for a catalog to kind of organize that information and get what you want all the more valuable. >> This has come up a lot, I want to get into the integration piece and how you're dealing with your partnerships, but the data lake integration has been huge, and having the catalog has come up with, has been the buzz. Foundationally if you will saying catalog is important. Why is it important to do the catalog work up front, with a lot of the data strategies? >> It's a great question, so, we see data cataloging as step zero. Before you can prep the data in a tool like Trifacta, PACSAT, or Kylo. Before you can visualize it in a tool like Tableau, or MicroStrategy. Before you can do some sort of cool prediction of what's going to happen in the future, with a data science engine, before any of that. These are all garbage in garbage out processes. The step zero is find the relevant data. Understand it so you can get it in the right format. Trust that it's good and then you can do whatever comes next >> And governance has become a key thing here, we've heard of the regulations, GDPR outside of the United States, but also that's going to have an arms length reach over into the United States impact. So these little decisions, and there's going to be an Equifax someday out there. Another one's probably going to come around the corner. How does the policy injection change the catalog equation? A lot of people are building machine learning algorithms on top of catalogs, and they're worried they might have to rewrite everything. How do you balance the trade off between good catalog design and flexibility on the algorithm side? >> Totally yes it's a complicated thing with governance and consumption right. There's people who are concerned with keeping the data safe, and there are people concerned with turning that data into real value, and these can seem to be at odds. What we find is actually a catalog as a foundation for both, and they are not as opposed as they seem. What Alation fundamentally does is we make a map of where the data is, who's using what data, when, how. And that can actually be helpful if your goal is to say let's follow in the footsteps of the best analyst and make more insights generated or if you want to say, hey this data is being used a lot, let's make sure it's being used correctly. >> And by the right people. >> And by the right people exactly >> Equifax they were fishing that pond dry months, months before it actually happened. With good tools like this they might have seen this right? Am I getting it right? >> That's exactly right, how can you observe what's going on to make sure it's compliant and that the answers are correct and that it's happening quickly and driving results. >> So in a way you're taking the collective intelligence of the user behavior and using that into understanding what to do with the data modeling? >> That's exactly right. We want to make each person in your organization as knowledgeable as all of their peers combined. >> So the benefit then for the customer would be if you see something that's developing you can double down on it. And if the users are using a lot of data, then you can provision more technology, more software. >> Absolutely, absolutely. It's sort of like when I was going to Stanford, there was a place where the grass was all dead, because people were riding their bikes diagonally across it. And then somebody smart was like, we're going to put a real gravel path there. So the infrastructure should follow the usage, instead of being something you try to enforce on people. >> It's a classic design meme that goes around. Good design is here, the more effective design is the path. >> Exactly. >> So let's get into the integration. So one of the hot topics here this year obviously besides cloud and AI, with cloud really being more the driver, the tailwind for the growth, AI being more the futuristic head room, is integration. You guys have some partnerships that you announced with integration, what are some of the key ones, and why are they important? >> Absolutely, so, there have been attempts in the past to centralize all the data in one place have one warehouse or one lake have one BI tool. And those generally fail, for different reasons, different teams pick different stacks that work for them. What we think is important is the single source of reference One hub with spokes out to all those different points. If you think about it it's like Google, it's one index of the whole web even though the web is distributed all over the place. To make that happen it's very important that we have partnerships to get data in from various sources. So we have partnerships with database vendors, with Cloudera and Hortonworks, with different BI tools. What's new are a few things. One is with Cloudera Navigator, they have great technical metadata around security and lineage over HGFS, and that's a way to bolster our catalog to go even deeper into what's happening in the files before things get surfaced and higher for places where we have a deeper offering today. >> So it's almost a connector to them in a way, you kind of share data. >> That's exactly right, we've a lot of different connectors, this is one new one that we have. Another, go ahead. >> I was going to go ahead continue. >> I was just going to say another place that is exciting is data prep tools, so Trifacta and Paxata are both places where you can find and understand an alation and then begin to manipulate in those tools. We announced with Paxata yesterday, the ability to click to profile, so if you want to actually see what's in some raw compressed avro file, you can see that in one click. >> It's interesting, Paxata has really been almost lapping, Trifacta because they were the leader in my mind, but now you've got like a Nascar race going on between the two firms, because data wrangling is a huge issue. Data prep is where everyone is stuck right now, they just want to do the data science, it's interesting. >> They are both amazing companies and I'm happy to partner with both. And actually Trifacta and Alation have a lot of joint customers we're psyched to work with as well. I think what's interesting is that data prep, and this is beginning to happen with analyst definitions of that field. It isn't just preparing the data to be used, getting it cleaned and shaped, it's also preparing the humans to use the data giving them the confidence, the tools, the knowledge to know how to manipulate it. >> And it's great progress. So the question I wanted to ask is now the other big trend here is, I mean it's kind of a subtext in this show, it's not really front and center but we've been seeing it kind of emerge as a concept, we see in the cloud world, on premise vs cloud. On premise a lot of people bring in the dev ops model in, and saying I may move to the cloud for bursting and some native applications, but at the end of the day there is a lot of work going on on premise. A lot of companies are kind of cleaning house, retooling, replatforming, whatever you want to do resetting. They are kind of getting their house in order to do on prem cloud ops, meaning a business model of cloud operations on site. A lot of people doing that, that will impact the story, it's going to impact some of the server modeling, that's a hot trend. How do you guys deal with the on premise cloud dynamic? >> Totally, so we just want to do what's right for the customer, so we deploy both on prem and in the cloud and then from wherever the Alation server is it will point to usually a mix of sources, some that are in the cloud like vetshifter S3 often with Amazon today, and also sources that are on prem. I do think I'm seeing a trend more and more toward the cloud and we have people that are migrating from HGFS to S3 is one thing we hear a lot about it. Strata with sort of dupe interest. But I think what's happening is people are realizing as each Equifax in turn happens, that this old wild west model of oh you surround your bank with people on horseback and it's physically in one place. With data it isn't like that, most people are saying I'd rather have the A+ teams at Salesforce or Amazon or Google be responsible for my security, then the people I can get over in the midwest. >> And the Paxata guys have loved the term Data Democracy, because that is really democratization, making the data free but also having the governance thing. So tell me about the Data Lake governance, because I've never loved the term Data Lake, I think it's more of a data ocean, but now you see data lake, data lake, data lake. Are they just silos of data lakes happening now? Are people trying to connect them? That's key, so that's been a key trend here. How do you handle the governance across multiple data lakes? >> That's right so the key is to have that single source of reference, so that regardless of which lake or warehouse, or little siloed Sequel server somewhere, that you can search in a single portal and find that thing no matter where it is. >> John: Can you guys do that? >> We can do that, yeah, I think the metaphor for people who haven't seen it really is Google, if you think about it, you don't even know what physical server a webpage is hosted from. >> Data lakes should just be invisible >> Exactly. >> So your interfacing with multiple data lakes, that's a value proposition for you. >> That's right so it could be on prem or in the cloud, multi-cloud. >> Can you share an example of a customer that uses that and kind of how it's laid out? >> Absolutely, so one great example of an interesting data environment is eBay. They have the biggest teradata warehouse in the world. They also have I believe two huge data lakes, they have hive on top of that, and Presto is used to sort of virtualize it across a mixture of teradata, and hive and then direct Presto query It gets very complicated, and they have, they are a very data driven organization, so they have people who are product owners who are in jobs where data isn't in their job title and they know how to look at excel and look at numbers and make choices, but they aren't real data people. Alation provides that accessibility so that they can understand it. >> We used to call the Hadoop world the car show for the data world, where for a long time it was about the engine what was doing what, and then it became, what's the car, and now how's it drive. Seeing that same evolution now where all that stuff has to get done under the hood. >> Aaron: Exactly. >> But there are still people who care about that, right. They are the mechanics, they are the plumbers, whatever you want to call them, but then the data science are the guys really driving things and now end users potentially, and even applications bots or what nots. It seems to evolve, that's where we're kind of seeing the show change a little bit, and that's kind of where you see some of the AI things. I want to get your thoughts on how you or your guys are using AI, how you see AI, if it's AI at all if it's just machine learning as a baby step into AI, we all know what AI could be, but it's really just machine learning now. How do you guys use quote AI and how has it evolved? >> It's a really insightful question and a great metaphor that I love. If you think about it, it used to be how do you build the car, and now I can drive the car even though I couldn't build it or even fix it, and soon I don't even have to drive the car, the car will just drive me, all I have to know is where I want to go. That's sortof the progression that we see as well. There's a lot of talk about deep learning, all these different approaches, and it's super interesting and exciting. But I think even more interesting than the algorithms are the applications. And so for us it's like today how do we get that turn by turn directions where we say turn left at the light if you want to get there And eventually you know maybe the computer can do it for you The thing that is also interesting is to make these algorithms work no matter how good your algorithm is it's all based on the quality of your training data. >> John: Which is a historical data. Historical data in essence the more historical data you have you need that to train the data. >> Exactly right, and we call this behavior IO how do we look at all the prior human behavior to drive better behavior in the future. And I think the key for us is we don't want to have a bunch of unpaid >> John: You can actually get that URL behavioral IO. >> We should do it before it's too late (Both laugh) >> We're live right now, go register that Patrick. >> Yeah so the goal is we don't want to have a bunch of unpaid interns trying to manually attack things, that's error prone and that's slow. I look at things like Luis von Ahn over at CMU, he does a thing where as you're writing in a CAPTCHA to get an email account you're also helping Google recognize a hard to read address or a piece of text from books. >> John: If you shoot the arrow forward, you just take this kind of forward, you almost think augmented reality is a pretext to what we might see for what you're talking about and ultimately VR are you seeing some of the use cases for virtual reality be very enterprise oriented or even end consumer. I mean Tom Brady the best quarterback of all time, he uses virtual reality to play the offense virtually before every game, he's a power user, in pharma you see them using virtual reality to do data mining without being in the lab, so lab tests. So you're seeing augmentation coming in to this turn by turn direction analogy. >> It's exactly, I think it's the other half of it. So we use AI, we use techniques to get great data from people and then we do extra work watching their behavior to learn what's right. And to figure out if there are recommendations, but then you serve those recommendations, either it's Google glasses it appears right there in your field of view. We just have to figure out how do we make sure, that in a moment of you're making a dashboard, or you're making a choice that you have that information right on hand. >> So since you're a technical geek, and a lot of folks would love to talk about this, so I'll ask you a tough question cause this is something everyone is trying to chase for the holy grail. How do you get the right piece of data at the right place at the right time, given that you have all these legacy silos, latencies and network issues as well, so you've got a data warehouse, you've got stuff in cold storage, and I've got an app and I'm doing something, there could be any points of data in the world that could be in milliseconds potentially on my phone or in my device my internet of thing wearable. How do you make that happen? Because that's the struggle, at the same time keep all the compliance and all the overhead involved, is it more compute, is it an architectural challenge how do you view that because this is the big challenge of our time. >> Yeah again I actually think it's the human challenge more than the technology challenge. It is true that there is data all over the place kind of gathering dust, but again if you think about Google, billions of web pages, I only care about the one I'm about to use. So for us it's really about being in that moment of writing a query, building a chart, how do we say in that moment, hey you're using an out of date definition of profit. Or hey the database you chose to use, the one thing you chose out of the millions that is actually is broken and stale. And we have interventions to do that with our partners and through our own first party apps that actually change how decisions get made at companies. >> So to make that happen, if I imagine it, you'd have to need access to the data, and then write software that is contextually aware to then run, compute, in context to the user interaction. >> It's exactly right, back to the turn by turn directions concept you have to know both where you're trying to go and where you are. And so for us that can be the from where I'm writing a Sequel statement after join we can suggest the table most commonly joined with that, but also overlay onto that the fact that the most commonly joined table was deprecated by a data steward data curator. So that's the moment that we can change the behavior from bad to good. >> So a chief data officer out there, we've got to wrap up, but I wanted to ask one final question, There's a chief data officer out there they might be empowered or they might be just a CFO assistant that's managing compliance, either way, someone's going to be empowered in an organization to drive data science and data value forward because there is so much proof that data science works. From military to play you're seeing examples where being data driven actually has benefits. So everyone is trying to get there. How do you explain the vision of Alation to that prospect? Because they have so much to select from, there's so much noise, there's like, we call it the tool shed out there, there's like a zillion tools out there there's like a zillion platforms, some tools are trying to turn into something else, a hammer is trying to be a lawnmower. So they've got to be careful on who the select, so what's the vision of Alation to that chief data officer, or that person in charge of analytics to scale operational analytics. >> Absolutely so we say to the CDO we have a shared vision for this place where your company is making decisions based on data, instead of based on gut, or expensive consultants months too late. And the way we get there, the reason Alation adds value is, we're sort of the last tool you have to buy, because with this lake mentality, you've got your tool shed with all the tools, you've got your library with all the books, but they're just in a pile on the floor, if you had a tool that had everything organized, so you just said hey robot, I need an hammer and this size nail and this text book on this set of information and it could just come to you, and it would be correct and it would be quick, then you could actually get value out of all the expense you've already put in this infrastructure, that's especially true on the lake. >> And also tools describe the way the works done so in that model tools can be in the tool shed no one needs to know it's in there. >> Aaron: Exactly. >> You guys can help scale that. Well congratulations and just how far along are you guys in terms of number of employees, how many customers do you have? If you can share that, I don't know if that's confidential or what not >> Absolutely, so we're small but growing very fast planning to double in the next year, and in terms of customers, we've got 85 customers including some really big names. I mentioned eBay, Pfizer, Safeway Albertsons, Tesco, Meijer. >> And what are they saying to you guys, why are they buying, why are they happy? >> They share that same vision of a more data driven enterprise, where humans are empowered to find out, understand, and trust data to make more informed choices for the business, and that's why they come and come back. >> And that's the product roadmap, ethos, for you guys that's the guiding principle? >> Yeah the ultimate goal is to empower humans with information. >> Alright Aaron thanks for coming on the Cube. Aaron Kalb, co-founder head of product for Alation here in New York City for BigData NYC and also Strata Data I'm John Furrier thanks for watching. We'll be right back with more after this short break.