>> Announcer: Live, from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. Okay, welcome back everyone, this is theCUBE, live here in Las Vegas at The Venetian, this is Informatica Worlds exclusive coverage with theCUBE, Informatica World 2018, I'm John Furrier, with my co-host Jim Kobielus, analyst at Wikibon, SiliconANGLE, and theCUBE, our next guest is Tracy Ring, Vice President at Deloitte Consulting, great to see you again. >> You as well! >> So, love havin' you on, last year, you know, we go through all the interviews and, you know it always comes up, and this is important, you know we are passionate about women in tech, inclusion and diversity, huge topic, the job's never done, in fact, I was in New York last week for a blockchain event, and I wore a shirt that said: Satoshi's Female. (Tracy laughing) And I literally was getting so many high fives and, but it's not just women in tech, there's a role that men play, this is, sort of an ongoing conversation so. What's the state of the industry, from your perspective, how do you see it? Obviously the data world is, indiscriminate data is data, >> Tracy: Absolutely. >> It should be 50/50. >> Yeah, you know I think that the, the opportunity is multi-faceted, right? So we're in a place where technology is changing unbelievably fast, we're graduating nearly as many men as women in, fields of science, data analytics, computer engineering, etc. But what we're not seeing, a combination of women in leadership roles as much as we would expect, we're not seeing the retention of women in those roles. And for me, I'm really passionate about the fact that supporting, attracting, and keeping women in those roles, is really critical, right? There's an interesting facet to how this all really, really plays together, Deloitte for 20 years has a women initiative, right? 20 years of supporting women, embracing them, helping them support leadership roles and, and I think that the time is now. If not, it's long overdue, to really support them within this field. I also think that women in data, an initiative that we're launching this year, and having our launch event today, is sort of super timely because women in data is not women who only become CIOs, or will only become CDOs, these are women that will be the Chief Marketing Officers, the CHROs, and using data to tell their stories. >> You know, we had a guest on earlier, who was a man, but he was the head of the CDO for the Ireland Bank, and Peter Burris asked the question, said hey, where did you come from technical? No, he came from the business side, who knows technology, this is what you're getting at, and I think this is something that we've been seeing as a pattern that you don't rise up through the ranks and be super nerdy, although that's cool too, and there's a lot more STEM action but there's also multiple vectors into the field. You can come from business, and know tech, and a lot more tech is consumable, and learnable, either online, or through some sort of other proficiency so, this is a big story and so, how do you guys, looking at that, at Deloitte, I know Deloitte's got the track record, but this all scales beyond Deloitte, right? It's an industry thing. >> Tracy: Absolutely. >> How are you guys seeing this? How are you looking at helping people, either connect the dots, or support each other? What's some of the latest and greatest? >> Yeah, I mean I think Informatica is part of what has created the case for change, right? We've democratized data integration, we have, you know, made self-service analytics, we've put data in the cloud in everyone's hands, right? So technology is out there more, every single day, and I think the unique part is, is that, when we think about diversity wholistically, and I think of diversity from ages, and geographic, and gender, etc. I think really being able to take all of that diverse experience, and be able to listen to business user's requirements in a way that they can hear it! And listen for something different, right? And brings skills to bare, that aren't necessarily there. I think if we can build better technology, that's more future-proofed, based on having a diverse crowd listening, and trying to build something that's far more compelling than, you know, I asked for X, build me X. I think when we really do our clients, and the world of justice is when we, you know, someone asks for X, and you ask them 10 more questions, and heavy--what about this? And what, and what, and what? And I think really being much more inquisitive, giving people the ability to be inquisitive, and bringing more opinions to the table to be inquisitive. >> And bringing more diversity of practice, makes the applications better, so that's clear. We see that in some of the conversations we have, but I got to ask about the question of roles, what are you seeing, kind of, you look at the trends, are there certain roles that are, that are being adopted with women in tech more than others? Less, trending down, up? What are some of the trend lines on, either roles in tech, for women? >> Yeah, you know, I think that over all, when I had the opportunity, so when we decided, we're going to launch a program within Informatica. We want the women who are going to be the Chief Data Officers of tomorrow. And it was a great question because, actually what we ended up saying is, the Chief Data Officers of tomorrow could be so many different current roles right now, right? And how do we really, kind of, attract the right women into this cohort, support them for a long year and, provide them the forum to network, connect with others, understand different career paths. You know, looking at what we're seeing, you know, with GDPR, and regulations, and all these other things happening, you know, the concepts and roles that didn't even exist years ago, right, so data governance leads and, Chief Analytic Officers, and all of these-- >> James: Or Chief AI Officers! >> Exac--(laughing) >> How do we bring women into the hottest fields like AI, deep learning? If you look at the research literature, out of, both the commercial and the academic world, many of the authors of the papers are men, I mean, more than the standard ratio of men to women in the corporate space, near as I can tell, from my deep reading. How do you break women into AI, for example, when they haven't been part of that overall research community? That's just a, almost like a rhetorical question. >> Yeah, how do you not, you know, it's just impossible to not bring them to bear, the skills, the talent, the ingenuity, I think it's absolutely mandatory, and someone said to me, they said well, why are the men not invited to this event? Why are they not in the cohort? And I said, you know, because there's a component of all this, that we want to grow and foster and support, and create opportunities. You know, one of the women that sat on our board today said, you know, I'm not somebody who's going to golf, I'm not someone who's going to go to a sports game, I'm going to meet you in the board room, and we're going to talk about compelling topics there. And so I think it's about, encouraging and fostering a new way of networking that's more aligned with what women are interested in, and what, you know, sometimes we do best and, I think creating an opportunity for a different type of everything, in the way that we operate is important. >> I think self-awareness, for men, and this also, creating a good vibe, right? Having a good vibe is critical, in my opinion, and also, you know, not judging people right, you know, based upon, you have some women say, hey I like to get dressed up and that's what I am, some people who don't want to go to sports and, some guys want this, so I think generally, there needs to be, kind of a reset, like hey, let's just have an open mind and a good vibe. >> It's like lunch and learns, you know, lunch and learns are, are a great enabler for centers of confidence, to get together on a regular basis, to talk about business and technical-related things, but also it's a social environment. How can you build more of those kinds of opportunities into the corporate culture, where, they're not skewing, the actual socializing, to traditionally male-dominated hobbies or interests, or traditionally female-dominated hobbies or interests? How can you have, sort of a balance, of those kinds of socialization opportunities in a professionally appropriate environment that also involve a fair amount of shop talk? 'cause that's what gets people bonding, promoted in their careers is that they do deep shop talk in the appropriate settings. >> Yeah, it's interesting, one of the women that I personally consider a mentor, she said if it wasn't for data, I wouldn't be where I am today. And she said, you know, I grew up in and industry where, unfortunately, I really didn't have a voice at the table, and my voice at the table came from data, it came from my ability to see connections, patterns, and detect things, and also for my ability to create networks of people, and make connections and pull things together in a way that my colleagues weren't doing. And, you know, when she tells that story I think that's, that's the template, right? >> John: That's the empowerment. >> We want to say, use everything at your bevy to bring the best value to your business end-users, and she's connecting the dots in a way that no one else had, and is using data as really, the impetis to really, solidify everything that she's saying, it's inarguable. >> That's a great story, it's a phenomenal story. >> It's just amazing. >> Once she got into power she really drove that hard, that's awesome. Well, let's take that to the next level, so, you know, I have a daughter as a junior at UCAL Berkeley, and she's a STEM girl, and so she's got a good vibe in there >> James: STEM girl, I have a stem girl too, mines 28 now. >> You know, and so, kind of aside, but she, turned away from computer science because, at, you know, in middle school the vibe wasn't there, right? And it was kind of a social thing, we mentioned social. You're advice to young women now? Because we're seeing people with the democratization, you see YouTube, you see all these tools, you got robots, you got makers, of course, you got data, you've seen a lot more touch points where people can, you know, ingratiate in unthreatened, un, you know, just, getting immersed in tech. So you have, you're starting to get people the taste of not being tracked into it. So, what's the advice for young folks trying to navigate? And is it networking groups, is it mentoring? What's the playbook in your mind? >> Yeah, I think it's a combination of everything that you've mentioned, right? I absolutely think that your network, and what one of my mentors calls your sleeper network, right? The network that's out there, the people that I worked with five years ago, and we worked, and were in a war room til two a.m. and you know, then I, I just got busy, right? And reactivating your sleeper networks, you know, having the courage to kind of, keep people apprised, using social media, in a way that people, you know, the number of people that say, oh I didn't know you were up to this, that, or the other, thank goodness you posted. And so, I think using all of the technology to your advantage. And I also think there's a component of someone, I mean, I had an MIS degree for undergrad, and I started out as a developer. >> You might have to explain what this is for the younger generation. (laughing) >> Oh, I know, how crazy is that! Oh my gosh, >> Is that in the DP department, was that in the DP department? >> Can you imagine. But I wasn't interested in technology that much, it was what was going to get me a job and, and I thought I would become a business analyst, I've stayed with it, and now really passionate about tech, but, I think there's a component of all this that, every job, you know, the CHROs, the CAOs, all of the roles that roll up, you know, every finance person I know that's exceptional, is phenomenal with data! Right? And so, I think, not only creating a network of people that are in the industry, but I think it's about telling the stories outside the industry, and telling the oh my gosh, you'll never believed what we learned today. And I think that's the magic of the stories, and being transparent. >> Well Tracy, you're an inspiration, thanks so much for coming on theCUBE, really love the story. I got to ask, what are you up to now? Tell us what's up with you, obviously you've moved on from MIS, Management Information Systems, part of the DP, Data Processing department, that's many computer days. >> Tracy: Oh my. >> Oh my God, we're goin' throwback there. >> Tracy: Absolutely. >> What're you up to now? What are you havin' fun with? >> Yeah, so my day job, I have the luxury of working across our cognitive analytic, and our PA alliances, which is an insane mouthful, but it means I get to work with some of our most exciting alliance partners that Deloitte is building solutions, and going to market, and getting really great customer stories under our belt. And I think really kind of blowing the doors off of, of what we did three years ago, five years ago, and 20 years ago, when MIS degrees were still being handed out, so. >> A lot more exciting now, isn't it. >> (laughing) It's way better now! So. >> I wish I was 23 again, you know, havin' a good time. (Tracy laughing) >> Yeah, so, really wholistically, seeing what we consider ecosystems and alliances, is, that's my day job. >> Tracy Ring, Vice President at Deloitte, great story, fun to have on theCUBE, also doing some great work, super exciting time, you got cloud, you got data, it really is probably one of the most creative times in the tech industry, it's super fun to get involved. This is theCUBE, here out in the open, at Informatica World in Las Vegas. I'm John Furrier with Jim Kobielus, be back with more, stay with us! From Vegas, we'll be right back. >> Tracy: Thank you. (bubbly music)

>> [Announcer] Live from Boston Massachusetts. This is theCUBE. Covering Sparks Summit East 2017. Brought to you by Databricks. Now here are your hosts, Dave Vellante and George Gilbert. >> [Dave] Welcome back to Boston everybody, this is Spark Summit East #SparkSummit And this is theCUBE. Ion Stoica is here. He's Executive Chairman of Databricks and Professor of Computer Science at UCal Berkeley. The smarts is rubbing off on me. I always feel smart when I co-host with George. And now having you on is just a pleasure, so thanks very much for taking the time. >> [Ion] Thank you for having me. >> So loved the talk this morning, we learned about RISELabs, we're going to talk about that. Which is the son of AMP. You may be the father of those two, so. Again welcome. Give us the update, great keynote this morning. How's the vibe, how are you feeling? >> [Ion] I think it's great, you know, thank you and thank everyone for attending the summit. It's a lot of energy, a lot of interesting discussions, and a lot of ideas around. So I'm very happy about how things are going. >> [Dave] So let's start with RISELabs. Maybe take us back, to those who don't understand, so the birth of AMP and what you were trying to achieve there and what's next. >> Yeah, so the AMP was a six-year Project at Berkeley, and it involved around eight faculties and over the duration of the lab around 60 students and postdocs, And the mission of the AMPLab was to make sense of big data. AMPLab started in 2009, at the end of 2009, and the premise is that in order to make sense of this big data, we need a holistic approach, which involves algorithms, in particular machine-learning algorithms, machines, means systems, large-scale systems, and people, crowd sourcing. And more precisely the goal was to build a stack, a data analytic stack for interactive analytics, to be used across industry and academia. And, of course, being at Berkeley, it has to be open source. (laugh) So that's basically what was AMPLab and it was a birthplace for Apache Spark that's why you are all here today. And a few other open-source systems like Mesos, Apache Mesos, and Alluxio which was previously called Tachyon. And so AMPLab ended in December last year and in January, this January, we started a new lab which is called RISE. RISE stands for Real-time Intelligent Secure Execution. And the premise of the new lab is that actually the real value in the data is the decision you can make on the data. And you can see this more and more at almost every organization. They want to use their data to make some decision to improve their business processes, applications, services, or come up with new applications and services. But then if you think about that, what does it mean that the emphasis is on the decision? Then it means that you want the decision to be fast, because fast decisions are better than slower decisions. You want decisions to be on fresh data, on live data, because decisions on the data I have right now are original but those are decisions on the data from yesterday, or last week. And then you also want to make targeted, personalized decisions. Because the decisions on personal information are better than aggregate information. So that's the fundamental premise. So therefore you want to be on platforms, tools and algorithms to enable intelligent real-time decisions on live data with strong security. And the security is a big emphasis of the lab because it means to provide privacy, confidentiality and integrity, and as you hear about data breaches or things like that every day. So for an organization, it is extremely important to provide privacy and confidentiality to their users and it's not only because the users want that, but it also indirectly can help them to improve their service. Because if I guarantee your data is confidential with me, you are probably much more willing to share some of your data with me. And if you share some of the data with me, I can build and provide better services. So that's basically in a nutshell what the lab is and what the focus is. >> [Dave] Okay, so you said three things: fast, live and targeted. So fast means you can affect the outcome. >> Yes. Live data means it's better quality. And then targeted means it's relevant. >> Yes. >> Okay, and then my question on security, I felt like when cloud and Big Data came to fore, security became a do-over. (laughter) Is that a fair assessment? Are you doing it over? >> [George] Or as Bill Clinton would call it, a Mulligan. >> Yeah, if you get a Mulligan on security. >> I think security is, it's always a difficult topic because it means so many things for so many people. >> Hmm-mmm. >> So there are instances and actually cloud is quite secure. It's actually cloud can be more secure than some on-prem deployments. In fact, if you hear about these data leaks or security breaches, you don't hear them happening in the cloud. And there is some reason for that, right? It is because they have trained people, you know, they are paranoid about this, they do a specification maybe much more often and things like that. But still, you know, the state of security is not that great. Right? For instance, if I compromise your operating system, whether it's in cloud or in not in the cloud, I can't do anything. Right? Or your VM, right? On all this cloud you run on a VM. And now you are going to allow on some containers. Right? So it's a lot of attacks, or there are attacks, sophisticated attacks, which means your data is encrypted, but if I can look at the access patterns, how much data you transferred, or how much data you access from memory, then I can infer something about what you are doing about your queries, right? If it's more data, maybe it's a query on New York. If it's less data it's probably maybe something smaller, like maybe something at Berkeley. So you can infer from multiple queries just looking at the access. So it's a difficult problem. But fortunately again, there are some new technologies which are developed and some new algorithms which gives us some hope. One of the most interesting technologies which is happening today is hardware enclaves. So with hardware enclaves you can execute the code within this enclave which is hardware protected. And even if your operating system or VM is compromised, you cannot access your code which runs into this enclave. And Intel has Intell SGX and we are working and collaborating with them actively. ARM has TrustZone and AMB also announced they are going to have a similar technology in their chips. So that's kind of a very interesting and very promising development. I think the other aspect, it's a focus of the lab, is that even if you have the enclaves, it doesn't automatically solve the problem. Because the code itself has a vulnerability. Yes, I can run the code in hardware enclave, but the code can send out >> Right. >> data outside. >> Right, the enclave is a more granular perimeter. Right? >> Yeah. So yeah, so you are looking and the security expert is in your lab looking at this, maybe how to split the application so you run only a small part in the enclave, which is a critical part, and you can make sure that also the code is secure, and the rest of the code you run outside. But the rest of the code, it's only going to work on data which is encrypted. Right? So there is a lot of interesting research but that's good. >> And does Blockchain fit in there as well? >> Yeah, I think Blockchain it's a very interesting technology. And again it's real-time and the area is also very interesting directions. >> Yeah, right. >> Absolutely. >> So you guys, I want George, you've shared with me sort of what you were calling a new workload. So you had batch and you have interactive and now you've got continuous- >> Continuous, yes. >> And I know that's a topic that you want to discuss and I'd love to hear more about that. But George, tee it up. >> Well, okay. So we were talking earlier and the objective of RISE is fast and continuous-type decisions. And this is different from the traditional, you either do it batch or you do it interactive. So maybe tell us about some applications where that is one workload among the other traditional workloads. And then let's unpack that a little more. >> Yeah, so I'll give you a few applications. So it's more than continuously interacting with the environment continuously, but you also learn continuously. I'll give you some examples. So for instance in one example, think about you want to detect a network security attack, and respond and diagnose and defend in the real time. So what this means is that you need to continuously get logs from the network and from the more endpoints you can get the better. Right? Because more data will help you to detect things faster. But then you need to detect the new pattern and you need to learn the new patterns. Because new security attacks, which are the ones that are effective, are slightly different from the past one because you hope that you already have the defense in place for the past ones. So now you are going to learn that and then you are going to react. You may push patches in real time. You may push filters, installing new filters to firewalls. So that's kind of one application that's going in real time. Another application can be about self driving. Now self driving has made tremendous strides. And a lot of algorithms you know, very smart algorithms now they are implemented on the cars. Right? All the system is on the cars. But imagine now that you want to continuously get the information from this car, aggregate and learn and then send back the information you learned to the cars. Like for instance if it's an accident or a roadblock an object which is dropped on the highway, so you can learn from the other cars what they've done in that situation. It may mean in some cases the driver took an evasive action, right? Maybe you can monitor also the cars which are not self-driving, but driven by the humans. And then you learn that in real time and then the other cars which follow through the same, confronted with the same situation, they now know what to do. Right? So this is again, I want to emphasize this. Not only continuous sensing environment, and making the decisions, but a very important components about learning. >> Let me take you back to the security example as I sort of process the auto one. >> Yeah, yeah. >> So in the security example, it doesn't sound like, I mean if you have a vast network, you know, end points, software, infrastructure, you're not going to have one God model looking out at everything. >> Yes. >> So I assume that means there are models distributed everywhere and they don't know what a new, necessarily but an entirely new attack pattern looks like. So in other words, for that isolated model, it doesn't know what it doesn't know. I don't know if that's what Rumsfeld called it. >> Yes (laughs). >> How does it know what to pass back for retraining? >> Yes. Yes. Yes. So there are many aspects and there are many things you can look at. And it's again, it's a research problem, so I cannot give you the solution now, I can hypothesize and I give you some examples. But for instance, you can look about, and you correlate by observing the affect. Some of the affects of the attack are visible. In some cases, denial of service attack. That's pretty clear. Even the And so forth, they maybe cause computers to crash, right? So once you see some of this kind of anomaly, right, anomalies on the end devices, end host and things like that. Maybe reported by humans, right? Then you can try to correlate with what kind of traffic you've got. Right? And from there, from that correlation, probably you can, and hopefully, you can develop some models to identify what kind of traffic. Where it comes from. What is the content, and so forth, which causes behavior, anomalous behavior. >> And where is that correlation happening? >> I think it will happen everywhere, right? Because- >> At the edge and at the center. >> Absolutely. >> And then I assume that it sounds like the models both at the edge and at the center are ensemble models. >> Yes. >> Because you're tracking different behavior. >> Yes. You are going to track different behavior and you are going to, I think that's a good hypothesis. And then you are going to assemble them, assemble to come up with the best decision. >> Okay, so now let's wind forward to the car example. >> Yeah. >> So it sound like there's a mesh network, at least, Peter Levine's sort of talk was there's near-local compute resources and you can use bitcoin to pay for it or Blockchain or however it works. But that sort of topology, we haven't really encountered before in computing, have we? And how imminent is that sort of ... >> I think that some of the stuff you can do today in the cloud. I think if you're on super-low latency probably you need to have more computation towards the edges, but if I'm thinking that I want kind of reactions on tens, hundreds of milliseconds, in theory you can do it today with the cloud infrastructure we have. And if you think about in many cases, if you can't do it within a few hundredths of milliseconds, it's still super useful. Right? To avoid this object which has dropped on the highway. You know, if I have a few hundred milliseconds, many cars will effectively avoid that having that information. >> Let's have that conversation about the edge a little further. The one we were having off camera. So there's a debate in our community about how much data will stay at the edge, how much will go into the cloud, David Flores said 90% of it will stay at the edge. Your comment was, it depends on the value. What do you mean by that? >> I think that that depends who am I and how I perceive the value of the data. And, you know, what can be the value of the data? This is what I was saying. I think that value of the data is fundamentally what kind of decisions, what kind of actions it will enable me to take. Right? So here I'm not just talking about you know, credit card information or things like that, even exactly there is an action somebody's going to take on that. So if I do believe that the data can provide me with ability to take better actions or make better decisions I think that I want to keep it. And it's not, because why I want to keep it, because also it's not only the decision it enables me now, but everyone is going to continuously improve their algorithms. Develop new algorithms. And when you do that, how do you test them? You test on the old data. Right? So I think that for all these reasons, a lot of data, valuable data in this sense, is going to go to the cloud. Now, is there a lot of data that should remain on the edges? And I think that's fair. But it's, again, if a cloud provider, or someone who provides a service in the cloud, believes that the data is valuable. I do believe that eventually it is going to get to the cloud. >> So if it's valuable, it will be persisted and will eventually get to the cloud? And we talked about latency, but latency, the example of evasive action. You can't send the back to the cloud and make the decision, you have to make it real time. But eventually that data, if it's important, will go back to the cloud. The other question of all this data that we are now processing on a continuous basis, how much actually will get persisted, most of it, much of it probably does not get persisted. Right? Is that a fair assumption? >> Yeah, I think so. And probably all the data is not equal. All right? It's like you want to maybe, even if you take a continuous video, all right? On the cars, they continuously have videos from multiple cameras and radar and lidar, all of this stuff. This continuous. And if you think about this one, I would assume that you don't want to send all the data to the cloud. But the data around the interesting events, you may want to do, right? So before and after the car has a near-accident, or took an evasive action, or the human had to intervene. So in all these cases, probably I want to send the data to the cloud. But for the most cases, probably not. >> That's good. We have to leave it there, but I'll give you the last word on things that are exciting you, things you're working on, interesting projects. >> Yeah, so I think this is what really excites me is about how we are going to have this continuous application, you are going to continuously interact with the environment. You are going to continuously learn and improve. And here there are many challenges. And I just want to say a few more there, and which we haven't discussed. One, in general it's about explainability. Right? If these systems augment the human decision process, if these systems are going to make decisions which impact you as a human, you want to know why. Right? Like I gave this example, assuming you have machine-learning algorithms, you're making a diagnosis on your MRI, or x-ray. You want to know why. What is in this x-ray causes that decision? If you go to the doctor, they are going to point and show you. Okay, this is why you have this condition. So I think this is very important. Because as a human you want to understand. And you want to understand not only why the decision happens, but you want also to understand what you have to do, you want to understand what you need to do to do better in the future, right? Like if your mortgage application is turned down, I want to know why is that? Because next time when I apply to the mortgage, I want to have a higher chance to get it through. So I think that's a very important aspect. And the last thing I will say is that this is super important and information is about having algorithms which can say I don't know. Right? It's like, okay I never have seen this situation in the past. So I don't know what to do. This is much better than giving you just the wrong decision. Right? >> Right, or a low probability that you don't know what to do with. (laughs) >> Yeah. >> Excellent. Ion, thanks again for coming in theCUBE. It was really a pleasure having you. >> Thanks for having me. >> You're welcome. All right, keep it right there everybody. George and I will be back to do our wrap right after this short break. This is theCUBE. We're live from Spark Summit East. Right back. (techno music)